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PMC010xxxxxx/PMC10292910.txt
==== Front Drug Resist Updat Drug Resist Updat Drug Resistance Updates 1368-7646 1532-2084 Elsevier Ltd. S1368-7646(23)00069-9 10.1016/j.drup.2023.100986 100986 Editorial New emerging SARS-CoV-2 variants and antiviral agents Vitiello A. 1 Ministry of Health, Directorate-General for Health Prevention, Viale Giorgio Ribotta 5, 00144, Rome, Italy Zovi A. Ministry of Health, Viale Giorgio Ribotta 5, 00144 Rome, Italy Rezza G. Ministry of Health, Directorate-General for Health Prevention, Viale Giorgio Ribotta 5, 00144, Rome,Italy 1 https://orcid.org/0000–0003-2623–166X 26 6 2023 9 2023 26 6 2023 70 100986100986 19 12 2022 9 6 2023 20 6 2023 © 2023 Elsevier Ltd. All rights reserved. 2023 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Keywords Covid-19 Sars-cov-2 Antivirals Drugs ==== Body pmcDear Editor, The continuous emergence of new Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) variants forces the health-care sector and the scientific world to constantly monitor the evolution, pathogenicity, virulence, and transmissibility of the virus in different countries. In parallel, there is a growing need for the rapid preparation of new diagnostic instruments and methods that may increase sensitivity and specificity in the detection and identification of CoronaVirus Disease (COVID-19) infections caused by the new SARS-CoV-2 variants/subvariants, which may present some difference in their clinical manifestations (SARS-CoV-2 variants of concern as of 1 June 2023 (europa.eu), Fernandes et al., 2022, Vitiello et al., 2022). Vaccines remain the main weapon available for health prevention of COVID-19 infection, to reduce mortality and severe cases of infection (Hadj Hassine, 2022, Vitiello et al., 2022). Recently, the European Medicine Agency (EMA) Committee for Medicinal Products for Human Use (Chmp) recommended the authorization of an adapted bivalent vaccine that therapeutically targets the induction and stimulation of antibody response against Omicron subvariants BA.4 and BA.5 in addition to the original SARS-CoV-2 strain (〈https://www.ema.europa.eu/en/news/adapted-vaccine-targeting-ba4-ba5-omicron-variants-original-sars-cov-2-recommended-approval〉), and monovalent vaccines against reccombinant subvariants regoing to be developed. As of 3 March 2023, the European Centre for Disease Prevention and Control (ECDC) removed BA.2. BA.4 and BA.5 from the list of SARS-CoV-2 variants of concern (VOC), as these parental lineages no longer have a significant impact on the epidemiological situation in terms of transmissibility and severity. To date, the list of variants of interest (VOI) includes all variants with available preliminary evidence on genomic, and epidemiological properties that could have a significant impact on transmissibility and epidemiological severity. To date, the list of VOI includes Omicron variant BA.2.75 with spike mutations of interest W152R, F157L, I210V, G257S, D339H, G446S, N460K, Q493, Omicron variant BQ.1 with spike mutations of interest K444T, N460K, Omicron variant XBB with spike mutations N460K, F490S, Omicron variant XBB.1.5 with spike mutations of interest N460K, S486P, F490S (SARS-CoV-2 variants of concern as of 1 June 2023 (europa.eu)). And finally, the latest variants identified and under close monitoring, and with very little evidence available, are Omicron variant CH.1.1 with spike muttions of interest K444T, L452R, Omicron variant XBB.1.16 with spike mutations of interest E180V, T478R, F486P and Omicron variant FE.1 with spike mutations of interest Q183E, F456L, F486P, F490S. In light of these emerging new variants of SARS-CoV-2, current challenges pose the need to periodically produce updated vaccines that can stimulate an appropriate antibody immune response that can effectively counter and prevent COVID-19 infection caused by these new variants. In addition, the efforts of the scientific community must always be directed in researching and developing new pharmacological therapeutic strategies that can effectively inhibit viral replication and lower the circulating load with new antiviral agents directed against the new variants and immunomodulating agents to manage the hyperinflammatory response of the individual that may develop in some cases. The various subvariants followed each other at different times as dominant variants in different countries around the world: first BA.1, then BA.2, and finally BA.5. All of the Omicron subvariants, including the latest emerging subvariants BQ.1.1 and XBB, have mutations of interest in the receptor binding domain of the spike(s) protein, the main target of vaccines and therapeutic monoclonal antibodies for coronavirus disease 2019 (Covid-19). These subvariants may therefore exhibit increased resistance and evasion from currently available pharmacological therapeutic strategies, with particular regard to monoclonal antibodies. In fact, an interesting recent study (Imai et al., 2022) showed that the efficacy of therapeutic monoclonal antibodies against the emerging subvariants of Omicron, namely BQ.1.1 and XBB, may not be effective in the clinical setting. Different is the case of antiviral drugs, such as Remdesivir (inhibitor of Rna-polymerase), Molnupiravir ( inhibitor of RdRp) whose efficacy has been recently questioned, and Nirmatrelvir (a major protease inhibitor of SARS-CoV-2), where current in vitro evidence shows good antiviral activity even against the latest BQ.1.1 and XBB, with decreased resistance rates. Another recent study demonstrated the antiviral activity of remdesivir, molnupiravir and nirmatrelvir against Omicron's new subvariants (Cho et al., 2023). Thus, the drugs are likely to retain their antiviral activity against newly emerging Omicron subvariants. Whether the new subvariants are less resistant to some antiviral drug treatments and more resistant to others should be better investigated (Cao et al., 2022, Takashita et al., 2022). This implies that the continued evolution of Omicron variants supports and reinforces the need to test new antiviral therapeutic strategies, and most importantly, clinicians must use currently available drugs always following the indications, dosages, and timing of administration reported in the summary of product characteristics. The development of better diagnostic tests, targeted and updated vaccines, and new antiviral therapeutic agents is a clear urgent need for the scientific world, which must know how to evolve in step with the pace of SARS-CoV-2 mutation. Continuous and ongoing research is needed to increase scientific knowledge on the emerging new variants of SARS-CoV-2, in order to rapidly implement effective preventive and therapeutic antiviral strategies. CRediT authorship contribution statement Antonio Vitiello: conceptualization; methodology; supervision; validation; writing – original draft; writing – review and editing; Andrea Zovi: methodology; validation; writing – original draft; writing – review and editing. Giovanni Rezza: Editing. Declaration of Competing Interest The authors declare no competing or financial interests. The authors declare that they have no known competing financial interests or personal relationships that could appear to have influenced the work reported in this paper. The authors declare that the opinions expressed are of a personal nature and do not in any way commit the responsibility of the Administrations to which they belong. Acknowledgement None. ==== Refs References 〈https://www.ema.europa.eu/en/news/adapted-vaccine-targeting-ba4-ba5-omicron-variants-original-sars-cov-2-recommended-approval〉. Cao Y. Yisimayi A. Jian F. BA.2.12.1, BA.4 and BA.5 escape antibodies elicited by omicron infection Nature 608 2022 593 602 35714668 Cho J. Shin Y. Yang J.S. Kim J.W. Kim K.C. Lee J.Y. Evaluation of antiviral drugs against newly emerged SARS-CoV-2 Omicron subvariants Antivir. Res. 214 2023 105609 10.1016/j.antiviral.2023.105609 Epub 2023 Apr 20. PMID: 37086978; PMCID: PMC10118056 Fernandes Q. Inchakalody V.P. Merhi M. Mestiri S. Taib N. Moustafa Abo El-Ella D. Bedhiafi T. Raza A. Al-Zaidan L. Mohsen M.O. Yousuf Al-Nesf M.A. Hssain A.A. Yassine H.M. Bachmann M.F. Uddin S. Dermime S. Emerging COVID-19 variants and their impact on SARS-CoV-2 diagnosis, therapeutics and vaccines Ann. Med. 54 1 2022 524 540 10.1080/07853890.2022.2031274 PMID: 35132910; PMCID: PMC8843115 35132910 Hadj Hassine I. Covid-19 vaccines and variants of concern: a review Rev. Med Virol. 32 4 2022 e2313 10.1002/rmv.2313 Epub 2021 Nov 9. PMID: 34755408; PMCID: PMC8646685 Imai M. Ito M. Kiso M. Yamayoshi S. Uraki R. Fukushi S. Watanabe S. Suzuki T. Maeda K. Sakai-Tagawa Y. Iwatsuki-Horimoto K. Halfmann P.J. Kawaoka Y. Efficacy of antiviral agents against omicron subvariants BQ.1.1 and XBB N. Engl. J. Med. 2022 10.1056/NEJMc2214302 Epub ahead of print. PMID: 36476720 SARS-CoV-2 variants of concern as of 1 June 2023 (europa.eu). Takashita E. Yamayoshi S. Simon V. Efficacy of antibodies and antiviral drugs against omicron BA.2.12.1, BA.4, and BA.5 subvariants N. Engl. J. Med 387 2022 468 470 35857646 Vitiello A. Ferrara F. Auti A.M. Di Domenico M. Boccellino M. Advances in the omicron variant development J. Intern Med. 292 1 2022 81 90 10.1111/joim.13478 Epub 2022 Mar 22. PMID: 35289434; PMCID: PMC9115048 35289434 Vitiello A. Porta R. Pianesi L. Ferrara F. COVID-19 pandemic: vaccine and new monoclonal antibodies, point of view Ir. J. Med Sci. 191 1 2022 487 488 10.1007/s11845-021-02584-5 Epub 2021 Mar 12. PMID: 33710481; PMCID: PMC7953943 33710481
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==== Front J Psychosom Res J Psychosom Res Journal of Psychosomatic Research 0022-3999 1879-1360 Elsevier Inc. S0022-3999(23)00282-9 10.1016/j.jpsychores.2023.111425 111425 Letter to the Editor Famotidine: A potential mitigator of mast cell activation in post-COVID-19 cognitive impairment Kow Chia Siang a⁎ Ramachandram Dinesh Sangarran b Hasan Syed Shahzad cd a School of Pharmacy, International Medical University, Kuala Lumpur, Malaysia b School of Pharmacy, Monash University Malaysia, Bandar Sunway, Selangor, Malaysia c School of Applied Sciences, University of Huddersfield, Huddersfield, United Kingdom d School of Biomedical Sciences & Pharmacy, University of Newcastle, Callaghan, Australia ⁎ Corresponding author at: School of Pharmacy, International Medical University, 126, Jalan Jalil Perkasa, Bukit Jalil, Kuala Lumpur, Malaysia. 26 6 2023 26 6 2023 11142522 6 2023 23 6 2023 © 2023 Elsevier Inc. All rights reserved. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmc
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==== Front Dialogues Health Dialogues Health Dialogues in Health 2772-6533 Published by Elsevier Inc. S2772-6533(23)00049-7 10.1016/j.dialog.2023.100145 100145 Article Perceptions of health care workers on maternal and child health services in Pakistan during COVID-19: A cross-sectional study Ahmed Jamil a Kumar Ramesh b⁎ Mehraj Vikram b Almarabheh Amer a Khowaja Sadiq Ali c Khan Shahzad Ali b Naeem Nawal d Pongpanich Sathirakorn e a Department of Family and Community Medicine, College of Medicine and Medical Sciences, Arabian Gulf University, Bahrain b Department of Public Health, Health Services Academy, Islamabad, Pakistan c Department of health, Government of Sindh, Pakistan d Fellow Public Health, Health Services Academy Islamabad, Pakistan e College of Public Health Sciences, Chulalongkorn University, Thailand ⁎ Corresponding author at: Health Services Academy, Islamabad, Pakistan. 26 6 2023 26 6 2023 10014515 5 2023 21 6 2023 24 6 2023 © 2023 Published by Elsevier Inc. . 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background Maternal and child healthcare service delivery in vulnerable and fragile health systems has suffered a tremendous impact owing to the shift in focus to curtail the COVID-19 pandemic. We aimed to evaluate the impact of the COVID-19 pandemic on maternal and child healthcare services to inform policy advice for a more resilient maternal and child healthcare service delivery in Pakistan. Methods A descriptive cross-sectional study was conducted. A structured and validated questionnaire was transformed into an online version and a link was first sent to about 300 healthcare professionals to achieve a sample size of 203, including medical doctors, nurses, and other paramedical staff working in public sector health facilities of the four provinces of Pakistan. The questionnaire was responded to by 195 participants. The Chi-Square test was used to determine the statistical differences between the categorical variables. Results Although about two-thirds of the participants reported a moderate adherence to protocols and procedures to prevent COVID-19 in their health facilities, the maternal and child health service delivery-related indicators declined during the pandemic. For instance, 66.8% and 62.4% of the participants, respectively, did not agree that a Neonatal Intensive Care Unit and an Intensive Care Unit to admit sick newborns and women with obstetric complications during the COVID-19 pandemic were available during the COVID-19 pandemic. In addition, 23% and 20% of the participants, respectively, reported that staff availability and the provision of cesarean section were moderate to extremely affected. The association between job designation and the impact of COVID-19 was statistically significant (χ2 p = 0.038). Conclusions The study suggests that maternal and child healthcare services including C-Section, perinatal care, and inpatient care of newborns in Pakistan may have been moderately affected by the COVID-19 pandemic. Keywords Maternal health Child health Women health care Covid-19 And healthcare workers ==== Body pmc1 Introduction During the acute phase of the COVID-19 pandemic, maternal and child health care (MNCH) service coverage has been disrupted or diminished owing to the unprecedented burden upon health staff and resources, including those necessary for infection prevention and control. MNCH services consist of antenatal, childbirth and postnatal care, including emergency obstetric and newborn care, normal and emergency care of women and babies around birth by skilled birth attendants, immunization, care of sick newborns and children under five years of age [1]. Additionally, the reduction in demand for and access to quality healthcare around birth has also been a major challenge to coverage of MNCH services during the pandemic [2]. The staff and other resources were either repurposed, redeployed, or prioritized for COVID-19-related service delivery. Although high-income countries reported a negative impact on the quality of maternal and newborn care [3] or maternal health indicators like a higher rate of gestational diabetes and pregnancy-induced hypertension during the pandemic [4], this impact has been harder on the countries with fragile and under-financed healthcare systems; for instance, some estimates of the impacts of COVID-19 indicate a 38.6% and 44.7% increase in maternal and child mortality, respectively, per month in 118 low- and middle-income countries [5]. Fear of physical proximity and contracting the virus among healthcare providers may limit or alter provision of care to mothers and their children. It has been reported that social distancing and the fear of spreading the virus may lead health workers to deny laboring mothers an outside birth companion or to separate newborns from mothers after birth [6,7]. Pregnancy cannot be postponed and essential care for every newborn including immediate skin-to-skin contact and early initiation of breastfeeding require close bonding between mothers and their newborn babies; any gaps in such bonding are associated with poor mental health outcomes in mothers [7,8]. This is especially critical for pre-term, underweight, and small babies whose survival relies on the practice of Kangaroo Mother Care, which includes – among other practices – early, continuous, and prolonged skin-to-skin contact between the mother and the newborn. Kangaroo mother care has shown to help reduce neonatal mortality regardless of the gestational age or neonatal weight; with studies concluding that duration must be at least 8 h a day [9]. Government-ordered curfews and lockdowns may prevent or inhibit women's access to routine antenatal care or emergency care or may cause them to give birth at home without the support of a trained midwife or health worker [10,11]. Not only that, but economic hardships from lost jobs and livelihoods limit financial access to health facilities [12]. All this disruption has been associated with an increase in maternal and child mortality, particularly in low- and middle-income countries. Our most severe scenario (coverage reductions of 39·3–51·9% and wasting increase of 50%) over 6 months would result in 1,157,000 additional child deaths and 56,700 additional maternal deaths [13]. Recent studies have evaluated the disruption of MNCH services during the pandemic and its impact on mothers, children, and their families [[14], [15], [16]]. For instance, in India most affected services were antenatal care services which faced a decline of 23% [14], whereas in Mozambique, the family planning and cesarean birth services dropped by 28% [15]. African regions mostly in South Asian and African regions, have reported wide-ranging disruptions to the delivery of the MNCH services impacting child and maternal health and survival for durations ranging from at least a month to several months. This disruption has particularly badly affected outpatient consultations for women and children, as well as childhood immunization. A study from Africa estimated 5.1 million outpatient consultations and 328,961 third-dose pentavalent vaccinations were missed during a five-month duration of the pandemic [16]. Other areas of MNCH services which suffered during the pandemic include facility births, and antenatal and postnatal care [16]. Most of these services were also affected in Pakistan in 2020. For instance, a study reported that treatment of children with pneumonia dropped by 82%, and cesarean sections declined by 57% during January–May 2020, or at the time of the first wave of COVID-19 compared to pre-pandemic levels [17]. It is essential to determine how pandemics and health emergencies pose significant risks to the routine MNCH services in the fragile healthcare systems of low- and middle-income countries. This type of investigation into how healthcare systems continue to function in the face of severe disruptions to service delivery and coverage may assist countries in developing evidence-based policies and, ultimately, interventions to create resilient and better-prepared systems [6,18,19]. In this study, we approached frontline healthcare workers to determine the impact of the COVID-19 pandemic on maternal and child healthcare services in Pakistan. We targeted frontline healthcare workers in this assessment as we expected them to provider most reliable opinion about the disruptions to services and any impact on the maternal and child health because their key role as maternal and child healthcare service delivery in Pakistan. 2 Methods 2.1 Study design We conducted a cross-sectional study based on a modified version of a validated structured questionnaire (rapid online global survey of maternal and newborn health professionals facing the COVID-19 pandemic) [20] to evaluate the impact of the COVID-19 pandemic on maternal and child healthcare services. 2.2 Study setting The study was conducted from April 2021 to March 2022 using an online questionnaire where a link was sent to the eligible study participants through a local research team in Pakistan. The first Covid-19 wave peaked in June 2020, and cases started climbing again in Pakistan, the government announced a second wave of COVID-19 on October 28, 2020 [19,20]. There were 245,987 registered medical doctors, 27,360 registered dentists, and 116,659 registered nurses in 2020. In the country, 92,949 Lady health workers are deployed at the community level providing maternal and child health services involved in COVID-19 [21]. The medical and paramedical staff in both public and private sector health facilities in the country were eligible to participate in the study. In Pakistan, although about three-quarters of the population seeks care from the private sector, the main maternal and child healthcare service providers in rural areas are public healthcare facilities, including Basic Health Units, Rural Health Centers, and Taluka and district hospitals. 2.3 Study population Our study population consisted of healthcare workers from various health facility departments, such as outpatients, maternal child health, pediatrics, and other in-patient departments directly involved in the care of pregnant and postpartum patients, as well as newborns and children. Workers from all four provinces of Pakistan were invited to participate in the study. The study sample is calculated using the formula for the simple random sampling approach Z×P1−P2E2. where Z = 1.96 (95% C.I), P = 15.6 (Expected proportion in population [21]), E = margin of error = 0.05. The total estimated sample size was 203 participants. We received the data from 203 participants. Among them, 8 participants provided incomplete data. Therefore, those responses were not included in the final sample. Hence, the final sample for the analysis turned out to be 195. 2.4 Data collection Healthcare workers from all four provinces of Pakistan were invited to participate in the study. We used existing email addresses of all cadres of healthcare workers available with the corresponding author from the previous training project on COVID-19 after receiving administrative permissions. Reminders were sent through emails and later by mobile phone text messages where the numbers were available. We used a validated structured data collection tool in English language to design the online survey [20]. This pre-tested survey of maternal and newborn health professionals collected data on demographic information and opinions of study participants about how various aspects of maternal and child healthcare service delivery were impacted during the COVID-19 pandemic. The validated rapid online global survey of maternal and newborn health professionals facing the COVID-19 pandemic was modified according to local culture and healthcare system. For instance, the list of service cadre and designations were edited, and geographical locations were added. We conveniently approached the participants and requested that they answer the questionnaire to the best of their knowledge. Data collection was stopped when no further responses were contributed by the potential participants over a two-week period. Thereafter, we relied on the existing sample to analyze the data, addressing our study objectives. The participants were also asked about their perceptions of limitations to the delivery of maternal and child healthcare services during the pandemic, testing their agreement on items using a 5-point Likert scale. When asked to what extent various MNCH services were impacted during the pandemic, the health professionals rated the impact by selecting the options from a Likert scale of extremely, moderately, slightly, somewhat affected, and not affected at all.. 2.5 Data analysis Responses recorded in the online form were exported to Microsoft Excel (Excel Version 2016) and then into the Statistical Package for Social Sciences version 28 (IBM, USA) for analysis. Descriptive statistics were used to summarize the sample characteristics, where categorical variables were presented as frequency and percentage, and continuous variables were presented as mean and standard deviation. A cluster bar chart was used to present a categorical variable. Inferential statistics were used to determine the significance of statistical differences across participant characteristics within affected versus not affected health facilities and services. The Chi-Square test was used to determine the degree of association between the categorical variables. A p-value of less than 0.05 was considered statistically significant. Opinions of healthcare workers were assessed using a 5-point Likert scale (good, fair, poor). We used five questions, and one mark was awarded for a correct answer to any of the five questions. Four or five marks were considered good knowledge, three marks were fair knowledge, and fewer marks were considered poor knowledge. 3 Results 3.1 Socio-demographics data Overall, 195 individuals participated in the study, therefore the response rate was 96%. We received a higher response rate from the province of Sindh. Most healthcare providers who responded worked in public healthcare facilities. The mean age of participants was 37.4 ± 9.7 years, and more than half of the participants (51.3%) were females. Most of the respondents were between the ages of 30 and 40 years (31.3%), followed by those aged less than 30 years (29.7%), 40–50 years (29.2%), and over 50 years (9.7%). Regarding years of working experience, one-third of the participants reported 4–10 years of experience, 23.6% reported less than four years, 23.1% reported 11–15 years of experience, and 20% reported more than 20 years. Most of the participants were from Sindh (63.1%), followed by Punjab (12.8%), Khyber Pakhtunkhwa (10.8%), Baluchistan (6.7%), and Islamabad (6.7%). Among those who were employed, more than half of the respondents (53.3%) were physicians, and about 23.6% were nurses. Regarding participants' type of facility, 35.9% were primary health care centers, 33.8% were tertiary care hospitals, and 30.3% were secondary health care centers. Most of the participants (84.6%) indicated that their work was affected by COVID-19 (Table 1 ).Table 1 The sociodemographic characteristics of the health professionals participating in the study. Table 1Characteristic n (%) (n = 195) Gender Male 95 (48.7) Female 100 (51.3) Age group (mean = 37.39 ± 9.68 years) <30 Year 58 (29.7) 30–40 Year 61 (31.3) 40–50 Year 57 (29.2) > 50 Year 19 (9.7) Years of Work Experience (mean = 9.95 ± 7.50) < 4 years 46 (23.6) 4–10 years 65 (33.3) 11–15 years 45 (23.1) > 15 years 39 (20) Current place of work Baluchistan 13 (6.7) Islamabad capital territory 13 (6.7) Khyber Pakhtunkhwa 21 (10.8) Punjab 25 (12.8) Sindh 123 (63.1) Job designation Consultant gynecologist 19 (9.7) Physician excluding pediatricians 104 (53.3) Medical student 13 (6.7) Nurse 46 (23.6) Pediatrician 13 (6.7) Type of facility Primary Health Care 70 (35.9) Secondary Health Care 59 (30.3) Tertiary care hospital 66 (33.8) Work affected (COVID-19) Yes 165 (84.6) No 30 (15.4) More than 80% of the healthcare providers reported that their work was affected during the pandemic. There were no statistically significant differences across gender, age group, current province or city of work, years of practice, or type of health facility as most of these participants reported their work was equally affected (P≥ 0.05). Nurses, medical students, and physicians reported more impact on their work during the COVID-19 pandemic compared to the work of pediatricians (specialist cadre providing clinical services to children only) (95.7%, 84.6%, and 83.7% vs. 61.5%). The association between job designation and the impact of COVID-19 was statistically significant (χ2 p = 0.038) (Table 2 ).Table 2 Comparison of socio-demographic characteristics of the health professionals by the impact of COVID-19 on work. Table 2Variables Work affected due to COVID-19 P value Not affected n (%) Affected n (%) Gender Male 17 (17.9) 78 (82.1) 0.344 Female 13 (13) 87 (87) Age group <30 Year 11 (19) 47 (81) 0.730 30–40 Year 7 (11.5) 54 (88.5) 40–50 Year 9 (15.8) 48 (84.2) > 50 Year 3 (15.8) 16 (84.2) Years of practice < 4 years 9 (19.6) 37 (80.4) 0.668 4–10 years 9 (13.8) 56 (86.2) 11–15 years 5 (11.1) 40 (88.9) > 15 years 7 (17.9) 32 (82.1) Current province/city of work Baluchistan 2 (15.4) 11 (84.6) 0.168 Islamabad capital territory 1 (7.7) 12 (92.3) Khyber Pakhtunkhwa 3 (14.3) 18 (85.7) Punjab 8 (32) 17 (68) Sindh 16 (13) 107 (87) Job designation Consultant gynecologist 4 (21.1) 15 (78.9) 0.038⁎ Physician (Medical doctors excluding pediatricians) 17 (16.3) 87 (83.7) Medical student 2 (15.4) 11 (84.6) Nurse 2 (4.3) 44 (95.7) Pediatrician 5 (38.5) 8 (61.5) Type of facility Primary Health Care 13 (18.6) 57 (81.4) 0.581 Secondary Health Care 9 (15.3) 50 (84.7) Tertiary care hospital 8 (12.1) 58 (87.9) ⁎ = p < 0.05. Further analysis showed that the majority of the participants (84.9%) said that healthcare staff in their health facilities answered patients' questions about the COVID-19 pandemic, and 77.9% of the participants reported that healthcare centers provided appropriate instructions to pregnant women. More than three-quarters of the participants (76.9%) indicated that they had been provided with appropriate training on how to deal with the COVID-19 pandemic, and 73.9% of the participants agreed that the routine cleaning of the maternity ward has been revised in response to COVID-19. Two-thirds of the participants (66.3%) indicated that the working staff was wearing N95 masks during COVID-19; 60.8% of the participants agreed that the facilities received maternity referral cases in the COVID-19 situation; and 59.8% of the participants reported that the facility reserved isolation rooms for COVID-19 suspected cases. More than half of the participants (55.9%) agreed that the facility set up a well-signposted general entrance and screening area for COVID-19 suspected cases. More than two-thirds (66.8%) of the participants did not agree that the facility had a neonatal intensive care unit during the COVID-19 pandemic. Finally, 62.4% of the participants indicated that their facility did not have an intensive care unit to admit women with obstetric complications during the COVID-19 pandemic (Table 3 ).Table 3 Health Professionals' opinions of the availability of services and staff related to MNCH in health facilities. Table 3Description of services at the health facilities⁎ Yes n (%) No n (%) Facility has an intensive care unit (ICU) which can admit women with obstetric complications in the COVID-19 situation. 70 (37.6) 116 (62.4) Facility has a neonatal intensive care unit (NICU) in the COVID-19 situation 62 (33.2) 125 (66.8) Facility receives maternity referral cases in the COVID-19 situation 110 (60.8) 71 (39.2) Feel that patients' questions about COVID-19 are being answered adequately by staff. 157 (84.9) 28 (15.1) Facility set up a well sign-posted general entrance and screening area for COVID-19 suspected cases 109 (55.9) 78 (44.1) Facility reserved isolation rooms for COVID-19 suspected cases 110 (59.8) 74 (40.2) Routine cleaning of the maternity ward changed in response to COVID-19? 144 (73.9) 51 (26.1) Working in emergency / Triage area, staff was wearing N95 Masks 118 (66.3) 60 (33.7) Description of staff at the health facilities Staff was provided any training on COVID-19 150 (76.9) 45 (23.1) Facility implemented COVID-19 guidelines for women's care during pregnancy 152 (77.9) 43 (22.1) ⁎ The Yes and No in the columns are for the N and percentages for the items in the rows. About half of the participants ranked the impact of the services from slightly to somewhat affected; a quarter said they were not affected at all. However, participants reported a higher impact on some services than others. For instance, 23% and 20% of the participants rated, respectively, staff availability and the provision of C-sections as moderate to extremely affected. A quarter (24.6%) of the participants reported a moderate or extreme impact was whether health professionals felt that they were sufficiently protected from infection with COVID-19 in their workplace (Fig. 1 ).Fig. 1 Health professionals' responses of how negatively the MNCH services were affected in their health facilities. Fig. 1 4 Discussion This study aimed to document the opinions of healthcare workers about their experience of disruptions observed at MNCH services during the COVID-19 pandemic in Pakistan. The study found that more than 80% of the healthcare workers opined that their work was affected by the pandemic. This was equally reported by male and female healthcare workers in all groups, with diverse years of practice, from almost all health facilities in the country. This study also found that most (84.9%) healthcare staff were able to answer their patients' questions about the COVID-19 pandemic in their health facilities, and three-quarters of the healthcare workers believed that their healthcare centers provided appropriate instructions to pregnant women. More than three-quarters of the participants (76.9%) indicated that they had been provided with appropriate training on how to deal with the COVID-19 pandemic, and 73.9% of the participants agreed that the routine cleaning of the maternity ward has been revised in response to COVID-19. More than two-thirds (66.8%) of the healthcare workers said that their facility did not have a dedicated neonatal intensive care unit during the COVID-19 pandemic; additionally, 62.4% of the facilities did not have an intensive care unit for women with obstetric complications during the COVID-19 pandemic. In response to the severity of the impact, about a quarter (23%) and 20% of the healthcare workers, respectively, opined that staff availability and the provision of C-sections suffered moderate to extreme impact. We report that although about three-fourths of the participants considered that their health facilities adhered to COVID-19-related health precautions and guidelines and were responsive to mothers and their newborns in providing COVID-19-related care or information, most responded that their health facilities did not have intensive care services for sick newborns or mothers during the pandemic. This supports the findings from a study from India, which compared data before and after the pandemic, showing a decline of about 23% in institutional births, antenatal care, and immunization which are essential maternal and child healthcare services [14]. Our results contrast with what Semaan, A. et al. reported from the sub-Saharan African region, where the healthcare system seemed to be more resilient in the face of the pandemic-related shocks. They reported a sustained availability of maternal care in referral hospitals that ensured that the care for the women and their babies was prioritized and did not suffer a negative impact [22]. The responses of health professionals to how negatively MNCH services were affected in their health facilities revealed a mixed pattern of response, with about a quarter of the healthcare workers reporting that services were either slightly or somewhat affected, and reported that staff availability (23%) and C-section provision (20%) were extremely or moderately affected. Specifically, 66.8% of the healthcare workers said that their facility did not have a dedicated neonatal intensive care unit and, 62.4% said their facilities did not have an intensive care unit for women with complications at birth. Staff shortages are linked to an inability to provide emergency obstetrical and newborn care services [6]. Also, availability of C-section and specialized care facilities for neonates and mothers at birth are crucial to save lives [23]. Other studies from low and middle-income countries found a similar pattern in the decrease in the availability of C-section services. Compared to a reported moderate to extreme impact of 20% on C-sections in our study, in Mozambique, for instance, c-sections declined by 28% during the pandemic, which is comparable to the findings from our study [15]. In yet another study that included district-level data from 13 sub-Saharan African countries, inpatient admissions showed the greatest reduction (17.0%) during the pandemic compared to the rest of the services, indicating that the care required for sick newborns and mothers around birth may have been considerably impacted [24]. However, there has been evidence of resilience by some healthcare systems to cope with the COVID-19-induced shocks to c-section provision. For instance, a study that assessed the resilience and vulnerability of maternal health services in Zimbabwe showed that although hospital bookings for pregnant women slightly decreased, the c-section provision did not change significantly from the pre-COVID-19 period. For instance, this study also showed that staff availability also did not change from before to during the pandemic [25]. It is also possible that these service delivery shortfalls may have compromised access for pregnant women and those with babies because of the fear of catching COVID-19 when visiting the health facilities. This is supported by a study that showed that 74.7% of pregnant women are afraid of contracting COVID-19 from public health facilities [26]. A study from Pakistan showed that compared to pre-pandemic levels, 33% fewer patients visited primary healthcare facilities in a typical month during the pandemic [21]. Studies have consistently shown that the burden of MNCH morbidity and maternal and child mortality may have increased during the pandemic [2,13,27]. However, in some countries the impact has been limited by organized efforts of healthcare systems; for instance, a study from Bangladesh reported a little impact on access to MNCH services, which shares similarities with the healthcare system of Pakistan [23]. Access to the MNCH service may also have diminished unequally for women in vulnerable communities, as a study with a refugee population in Kenya reported that more women had to give birth at home during the pandemic [28]. Recent evidence suggests that these disruptions in MNCH service delivery resulting from pandemics are avoidable with existing interventions. These successful interventions such as the development of updated policy guidelines by some countries have prevented major disruptions to MNCH service delivery. For example, a recent review identified that preserving quality care for pregnant women around birth and afterward has been possible for countries like Kenya, Mozambique, Uganda, and Zimbabwe, which like Pakistan have had a high burden of maternal and child morbidity and mortality. The review showed a pattern of resilience as depicted in the policy guidelines listing antenatal and intrapartum care, family planning, and immunization as essential services during any pandemic [6]. A study documented these interventions and highlighted the importance of implementing virtual patient encounters, improved patient triaging, dedicated maternity centers, or maternity schools; however, these interventions have not bee effective [18]. Therefore, it is critical that these policy guidelines are implemented using evidence-informed interventions to ensure a resilient and quality maternal and child healthcare service delivery [18]. Also considering the contextual factor such as local resource availability is crucial to develop any interventions for MNCH service improvement during pandemics [29]. 5 Strengths and weaknesses The use of a structured, validated tool and the de-identified responses from frontline health workers are strengths of this study. The study also had some limitations. These included non-random sampling using an online questionnaire where it is expected that the participants may respond from a non-representative population, which may indicate that results could be cautiously generalized. Another limitation was a low response rate (as we originally invited about 500 participants)resulting in small sample size. Considering these limitations, we expect that although the findings from our study are suggestive of a perceived impact on MNCH service delivery, it may not be the experience of all the healthcare workers. 5.1 Conclusions and implications MNCH services in Pakistan seemed to have been moderately affected by the COVID-19 pandemic. Although 84.9% healthcare staff were able to answer their patients' questions about the COVID-19 pandemic in their health facilities, and three-quarters responded that their healthcare centers provided appropriate instructions to pregnant women, more than 80% of the participants found their work was affected by the pandemic. The study also concluded that 66.8% facilities lacked a dedicated neonatal intensive care unit and 62.4% of the facilities did not have an intensive care unit for women with obstetric complications during the COVID-19 pandemic. Lastly about a quarter of the participants believed that staff availability and the provision of C-sections suffered moderate to extreme impact during the pandemic. We recommend that to avoid such a high impact on healthcare workers' work during the COVID-19 pandemic, pandemic preparedness needs to be implemented and must include strategies to protect MNCH services in the face of shocks to enable a resilient and sustainable healthcare service delivery system. We also recommend the establishment of intensive care units for neonates and women requiring specialized care around birth where they are not currently available or accessible to catchment populations. Further research is required to understand the long-term impact of the COVID-19 pandemic on maternal and child health and to measure the availability of MNCH services, especially emergency and specialized care services around birth post-COVID-19. Ethical approval This study was approved by the Institutional Review Board of Health Services Academy, Islamabad Pakistan (7–82/IERC-HSA-2020-22). Authors' contributions JA conceptualized this study; AA, NN analyzed the data and VM, and SP contributed to drafting and critically revising the manuscript. SK, RK, and SAK were involved in data collection, and they revised the manuscript. FA revised the manuscript. All authors read and approved the final manuscript. Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ramesh Kumar reports statistical analysis and writing assistance were provided by Health Services Academy. Ramesh Kumar reports a relationship with Health Services Academy that includes: employment. NA. Acknowledgements We are grateful to Associate Professor Lenka Benova at the Institute of Tropical Medicine in Antwerp, Belgium for the study instrument that we adapted from the validated version used for a global study and the support by Ratchadapisek Somphot Fund for Postdoctoral Fellowship, Chulalongkorn University Thailand. ==== Refs References 1 World Health Organisation Maternal, newborn and child health (MNCH) strategy 2021-2025 Available from: https://pmnch.who.int/docs/librariesprovider9/governance/2023022122-workplanning-retreat-mnch-advocacy-strategy.pdf?sfvrsn=fc799bcf_5 2023 2 Roberton T. Carter E.D. Chou V.B. Stegmuller A.R. Jackson B.D. Tam Y. Early estimates of the indirect effects of the COVID-19 pandemic on maternal and child mortality in low-income and middle-income countries: a modelling study Lancet Glob Health 8 7 2020 e901-e8 3 Lazzerini M. Covi B. Mariani I. Drglin Z. Arendt M. Nedberg I.H. 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==== Front J Migr Health J Migr Health Journal of Migration and Health 2666-6235 The Author(s). Published by Elsevier Ltd. S2666-6235(23)00044-2 10.1016/j.jmh.2023.100194 100194 Article Socio-ecological barriers to access COVID-19 vaccination among Burmese irregular migrant workers in Thailand Khai Tual Sawn PhD Candidate in Sociology and Social Policy, School of Graduate Studies, Lingnan University, Hong Kong, People's Republic of China 26 6 2023 26 6 2023 100194© 2023 The Author(s). Published by Elsevier Ltd. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Thailand is a migration hub in ASEAN (Association of Southeast Asian Nations), with more than 3.9 million migrant workers, accounting for 10% of the country's workforce. The government of Thailand has moved from a pandemic to an endemic state of living with the SAR-CoV2 virus as a new normal since over half of the population has been vaccinated. There is, however, an estimated 1.3 million irregular migrant workers in Thailand who are not covered by Social Security Schemes (SSS) and are likely to have not been vaccinated. This study examines the socio-ecological barriers to accessing vaccination among Burmese irregular migrant workers in Thailand. Qualitative and quantitative data were collected from NGO (Non-Government Organizations) workers and Burmese irregular migrants through an online survey and in-depth interviews. The study reported that over 90% of Burmese irregular migrants were unvaccinated. The main reasons for the low vaccination rate include exclusion from the vaccine distribution program, high cost of the vaccine, perceived low quality of vaccine, language barriers, lack of vaccine information, private and public discrimination against migrants, fear of being detained and deported, and difficulties in finding time and transportation to go to vaccination centres. The Thai government should employ culturally competent interpreters to disseminate vaccine information and potential side effects to prevent further casualties and curb the global health crisis. Moreover, the Thai government should provide free vaccines to all immigrants regardless of their status and amnesty from deportation and detention during the vaccination period. Keywords COVID-19 vaccine barriers vaccination Thailand irregular migrant workers Burmese/Burma ==== Body pmc1 Background Over the last two years, the world has witnessed an unprecedented global health catastrophe caused by COVID-19, which has significantly impacted the lives of millions of people. Approximately 6.17 million people have died due to COVID-19, and over 494 million infections have been confirmed as of 5 Apr 2022 (Worldometer, 2022a). Globally, migrant workers are vulnerable to poor health due to precarious working conditions and overcrowded living conditions (Lassale et al., 2020; Lewis et al., 2020), limited social distancing or self-isolation capacity (De Vito et al., 2017), and are at a higher risk of contracting COVID-19 than the host population in their destination (European Centre for Disease Prevention and Control, 2021). For example, during the early outbreak of COVID-19 in Singapore, 90% of the cases were found among migrant workers living in dormitories (Singapore Ministry of Health, 2021). Additionally, even before the COVID-19 pandemic, access to healthcare and vaccinations was a barrier for them due to an inability to pay out of pocket, discrimination, language and cultural barriers, and concerns about one's legal status (Chuah et al., 2018; European Centre for Disease Prevention and Control, 2018; Kohlenberger et al., 2019; Lebano et al., 2020; Pocock et al., 2020; Rivenbark & Ichou, 2020). In particular, this pandemic has exposed vulnerabilities among those of irregular status (World Health Organization, 2021a). As a regional migration hub in ASEAN (Association of Southeast Asian Nations), Thailand hosts more than 3.9 million migrant workers, representing 10% of the country's workforce. Most come from Burmese, Cambodia, Laos, and Vietnam, contributing between 4.3 and 6.6 % of the country's gross domestic product. Only approximately 1.97 million migrant workers were enrolled in public health insurance (United Nations Thematic Working Group on Migration in Thailand, 2019). An estimated 1 to 2.5 million were irregular status conditions (International Organization for Migration, 2021b), and this number seems higher following the COVID-19 disruption due to loss of employment and work permit expiration. Moreover, this unprecedented COVID-19 virus has negatively impacted global health and the economy (International Monetary Fund, 2020). To recover the country's economy from COVID-19, Thailand has transited to an endemic condition or to adapt to living with the virus as a new normal (Bangkok Post, 2022). To address this issue, the Thai government has vaccinated more than 60% of its population against COVID-19 (Ritchie et al., 2022) and announced vaccination for migrant workers who are insured with Social Security Scheme (SSS) (Boonwara Sumano & Naneksomboonph, 2021). As part of a new agreement to mitigate labour shortages and strengthen economic recovery, the government has allocated 500,000 vaccines to new migrant workers from neighbouring countries (Reuters, 2021b). However, an estimated 1.3 million irregular migrants uninsured by SSS do not receive any vaccinations in Thailand (Bangkok Post, 2021b), despite the Thai government's commitment to living with a virus in the long term. In the meantime, the COVID-19 infection rate in Thailand remains high daily. As of April 2022, there were 3,757,575 infections detected and 25,603 deaths. A total of 21,088 additional confirmed cases were reported, of which 20,995 were transmitted locally (Worldometer, 2022b). Furthermore, an analysis revealed that the COVID-19 fatality rate is higher among unvaccinated populations across the globe (Our World in Data, 2021). In comparison with the general population, migrant workers demonstrate a lower level of vaccination acceptance or higher levels of vaccine hesitancy globally (Loiacono et al., 2020; Noori et al., 2021). Therefore, it is extremely important to include all immigrants in vaccination campaigns, regardless of their immigration status, to enable a healthy labour force and to minimise the risk of severe disease and further casualties caused by COVID-19. The purpose of this article was to explore the barriers that Burmese irregular migrant workers (BIMWs) encounter when it comes to accessing COVID-19 vaccination in Thailand. 2 Theoretical Framework This study adapted McLeroy's Socio-Ecological Model (SEM) to understand the barriers to accessing COVID-19 vaccination among BIMWs in Thailand. The model described the multifactored interrelationship between intrapersonal, interpersonal, organisational, community, and policy environments and how they affect the population's well-being (McLeroy et al., 1988). In this study, participants were NGO (Non-Governmental Organizations) workers and irregular migrants who lacked legal documents and were uninsured for health insurance and social security. Several variables may affect irregular migrant workers' willingness to get and access vaccination. Therefore, to improve access to vaccination among BIMWs, interventions should consider all five levels of the SME model. This model guides interview questions to explore the participant's experiences and barriers to getting vaccination that is not attainable from the quantitative data. Moreover, Glanz et al. (2015) stated that this model is the most relevant for investigating factors that influence health behaviour within a population and can formulate interventions strategically. The model has also been utilised in many existing studies to explore the barriers to accessing healthcare services among the migrant population in various countries and contexts (Gany et al., 2006; Barker & Horton, 2008; Greves et al., 2007; Shtarkshall et al., 2009; Pitkin Derose et al., 2009; Reininger et al., 2014; Viken et al., 2015; Yu et al., 2019). 3 Material and methods 3.1 Study Setting and study method This study used a convergent mixed-method design to explore the factors that prevent BIMWs from accessing the COVID-19 vaccine in Thailand. Both qualitative and quantitative data were collected between September 2021 to January 2022. In total, 23 BIMWs and 17 NGO workers participated in qualitative interviews (see Table 1 ), while 398 completed the online questionnaire (see Table 2 ). The rationale behind using a convergent mixed-method study is that it enables both qualitative and quantitative data to be collected concurrently, analysed separately, and interpreted to support and compare the results (Creswell, 2018).Table 1 Demographic characteristics of the qualitative participants (N=40) Table 1Characteristics Number BIMWs (N=23) Gender  Male 11  Female 12 Current Employment Status  Unemployed 15  Employed 6 Vaccination status  Fully vaccinated (two doses) 1  One dose 0  None 22  An average year in Thailand 5 NGO participants (N=17) Gender  Male 8  Female 9 Nationality  Burmese 11  Thai 5  USA 1 Employment sector  Health sector 6  Labour protection sector 6  Local journalist on the migrant issue 1  Both labour protection and the health sector 3  Legal officer 1 Table 2 Demographic characteristics of the quantitative participants among irregular migrant workers (N=398) Table 2Sample Characteristic Frequency Percentage Gender  Male 188 47.2  Female 210 52.8 Age  18-29 139 34.9  30-39 145 36.4  40-49 80 20.1  50 above 34 8.5 Current employment status  Unemployed 156 39.2  Employ 242 60.8 Current employment sector (Top 5 answer only)  Construction 56 23.1  Agriculture sector 49 20.2  Domestic work 34 14.0  Factory/ garment production 26 10.7  Retail trade and vendor 14 4.8 Vaccination status  Fully vaccinated (two doses) 12 3.0  One does 25 5.3  None 361 90.7 Able to speak Thai  Yes 36 9  No 362 91.0 The inclusion criteria for this study included irregular migrant workers who were originally from Burmese and had migrated to Thailand before the COVID-19 pandemic or had been in Thailand for at least two years. The BIMWs were chosen specifically because the Burmese migrant workers population accounts for 68% of Thailand's entire migrant worker population, with at least 1 million were estimated to be in irregular status (United Nations Thematic Working Group on Migration in Thailand, 2019). The selection for NGO (Non-Govermnent Organization) participants includes individuals currently engaged in the healthcare, legal rights, and labour-protection sectors for migrant populations in Thailand. 3.2 Data collection procedure This study employed a multistage sampling procedure for data collection. The study employed an online data collection approach considering travel restrictions enforcement and the rising number of COVID-19 infections in Thailand during the data collection period. Initially, the researcher sent invitations for interview participation and online questionnaires to several organisations that provide healthcare and protection for migrants, as well as community-based organisations led by migrants, to help distribute the survey on their respective platforms to obtain voluntary responses. Moreover, the researcher distributed the Burmese language online questionnaires survey (multiple-choice questions) to several migrant communities in Thailand via social media (Facebook groups, messenger chat groups, WhatsApp groups). As a last step in the online survey, the respondents were asked to share it with their community and provide their contact information if they were interested in voluntarily participating in an in-depth interview. BIMW's online survey respondents who indicated a willingness to participate were conducted in-depth interviews (IDIs) individually based on their availability. Interview questions were based on survey questionnaires, with some modifications and follow-ups based on the respondents' responses.  NGO workers who responded to the invitation for voluntary participation were interviewed regarding their experiences and perceptions of the major challenges faced by BIMW in obtaining vaccinations. A separate focus group discussion (FDG) interview was conducted for BIMWs and NGOs whose schedules were concurrent. The study adopted an online interview approach through Zoom or WhatsApp based on the participants' preferences; both provide end-to-end encryption to ensure that the participants' stories are secure and data remain confidential. During the interview, the researcher also took some notes. The Burmese language was primarily used to communicate with the migrant participants. An English-language interview was conducted with participants from Thai NGOs. The participants were encouraged to share the online survey with others and recruit more members who meet the selection criteria. For each participant, the interview lasted between 25 and 35 minutes. Upon repetition of narratives or data saturation, the interview was concluded as suggested by (Saunders et al., 2018). To cover internet access costs, the researcher provided each participant with a mobile phone card credited with 100 Thai Bath. 3.3 Ethical Consideration Before the interview, the researcher explained the purpose of the study and the interview procedure. This included the participants' right to withdraw from the interview without hesitation. As part of the interview procedure, the researcher obtained either verbal or written consent from the participant, depending on their preferences for participation and the interview audio recording. Research Ethics Sub-Committee approved the research ethical protocol, Postgraduate Student Committee (PSC) of Lingnan University in Hong Kong, China. 4 Data analysis procedure 4.1 Quantitative data Quantitative data analysis was performed using IBM SPSS Statistic for Windows, Version 26. Online questionnaires were converted into Microsoft Excel, assigning numbers to variables. For example, vaccination status (completely vaccinated = 1, one does vaccinate = 2, none = 3), and data were exported into SPSS. Descriptive statistics were used rather than regression analysis to understand vaccination rates and the significant barriers to accessing vaccination. The quantitative data are presented in percentages and frequencies to describe sociodemographic characteristics, barriers to vaccination, and willingness to receive vaccination. 4.2 Qualitative data The researcher listened to the audio recording several times before transcribing and translating it to gain a deeper understanding of the qualitative data. After the audio interview recording was transcribed verbatim, it was translated into English. In order to clarify any transcription errors that may have occurred, the transcribed transcripts were read aloud, and the audio tape was played several times. To increase the credibility of the study's findings, the transcription was returned to the NGO participants for confirmation that the researcher's translation accurately reflected their experiences and perceptions. The transcriptions were not returned to the migrant participants as they were occupied with their jobs and had no computer access. The researcher read the field notes and transcribed data line by line, merging similar data into consolidated data and analysing them thematically using the methodology provided by (Clarke et al., 2015), and the socio-ecological model. Inductive analysis was performed, with codes and themes derived from the data rather than the socio-ecological framework. 5 Results 5.1 Participants' demographic characteristics A total of 40 individuals participated in this study. Among the participants, there were 23 BIMWs and 17 NGO workers. Tables 1 and 2 below provide an overview of the demographic characteristics of all participants. 5.2 Quantitative findings 5.2.1 Vaccination status According to the quantitative findings, only 3.0 % (n=12) of 398 survey participants received a full vaccination, 5.3% (n=25) received one dose, and 90.7% (n=361) were unvaccinated due to numerous barriers. To understand the significant barriers hindering BIMWs from accessing vaccinations, this study utilised thematic qualitative analysis using a socio-ecological model, emphasising factors relating to intrapersonal, interpersonal, institutional, community, and policy issues. These major themes are further subdivided into 11 sub-themes, as illustrated in (figure 1 ).Figure 1 Socio-ecological barriers to access COVID-19 vaccination among Burmese irregular migrant workers in Thailand Figure 1 5.3 Qualitative findings 5.3.1 Individual-level 5.3.1.1 Financial burden due to free vaccine delivery is not accessible throughout the country Participants reported that the Thai government only provides the COVID-19 vaccine to residents in certain districts regardless of immigration status, including irregular migrants. Some areas, however, offer free vaccinations to migrant workers, while others charge a fee, and others do not offer any vaccinations at all. The study found that 39.2% (n=156) of participants were unemployed or became unemployed due to the disruption caused by COVID-19. Others were fired by their employers after testing positive for COVID-19. As a result, the financial burden was one of the significant hurdles to COVID-19 vaccination among the BIMWs due to a lack of income to pay the vaccine fee. 5.3.1.2 The employer's reluctance to cover the vaccination cost The employer's reluctance to cover the costs of COVID-19 tests, vaccines, and registration is another barrier to vaccination among the currently employed. Participants reported that most multinational companies or factories cover all costs of hiring migrant workers, including vaccinations. There are, however, a relatively small number of such factories located primarily in major metropolitan areas. Most participants work in low- and middle-income industries in remote locations.My employer informed me that the cost would be deducted from my salary if I wanted to receive the COVID-19 vaccine. Therefore, I must refuse to accept the vaccine since I am more concerned with food and housing costs for my daily survival. P10 (Female) 5.3.1.3 Rumours and unavailability of vaccine side effects in the migrant's mother tongue language Additionally, 79.4% (n= 316) of the participants did not receive or have access to information regarding the side effects of the COVID-19 vaccine in their native languages. Several factors contribute to migrant workers' unwillingness to receive vaccination, such as a lack of access to adequate information and rumours about the vaccine spreading among them.One of the challenges among the Burmese immigrant population is living with rumours regarding vaccines. (P8, Executive Director) Several participants from NGO organisations indicated that Burmese migrant workers believe rumours stating that vaccinations from China or India are ineffective and that someone had died because of the vaccination. In Mae Sot City, where the Thai population and the migrant population are almost equal, a group of community-based organisations distributed Sinopharm vaccines to migrant workers. As an example, one of the NGO participants shared his experience in mobilising migrant workers for vaccination due to vaccine rumours:It is extremely difficult to communicate about the vaccine with migrant workers. They repeatedly asked what type of vaccine they would receive. It was challenging to gather a list of names willing to receive vaccinations when we mentioned vaccine names such as Sinopharm. (P4, Health officer) 5.3.2 Interpersonal level 5.3.2.1 Language barriers The language barrier is one major obstacle to getting vaccination among participants. More than 91.0% (n=362) of participants indicated they did not understand Thai and had difficulty understanding the announcement about the COVID-19 vaccination procedure.There has been a high rate of migrant workers who have failed to receive the COVID-19 vaccination due to language barriers and a lack of knowledge regarding whether the vaccine is free, where to obtain it, or how to register. (P2, healthcare workers) 5.3.2.2 Factory lockdowns On the other hand, the lockdown of factories presents another challenge for migrant workers who wish to receive a vaccination. Participants mentioned employers are concerned about the factory closing during a disease outbreak or infection among migrant workers. Consequently, some companies prohibit workers from leaving their manufacturing sites, including seeking medication or vaccination.Although I have learned that my local district government offers free vaccinations, it is difficult for me to obtain vaccinations because I cannot take time off work or off days. (P7, Male) Aside from factories being locked down, NGO participants reported that most workplaces prohibited them from entering, making it difficult for them to distribute vaccines and for many migrant workers to obtain vaccinations.Many employers did not provide their workers with the COVID-19 test and vaccination. Furthermore, it was also difficult for NGO workers to access the workplace to deliver vaccination. (P4, labour rights officer). 5.3.3 Organisation level 5.3.3.1 Inaccessibility of vaccination centres More than half, 76.9% (n= 306) of the participants indicated that NGOs' geographical challenges in reaching free COVID-19 vaccination centres posed a barrier to vaccination.The cost of transportation is a concern for some migrant workers due to geographical challenges. In addition, some of them must take a ferry or a boat to the designated vaccination centre for COVID-19 even though the vaccination would be free. (P5, health officer) The participants also mentioned that some NGO health clinics, such as "Mae Taw Clinic" in Mae Sot City, offer free health care and vaccination services to all Burmese migrants. However, migrant workers from other areas, such as those working in rural agriculture, have difficulty accessing the program due to travel restrictions, lack of legal documentation, and transportation issues. 5.3.3.2 Unfamiliarity with the registration process and experiences of discrimination An unfamiliar registration process and a complex vaccination booking system hinder migrant workers from receiving vaccinations. Many participants, 81.4% (n= 325), expressed concerns about contacting the provincial administration health office for vaccinations due to a lack of social support for interpretation services. Also, 63.8% (n=254) of the participants indicated they would resent approaching a government healthcare facility for vaccination even though it would be free of charge because of discriminatory treatment experiences and being labelled as viral spreaders after COVID-19. 5.3.4 Community-level 5.3.4.1 Fear of arrest and deportation A total of 91.2 % (n=363) participants stated that the fear of being arrested on their way to getting vaccinated and being deported back to their home countries are impediments to getting vaccinated. Similarly, most NGO participants reported that migrant workers have little trust in the Thai government's announcement that they can receive vaccinations at a nearby public health facility due to the lack of legal documents and ongoing arrest and deportation among irregular migration workers.Irregular migrants do not trust government announcements and are concerned about being arrested. (P1, Head of the Labour) Some participants expressed scepticism regarding the Thai government's announcement that vaccinations would be administered in the community. The Thai government has not announced any safety measures, and the police are positioned at every street corner monitoring irregular migrants to extort money. Participants described that police are similar to money robbers.The police will likely demand money of at least 2,000 to 5,000 baht if they apprehend us. We were threatened with deportation back to Burmese if we did not pay. If we do not have any money, the police will take whatever we possess, such as watches, rings, and jewellery. The police's behaviour is reminiscent of a robbery. (P9, Female) 5.3.4.2 Discrimination in public spaces Furthermore, over three-quarters of participants, 86.4% (n=344), reported discrimination experiences in the workplace, public areas, and transportation following an infection among migrant workers in the seafood processing industry.The public has begun to blame migrant workers from Burmese for spreading the disease, despite the fact that positive COVID-19 cases have also been detected among Thai citizens. (P3, Labour protection officer) The participants reported that discrimination against Burmese migrant workers was high and that it was difficult for them to obtain even a taxi. For example, one participant described her experiences with the taxi driver in this way:The taxi driver told me that I did not need your money. It is quite challenging to get even a taxi for transportation" " (P5, Female) 5.3.5 Public policy level 5.3.5.1 Documentation requirements and vaccine combinations Nearly half of the participants, 46.5% (n=185), indicated that the complicated announcement about combining Sinovac and AstraZeneca vaccines confuses and decreases their willingness to vaccinate. Aside from the issue of mixed vaccines, migrant workers must possess a legal document or registration card to make an appointment for vaccination.For irregular migrants, it is challenging to schedule vaccination appointments due to a lack of legal documentation and the inability to afford registration to obtain legal status. (P2, Female) This requirement poses a significant challenge because many migrant workers have found themselves in irregular conditions because of the loss of employment since the COVID-19 disruption and the financial hardships associated with completing the Thai cabinet resolution registration. 6 Discussion The study's findings indicate that participants face several barriers to accessing vaccination. This study found that 90.7 % (n= 361) of participants were unvaccinated. This finding is similar to a previous rapid assessment of migrant workers from a construction site in the Bangkok Metropolitan Region in early 2021 (International Organization for Migration, 2021a). The primary barriers factors are discussed below: 6.1 Individual-level The study revealed that a significant number (39.2%) of participants were unemployed due to the COVID-19 pandemic disruption. A similar finding was found in a previous assessment of Burmese migrant workers' vulnerability in Thailand in March 2021(International Organization for Migration, 2021c). A significant number of Burmese migrant workers were trapped in Thailand due to the closure of the border and travel restrictions. Most of these individuals live in precarious living conditions and cannot cover the cost of extending their work visas or registering for work permits. The findings reveal that one of the most significant barriers to vaccination is the inability to afford out-of-pocket expenses resulting from financial challenges faced by low-income migrant workers (Ang et al., 2017; Han et al., 2021; World Health Organization, 2021c). Similarly, this study indicates that employers' reluctance to cover health insurance and vaccination costs, as well as factory lockdowns and difficulties accessing the outdoors, are barriers to vaccinations for workers currently employed (Kunpeuk et al., 2020; United Nations Thematic Working Group on Migration in Thailand, 2019). Moreover, the study reported that the COVID-19 pandemic exacerbated the hardships of millions of migrant workers and refugees in Thailand. These hardships included a lack of access to medical care related to the coronavirus and a loss of employment and essential income that adversely affected their mental health and well-being (Quinley, 2021). Therefore, it is likely that irregular migrants in Thailand will continue to face barriers to vaccination unless vaccines are freely distributed, as they cannot afford vaccinations. Their primary concern is to obtain food and shelter daily. In light of this, the Thai government should consider including all migrant populations in the national health plans and distributing free COVID-19 vaccines as part of the global call to reduce the global health crisis (Al-Oraibi et al., 2021). Furthermore, migrants are not included in the COVID-19 national response and do not have global access to COVID-19-related precaution information in their mother tongues (Kondilis et al., 2021; Maldonado et al., 2020). The study also found that (79.4 %) of participants did not receive information about the side effects of the COVID-19 vaccine in the language of their community. A recent study of Burmese migrant workers in July 2021 also revealed that 53% could not obtain information about COVID-19 precautions in their native language (International Organization for Migration, 2021c). The present study's findings are consistent with previous studies conducted in different countries, which have found that some participants were not confident in the effectiveness of the COVID-19 vaccine. This was due to a lack of information about vaccination side effects in their native languages and the manipulation of rumours that some people died due to vaccination. However, some participants said the vaccine had no side effects (Abba-Aji et al., 2022; Han et al., 2021). Therefore, an explicit and accessible vaccine information campaign in their native language is essential to increase the vaccination rate among irregular migrant workers. The Thai government could implement this in conjunction with other diaspora organisations and by recruiting migrant workers themselves to disseminate the side effects. 6.2 Interpersonal level Additionally, participants face a significant language barrier that makes accessing information on COVID-19 precautions and vaccine side effects difficult. About 91.0% of participants in this study did not understand the COVID-19 vaccine announcement, the registration process for making reservations, or where to obtain the vaccine. Further, the study revealed that (81.4%) of participants were unfamiliar with the registration process, such as contacting the district health office or registering online. According to a study conducted in the United Kingdom, the digitisation of healthcare services has reduced access to information and the COVID-19 vaccine for minority migrant populations owing to language barriers (Knights et al., 2021). In this respect, the Thai government should consider recruiting more volunteers in collaboration with various diaspora organisations, including cultural competence interpreter volunteers from within the migrant community to assist in delivering COVID-19 vaccine information and facilitating the vaccination booking process, as implemented by UNICEF in Thailand (UNICEF, 2022). 6.3 Organisational level According to the study, 76.9% of participants had difficulty accessing vaccination sites due to geographical barriers, and 81.4% cited unfamiliarity with the booking process as a barrier. Similarly, these barriers have been identified as substantial impediments to receiving vaccines among the migrant population globally (Deal et al., 2021; Williams et al., 2021; World Health Organization, 2021a). Moreover, this study found that over half (63.3%) are unwilling to accept vaccinations even if they are free of charge due to discrimination experiences such as virus spreaders and the fear of being detained. On the contrary, a recent 2021 survey conducted by the International Organization for Migration (IOM) among construction migrant workers revealed that 77% were unvaccinated, while only 23% had received at least one vaccination. When asked whether they would take a vaccine if it were free of charge and available in the community, only 12% of those surveyed were reluctant to receive immunisations due to worries about interaction with other medicine, side effects, and though the vaccine is unnecessary (International Organization for Migration, 2021a). Nevertheless, the participants in this study are regular migrant workers who have no fear of going to the doctor to have a vaccination or vaccination is conducted in the factory. The situation of irregular migrants differs from that of regular workers. The restricted travel restrictions and their lack of legal status prevent some of them from accessing even free NGO vaccine clinics within their communities. On the other hand, only a few NGO clinics and the Thai Red Cross Society provide free vaccinations to immigrant populations regardless of their status in Thailand (Reuters, 2021a). 6.4 Community-level The majority (91.2%) of those who participated in this study expressed concerns about being detained at a vaccination clinic, imprisoned, and deported due to the lack of legal documentation. These concerns have been cited as barriers to accessing vaccination among irregular migrants worldwide (Khai, TUAL, 2021; World Health Organization, 2021b). In particular, the Thai government continues raids and crackdowns on irregular migrant communities (Khmer Times, 2021) and has deported over 30,000 irregular migrant workers, primarily from Burma, by 2021(Bangkok Post, 2021a). When fighting the COVID-19 pandemic, the Thai government should prioritise vaccine distribution to those irregular immigrant populations through transparent procedures and protocols that respect human rights, gender, and cultural perspectives, while following WHO vaccine guidelines to ensure maximum success (World Health Organization & United Nations Children's Fund, 2021). The study also found that (86.4%) of participants were concerned about public discrimination regarding going to medical facilities and getting outside of their community for health care, as reported (Wongsamuth, 2021; Khai, Tual Sawn & Asaduzzaman, 2022). These factors make it difficult for the migrant population to access vaccinations, even though they are free of charge in some areas. 6.5 Public policy level In contrast to other countries, the Thai government introduced a vaccine combination to combat the COVID-19 pandemic, particularly during the fifth wave. Due to this policy introduction, the Thai population has also become more hesitant to receive vaccines (Bangkok Post, 2021c). Consequently, half of the participants (46.5%) reported being unwilling to accept free vaccines because of the combination. In addition to requiring proper documentation, such as a passport and a pin card, the Thai government has announced the availability of walk-in vaccinations and reservations for residents, including foreign workers. Meanwhile, the Thai government offers registration to irregular migrant workers to obtain pin cards and work permits until 2023, but they must undergo a health examination that costs more than 7,200 baths (240 dollars) (Wongsamuth, 2020). These offers are unaffordable and inaccessible to the migrant population considering COVID-19 due to the loss of employment and livelihoods. As WHO (World Health Organization) recommended, the Thai government should consider the distribution of free vaccines in partnership with diaspora organisations and introducing an amnesty period to encourage vaccination (World Health Organization, 2021d). 7 Policy implications Firstly, to prevent further casualties and halt the pandemic, vaccination of all populations, regardless of nationality or immigration status, would be crucial if living with a virus condition became the new norm. As such, the Thai government should promote vaccine awareness in the migrant's language in collaboration with diaspora organisations and community-based health workers and employ linguistically and culturally competent interpreters. Secondly, to motivate and increase vaccination rates among irregular migrant workers, the government should launch a vaccination campaign with a specific amnesty period from arrest and detention. This is because irregular migrants are most concerned about arrest and deportation as they lack legal documentation and the ongoing deportation of those detained by the Thai authorities. Additionally, the livelihood opportunities in their home country have been exacerbated by political turmoil following the military coup in 2021. 8 Strengths and limitations To the author's knowledge, this is the first study to examine factors that hinder or prevent Burmese irregular migrant workers from accessing vaccinations in Thailand and Southeast Asia countries. There is a lack of studies, although Burmese migrant workers constitute more than half of Thailand's migrant worker population. The study presents several novel insights and practical implications that can be incorporated into policy and interventions to address the factors preventing irregular migrants and asylum seekers from receiving COVID-19 vaccinations. A further strength of this study is the participation of NGO workers, Thai citizens, and BIMWs, which allows for capturing different perspectives and experiences, thereby enhancing the research findings for policy implications.  Using a mixed-method case study design enabled the identification of key barriers preventing vaccination among BIMWs. In addition, the socio-ecological model employed in this study provides a framework for interpreting the findings and guiding interventions to address vaccination barriers among irregular migrants in Thailand. However, given the travel restrictions during this research, the study relied on online interviews and could only interview individuals who had access to the internet. Thus, this study uncovered the experiences and concerns of migrants who could not access the internet or did not access the survey questions. It would be ideal to conduct a survey and interview in person to understand better the various barriers that hinder the BIMWs from getting vaccinated. As a result, this study cannot generalise barriers to BIMWs' access to COVID-19 vaccination across Thailand. It is necessary to conduct further research in remote areas using the same method of collecting fieldwork data to fully understand the research problem and identify a practical route to counter this unprecedented pandemic in Thailand or other countries by addressing vaccine barriers among migrant workers. 9 Conclusion Thailand has a high rate of irregular migrant workers who are marginalised and vulnerable to healthcare access. COVID-19 has exacerbated this situation, and the Thai government has taken advantage of the moment control order to enforce more raids and arrests of irregular migrants.  The study identified some of the most significant barriers that BIMWs face when it comes to obtaining COVID-19 vaccinations, which can be applied to other irregular migrants around the world as well. The absence of vaccination provision, excessive costs associated with vaccinations, geographical barriers, factory lockdowns, and inaccessible and unavailable in all districts of free vaccination distribution from NGOs are major barriers to vaccination for irregular migrant workers. Other significant barriers to vaccination for irregular migrants are rumours, language barriers, complex registration procedures, public discrimination, and fear of arrest. The findings of this study suggest that providing free vaccinations to all individuals regardless of their immigrant status and protecting them from detention and arrest will increase vaccination among irregular migrants and may enable the fight against the COVID-19 virus to succeed. Ethical approval and consent to participate: Ethical approval was obtained from the Postgraduate Student Committee (PSC) of the research ethics committee of Lingnan University in Hong Kong. Oral informed consent from all participants was obtained before conducting an interview and audio recording aligned with the research ethics. All the participants' names were assigned pseudonyms. All methods are well carried out according to relevant guidelines and regulations. Consent to publication: Not applicable. Availability of data and material: All data generated or analysed during this study are included in this published article. Funding: This research received no funding. Uncited References Glanz, 2015 Declaration of Competing Interest The author declares that he has no known completing financial interests or personal relationships that could have influenced the work reported in this paper. Acknowledgement The author's sincere gratitude goes to all Burmese irregular migrant workers and NGOs who participated in sharing their perspectives, experiences, and valuable time in this study. ==== Refs References Abba-Aji M. Stuckler D. Galea S. McKee M. 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==== Front Heliyon Heliyon Heliyon 2405-8440 The Authors. Published by Elsevier Ltd. S2405-8440(23)04775-8 10.1016/j.heliyon.2023.e17567 e17567 Article Factors associated with COVID-19 vaccine uptake among foreign migrants in China Akintunde Tosin Yinka ab∗ Chen Ji-Kang b Ibrahim Elhakim c Isangha Stanley Oloji d∗∗ Sayibu Muhideen e Musa Taha Hussein f a Department of Sociology, School of Public Administration, Hohai University, China b Department of Social Work, Chinese University of Hong Kong, Hong Kong c Department of Demography, College for Health, Community and Policy, The University of Texas, San Antonio, TX, United States d Department of Social and Behavioral Sciences, College of Liberal Art and Social Sciences, City University of Hong Kong, Kowloon, Hong Kong e Department of Sci-Tech Communication and Policy, University of Science and Technology of China, Anhui, Hefei, China f Department of Epidemiology and Health Statistics, Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China ∗ Corresponding author. Department of Sociology, School of Public Administration, Hohai University, China. ∗∗ Corresponding author. 26 6 2023 26 6 2023 e175671 11 2022 14 6 2023 21 6 2023 © 2023 The Authors. Published by Elsevier Ltd. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background/Purpose The COVID-19 outbreak created unique policy challenges for vaccinating special groups like migrants. As part of sustainable development goals, the equitable distribution of the COVID-19 vaccine can contribute to ensuring health for all. This study examined COVID-19 vaccine uptake among foreign migrants in China based on sociodemographics, cultural beliefs, past vaccine behaviors, and psychosocial factors. Design An online cross-sectional survey was conducted among foreign migrants in mainland China via social media platforms from 21 November through 20 December 2021. Bivariate (unadjusted odd-ratio) and multivariate logistic regression analyses were performed to establish the correlates of COVID-19 vaccine uptake. Result Surveyed foreign migrants that are culture neutral (AOR: 2.5, CI: 95%, 1.02–5.90, p = 0.044), willing to pay for vaccination (AOR: 2.27, CI: 95%, 1.18–3.98, p = 0.012), believe in vaccine efficacy (AOR: 3.00, CI: 95%, 1.75–5.16, p < 0.000), have poor psychological health (AOR: 1.96, CI: 95%, 1.14–3.38, p = 0 0.014), and have higher perceived seriousness of COVID-19 (AOR: 2.12, CI: 95%, 1.26–3.57, p = 0.005) are more likely to receive COVID-19 vaccine. Those migrants with a history of declining vaccination (AOR: 0.34, CI: 95%, 0.18–0.65, p = 0.000) and middle-income earners $1701–3500 (AOR: 0.43, CI: 95%, 0.23–0.82, p = 0.010) are less likely to receive the COVID-19 vaccine. Conclusion This study brings a unique perspective to understanding vaccine behavior among international migrants in China. There is an urgent call from the World Health Organization and countries for complete vaccination and efforts to improve vaccine coverage. However, fewer studies have been conducted globally on the vaccination of migrant populations. The current study provides empirical information to increase the knowledge of the correlates of vaccine behavior among immigrants in countries around the globe. Future studies should conduct cross-country comparisons to understand the factors associated with increasing vaccination rates among immigrant populations to formulate a strong policy to increase vaccine coverage among immigrant populations across countries. Keywords COVID-19 Vaccine uptake Migrants Psychosocial Foreigners China ==== Body pmc1 Introduction The unique composition of the global migrant population has created an enormous policy concern toward ensuring all-inclusive participation in the COVID-19 vaccination. While migrants are distinct groups, diverse in their historical background, culture, beliefs, and experiences [1], the international organization for migration has shown concern about migrants’ vulnerabilities during the COVID-19 pandemic [2] and how they could access health services. The COVID-19 vaccines roll-out and the complexities associated with COVID-19 variants require consideration of migrants to ensure their safety and participation in the vaccination program [3]. The approach to solving the complexities of involving migrants in COVID-19 vaccination hinges on inclusion and equitable health promotion [4]. Thus, equitable distribution of the COVID-19 vaccine is crucial to easing the global crisis and ensuring health for all [5]. Additionally, equity in vaccine distribution supports the notion that everyone, regardless of age, socioeconomic status, race, or location, should participate in the global effort to prevent and control the spread of COVID-19. However, significant stumbling blocks were encountered worldwide concerning equitable vaccination. A concern associated with the equitable distribution of the COVID-19 vaccine is the lack of access to vaccines and healthcare for migrants [[6], [7], [8], [9]]. The hesitancy to receive vaccines and barriers to COVID-19 vaccination among international migrants have also been the subject of ongoing debates [10]. Prioritizing COVID-19 vaccination poses health policy problems without understanding the peculiarities, dynamics, and composition of foreign migrants worldwide. Several studies have examined the experiences of migrants in non-Asian regions, finding that short waiting periods and free vaccination influence acceptance, whereas concern about side effects, perceived safety, and the severity of COVID-19 encourages vaccine hesitancy [11]. Situational peculiarities of migrants, specifically stigmatization, have been identified in other studies as factors that hinder their willingness to receive vaccinations [12]. The sensitivity of the global migrant population's position on vaccine uptake consolidated the growing need to examine their circumstances in diverse regions to inform global health policies [13,14]. Despite this research needs, few evidence-based studies have paid attention to culture, beliefs, gender minorities, past vaccination behavior, and multi-level psychosocial indicators that may aid in promoting or discouraging vaccination among migrants [9,15]. Several studies suggest that introducing new vaccines to combat emerging diseases could lead to vaccination hesitancy and refusal [16,17]. In addition, low acceptance rates of the COVID-19 vaccine have been reported in the Middle East (i.e., 23.6% in Kuwait and 28.4% in Jordan) and Africa (i.e., 27.7% in Congo), while some Asian regions have an average acceptance rate of 90% among adults [18]. Like any other vaccine, COVID-19 vaccine hesitancy has been attributed to concern about side effects [19] and misinformation [20]. A preponderance of evidence also suggests that regional COVID-19 vaccine hesitancy, especially in Africa, is primarily due to a multiplicity of distrust and misconceptions about the COVID-19 outbreak [21], concern about side effects [16], and African cultural and religious prejudice [22]. In developed countries, age, gender, ethnicity, and education have been identified as factors contributing to vaccine hesitancy [23]. In South Korea, lack of confidence, fear factors, earnings, and health status were causes of vaccine hesitancy [24]. Considering the robust evidence on COVID-19 vaccination and hesitancy, a global scale-up of vaccine safety information has been proposed [20,25]. In China, the nationwide demand for the COVID-19 vaccine is high. Residents believe the COVID-19 vaccine decreases the risk of infection, and those not concerned about vaccine efficacy are more likely to obtain vaccination [26]. In a comparative study of vaccine hesitancy in the US and Chinese populations, less than 20% of Chinese individuals are vaccine-hesitant, compared to about 30% in the US [27]. Research evidence suggests that 89.5% of the Chinese believed vaccination against COVID-19 would effectively curtail the spread of the disease [28]. Therefore, the willingness of the Chinese population to be vaccinated increased as the pandemic progressed through various waves [29]. Most Chinese people with increased trust in the healthcare system are more receptive to the COVID-19 vaccination [30]. However, in Hong Kong, due to increased concerns about the safety of the COVID-19 vaccine, vaccination acceptance among the working population decreased in the third wave compared to the first wave [31]. According to research on the Chinese population, misinformation and an out-of-pocket vaccination approach are the primary psychosocial factors responsible for vaccine hesitancy [32]. To document their unique circumstances and support global intervention, it is necessary to conduct extensive research on the vaccination experience of the migrant population in China. Based on a comprehensive survey of willingness to pay (WTP), vaccination among internal migrants in Shanghai may be hindered by the high cost of vaccination and safety concerns [33]. Considering that the research subjects were internal migrants, the scope of the study was limited; therefore, more study is needed to support the existing evidence regarding the need for continuing support for foreign migrant groups. This study aimed to identify determinants of vaccination uptake among foreign migrant populations in China based on historical, cultural, social, and psychological factors. There are significant gaps in our understanding of psychosocial factors that may influence the uptake of the COVID-19 vaccine in China due to the diversity of migrants and less research focus among the population. Therefore, the current study examined indicators such as culture, beliefs, past vaccinations, past vaccine refusals, accessibility, personal health, psychological health, and perceived seriousness of COVID-19 infection to enhance migrants’ participation in COVID-19 vaccination. This study extends empirical evidence regarding the unique circumstances of migrants towards COVID-19 vaccination and provides templates for developing support for exclusive global vaccination against COVID-19 among the group. The objective of this study was to examine the likelihood that foreign migrants in China will receive vaccination against COVID-19 in light of a variety of socio-demographic characteristics, past vaccination behaviors, cultural and psychosocial factors. 2 Materials and methods 2.1 Study design, population, and sample size The current research adopted a web-based cross-sectional approach to recruit migrants in China from 21 November through 20 December 2021. The distribution of the migrant population necessitated social media approaches as they are easily accessible via these channels based on convenience and snowballing. Statistics from the international organization for migration reported about one million foreign migrants in China [34]. Similar studies have used social media platforms to recruit a population based on the convenience of accessing people during the pandemic [[35], [36], [37]]. The foreign migrants included in the survey were students, ex-pats, and business owners. The number of foreign migrants in China was estimated at 1.4 million [38], and sample size (n) was evaluated using an online-based sampling estimator [39]. The sample size was 498, with a population proportion of 60% and a margin of error of 4.38% at a 95% confidence interval [40]. The recruited migrant population was nested in foreigner groups on WeChat, and the invitation was sent for voluntary participation. As the total population of students, ex-pats, and business owners is unknown, the distribution of these groups is not proportional. The total number of migrants recruited comprised 37.1% students, 43% ex-pats, and 19.9% business owners. Data were collected from foreign groups by targeting groups of students, ex-pats, and business owners. 2.1.1 Ethics approval and consent to participate The study was conducted following the Helsinki Declaration and was approved by the School of Public Administration Review Board of Hohai University (reference No: CCF_000027). Informed consent was obtained from all participants involved in the study as requirement for filling research questionnaire and participation. 2.2 Measure 2.2.1 Outcome variable Supported by previous studies, the outcome of interest was based on the current vaccination status if respondents received at least one dose of the COVID-19 vaccine. The measure was premised on a dichotomous variable (No = 0 or Yes = 1), “have you received the first dose of the COVID-19 Vaccine?” [41,42]. The binary step supports the exploration of the likelihood of receiving COVID-19 vaccination. 2.2.2 Predictor variables Multiple indicators were conceptualized based on literature to examine the determinant of COVID-19 vaccination among the study population. The questionnaire development were guided by existing literature exploring the determinants of psychosocial and health behaviors. Essential socio-demographic characteristics such as gender identity, age, education, employment, and earning status were captured from previous studies examining socioeconomic determinants of vaccine behaviors [43,44]. Subjective cultural and psychosocial indicators were preference for traditional medicine over modern, vaccine development in an individual country, culture, and personal belief in the COVID-19 vaccine guided by literature on culture and COVID-19 vaccination [45]. Other measurement like free vaccination, willingness to pay (WTP), experience with vaccine adverse effects, and relatives/family opposing vaccination were examined as explored in similar studies [46,47]. Further consideration was given to the history of vaccination against flu and hepatitis, past refusals, and vaccine accessibility [48]. Questions addressing personal health [49], perceived sensitivity [50], the seriousness of COVID-19 [51], social anxiety, and psychological health [52] were sourced from existing empirical evidence. Questionnaire information are detailed in Supplementary File 1. 2.3 Data management and analysis Using percentage distribution and Chi-square estimations, a bivariate analysis was conducted. The analytical measures were premised on both Unadjusted Odds Ratio (UOR) and Adjusted Odds Ratio (AOR) to appraise the unconditional and conditional influence of the predictors (socio-demographic and psychosocial indicators) on the outcome variable (COVID-19 Vaccine uptake) among the foreign migrant population. The UOR explored the independent influence of the predictors on the outcome variables, whereas the AOR result presents the total net effect of all the predictors on vaccine uptake in the study population. The bivariate and multivariate logistic models’ outcome results were presented as Odds ratios (and confidence intervals), where an outcome above/below 1 is a comparative category of a specific attribute that indicates a higher/lower probability of a result. We benchmarked statistical significance as p < 0.05. The analysis was conducted using SPSS version 25 and STATA 17 statistical computing software. 3 Results 3.1 Socio-demographic attributes The data characteristics of the study population (N = 498) were included in the analysis. The age distribution shows that most participants were 25–34 (55.8%), while males were 47.6% and females were 45.2%. Meanwhile, the education level of the majority was university/postgraduate (92%). Students represented in the survey were 37.1%, and those employed were 43% (Table 1 ).Table 1 Characteristics of Migrants participants in China N = 498. Table 1Variables Attributes Freq. (%) Age 15–24 68 (13.7) 25–34 278 (55.8) 35–44 141 (28.3) 44+ 11 (2.2) Gender Identity Male 237 (47.6) Female 225 (45.2) Gender minorities 36 (7.2) Earning/Month $3500+ 202 (40.6) $1700–3500 128 (25.7) $300–1700 168 (33.7) Employment Employed (Ex-pats) 214 (43) Self-employed (Business Owners) 75 (15.1) Students 185 (37.1) Unemployed/Business 24 (4.8) Education University/Postgraduate 458 (92) Technical Training 18 (3.6) Highschool 22 (4.4) Based on chi-square analysis, socio-demographic attributes such as gender, education, and earnings were associated with COVID-19 uptake (Table 2 ). Other facilitators of COVID-19 vaccine uptake were further presented in the table.Table 2 Characteristics of Study Participants and facilitating factors of COVID-19 vaccine uptake among Migrants population in China (N = 498). Table 2Variables Categories Vaccine Uptake X2(p-value) No Yes Total(498)% (N = 153)% (N = 345)% Gender 7.206* Male 237 (47.6) 66 (43.1) 171 (49.6) Female 225 (45.2) 69 (45.1) 156 (45.2) Others 36 (7.2) 18 (11.8) 18 (5.2) Age 3.229 15–24 68 (13.7) 19 (12.4) 49 (14.2) 25–34 278 (55.8) 84 (54.9) 194 (56.20) 35–44 141 (28.3) 44 (28.8) 97 (28.1) 44> 11 (2.2) 6 (3.9) 5 (1.4) Education 6.258* University/Postgraduate 458 (92) 135 (88.2) 323 (93.6) College/Technical Training 18 (3.6) 6 (3.9) 12 (3.5) Secondary/Highschool 22 (4.4) 12 (7.8) 10 (2.9) Employment 2.599 Employed 214 (43) 65 (42.5) 149 (43.2) Self-employed 75 (15.1) 25 (16.3) 50 (14.5) Students 185 (37.1) 59 (38.7) 126 (36.5) Unemployed/Business 24 (4.8) 4 (2.6) 20 (5.8) Earning/Month $3500 202 (40.6) 47 (30.7) 155 (44.9) 9.178* $1701–3500 128 (25.7) 48 (31.4) 80 (23.2) $300 - 1700 168 (33.7) 58 (37.90 110 (31.9) Preference for TM 9.708** No 371 (74.5) 100 (65.4) 271 (78.6) Yes 127 (25.5) 53 (34.6) 74 (25.5) Home Made Vaccine 3.296 No 234 (48.8) 84 (54.9) 159 (46.1) Yes 255 (51.2) 69 (45.1) 189 (53.9) Culture oppose Vaccine 32.920*** No 387 (77.7) 102 (66.7) 285 (82.6) Neutral 59 (11.8) 17 (11.1) 42 (12.2) Yes 52 (10.4) 34 (22.2) 18 (5.2) Belief oppose Vaccine 36.583*** No 371 (74.5) 92 (60.1) 279 (80.9) Neutral 65 (13.1) 22 (14.4) 43 (12.5) Yes 62 (12.4) 39 (25.5) 23 (6.7) Free Vaccine 5.819* No 361 (72.5) 122 (79.7) 239 (69.3) Yes 137 (27.5) 31 (20.3) 106 (30.7) WTP 28.890*** No 175 (35.3) 80 (52.6) 95 (27.6) Yes 321 (64.7) 72 (47.4) 249 (72.4) The vaccine has Adverse effect No 375 (75.7) 114 (74.5) 261 (75.7) 0.074 Yes 123 (24.7) 39 (25.5) 84 (24.3) Family Oppose Vaccine 3.513 No 190 (38.2) 49 (32) 141 (40.9) Yes 308 (61.8) 104 (68) 204 (59.1) Vaccines Efficacy 46.719*** No 149 (29.9) 78 (51) 71 (20.6) Yes 349 (70.1) 75 (49) 274 (79.4) Past Vaccination (HPV/Flu etc.) 26.295*** No 143 (28.7) 68 (44.4) 75 (21.7) Yes 355 (71.3) 85 (55.6) 270 (78.3) Refused Vaccine Before 34.389*** No 415 (83.3) 105 (68.6) 310 (89.9) Yes 83 (16.7) 48 (31.4) 35 (10.1) Vaccines are Accessible 10.454** No 173 (34.7) 69 (45.1) 104 (30.1) Yes 325 (65.3) 84 (54.9) 241 (69.9) Personal Health 19.991*** Poor Health 138 (27.7) 63 (41.2) 75 (21.7) Good Health 360 (72.3) 90 (58.8) 270 (78.3) Health compared to others 8.898** Poor Health 256 (51.4) 94 (61.4) 162 (47) Good Health 242 (48.6) 59 (38.6) 183 (53) Perceived Sensitivity to COVID-19 1.273 Low Sensitivity 133 (26.7) 46 (30.1) 87 (25.2) High Sensitivity 365 (73.3) 107 (69.9) 258 (74.8) Perceived Seriousness of COVID-19 25.655*** Low Seriousness 176 (35.3) 79 (51.6) 97 (28.1) High Seriousness 322 (64.7) 74 (48.4) 248 (71.9) Social Anxiety 0.016 High Anxiety 308 (61.8) 94 (61.4) 214 (62) Low Anxiety 190 (38.2) 59 (38.6) 131 (38) Psychological Health 0.458 Good 194 (39) 63 (41.2) 131 (38) Poor 304 (61) 90 (58.8) 214 (62) *p < 0.05, **p < 0.01, ***p < 0.001; TM: Traditional Medicine, WTP: Willingness to Pay, Other: Gender minorities. 3.2 Bivariate and multivariate logistic regression The data analysis and the independent effects (UORs) of the determinants of vaccine uptake indicated that gender minorities (others), secondary/high school, earning less than $3,500, and preferred TM over modern medicine were less likely to have received COVID-19 vaccines. Similarly, those with culture and beliefs against vaccination and a history of vaccine refusal were less likely to be vaccinated. Those migrants more likely to be vaccinated have higher perceived Seriousness of COVID-19, good health status, believe vaccines are accessible and efficacious, have received vaccination before, WTP, and will receive COVID-19 vaccine is made freely available (Table 3 ).Table 3 Bivariate and multivariate analysis of psychosocial determinants of COVID-19 vaccination (N = 498). Table 3 Bivariate Multivariate UOR (95% CI) p-value AOR (95% CI) p-value Socio-demographic Attributes Gender  Male 1 1  Female 0.87 (0.58–1.30) 0.506 0.96 (0.57–1.59) 0.863  Others 0.39 (0.19–0.79) 0.009 1.01 (0.37–2.77) 0.989 Age  15-24 1 1  25-34 0.89 (0.49–1.61) 0.713 1.07 (0.51–2.24) 0.852  35-44 0.85 (0.45–1.62) 0.630 0.83 (0.36–1.92) 0.665  44> 0.32 (0.45–1.19) 0.088 1.45 (0.19–10.91) 0.714 Education  University/Postgraduate 1 1  College/Technical Training 0.84 (0.31–2.27) 0.725 0.67 (0.18–2.51) 0.555  Secondary/Highschool 0.34 (0.15–1.83) 0.017 0.68 (0.21–2.16) 0.508 Employment Status  Employed 1 1  Self-employed 0.87 (0.49–1.53) 0.634 0.84 (0.40–1.75) 0.638  Students 0.93 (0.61–1.4) 0.744 0.70 (0.38–1.27) 0.246  Unemployed/Business 2.18 (0.72–6.63) 0.169 1.75 (0.48–6.35) 0.392 Earning  $3500 1 1  $1701–3500 0.51 (0.31–0.82) 0.006 0.43 (0.23–0.82) 0.010  $300-1700 0.58 (0.37–0.91) 0.017 0.67 (0.36–1.22) 0.190 Transcultural Attributes Preference for TM  No 1 1  Yes 0.52 (0.34–0.78) 0.002 1.14 (0.61–2.12) 0.677 Home Made Vaccine  No 1 1  Yes 1.42 (0.97–2.09) 0.070 1.06 (0.64–1.77) 0.809 Culture oppose vaccine  No 1 1  Neutral 0.88 (0.48–1.62) 0.691 2.5 (1.02–5.90) 0.044  Yes 0.19 (0.10–0.35) 0.000 0.83 (0.28–2.44) 0.738 Belief oppose vaccine  No 1 1  Neutral 0.65 (0.37–1.13) 0.128 0.67 (0.30–1.49) 0.332  Yes 0.19 (0.11–0.34) 0.000 0.43 (0.15–1.21) 0.110 Vaccine behavior Free Vaccine  No 1 1  Yes 1.75 (1.11–2.76) 0.017 1.52 (0.83–2.81) 0.171 Willingness to pay (WTP)  No 1 1  Yes 2.91 (1.96–4.33) 0.000 2.27 (1.18–3.98) 0.012 The vaccine has Adverse effect  No 1 1  Yes 0.94 (0.61–1.46) 0.785 0.91 (0.52–1.58) 0.728 Family Oppose Vaccine  No 1 1  Yes 0.68 (0.46–1.01) 0.062 0.81 (0.48–1.35) 0.420 Vaccines Efficacy  No 1 1  Yes 4.01 (2.66–6.05) 0.000 3.00 (1.75–5.16) 0.000 Received Vaccine Before  No 1 1  Yes 2.88 (1.91–4.33) 0.000 2.13 (1.23–3.67) 0.007 Refused Vaccine Before  No 1 1  Yes 0.25 (0.15–0.40) 0.000 0.34 (0.18–0.65) 0.001 Vaccines are Accessible  No 1 1  Yes 1.90 (1.29–2.82) 0.001 1.38 (0.83–2.29) 0.213 Psychosocial Attributes Personal Health  Poor Health 1 1  Good Health 2.52 (1.67–3.80) 0.000 1.91 (1.06–3.40) 0.029 Health compared to Others  Poor Health 1 1  Good Health 1.79 (1.22–2.65) 0.003 1.79 (1.02–3.15) 0.044 Perceived Sensitivity to COVID-19  Low Sensitivity 1 1  High Sensitivity 1.28 (0.84–1.95) 0.260 0.91 (0.52–1.57) 0.725 Perceived Seriousness of COVID-19  Low Seriousness 1 1  High Seriousness 2.73 (1.83–4.05) 0.000 2.12 (1.26–3.57) 0.005 Social Anxiety  High Anxiety 1 1  Low Anxiety 0.98 (0.66–1.44) 0.900 1.12 (0.66–1.88) 0.677 Psychological Health  Good 1 1  Poor 1.14 (0.78–1.69) 0.499 1.96 (1.14–3.38) 0.014 *p < 0.05, **p < 0.01, ***p < 0.001, CI: Confidence Interval, Ref −1: reference; UOR: Unadjusted Odds-ratio; AOR: Adjusted Odds-ratio; TM: Traditional Medicine, WTP: Willingness to Pay, Other: Gender minorities. In Table 3, the net effect of the predictors projects a different scenario based on the outcomes of the AOR. The foreign migrants, based on multivariate estimates who are less likely to be vaccinated earn between $1701–3500 (AOR: 0.43, CI: 95%, 0.23–0.82, p = 0.010) and have a history of declining vaccination (AOR: 0.34, CI: 95%, 0.18–0.65, p = 0.000). Those foreign migrants who are likely to be vaccinated are those who are culture neutral (AOR: 2.5, CI: 95%, 1.02–5.90, p = 0.044), willing to pay for vaccination (WTP) (AOR: 2.27, CI: 95%, 1.18–3.98, p = 0.012), believe in vaccine efficacy (AOR: 3.00, CI: 95%, 1.75–5.16, p < 0.000), received vaccine before (AOR: 2.13, CI: 95%, 1.23–3.67, p = 0.001), good personal health status (AOR: 1.91, CI: 95%, 1.06–3.40, p = 0.029), better health compared to same age-group (AOR: 1.79, CI: 95%, 1.02–3.15, p = 0.044), high perceived Seriousness to COVID-19 (AOR: 2.12, CI: 95%, 1.26–3.57, p = 0.005) and have poor self-rated psychological health (AOR: 1.96, CI: 95%, 1.14–3.38, p = 0 0.014). 4 Discussion This study contributes to the empirical evidence concerning the behavioral, psychosocial, and cultural determinants of vaccine uptake among foreign migrants in China. The mutations and variants of COVID-19 have led to health experts recommending a global vaccination program to increase resistance and immunity to COVID-19 infection [53]. An evaluation of foreign migrants' multi-level attributes was conducted based on perceptions about their culture, beliefs, past vaccinations or refusals, accessibility, personal health, psychological health, and perceived severity of COVID-19 infection. Based on the recruited population, the study found that less than 17% of migrants had a history of vaccine refusal, and 30.7% had not received COVID-19 vaccines. This result shows the low vaccination refusal among the research population, although it is problematic to compare hesitancy among foreign migrants and the Chinese people based on limited research evidence among the former group. Foreigners who are culture neutral, WTP, believe in vaccine efficacy, have poor psychological health, and have a higher perception of COVID-19's seriousness were more likely to receive the COVID-19 vaccine. In contrast, migrants with a history of declining vaccination and those with an average income ($1701 - $3500) are less likely to receive the COVID-19 vaccine. Foreign migrants with average incomes were less likely to receive COVID-19 vaccination than those with above-average incomes. The findings of this study are in agreement with those found among Chinese citizens, who may not receive health care services if they earn a low income [54]. However, the findings of the current study differ from those obtained from research conducted in Russia, since the majority of COVID-19 vaccine recipients were low-income earners in the region [55]. The level of financial security plays an important role in determining the intent to be vaccinated, and average earners may feel too burdened to bear the cost of vaccination, especially if vaccines are not readily available. This study supports the evidence that high income is associated with an increased willingness to pay for vaccinations [56]. Research evidence show that vaccine refusals predate the COVID crisis, which explains the consistency of our findings [57]. This study indicates that those who have previously refused vaccinations are less likely to have received COVID-19 vaccines. Negative perceptions about vaccines may lead to vaccine refusal [58]. However, one-time vaccination of migrants, especially those who have previously received hepatitis B or flu vaccines, is likely to increase their uptake of the COVID-19 vaccine. Contrary to narratives concerning attributes of vaccine refusers, the underpinning cultural factor can serve as a bridge to understanding some complexity in vaccine hesitancy among the immigrant, given that they are a composition of culturally diverse groups. Culture based on vaccine acceptability has been proposed as a potential attribute of anti-vaxxers related to ideologies and how it may affect global herd immunity [59]. Additionally, based on the findings of our study, culture-neutral people are more likely to be vaccinated than those who are culturally inclined. Furthermore, it is imperative to note that cultural beliefs (i.e., not opposing vaccination) may not necessarily promote vaccination compared to cultural neutrality. There is evidence that specific cultural values contribute to hesitancy among certain individuals regarding other vaccinations, such as the Human Papillomavirus (HPV) vaccine [60]. The willingness to pay (WTP) for vaccination and perceived efficacy have also been investigated among diverse groups and regions to promote vaccination participation [61,62]. Several studies have revealed that foreign migrants in China with WTP for vaccination are more likely to have received the COVID-19 vaccine than those with no WTP [63]. Disseminating information about vaccine efficacy globally can reduce vaccine hesitancy by considering the cost implications of vaccination [26]. Vaccination against COVID-19 is more likely to be undertaken by individuals who believe that vaccines provide adequate protection against infection. Without knowing vaccine functions and clarifying misconceptions about vaccines, people from diverse backgrounds may not understand vaccine efficacy [25]. The subjective health of vaccine recipients takes prominence in encouraging vaccination. Evidence suggests that hindrances to vaccination are associated with self-rated health statuses [64]. The role of personal health status in decision-making has been demonstrated to be important in determining health behaviors among diverse populations [65]. The likelihood of receiving vaccinations was higher among foreign migrants who rated their subjective health status as good and felt healthier than others in their age group. Vaccination rates were higher among foreign migrants who rated their subjective health status as good and felt healthier than their peers in their age group. There is an agreement between the findings of this study and those of other studies that report that self-rated health is a determinant of vaccine acceptance in the elderly population of Singapore and among local Chinese residents [66,67]. Thus, healthy individuals consistently strive to maintain their health and are willing to invest in it. The likelihood of non-vaccination is high among people who feel they are not as healthy or do not compare with others [24]. One might speculate why unhealthy individuals would not desire to receive a vaccination that would improve their health status. Several other studies have shown that self-rated health does not predict readiness to receive COVID-19 vaccination among different racial groups [68]. However, attempts should be made to address the unhealthy population of society immediately, assuming that reducing ignorance about vaccination as a protective measure will result in improved health outcomes. Depending on a person's health status, age, and immune response, as well as changing global perceptions regarding COVID-19's severity, COVID-19's perceived seriousness varies widely [69]. When infected with COVID-19, one faces financial hardship as well as an increased risk of mortality. The perception of the seriousness of COVID-19 is an important factor in empirical discourse. Foreign migrants who believe COVID-19 infection can be severe are more likely to receive vaccination than those who believe it is not painful. The evidence suggests that COVID-19 is a serious disease, and it is important to take steps toward vaccination to mitigate the consequence [70]. COVID-19 has caused psychological distress that cannot be ignored [71]. In the context of the COVID-19 crisis, there is an interaction between mental health status and the willingness to participate in vaccination. Consequently, foreign migrants living in China with psychological distress were more likely to receive COVID-19 vaccinations. Studies have indicated that those with little or no fear of COVID-19 are more likely to hesitate to receive vaccinations [24]. In addition, people who display psychological resistance and distrust authoritative information are more likely to be vaccine-hesitant in countries such as the United Kingdom and Ireland [72]. Consequently, the current study focusing on the psychological health of foreign migrants revealed a high level of despair and depressive symptoms among the study group that led to the purchase of the COVID-19 vaccine. In addition, we argue that vaccination prevents psychological trauma resulting from infectious diseases [73]. This study identifies some critical factors contributing to vaccine uptake among foreign migrant residents in China. However, certain limitations should be considered when using the findings. Due to the cross-sectional nature of the study and the snowballing approach used to recruit participants, the cross-sectional design and data represent a subset of the total number of foreign migrants in China. Several vital details about the migrant population have been omitted in order to maintain the confidentiality of participant records. These details include the country of origin and the length of stay. To better understand the vaccine behavior of migrants, future research should examine more psychosocial variables. Nevertheless, the study's results offer valuable insight into the dynamics of migrants, which may contribute to improving health policy interventions. 4.1 Policy implication The study's results suggest that attributes such as culture neutrality, willingness to pay, and confidence in the efficacy of vaccines can promote vaccination among foreigners, particularly regarding global health and policy. Migrants with poor subjective health may require health-promoting interventions to identify their health needs to facilitate their transition into good health. Governments, global health stakeholders, and policymakers can pay attention to these factors to improve vaccine uptake among migrants in host countries. Income and previous vaccination refusal should be considered when addressing hesitancy among migrants at the global level. As part of universal health coverage, policy decisions must ensure that migrant populations are considered part of the COVID-19 vaccine roll-out. To improve their vaccine participation for health equity, foreign migrants with a history of vaccine refusal require further investigation to understand the reasons for past refusal. The study underscores the need for global policy to support minority and unique groups in health promotion programs to improve their experiences and quality of life. To encourage vaccine participation, policies centered on the cost-effective vaccine should be implemented for minority groups. To increase positive knowledge of vaccine efficacy, vaccination awareness, and education are necessary worldwide and in migrant communities. Efforts should be intensified to disseminate vaccine safety information, and micro-level strategies should be adopted to improve the reach of vaccine safety information, especially among minority groups and hard-to-reach population groups. It is imperative to pay attention to migrants who may not have access to health and social services as they do in their home countries to ensure they are supported through the COVID-19 crisis. 6 Conclusions This study provides insight into vaccine behavior among foreigners in China during the roll-out of COVID-19 vaccines. Migrant populations must be targeted by considering cultural attributes, income status, and vaccination history. The study findings and the unique circumstance of the study population indicate that critical psychosocial factors such as psychological health, personal health, perceived seriousness, and willingness to pay are essential domains to reduce vaccine hesitancy and negative perceptions and increase vaccine uptake globally and among unique populations, including migrants studied in this study. Vaccination rates are higher among migrants in China if they are culture neutral, willing to pay for the COVID-19 vaccine, believe the vaccine is effective, have poor psychological health, perceive the outbreak of COVID-19 as being more severe, and are more likely to engage in vaccination practices. The likelihood of foreign migrants receiving at least one shot of the COVID-19 vaccine is also lower among those with a history of refusing vaccination and who earn an average income. Further research should be conducted on the migrant population to better understand salient concerns about vaccination through interviews and other variables not addressed in this study. Author contribution statement Tosin Yinka Akintunde: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper. Ji-Kang Chen, Stanley Oloji Isangha, Muhideen Sayibu,: Contributed reagents, materials, analysis tools or data; Wrote the paper. Elhakim Ibrahim: Conceived and designed the experiments; Performed the experiments; Wrote the paper. Taha Hussein Musa: Performed the experiments; Analyzed and interpreted the data; Wrote the paper. Data availability statement Data will be made available on request. Declaration of competing interest The authors declare that no conflict of interest in respect of this manuscript. 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==== Front Heliyon Heliyon Heliyon 2405-8440 The Authors. Published by Elsevier Ltd. S2405-8440(23)04339-6 10.1016/j.heliyon.2023.e17131 e17131 Article Factors associated with severe acute respiratory syndrome in pregnant/postpartum women with COVID-19 receiving care at referral centers in northeastern Brazil: Secondary analysis of a cohort study Cunha Carolina Maria Pires ab Amorim Melania Maria Ramos abc de Azevedo Guendler Julianna ab Katz Leila ab∗ a Instituto de Medicina Integral Professor Fernando Figueira (IMIP), Recife, Pernambuco, Brazil b Stricto Sensu Postgraduate Program, IMIP, Recife, Pernambuco, Brazil c Federal University of Campina Grande (UFCG), Campina Grande, Paraíba, Brazil ∗ Corresponding author. Rua Barão de Itamaracá 160/1501, Espinheiro, 52020-070, Recife, Pernambuco, Brazil. 26 6 2023 26 6 2023 e171311 11 2022 1 6 2023 8 6 2023 © 2023 The Authors. Published by Elsevier Ltd. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background At the beginning of the COVID-19 pandemic, the greater risks associated with the new SARS-CoV-2 pathogen in pregnant women were as yet unclear. This study analyzed factors associated with severe acute respiratory syndrome (SARS) in pregnant/postpartum women with COVID-19. Methods A prospective and retrospective cohort study was conducted in eight referral centers in northeastern Brazil between April 2020 and December 2021 involving pregnant/postpartum women with a positive COVID-19 RT-PCR test. A multivariate analysis was then conducted using a hierarchical logistic regression model to evaluate the association between the independent variables and the presence of SARS. Findings Of 611 patients included, 522 were pregnant and 83 were postpartum, at the time of admission. Criteria for SARS were present in 215 patients (35·2%). Factors associated with SARS included overweight and/or obesity (adjusted odds ratio/AOR: 1·95; 95%CI: 1·21–3·12; p = 0·0054), parity ≥2 (AOR: 1·72; 95%CI: 1·21–2·45; p = 0·0025), gestational age <34 weeks (AOR: 3·54; 95%CI: 2·47–5·07; p < 0·0001) and duration of symptoms >7 days (AOR: 1·97; 95%CI: 1·35–2·89; p = 0·0004). SARS increased the likelihood of requiring oxygen therapy (RR = 8·80; 95%CI: 6·25–12·40; p = 0·0000), mechanical ventilation (RR = 8·15; 95%CI: 4·67–14·21; p = 0·0000), and admission to an ICU (RR = 6·54; 95%CI: 4·70–9·11; p = 0·0000), and of maternal near miss (RR = 10·82; 95%CI: 1·20–22·47; p = 0·0000) and maternal death (RR = 8·12; 95%CI: 3·11–21·09; p = 0·0000). Interpretation In patients with COVID-19, parity ≥2, overweight/obesity, gestational age <34 weeks and duration of symptoms >7 days increased the risk of SARS. Cesarean sections, oxygen therapy, and mechanical ventilation were more common in patients with SARS. Keywords COVID-19 SARS-CoV-2 Severe acute respiratory syndrome Pregnancy ==== Body pmcResearch in context Evidence before this study Various publications have already described severe forms of COVID-19 in pregnancy, with an increased risk of severe acute respiratory syndrome (SARS) and a need for mechanical ventilation. However, there is a lack of studies evaluating the factors associated with SARS during pregnancy and in the postpartum. Added value of this study This study analyzed the factors associated with SARS in pregnant/postpartum women with COVID-19 receiving care at referral centers in northeastern Brazil and found that parity ≥2, overweight/obesity, gestational age <34 weeks and duration of COVID-19 symptoms >7 days increased the risk of SARS. Cesarean sections and need for oxygen therapy and mechanical ventilation were more common in patients with SARS. Implications of all the available evidence This study was innovative in investigating the factors associated with SARS in pregnant/postpartum women with COVID-19. These findings on the clinical course of the disease may contribute to improving the adequate allocation of resources and the provision of timely care to pregnant/postpartum women affected by COVID-19 in order to prevent severe maternal outcomes. 1 Introduction At the beginning of the COVID-19 pandemic, the greater risks associated with the new SARS-CoV-2 pathogen in pregnant women were as yet unclear [1]. However, global evidence has now shown the various degrees to which the disease can affect pregnant and postpartum women, as well as the nature of possible adverse outcomes including preeclampsia, premature delivery, stillbirths, Cesarean sections, admission to an intensive care unit (ICU), mechanical ventilation and death [2,3]. Pregnancy triggers physiological changes in the immune and cardiopulmonary systems, with pregnant women representing a group at risk of developing severe forms of viral respiratory diseases. Changes in respiratory mechanics in pregnant women need to be taken into consideration and become even more relevant as pregnancy advances, with an increase in the transverse diameter of the thoracic cage and the upward displacement of the diaphragm, reducing the woman's tolerance to hypoxia [4]. In addition, other factors including anatomical and physiological cardiorespiratory changes, as well as increased blood coagulability, can lead to a poorer prognosis of the disease [5]. This factor was evident during the H1N1 flu virus pandemic in 2009 as well as with the SARS-CoV and MERS-CoV infections, with a consequently greater need for mechanical ventilation and admission to ICU, and a higher incidence of kidney failure and death compared to the general population. Consequently, pregnant women were classified as a vulnerable group and advised to take additional precautions during the COVID-19 pandemic [6]. Evidence has been mounting both in the sense that pregnancy worsens the course of COVID-19 infection and that COVID-19 infection involves a greater risk of complications in pregnancy [[7], [8], [9]]. The risk of severe COVID-19 was found to be significantly greater in women over 35 years of age, obese women, smokers, women with diabetes and those with preeclampsia [8]. A cohort study involving 43 institutions from 18 countries investigated the risks associated with COVID-19 in pregnancy, evaluating maternal and neonatal outcomes in pregnant women with COVID-19 compared to pregnant women unaffected by the virus. A total of 706 pregnant women with a diagnosis of COVID-19 and 1424 pregnant women without COVID-19 were included. Women with a diagnosis of COVID-19 were at a greater risk of preeclampsia/eclampsia, of severe infection, of requiring to be admitted to an ICU, of delivering prematurely and of dying [3]. Although various publications have already described severe forms of COVID-19 in pregnancy, with an increased risk of severe acute respiratory syndrome (SARS) and a need for mechanical ventilation, a search on Pubmed and Scopus using the descriptors COVID-19 and pregnancy, on November 2022 failed to reveal any study that adequately evaluated the factors associated with SARS during pregnancy and in the postpartum. Therefore, the objective of the present study was to analyze the factors associated with SARS in pregnant/postpartum women with COVID-19 receiving care at referral centers in northeastern Brazil. 2 Methods The present study consists of a secondary analysis of a database created for a multicenter cohort study that was initiated in 2020 and is registered at Clinical Trials under reference NCT04462367. Data were collected at eight referral centers in the following states of northeastern Brazil: two in Pernambuco, four in Paraíba and one in Ceará. The study involved retrospective and prospective stages and was conducted between April 2020 and December 2021. The institutional review board approved the study protocol under reference CAAE 58466822.0.0000.5201. Since the study consisted of a secondary analysis of the database previously constructed for the original study, the need for signed informed consent was waived. The inclusion criteria were: being pregnant or in the postpartum, having COVID-19 confirmed by a positive real-time polymerase chain reaction test and having been admitted to the COVID-19 sector of one of the institutions included in the study during the defined period. Hospital records that could not be located or were incomplete were excluded from this analysis. In the first stage of the statistical analysis, the independent or predictive variables taken into consideration were: sociodemographic characteristics (skin color/ethnicity, schooling, marital status and the location of the patient immediately prior to admission), obstetric characteristics (number of pregnancies, parity, number of prenatal consultations and gestational age), and clinical characteristics (the presence of comorbidities). The dependent or endpoint variable was the presence of SARS. In the second stage of the analysis, SARS was considered an independent variable while hospitalization, maximum duration of hospitalization, need for non-invasive and invasive mechanical ventilation, maternal near miss and death were endpoints analyzed according to whether or not SARS was present. SARS was defined as a flu-like syndrome (fever, cough, dyspnea and other non-specific symptoms) together with oxygen saturation (SpO2) <95%, respiratory distress or tachypnea, hypotension and worsening clinical conditions of the primary disease [10]. Epi-Info, version 7.2.5 and Medcalc, version 20.112 were used. Measures of central tendency and dispersion were calculated to describe the numerical variables, while tables of frequency distribution were constructed for the categorical variables. The chi-square test of association was used to evaluate the association between the categorical variables, with Fisher's exact test being used when pertinent. All p-values were two-tailed. The significance level adopted was 5%. To determine the strength of the association, risk ratios (RR) were calculated as a measure of relative risk, together with their 95% confidence intervals (95%CI). A multivariate analysis was then conducted using a hierarchical logistic regression model to evaluate the association between the independent variables and the presence of SARS following adjustment for potential confounding factors, based on the causal model (Fig. 1 ). The variables included in the model were age, years of schooling, absence of a steady partner, being overweight/obesity (as described on records), number of prenatal visits, parity, ethnicity/skin color, gestational age at admission, admission in postpartum, cesarean section, duration of symptoms, comorbidities (asthma, diabetes and hypertension) and being referred form another healthcare unit. We tested various models and the one presented was the one that predicted a larger proportion of cases.Fig. 1 Prediction model. Fig. 1 3 Results The study included 611 patients admitted after testing positive for COVID-19. Of these, 522 were pregnant and 83 were in the postpartum, at the time of admission (this information was missing in six patients). Criteria for SARS were present in 215 patients, representing 35·2% of the sample (Fig. 2 ). The mean age of the patients with SARS was 29·4 ± 6·7 years compared to 26·3 ± 6·8 years for those without SARS (p = 0·000). In the bivariate analysis, overweight or obesity (RR = 1·52; 95%CI: 1·15–2·02; p = 0·004), parity ≥2 (RR = 1·43; 95%CI: 1·14–1·78; p = 0·0014), gestational age <34 weeks (RR = 2·39; 95%CI: 1·87–3·06; p = 0·0000), duration of symptoms >7 days (RR = 1·69; 95%CI: 1·31–2·18; p = 0·00004), age >35 years (RR = 1·4; 95%CI: 1·08–1·82; p = 0·017), having attended fewer than six prenatal visits (RR = 1·5; 95%CI: 1·12–1·99; p = 0·005) and having been referred from another healthcare unit (RR = 0·59; 95%CI: 0·40–0·86; p = 0·014) were all factors that increased the risk of having SARS (Table 1 ).Fig. 2 Flow chart of patient recruitment to study. Fig. 2 Table 1 Association of the characteristics of the patients with SARS and COVID-19 who received care at one of the referral centers in northeastern Brazil. Table 1Characteristics Patients with SARS (n = 215) Patients without SARS (n = 396) RR 95%CI p-value Agea (mean/SD) (Years) 29·4 6·7 26·3 6·8 ·· ·· 0·000 Age <19 yearsa (n/%) 17 7·9 73 18·5 0·49 0·31–0·77 0·0004 Age >35 yearsa (n/%) 38 17·7 43 10·9 1·4 1·08–1·82 0·017 Years of schoolingb (median/IQR) 12 6–12 12 7–12 ·· ·· 0·64* Years of schooling <8 (n/%)b 35 30·4 56 28·3 1·06 0·78–1·46 0·68 No steady partner (n/%)c 49 36·6 108 43·0 0·83 0·62–1·11 0·21 Overweight/obesity described on records (n/%)d 44 36·1 49 21·9 1·52 1·15–2·02 0·004 Number of pregnancies (Median/IQR)e 3 2–4 2 1–3 ·· ·· 0·0000* Parity (Median/IQR)f 2 1–3 1 0–2 ·· ·· 0·0000* Parity ≥2 births (n/%)f 105 51·2 143 37·5 1·43 1·14–1·78 0·0014 Number of prenatal visits (Median/IQR)g 5 3–6 5 7–12 ·· ·· 0·08 Prenatal visits <6 (n%)g 66 51·9 94 36·8 1·5 1·12–1·99 0·005 Ethnicity/skin color (black) (n/%)h 14 9·0 21 6·9 1·2 0·78–1·84 0·42 Gestational age at admission (weeks) (median/IQR) 33 29–36 37 34–39 ·· ·· 0·0000 Gestational age at admission <34 weeks (n/%) 115 63·5 95 29·8 2·39 1·87–3·06 0·0000 Gestational age at delivery (weeks) (median/IQR)i 35 32–38 38 37–40 – – 0·0000 Gestational age at delivery <34 weeks (n/%)i 51 35.6 28 10.4 2.3 1.84–2.96 0·0000 Admission in postpartum (n/%) 29 13·5 51 13·4 1·02 0·74–1·39 0·88 Cesarean section (n/%) 140 90.3 205 65.7 3.30 2.02–5.39 0·0000 Duration of symptoms (days) (median/IQR) 4 7–18 3 5–14 ·· ·· 0·0013 Duration of symptoms ≥7 days (n/%)j 86 58·2 92 35·4 1·69 1·31–2·18 0·00004 Hypertensive disorders (n/%)k 79 40·9 132 35·6 1·16 0·92–1·46 0·21 Clinical or gestational diabetes (n/%)l 28 15·6 50 13·7 1·10 0·80–1·53 0·54 Asthma (n/%)m 12 6·6 19 5·1 1·18 0·74–1·87 0·48 Referred from another healthcare unit (n/%)n 96 84·6 229 93·1 0·59 0·40–0·86 0·014 SARS: severe acute respiratory syndrome; RR: risk ratio; 95%CI: 95% confidence interval. *Mann-Whitney. a–m Data available for: a 608 cases; b 313 cases; c 385 cases; d 345 cases; e 586 cases; f 585 cases; g 382 cases; h 459 cases; i 412 cases j 413 cases; k 564 cases; l 544 cases; m 551 cases; n499 cases. The gestational age at admission of the patients with SARS was significantly lower than that of those without SARS (a median of 33 weeks; interquartile range [IQR] 29–36 weeks versus a median of 37 weeks; IQR 34–39 weeks; p = 0·0000). The median number of pregnancies in the patients with SARS was 3 (IQR 2–4) versus a median of 2 (IQR 1–3) for the women without SARS (p = 0·0000). In relation to parity, patients with SARS had had a median of 2 births (IQR 1–4) compared to 1 (IQR 0–2) for those without SARS (p = 0·0000). No statistically significant differences were found for any of the other endpoints evaluated here. Of the patients who delivered their child during hospitalization, (n = 467), 73·4% had a Cesarean section. Overall, 90·3% of the patients with SARS had their child delivered by Cesarean section compared to 65·7% of those without SARS (RR = 1·37; 95%CI: 1·25–1·51; p = 0·0000) (Table 1). In the multiple logistic regression analysis performed to control for potential confounders, the factors that remained associated with SARS were overweight and/or obesity (adjusted odds ratio [AOR] = 1·95; 95%CI: 1·21–3·12; p = 0·0054), parity ≥2 (AOR = 1·72; 95%CI: 1·21–2·45; p = 0·0025), gestational age <34 weeks (AOR = 3·54; 95%CI: 2·47–5·07; p < 0·0001) and duration of symptoms >7 days (AOR = 1·97; 95%CI: 1·35–2·89; p = 0·0004) (Table 2 ). This model correctly predicted 71·6% of the cases, with an area under the curve of 0·72 (95%CI: 0·693–0·762).Table 2 Conditions associated with SARS in patients with COVID-19 following multivariate analysis. Table 2Factor Coefficient Standard error Odds ratio 95%CI p-value Gestational age ≤34 weeks 1·26 0·18 3·54 2·47–5·07 <0·0001 Parity ≥2 0·54 0·18 1·72 1·21–2·45 0·0025 Overweight/obesity 0·66 0·24 1·95 1·21–3·12 0·0054 Duration of symptoms >7 days 0·68 0·19 1·97 1·35–2·89 0·0004 Constant −1·75236 0·15310 <0·0001 95%CI: 95% confidence interval. Cases correctly predicted = 71·6%/Area under the curve = 0·72 (95%CI = 0·693–0·762). A diagnosis of SARS increased the likelihood that the patient would require oxygen therapy (RR = 8·80; 95%CI: 6·25–12·40; p = 0·0000) or mechanical ventilation (RR = 8·15; 95%CI: 4·67–14·21; p = 0·0000), be admitted to the ICU (RR = 6·54; 95%CI: 4·70–9·11; p = 0·0000), be classified as a maternal near miss (RR = 10·82; 95%CI: 1·20–22·47; p = 0·0000) or die (RR = 8·10; 95%CI: 3·11–21·09; p = 0·0000). The median time the patient remained in the ICU was 6·5 days (IQR 2·5–12) for the women with SARS compared to 6 days (IQR 3–9) for those without SARS; however, this difference was not statistically significant (Table 3 ).Table 3 Clinical characteristics and obstetric outcomes in patients with SARS and COVID-19 at referral centers in northeastern Brazil. Table 3Clinical characteristics of the patients, management and outcomes Patients with SARS (n = 215) Patients without SARS (n = 396) RR 95%CI p-value Non-invasive oxygen therapy (n/%) 153 76·8 32 8·3 8·80 6·25–12·40 0·0000 Mechanical ventilation (n/%) 62 33·2 14 3·7 8·15 4·67–14·21 0·0000 Admission to ICU (n/%) 128 64·9 36 9·5 6·54 4·70–9·11 0·0000 Near miss (n/%) 47 24·8 8 2·1 10·82 1·20–22·47 0·0000 Maternal death (n/%) 22 10·6 5 1·3 8·10 3·11–21·09 0·0000 Duration of hospital stay (days) (Median/IQR) 6·5 2·5–12 6 3–9 – – 0·59* SARS: severe acute respiratory syndrome; RR: risk ratio; 95%CI: 95% confidence interval; SpO2: oxygen saturation; IQR: interquartile range. a Data available for 467 patients. 4 Discussion Research into COVID-19 during pregnancy and in the postpartum is particularly important, since the adaptive physiological and anatomical changes, together with the woman's state of immunosuppression, negatively affect this population. In the present cohort study, based on data from eight referral centers in northeastern Brazil, the overall frequency of women with SARS in the population of hospitalized pregnant/postpartum women testing positive for COVID-19 was 35·2%. This is higher than the prevalence rate for Brazil as a whole of 14·4% for this group [11]. Brazil is a country in which the rate of admissions to hospital involving pregnant and postpartum women with COVID-19 is high, higher than rates reported from other countries [12]. Indeed, although some international studies have shown high morbidity and mortality rates in pregnant and postpartum women with severe COVID-19,1 the data from Brazil could have been aggravated by inadequate policies which, in addition to failing to implement social isolation measures or guaranteeing paid leave for pregnant women, also delayed the beginning of vaccination [11,12]. Within this scenario, these women, who are already known to be more vulnerable to respiratory infections, were placed at even more risk [13]. Comorbidities have been found to increase the risk of a patient developing more severe respiratory distress, needing to be admitted to hospital and dying from COVID-19 [14,15]. The present study showed an association between the presence of SARS during pregnancy and in the postpartum and obesity and/or overweight. A cohort study conducted in the United States between March and April 2020 involving 12 referral maternity hospitals investigated the clinical course of pregnant women admitted to hospital with a diagnosis of COVID-19 and SARS. Obesity was shown to be a risk factor for maternal death, together with diabetes mellitus, cardiorespiratory disease and thromboembolic complications in pregnant women with SARS [16]. The comorbidities most associated with COVID-19 and SARS in the Brazilian population have been reported as hypertension or other cardiovascular diseases, chronic respiratory diseases, diabetes and obesity (9·9%, 6·9%, 6·5% and 4·1%, respectively) [12]. However, in the present sample, no increased risk of SARS was found in patients with hypertension, gestational or clinical diabetes, or asthma, with the only association found being with obesity. This may be because the absolute number of individuals in this sample with these diseases was small compared to the numbers in the population-based studies that reported this association. Being referred from another healthcare unit decreased the risk of SRAS in the bivariate analysis. Although after multivariate analysis this was not significant, an explanation for this is the two patient profiles that the involved units were attending. First pregnant and postpartum patients with respiratory symptoms, generally more serious, but often coming from their home, and high risk pregnancies (hypertensive disorders, twin pregnancies, preterm labor, diabetics or other conditions), asymptomatic for COVID-19, but with a positive COVID-19 test and these patients were usually less severe cases and were mostly referred from other units. This may explain our results. The explanation for the increased risk of severe disease and mortality in patients with comorbidities is currently unknown; however, it appears to be related to changes in the immune response. Chronic diseases are associated with a proinflammatory state similar to that seen with COVID-19. Furthermore, a weakening of the innate immune response is often seen in chronic conditions [17]. The increase in inflammation and the reduction in the innate immune response may provide answers for the increased susceptibility to severe SARS-CoV-2 disease in patients with these conditions, including women during pregnancy [17]. Advanced gestational age is an important risk factor for respiratory infections, since the physiological changes of this phase result in a reduction in functional residual capacity, rapid oxygen consumption, an increase in uterine pressure and physiological respiratory alkalosis [18], as well as the presence of changes in the mechanics of ventilation such as rib cage expansion and upward displacement of the diaphragm. Therefore, it is expected that as gestational age progresses in the third trimester, patients will be more likely to develop SARS. In the present study, the median gestational age at admission and at delivery of the patients with SARS was lower than that of those without SARS (33 weeks versus 37 weeks, respectively; p < 0.01 and 35 weeks versus 38 weeks, respectively; p < 0.01). Th. A considerable number of studies have shown that the majority of patients with SARS were in the third trimester of pregnancy; however, those studies failed to perform a more detailed analysis or to compare those patients with others who did not have SARS [1,12,16]. An observational study conducted within a surveillance program for pregnant women with SARS in Canada described the factors associated with the development of the disease in this population. Of the 6012 patients analyzed, the risk of requiring hospitalization and oxygen therapy was greater in women at 28 weeks of pregnancy or more compared to those at 15–27 weeks [1]. A multicenter cohort study conducted in the United States found that the majority of the pregnant women hospitalized with a SARS-CoV-2 infection were at the end of the second trimester of pregnancy or in the third trimester [16]. Data from Brazil show a higher frequency of SARS in COVID-19 patients in the third trimester of pregnancy [12], corroborating data published by the Ministry of Health in 2021 [19] and the present findings. A study conducting a more detailed investigation into gestational age and the risk of developing adverse outcomes showed that SARS-CoV-2 infections after 20 weeks of pregnancy significantly increased the risk of adverse obstetric outcomes [20]. Although in the present cohort no difference was found with respect to schooling between the patients with SARS and those without it, the amount of missing data on education could have affected the results. Previous evidence has shown that socioeconomic status and poor education level can be associated with worsening maternal health, since these variables affect self-care as well as the quality of prenatal care and the effectiveness of treatment [21]. Providing individualized prenatal care and adequately monitoring the progress of pregnancy is of the utmost importance. The risk of maternal mortality is known to be greater in multiparous women [22]. A statistically significant association was found in the present study between parity (≥2) and the risk of developing SARS. This may be because multiparous women tend to be older than primiparas or nulliparous women. Another factor seen here was that the women with SARS tended to attend fewer prenatal visits. An explanation for this finding could be the number of pregnant women at <34 weeks of pregnancy at admission to hospital. In the multivariate analysis, however, this variable no longer remained associated with the risk of developing SARS. It is possible that the role of gestational age overlapped with the number of prenatal visits, eliminating this variable in a multivariate analysis model. Having had symptoms for more than seven days was a finding that remained strongly associated with developing SARS in the multivariate analysis. One possible hypothesis to explain this is that the course of COVID-19 consists of three periods and five phases: the period of pre-exposure, the incubation period and the period of detectable viral replication; the symptomatic phase, the initial inflammatory phase, the secondary infection phase, the multi-system inflammatory phase and the tail phase. The inflammatory phase begins between the 7th and 14th day after symptoms develop, with this phase beginning earlier in vulnerable populations such as the elderly and individuals with comorbidities. The most severe clinical manifestations of this phase are usually pulmonary, with the onset and aggravation of hypoxemia. Cases can progress rapidly and may require intensive care [23]. This could explain the findings of the present study. The management of these pregnant women reflects the expressive increase in the relative risk of the patient with SARS requiring oxygen therapy, mechanical ventilation and admission to an ICU compared to the women without SARS. This could be explained because the presence of pneumonia, clinically classified as severe according to the SARS criteria, is associated with a high maternal mortality rate and adverse perinatal outcomes, requiring multifactorial treatment that includes oxygen therapy and ventilatory support [24]. Therefore, a higher frequency of these findings would be expected. The results of the Canadian study on factors associated with SARS in pregnant women found a greater risk of hospitalization and admission to the ICU in pregnant women affected by SARS-CoV-2 compared to non-pregnant women of reproductive age with SARS-CoV-2 infection [1]. The high rate of admission to an ICU in the present study could be explained by the fact that data collection was performed in referral centers for pregnant women, where a considerable number of ICU beds are available compared to the other healthcare units in the country. The mean duration of hospital stay was 6·5 days. Data reported from the study conducted in Canada were similar, with pregnant women with COVID-19 and SARS spending a median of six days in hospital [1]. Conversely, a cohort study conducted by the University of Texas analyzed maternal and neonatal outcomes in pregnant women with SARS and reported that 6% of the women with SARS were hospitalized for 14 days to treat COVID-19 [25]. In principle, if maternal and fetal well-being are closely monitored, vaginal delivery may be a safe strategy. However, most of the patients in both groups in the present study were submitted to Cesarean section: 90·3% of the patients with SARS and 65·7% of those without SARS. In fact, Cesarean section can be indicated in this context when there is a greater need for oxygen therapy and a potential for complications at the moment of birth. This finding is in agreement with studies published around the world [26] and with the average figures for Brazil [12]. On the other hand, the rate was high even in patients without SARS, contradicting general recommendations. The risk of death increased 8-fold in the pregnant and postpartum women with COVID-19 and SARS. This has already been shown in data from the United States published in February 2022 that confirmed an increase in the rate of maternal mortality in 2020 compared to data from 2019 [16]. A publication from Brazil also showed a clear increase in the number of maternal deaths in the country, with the number being 3.4 times greater than the total number of COVID-19-related maternal deaths in the entire rest of the world [27]. In Brazil, the high rates of maternal mortality can be explained by re-adaptations in the healthcare facilities or failure to allocate resources, difficulty in restructuring women's healthcare services to return to pre-pandemic levels, failure to implement social distancing policies and/or quarantine and delay in implementing vaccination [28,29] in addition to the discrimination associated with COVID-19 and its effects on patients and healthcare providers. The limitations of the present study include the inherent disadvantages of retrospective studies (since part of the data was obtained retrospectively), including the inability to obtain some of the data of interest in the hospital records of the patients selected for this study. It proved impossible to collect data for all the patients on characteristics such as marital status, occupation or the location of the patient prior to admission to hospital. Since the distribution of healthcare equipment in Brazil tends to differ between metropolitan and non-metropolitan regions, such information could have answered some interesting questions on transportation and access to intensive care in the country. Prospective studies need to be conducted to guarantee a more complete data set. Nevertheless, a strongpoint of the present study was the fact that an expressive number of patients was included, an unusually large sample of pregnant/postpartum women with a diagnosis of COVID-19 and SARS. The data shown here are innovative with respect to the factors associated with SARS during pregnancy and the postpartum. In addition, this is a robust sample of cases of adverse maternal outcomes obtained from tertiary referral centers providing care for pregnant and postpartum women with COVID-19 in northeastern Brazil. Furthermore, these data refer to pregnant women receiving care at eight referral centers, highlighting the actual situation of the Brazilian public healthcare system in northeastern Brazil. 4 Conclusion The results of this multicenter cohort study conducted in northeastern Brazil suggest an association between the presence of SARS and parity ≥2, overweight or obesity, gestational age <34 weeks and duration of symptoms >7 days. Cesarean section rates and the need for oxygen therapy and mechanical ventilation were also significantly higher in the patients with SARS. We highlight the importance of vaccination and protective measures for pregnant women, as this is a group more susceptible to respiratory pathogens and, consequently, to infection by the new coronavirus. Role of the funding source The authors declare that CNPq played no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. Author contribution statement Carolina Maria Pires Cunha Cunha: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper. Melania Amorim: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper. Julianna Guendler: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper. Leila Katz: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper. Data availability statement The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Funding 10.13039/501100003593 National Council for Scientific and Technological Development (CNPq). Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Melania Amorim reports financial support was provided by 10.13039/501100003593 CNPq . Acknowledgements Funding source: The study received funding through a “Productivity in Research” grant from the 10.13039/501100003593 National Council for Scientific and Technological Development (CNPq). ==== Refs References 1 McClymont E. Albert A.Y. Alton G.D. Association of SARS-CoV-2 infection during pregnancy with maternal and perinatal outcomes JAMA 327 2022 1983 1991 35499852 2 Wei S.Q. Bilodeau-Bertrand M. Liu S. Auger N. The impact of COVID-19 on pregnancy outcomes: a systematic review and meta-analysis CMAJ (Can. Med. Assoc. J.) 193 2021 E540 E548 33741725 3 Villar J. Ariff S. Gunier R.B. Maternal and neonatal morbidity and mortality among pregnant women with and without COVID-19 infection: the INTERCOVID Multinational Cohort Study JAMA Pediatr. 175 2021 817 826 33885740 4 Zaigham M. Andersson O. Maternal and perinatal outcomes with COVID-19: a systematic review of 108 pregnancies Acta Obstet. Gynecol. Scand. 99 2020 823 829 32259279 5 South A.M. Diz D.I. Chappell M.C. COVID-19, ACE2, and the cardiovascular consequences Am. J. Physiol. Heart Circ. Physiol. 318 2020 H1084 H1090 32228252 6 Di Mascio D. Khalil A. Saccone G. Outcome of coronavirus spectrum infections (SARS, MERS, COVID-19) during pregnancy: a systematic review and meta-analysis Am J Obstet Gynecol MFM 2 2020 100107 7 Ko J.Y. DeSisto C.L. Simeone R.M. 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PMC010xxxxxx/PMC10292916.txt
==== Front Health Policy Health Policy Health Policy (Amsterdam, Netherlands) 0168-8510 1872-6054 The Author(s). Published by Elsevier B.V. S0168-8510(23)00148-3 10.1016/j.healthpol.2023.104863 104863 Article COVID-19 and Healthcare Worker Mental Well-being: Comparative case studies on interventions in six countries in the WHO European Region Byrne John-Paul Lecturer ⁎ Humphries Niamh Senior Lecturer McMurray Robert Professor of Management & Organisation Scotter Cris Adjunct Lecturer Graduate School of Healthcare Management, RCSI University of Medicine and Health Sciences, Dublin, Ireland ⁎ Corresponding author: Postal Address: Dr John-Paul Byrne, Graduate School of Healthcare Management, RCSI, Ballymoss Road, Sandyford Industrial Est. Dublin 18, Ireland. Telephone number: 00353879902751 26 6 2023 26 6 2023 10486314 11 2022 9 6 2023 21 6 2023 © 2023 The Author(s). Published by Elsevier B.V. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Healthcare worker (HCW) mental well-being has become a global public health priority as health systems seek to strengthen their resilience in the face of the COVID-19 pandemic. Analysing data from the Health System Response Monitor, we present six case studies (Denmark, Italy, Kyrgyzstan, Lithuania, Romania, and the United Kingdom) as a comparative review of policy interventions supporting HCW mental health during the pandemic. The results illustrate a wide range of interventions. While Denmark and the United Kingdom built on pre-existing structures to support HCW mental wellbeing during the pandemic, the other countries required new interventions. Across all cases, there was a reliance on self-care resources, online training tools, and remote professional support. Based on our analysis, we develop four policy recommendations for the future of HCW mental health supports. First, HCW mental health should be seen as a core facet of health workforce capacity. Second, effective mental health supports requires an integrated psychosocial approach that acknowledges the importance of harm prevention strategies and organisational resources (psychological first aid) alongside targeted professional interventions. Third, personal, professional and practical obstacles to take-up of mental health supports should be addressed. Fourth, any specific support or intervention targeting HCW's mental health is connected to, and dependent on, wider structural and employment factors (e.g. system resourcing and organisation) that determine the working conditions of HCWs. ==== Body pmc1 Introduction In the beginning, I often had nightmares, Even while asleep I'd continue working. We had to make decisions under a lot of pressure, Which patient to admit? Who could endure the longest out of the ICU? Whether such a decision was ours to make at all? I…don't feel like myself, The tiredness and the sadness. You go home burdened with the thought of bringing it with you. There are specific moments that have left more marks on me. The pregnant women who have died, Two lives at once. I will never forget that pain. We need to face our emotions, Talking about how hard it was - it makes me stronger. Any organisation is only as good as its people. We are only human. We cry under the masks. Note: Composite poem comprised of verbatim statements from the testimonies of health workers featured in a film by the WHO Regional Office for Europe. See: https://www.youtube.com/watch?v=mKKgx2DpP8k]. The COVID-19 pandemic has transformed healthcare, placing immense pressure on the capacity and resilience of national health systems [1]. At the forefront of the pandemic response were healthcare workers (HCWs) who, over the course of the pandemic, have been heroes and virus vectors, applauded and stigmatised, and empowered and traumatised. The pandemic has had a profound and pernicious impact on the mental well-being of HCWs across the World Health Organisation (WHO) European region leaving many ‘exhausted, stunned – shell-shocked’ [2]. Deterioration in the mental well-being of HCWs has been observed to exacerbate pre-existing system stresses relating to resource constraints, crisis management, growing demand, recruitment and retention [3,4]. Failure to address HCW well-being may present a material risk to the functioning of national healthcare systems, including their ability to care for those in need. As such, HCW mental well-being has now been recognised as an ‘urgent global public health priority’ [5] with policies and interventions addressing this issue identified as a ‘top priority and a long-term concern’ [6] for national health systems. This article assesses these issues by exploring how six WHO European region countries supported the mental well-being of their HCWs during the COVID-19 pandemic. The objectives of the article are to consider the policy responses adopted in Denmark, Italy, Kyrgyzstan, Lithuania, Romania, and the United Kingdom to support HCWs’ mental health during the pandemic and, based on the lessons from these countries, outline future considerations and actions for member states of the WHO European region regarding the HCW mental health. We use the following WHO definition of mental health; ‘a state of well-being in which an individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and is able to make a contribution to his or her community’ [7]. Subsequently, we use the broad terms of mental well-being, mental health, and psychological well-being interchangeably throughout the article. We also adopt the broad WHO definition of HCW for the purposes of this article to include health service providers such as: ‘…doctors, nurses, midwives, public health professionals, lab-, health- and medical and non-medical technicians, personal care workers, community health workers, healers and some practitioners of traditional medicine … those employed in long-term care, public health, community-based care, social care and home care and other occupations in the health and social work sectors…’ [8]. 2 Background & Literature Health systems globally relied on their workforce to adapt and provide care to COVID-19 patients within extremely pressurised and uncertain circumstances [9], resulting in a heightened risk of psychological distress particularly for those working in under-resourced health systems [6,[10], [11], [12]]. Subsequently, attention has now turned to focus on how we avoid replacing waves of COVID-19 infections with waves of psychological morbidity among HCWs [13]. Within this context, addressing the mental health needs of HCWs first requires an understanding of the unique constellation of stressors they have faced, and continue to face, on the frontline. 2.1 COVID-19 Work-life Stressors for HCWs Work-life stressors are extrinsic threats, demands or structural constraints that challenge the psychological well-being of individuals, but may not necessarily result in an immediate physiological response [14]. Figure 1 synthesises recent literature to illustrate the unique set of work-life stressors experienced by HCWs during COVID-19 across three levels: i) public health measures: those experienced by citizens generally to prevent the transmission of COVID-19; ii) frontline work: those experienced within healthcare contexts, and; iii) being a health worker: those experienced specifically by HCWs.Figure 1 A summary cascade of stressors faced by HCWs during COVID-19 Figure 1: During COVID-19, HCWs faced a cascade of stressors across their social, work and professional lives that often moved well beyond the ‘normal stresses of life’ [7], and subsequently made them more vulnerable to adverse mental health outcomes. Key stressors included: lockdowns and alterations to family life, the use – and absence –of personal protective equipment (PPE), fear of infection and transmission at home, reduced breaks, isolation at work and at home, aggression from patients and colleagues, ever-changing treatment protocols and ward environments, the sense of failing patients, and the ‘deathscapes’ of high critical care [12,13,[15], [16], [17]]. Concerns around COVID-19 transmission also led to some HCWs being stigmatised as vectors of disease by family, friends, and wider society [12,18]. In some cases, HCWs spent time separated from their families and support networks in order to reduce the risk of transmission [12,19]. The impact of pandemic work varied across HCW subgroups. For example, critical care nurses in the UK reported that excluding families from ICU wards and having to use Facetime to connect family members with their dying relatives was emotionally overwhelming [20]. Hospital doctors on the frontline reported a sense of helplessness [12,17]. Women, ethnic minority and older workers were disproportionately affected due to their higher representation in frontline roles such as nursing, midwifery, home care and paramedicine [21,22]. Highlighting how COVID-19 may have had a different impact on men and women working in healthcare [23], studies have found a higher risk of psychological distress, mental health burden and stress for nurses, women, and those working directly in COVID-19 patient environments [10,13,21,24,25]. Feminist research has highlighted these gendered dimensions of COVID-19 that impact the mental health of HCWs [26]. Women comprise the majority of the healthcare workforce, facing increased risk of exposure whilst continuing to hold primary responsibility for childcare, which was also disrupted by school closures and lockdowns [23,27,28]. In addition, the United Nations also highlighted the increased risk of domestic violence and threats of violence in the workplace for women during the pandemic [26,29]. Overall, these system-wide and sub-group specific stressors exacerbated long-standing pressures of overwork, long hours and high stress within many healthcare settings throughout Europe [4], [5], [6]. Research has reported a higher prevalence of HCW depression, stress, anxiety, insomnia, and post-traumatic stress disorder during the pandemic [13,30,31] and, subsequently, the potential for increasing workforce attrition. 2.2 Interventions to Support HCW Mental Health Many countries, institutions, and organisations across the world developed interventions to support HCW mental health during the pandemic. Williams et al. draw on data from the European Observatory on Health Systems and Policy's COVID-19 Health System Response Monitor (HSRM) to highlight initiatives developed outside of clinical settings to support the mental health of HCWs during the pandemic [32]. These national interventions included remote access to professional support, digital and online tools for managing mental well-being, peer-support groups, as well as social and financial supports (e.g. childcare provision and bonus payments). Moving beyond national health systems, David et al.’s narrative review of mental and emotional well-being interventions for HCWs during the pandemic highlights a ‘global surge of creativity’ [25]. The authors use La Montagne's integrated workplace mental health framework [33] to categorise interventions as one of three types: harm prevention, illness-focused mental health management, or promoting positivity. Interestingly, unlike Williams et al. [32], this review emphasises HCW prioritisation of professional and personal needs shaped at the health system and healthcare organisation level such as PPE, childcare, additional training, accommodation, transport, relaxation spaces, work-life balance and rest [25]. The second category, illness-focused mental health management, refers to interventions that promoted mental health literacy such as psychological first aid, as well as psychological support hotlines, remote counselling and centralised resources on stress management. Importantly the authors emphasise the need to minimise obstacles that prevent HCWs taking up these supports. These include a sense of professional stigma associated with seeking help, logistical barriers in terms of lack of protected time or scheduling conflicts, and personal obstacles around issues such as childcare. Finally, interventions such as peer support initiatives were found to promote positivity, especially where they harness social media to connect and empower HCWs [25]. At the organisational level, O'Hayer et al. [34] describe the comprehensive efforts of Jefferson University Hospital in Philadelphia in supporting the mental health of employees during the pandemic. The Department of Psychiatry and Human Behavior, in collaboration with the Human Resources department, established a four-tier intervention model that encapsulated individual (e.g. self-help resources aimed at prevention), team (e.g. roadmaps for help and training), peer (e.g. profession-specific support groups), and professional-based supports (e.g. counselling and employment assistance) [34]. Importantly, the authors note how engagement with these interventions improved considerably when the ‘burden of request’ for supports was taken away from the individual HCW. Supporting the findings of David et al. [25], O'Hayer et al. note that support requests that came from local units had a number of knock-on effects, including: HCWs being provided with protected time to engage, a reduction in stigma due to a whole team approach, and interventions being tailored to specific issues or specialties. However, the authors note that barriers remain including time constraints, scheduling conflicts, pandemic exhaustion, technological issues, and childcare challenges [34]. This literature points to the need for an approach that acknowledges that psychological supports can only be effective if embedded within social and occupational conditions that limit the impact of the work-life stressors identified in Figure 1. This literature also highlights the complexity of developing relevant interventions to support HCWs’ mental health during the pandemic and the subsequent need for cross-sectoral governance structures [35]. Drawing on the work of Greer et al. [35], we can define HCW mental health as a problem of intersectoral governance as it involves an enormous range of stakeholders including: medical and nursing schools, professional training bodies and associations, unions, healthcare organisations, regional bodies and government departments. Yet, this very complexity may act as a barrier to the effective co-ordination of multiple-level action in support of HCW mental health. It may be particularly difficult to involve diverse HCWs and healthcare organisations in the development, implementation and evaluation of interventions at a national and local level. It is against this background that we set out to compare the interventions of six countries in the WHO European Region. 3 Methods Following the approach set out by van Ginneken et al. in the recent Health Policy Special Issue, Lessons learned from the COVID-19 pandemic [36], this article draws primarily on data from the COVID-19 Health System Response Monitor (HSRM) [37]. The HSRM is a joint initiative of the WHO Regional Office for Europe, the European Observatory on Health Systems and Policies, and the EU Commission, which collates information on how countries responded to the COVID-19 pandemic via standardised health system function headings e.g. preventing transmission, ensuring sufficient physical infrastructure and workforce capacity, and governance [37]. For the purposes of this article, we analysed data collated under the “Workforce” subsection within the “Ensuring sufficient physical infrastructure and workforce capacity” of each country's response. 27 country profiles from a total of fifty (excluding USA and Canada) noted some form of support for the mental health of HCWs. Following Burau et al. [9], the countries were selected to capture a variation of health systems (i.e., social health insurance vs. tax-financed, multi- vs. single payer, centralised vs. decentralised) while also ensuring geographical distribution across the WHO European region. The authors feel this approach provided a breadth of context and intervention types to meet the article objectives. Thus, we provide six brief country case studies - Denmark, Italy, Kyrgyzstan, Lithuania, Romania, and the United Kingdom (UK) - to further our understanding of interventions to support HCW's mental health during COVID-19. Figure 2 provides a brief overview of the approach employed to meet the article objectives.Figure 2 Methdological Process Figure 2: To provide consistency we used the following categories of analysis in presenting the case study country data: health system type, health system resources prior to/during the pandemic (Table 1 ), the impact of COVID-19 on the health system and HCWs and finally, the interventions adopted to support HCW mental health during the pandemic. The final category of analysis, focused on interventions, was kept purposefully broad to allow for a full range of intervention types – as described in the HSRM data. For example, in some countries remuneration is noted as a support for HCWs during the pandemc, while in others this is absent. Additionally, we supplemented HSRM data with academic and policy literature that enhanced our understanding of policy responses in the respective case study countries.Table 1 Selected Macro Health Sector Resource Indicators Table 1: Doctors per 1,000 pop Nurses per 1,000 pop Hospital beds per 1,000 pop Denmark 4.2a 10.1a 2.6b Italy 4.1c 6.2c 3.2c Kyrgyzstan* 2.19a 3.97a 3.85a Lithuania 4.6c 7.7c 6.4c Mean (EU) 3.9c 8.4c 5.3c Romania 3.2c 7.5c 7c UK 3.18a 8.68a 2.34a Years: a 2021 b 2020 c2019 d2018; Sources: OECD & European Observatory on Health Systems Policy (2021) Country Profiles on Denmark [38], Italy [42], Lithuania [43] and Romania [44]; Moldoisaeva et al. (2022) on Kyrgyzstan [45]; OECD Statistics for UK [46] *Converted from per 100,000 rate for comparison purposes. 3.1 Limitations It must be noted that the level of detail of information reported in the HSRM is not systematically harmonised across countries. Country authors used different approaches to collect information and report on their health systems [11]. While we sought balance across our selected case countries, the range and depth of information was limited by differing country contributions to the HSRM, as well as a reliance on English language publications and material. The HSRM data does not consider the gendered dimensions of HCW's mental health needs in any detail. HSRM data collation ranges from spring 2020 to winter 2021, with a variation in updates across this time period. The focus on national level interventions means that local organisation or unit initiatives – which are undoubtedly important – may be under-reported. Nonetheless, this article represents an important scoping exercise which: i) expands on the Health Policy special issue [36] in exploring the HSRM data to highlight how HCW mental health is a fundamental, but often underplayed, feature of health system workforce capacity, and; ii) sets the agenda for future research and policy recommendations across the WHO European region. 4 Results We present six case study countries (Denmark, Italy, Kyrgyzstan, Lithuania, Romania, UK) which explore the policy responses adopted to support HCWs’ mental health during the pandemic. 4.1 Denmark The Danish health system provides universal health care (UHC), financed mainly through general income taxation. The system is regulated nationally, while the organisation and delivery of healthcare services are managed regionally [38]. In 2021, Denmark had above (EU) average doctor and nurse density but below average hospital bed density (2020) – see Table 1. However, acute and intensive care bed surge capacity were planned and sufficient throughout the first and second waves [38]. A COVID-19 Intensive Task Force was established during the pandemic comprising of national and regional health authorities to provide governance of resources [38]. COVID-19 had a relatively smaller impact on health and mortality in Denmark for the first 20 months of the pandemic [38]. In contrast to all other case countries, Denmark's peaks throughout this timescale are relatively low and short-lived. Denmark's approach was centered around the five Danish regions, each of whom established psychosocial support services for HCWs [37]. The Danish pandemic response built on an institutional structure acknowledging the importance of the psychosocial work environment for all workers via: the Work Environment Act 2010, regulatory bodies (the Work Environment Authority), and organisational mechanisms (works councils and work environment committees) [39,40]. For example, North Jutland's psychosocial preparedness plan during the pandemic was based on four key aspects organised at the level of prevention, group, individual, and emergency:1 Preventive: this included additional planning by managers and work environment committees to assess the impact of the work environment on HCWs. 2 Group: Virtual group-based efforts e.g. debriefing or professional supervision after traumatic events. 3 Individual: psychological first aid by addressing instances of prolonged strain that manifests acutely, or through an acute traumatic experience. 4 Emergency: referral to the Psychiatric Emergency Room for staff who became severely mentally distressed. Access to this emergency room was via their manager or work environment organization and contact was via email or telephone [41]. At a national level, the Danish Health Authority produced a leaflet and poster campaign addressed to all citizens, including HCWs, on how to maintain mental well-being during the pandemic [37]. In addition, a 24-hour psychosocial crisis hotline was established for HCWs. COVID-19 was also recognised as work-related injury in Denmark, providing HCWs with access to associated benefits [32]. 4.2 Italy Italy's National Health Service (NHS) provides universal healthcare, financed through publicly funded general taxation. Planning and delivery of healthcare services is decentralised with regions responsible for governance of local health units and public hospitals [42]. As Table 1 indicates, in 2019 Italy had a relatively high density of doctors but was below the EU average for density of nurses and hospital beds. In 2020, Italy had 8.6 intensive care beds per 100,000 population, far below Germany's rate of 33.9, or the OECD average of 12 [47]. The pandemic exposed these workforce and infrastructure weaknesses in the Italian health system, despite modern healthcare infrastructure in regions such as Lombardy [42]. Italy was one of the first European countries impacted by COVID-19; with ‘rapid saturation of hospital capacity’ and approximately 800 deaths per day reported at the end of March 2020, and a death rate that exceeded the EU average – especially during the first wave [42]. The Italian government have subsequently invested €5.4 billion into the health system [42]. Italian HCWs reported significant work-related physical illness, psychological distress, emotional exhaustion and somatic symptoms – especially from women and those on the frontline [48,49]. Among those working directly with COVID-19 patients, a higher proportion considered asking for psychological support, while exhibiting higher levels of stress, burnout, secondary trauma and anxiety [50]. Italy introduced a number of supports targeting HCWs’ mental health in response to the pandemic, including:• The Istituto nazionale per l'assicurazione contro gli infortuni sul lavoro (INAIL), translated as the National Institute for Insurance against Accidents at Work, and the National Council of the Order of Psychologists (CNOP) developed psychological support services for HCWs. These included directives and tools enabling healthcare facilities to capacity-build stress management and burnout prevention services for HCWs [37]. • INAIL provided online training materials for psychologists related to managing the impact of COVID-19 on patients, including a specifically designed triage system for psychologists to assess and monitor patients [37]. • Healthcare facilities were advised to develop in-house psychologist task forces to provide professional guidance and remote counselling to HCWs [37]. Data from the HSRM also notes that a dedicated e-mail was created to encourage the establishment of a network which could learn from independent and online initiatives launched at the organisational level [37]. Doctors, nurses, pharmacists and dentists who worked during the pandemic were also awarded 50 continuing medical education (CME) credits for 2020 [32]. 4.3 Kyrgyzstan The Ministry of Health and Social development governs and regulates the Kyrgyzstan health system. Following reforms over the last two decades, a Mandatory Health Insurance Fund (MHIF) covers the purchasing of health services and underpins the State Guaranteed Benefits Programme (SGBP) which entitles citizens to a set of essential health services including primary care and emergency care [51]. However, the SGBP's entitlements are limited, with co-payments required for in-patient care charges, and around a third of the population not covered by the MHIF. In 2021, Kyrgyzstan had fewer doctors and nurses than the other case countries, and a hospital bed density below the EU average (Table 1). COVID-19 peaked in Kyrgyzstan in July 2020 with one-in-four of those infected identified as a HCW [52]. In a post-peak survey of HCWs, 48% reported anxiety-depression spectrum symptoms [53]. In a qualitative study of HCWs who had been infected with COVID-19, interviewees emphasised the significant psychological impact of their experience with many highlighting the high levels of stress and mood changes which remained post infection, the impact on their families, the stigma of impaired health as a HCW, and the importance of seeking psychological support [52]. A World Bank report also highlights the mental strain on doctors who were redeployed to work on COVID-19 ‘red zones’ - often away from family and friends for months at a time, working long shifts, and at high risk of infection [19]. In response to the pandemic, Kyrgyzstan introduced a number of initiatives to support the mental well-being of their HCWs:• Legislation setting out the provision of medical, psychological and psychotherapeutic assistance to the Kyrgyz Republic during the pandemic was signed on 24 April 2020. This was followed on 4 May 2020 by the protocol for psychological support for the general population – including HCWs. • The Republican Centre of Mental Health organized online psychiatrist and clinical psychologist consultations. • The Ministry of Health appointed a group of doctors tasked with providing remote consultations and support to clinicians specifically. • HCWs received a one-off bonus payment for working during the pandemic with rates varying by profession, and doctors paid the highest amount [32]. 4.4 Lithuania The Lithuanian health system is based on a single-payer public financing system – the National Health Insurance Fund (NHIF) which covers the entire population [43]. The NHIF generates revenue primarily through compulsory health insurance contributions. Governance of the health system is centralised through the Ministry of Health, while municipalities play a role in service delivery – owning some of the primary care centres and smaller hospitals [43]. In 2019, while Lithuania's density of nurses was lower than the EU average, its density of doctors and, in particular, hospital beds exceeded it (Table 1). COVID-19 had a major impact on health and mortality in Lithuania, evidenced by a 17 month reduction in life expectancy between 2019 and 2020 [43]. Like the other Baltic countries, Lithuania experienced heavier subsequent waves with COVID-19 case-incidence rates among the highest in the EU in Nov-Dec 2020 [43,54]. Despite having a relatively high number of hospital beds, many hospitals reached full capacity (in terms of acute and intensive care beds) in December 2020 [43]. An online survey of medical students and resident doctors conducted between December 2020 and February 2021 found poor subjective psychological well-being associated with sleep difficulties, higher depression scores and anxiety symptoms for women, and higher depression scores for men [55]. Lithuania introduced a number of supports for HCWs in response to the pandemic. These included;• A 60-100% temporary salary increase for those providing care to COVID-19 patients with rates based on the nature of their work and risks of contracting the disease [43]. • HCWs received vouchers for tourism and leisure activities worth €200 [43]. • To ensure availability of HCWs, the Vilnius Municipality mandated schools and kindergartens to continue providing education and childcare services for the children of HCWs during the national quarantine [37]. • COVID-19 was also recognised as a work-related injury, providing HCWs with associated benefits [32]. • In terms of mental health supports, HSRM data notes Lithuania's emphasis on developing voluntary psychological assistance initiatives for medical professionals [37]. • A recommendation from the Ministry of Health, adopted on the 1st of April 2020, highlighted the importance of the work of psychologists in supporting doctors, especially in primary care settings [37]. However, the Ministry of Health noted in July 2020 that medical professionals were not availing of psychological supports from public services due to concerns around how that might impact their licence to practice. To encourage take-up of these supports the Ministry amended regulations to remove mental disorders from the list of conditions affecting medical professional licensing [37]. 4.5 Romania Romania's health system is characterised by centralised governance through the Ministry of Health, financed through a compulsory social insurance – the National Health Insurance Fund (NHIF), and service delivery through local public health authorities [44]. A relatively small working age population, and a reliance on employee contributions, has led to long-standing underfunding of the health system [44]. In 2019, Romania's density of doctors and nurses fell below the EU average, however its density of hospital beds was the highest across the case countries included in this article (Table 1). Despite training high numbers of doctors and nurses, HCW emigration is a major challenge for workforce retention and healthcare access in Romania [44,56]. COVID-19 noticeably impacted life expectancy in Romania with a reduction of 1.4 years between 2019 and 2020 to 74.2 – one of the lowest in the EU [44]. Like Lithuania, Romania managed to avoid the worst impact of the first wave of COVID-19, however, by autumn 2020, infection and death rates rose dramatically. The impact of these waves were also shaped by low vaccination coverage with only 27% vaccinated (two doses) by August-end 2021 – compared to 54% in the EU [44]. Securing adequate numbers of HCWs – especially intensive care staff – was the biggest challenge for the Romanian health system response to the pandemic [44]. A March 2021 survey of ICU staff from Târgu Mureș Emergency Clinical County Hospital found increased rates of anxiety and post-traumatic stress disorder for all staff working in intensive care with COVID-19 patients. Despite redeployment, and workforce surge strategies that included an additional 2,000 temporary HCW jobs, access to care through the peak of autumn-winter 2020 was a challenge [44,56]. The result was a disparity between HCWs who were chronically overworked – some of whom were not fully trained – and those who experienced significantly reduced workloads [56]. The high rates of redeployment and creation of temporary roles as part of the pandemic response highlighted pre-existing workforce bottlenecks created by weak workforce retention. Romania introduced a number of supports for HCWs, both general and mental health specific, including:• Financial bonuses and accommodation for those working directly with COVID-19 patients. • Free accommodation for HCWs isolating from their families, while childcare costs were covered where neither parent was able to take paid leave [32]. • According to the HSRM, initiatives to support the mental health of HCWs were provided by: the National Center of Psychology and Behavioural Health of the Ministry of Defence, psychiatry hospitals, centers of psychological counselling, support groups at medical universities, NGOs in the field of mental health, and private medical clinics [37]. There is little detail on the specific nature of these supports other than that psychological counselling, support, and resources were offered online, or through dedicated phonelines for HCWs. 4.6 United Kingdom The UK's National Health Service (NHS) provides universal healthcare access to all ordinarily resident citizens based on clinical need [57]. The NHS is funded mainly through progressive taxation, however since 1999 healthcare responsibility and organisation has been devolved with each of the four countries in the UK developing their own planning, governance and delivery [58]. In 2021, the UK had a relatively low density of doctors and hospital beds. However, it also had a nurse density above the EU average (Table 1), with a high reliance on internationally recruited HCWs [57,59]. Pre-existing infrastructural weaknesses, coupled with a slow take up of mask use and social distancing, challenged the UK's ability to respond efficiently to the challenges of COVID-19 [58,59]. The UK experienced one of the highest COVID-19 death rates (excess and direct) in the world, peaking in January and February 2021 [57,59]. The pandemic placed additional pressure on an already overburdened healthcare workforce now charged with balancing their own and their patients’ well-being on a daily basis with limited resources and constantly changing guidelines [16]. To illustrate the risk to HCWs health, research has pointed to a COVID-19 prevalence 3.3 times higher for HCWs compared to the wider UK population [59]. A longitudinal qualitative study of the impact of COVID-19 on nurses in the UK describes in detail the mental and emotional toll of interviewees who report being ‘forever altered’ by the persistent strain, exhaustion and moral distress of providing ‘diminished’ care, and attempting some form of ‘self-preservation’ [15]. Key findings regarding the impact of COVID-19 on the mental health of HCWs included the importance of self-developed, informal social media networks in providing a sense of empowerment and shared experience for HCWs, and a reticence to disclose mental health issues to managers due to the stigma [15]. According to HSRM data, mental health supports for HCWs relied on utilising and tailoring pre-existing resources across three key areas:• Prevention (awareness, mental health promotion, stigma reduction): The NHS collaborated with a range of mental health organisations to develop apps and online support services for HCWs (e.g. a ‘How are you feeling today’ tool and “Head First’ resources emphasising emotional and mental well-being)[37]. • Interventions (to manage burnout and promote resilience): A collaboration between Frontline 19 (a national psychological support service for NHS and frontline staff), Practitioner Health (a primary care mental health and addiction service for NHS staff) and Helpforce (a charity promoting volunteering in healthcare), developed free online psychological services delivered by trained therapists specifically for frontline workers. Psychological support helplines, and a 24/7 text service, provided psychological support for NHS staff - including referral to additional mental health services such as remote counselling sessions. The Royal College of Nursing provided free counselling for nurses [37]. • Self-care culture (the use of apps, mindfulness and lifestyle changes): including apps for emergency and critical care services, which signpost to various mindfulness and wellbeing resources. The Royal College of Psychiatrists also produced guidance on maintaining organisational wellbeing during the pandemic [37]. 5 Discussion This article provides a snapshot of the policy responses adopted by six countries across the WHO European region to support the mental health of their HCWs during the COVID-19 pandemic. The results illustrate a wide range of intervention types. While Denmark and the UK were able to harness and tailor pre-existing structures and resources to develop supports, the four other countries required the introduction of new interventions. In line with the findings of Williams et al. [32], the most common intervention types across the six countries were self-care resources (e.g. mindfulness and stress management apps), online training and tools (e.g. mental health literacy and emotional health in the workplace), peer support groups, and remote psychological support. The results also show how the pandemic not only exposed pre-existing weaknesses in health system capacity (e.g. staffing levels, beds, HCW migration), it also highlighted pre-existing deficiencies in psychosocial supports for HCWs (e.g. tailored counselling, peer support initiatives). Applying David et al.’s [25] previously discussed categorisation of HCW mental health interventions, the results point to an over reliance on illness-focussed mental health management strategies (e.g. stress management resources and remote counselling) with limited attention paid to harm prevention factors (e.g. work-life balance), or the obstacles that may impede engagement and take-up of supports. Additionally, following O'Hayer et al. [34], most supports relied on individual HCW take-up, ignoring the important ‘burden of request’ which also hinders engagement with support strategies. Analysing the nature and range of interventions adopted, it is possible to draw four conclusions that contribute to our understanding of supports for HCW mental health. First, there is a clear need to view HCW mental health as a core element of workforce capacity if we are to ensure effective healthcare system functioning and patient treatment. Second, supports for HCW mental health require an integrated approach comprising basic working conditions that limit work-life stressors, in addition to providing individual, organisational, peer and professional level supports [25,32,34]. Third, those from local managers to national policymakers need to recognise – and address – the personal, professional and practical obstacles to accessing and use of mental health supports. Finally, there is strong evidence to suggest that the effectiveness and utility of mental health supports for HCWs relies on addressing the longer-term, pre-existing deficits in system resourcing, organisation and management. These conclusions support Bodenheimer and Sinsky's call for HCW's working lives to be included as the fourth pillar of health system optimisation – alongside improving population health, the patient experience, and reducing costs [60]. Winkelmann et al. highlight how the supply of health services relies on the availability of HCWs, their productivity, and provider capacity (e.g. beds, theatres etc.) [6,11]. The implication is that workforce capacity, and therefore health system capacity, is about more than just the number of HCWs; it is about HCW well-being, the sustainability of their workloads and the range of supports available to them [12,32,61]. COVID-19 negatively affected each of these health service elements. A sustainable approach to health workforce capacity and planning must go beyond numbers to focus on the working lives and well-being of HCWs. If COVID-19 is to be used as an opportunity for learning and long-lasting health transformation, we need to reconfigure our definition of workforce capacity to encompass the stressors and mental well-being of HCWs. We need to recognise HCW mental health as a key part of health workforce capacity and health system resilience [9,11,36] and provide psychosocial supports to help HCWs maintain their mental wellbeing while working in challenging circumstances. Extending the findings of previous research [25,32,34], the interventions outlined in this article point to the importance of developing an integrated suite of psychosocial support-types and intervention levels for HCWs – not just in response to the pandemic but for HCW wellbeing more generally. The cases highlight a tendency to rely on secondary and tertiary interventions that focus on an individual's response to stress (e.g. mindfulness apps and online stress management resources), or periods of crisis (e.g. remote counselling and psychological support). However, effective mental health provision for HCWs requires that psychological supports are embedded within broader social and organisational supports as structural resource deficiencies cannot be overcome by psychological interventions alone [25]. For example, apps and online training on stress management will have little effect in contexts where HCWs are under daily strain due to understaffing, limited PPE and systemic organisational or managerial failures. To diminish the constellation of work-life stressors for HCWs (Figure 1), an integrated and embedded suite of psychosocial supports is required – one which includes the ‘harm prevention’ [25] conditions of appropriate training, PPE, and childcare supports as well as self-help resources, organisational supports, formal and informal peer supports, and access to professional expertise where required. Figure 3 illustrates what such a suite of supports might comprise. With health system resourcing, organisation and management as an overarching influence, this suite captures macro, meso, and micro support systems leading to targeted individual interventions in times of crisis.Figure 3 Psychosocial Suite of Supports for HCW Mental Health – adapted from David et al. [25], O'Hayer et al[34], and Williams et al.[32] - based on results. Figure 3: As the six cases highlight, mental well-being supports for HCWs during the pandemic were as much about organisational supports, PPE and childcare as they were about helplines and remote counselling. This is particularly important to address the predominance of adverse mental health outcomes among women, nurses, and those in closest contact with COVID-19 patients [10,13,24]. Interestingly, a number of countries offered salary increases or one-off bonuses to their HCWs during the pandemic. While these financial supports do not diminish the strain involved in working as a HCW during the pandemic, they indicate a public and political recognition of their efforts, and thus may have had a positive effect for HCWs. Finally, responsibility for accessing mental health supports cannot reside solely with the individual. Sourcing mental health supports, or the ‘burden of request’, must also reside at the managerial, departmental and organisational level to promote take-up by minimising stigma and ensuring protected time [34]. An effective approach to psychosocial supports must acknowledge and address the practical, professional, and personal obstacles that inhibit HCW engagement with mental health supports. Research points to the obstacles of professional stigma, unprotected time, scheduling conflicts, inability to leave the ward, family and childcare arrangements, and pandemic exhaustion [15,25,34], all of which can impede HCWs from accessing the supports they need. The Lithuanian and UK case studies support these findings. In Lithuania, the Ministry of Health amended license-to-practice regulations relating to doctors’ mental health to encourage take-up of public supports [37]. In the UK, nurses noted how time constraints and the stigma of counselling being viewed as ‘a sign of weakness’ prevented the take up of mental health supports during the first wave of COVID-19 [15]. These findings indicate how the range of mental health supports for HCWs in the UK may look adequate on paper while remaining under-utilised on the ground. Identifying and minimising these challenges will be key to engaging HCWs in much-needed mental health supports [62]. Finally, and most importantly, the suite of psychosocial supports discussed will be ineffective if pre-existing health system stresses and infrastructural weaknesses (e.g. staffing, resourcing) are not addressed [6,10]. Figure 3 illustrates this point with the suite of support levels overarched by health system resourcing, organisation and management. Systemic failures through underfunding, poor resource allocation and defective organisation shape HCWs’ daily experiences of pain and stress [4,63]. Put simply, health system resources shape the provision of working conditions that limit the harm caused by work-life stressors (e.g. working hours and work-life balance, PPE supply). A fully integrated psychosocial approach to supporting the mental health of HCWs must also address these wider health system issues in order to avoid relying on individualised, self-care supports being tacked on to working conditions which continue to actively damage HCW mental wellbeing. With so many policy areas and organisations involved, aligning multiple interests towards these goals will require an emphasis on the key features of intersectoral governance (e.g. accountability, participation and policy capacity) [35] – across national and international structures and contexts. Future research can build on the integrated approach to psychosocial supports considered here by evaluating the effectiveness of national interventions versus local well-being supports, while also assessing the value of national interventions in respect of different HCW sub-groups (doctors, nurses, allied health professionals) and health system strata. 6 Conclusion & Policy Considerations The COVID-19 pandemic brought a unique constellation of work-life stressors for HCWs, increasing the risk and prevalence of adverse mental health outcomes, especially for women, nurses, and those working directly with COVID-19 patients. As such, it represents an opportunity for change regarding how we support HCW mental well-being, if, that is, we can learn from the experience of the pandemic from policy formulation to the frontline. Such learning requires a holistic psychosocial working life approach that bridges structural capabilities and individual solutions with a view to maintaining and improving HCW mental health well beyond the COVID-19 pandemic. An integrated suite of psychosocial supports for HCWs should include harm prevention conditions relating to staffing, PPE and training, as well as self-care resources, organisational training, peer support, and access to professional expertise where required. Importantly, organisational interventions should foster team and/or department-based engagement with mental health supports to overcome an individualised burden of request. Collaborating with medical schools, training bodies, professional associations, and healthcare organisations may help address the personal (childcare), professional (competitive culture, stigma,) and practical (protected time, scheduling conflicts) obstacles to take-up of mental health supports. However, the take-up and effectiveness of any supports will be undermined where pre-existing system stresses, infrastructure weaknesses (e.g. staffing, resourcing) and obstacles to access are not tackled. This is akin to applying a topical treatment to mask a systemic disease that is left to fester. In short, there is limited efficacy in attending to the symptoms of HCW mental health distress at a local, national or international level if the underlying causes are not also addressed for the longer-term. Declaration of Competing Interet None Acknowledgements Thanks to WHO Europe for supporting this project. Thanks also to the COVID-19 Health System Response Monitor (HSRM) network who provided country-level workforce capacity data, as per van Ginneken et al [36]: Denmark: Allan Krasnik (University of Copenhagen), Hans Okkels Birk (University of Copenhagen), Signe Smith Jervelund (University of Copenhagen), Karsten Vrangbæk (University of Copenhagen) Italy: Giovanni Fattore (Bocconi University), Antonio Giulio de Belvis (Universita `Cattolica del Sacro Cuore), Alisha Morsella (Universita Cattolica del Sacro Cuore), Gabriele Pastorino (WHO Regional Office for Europe), Andrea Poscia (Universit` a Cattolica del Sacro Cuore), Andrea Silenzi (Universita ` Cattolica del Sacro Cuore), Walter Ricciardi (Fondazione Policlinico Universitario A.Gemelli) Kyrgyzstan: Aliina Altymysheva (WHO Regional Office for Europe), Nazira Artykova (WHO Regional Office for Europe), Tasnim Atatrah (WHO Regional Office for Europe), Akbar Esengulov (WHO Regional Office for Europe), Kaliya Kasymbekova (WHO Regional Office for Europe), Monolbaev Kuban (WHO Regional Office for Europe), Moldoisaeva Saltanat (WHO Regional Office for Europe), Salieva Saltanat (WHO Regional Office for Europe), Aigul Sydykova (WHO Regional Office for Europe), Nurshaim Tilenbaeva (WHO Regional Office for Europe). Lithuania: Laura Miščikienė (Lithuanian University of Health Sciences), Agnė Slapšsinskaitė (Lithuanian University of Health Sciences), Mindaugas Štelemėkas (Lithuanian University of Health Sciences) Romania: Silvia Gabriela Scintee (National School of Public Health), Dana Farcasanu (Centre for Health Policy and Services) United Kingdom: Natasha Curry (The Nuffield Trust), Selina Rajan (London School of Hygiene and Tropical Medicine ==== Refs References 1 Sagan A Webb E Azzopardi-Muscat N de la Mata I McKee M Figueras J. Health systems resilience during COVID-19: Lessons for building back better 2021 World Health Organisation Copenhagen 2 Clarke R. 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Romania: Country Health Profile 2021 OECD iLibrary2021 [Available from: https://www.oecd-ilibrary.org/content/publication/74ad9999-en. 45 Moldoisaeva S, Kaliev M, Sydykova A, Muratalieva E, Ismailov M, Madureira Lima J, et al. Kyrgyzstan: Health System Summary, 2022. Copenhagen; 2022. 46 OECD Statistics Health Care Resources Dataset 2023 OECD Stat 47 Beyond Containment: Health systems responses to COVID-19 in the OECD 2020 OECDOECD https://www.oecd.org/coronavirus/policy-responses/beyond-containment-health-systems-responses-to-covid-19-in-the-oecd-6ab740c0/ [Accessed on 04-08-22] 48 Barello S Palamenghi L Graffigna G. Burnout and somatic symptoms among frontline healthcare professionals at the peak of the Italian COVID-19 pandemic Psychiatry Research 290 2020 113129 49 Rossi R Socci V Pacitti F Di Lorenzo G Di Marco A Siracusano A Mental Health Outcomes Among Frontline and Second-Line Health Care Workers During the Coronavirus Disease 2019 (COVID-19) Pandemic in Italy JAMA Network Open 3 5 2020 e2010185-e 50 Trumello C Bramanti SM Ballarotto G Candelori C Cerniglia L Cimino S Psychological Adjustment of Healthcare Workers in Italy during the COVID-19 Pandemic: Differences in Stress, Anxiety, Depression, Burnout, Secondary Trauma, and Compassion Satisfaction between Frontline and Non-Frontline Professionals International journal of environmental research and public health 17 22 2020 51 European Observatory on Health Systems and Policies Health Systems in Action - Kyrgzstan European Observatory on Health Systems and Policies 2021 World Health Organization [Available from: https://eurohealthobservatory.who.int/publications/i/health-systems-in-action-kyrgyzstan 52 Molchanova ES Kharsun VS Kenzhebaeva ZS Alikanova AS. Experiences of Kyrgyzstani Frontline Healthcare Workers during the “Black July” of 2020: a Qualitative Study Consortium Psychiatricum 3 2 2022 97 110 53 Molchanova ES. Preliminary results of the nationwide survey on COVID-19 consequences Kyrgyz State Medical Academy Annual Meeting Bishkek 2021 54 Webb E Winkelmann J Scarpetti G Behmane D Habicht T Kahur K Lessons learned from the Baltic countries’ response to the first wave of COVID-19 Health Policy 126 5 2022 438 445 35101287 55 Stanyte A Podlipskyte A Milasauskiene E Király O Demetrovics Z Ambrasas L Mental Health and Wellbeing in Lithuanian Medical Students and Resident Doctors During COVID-19 Pandemic Frontiers in Psychiatry 13 2022 56 Džakula A Banadinović M Lovrenčić IL Vajagić M Dimova A Rohova M A comparison of health system responses to COVID-19 in Bulgaria, Croatia and Romania in 2020 Health Policy 126 5 2022 456 464 35221121 57 Anderson M Pitchforth E Edwards N Alderwick H McGuire A E M The United Kingdom: Health system review 2022 World Health Organization https://eurohealthobservatory.who.int/publications/i/united-kingdom-health-system-review-2022 [Accessed on 08-08-22]Contract No.: 1 58 OECD & European Observatory on Health Systems Policies United Kingdom: Country Health Profile 2019 2019 State of Health in the EU, European Observatory on Health Systems and Policies OECD Publishing [Available from: https://eurohealthobservatory.who.int/publications/m/united-kingdom-country-health-profile-2019 59 Unruh L Allin S Marchildon G Burke S Barry S Siersbaek R A comparison of 2020 health policy responses to the COVID-19 pandemic in Canada, Ireland, the United Kingdom and the United States of America Health Policy 126 5 2022 427 437 34497031 60 Bodenheimer T Sinsky C. From Triple to Quadruple Aim: Care of the Patient Requires Care of the Provider The Annals of Family Medicine 12 6 2014 573 576 25384822 61 Byrne J-P Creese J Matthews A McDermott AM Costello RW Humphries N. ‘…the way it was staffed during COVID is the way it should be staffed in real life…’: a qualitative study of the impact of COVID-19 on the working conditions of junior hospital doctors BMJ Open 11 8 2021 e050358 62 Arnold-Forster A Moses JD Schotland SV Obstacles to Physicians’ Emotional Health — Lessons from History The New England Journal of Medicine 386 1 2022 4 7 34979069 63 Byrne J-P Conway E McDermott AM Matthews A Prihodova L Costello RW How the organisation of medical work shapes the everyday work experiences underpinning doctor migration trends: The case of Irish-trained emigrant doctors in Australia Health Policy 125 4 2021 467 473 33551205
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==== Front Sci Total Environ Sci Total Environ The Science of the Total Environment 0048-9697 1879-1026 Published by Elsevier B.V. S0048-9697(23)03795-6 10.1016/j.scitotenv.2023.165172 165172 Article Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism Acosta Nicole a Dai Xiaotian b Bautista Maria A. c Waddell Barbara J. a Lee Jangwoo a Du Kristine a McCalder Janine ac Pradhan Puja ac Papparis Chloe ac Lu Xuewen b Chekouo Thierry bd Krusina Alexander ef Southern Danielle ef Williamson Tyler efg Clark Rhonda G. c Patterson Raymond A. h Westlund Paul i Meddings Jon f Ruecker Norma j Lammiman Christopher k Duerr Coby k Achari Gopal l Hrudey Steve E. mn Lee Bonita E. opq Pang Xiaoli mqr Frankowsk Kevin s Hubert Casey R.J. c Parkins Michael D. aft⁎ a Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada b Department of Mathematics and Statistics, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada c Department of Biological Sciences, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada d Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St. S.E., Minneapolis, MN 55455, USA e Department of Community Health Sciences, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada f Department of Medicine, University of Calgary and Alberta Health Services, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada g O'Brien Institute for Public Health, University of Calgary, 3280 Hospital Dr NW, Calgary, Alberta T2N 4Z6, Canada h Haskayne School of Business, University of Calgary, SH 250, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada i C.E.C. Analytics Ltd., Calgary, Canada j Water Services, City of Calgary, 625 25 Ave SE, Calgary, Alberta T2G 4k8, Canada k Calgary Emergency Management Agency (CEMA), City of Calgary, 673 1 St NE, Calgary, Alberta T2E 6R2, Canada l Department of Civil Engineering, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada m Department of Laboratory Medicine and Pathology, University of Alberta, 116 St. and 85 Ave, Edmonton, Alberta T6G 2R3, Canada n Analytical and Environmental Toxicology, University of Alberta, 116 St. and 85 Ave, Edmonton, Alberta T6G 2R3, Canada o Department of Pediatrics, University of Alberta, 116 St. and 85 Ave, Edmonton, Alberta T6G 2R3, Canada p Women & Children's Health Research Institute, 116 St. and 85 Ave, Edmonton, Alberta T6G 2R3, Canada q Li Ka Shing Institute of Virology, University of Alberta, 116 St. and 85 Ave, Edmonton, Alberta T6G 2R3, Canada r Alberta Precision Laboratories, Public Health Laboratory, Alberta Health Services, 116 St. and 85 Ave, Edmonton, Alberta T6G 2R3, Canada s Advancing Canadian Water Assets, University of Calgary, 3131 210 Ave SE, Calgary, Alberta T0L 0X0, Canada t Snyder Institute for Chronic Diseases, University of Calgary and Alberta Health Services, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada ⁎ Corresponding author at: Departments of Medicine and Microbiology, Immunology & Infectious Diseases, Cumming School of Medicine, University of Calgary, Section Chief, Infectious Diseases, Calgary Zone, Alberta Health Services, Postal address: 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada. 26 6 2023 26 6 2023 16517221 1 2023 21 6 2023 25 6 2023 © 2023 Published by Elsevier B.V. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Wastewater-based surveillance (WBS) of infectious diseases is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19's impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.4 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5 % (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4524 unrelated absences COVID-19 cases were recorded. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P < 0.0001). The Poisson regression with wastewater as a one-week leading signal has an Akaike information criterion (AIC) of 858, compared to a null model (excluding wastewater predictor) with an AIC of 1895. The likelihood-ratio test comparing the model with wastewater signal with the null model shows statistical significance (P < 0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19. Graphical abstract Unlabelled Image Keywords Epidemiology Sewage SARS-CoV-2 Staffing Labour scheduling Prediction Editor: Warish Ahmed ==== Body pmcData availability Data will be made available on request.
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==== Front Heliyon Heliyon Heliyon 2405-8440 Published by Elsevier Ltd. S2405-8440(23)04733-3 10.1016/j.heliyon.2023.e17525 e17525 Article Implications of the COVID-19 pandemic on the shanghai, New York, and Pakistan stock exchanges Aamir Muhammad a Khan Nazeem a Naeem Muhammad a Bilal Muhammad ab Khan Faisal c Abdullah Saleem d∗ a Department of Statistics, Abdul Wali Khan University Mardan, Pakistan b Department of Mathematical Sciences, Balochistan University of Information Technology, Engineering and Management Sciences (BUITEMS), Quetta, Pakistan c Department of Electrical and Electronic Engineering, College of Science and Engineering, National University of Ireland Galway, Ireland d Department of Mathematics, Abdul Wali Khan University Mardan, Pakistan ∗ Corresponding author. 26 6 2023 26 6 2023 e1752522 8 2022 19 6 2023 20 6 2023 © 2023 Published by Elsevier Ltd. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. This research aims to determine the impact of COVID-19 on the stock markets of Pakistan (Islamabad), China (Shanghai), and the United States of America (New York). These three stock markets were chosen to demonstrate the variation in the degree of influence based on varied times in which the respective nations were impacted by COVID-19. COVID-19, a pandemic virus, was still present in China in December 2020. The one-year timeline helps us understand the pattern of the effect on different stock markets that show onward to guide us to indicate that in this situation, the lack of economic movement (due to the lockdown) had a more negative effect on stock prices than the increase in the number of new confirmed cases of the COVID-19 virus. This study was carried out to assess the influence of COVID-19 on the financial sectors, including the stock market. The effects were assessed by employing the Autoregressive Distributed Lag Model (ARDL) to demonstrate correlations between three stock markets (Pakistan, Shanghai, and New York) and COVID-19 instances. The study's major goal is to demonstrate the differences in the three countries' levels of influence. We got empirical results and discovered that the confirmed cases had a detrimental influence on three stock exchanges. However, all three countries saw an increase in the number of recovery cases. The number of deaths was minor for Pakistan and China but had a detrimental impact on the New York Stock Exchange. Keywords Auto-regressive distributed lag (ARDL) COVID-19 New York stock exchanges Pakistan And shanghai ==== Body pmc1 Introduction Numerous sicknesses or diseases, pandemics, and epidemics have struck the world, causing not only economic disruption but also the emergence of various diseases as a result of pandemic clues, damage, psychological issue, disaster, unrest, and terror fear. Some annihilative pandemics and epidemics were still expected to erupt in China in December 2020, according to COVID-19. The viral propagation of this pandemic is frequently quicker than that of any prior pandemic. This pandemic has infected millions of people and is presently expanding rapidly. A pandemic poses a significant risk not just to a single nation, but to the entire world, because globalization connects the country of one individual, and the virus has a secondary impact. The pandemic had a financial impact on many economies throughout the world, producing suffering, misery, and high mortality rates. In December 2019, a new viral case was detected in Wuhan, China. The virus is a cold and severe acute respiratory syndrome (SARS) virus [1]. Coronavirus symptoms include shortness of breath or difficulty breathing, cold or fever, sore throat, fatigue, muscle or bodily aches, loss of smell or taste, headache, diarrhea, and nausea or vomiting. A patient died as a result of COVID-19 on January 11, 2020, and the therapy was unsuccessful [2]. During the outbreak, COVID-19 cases were reported in South Korea, Australia, France, Malaysia, Singapore, Taiwan, and Nepal. Thousands of people died and millions were sick by the end of April. Following that, South Korea was the second country to face a huge COVID-19 pandemic, with around 9 million positive COVID-19 cases reported globally, as well as 472, 539 fatalities [3]. The number of positive cases and recoveries has increased since the disease's inception. However, the number of cases in Europe has declined while it has increased in Asia and America. On March 11, 2020, WHO declared the outbreak to be worldwide, affecting more than 170 countries [4]. COVID-19 has pushed the world towards low-cost markets, followed by a catastrophic slump. Protective events, such as community separation and lockdowns, must illustrate their importance to a larger extent, but they come at a cost in the form of reduced commercial sales and smoothing the stability of diverse organizations. COVID-19-related financial expenditures have also had an influence on global stock markets. Confirmed pandemic instances on global stock markets have occurred in practically every place, and the Pakistani stock exchange is also one of the linkages impacted by COVID-19. The Autoregressive Distributed Lag Model (ARDL) is an ordinary least square (OLS) based model that is appropriate for both stationary and non-stationary time series with mixed order of variables I(0), I(1) to estimate relationships between three stock exchanges (Pakistan, Shanghai, and New York) and COVID-19, which contains new confirm, death, and recover cases in due course, to investigate the impact of COVID-19 on the financial sectors, including the stock market. The goal of this study is to see how COVID-19 affects three stock markets. The pandemic has touched almost every business on the earth. As a consequence, the current study will help in evaluating the relationship between COVID-19 events and stock market exchanges, particularly for the three COVID-19-affected countries. The research perspective is that stock analysts use stock analysis to forecast the future activities of an instrument, industry, or market for investors. And the traders decide which stocks to purchase and sell. By studying and analyzing past and current market data, investors and traders may get a competitive advantage in the market. 1.1 Literature review Several incidents were cited to indicate how the ongoing epidemic is harming the stock market. Forecasting COVID-19 is also important, and numerous ways have been developed by researchers. The researchers [5] developed and implemented the Kalman filter in the top four countries: the United States, India, Pakistan, and Russia. The Kalman filter is applied to both the smoothed data and the filter technique. The researchers also find the forecast for the top four countries for the following fifteen days. Confirmed cases grow in the United States and India while decreasing in Brazil and Russia; recover cases increase in the United States, India, and Brazil while decreasing in Russia; and deaths decrease in the United States while increasing in Russia, India, and Brazil. Similarly, the authors [6] devised an ensemble learning-based strategy and compared the results to the standard ARIMA model. Furthermore, based on their findings, ensemble learning outperforms the traditional ARIMA model. Furthermore, the author forecasted recovered instances for Pakistan in the next fifteen days. The influence of COVID-19 on the stock market and potential participation plans by studying various companies such as travel-related enterprises, technology, and gold performance [7]. The learning is systematic, and the pandemic might have opposing impacts in the long run and in the short run. COVID-19 has impacted the stock markets of 21 nations. It has been discovered that the epidemic had a detrimental impact on the affected country's stock market, causing a quick drop in stock prices and return. The researchers [8] utilized a simple regression model to determine the effect of COVID-19 on the Chinese and American economies and while [9] used regression model as an input machine learning approaches to predict the COVID cases. The study examined short-term data and discovered that COVID-19 had a considerable favorable influence on the New York Dow Jones index and the Shanghai stock market. Authors [10] analyzed the stock market during the epidemic. They employed algorithms to determine the impact of the epidemic on the US stock market. According to the research study, the major source of government limits on marketable activity in a service-oriented economy was voluntary social isolation. The COVID-19 virus shook global financial markets, resulting in an irregular or unexpected backdrop with significant fluidity points. Authors [11] investigated pollution and its impact on individuals. Sequential and geographical autocorrelations to investigate the dynamics of country-wide infection growth rates and discovered significant values [12]. The researchers [13] employed price gradient analysis to determine how the current pandemic affects housing worth. The authors [14] underlined that COVID-19 has an impact on registered insurance manufacturing enterprises. The researchers [15] investigated the total number of 10th developing marketplaces and discovered that COVID-19 affected the most firms. COVID-19 to research emerging markets. COVID-19 variation has an impact on economic markets [16]. According to Ref. [17], public concern, together with constraints and isolation, has an impact on market companies. They [18] showed that gold and oil were useless during the pre-epidemic corona. Later, depositors might discover moneymaking methods based on market shortcomings to achieve uneven returns. On the contrary [19], proposed that ideal savings include sightseeing manufacturing, skill sector, industrial holidays, and gold. Authors [20] criticizes the firms' response to COVID-19 in numerous ways, because many segments were shielded during the separation stage, and it verified that the companies will be vulnerable to the epidemic. The energy sector had the most irregular negative profits of any category [21]. Chinese industrial section was hardly affected by COVID-19, although the other sectors, such as building construction, computer services and software, fitness maintenance, and community service, were less affected [22]. Economic markets resisted the flight-to-safety phenomena, which resulted in a clear failure in asset values and increased global instability. Authors [23] investigated and concluded that the casualty rate had a significant impact on economic instability, but the provenance of olive stocks was not as influenced. An economy is a government act, with subsequent responses reliant on experts [24], responds that a recurrence of possible illnesses is associated with a 4%–11% decrease in aggregate market value. COVID-19 instances are more than only death cases, indicates that COVID-19 affected the stock market [25]. The researchers [26] demonstrate that there are disparities in the number of death cases and new cases. Furthermore, people were severely affected by the coronavirus, but this had little effect on stock market yields outside of the United States, omitting the number of cases in China. The global expansion of COVID-19 resulted in a higher growth in earnings on independent safeties than in emerging and developing nations. The authors [27] investigated the stock market impact of COVID-19 by studying several manufacturers such as transportation, manufacturing, metals, and technology, entertainment. In his investigation, the author discovered that the pandemic might have opposite effects in the short term and rises in the long run. The researchers [28] investigated COVID-19 and its effects on stock market conditions. They utilized the regression approach and discovered that the epidemic has a significant negative impact on the valuable country's stock market and can cause a direct fall in stock prices and returns. The authors [29] explored the commercial issues caused by COVID-19. In the first quarter of 2020, they used data from over 6,000 businesses from 56 countries. They were interested in learning how COVID-19 cases affected stock prices and corporate attributes. According to the study's findings, enterprises with superior pre-2020 finances had less experience with pandemics, fewer fixed managers, and larger social charge activities, and they were able to manage a little pandemic-induced stock price drop. Furthermore, the statistics reveal that corporations with larger corporate possession outperformed organizations with developed tenure of dodge assets. All the past literature helps in carrying out this study on COVID-19 pandemic impacts on the Shanghai, New York, and Pakistan Stock Exchanges. 1.2 General model formulation The Autoregressive Distributed Lag (ARDL) model is a time-series data model in statistics and econometrics that illustrates a link between explanatory and dependent variables based on both current and lagged values of both explanatory and dependent variables. The general form of ARDL is as under in eq. (1):(1) logyt=α0+∑i=0mβilogyt−i+∑j=0nδjlogct−j+∑j=0nγjlogdt−j+∑j=0nωjlogrt−j+εt Model (1) is a distributed lag model with finite lags, which is further simplified as follows in eq. (2).(2) yt=α0+β1yt−1+β2yt−2+…,βjyt−j+δ1ct+δ2ct−1…,δict−j+γ1dt+γ2dt−1+…,γidt−j+ω1rt+ω2rt−1+…,ωirt−j+εt Where yt represents the number of daily stock exchanges in three separate countries at the time t: Pakistan, Shanghai, and New York while α0 represents the intercept term in the same course and β1yt−1+β2yt−2+…,βjyt−j are the qth autoregressive lag order of the model. The regressors “confirm” “death” and recovery cases are denoted by ct, dt and rt respectively. Whereas δ1ct+δ2ct−1+…,δict−j,γ1dt+γ2dt−1+…,γidt−jandω1rt+ω2rt−1+…,ωirt−j are the lags in order of ct, dt and rt respectively. The parameters β, δ, γ, and ω coefficients of the stock exchange, confirm, death and recovery while εt denotes the error term. The explicit form of the ARDL model is as follows in eq. (3):(3) yt=α+∑i=1pπiyt−1+∑j=0rβjxt−j+εt Where yt and xt of the values of regressand and regressor and εt is the error term. 1.3 Research methodology This section explains the research methodology and estimation strategy, which covers the unit root test and auto-regressive distributed lag (ARDL). The ARDL model may be extended to account for different stock exchange yt, confirm ct, death dt and recover rt. Pakistan Stock Exchange (PSX) model is given as in eq. (4):(4) yt(psx)=−α0−β1yt−1+β2yt−2−β3yt−3−β4yt−4−δ1ct−1−δ2ct−2+ω1rt−1+εt Shanghai Stock exchange (SSE) model is given as in eq. (5):(5) yt(sse)=−α0−β1yt−1−β2yt−2−β3yt−3−β4yt−4−δtct+δ1ct−1+δ2ct−2+ω3rt−3+εt New York Stock Exchange (NYSE) model is given as in eq. (6):yt(nyse)=−α0−β1yt−1−β2yt−2−β3yt−3−β4yt−4−β5yt−5+δ2ct−2+δ3ct−3+δ4ct−4 (6) +δ5ct−5+γ4dt−4+ω4rt−4+εt The negative coefficients of the variables indicated that there is a negative association between stock exchanges and the number of COVID-19 instances in eqs. (4), (5), (6). 1.4 Unit root test Each variable in the model was tested for stationarity and non-stationarity using the Augmented Dickey-Fuller test and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test and the results are presented in Table 1, Table 2 .Table 1 Summary statistics of PSX and COVID-19 cases. Table 1Variables Mean Median Minimum Maximum Std. Dev Obs PSX 38891 40095 27229 46934 4839 366 Confirm 1591 1236 0 8687 1405.81 366 Death 34.17 27 0 153 30.77 366 Recovery 1473 859 0 16813 2021.59 366 Table 2 ADF and KPSS test for Pakistan Stock Exchange (PSX). Table 2 ADF Test KPSS Test Variables ADF statistics P-Value Decision KPSS statistics P-Value Decision PSX −12.47 0.01 Stationary 0.01015 0.1 Stationary Confirm −15.91 0.01 Stationary 0.00917 0.1 Stationary Death −14.694 0.01 Stationary 0.00961 0.1 Stationary Recovery −15.986 0.01 Stationary 0.00961 0.1 Stationary Table 1 shows five number summary statistics, while Table 2 shows the Kwiatkowski-Phillips-Schmidt-Shin and Augmented Dickey-Fuller unit results. Because all of the variables are stationary, the model (4) result is shown in Table 3 .Table 3 ARDL model of Pakistan stock exchange. Table 3Coefficients Estimate Std. Error t-value p-value Intercept −7.865e-05 7.334e-04 −0.107 0.9147 LOG (Ct.t) - 6.930e-03 3.269e-03 −2.120 0.0348 * LOG (Ct.1) −1.079e-02 5.184e-03 −2.081 0.0382 * LOG (Ct.2) −1.324e-02 6.075e-03 −2.180 0.0301 * LOG (Rt.1) 2.937e-03 1.844e-03 1.592 0.0112 * LOG (Yt.1) −1.240e+00 5.367e-02 −23.108 <2e-16 *** LOG (Yt.2) −1.109e+00 7.745e-02 −14.314 <2e-16 *** LOG (Yt.3) −8.154e-01 7.661e-02 −10.644 <2e-16 *** LOG (Yt.4) −3.786e-01 5.290e-02 −7.158 6.36e-12 *** Residual standard error: 0.01319 Multiple R-squared 0.6871 Adjusted R-squared: 0.662 F-statistic: 27.36 p-value <2.2e-6 Source: Author’s computation using R (dLagM) package *** represent 1%, **5% and *10% level of significance respectively The coefficient is adversely connected to the confirming cases LOG (Ct) and its current, first, and second lag at the 10% level, while the first lag of the recovery LOG (Rt) is positively significant at the 10% level and the deaths cases are inconsequential. Furthermore, from the first to fourth lags, the coefficient of a stock exchange response variable LOG (Yt) is significant (negative) at the 1% level. The whole model is highly significant at the 1%, 5%, and 10% levels, with p-values less than 2.2e-16 and Adjusted R-squared values of 66.2% and 68.71%, respectively. The fitted model is denoted by eq. (7) and represented graphically in Fig. 1 :yt(psx)=−0.0007−1.240yt−1−1.10yt−2−0.082yt−3−0.037yt−4−0.0069ct−0.017ct−1 (7) −0.0133ct−2+0.0029rt−1 Fig. 1 Pakistan stock exchange verses COVID-19 cases. Fig. 1 Fig. 1 depicts the complete panel of the number of new pandemic i.e. confirmed, mortality, and recovery cases, which are compared to monthly movements in the Pakistan stock market. The figure demonstrates temporal similarities and differences in the trend line for each number of factors. The Pakistan stock market is plotted against the number of COVID-19 cases, having effect from February 2020 to February 2021. It is obvious that in the first month of March 2020 confirm cases began the Pakistan stock market went down with less record value on April 2, 6260 and confirm is 0 after April the trend line of the Pakistan stock market increased slowly fluctuating with the highest value of 46934 in February 2021 and 27229 at its lowest in March 2020 it fluctuated up to 72.3% in this period. We may deduce from the graph that when the number of pandemic cases increases or decreases after May 2020, the Pakistan stock market rises till the conclusion of the timeline period. Shanghai Stock Exchange and COVID-19 implications summary statistics and corresponding Augmented Dickey-Fuller test and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test results are presented in Table 4 and Table 5 respectively.Table 4 Summary statistics of shanghai stock exchange and COVID-19 cases. Table 4Variables Mean Median Minimum Maximum Std. Dev Obs SSE 3144 3227 2660 3621 253.5 366 Confirm 83143 85800 547 98800 18006.55 366 Death 4139 4648 0 4803 1179.58 366 Recovery 228 19 0 3622 582.58 366 Table 5 ADF and KPSS tests for shanghai stock market. Table 5 ADF Test KPSS Test Variables ADF statistics P-Value Decision KPSS statistics P-Value Decision SSE −10.63 0.01 Stationary 0.0135 0.1 Stationary Confirm −6.89 0.01 Stationary 0.0083 0.1 Stationary Death −8.16 0.01 Stationary 0.0093 0.1 Stationary Recovery −11.43 0.01 Stationary 0.0103 0.1 Stationary Table 4 shows five number summary statistics for SSE, while Table 5 shows the Kwiatkowski-Phillips-Schmidt-Shin and Augmented Dickey-Fuller unit results. Because all of the variables are stationary, the model (5) result is shown in Table 6 .Table 6 ARDL model of SSE & COVID-19 Cases. Table 6Coefficients Estimate Std. Error t-value P-value Intercept −7.865e-05 7.334e-04 −0.107 0.9147 LOG (Ct.t) −6.930e-03 3.269e-03 −2.120 0.0348 * LOG (Ct.1) −1.079e-02 5.184e-03 −2.081 0.0382 * LOD (Ct.2) −1.324e-02 6.075e-03 −2.180 0.0301 * LOG (Rt.3) 4.084e-03 5.569e-03 0.733 0.0639 * LOG (Yt.1) −1.240e+00 5.367e-02 −23.108 <2e-16 *** LOG (Yt.2) −1.109e+00 7.745e-02 14.314 <2e-16 *** LOG (Yt.3) −8.154e-01 7.661e-02 −10.644 <2e-16 *** LOG (Yt.4) −3.786e-01 5.290e-02 −7.15 6.36e-12 *** Residual standard error: 0.0131 Multiple R-squared: 0.6871 Adjusted R-squared: 0.662 F-statistic: 27.36 p-value: <2.2e-16 Source: Author’s computation using R (dLagM) package *** represent 1%, **5% and *10% level of significance respectively The coefficients of the confirmed cases LOG (Ct) and its current, first, and second delays are all significant at the 10% level, but the third lag of the recovery LOG (Rt) is not. Furthermore, from the first to fourth lags, the coefficient of the China stock exchange response variable LOG (Yt) is significant (negative) at 1%. At the 1%, 5%, and 10% levels, the model is highly significant, with p-values less than 2.2e-16 and Adjusted R-squared values of 66.2% and 68.71%, respectively. The fitted model is denoted by eq. (8) and is explicitly illustrated in Fig. 2 :yt(sse)=−0.00007−1.240yt−1−1.10yt−2−0.082yt−3−0,037yt−4−0.0069ct (8) −0.017ct−1−0.0133ct−2+0.0004rt−3 Fig. 2 Shanghai stock exchange against COVID-19 cases. Fig. 2 Fig. 2, in complete panel, portrays the pandemic's influence on the Shanghai stock exchange markets in terms of new confirmed, death, and recovery cases. The confirmed cases began to rise abruptly and considerably in January 2020 and continued to rise steadily until August 2020, when suddenly began to fall sharply, with the number of confirmed cases falling to 547, and then rising steadily until January 2021. Furthermore, we can see that in the early phases of the pandemic, the Shanghai stock market has a negative effect between February and April 2020, then steadily increases and decreases until the conclusion of the timeframe. The Shanghai stock exchange achieved a high of 3621 in January 2021 and a low of 2660 in March 2020. During this time, it fluctuated up to 36.12%. The figure shows that the Shanghai stock market had a negative impact in the early phases of recovery owing to new infected and death cases. After July 2020, the confirmed cases and April death cases stay stable and had no negative influence on the stock market. New York Stock Exchange (NYSE) and COVID-19 implications summary statistics and corresponding Augmented Dickey-Fuller test and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test results are presented in Table 7 and Table 8 respectively.Table 7 Summary statistics of NYSE and COVID-19 cases. Table 7Variable Mean Median Minimum Maximum Std.Dev Obs NYSE 26988 27443 18592 31188 2627.6 366 Confirm 65729 39969 0 402270 71813.48 366 Death 1196 931 0 40313 2271.36 366 Recovery 64532.99 18425 0 501134 96869.9 366 Table 8 ADF and KPSS tests for NYSE. Table 8 ADF Test KPSS Test Variables ADF statistics P-Value Decision KPSS statistics P-Value Decision NYSE −14.76 0.01 Stationary 0.0930 0.1 Stationary Confirm −1.71 0.01 Stationary 0.0097 0.1 Stationary Death −14.69 0.01 Stationary 0.0084 0.1 Stationary Recovery −21.91 0.01 Stationary 0.0091 0.1 Stationary Table 4 shows five number summary statistics for NYSE, while Table 5 shows the Kwiatkowski-Phillips-Schmidt-Shin and Augmented Dickey-Fuller unit results. Because all of the variables are stationary, model (6) result is shown in Table 9 .Table 9 ARDL model of NYSE & COVID-19 Cases. Table 9Coefficients Estimate Std. Error t value P-value (Intercept) −0.0002981 0.0008780 −0.340 0.73444 LOG (Ct.2) - 0.0245288 0.0100759 2.434 0.01556 * LOG (Ct.3) 0.0324619 0.0099030 3.278 0.00118 ** LOG (Ct.4) 0.0296426 0.0091047 3.256 0.00127 ** LOG (Ct.5) 0.0135858 0.0069112 1.966 0.05033 LOG (Dt.2) −0.0060942 0.0034588 2.434 0.07920. LOG (Dt.4) −0.0081194 0.0033701 −2.409 0.01664 * LOG (Dt.5) −0.0059702 0.0027781 −2.149 0.03251 * LOG (Rt.4) 0.0015286 0.0023315 −0.656 0.01664 * LOG (Yt.1) −1.2221861 0.0571255 −21.395 <2e-16 *** LOG (Yt.2) −1.2651525 0.0838761 −15.084 <2e-16 *** LOG (Yt.3) −0.8877147 0.0937629 −9.468 <2e-16 *** LOG (Yt.4) −0.5102230 0.0815206 −6.259 1.49e-09 *** LOG (Yt.5) −0.1453392 0.0540557 −2.689 0.00761 ** Residual standard error: 0.01519 Adjusted R-squared: 0.6522 Multiple R-squared: 0.6824 F-statistic: 22.64 p-value: <2.2e-16 Source: Author’s computation using R (dLagM) package *** represent 1%, **5% and *10% level of significance respectively The coefficient favorably connected of confirm cases LOG (Ct) at the lags from third to fifth and its second lag is negatively significant at the level of 5%, third, and fourth lags are significant at the level of 1%, and lag fifth is significant at the level of 10% in Table 9. LOG (Dt) death is adversely significant at the 10% level at the second and fourth delays and the 5% level at the fifth lag. Furthermore, LOG (Rt) recovery is favorably significant at lag fourth at the level of 5%, whereas LOG (Yt) is significantly negative from lags first to fourth at the level of 0.1% and lag fifth at the level of 1%, with the adjusted R-squared 65.22% and multiple R-squared 68.24%. The fitted model is denoted by eq. (9) and represented graphically in Fig. 3 :(nyse)=−0.0029−1.2220yt−1−1.265yt−2−0.888yt−3−.0510yt−4−0.145yt−5−0.024ct−2 (9) +0.032ct−3+0.029ct−4−0.006dt−2−0.008dt−4−0.005dt−5+0.001rt−4 Fig. 3 New York stock exchange against COVID-19 cases. Fig. 3 Fig. 3 shows the panel of the number of newly infected, death, and recovery cases, which are compared to the monthly change in the New York stock market. The plots demonstrate a variation in the trend line of each factor. The above figure indicates the New York stock exchange, while the below graph represents pandemic factors, and it is clear that the pandemic began in February 2021 and increased in April. The stock price of New York was 18592 in January 2021, with the highest being 31188 in January 2021, indicating a 67.7% fluctuation in stock prices during this period. Following April, the stock price of New York has no negative influence as compared to the volatility of pandemic instances. The numbers compare the impacts of COVID-19, and all stock exchanges fell with the peak of COVID-19 cases and then rose with the decrease of COVID-19 instances. This demonstrates that the COVID-19 epidemic impacted all stock markets. Fig. 4 shows the complete panel of the three stock markets (Pakistan, Shanghai, and New York) had a negative substantial influence at the same time in April because of COVID-19 incidents. Furthermore, there is an increase at various times with distinct fluctuation, we can say about the fluctuation in the three stock markets Pakistan at 72.3%, Shanghai at 36.12%, and New York at 67.7% in the research period owing to COVID-19 instances. Furthermore, the statistics suggest that all nations were impacted, and the number of new confirmed cases grew over time. The United States of America is the most afflicted country, with a high number of confirmed deaths.Fig. 4 Pakistan, shanghai, and New York stock exchange. Fig. 4 In Fig. 5 , the panel portrays a one-year trajectory of newly infected COVID-19 cases beginning, rising, and falling, and so on for the three countries Pakistan, the United States (US), and China. China was the first nation where the pandemic began; in August, it dropped to a lower value of 547, and the trend line returned to its original level, remaining constant until January 2021. Following the United States, the number of cases continues to rise and fall from February 2020 to January 2021, reaching a peak point in December. Pakistan is afflicted immediately after China, with the highest number of persons sick owing to COVID-19 in June and July gradually decreasing to October and fluctuating again up to January 2021. We find that Pakistan is doing better than China and the United States in terms of new confirmed cases. There might be a variety of causes for disparities in mortality instances between countries, such as geographic locations, the number of lockdowns, isolation centers, rehabilitation, and so on.Fig. 5 Pakistan, China, and the USA confirm Cases. Fig. 5 The complete panel represented by Fig. 6 illustrates a one-year timeline for new COVID-19 mortality cases, showing the rise and decline and so on for three countries: Pakistan, China, and the United States. According to the graph, China had more mortality instances than the other two nations. It reached its peak in April and then remained on the street until the survey ended. The United States experienced its first death case at the end of March, with the largest number reported in April and then drastically decreasing up to the end of time. The death cases in Pakistan with the highest value of 153 then fluctuate further and it is observed that at the same time in the United States and China, death cases pick up in the United States highly and down from China; further China remains up until the finish. In Pakistan, the number of instances fluctuates between the two nations, with a growing and declining trend. The three variables were compared at the same time in the figure to show the variation. The next graph is made up of three panels for recovery scenarios, each of which plays a significant part in the stock market and has a beneficial influence on the stock exchange.Fig. 6 Pakistan, China, and the USA deaths cases. Fig. 6 In Fig. 7 , the panel demonstrates the one-year timeframe for COVID-19 recovery cases beginning to grow and decline in three countries: Pakistan, China, and the United States. The following two graphs show that in July 2020, the number of recovery cases in the United States and Pakistan is growing and dropping at the same time. In December, the number of cases increased significantly, but there were fewer of them in Pakistan. And China registered a peak figure of 3622 in March, with no further change.Fig. 7 Pakistan, China, and the United States recovery cases. Fig. 7 Table 10 provides an overall overview and comparison highlight, demonstrating variation, Coefficient of Variation, and effect across three stock exchanges: Pakistan (PSX), New York (NYSE), and Shanghai (SSE), as well as COVID-19 examples. Confirm cases had a negative significant impact on the three stock markets, recoveries had a positive significant impact, and death cases had a negative significant impact (NYSE). While volatility and coefficient of variation are at an all-time high in the Pakistan stock market, followed by NYSE, and SSE respectively.Table 10 Overall summary of stock exchanges and COVID-19 cases. Table 10Stock Exchanges Confirms Deaths Recoveries Fluctuation C.V PSX -ve significant Insignificant +ve significant 72.3% 12.44% NYSE -ve significant -ve significant +ve significant 67.7% 9.7% SSE -ve significant insignificant +ve significant 36.1% 8.06% 2 Conclusion The significance of COVID-19 on the three stock exchanges in Pakistan, Shanghai, and New York is examined in this research. The Auto-Regressive Distributed Lag (ARDL) model was used in this study, and data were collected from January 2020 to January 2021 to capture the influence of three stock markets. This study aims to determine the relationship between COVID-19, stock exchange fluctuations, and changes in economic policy. According to the ARDL model, the pandemic had a negative influence on three stock markets at the start of COVID-19 instances since the virus transmits very swiftly from person to person daily. Furthermore, the graph shows that the stock market has a negative influence at the start of COVID-19, followed by a gradual upward trend up to the end of the research in the three stock markets. The study also looks at the fluctuation parallels and dissimilarities that develop with a high degree in Pakistan's stock trend 72.3%, USA's 67.7%, and China's 36.1%. We concluded that the confirmed cases harm stock markets, recovery is positive, and the fatalities effect is modest in China and Pakistan but substantial in New York. Author contribution statement All authors listed have significantly contributed to the development and the writing of this article. 3 Data availability statement The data was taken from Pakistan, Shanghai, and New York stock exchanges website whereas the COVID-19 pandemic data was taken from respective countries health department websites. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ==== Refs References 1 Lei C. Protocol of a randomized controlled trial testing inhaled Nitric Oxide in mechanically ventilated patients with severe acute respiratory syndrome in COVID-19 (SARS-CoV-2) medRxiv 2020 2 Aziz A. Islam M.M. Zakaria M. COVID-19 exposes digital divide, social stigma, and information crisis in Bangladesh Media Asia 47 3–4 2020 144 151 3 Hopkins J. 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==== Front IDCases IDCases IDCases 2214-2509 Published by Elsevier Ltd. S2214-2509(23)00154-3 10.1016/j.idcr.2023.e01830 e01830 Case Report Alopecia Universalis after Injection of messenger RNA COVID-19 Vaccine. A Case Report Iwata Kentaro a⁎ Kunisada Makoto b a Division of Infectious Diseases, Kobe University Hospital, 7-5-2 Kusunokicho, Chuoku, Kobe, Japan b Department of Dermatology, Kobe University Hospital, 7-5-2 Kusunokicho, Chuoku, Kobe, Japan ⁎ Correspondence to: Division of Infectious Diseases Therapeutics, Kobe University Graduate School of Medicine, Kusunokicho 7-5-2, Chuoku, Kobe, Hyogo 650-0017 Japan. Fax.: (+81-78-382-6298). 27 6 2023 27 6 2023 e0183017 5 2023 26 6 2023 © 2023 Published by Elsevier Ltd. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Messenger RNA vaccines against SARS-CoV-2 infection, or COVID-19 dramatically changed the landscape of the fight against COVID-19 pandemic. However, they might be associated with various side effects, such as myocarditis. Herein we report a case of alopecia universalis occurring after injection of an mRNA COVID-19 vaccine in a Japanese patient. Healthcare workers should be aware of this rather rare complication after vaccinations. Keywords alopecia universalis COVID-19 mRNA vaccines ==== Body pmcIntroduction Messenger RNA vaccines against SARS-CoV-2 infection, or COVID-19 dramatically changed the landscape of the fight against COVID-19 pandemic. The vaccines are well tolerated with a fairly good safety profile [1]. However, it is not without significant adverse side effects like any other vaccines, and healthcare workers should remain to be aware of their potential risks. Here, we report a case of alopecia universalis occurring in a female who received mRNA COVID-19 vaccines. Case Report A Japanese female patient in her 40 s without significant past medical history was referred to the Kampo clinic of the hospital with hair loss in her body. She was in her usual state of health but developed acute onset hair loss 1 week after the receipt of the first dose of mRNA-1273 intramuscularly in August 2021. She lost her head hair, eyebrows, eyelashes, axillary hair, pubic hair, and hair on the arms and legs. She again received the second dose of the same vaccine a month later and there was no additional event except for a mild fever for a few days. She denied any significant past medical and family history. She had hair dye one year prior to the vaccination without any event, and she denied any further hair dye afterward. She was not on any medication, supplements, or herbs. She also denied any change of shampoo, hair conditioner, soap, or cosmetics. She visited a dermatologist and was diagnosed with alopecia totalis. Blood tests were unremarkable with a negative rapid plasma reagin (RPR) test, and anti-nuclear antibody (ANA) titer of 1:40. Thyroid stimulating hormone (TSH) level was also normal (0.456 μIU/mL). She was given oral prednisone of an unknown dosage with no improvement. She also received excimer lamp treatment with few effects. She was later prescribed oral cepharanthin, and monoammonium glycyrrhizinate without success. She wished to try herbal medications and was referred to our clinic in August 2022. On physical examination, her vital signs were normal. She had scarce gray hair on her head. Her eyebrows were also scarce, with short eyelashes. There was no axillary hair and her arms and legs also lacked hair growth ( Fig. 1). Her genitalia was not examined. She has no skin lesions. The remainder of the physical examination was unremarkable. She declined a skin biopsy.Fig. 1 The head of the patient on the initial visit. Fig. 1 Shimotsuto, or Siwo-Tang, a Kampo herbal medicine, consisting of Rhemannia root, Peony root, Cnidium Rhizome, and Japanese Angelica root, was prescribed. She continued to receive excimer lamp treatment regularly at a dermatologist. Her alopecia improved a little but no significant change has occurred as of this writing. Discussion We here report a case of alopecia universalis presumably induced by mRNA-1273 injection. Alopecia areata is a common autoimmune disease that presents as nonscarring hair loss, although the exact pathogenesis of the disease remains unclear. Alopecia totalis and alopecia universalis are subtypes of alopecia areata and there is a 100% loss of all scalp hair in the former, and a 100% loss of all scalp and body hair in the latter [1]. Tinea capitis and trichotillomania are two important differential diagnoses, and they are mainly seen in children. This patient did not have inflammation or scaling of the skin, which makes tinea capitis unlikely. There was no irregularity of hair loss or broken hairs, and trichotillomania is also unlikely in the patient. Systemic lupus erythematosus (SLE) and secondary syphilis are also considered in the differential diagnosis but blood tests and lack of physical findings make these also unlikely [1]. Therefore, it is reasonable to conclude that this patient suffered from alopecia universalis. Alopecia areata could occur with multiple risk factors. Genetic factors might play a role although this patient did not have any family history of alopecia. Comorbidities such as hyperthyroidism, hypothyroidism, vitiligo, psoriasis, rheumatoid arthritis, and inflammatory bowel disease, are associated with alopecia areata, although none of these conditions existed in the patient [2]. Prevalence of atopic dermatitis, asthma, and rhinitis are common among patients with alopecia areata [2]. Herpes zoster may also be associated with alopecia areata [3]. We were not able to identify any triggering factors other than COVID-19 vaccination before the onset of alopecia universalis in the patient. Therefore, although not conclusive, it is a reasonable assumption that the vaccine had an inducing effect to develop alopecia universalis in the patient. Alopecia areata after vaccinations such as tetanus, hepatitis B, and smallpox have been reported [4], [5]. Alopecia areata also has been reported after COVID-19 vaccinations [6], [7], [8]. Gallo et al. reported a case of a male patient with alopecia areata after receiving BNT172b, another mRNA vaccine [6]. Essam et al. also reported a case of alopecia areata in a female patient after receiving ChAdOx1 nCoV-19, an adenovirus vector vaccine [7]. Rossi et al. also reported 3 cases of alopecia areata, after the receipt of either BNT172b or ChAdOx1 nCoV-19 [8]. A cross-sectional study in Italy found 24 patients who developed alopecia areata which developed within 16 weeks after COVID-19 vaccination. Fifteen patients received BNT162b2 and 5 received mRNA-1273, and 4 received ChAdOx1 nCoV-19. Four out of these 24 developed alopecia universalis. Half of these 24 patients had underlying autoimmune diseases such as Hashimoto thyroiditis, and celiac disease [9]. Another case series reported 9 cases of alopecia areata following COVID-19 vaccination [10]. Three of them received mRNA-1273 and the rest received BNT162b2. Alopecia universalis occurred in 2 patients and one developed after the receipt of mRNA-1273. The patient was a female in her 60 s and had a remote history of alopecia areata. According to the Vaccine Adverse Event System Reporting System at Centers for Disease Control and Prevention, there were 3,823 alopecia, and 44 alopecia universalis reported after COVID-19 vaccine as of the search on March 15, 2023 (12). Although the causality of these reports is not definite, it is judicious to consider that COVID-19 vaccines, either mRNA vaccines or others, could cause alopecia areata and its subtypes. As far as our literature search could find, this is the first to report the case of alopecia areata occurring in Japanese persons. COVID-19 vaccines may play a role in the activation of immunological events leading to an aberrant autoimmune response in susceptible individuals. Both mRNA vaccine and adenovirus vector vaccine deliver the gene encoding the S protein, which induces the immune system, with antibodies production and Th1 cells activation with the release of pro-inflammatory cytokines [11]. This could explain the occurrence of autoimmune diseases including alopecia areata. The incidence of alopecia areata and alopecia universalis after COVID-19 vaccines are unknown but are considered to be rare. Because the dosage of the vaccine is higher (59 μg) in mRNA01273 than that of BNT172b2 (30 μg), the frequency of adverse reactions such as fever, arthralgia, chill, and myalgia appear higher in the former than the latter [12]. Whether mRNA1273 causes more alopecia than other COVID-19 vaccines should be investigated in future studies. Of note, alopecia is a common phenomenon after SARS-CoV-2 infection, with a potentially similar mechanism [13]. It is still rational to prevent the infection by vaccination, and the avoidance of vaccination due to a fear of alopecia is not justifiable. The treatment of alopecia areata or universalis remains a challenge. Oral JAK inhibitors such as baricitinib, topical or systemic corticosteroids, laser treatment, and other options exist with various efficacy. The best approach to refractory diseases remains unknown [2]. In conclusion, we report a case of alopecia universalis in a Japanese patient after receipt of the mRNA COVID-19 vaccine. Further studies will be necessary to elucidate its incidence and prevalence, and its best clinical approach. Ethics statement The current study is exempted from the ethics committee approval since this is a case report. A written informed consent was given by the patient to publish this work. CRediT authorship contribution statement Conception of the report: KI. Drafting of the manuscript: KI. The revision and the approval of the manuscript: KI and MK Declaration of competing interest We have no conflicts of interest. Acknowledgments The authors thank the patient to approve the publication of the current work. ==== Refs References 1 Alkhalifah A. Alsantali A. Wang E. McElwee K.J. Shapiro J. Alopecia areata update: Part I. Clinical picture, histopathology, and pathogenesis J Am Acad Dermatol 62 2010 177 188 10.1016/j.jaad.2009.10.032 20115945 2 Pratt C.H. King L.E. Messenger A.G. Christiano A.M. Sundberg J.P. Alopecia areata Nat Rev Dis Primer 3 2017 17011 10.1038/nrdp.2017.11 3 Chen C.-H. Wang K.-H. Hung S.-H. Lin H.-C. Tsai M.-C. Chung S.-D. Association between herpes zoster and alopecia areata: A population-based study J Dermatol 42 2015 824 825 10.1111/1346-8138.12912 25959999 4 Wise R.P. Kiminyo K.P. Salive M.E. Hair loss after routine immunizations JAMA 278 1997 1176 1178 9326478 5 Sánchez-Ramón S. Gil J. Cianchetta-Sívori M. Fernández-Cruz E. [Alopecia universalis in an adult after routine tetanus toxoid vaccine] Med Clin (Barc) 136 2011 318 10.1016/j.medcli.2010.03.010 20451934 6 Gallo G. Mastorino L. Tonella L. Ribero S. Quaglino P. Alopecia areata after COVID-19 vaccination Clin Exp Vaccine Res 11 2022 129 132 10.7774/cevr.2022.11.1.129 35223675 7 Essam R. Ehab R. Al-Razzaz R. Khater M.W. Moustafa E.A. Alopecia areata after ChAdOx1 nCoV-19 vaccine (Oxford/AstraZeneca): a potential triggering factor? J Cosmet Dermatol 20 2021 3727 3729 10.1111/jocd.14459 34559937 8 Rossi A. Magri F. Michelini S. Caro G. Di Fraia M. Fortuna M.C. Recurrence of alopecia areata after covid-19 vaccination: A report of three cases in Italy J Cosmet Dermatol 20 2021 3753 3757 10.1111/jocd.14581 34741583 9 Tassone F. Cappilli S. Antonelli F. Zingarelli R. Chiricozzi A. Peris K. Alopecia Areata Occurring after COVID-19 Vaccination: A Single-Center, Cross-Sectional Study Vaccines 10 2022 1467 10.3390/vaccines10091467 36146545 10 Scollan M.E. Breneman A. Kinariwalla N. Soliman Y. Youssef S. Bordone L.A. Alopecia areata after SARS-CoV-2 vaccination JAAD Case Rep 20 2022 1 5 10.1016/j.jdcr.2021.11.023 34931171 11 Talotta R. Do COVID-19 RNA-based vaccines put at risk of immune-mediated diseases? In reply to “potential antigenic cross-reactivity between SARS-CoV-2 and human tissue with a possible link to an increase in autoimmune diseases Clin Immunol 224 2021 108665 10.1016/j.clim.2021.108665 12 gianni. COVID-19 vaccines: comparison of biological, pharmacological characteristics and adverse effects of Pfizer/BioNTech and Moderna Vaccines Eur Rev 2021 〈https://www.europeanreview.org/article/24877〉 accessed March 16, 2023 13 Nguyen B. Tosti A. Alopecia in patients with COVID-19: A systematic review and meta-analysis JAAD Int 7 2022 67 77 10.1016/j.jdin.2022.02.006 35224518
PMC010xxxxxx/PMC10293119.txt
==== Front Eur J Integr Med Eur J Integr Med European Journal of Integrative Medicine 1876-3820 1876-3839 Published by Elsevier GmbH. S1876-3820(23)00049-5 10.1016/j.eujim.2023.102273 102273 Article Traditional Chinese medicine for the COVID-19 pandemic: An online cross-sectional survey among health care workers Jin Xinyan ab Xu Leqin b Lu Chunli ac Xue Xue d Liu Xuehan a Zhou Yuzhen b Hu Xiaoyang e Liu Jianping a⁎⁎ Pei Xiaohua b⁎ a Centre for Evidence-based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China b Xiamen Hospital Affiliated of Beijing University of Chinese Medicine, Xiamen, 361001, China c Institute of Chinese medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China d The First Clinical Medical School, Hubei University of Chinese Medicine, 430074, Wuhan, China e School of Primary Care, Population Sciences and Medical Education, Aldermoor Health Centre, University of Southampton, Southampton, SO17 1BJ, United Kingdom ⁎ Corresponding author. Centre for Evidence-based Chinese Medicine, Beijing University of Chinese Medicine. No.11, Bei San Huan Dong Lu, Chaoyang District, Beijing, 100029, China ⁎⁎ Corresponding author. JP Liu, Xiamen Hospital Affiliated of Beijing University of Chinese Medicine. No. 1739, Xian Yue Lu, Huli District, Fujian, 361001, China 27 6 2023 27 6 2023 1022739 3 2023 19 6 2023 23 6 2023 © 2023 Published by Elsevier GmbH. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background : During the coronavirus disease (COVID-19) pandemic, health care workers (HCWs) have faced a heightened risk of infection. Preventative measures are critical to mitigate the spread of COVID-19 and protect HCWs. Traditional Chinese medicine (TCM) has been recommended to prevent and treat COVID-19 in China. We conducted this survey to investigate the use of infection control behaviors, preventative and therapeutic interventions, and outcomes among HCWs during the surge of Omicron variant infections to explore the association of preventative measures with outcomes and to investigate the factors influencing the adoption of TCM as a preventative measure. Methods : The questionnaire consisted of 23 sections with 154 questions intended for HCWs. The targeted respondents comprised all HCWs from Xiamen Hospital Affiliated of Beijing University of Chinese Medicine. The recruitment process was open between March 17 and June 1, 2022. Chi-square test was used to estimate the relationship between prevention and outcomes. Multivariable logistic regression was used to investigate factors influencing the use of TCM as a preventative measure. Results : Among the 1122 participants who completed the questionnaire, 79.71% took preventative measures, including TCM (56.21%), physical activities (52.37%) and food supplements (26.99%). Xiamen preventative formula (a government-approved fixed prescription) (45.22%) and Lianhua Qingwen preparations (18.95%) were the most commonly used Chinese medicines. Thirty-six participants reported flu-like symptoms and three were diagnosed with COVID-19. Flu-like symptoms were not associated with prevention, vaccination, or TCM. Frontline working experience (OR = 0.61, 95% CI: 0.46–0.80), good knowledge of post-COVID-19 syndrome (OR = 0.57, 95% CI: 0.39–0.84), Western medicine qualifications (OR = 2.41, 95% CI: 1.51–3.86), nurses (OR = 1.70, 95% CI: 1.21–2.40), and medical technicians (OR = 2.27, 95% CI: 1.25–4.10) were associated with the willingness of using TCM as a preventative measure. Conclusion : Complementary medicine, especially TCM, could be used for COVID-19 prevention. Knowledge of COVID-19 may prompt people to use TCM to prevent COVID-19. Multicenter studies and prospective cohort follow-up studies are needed to provide further insights into the use of TCM for COVID-19 management. Graphical abstract Image, graphical abstract Keywords retrospective treatment outcome complementary therapy Chinese herbs non-pharmacological intervention healthcare personnel Omicron variant Abbreviations BMI body mass index BUCM Beijing University of Chinese Medicine CHERRIES Checklist for Reporting Results of Internet E-surveys checklist CI confidence interval COVID-19 coronavirus disease HCWs health care workers IP Internet protocol IQR interquartile range OR odds ratio STROBE Strengthening the Reporting of Observational Studies in Epidemiology TCM traditional Chinese medicine, WHO, World Health Organization ==== Body pmc1 Introduction Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. As of February 17, 2023, the pandemic had cumulatively infected 756.6 million people and resulted in 6.8 million deaths worldwide [1]. SARS-CoV-2 has undergone various mutations, among which the Omicron variant has been the predominant strain since 2022 [2]. Despite having a lower risk of severe COVID-19 and mortality compared to previous variants, the increased transmission rate of the Omicron variant has contributed to a substantial burden on the health care system [3], [4], [5], [6]. Since the onset of the COVID-19 pandemic, health care workers (HCWs) have been at an increased risk of exposure to the SARS-CoV-2 virus, leading to physical and psychological strain due to overwork, which may make them more vulnerable to infection [7,8]. Considering the substantial transmission advantage of Omicron, preventing COVID-19 is especially critical for HCWs. The World Health Organization (WHO) living guideline recommends implementing infection prevention and control measures, such as mask wearing, environmental cleaning, physical distancing, hand hygiene, respiratory etiquette, and personal protective equipment, to prevent the spread of COVID-19 [9]. However, as yet, no method other than vaccination has proven effective in preventing COVID-19 [10]. Notably, the efficacy of vaccines against Omicron is more preserved against severe disease than against infection and the effectiveness wanes as antibody titers decrease [3,11]. The effectiveness of vaccines against infection by Omicron variants is still controversial [12,13]. The National Health Commission of the People's Republic of China has recommended traditional Chinese medicine (TCM) as a preventative and therapeutic approach for COVID-19 in its Guidelines for Integration of Chinese and Western Medicine [14,15,16,17,18,19,20]. For example, Lianhua Qingwen granule and Jinhua Qinggan granule are among the remedies recommended for patients with suspected or confirmed COVID-19, and have been proven to help regulate the immune system and exert antiviral effects by clinical studies and experiments [21,22,23,24,25]. Our previous online cross-sectional survey aimed to investigate the use of infection control behaviors, preventative and therapeutic interventions, and outcomes in the community during the COVID-19 pandemic in China and found that 22.3% (76/341) of respondents chose TCM as a means of prevention. The respondents who reported their occupations were predominantly HCWs (138/155) suggesting that HCWs represent a conducive population to collect data from. Furthermore, the variants involved in this study were undefined, and the association of outcomes with preventative interventions was unclear. Notably, the data may vary across different waves of the pandemic and target population [26]. For instance, HCWs in Xiamen accumulated experience in combating COVID-19 as a result of the localized outbreaks that occurred in Xiamen, Fujian province, China in September 2021 and May 2022. Therefore, we aimed to conduct this online cross-sectional survey specifically among HCWs in Xiamen during the Omicron pandemic to investigate infection control behaviors, preventative and therapeutic interventions, and outcomes. We will explore the association of preventative measures with outcomes and the factors influencing the adoption of TCM as a preventative measure. 2 Methods The study is reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement, and the findings are reported following the Checklist for Reporting Results of Internet E-surveys checklist (CHERRIES) [27,28]. 2.1 Study design An international retrospective survey of prevention, treatment, occurrence, and outcomes of COVID-19 in the community (RTO-COVID-19) involving 14 countries was conducted by researchers at the University of Southampton and the University of Geneva. The original survey in the English version (available at: https://www.rtocovid19.com/) was translated into the simplified Chinese version and adapted for HCWs by authors at Beijing University of Chinese Medicine (BUCM) in May 2020 and January 2022 [26]. 2.2 Study setting The study was conducted online at Xiamen Hospital Affiliated of BUCM (Xiamen Hospital). The outbreak of COVID-19 occurred in Xiamen during the study period. Xiamen Hospital, as the only traditional Chinese hospital in Xiamen, is representative of the implementation of TCM preventative strategies in the city and can be used to provide a reference for the outbreak of future pandemics. Additionally, many data were missing from our previous study, which limited the interpretation of the results. Therefore, Xiamen Hospital, as an affiliated hospital of BUCM, was a feasible study site to ensure that the data can be collected completely and reliably. 2.3 Participants All potential HCWs, including clinicians, TCM practitioners, nurses, pharmacists, medical technicians, administrative staff, and medical students, in Xiamen Hospital were able to access the website of this survey without limitation. Purposive sampling was used, and there was no human-made selection bias on the participants. 3 Inclusion criteria 1) Any HCW in Xiamen Hospital with access to the questionnaire; 2) any HCW who can read, understand, and provide information about the questionnaire; 3) HCWs who checked “agree” on the informed consent page; and 4) adults aged ≥ 18 years. 3.1 Ethics approval and informed consent The study has been approved by Xiamen Hospital (2022-K00401, February 16, 2022) and University of Southampton (ERGO 56975, May 2020). All participants were invited to read the overview, benefits and risks, and confidentiality of the survey. Only those who provided informed consent were permitted to proceed to the questionnaire. All participants were free to withdraw at any time. 3.2 Development of the survey 3.2.1 Adaptation for health care workers The online questionnaire was translated into simplified Chinese and localized to suit HCWs and Chinese policies during the COVID-19 pandemic. First, considering that the participants of the questionnaire were HCWs and had appropriate knowledge of COVID-19, we supplemented questions about health professional qualifications and professional titles and simplified the choices of preventative measures. For example, in the Chinese herb section, we presented options by names of Chinese medicine rather than formulations. Then, we updated preventative measures according to the available evidence, e.g., Chinese patent injections, antibiotics, and antivirals (chloroquine was not recommended for prevention). The questionnaire was designed on wjx.cn. At least seven authors pretested the survey in different scenarios to improve the logic and wording before pilot testing. This procedure was repeated four times. 3.2.2 Pilot testing for health care workers The questionnaire was tested by 12 HCWs before dissemination. The pilot testers checked the logic between questions and ambiguous expressions and verified medical issues. They tested the questionnaires based on their conditions. 3.3 Recruitment process The open questionnaire was released on wjx.cn from March 17, 2022, and lasted for 3 months. Each participant had only one account through which to answer questions. The survey was disseminated via the Office Automation System of the hospital. The authors and participants were also invited to forward the QR code posters via WeChat (the most popular social application in China, available from https://weixin.qq.com/). All HCWs in Xiamen Hospital were invited to participate in this study through both methods. 3.4 Survey administration After the participants had submitted their answers to wjx.cn, a public, open access online survey platform in China, they received the prompt “Your answer has been submitted and thank you for your participation,” with non-monetary incentives. To prevent bias, the answers to questions were arranged in a random order. The questionnaire consisted of 23 sections with 154 questions, which additionally included one page of welcome and thanks. Participants could skip some questions due to the pre-designed logic and their conditions. The questions appropriate for the participants were mandatory, but a non-response option was provided. All participants could return or review the questionnaire but could not change the answers once submitted. A recent entry was retained for duplicate entries from the same Internet Protocol (IP) address. 3.5 Sample size Referring to sample size estimation of multivariable regression, the sample size was 5 to 10 times as large as the number of variables, with a larger sample size considered to result in greater accuracy of the results. In this questionnaire, there were at least 230 questions with 23 variables [29]. We planned to maximize the sample size but were limited by the project timeline; the recruitment period was from March to June 2022. 3.6 Data collection and analysis 3.6.1 Data collection The following data were collected:(1) Response rate (the number of visitors, participation rate, completion, and completeness rate) (2) Basic characteristics, including health professional qualifications, professional titles, physical and mental conditions, COVID-19 impact on individual development and behaviors, and experience of frontline work (3) Diagnosis (detection of SARS-CoV-2) and/or flu-like symptoms (4) Preventative measures (TCM, other complementary therapies, vaccination) (5) Relationship between preventative measures and outcomes (6) Influencing factors affecting TCM adoption as preventative measures (7) Treatments (TCM, conventional treatments, other complementary therapies) and outcomes (hospitalization, aggravation, and recovery conditions) It is possible that the retrospective nature of the study led to self-reporting bias and recall bias. To minimize potential bias, the questionnaire was designed explicitly and carefully. Moreover, a suggestion that participants could complete the questionnaire with the help of medical records was given to participants at the beginning of the questionnaire. 3.6.2 Confidentiality Wjx.cn is committed to the security of data stored on the Web server. The data security system has been officially certified according to Chinese laws. Wjx.cn cannot change the security classification of data without permission and cannot disclose data to any other party. The obligation of privacy policy or data security is permanent and it supported IP addresses as unique identifiers, while also excluding individual multiple visits. Information that identified individuals (e.g., IP address, WeChat account) were not disclosed to anyone other than the members of the study team. The investigators, sponsor, and ethics committee were permitted to access the data to ensure its authenticity and accuracy, but no individual information was shared. All data collected were managed by the BUCM and Xiamen Hospital teams. 3.6.3 Data analysis Data were managed in Excel Microsoft 365 and analyzed with wjx.cn and IBM SPSS Statistics Premium 28.0. Incomplete survey data were not collected for analysis, and the data from the pilot testing were not included in the final analysis. Descriptive analyses are presented as numbers and percentages, means and standard deviations, or median and interquartile range (IQR). The descriptive analyses included basic characteristics, diagnosis (detection of SARS-CoV-2), and/or flu-like symptoms, behavior measures, preventative measures, and treatments for symptoms. To estimate behavior changes due to COVID-19, the McNamar Bowker test was used to compare individual behavior changes before and after 2020. Chi-squared test was performed to estimate the relationship between preventative measures and outcomes. Chi-squared test or t-test was performed to investigate potential influencing factors between the TCM and non-TCM preventative groups. The variables included visit time points (before or after the May 2022 Xiamen outbreak), age, sex, body mass index (BMI), smoking status, alcohol consumption, working years, health professional qualifications, highest education level, professional titles, physical and mental condition, experience of frontline work, vaccination status, and frequency of flu-like symptoms. The significant variables were entered into a stepwise multivariable linear regression model to identify the factors influencing the willingness to use TCM as a preventative measure. As for the relationship between preventative measures and outcomes and the influencing factors affecting the willingness to use TCM as a preventative measure, sensitivity analysis was conducted in doctors and all respondents to test the robustness of the results. A two-sided P-value < 0.05 was considered significant. Odds ratios (ORs) and 95% confidence intervals (CIs) were used. Missing data in the analyzed items are presented as non-values. 4 Results 4.1 Response rates The survey was available online from March 17, 2022 to June 1, 2022 with 4131 visits. Among the overall visits, 1176 completed the questionnaire and submitted their answers, and 54 participants noted that they would like to withdraw their questionnaires; therefore, we removed those data. A total of 1122 questionnaires were finally collected. The participation rate, defined as the percentage of all unique visitors who agreed to complete the survey, was 28.47%. The completion rate was 100%, and the completeness rate was 95.41%. The generic information of participants is presented in Table 1 .Table 1 The basic characteristics of 1122 participants. Table 1Items Summary data (number and %, mean ± standard deviations, and range) Age (years) 33.00 ± 7.07 (18–60) Gender Men 848 (75.58) Women 274 (24.42) Weight (kg) Mean weight 60.39 ± 14.79 (40–138) Normal (self-reported) 578 (51.52) Underweight (self-reported) 68 (6.06) Overweight (self-reported) 226 (20.14) Not reported 250 (22.28) Height (cm) 162.48 ± 7.40 (106–188) BMI 22.80 ± 5.10 (15.59–48.93) Smoking Mean cigarette amount 7.82 ± 4.10 (1–20) Yes 29 (2.58) No 1093 (97.42) Alcohol consumption Yes 220 (19.61) No 902 (80.39) Highest education level Bachelor 756 (67.38) Master 201 (17.91) Doctor 11 (0.98) Others 154 (13.73) Health professional qualifications TCM 266 (23.71) Integration of traditional and Western medicine 67 (5.97) Western medicine 115 (10.25) Nursing 414 (36.90) Chinese medicine pharmacist 25 (2.23) Western medicine pharmacist 12 (1.07) Medical technician 60 (5.35) Students a 99 (8.82) Not health professionals 60 (5.35) Not reported 4 (0.36) Professional titles Senior 149 (13.28) Intermediate 297 (26.47) Junior 344 (30.66) Student 254 (22.64) Not reported 78 (6.95) Working years Mean years 12.52 ± 8.93 (0–40) 0–20 711 (711/868, 81.91) 21–40 157 (157/868, 18.09) Physical conditions Chronic disease 183 (16.31) Healthy 939 (83.69) Mental conditions Mental health problem 98 (8.73) Healthy 1024 (91.27) Working/living status Frontline 765 (68.36) Medium/high risk b 190 (16.98) Low risk 471 (42.09) SARS-CoV-2 nucleic acid test Negative 1119 (99.73) Positive 3 (0.27) Flu-like symptoms Symptoms occurred 36 (36/1119, 3.22) Healthy 1083 (1083/1119, 96.78) Flu vaccination 334 (29.77) The first dose of COVID-19 vaccine 1099 (97.95) Beijing Institute of Biological Products 810 (808/1099, 73.52) Beijing Sinovac 227 (227/1099, 20.66) Wuhan Institute of Biological Products 16 (16/1099, 1.46) Sinopharm 7 (7/1099, 0.64) CanSinoBIO 7 (7/1099, 0.64) Pfizer BioNTech 7 (7/1099, 0.64) Anhui Zhifei Longcom Biopharmaceutical 5 (5/1099, 0.45) Janssen 2 (2/1099, 0.18) Others 18 (18/1099, 1.64) The second dose of COVID-19 vaccine 1086 (1086/1099, 98.82) Beijing Institute of Biological Products 758 (808/1086, 69.80) Beijing Sinovac 257 (227/1086, 20.90) Wuhan Institute of Biological Products 30 (30/1086, 2.76) Pfizer BioNTech 9 (9/1086, 0.83) Anhui Zhifei Longcom Biopharmaceutical 6 (6/1086, 0.55) Sinopharm 5 (5/1086, 0.46) Bharat Biotech 2 (2/1086, 0.18) Others 19 (64/1086, 1.75) The third/booster dose of COVID-19 vaccine 934 (934/1086, 86.00) Beijing Institute of Biological Products 334 (334/934, 35.76) Beijing Sinovac 262 (262/934, 28.05) Changchun Institute of Biological Products 213 (213/934, 22.81) Wuhan Institute of Biological Products 41 (41/934, 4.39) Anhui Zhifei Longcom Biopharmaceutical 10 (10/934, 1.07) Sinopharm 5 (5/934, 0.54) Pfizer BioNTech 4 (4/934, 0.43) Others 65 (65/934, 6.96) COVID-19 impact on personal development 6.37 ± 2.44 (0–10) No problems 353 (31.46) Mild impact 69 (6.15) Moderate impact 234 (20.86) Severe impact 278 (24.78) Not reported 188 (16.76) Relationship between COVID-19 pandemic and flu-like symptoms Higher frequency 57 (5.08) No change 758 (67.56) Lower frequency 185 (16.49) Not applicable 122 (10.87) Note: BMI, body mass index. a. students indicate medical or nursing students from medical universities. b. medium risk indicates coming in contact with less than 50 COVID-19 patients within a 14-day period or coming in contact with more than 50 COVID-19 patients over a long period; high risk indicates coming in contact with more than 50 COVID-19 patients within a 14-day period. 4.2 Basic characteristics of the included participants 4.2.1 Demographic characteristics and medical history The participants’ demographic characteristics, medicine background, and physical and mental conditions are detailed in Table 1. A total of 448 (39.93%) doctors and 414 (36.90%) nurses participated in the survey. The majority (266/448, 59.38%) of doctors possessed TCM qualifications. Moreover, 886 participants (78.97%) reported no chronic diseases or mental health problems, approximately half (91/183, 49.73%) suffered from chronic diseases, and one third (34/98, 34.69%) of those suffering from mental health problems reported that the conditions were under control. 4.2.2 Impact of COVID-19 on personal development and behaviors Nearly half of the participants (512/1122, 45.64%) believed that the COVID-19 pandemic influenced their personal development with moderate to severe negative effects, including income or welfare (29.50%), position promotion (14.97%), and graduation (12.92%), as detailed in Appendix Fig. 1. Furthermore, 765 participants (68.36%) were involved in frontline work. Regarding the relationship between the COVID-19 pandemic and flu-like symptoms, 185 participants (16.49%) reported a lower frequency of symptoms, 59 (5.08%) reported a higher frequency and most participants (67.56%) reported no change (Table 1). Additionally, after 2020, 1083 participants reported behavior changes due to COVID-19, including washing hands and wearing masks, maintaining social distancing, and ventilating and disinfecting workplaces (P < 0.001), as shown in Appendix Table 1. 4.3 Diagnosis (detection of SARS-CoV-2) and/or flu-like symptoms According to China's policies during the COVID-19 pandemic, all participants received multiple SARS-CoV-2 nucleic acid tests, among which, 1119 (99.73%) were negative and three were diagnosed with COVID-19 infection once, including two asymptomatic infections and one symptomatic infection. Three confirmed participants recovered from COVID-19 in less than 7 days. The reasons for COVID-19 detection are presented in Appendix Fig. 2. Among the 1119 participants with negative SARS-CoV-2 nucleic acid tests, only a few participants (36/1119, 3.22%) presented flu-like symptoms during the COVID-19 pandemic (Appendix Fig. 3). The symptoms lasted for an average of 16.33 (1–365) days and restricted the participant's normal activities for an average of 15.39 (0–365) days. The participants reported that flu-like symptoms caused varying degrees of discomfort, and the average symptom severity was 4.53, where a score of 0 is the best and 10 is the worst. The average score for concern about symptoms was 3.00, where a score of 0 is no concern and 10 is extremely concerned. 4.4 Preventative measures and outcomes 4.4.1 Overview of preventative measures We collected data from 1119 participants, among whom 79.71% (892/1119) took preventative measures, including 56.21% who chose TCM and 58.18% who chose other measures to prevent COVID-19, as detailed in Table 2 . Lianhua Qingwen preparations (granule or capsule) were most commonly used (212/1119, 18.95%) as a Chinese patent medicine. Xiamen preventative formula (a fixed prescription approved by local government) was the most commonly used (506/1119, 45.22%) as a Chinese herbal decoction whose herbal composition is detailed in Appendix Table 2. Among non-pharmacological measures, moxibustion was most commonly used (234/1119, 20.91%). The acupuncture points mainly included Zusanli (ST36), Zhongwan (RN12), Tianshu (ST25), Danzhong (RN17), and Dazhui (DU14). Regarding other measures, physical activities (586/1119, 52.37%) and food supplements (302/1119, 26.99%) were used quite often, as detailed in Table 2.Table 2 Preventative measures of 1119 participants. Table 2Preventative measures Summary data (number %) Traditional Chinese Medicine 629 (56.21) Chinese herbal decoction 544 (86.49) Xiamen preventative formula 506 (93.01) Self-prescription 62 (11.40) Chinese patent medicine 265 (42.13) Lianhua Qingwen preparation 212 (80.00) Huoxiang Zhengqi preparations 111 (41.89) Shuanghuanglian preparation 102 (38.49) Banlangen (Isatidis Radix) granule 98 (36.98) Shufeng Jiedu preparation 68 (25.66) Ganmao Qingre granule 61 (23.02) Jinhua Qinggan granule 58 (21.89) Qingfei Paidu granule 1 (0.38) Shengmaiyin liquid 1 (0.38) Others 15 (5.66) Moxibustion 234 (37.20) Tuina 124 (19.71) Cupping 117 (18.60) Acupuncture 115 (18.28) Manual acupuncture 74 (64.35) Needle warming moxibustion 67 (58.26) Electroacupuncture 54 (46.96) Press needle 35 (30.43) Laser acupuncture 10 (8.70) Tai Chi 81 (12.88) Other measures 651 (58.18) Physical activities 586 (90.02) Walking, hiking 442 (75.43) Aerobic exercise 303 (51.71) Anaerobic exercise 164 (27.99) Yoga 135 (23.04) Meditation, mindfulness 98 (16.72) Others 48 (8.19) Food supplements 302 (46.39) Vitamin supplements 225 (74.50) Vitamin C 193 (85.78) Vitamin D 131 (58.22) Vitamin E 110 (48.89) Vitamin B12 107 (47.56) Vitamin A 104 (46.22) Multivitamin 89 (29.47) Probiotics 139 (46.03) Amino acid, protein 134 (44.37) Mineral supplements 96 (31.79) Calcium 91 (94.79) Zinc 72 (75.00) Magnesium 62 (64.58) Selenium 57 (59.38) Copper 44 (45.83) Omega-3 fatty acid 72 (23.84) Special food 193 (29.65) Fruits and vegetables 176 (91.19) Oranges 169 (96.02) Carrots 143 (81.25) Celery 119 (67.61) Lemons 104 (59.09) Onions 99 (56.25) Soup 122 (63.21) Broth soup 107 (87.70) Vegetable soup 105 (84.07) Ginger soup 68 (55.74) Onion soup 36 (29.51) Tea 96 (49.74) Black tea 81 (84.38) Green tea 74 (77.08) Fermented tea 29 (30.21) Bee products 64 (33.16) Honey 60 (93.75) Propolis 32 (50.00) Royal jelly 29 (45.31) Spices 59 (30.57) Ginger (Zingiberis rhizoma recens) 56 (94.92) Garlic (Allii sativi bulbus) 54 (91.53) Green Chinese onion (Allium fistulosum) 48 (81.36) Hot pepper (Capsici fructus) 43 (72.88) Turmeric (Curcumae longae rhizoma) 25 (42.37) Special diets 36 (18.56) Home remedies 192 (29.49) Nasal rinse 142 (73.96) Inhalation (steam) 53 (27.60) Essential oils 44 (22.92) Tea Tree (Melaleuca alternifolia) 33 (75.00) Savory (Satureja hortensis) 28 (63.64) Lemon (Citrus limonum) 23 (52.27) Thyme (Thymus vulgaris) 14 (31.82) Marjoram (Origanum majorana) 13 (29.55) Ravensara (Ravensara aromatica) 13 (29.55) Eucalyptus (Eucalyptus globulus) 12 (27.27) Gelodurat® 11 (25.00) Oregano (Origanum vulgare) 10 (22.73) No special treatments 227 (20.29) The COVID-19 vaccination rate for different doses and vaccine brands is shown in Table 1. The reasons for vaccination are shown in Appendix Fig. 4 and those for non-vaccination included pregnancy, allergy, and cancer. Concerns about COVID-19 decreased with vaccination, while the majority of participants (67.61%–73.89%) reported that infection control behaviors were unaffected, as detailed in Appendix Table 3. 4.4.2 Preventative measures and flu-like symptoms A few participants (36/1119, 3.22%) developed flu-like symptoms, although these were not associated with preventative measures, vaccination, or TCM (P = 0.475, P = 0.377, P = 0.547). 4.5 Influencing factors of TCM We identified four variables with P < 0.05 by univariate analysis, including experience of frontline work (P < 0.001), health professional qualifications (P = 0.003), knowledge of post-COVID-19 syndrome (P = 0.002), and frequency of flu-like symptoms (P < 0.001) (Appendix Table 4). Then, we used the stepwise multivariable linear regression model and found that experience of frontline work, health professional qualifications, and knowledge of post-COVID-19 were independent factors associated with TCM adoption. Participating in frontline working (OR = 0.61, 95% CI: 0.46–0.80, P < 0.001) and good knowledge of post-COVID-19 syndrome (OR = 0.57, 95% CI: 0.39–0.84, P = 0.005) were the factors that contributed most to TCM adoption. In contrast participants who were licensed to Western medicine (OR = 2.41, 95% CI: 1.51–3.86, P < 0.001), nurses (OR = 1.70, 95% CI: 1.21–2.40, P = 0.002), and medical technicians (OR = 2.27, 95% CI: 1.25–4.10, P = 0.007) tended to not prefer TCM as a preventative measure. 4.6 Treatments and outcomes Among the 36 participants who developed flu-like symptoms, five did not take any measures and 31 reported their treatments. Twenty-two participants chose TCM, and 21 participants chose Western medicine. At the same time, 14 participants chose Integration of Traditional and Western Medicine, as detailed in Appendix Table 5. The source of treatments used by participants for flu-like symptoms are presented in Appendix Table 6. The average days from the occurrence of flu-like symptoms to receive treatment was 2.55 (1–30) days, and the average length of recovery was 21.93 (0–335) days. None of the participants were hospitalized for flu-like symptoms. Thirty-four participants, including five without any treatment, had recovered or improved, while two reported still suffering from symptoms of shortness of breath and anxiety. 4.7 Additional analysis Regarding the relationship between preventative measures and outcomes and the influencing factors affecting the willingness to use TCM as a preventative measure, sensitivity analysis was conducted in doctors to test the robustness of the results. The occurrence of symptoms was not associated with preventative measures or vaccination or TCM (P = 0.506, P > 0.999, P = 0.203). Good knowledge of post-COVID-19 syndrome (OR = 0.35, 95% CI: 0.21–0.60, P < 0.001) was the main factor that contributed to the adoption of TCM as a preventative measure, while participants licensed to Western medicine (OR = 2.46, 95% CI: 1.56–3.88, P < 0.001) tended to not prefer to choose TCM as preventative measures; details are presented in Appendix Table 7. The results in doctors were consistent with those in all respondents, except in cases where frontline experience contributed to TCM adoption. 5 Discussion To the best of our knowledge, few studies have investigated the preventative measures selected by HCWs who encounter Omicron variants of COVID-19. Our previous study suggested that HCWs could be used as a conducive population to collect data, although the association of outcomes with preventative interventions was unclear. In this study, we aimed to conduct this online cross-sectional survey specifically among HCWs in Xiamen during the Omicron pandemic to investigate infection control behaviors, preventative and therapeutic interventions, and outcomes. We additionally explored the association of preventative measures with outcomes and the factors influencing the adoption of TCM as a preventative measure. With a participation rate of 28.47%, our results showed that the majority of HCWs took preventative measures, such as TCM, physical activities, and food supplements, during the Omicron pandemic. Three respondents were diagnosed with COVID-19 and 36 developed flu-like symptoms, although these were unrelated to the preventative measures, vaccination, and TCM. The knowledge of post-COVID-19 and doctors licensed to Western medicine were independent factors associated with TCM adoption, which is consistent with the results of sensitivity analysis. Considering the heavy workload and convenience of portable devices, we continued to use the wjx.cn platform to disseminate and collect the data. The response rate was 28.47%, which although low was acceptable. As reported by a previous study, the response rate of surveys of health professions trainees ranged from 26.6% to 100%, with a mean response rate of 73.1% [30]. The reasons for the various response rates may result from unexplicit definitions and different recruitment methods, where lower response rates are observed for web surveys [30]. Furthermore, the response rates for surveys have decreased over the past decade, which may result from the use of long questionnaires and a surge of questionnaire surveys leaving people fatigued [31,32]. The low response rate observed in this survey has two likely explanations. First, the total visits were 4131 and the total number of HCWs in Xiamen Hospital was 1583, which indicates a relatively high number of irrelevant people visiting the questionnaire. As the interested participants were HCWs in Xiamen Hospital, we illustrated those on the welcome page. Thus, some irrelevant people may visit the questionnaire without submission. Second, this was a comprehensive questionnaire covering diagnosis, preventative and treatment interventions, and outcomes, which took approximately 16 to 20 min to complete. The behavior fatigue that may be experienced by participants during the completion of the questionnaire may have resulted in reluctance to complete the questionnaire. Moreover, to collect as many questionnaires as possible, we assumed that it was difficult for HCWs to spend more than 15 min completing the questions in one sitting. As a result, the questionnaire was designed to allow intermittent responses with only one submission. Our findings demonstrated that 79.71% of participants took preventative measures. The measures were comprehensive and varied, which differed from previous findings in which 32.84% (112/341) took preventative measures in the general population with limited data. A previous study using data available from Turkey and Saudi Arabia, showed that 39.3% and 22.1% of participants among the general population chose traditional and complementary medicine during the COVID-19 pandemic [33,34]. It seemed that compared to the general population, HCWs paid more attention to COVID-19 prevention and self-care, with a good knowledge of available measures. As for the willingness to use TCM in contrast to the general population, we did not find a significant difference between TCM and non-TCM groups in terms of sex, age, highest education level, and physical condition among HCWs [33]. However, knowledge of COVID-19 or post-COVID-19 was positively correlated with the adoption of herbal products or TCM as preventative measures [33]. Additionally, we found that the health professional qualifications may be independently correlated with TCM adoption, which could be unique to the HCW population. TCM, physical activities, and food supplements were common preventative measures for HCWs, all of which may represent effective self-management measures in preventing respiratory infections. We found that 52.37% of participants chose physical activities. Indeed, it has been reported that moderate intensity exercise can be recommended as a non-pharmacological, inexpensive, and viable way to cope with COVID-19. Furthermore, better exercise capacity was associated with lower risks of hospitalization due to COVID-19 [35,36]. We found that approximately 26.99% of participants chose food supplements as a preventative measure, including vitamin C, vitamin D, zinc, garlic, tea tree, and turmeric. There is evidence to support the use of food supplements as potential effective and preventative agents against COVID-19, although this requires experimental validation [37]. The food supplements mentioned above have been proven to play an important role in immunity against virus infection [38,39,40,41,42]. For example, vitamin D deficiency has been recognized as a risk factor for COVID-19, and some trials have been conducted to confirm its effectiveness [43,44]. However, ongoing trials require further follow-up for conclusive results [45,46]. Regarding our findings, 56.21% of participants chose TCM as a preventative measure, including 45.22% who used the Xiamen preventative formula, which was modified based on Yupingfeng Powder, with the addition of dampness-expelling herbs. The most commonly used Chinese patent medicine was Lianhua Qingwen preparations, followed by Huoxiang Zhengqi preparations. COVID-19 was categorized as a “plague” in febrile disease, with cold-dampness as the syndrome [47,48]. Reinforcing qi and expelling cold-dampness to enhance body resistance against virus was essential for COVID-19 prevention, which is the action of the Xiamen preventative formula and Lianhua Qingwen preparations [49]. Moreover, modified Yupingfen Powder, the Chinese classical prescription, has showed potential preventative effectiveness on iatrogenic infection in HCWs during the severe acute respiratory syndrome pandemic [47]. Another cohort study showed that Huoxiang Zhengqi liquid combined with Jinhao Jiere granules may have potential preventative effects on cold during the COVID-19 pandemic [50]. However, in the current study, we failed to investigate whether preventative measures were related to the onset of COVID-19 or flu-like symptoms because of the lack of confirmed cases. Indeed, in China, only a few participants developed COVID-19 or experienced flu-like symptoms due to the effective prevention and control measures for COVID-19, as well as standardized medical procedures [9,51]. This study is a preliminary investigation. Due to the strict public health measures taken by HCWs, the results showed that only three respondents were diagnosed with COVID-19 and 36 respondents developed flu-like symptoms. Limited data suggest few relationships between preventative measures and outcomes. Consequently, we will further explore whether complementary therapies can boost the immune system to prevent infectious diseases based on effective public health measures. The interventions of complementary medicine include physical activities, vitamin D, and TCM. Regarding TCM, it would be appropriate to start with a literature review to identify interventions for respiratory infectious diseases based on ancient Chinese books and published reports. We will then conduct investigator-initiated prospective cohort studies or randomized controlled trials to evaluate the preventative effect of TCM, including Chinese herbs and non-pharmacological TCM measures. Moreover, to improve compliance, it would be beneficial to recruit participants with good knowledge of targeted disease or those who are licensed to TCM or who integrate Chinese and Western qualifications. Regarding public health policy, there are insufficient evidence-based studies on complementary medicine for preventing COVID-19, aligning with the results of the clinical practice guidelines developed in China [52]. Based on current evidence, it is not recommended for HCWs to use complementary therapies, such as Chinese herbs or TCM sachets, for the prevention of COVID-19. This study has several limitations that warrant discussion. First, this was a retrospective survey, which cannot identify causal inference between variables. Moreover, it was unclear whether HCWs took preventative measures before or after exposure to the SARS-CoV-2 virus; thus, it was also unknown whether these preventative measures focused on the prevention of the general population or the high-risk population. Second, there was an inevitable potential recall bias limited by the survey questions asking participants to recall information (behavior measures, preventative measures or treatments). Finally, we collected data only from one center in Xiamen, Fujian province. The preventative measures of HCWs may be insufficient to represent those of all HCWs because of the diversities of geography and individual experience. Further multicenter, prospective cohort studies could be conducted to provide more representative and comprehensive information. Future studies should also focus on exploring the association between preventative measures and outcomes, with the aim to accumulate experience in response to public health emergencies. Conclusion The findings of this survey demonstrated that a considerable proportion of HCWs took measures, especially TCM, physical activities, and food supplements to prevent COVID-19. Knowledge of COVID-19 may prompt people to use TCM as a preventative measure. Multicenter studies and prospective cohort follow-up studies are needed to provide more insights on COVID-19 management in TCM. CRediT authorship contribution statement Xinyan Jin: Writing – original draft, Visualization, Validation, Software, Project administration, Methodology, Formal analysis, Data curation, Conceptualization. Leqin Xu: Project administration, Investigation. Chunli Lu: Writing – review & editing, Validation, Methodology, Conceptualization. Xue Xue: Writing – review & editing, Validation. Xuehan Liu: Writing – review & editing, Validation. Yuzhen Zhou: Investigation. Xiaoyang Hu: Writing – review & editing, Methodology. Jianping Liu: Writing – review & editing, Supervision, Resources, Methodology. Xiaohua Pei: Resources, Investigation, Funding acquisition, Data curation. Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Xiaohua Pei reports article publishing charges was provided by Xiamen Municipal Bureau of Science and Technology. Co-responding author Jianping Liu is one of editorial board members of the European Journal of Integrative Medicine. Co-author Xiaoyang Hu is one of editorial board members of the European Journal of Integrative Medicine. Co-author Chun-li Lu is the managing editor of the European Journal of Integrative Medicine. Appendix Supplementary materials Image, application 1 Image, application 2 Image, application 3 Image, application 4 Author contributions Conceptualization: Chunli Lu, Xinyan Jin. Data curation: Xiaohua Pei, Xinyan Jin. Formal analysis: Xinyan Jin. Funding acquisition: Xiaohua Pei. Investigation: Leqin Xin, Yuzhen Zhou, Xiaohua Pei. Methodology: Jianping Liu, Chunli Lu, Xiaoyang Hu, Xinyan Jin. Project administration: Leqin Xu, Xinyan Jin. Resources: Xiaohua Pei, Jianping Liu. Software: Xinyan Jin. Supervision: Jianping Liu. Validation: Chunli Lu, Xuehan Liu, Xue Xue, Xinyan Jin. Visualization: Xinyan Jin. Writing – Original Draft: Xinyan Jin. Writing – Review & Editing: Jianping Liu, Xiaoyang Hu, Chunli Lu, Xuehan Liu, Xue Xue. Financial support This research was supported by the Xiamen Municipal Bureau of Science and Technology (No. 3502Z2021YJ12). The funding source was not involved in the study design, in the collection, analysis or interpretation of data, in the writing of the report, or in the decision to submit the article for publication. Acknowledgements We gratefully acknowledge the retrospective treatment outcomes of COVID-19 (RTO-COVID-19) project for survey development. We gratefully acknowledge Dr. Ning Dai and Ms. Ruoxiang Zheng (Centre for Evidence-Based Chinese Medicine, BUCM) for survey pre-testing and Mr. Xiaohui Lu (Department of Research and Education, Xiamen Hospital) for survey dissemination. Special thanks to all participants in this survey. Data availability The survey respondents were assured that personal data would not be shared. Data supporting the findings of this study are available from the corresponding author upon reasonable request. 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Jin X.J. Zhao L. Xia P.F. Luo H. Li C.X. The pharmacological material basis of Lianhua Qingwei Granules for the treatment of COVID-19 based on network pharmacology Chinese Traditional Patent Medicine 44 2022 1326 1331 22 Lin J.R. Zheng W.W. Zeng G.X. Lin Q.Z. Study on the network pharmacology of Jinhua Qinggan Granules in the treatment of COVID-19 Journal of Chinese Medicinal Materials 43 2020 2070 2076 10.13863/j.issn1001-4454.2020.08.051 [In Chinese, English abstract] 23 Hu K. Guan W.J. Bi Y. Zhang W. Li L.J. Zhang B.L. Efficacy and safety of Lianhuaqingwen capsules, a repurposed Chinese herb, in patients with coronavirus disease 2019: A multicenter, prospective, randomized controlled trial Phytomedicine 85 2021 153242 10.1016/j.phymed.2020.153242 24 An X.D. Xu X. Xiao M.Z. Min X.J. Lyu Y. Tian J.X. Efficacy of Jinhua Qinggan Granules Combined with Western Medicine in the Treatment of Confirmed and Suspected COVID-19: A Randomized Controlled Trial Front Med (Lausanne) 8 2021 728055 10.3389/fmed.2021.728055 25 Luo H. Tang Q.L. Shang Y.X. Liang S.B. Yang M. Robinson N. Can Chinese Medicine Be Used for Prevention of Corona Virus Disease 2019 (COVID-19)? A Review of Historical Classics, Research Evidence and Current Prevention Programs Chin J Integr Med 26 2020 243 250 10.1007/s11655-020-3192-6 32065348 26 Lu C.L. Zheng R.X. Xue X. Zhang X.W. Liu X.H. Jin X.Y. Traditional Chinese medicine for COVID-19 pandemic and emerging challenges: An online cross-sectional survey in China Integr Med Res 10 Suppl 2021 100798 10.1016/j.imr.2021.100798 27 von Elm E. Altman D.G. Egger M. Pocock S.J. Gøtzsche P.C. Vandenbroucke J.P. STROBE Initiative (2014, The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies Int J Surg 12 2014 1495 1499 10.1016/j.ijsu.2014.07.013 25046131 28 Eysenbach G. Improving the quality of Web surveys: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES) J Med Internet Res 6 2004 e34 10.2196/jmir.6.3.e34 15471760 29 J. L. Zhao, Clinical epidemiology, fourth ed., Shanghai, China, 2014. 30 Phillips A.W. Friedman B.T. Utrankar A. Ta A.Q. Reddy S.T. Durning S.J. Surveys of Health Professions Trainees: Prevalence, Response Rates, and Predictive Factors to Guide Researchers Acad Med 92 2017 222 228 10.1097/ACM.0000000000001334 27532869 31 Marcano Belisario J.S. Jamsek J. Huckvale K. O'Donoghue J. Morrison C.P. Car J. Comparison of self-administered survey questionnaire responses collected using mobile apps versus other methods Cochrane Database Syst Rev 7 2015 10.1002/14651858.MR000042.pub2 MR000042 32 Sammut R. Griscti O. Norman I.J. Strategies to improve response rates to web surveys: A literature review Int J Nurs Stud 123 2021 104058 10.1016/j.ijnurstu.2021.104058 33 Karataş Y. Khan Z. Bilen Ç. Boz A. Özagil E.S.G. Abussuutoğlu A.B. Traditional and complementary medicine use and beliefs during COVID-19 outbreak: A cross-sectional survey among the general population in Turkey Adv Integr Med 8 2021 261 266 10.1016/j.aimed.2021.09.002 34567968 34 Alyami H.S. Orabi M.A.A. Aldhabbah F.M. Alturki H.N. Aburas W.I. Alfayez A.I. Knowledge about COVID-19 and beliefs about and use of herbal products during the COVID-19 pandemic: A cross-sectional study in Saudi Arabia Saudi Pharm J 28 2020 1326 1332 10.1016/j.jsps.2020.08.023 32904846 35 Rahmati-Ahmadabad S. Hosseini F. Exercise against SARS-CoV-2 (COVID-19): Does workout intensity matter? (A mini review of some indirect evidence related to obesity) Obes Med 19 2020 100245 10.1016/j.obmed.2020.100245 36 Brawner C.A. Ehrman J.K. Bole S. Kerrigan D.J. Parikh S.S. Lewis B.K. Inverse Relationship of Maximal Exercise Capacity to Hospitalization Secondary to Coronavirus Disease 2019 Mayo Clin Proc 96 2021 32 39 10.1016/j.mayocp.2020.10.003 33413833 37 Panyod S. Ho C.T. Sheen L.Y. Dietary therapy and herbal medicine for COVID-19 prevention: A review and perspective J Tradit Complement Med 10 2020 420 427 10.1016/j.jtcme.2020.05.004 32691006 38 Jeffery L.E. Burke F. Mura M. Zheng Y. Qureshi O.S. Hewison M. 1,25-Dihydroxyvitamin D3 and IL-2 combine to inhibit T cell production of inflammatory cytokines and promote development of regulatory T cells expressing CTLA-4 and FoxP3 J Immunol 183 2009 5458 5467 10.4049/jimmunol.0803217 19843932 39 Razzaque M.S. COVID-19 Pandemic: Can Maintaining Optimal Zinc Balance Enhance Host Resistance? Tohoku J Exp Med 251 2020 175 181 10.1620/tjem.251.175 32641644 40 Rasool A. Khan M.U. Ali M.A. Anjum A.A. Ahmed I. Aslam A. Anti-avian influenza virus H9N2 activity of aqueous extracts of Zingiber officinalis (Ginger) and Allium sativum (Garlic) in chick embryos Pak J Pharm Sci 30 2017 1341 1344 29039335 41 Romeo A. Iacovelli F. Scagnolari C. Scordio M. Frasca F. Condò R. Potential Use of Tea Tree Oil as a Disinfectant Agent against Coronaviruses: A Combined Experimental and Simulation Study Molecules 27 2022 3786 10.3390/molecules27123786 35744913 42 Gupta H. Gupta M. Bhargava S. Potential use of turmeric in COVID-19 Clin Exp Dermatol 45 2020 902 903 10.1111/ced.14357 32608046 43 D'Avolio A. Avataneo V. Manca A. Cusato J. De Nicolò A. Lucchini R. 25-Hydroxyvitamin D Concentrations Are Lower in Patients with Positive PCR for SARS-CoV-2 Nutrients 12 2020 1359 10.3390/nu12051359 32397511 44 Shakoor H. Feehan J. Al Dhaheri A.S. Ali H.I. Platat C. Ismail L.C. Immune-boosting role of vitamins D, C, E, zinc, selenium and omega-3 fatty acids: Could they help against COVID-19? Maturitas 143 2021 1 9 10.1016/j.maturitas.2020.08.003 33308613 45 Feng Z.T. Yang J. Xu M.Z. Lin R. Yang H.J. Lai L.T. Dietary supplements and herbal medicine for COVID-19: A systematic review of randomized control trials Clin Nutr ESPEN 44 2021 50 60 10.1016/j.clnesp.2021.05.018 34330513 46 Murai I.H. Fernandes A.L. Sales L.P. Pinto A.J. Goessler K.F. Duran C.S.C. Effect of a Single High Dose of Vitamin D3 on Hospital Length of Stay in Patients With Moderate to Severe COVID-19: A Randomized Clinical Trial JAMA 325 2021 1053 1060 10.1001/jama.2020.26848 33595634 47 Zhao Z.Y. Li Y.D. Zhou L.Y. Zhou X.T. Xie B.W. Zhang W.J. Prevention and treatment of COVID-19 using Traditional Chinese Medicine: A review Phytomedicine 85 2021 153308 10.1016/j.phymed.2020.153308 48 Li B. Qiu R.L. TCM prevention measures of novel coronavirus pneumonia based on the “Natural Factor” and the “Human Factor” Acta Chin Med 35 2020 477 482 10.16368/j.issn.1674-8999.2020.03.106 49 Shen X.H. Yin F.G. The mechanisms and clinical application of Traditional Chinese Medicine Lianhua-Qingwen capsule Biomed Pharmacother 142 2021 111998 10.1016/j.biopha.2021.111998 50 Yan B.H. Jiang Z.W. Zeng J.P. Tang J.Y. Ding H. Xia J.L. Large-scale prospective clinical study on prophylactic intervention of COVID-19 in community population using Huoxiang Zhengqi oral liquid and Jinhao Jiere granules Zhongguo Zhong Yao Za Zhi 45 2020 2993 3000 10.19540/j.cnki.cjcmm.20200430.501 32726003 51 The State Council of the People's Republic of China, Notice on doing a good job in guaranteeing service for the people who staying in local region during Chinese New Year, 2021. http://www.gov.cn/xinwen/2021-01/25/content_5582497.htm, 2021 (accessed August 26, 2022). 52 Jin Y.H. Zhan Q.Y. Peng Z.Y. Ren X.Q. Yin X.T. Cai L. 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==== Front J Infect J Infect The Journal of Infection 0163-4453 1532-2742 The British Infection Association. Published by Elsevier Ltd. S0163-4453(23)00341-9 10.1016/j.jinf.2023.06.017 Article Genomic characteristics of SARS-CoV-2 variants and their clinical impact on patients with COVID-19 in Taiwan Su Hung-Chieh Division of Infectious Diseases, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan Lai Zi-Lun Chang Yu-Chang Department of Laboratory Medicine, China Medical University Hospital, Taichung, Taiwan Cheng Meng-Yu Hsih Wen-Hsin Chen Yi-Jhen Chou Chia-Huei Division of Infectious Diseases, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan Chen Chieh-Lung Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan Lin Yu-Chao Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan School of Medicine, China Medical University, Taichung, Taiwan Lin Tsai-Hsiu Hsiao Chiung-Tzu Department of Laboratory Medicine, China Medical University Hospital, Taichung, Taiwan Ho Mao-Wang Division of Infectious Diseases, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan School of Medicine, China Medical University, Taichung, Taiwan Shih Hong-Mo School of Medicine, China Medical University, Taichung, Taiwan Department of Emergency Medicine, China Medical University Hospital, Taichung, Taiwan Department of Public Health, China Medical University, Taichung, Taiwan Hsueh Po-Ren ⁎ Division of Infectious Diseases, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan Department of Laboratory Medicine, China Medical University Hospital, Taichung, Taiwan School of Medicine, China Medical University, Taichung, Taiwan PhD Programme for Aging, School of Medicine, China Medical University, Taichung, Taiwan Department of Laboratory Medicine, School of Medicine, China Medical University, Taichung, Taiwan Cho Der-Yang Department of Neurosurgery, China Medical University Hospital, Taichung, Taiwan ⁎ Correspondence to: the Departments of Laboratory Medicine and Internal Medicine, China Medical University Hospital, School of Medicine, China Medical University, Taichung, Taiwan. 27 6 2023 27 6 2023 21 6 2023 23 6 2023 © 2023 The British Infection Association. Published by Elsevier Ltd. All rights reserved. 2023 The British Infection Association Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcDear Editor, We read with great interest the article by Kang et al. that described how health care workers infected with the Omicron BA.5 variant had more severe and systemic symptoms and a shorter viable virus shedding period than those infected with the BA.1/BA.2 variant, suggesting potential variations of variants in their clinical impact.1 Furthermore, the increased severity of patients infected with the BA.4 and BA.5 variants might be due to their spike protein mutation associated with immune evasion.1 However, there were no significant differences in the risk of hospital admission, death, or oxygen supplementation between BA.4/BA.5 and BA.2 patients presenting to emergency care, contradicting these previous findings.2 With the ongoing evolution of SARS-CoV-2 and the constant emergence of new variants, it is imperative to continuously monitor and swiftly assess genetic changes for an effective public health response and health care management.3 The rapid classification and detection of SARS-CoV-2 variants plays a crucial role in understanding the transmission dynamics of the virus. Moreover, the clinical presentation of the infection is influenced by various factors, including comorbidities, age, immune status, diabetes, and the specific variant responsible for the infection. As a result, clinical management approaches may need to be tailored to address the unique characteristics of these new variants.3 The Centers for Disease Control and Prevention collaborated with laboratories to sequence the SARS-CoV-2 genome from positive samples to investigate the impact of variants on disease severity and evaluate the efficacy of vaccines and treatments.4 This involved performing viral whole genome SARS-CoV-2 sequencing using the Illumina COVIDSeq™ protocol (Illumina Inc., San Diego, CA, USA) on the NextSeq 550 system or NovaSeq 6000 system, with confirmation of positive patient samples through reverse transcription polymerase chain reaction (RT—PCR) (N gene Ct value<30).4 In alignment with Taiwan's public health policies, the Illumina COVIDSeq™ assay (Illumina Inc.) was employed during the later stage of the COVID-19 pandemic, marking its inaugural use in Taiwan for COVID-19 research purposes. To investigate the potential impact of viral strains on the severity of clinical outcomes, we conducted this study at a reputable medical centre in Central Taiwan. A total of 87 patients with COVID-19 were included in this study. Eighteen patients who visited the emergency department from May to August 2022 were randomly selected. Sixty-nine patients who were hospitalized in general wards in February 2023 were included. All patients tested positive for SARS-CoV-2 using either the cobas® SARS-CoV-2 real-time RT—PCR assay (cobas 6800 automation system) or the Liat cobas® SARS-CoV-2 & Influenza A/B assay (Roche Molecular Systems, Inc., Branchburg, NJ, USA). Nucleic acid extracted from nasopharyngeal and oropharyngeal swabs was utilized for viral whole genome SARS-CoV-2 sequencing by the Illumina COVIDSeq™ protocol on a NextSeq 550 system (Illumina Inc.). This comprehensive genomic analysis aimed to provide insights into the correlation between different viral strains and variations in disease severity in the patient population. The Illumina COVIDSeq™ Assay (96 samples) is a next-generation sequencing (NGS) method with a low- to mid-throughput capacity. It utilizes ARTIC V3 and V4 primer pools (https://artic.network) to ensure consistent coverage of the spike protein region in the viral genome (illumina-covidseq-data-sheet-m-gl-00243.pdf). The ARTIC V3 primer pool also includes primers for 11 human control genes. The assay is performed with a read length of 2 ×151 bp, allowing for accurate and comprehensive sequencing of the targeted genomic regions. Due to a shorter read length per sample generated on the NextSeq 550 compared to the NovaSeq 6000, samples run on the NextSeq 550 showed a slightly higher fraction of masked nucleotides in the consensus sequence generated. However, the NextSeq 550 instrument exhibited a similar global average SARS-CoV-2 genome coverage to that of both instruments combined.4 Based on the data from the Global Initiative on Sharing All Influenza Data (GISAID, https://gisaid.org/) and utilizing the Phylogenetic Assignment of Named Global Outbreak Lineages (PANGOLIN, Pango lineage) system (https://pango.network), our study period revealed a predominant presence of Omicron BA.5 and its descendant lineages among the 50 circulating SARS-CoV-2 variants of concern (VOCs) and Omicron subvariants under surveillance in Asia. Notably, during our study period in February 2023, a novel recombinant variant, XBB.1, emerged. Additionally, strains BA.5.1, BA.5.2, BA.5.2.1, BN.1.2, BN.1.2.3, and BN.1.3 consistently appeared in Taiwan and other parts of Asia. Furthermore, due to the possible prevalence of these strains in South Korea and Japan, we also noted a higher frequency of BN.1.2.3 with its parental lineage and BN.1.3 in our observations ( Fig. 1).5, 6 Fig. 1 The rectangular phylogenetic trees represent the SARS-CoV-2 variants among the 87 strains identified in this study. (A) The phylogenetic tree displays the clade of the 87 SARS-CoV-2 strains (https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-classifications.html). (B) The phylogenetic tree illustrates the lineage of SARS-CoV-2 strains based on the Pango lineage naming system (https://pango.network). The branches on both trees are scaled by genetic divergence and encompass the 87 identified strains. Fig. 1 The baseline characteristics of SARS-CoV-2 strains among the 87 patients with COVID-19 are presented in Table 1. The age of these patients ranged from 1 to 98 years, and 46 (52.9%) were male. Of the 87 patients, 86 (98.9%) were infected with the Omicron strain. Among the 18 viral strains identified from the 18 patients with COVID-19 diagnosed from May to August 2022, all belonged to the Omicron BA.2 lineage, specifically the BA.2.3.7 variant. Among these 18 patients, 10 (55.6%) had moderate illness, 7 (38.9%) had severe illness, and 1 (5.6%) had critical illness based on the severity classification by the National Institutes of Health (https://www.covid19treatmentguidelines.nih.gov/overview/clinical-spectrum/). Regarding the 67 patients with COVID-19 diagnosed in February 2023, 27 (39%) were infected with the Omicron BA.5 variant and its sublineages, 41 (59%) were infected with Omicron BA.2, and one was infected with a recombinant lineage, XBB.1.5.15. In terms of disease severity, for the BA.2 strain, 5 (12.2%) cases were classified as moderate, 22 (53.7%) as severe, and 14 (34.1%) as critical illness. Regarding the BA.5 strain, there were 13 (48.1%) cases classified as severe and 14 (51.9%) as critical. BA.5 was associated with a more critical illness (51.9%) than BA.2 (34.1%) in 2023, and the difference was also statistically significant (p = 0.0101). Patients infected with Omicron BA.5 had a significantly higher mortality rate (40.7%) compared to those infected with BA.2 (11.1%) in 2022 (p<0.0001) and (17.1%) 2023 (p = 0.0002) (Table 1), which is consistent with previous research.7 Table 1 Demographic characteristics of the 87 patients with COVID-19 regarding the SARS-CoV-2 strains identified. Table 1Year (no. of patients) Omicron variant (no. of strains) PANGO phylogeny of subsequent lineage (no. of strains) Mean Ct value of the samples Range of age, years (mean) No. (%) of patients with 28-day mortality 2022 (18) BA.2 (18) BA.2.3.7 (18) 18.5 1-87 (55.7) 2 (11.1) 2023 (69) BA.2 (41) BA.2.75.2 (1), BA.2.75.3 + BA.2.75.5 (1), BN.1.1 (4), BN.1.1.1 (2), BN.1.2 (8), BN.1.2.3 (18), BN.1.3 (6), BN.1.3.5 (1) 14.8 44-93 (73.2) 7 (17.1) BA.5 (27) BA.5.1 (3), BA.5.2 (3), BA.5.2.1 (2), BA.5.2.1+BF.12 (1), BA.5.2.1+BF.38 (2), BA.5.2.27 (1), BA.5.2.57 (1), BF.10 (1), BF.27 (1), BF.7.14 (1), BF.7.4.3 (1), BQ.1.1.35 (1), BQ.1.1.46 (1), BQ.1.1.5 (1), CM.12 (1) CP.1 (1), EA.1 (1), FB.1 (4) 15.7 56-98 (76.6) 11 (40.7) Nonrecombinant descendent lineages of a recombinant lineage (1) XBB.1.5.15 (1) 13.5 90 (90.0) 0 (0) Ct, Cycle threshold The current strains exhibit remarkable diversity, raising questions about the adequacy of vaccine protection.8, 9 During different periods of viral strain prevalence, the diversity and types of vaccines led to varying responses in terms of neutralizing antibodies and their effectiveness. For instance, when compared to mRNA-based or viral vector vaccines, the protein-based Medigen MVC-COV1901 vaccine (Medigen Vaccine Biologics Corporation, Xinzhu, Taiwan) developed in Taiwan exhibited distinct characteristics in its response against the Delta and Omicron variants.10 Our study had several limitations. First, the small sample size and short study period may have introduced survival bias and limited the ability to capture the true extent of SARS-CoV-2 epidemics in Taiwan. Second, the NextSeq 550 instrument used in the study had a shorter read length than the NovaSeq 6000, resulting in lower SARS-CoV-2 genome coverage and a higher rate of ambiguity. Last, the absence of live virus samples for confirmation of variants using NGS methods was another limitation in this study. In conclusion, while the current prevailing sublineages of Omicron in Taiwan may not demonstrate increased severity compared to the previous dominant variants, such as Alpha and Delta, it is crucial to maintain vigilance and avoid complacency. Future viral lineages may exhibit different patterns, necessitating ongoing monitoring of severe outcomes associated with novel SARS-CoV-2 variants. The utilization of the COVIDSeq™ methodology employed in this study holds immense value in assessing the severity of COVID-19 in individuals requiring emergency care, particularly during periods of limited testing resources. Such continuous surveillance is pivotal in shaping effective public health strategies to address emerging variants in the future. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ==== Refs References 1 Kang S.W. Park H. Kim J.Y. Lim S.Y. Lee S. Bae J.Y. Comparison of the clinical and virological characteristics of SARS-CoV-2 Omicron BA.1/BA.2 and omicron BA.5 variants: A prospective cohort study J Infect 86 5 2023 e148 e151 10.1016/j.jinf.2023.01.015 36669564 2 Abdul Aziz N. Nash S.G. Zaidi A. Nyberg T. Groves N. Hope R. Risk of severe outcomes among SARS-CoV-2 Omicron BA.4 and BA.5 cases compared to BA.2 cases in England J Infect 87 1 2023 e8 e11 10.1016/j.jinf.2023.04.015 37100176 3 Fourati S. Audureau E. Arrestier R. Marot S. Dubois C. Voiriot G. SARS-CoV-2 Genomic characteristics and clinical impact of SARS-CoV-2 viral diversity in critically ill COVID-19 patients: a prospective multicenter cohort study Viruses 14 7 2022 1529 10.3390/v14071529 35891509 4 Goswami C. Sheldon M. Bixby C. Keddache M. Bogdanowicz A. Wang Y. Identification of SARS-CoV-2 variants using viral sequencing for the Centers for Disease Control and Prevention genomic surveillance program BMC Infect Dis 22 1 2022 404 10.1186/s12879-022-07374-7 35468749 5 World Health Organization, COVID-19 Weekly Epidemiological Update, Edition 131 published 22 February 2023 6 Khare S. Gurry C. Freitas L. Schultz M.B. Bach G. Diallo A. GISAID's role in pandemic response China CDC Wkly 3 49 2021 1049 1051 10.46234/ccdcw2021.255 34934514 7 Robertson C. Kerr S. Sheikh A. Severity of Omicron BA.5 variant and protective effect of vaccination: national cohort and matched analyses in Scotland Lancet Reg Health Eur 28 2023 100638 10.1016/j.lanepe.2023.100638 8 Garcia-Beltran W.F. Lam E.C. St Denis K. Nitido A.D. Garcia Z.H. Hauser B.M. Multiple SARS-CoV-2 variants escape neutralization by vaccine-induced humoral immunity e9 Cell 184 9 2021 2372 2383 10.1016/j.cell.2021.03.013 33743213 9 Shao W. Zhang W. Fang X. Yu D. Wang X. Challenges of SARS-CoV-2 Omicron Variant and appropriate countermeasures J Microbiol Immunol Infect 55 3 2022 387 394 10.1016/j.jmii.2022.03.007 35501267 10 Song Y.C. Liu S.J. Lee H.J. Liao H.C. Liu C.T. Wu M.Y. Humoral and cellular immunity in three different types of COVID-19 vaccines against SARS-CoV-2 variants in a real-world data analysis Mar 31:S1684-1182(23)00075-0 J Microbiol Immunol Infect 2023 10.1016/j.jmii.2023.03.008
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==== Front Results Chem Results Chem Results in Chemistry 2211-7156 The Author(s). Published by Elsevier B.V. S2211-7156(23)00258-8 10.1016/j.rechem.2023.101019 101019 Article A sensitive UPLC-MS/MS method for the simultaneous assay and trace level genotoxic impurities quantification of SARS-CoV-2 inhibitor-Molnupiravir in its pure and formulation dosage forms using Fractional Factorial Design Nakka Srinivas a Krishna Muchakayala Siva ab Babu Manabolu Surya Surendra a⁎ a Department of Chemistry, School of Science, GITAM Deemed to be University, Hyderabad-502329, India b Analytical Research and Development, Catalent Pharma Solutions, 1100 Enterprise Drive, Winchester, Kentucky, 40391, USA ⁎ Corresponding author at: Department of Chemistry, School of Science, GITAM Deemed to be University 27 6 2023 27 6 2023 10101917 5 2023 22 6 2023 © 2023 The Author(s) 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Graphical abstract Two potential genotoxic impurities were identified (PGTIs)-viz. 4-amino-1-((2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)tetrahydrofuran-2-yl)pyrimidin-2(1H)-one (PGTI-1), and 1-(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)tetrahydrofuran-2-yl)pyrimidin-2,4(1H,3H)-one (PGTI-II) in the Molnupiravir (MOPR) synthetic routes. COVID-19 disease was treated with MOPR when mild to moderate symptoms occurred. Two (Q)-SAR methods were used to assess the genotoxicity, and projected results were positive and categorized into Class-3 for both PGTIs. A simple, accurate and highly sensitive ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) method was optimized for the simultaneous quantification of the assay, and these impurities in MOPR drug substance and formulation dosage form. The multiple reaction monitoring (MRM) technique was utilized for the quantification. Prior to the validation study, the UPLC-MS method conditions were optimised using fractional factorial design (FrFD). The optimized Critical Method Parameters (CMPs) include the percentage of Acetonitrile in MP B, Concentration of Formic acid in MP A, Cone Voltage, Capillary Voltage, Collision gas flow and Desolvation temperature were determined from the numerical optimization to be 12.50 %, 0.13 %, 13.6 V, 2.6 kV, 850 L/hr and 375 °C, respectively. The optimized chromatographic separation achieved on Waters Acquity HSS T3 C18 column (100 mm × 2.1 mm, 1.8 µm) in a gradient elution mode with 0.13% formic acid in water and acetonitrile as mobile phases, column temperature kept at 35 °C and flow rate at 0.5 mL/min. The method was successfully validated as per ICH guidelines, and demonstrated excellent linearity over the concentration range of 0.5-10 ppm for both PGTIs. The Pearson correlation coefficient of each impurity and MOPR was found to be higher than 0.999, and the recoveries were in between the range of 94.62 to 104.05% for both PGTIs and 99.10 to 100.25% for MOPR. It is also feasible to utilise this rapid method to quantify MOPR accurately in biological samples. Keywords Fractional factorial design Molnupiravir Potential genotoxic impurities (Q)-SAR UPLC-MS/MS ==== Body pmcIntroduction In the research of pharmaceutical active ingredients and formulation dosage forms, the control of potentially genotoxic impurities has become more significant in recent years. The impurities that may result from process reagents, intermediates, by-products, and stability storage can be carried over to the final drug substance and drug products. Some of these impurities are potentially genotoxic and or mutagenic [1]. The acceptable limits of these impurities by the regulatory agencies are very low when compared to other impurities. Developing an analytical method for trace-level potential genotoxic impurities is quite challenging. In addition to the quantification of low-level impurities and their sensitivity problems, the reactivity or instability causes difficulty in developing a suitable analytical method [2]. Guidelines and expectations on regulatory concerns regarding the presence and limit of GTIs in new drug products have been issued by the United States Food and Drug Administration (USFDA) [3], [4], International Conference on Harmonization (ICH) M7 guideline [5], and European Medicines Agency (EMA) [6]. According to the ICH M7 guideline, “A computational toxicology evaluation should be accomplished using Quantitative Structure-Activity Relationships ((Q) SAR) methods to forecast the result of bacterial mutagenicity assay for the active drug substance“. Both expert rule-based and statistically-based prediction methods are used by (Q)-SAR models. Additionally, a Threshold of Toxicological Concern (TTC), referring to a threshold exposure limit of 1.5 µg per day, was established [7]. Molnupiravir (MOPR) is chemically ((2R,3S,4R,5R)-3,4-Dihydroxy-5-(4-(hydroxyamino)-2-oxopyrimidin-1(2H)-yl) tetrahydrofuran-2-yl)methyl isobutyrate having molecular formula and molecular weights are C13H19N3O7 and 329.3 g mol−1, respectively. MOPR is an orally-administrated antiviral prodrug of β-D-N4-hydroxycytidine (NHC) [8], which rapidly hydrolyses in the plasma by the Host’s esterase . NHC further forms an NHC-triphosphate by the Host’s kinase, which, by inducing viral errors, prevents the replication of RNA viruses [9]. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been shown to be resistant to the small-molecule ribonucleoside prodrug MOPR in vitro and in animal models. In favour of the prevention and treatment of COVID-19, it was created by Drug Innovations at Emory (DRIVE), LLC, and further developed and sponsored by Merck and Ridgeback Biotherapeutics [10]. To treat COVID-19 patients, since there is no definite medication for SARS-CoV-2 in the market, the researchers have tested nearly 250 medications of therapeutically active compounds for their antiviral properties against the feline infectious peritonitis coronavirus (FIPV) and the human coronavirus OC43 (HCoV-OC43) [11], [12]. Similar to SARS-CoV-2, HCoV-OC43 is belongs to the beta coronavirus. Both FIPV and HCoV-OC43, which have been therapeutically used for their respective indications and are connected to known dosing regimens, side effects, or toxicity profiles in human beings, have been shown to be susceptible to the MOPR. It has also been demonstrated that these viruses can be inhibited by the MOPR [13], [14]. In the era of Omicron and in real-life settings, MOPR might be effective in reducing the risk of severe COVID-19 and COVID-19-related mortality, particularly in specific subgroups. During the COVID-19 clinical trials, MOPR treatment is well tolerated by patients [15], [16]. International phase II/III randomly assigned, double-blind and studies with placebo control have shown that MOPR reduces the chance of hospitalisation or death in COVID-19 patients [17], [18], [19]. An RNA-dependent RNA polymerase (RdRp) executes the replication and transcription of the new coronavirus's genes. In order to combat SARS-CoV-2, RdRp is a possible target for both the development of new antiviral drugs and the repurposing of existing ones [20], [21]. In the UK, the MOPR was approved on November 4th, 2021, for the treatment of mild to moderate COVID-19 symptoms in individuals with a positive result on the SARS-CoV-2 diagnostic test. It was filed for approval for the medication of COVID-19 and has emergency use authorization in a number of countries, including Japan, Europe and USA [22]. MOPR ((2R,3S,4R,5R)-3,4-Dihydroxy-5-(4-(hydroxyamino)-2-oxopyrimidin-1(2H)-yl) tetrahydrofuran-2-yl)methyl isobutyrate, PGTI-1(4-amino-1-((2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)tetrahydrofuran-2-yl) pyrimidin-2 (1H)-one), and PGTI-2 (1-(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxy methyl)tetrahydrofuran-2-yl) pyrimidin-2,4 (1H,3H)-one) are shown in Fig.1 . The maximum daily dose (MDD) of MOPR is 1.6 g (800 mg twice) over a period of 5 days to treat COVID-19 [23]. Considering the shorter period of usage, the daily acceptable intake of impurity is considered 120 µg/day as per ICH M7 guidelines. The control limit of these impurities is calculated as 120 µg/day/MDD i.e.,75 µg/g. Both the impurities should be controlled below 7.5 µg/g; therefore, 5 µg/g has been used as the specification limit for the two PGTIs.Fig. 1 Structures of (a) MOPR API, (b) PGTI-1, and (c) PGTI-2 A detailed literature review indicated that the MOPR quantification could be done using various analytical techniques such as HPLC [24], [25], the mathematical assisted UV spectroscopic method [26], Voltammetric method [27], HPTLC [28], and the hyphenated technique LC-MS method in human plasma and saliva [29], [30]. However, no publications were reported on the separation and simultaneous assay and trace-level quantitative analysis of MOPR and its two PGTIs. To the best of our knowledge, this is the first Fractional factorial design (FrFD) optimized UPLC-MS/MS method for the simultaneous assay and trace-level quantification of two genotoxic impurities in MOPR drug substance and formulation dosage forms. A comparison study between the literature methods and the method developed in this work is shown in Table 1 .Table 1 A comparison between literature available methods and the method developed in this study Technique Mobile phase Stationary phase Linearity range Ref. MOPR (μg/mL) PGTI-1 (ng/mL) PGTI-2 (ng/mL) RP-HPLC-UV 20 mM phosphate buffer pH 2.5: acetonitrile; 80:20, v/ v% Inertsil C18 column (150 × 4.6 mm, 5 mm) 0.2–80 - - 24 RP-HPLC ACN:Water (20:80 v/v) mixture-isocratic mode Discovery® HS C18 Column (75 ×4.6 mm, 3 µm) 0.1–60.0 - - 25 UV-Spectroscopic method isocratic mixed micellar mobile phase composed of 0.1 M SDS, 0.01 M Brij-35, and 0.02 M monobasic potassium phosphate pH 3.1 RP-C18 core-shell column (5 μm, 150 × 4.6 mm) 5-25 - - 26 Voltametric method - - 0.25 - 750 µM - - 27 HP-TLC methylene chloride:ethyl acetate:methanol:25% ammonia (6:3:4:1, v/v/v/v) Silica gel 60F254 3.75–100.00 - - 28 LC-MS 1 mM Ammonium acetate inwater (pH4.3) and 1 mM Ammonium acetate in acetonitrile Atlantis C18 column 2.5–5000 ng/mL - - 29 LC-MS Methanol 0.2 % acetic acid (5:95, v/v) Zorbax Eclipse plus C18, 4.6 × 150 mm, (5 µm) 20.0–10000.0 ng/mL - - 30 LC-MS 0.13% formic acid in water and acetonitrile in Gradient elution Waters Acquity HSS T3 C18 column (100 mm × 2.1 mm, 1.8 µm) 2.25-100 1-20 ng/mL 1-20 ng/mL This study The importance of quality by design (QbD) driven design of experiments (DoEs) across the optimization, screening, and robustness methodology conditions of the analytical procedure has significantly increased in recent years. The one-factor-at-a-time (OFAT) or trial-and-error (TAE) approaches are the basis of the conventional chromatographic procedure [31]. These methods, however, do not reveal the interaction effects of various factors on separation and elution. To conduct a minimum number of trials and quantitatively identify the effects of variable interactions, AQbD-established experiments are used [32], [33], [34], [35]. A statistical quality-by-design is an effective technique for identifying probable risks and failures and understanding their interaction effect. In the current study, we used an expert rule-based Deductive Estimation of Risk from Existing Knowledge (DEREK) and knowledge-based Sarah software to predict the toxicity of PGTIs [36]. In addition, a QbD-based fractional factorial model was used to understand the distinct and interaction impacts of CMPs and to optimize a selective, precise and accurate UPLC-MS/MS method to determine PGTIs in MOPR drug substance and formulation dosage forms by following the regulatory requirements. The detailed workflow is shown in Fig. 2 , from the genotoxicity evaluation of PGTIs to optimized method efficiency to MOPR formulation samples.Fig. 2 Workflow diagram Material and methods Instrumentation and software Both Knowledge: Derek KB 2022 1.0, Derek version: Derek Nexus: 6.2.0, Knowledge version: 1.0 with Knowledge Date: 06 January 2022 and Model: Sarah Model 2022.1, Nexus version: Nexus: 1.9, Sarah version: Sarah Nexus: 3.2.0 were the two main (Q) SAR methods used. Bacteria are the species, and in vitro, mutagenicity is the endpoint. Waters Acquity H-Class UPLC (Waters Corporation, Milford, USA) with a temperature-regulated autosampler and column thermostat was used for the chromatographic separation study. Waters Xevo TQ-XS MS (Waters Corporation, Milford, MA) with an ESI ion source was employed for the overall method optimization and validation study. To acquire and process the mass data, used Masslynx software version 4.2.1. The design of experiments and optimization data for the FrFD were attained from Design-Expert software version 13 (Stat-Ease Inc, Minneapolis, USA). ChemDraw Professional 15.0 was used to represent the chemical structure of MOPR and its PGTIs. Reagents Analytical reference standards of MOPR, PGTI-1 and PGTI-2, having the purities 99.85%, 99.63% and 99.72%, respectively, were procured from M/S Synpure laboratories, India. MOPR, PGTI-1, and PGTI-2 were characterized by NMR data shown in Fig. S1-S3. Commercially available MOPR formulation tablets of two different brands were purchased from a local pharmacy. Formic acid (≥98%) was acquired from Merck life science (Mumbai, India). LCMS-grade acetonitrile and methanol were purchased from J.T. Baker (Hyderabad, India). A 0.22 µm Millex Syringe, Durapore® (PVDF) membrane filters were procured from Millipore (Burlington, Massachusetts, USA). Throughout the analysis, high pure Milli-Q water from the Milli-Q purification device (Bedford, MA, USA) was used. Preparation of Mobile phase To 1000 mL of water, 1.0 mL of formic acid was added to prepare mobile phase solution A. Mobile phase solution B was acetonitrile, and both were ultra-sonicated for degassing. Diluent A mixture of Mobile phase-A and Methanol (10:90 v/v). Standard preparation The appropriate amounts of each impurity were dissolved in a diluent solution to prepare a stock mixture of PGTI-1 and PGTI-2 (1000 ng/mL). The stock solution was further diluted to prepare 100 ng/mL diluted stock solution. Accurately weighed about 50 mg of MOPR standard to a 20mL volumetric flask, added 5 ml of diluent, and then sonicated for 15 minutes. Equilibrated the flask to room temperature and made up to the mark. For MOPR Assay quantification, the above solution further dilutes from 2.5 mL to 100 mL with diluent (50 µg/mL). The final standard solutions were filtered with a 0.22 µm Millex Syringe Filter, Durapore® (PVDF), prior to injecting into the UPLC-MS system. Sample preparation A sample solution of 2 mg/mL was prepared as accurately weighed about 50 mg of MOPR API into a 25 mL of volumetric flask and then dissolved in 15 mL of diluent and sonicate for about 15 min. Makeup with diluent to the mark after bringing the flask to room temperature. For MOPR Assay quantification, the above sample solution was further diluted from 2.5 mL to 100 mL with diluent (50 µg/mL). The resultant sample solution was filtered through 0.22 µm containing Millex Syringe and Durapore® (PVDF) syringe filters. Formulation tablet sample preparation Ground the 10 tablets of commercially available two different brand tablets separately using mortar and pestle to become a fine powder. Weighed equivalent to 50 mg of powder into a 25 mL volumetric flask, added 15 mL of diluent, and sonicated for 15 minutes to dissolve. Makeup with diluent to the mark after bringing the flask to room temperature. For MOPR Assay quantification, the above sample solution was further diluted from 2.5 mL to 100 mL with diluent (50 µg/mL). The resultant sample solution was filtered through 0.22 µm containing Millex Syringe and Durapore® (PVDF) syringe filters. UPLC-MS/MS-tuned conditions of operation The chromatographic separation study was performed using Waters Acquity H-Class UHPLC equipment with a temperature-regulated autosampler and column thermostat. ESI ion source-equipped Waters Xevo TQ-XS MS instrument was employed for the multiple reaction monitoring (MRM) technique. The Waters Acquity HSS T3 C18 column (100 mm × 2.1 mm, 1.8 μm) was utilized to achieve the chromatographic peak separation. As the mobile phase, 0.1% formic acid in water and acetonitrile were used in a gradient program with a pump flow rate of 0.5 mL min-1 for a chromatographic run time of 10 minutes. While the autosampler temperature was controlled at 15 °C, the column temperature was kept at 35 °C with a 5 µL of injection volume and initial gradient program as follows: 0.0/90, 4.0/90, 6.0/40, 12.0/10, 12.1/90, and 15.0/90. The quantification of both PGTIs in MOPR used the MRM technique with ESI+ mode. The MRM transitions 244.03/111.95 for PGTI-1, 245.03/112.95 for PGTI-2, and 330.03/127.62 for MOPR were chosen for the quantification. The fragment ions of MRM transition for MOPR and both impurities are shown in Fig. S4. The capillary voltage, desolvation temperature and desolvation gas flow of the electrospray source were kept at 2.8 kV, 400 °C and 800 L/hr, respectively. The set cone voltage (kV) was 15, and the collision energy was 14 eV for both PGTI-1 and PGTI-2. The set cone voltage (kV) was 22, and the collision energy was 18 eV for MOPR. The mass Lynx (version 4.2) software was used to measure each acquisition and processing parameter for the MS. Results and discussion Assessment of Q) SAR data (Q)-SAR predictions are essential for evaluating the mutagenicity/toxicity of impurities in drug products according to ICH M7(R1). (Q)-SAR techniques with high throughput capabilities can rapidly forecast hundreds of impurities. Two complementary (Q) SAR methods, one centred on expert knowledge and another on statistics, are commonly used to establish the bacterial reversion mutation test. Derek is an expert knowledge-based software that encompasses data from published, confidential and non-confidential data donated by the organizations. While Sarah software comprises public and non-confidential data contributed by organizations. The positive results of both impurities indicate that the structures were predicted to be positive in the Ames test. Nexus 2.5.0 was utilized for submitting MOPR, PGTI-1, and PGTI-2, and the ICH classification model was employed to make predictions. The predicted results are demonstrated in Table 2 . The predicted outcomes of both PGTIs, Derek’s plausible and Sarah's positive. The nexus system is an integrated platform of Derek, and Sarah predicted as positive and assigned both impurities to the Class-3 category, which indicated that the impurities are potentially mutagenic.Table 2 (Q) SAR prediction summary for genotoxicity of MOPR both impurities Proposed Structure ICH M7 Class Coherent of concern Derek prediction Sarah Prediction Experimental data Similar to API Overall in Silico Class-3 No Carc: Unspecified Ames: Unspecified Alert(s) not found in API Positive Class-3 No Carc: Unspecified Ames: Unspecified Alert(s) not found in API Positive Method development and optimization Optimization of sample preparation Sample preparation plays a crucial part in trace-level quantification analytical method development since the matrix effects in trace analysis can be enlarged and affect the detection of sensitivity [37]. Solubility can be altered by a number of variables, including pH, temperature, and the presence of other compounds or excipients in a formulation. MOPR is freely soluble in dimethyl sulfoxide (DMSO) and methanol. It is soluble in water, sparingly soluble in ethyl acetate, and 2-propanol. It is practically soluble in dichloromethane, n-hexane, and n-heptane. The higher concentration of MOPR contributes to the sensitivity of the detection, so screened different diluents in order to achieve better extraction efficiency. An aqueous solution containing 0.1% formic acid and methanol was screened at various concentrations. Finally, a mixture in the ratio of 10:90 v/v of 0.1% formic acid in an aqueous solution and methanol was selected as the diluent to achieve a MOPR concentration of 2 mg/mL in consequence of solvent effects and solubility. Both PGTIs are soluble in the optimised diluent. Different sonication times were evaluated and finalized at 15 min for sample preparation bearing in mind the solubility and extraction efficiency. The Filter study was evaluated against the unfiltered centrifuged solution of MOPR, and both PGTIs peak areas are comparable. MOPR and both genotoxic impurities are not retained by the Durapore PVDF membrane. Therefore, the filter is appropriate for the current study without retaining MOPR and its genotoxic impurities. Method development of chromatographic separation The intended purpose of this study was to acquire a simple, selective, and accurate LC-MS/MS method that can effectively separate and quantify trace levels of two PGTIs in MOPR simultaneously. The poor peak shape of MOPR and similar retention times were observed while working with the Acquity BEH C18 (100 mm × 2.1 mm, 1.7µm) column. Both PGTI-I and PGTI-2 have co-eluting during the usage of Symmetry C18 (100 mm × 2.1 mm, 1.7 µm) column, which is challenging in accurate quantification. In terms of separation, analyte response and peak shape, the Waters HSS T3 C18 (100 mm × 2.1 mm, 1.8 µm) column was found to have acceptable applicability. The acquisition time of MS was established to 0 to 6 mins for initial development to eliminate the MOPR contamination. The assessed and composed mobile phases included 0.1% trifluoro acetic acid in the water, 10 mM ammonium acetate, and 0.1% formic acid in the water. As organic phases, acetonitrile was used instead of methanol due to its significantly better elution efficacy. When the chromatographic efficiency was assessed using different diluents, the mobile phase-A and methanol in the proportion of 10:90 v/v showed higher sensitivity and was suitable for the current study. The outcomes established that the mobile phases were controlled in gradient mode with 0.1% formic acid in water and acetonitrile as aqueous and organic mobile phases A and B, respectively. This was due to the method's proficiency in producing chromatograms with the highest level of chromatographic resolution, sharp peaks, and virtuous response. The pump was operating at a constant flow of 0.5 mL min-1, while the column thermostat was set to 35 °C. During the optimization of MOPR MRM transitions, it has shown four transitions (m/z 330.03/70.94, m/z 330.03/82.03, m/z 330.03/110.85, and m/z 330.03/127.92). Among these transitions, m/z 330.03/127.92 was chosen since the response was below compared to other transitions, which is suitable for accurate and precise MOPR assay determination. Method optimization using Fractional factorial design (FrFD) approach The FrFD has been employed in the current study to optimize and finalize the UPLC-MS method conditions. DoEs were mainly employed to discriminate and enhance the CMPs. To understand the critical significance of the analytical method, a FrFD was employed to find CMPs and each of their unique interaction effects. The FrFD was implemented in the present study in order to enhance UPLC-MS/MS conditions. The designated CMPs that were identified based on the initial experiments and the adherences; the percentage of acetonitrile composition in MP-B (A), the concentration of formic acid in MP-A (B), the cone voltage (V) (C), the capillary voltage (kV) (D), the collision gas flow (L/hr) (E), and the desolvation temperature (°C) (F). According to USP guideline <621>, chromatographic parameters are altered from lower to higher. The Critical Quality Attributes (CQAs) are identified as PGTI-1 content in ppm (µg/g) (R1), and PGTI-2 content in ppm (µg/g) (R2). CQAs are measurable as numerical variables and are quantitative. Since the trace level quantification of both impurities is critical, the contents of both impurities were measured as CQAs. Therefore, MOPR content or assay was not included in the method optimization. The centre point conditions were the percentage of Acetonitrile (10%) in MP-B (A), the Concentration of Formic acid (0.10%) in MP-A (B), Cone Voltage (15 V) (C), Capillary Voltage (2.8 kV) (D), Collision gas flow (850 L/hr) (E), and Desolvation temperature (400 °C) (F). The monitored responses (R) were PGTI-1 (R1) and PGTI-2 (R2) contents in ppm. A total of 19 experiments were performed, which include 2n-2 (n=6), 16 experiments, and 3 center points. The DoE study employed the standard solution that contained 2 mg/mL of MOPR spiked with 5 ppm (µg/g) of each PGTI impurity. The experimental designs with variables (CMPs) and replies (CQAs) are shown in Table 3 .Table 3 The experimental designs with variables (CMPs) and replies (CQAs) Std Run Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Response 1 Response 2 A: Acetonitrile in MP B (%) B: Formic acid in MP A (%) C: Cone Voltage (V) D: Capillary Voltage (KV) E: Collision gas flow (mL/min) F: Desolvation temperature (°C) R1 R2 4 1 15 0.13 13 2.6 950 450 5.01 5.11 17 2 10 0.10 15 2.8 850 400 4.93 5.01 18 3 10 0.10 15 2.8 850 400 4.95 5.04 7 4 5 0.13 13 2.6 750 450 4.97 5.01 1 5 5 0.07 13 2.6 750 350 4.95 4.97 12 6 15 0.13 17 2.6 750 350 5.11 4.99 11 7 5 0.13 17 3.0 750 450 4.88 4.93 6 8 15 0.07 13 2.6 950 350 4.95 5.01 5 9 5 0.07 13 3.0 950 450 4.89 4.96 14 10 15 0.07 17 2.6 750 450 4.81 5.01 13 11 5 0.07 17 3.0 750 350 4.79 4.99 16 12 15 0.13 17 3.0 950 450 4.72 5.03 8 13 15 0.13 13 3.0 750 350 5.01 5.11 2 14 15 0.07 13 3.0 750 450 4.78 4.86 9 15 5 0.07 17 2.6 950 450 4.89 5.01 10 16 15 0.07 17 3.0 950 350 5.05 5.15 3 17 5 0.13 13 3.0 950 350 4.91 5.11 15 18 5 0.13 17 2.6 950 350 4.85 5.05 19 19 10 0.10 15 2.8 850 400 4.91 5.04 The chosen model was analysed, and the ANOVA test demonstrated that the p-values are significant with <0.05 for both responses R1 and R2. Similarly, the lack of fit p-values is not significant, with >0.05 for both responses R1 and R2. The model and lack of fit p-values demonstrate that the model is suitable for the current study. Examined and navigated the design model using the adjusted R2, predicted R2, regression coefficient (R2), and adequate precision. The data seemed to have excellent, as evidenced by the closeness of the values between the predicted R2 (0.8247, 0.8676 for response R1, R2) and adjusted R2 values (0.9555, 0.9437 for response R1, R2), which was obtained to be <0.2. A ratio of more than 4 is enviable when measuring the signal-to-noise ratio with allowable precision. The perceived ratio (22.396 and 23.027 for responses R1 and R2) shows that an ample signal and design can be used to traverse design space. Data from the ANOVA, including F and P values, model R2, Lack of fit, predicted R2, and modified R2, are shown in Table 4 . To distinguish the interactions between the variables and the effect of factors, examined half-normal plots and numerical plots for each response.Table 4 Summary of ANOVA results Response Source Sum of Squares df Mean Square F-value p-value R2 Adjusted R2 Predicted R2 R1 Model 0.1687 13 0.0130 30.74 0.0007* 0.9876 0.9555 0.8247 A-Acetonitrile in MP B 0.0060 1 0.0060 14.23 0.0130 B-Formic acid in MP A 0.0077 1 0.0077 18.14 0.0080 C-Cone Voltage 0.0086 1 0.0086 20.27 0.0064 D-Capillary Voltage 0.0163 1 0.0163 38.51 0.0016 F-Desolvation temperature 0.0281 1 0.0281 66.46 0.0005 AB 0.0018 1 0.0018 4.28 0.0934 AC 0.0039 1 0.0039 9.25 0.0287 AD 0.0011 1 0.0011 2.50 0.1745 AF 0.0541 1 0.0541 128.04 < 0.0001 BC 0.0060 1 0.0060 14.23 0.0130 BD 0.0068 1 0.0068 16.12 0.0102 ABC 0.0068 1 0.0068 16.12 0.0102 ABD 0.0218 1 0.0218 51.53 0.0008 Residual 0.0021 5 0.0004 Lack of Fit 0.0013 3 0.0004 1.09 0.5106** Pure Error 0.0008 2 0.0004 R2 Model 0.0827 9 0.0092 34.53 < 0.0001* 0.9719 0.9437 0.8676 A-Acetonitrile in MP B 0.0036 1 0.0036 13.53 0.0051 B-Formic acid in MP A 0.0090 1 0.0090 33.92 0.0003 C-Cone Voltage 0.0000 1 0.0000 0.0940 0.7662 E-Collision gas flow 0.0196 1 0.0196 73.66 < 0.0001 F-Desolvation temperature 0.0132 1 0.0132 49.70 < 0.0001 AB 0.0001 1 0.0001 0.3758 0.5550 AC 0.0016 1 0.0016 6.01 0.0366 BC 0.0306 1 0.0306 115.10 < 0.0001 ABC 0.0049 1 0.0049 18.42 0.0020 Residual 0.0024 9 0.0003 Lack of Fit 0.0018 7 0.0003 0.8546 0.6356** Pure Error 0.0006 2 0.0003 The model graphs revealed that the response R1 and R2 increases with the increase of organic percentage (CMP-A) and concentration of formic acid (CMP-B). R1 decreases with a rise in cone voltage (CMP-C), and Response R2 is not affected; R1 decreases with an increase in capillary voltage (CMP-D), and Response R2 is not affected; R2 increases with an increase in collision gas flow (CMP-E), and Response R1 is not affected. Both R1 and R2 decrease with the increase in desolvation temperature (CMP-F). Half-normal plots, the Effect of factors on responses, and numerical optimization graphs are shown in Fig. 3, Fig. 4, Fig. 5 .Fig. 3 Half-normal plots Fig. 4 (A) The effect of factors on response R1, (B) The Effect of factors on response R2 Fig. 5 Numerical optimization graphs In order to maximise the responses of R1 and R2, the best optimum chromatographic conditions have been found using the numerical optimisation approach. CMP-A (percentage of Acetonitrile in MP B) (ACN) (%), CMP-B (Concentration of Formic acid in MP A) (%), CMP-C (Cone Voltage) (V), CMP-D (Capillary Voltage) (kV), CMP-E (Collision gas flow) (L/hr) and CMP-F (Desolvation temperature) (°C) were determined from the numerical optimization to be 12.79 %, 0.13 %, 13.62 V, 2.60 kV, 846.36 L/hr and 373.23 °C, respectively. The closest values, such as CMP-A, CMP-B, CMP-C, CMP-D, CMP-E, and CMP-F, were 12.5 %, 0.13 %, 13.6 V, 2.6 kV, 850 L/hr and 375 °C, respectively were finalized from the fractional factorial design for the method validation use of MOPR drug substance and formulation dosage form. The optimized chromatographic conditions and MS conditions from fractional factorial design were shown in Table 5, Table 6 , respectively. As shown in Fig. S5, the final optimized chromatogram and the retention times of PGTI-1, PGTI-2 and MOPR were found at 3.39, 4.12, and 8.08 min, respectively.Table 5 Optimized chromatographic conditions from Fractional factorial design Time (min) Flow rate (mL/min) Mobile phase-A(0.13% Formic acid in water) Mobile phase-B(Acetonitrile) 0.0 0.5 87.5 12.5 2.0 0.5 87.5 12.5 5.0 0.5 10.0 90.0 8.0 0.5 10.0 90.0 8.1 0.5 87.5 12.5 10.0 0.5 87.5 12.5 Table 6 Optimized mass spectrometry conditions for PGTI-1, PGTI-2, and MOPR in electron spray positive ion mode from Fractional factorial design Compound Precursor ion (m/z) Product ion (m/z) Collision energy (V) Cone Voltage (V) Capillary Voltage (kV) Collision gas flow (L/hr) Desolvation temperature (°C) PGTI-1 244.03 111.95 15 13.6 2.60 850 375 PGTI-2 245.03 112.95 15 13.6 2.60 850 375 MOPR 330.03 127.62 18 22 2.60 850 375 Method validation The current enhanced LC-MS/MS method originated using a fractional factorial design, and it has been fully validated in compliance with ICH quality standard guidelines [38] and published research articles [39], [40], [41], [42]. This method was verified for specificity, Linearity, detection and quantification limits, accuracy, Method and intermediate precision, and solution stability. Specificity Specificity demonstrates an analytical method's capability to separate target components in the context of sample matrix components. The analytical method's specificity was proven by showing that the LC-MS chromatographic system could distinguish between the diluent, individual impurities, and a sample spiked with impurities. The specificity of the method was evaluated by examining the retention times of both PGTI-1 and PGTI-2 across the chromatography of diluent in addition to spiked (PGTI-1-5 ppm (µg/g), PGTI-2-5 ppm (µg/g), and MOPR 2 mg/mL) solutions. As a result of the LC/MS analysis of the above solutions, PGTI-1, PGTI-2, and MOPR were not co-eluted, thus facilitating the precise and accurate quantification of these two impurities in the MOPR drug substance and tablets. The typical MRM chromatograms demonstrating the method specificity can be found in Fig. 6 A–G.Fig.6 Typical MRM Chromatogram of (A) PGTI-I diluent blank, (B) PGTI-1 at the specification level, (C) MOPR drug spiked with PGTI-1, (D) PGTI-2 diluent blank, (E) PGTI-2 at the specification level, and (F) MOPR drug spiked with PGTI-2 and (G) MOPR Determination of limit of detection (LOD) and limit of quantitation (LOQ) The LOD and LOQ for PGTI-1 and PGTI-2 were established using the signal-to-noise ratios (S/N) method of 3 and 10, correspondingly. These ratios were determined by preparing reference solutions with known concentrations and injecting them into the LC-MS spectrometry. Six injections of these impurities were used to determine the repeatability of the LOQ values and calculated the % RSD value found to be less than 2. Based on the result of calculations, LOD and LOQ were determined to be 0.16 ppm (µg/g) and 0.5 ppm (µg/g) for PGTI-1 and 0.12 ppm (µg/g) and 0.4 ppm (µg/g) for PGTI-2, respectively. From the linearity regression data, the LOD and LOQ values of MOPR were determined. The obtained values are 0.68 and 2.25 µg/g, respectively. Hence, the optimized method is sensitive enough to determine both PGTIs. Typical LOQ chromatograms of PGTI-1 and PGTI-2 are shown in Fig. 7 .Fig. 7 LOQ chromatograms of (a) PGTI-1 and (b) PGTI-2 Linearity The linearity of the two PGTIs was investigated over a concentration range between 1 to 20 ng/mL, or 0.5 to 10 ppm (µg/g), against a 2 mg/mL sample concentration. In the construction of a set of calibration standards, 100 ng/mL of diluted stock solution was prepared as described in section 2.5. Further, diluted to produce the following final concentrations: 1, 2, 5, 10, 12, 15, and 20 ng/mL (0.5, 1.0, 2.5, 5, 6, 7.5, and 10 ppm (µg/g), respectively). A concentration equivalent to 2 mg/mL of MOPR was used for preparing sample solutions in the diluent. Six different concentrations, ranging from 10 µg/mL to 100 µg/mL, were used to determine the linearity of MOPR. Using peak areas vs. their corresponding concentrations, the calibration curves of PGTI-1 and PGTI-2 were evaluated. The method linearity for both PGTIs was examined at six distinct concentrations varying from 0.5 to 10 ppm (µg/g). The slope (a), intercept (b), and Pearson correlation coefficient (r) were calculated using the linear regression equation with least squares. According to the regression equation, Y= a.X+ b. The regression equation for PGTI-1 is Y= 313.61 X +77.616. Similarly, Y= 297.11 X +52.746 for PGTI-2 and Y= 607.19 X-4357.4 for MOPR. The Pearson coefficient was 0.9996, 0.9995 and 0.9997 for PGTI-1, PGTI-2, and MOPR, respectively. The aforementioned results show an excellent correlation between the peak areas vs the concentrations of both impurities. The calibration curves of MOPR, PGTI-1, and PGT-2 were shown in Fig. S6 Precision Method precision of the current optimized method was evaluated by calculating the % RSD of the contents of PGTI-1 and PGTI-2 from six replicate injections of spiked solution at the specification level was used. A diluted sample with a concentration of 50 µg/mL and analysis in accordance with the method was used to show the precision of the MOPR. Furthermore, intermediate precision was measured by determining the % RSD of the contents of PGTI-1, PGTI-2 and MOPR from six replicate injections of spiked solutions at the specification level by the different analyst on different day. The % RSD values for the method precision and intermediate precision were less than 2.0, and the results are briefly summarised in Table 7 . The precision outcomes show that the method used is precise.Table 7 Method validation summary for Specificity, precision, accuracy, and solution stability Method Validation parameter Results PGTI-1 PGTI-2 MOPR Specificity Should be no interference from the diluent No interference No interference No interference Method Precision (n=6, % RSD <10.0 for both PGTIs and <2.0 for MOPR) Analyst-1Analyst-2 1.021.25 0.981.13 0.820.79 Intermediate Precision (n=6, % RSD <10.0 for both PGTIs and <2.0 for MOPR) Analyst-1Analyst-2 1.111.24 1.031.18 1.020.98 Accuracy in pure MOPR (% recovery) (n=3, mean recovery ± STDEV) The level at 50% mean ± SDThe level at 100% mean ± SDThe level at 150% mean ± SD 94.62 ± 1.1896.32 ± 1.02104.05 ± 1.42 95.86 ± 1.1897.86 ± 1.12101.92 ± 1.05 99.86 ± 1.0899.79 ± 1.22100.05 ± 1.05 Accuracy in Formulated MOPR (% recovery) (n=3, mean recovery ± STDEV) The level at 50% mean ± SDThe level at 100% mean ± SDThe level at 150% mean ± SD 95.32 ± 1.1897.12 ± 1.8598.89 ± 2.35 96.25 ± 1.08100.15 ± 1.0299.35 ± 1.25 99.10 ± 1.18100.25 ± 1.0299.68 ± 1.05 Solution stability Peak area (0 Hr to 48 Hrs, % difference with initial <10 for both PGTIs and MOPR) 1.38 1.53 1.08 Accuracy study “The analytical procedure’s accuracy demonstrates the proximity agreement among the reference value and the obtained value”. The current method’s accuracy was assessed at three levels-LOQ (0.5 µg/g), 5 µg/g, and 10 µg/g of pure and formulated samples of MOPR in triplicate determinations by using the standard addition method. Each concentration's triplicate preparations were all injected at once. The recovery concurrence level was between 80 and 120%. Similarly, the recovery of MOPR was demonstrated on a placebo, spiked MOPR at a concentration of 25 µg/mL, 50 µg/mL, and 75 µg/mL. The obtained mean recoveries ranged from 94.62 to 104.05%, 95.86 to 101.92% and 99.10 to 100.25% for PGTI-1, PGTI-2, and MOPR, correspondingly. Based on the accuracy results shown in Table 7, the method is accurate. Solution stability The sample solution stability was attained by making the spiked sample solution with both impurities, keeping it at 25 °C, and injecting it into the LCMS at intervals of up to 48 hours. When compared to a freshly prepared standard solution for each impurity, there was no significant difference in the concentration of both impurities. The results were summarized in Table 7. The sample solution has been confirmed to be stable at room temperature for at least 48 hours. Applicability of the optimized method Two commercially available branded formulation tablets were analyzed in triplicate preparations in duplicate injections employing the optimized UPLC-MS/MS method. The contents of the PGTI-1 and PGTI-2 are summarized in Table S1. Conclusion In the current study, two complementary (Q) SAR methodologies were employed to predict and categorize the genotoxicity of two impurities raised during the Molnupiravir synthetic process. The predicted (Q)-SAR outcomes of both PGTIs are Derek plausible, and Sarah is positive and comes under the Class-3 category, according to the ICH M7(R1) classification. The multiple reaction monitoring technique with an electron spray ionization in positive mode was used for quantification. Fractional factorial Design was employed to optimize the developed method prior to method validation. To comprehend the effects of the identified CMPs individually and in combination, the information was statistically assessed using best-fit ANOVA models. Numerical optimization was conducted in order to obtain the maximum responses of R1(Content of PGTI-1) and R2(Content of PGTI-2). The optimized rapid UPLC-MS/MS method was successfully validated, and its specificity, precision, linearity, and accuracy were all found to meet current ICH quality standards. The developed method was effectively applied for the simultaneous assay and trace-level quantification of PGTIs from the commercially formulated tablet dosage forms, further demonstrating its high efficacy. Therefore, the current method can assist as a rapid, accurate and highly sensitive to quantify assay and two PGTIs simultaneously from the marketed tablet dosage forms of MOPR for commercial release and stability sample testing. Furthermore, It is also feasible to utilise this rapid method to quantify MOPR accurately in biological samples. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. Acknowledgement Sincere thanks to Gitam University management ==== Refs References 1 Pierson D.A. Olsen B.A. Robbins D.K. DeVries K.M. Varie D.L. Approaches to Assessment, Testing Decisions, and Analytical Determination of Genotoxic Impurities in Drug Substances Org. Process Res. 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PMC010xxxxxx/PMC10293122.txt
==== Front Diabetes Res Clin Pract Diabetes Res Clin Pract Diabetes Research and Clinical Practice 0168-8227 1872-8227 The Author(s). Published by Elsevier B.V. S0168-8227(23)00572-7 10.1016/j.diabres.2023.110809 110809 Article Diabetes care practices and outcomes in 40.000 children and adolescents with type 1 diabetes from the SWEET registry during the COVID-19 pandemic Chobot Agata ab⁎ Lanzinger Stefanie cd Alkandari Hessa e Todd Alonso G. f Blauensteiner Nicole g Coles Nicole h De Sanctis Luisa i Mul Dick j Saboo Banshi k Smart Carmel l Tsai Meng-Che m Zabeen Bedowra n Dovc Klemen o1 a Institute of Medical Sciences, University of Opole, Department of Pediatrics, Opole, Poland b University Clinical Hospital in Opole, Department of Pediatrics, Opole, Poland c Institute of Epidemiology and Medical Biometry, ZIBMT, Ulm University, Ulm, Germany d German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany e Dasman Diabetes Institute, Department of Populational Health, Kuwait f University of Colorado, Anschutz Medical Campus, Barbara Davis Center, Aurora, CO, USA g Department of Pediatrics and Adolescent Medicine, Medical University Vienna, Vienna, Austria h Markham Stouffville Hospital, Markham, Ontario, Canada i Regina Margherita Children Hospital, Torino - Department of Public Health and Pediatric Sciences, University of Torino, Italy j Diabeter, centre for pediatric and adult diabetes care and research, Rotterdam, The Netherlands k Diabetes Care & Hormone Clinic, Ahmedabad, Gujarat, India l Department of Paediatric Endocrinology and Diabetes, John Hunter Children's Hospital and School of Health Sciences, University of Newcastle, Newcastle, New South Wales, Australia m Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan n BADAS Paediatric Diabetes Care and Research Center, BIRDEM Hospital, Dhaka, Bangladesh o University Medical Center Ljubljana, University Children’s Hospital, and Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia ⁎ Corresponding author at: Department of Pediatrics, Institute of Medical Sciences, University of Opole, Al. W. Witosa 26, 45-418 Opole, Poland 1 A complete list of contributing centers for the SWEET Study Group can be found in the Appendix. 27 6 2023 27 6 2023 11080926 5 2023 21 6 2023 23 6 2023 © 2023 The Author(s) 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Aims This study aimed to provide a global insight into initiatives in type 1 diabetes care driven by the COVID-19 pandemic and associations with glycemic outcomes. Methods An online questionnaire regarding diabetes care before and during the pandemic was sent to all centers (n=97, 66,985 youth with type 1 diabetes) active in the SWEET registry. Eighty-two responded, and 70 (42,798 youth with type 1 diabetes) had available data (from individuals with type 1 diabetes duration >3 months, aged ≤21 years) for all 4 years from 2018 to 2021. Statistical models were adjusted, among others, for technology use. Results Sixty-five centers provided telemedicine during COVID-19. Among those centers naive to telemedicine before the pandemic (n=22), four continued only face-to-face visits. Centers that transitioned partially to telemedicine (n=32) showed a steady increase in HbA1c between 2018 and 2021 (p<0.001). Those that transitioned mainly to telemedicine (n=33%) improved HbA1c in 2021 compared to 2018 (p<0.001). Conclusions Changes to models of care delivery driven by the pandemic showed significant associations with HbA1c shortly after the pandemic outbreak and 2 years of follow-up. The association appeared independent of the concomitant increase in technology use among youth with type 1 diabetes. Keywords type 1 diabetes children HbA1c diabetes ketoacidosis diabetes care covid-19 Abbreviations BMI SDS body mass index standard deviation score calculated using the World Health Organization reference values CGM use of continuous glucose monitoring CPCG Clinical Practice Consensus Guidelines DID Daily Insulin Dose DKA diabetes ketoacidosis HbA1c haemoglobin A1c ISPAD International Society for Pediatric and Adolescent Diabetes SH severe hypoglycemia SWEET Better control in Pediatric and Adolescent diabeteS: Working to crEate CEnTers of Reference ==== Body pmc1 1. Introduction The COVID-19 pandemic, due to lockdowns of variable extent and duration in different countries, forced the modification of pediatric care worldwide [1], [2], [3]. Medical teams, including multidisciplinary teams for diabetes care, addressed these challenges [4], [5], [6]. Traditional models of diabetes care once delivered routinely in person, were forced to adopt a diverse array of ad hoc solutions during the pandemic [7], [8]. Over the past few years, a significant increase in telemedicine use (also: digital/virtual clinic, remote monitoring, and telecare) in pediatric diabetes care has been observed. Advances in continuous glucose monitoring (CGM) technology, increased clinical uptake, and cloud-based data exchange, have decisively contributed to and accelerated this advancement [8], [9] and were swiftly incorporated in many institutions with the pandemic outbreak [4], [10], [11]. The results of these rapidly and unscheduled implemented changes, under the restraints of healthcare systems and real-time regulations to the COVID-19 pandemic, were challenging to predict and caused uncertainty for care providers and consumers [5], [12]. Initially, there was concern about the potential to impact glycemic outcomes negatively, but the majority of reports published to date have demonstrated stable glycemic control among youth with type 1 diabetes [13], [14], [15], [16]. Some authors even noted temporary improvements in glycemic control [15], [17], [18], [19]. Furthermore, there is increasing evidence that diabetes care teams [10], [20] and individuals with type 1 diabetes [12] seem to be satisfied with telemedicine. However, evidence from extensive multinational, multicultural studies is still being determined. Our study primarily focused on changes in pediatric ambulatory and inpatient type 1 diabetes care driven by the COVID-19 pandemic, including telemedicine and psychological support, and their potential associations with HbA1c and acute diabetes complications. The study aim was pursued using data from centers from the international pediatric SWEET registry, providing an over-arching, international insight into different diabetes practices. 2 2. Subjects, Materials and Methods 2.1 2.1. Materials and Methods Data from individuals with type 1 diabetes, aged ≤ 21 years, and with type 1 diabetes duration > 3 months were extracted from the SWEET database. For each individual, the following data were aggregated: current age, age at diagnosis, biological sex, diabetes duration, HbA1c [mmol/mol] and [%], use of insulin pump and use of CGM (including both real-time and intermittently scanned glucose monitoring; as binary variables), the prevalence of diabetic ketoacidosis (DKA at diabetes onset was not taken into account) and severe hypoglycemia (SH), body mass index standard deviation score (BMI SDS) calculated using the World Health Organization reference values [21] and daily insulin dose [U/kg] (DID). DKA (pH < 7.3 or serum bicarbonate < 15 mmol/l) and SH (event during which assistance of another person is required to administer carbohydrates, intravenous glucose, or glucagon) followed the definitions presented in the ISPAD 2022 Clinical Practice Guidelines [22], [23]. The proportion of individuals with at least one event in the respective treatment year was analyzed. Additionally, a short, structured questionnaire regarding diabetes care and telemedicine was sent online (Google Forms, Google LLC, California, United States) between November 2020 and January 2021 to all centers that were active in the SWEET registry at that time (97 centers worldwide, overall 66,985 children with type 1 diabetes). The survey is available in detail in the online Supplementary materials. To capture fixed trends in glycemic control potentially associated with changes to diabetes care delivery, we applied a prolonged observation period – two years before the pandemic and the first two years of the pandemic. This interval selection was supported by the ability to visualize the results of not only the moment of the pandemic outbreak and first wave of lockdowns but also to capture longer-term changes in diabetes care provided to children and adolescents with type 1 diabetes. 2.2 2.2. Statistical analysis To compare the pre-pandemic and pandemic period from 2018 to 2021, data were aggregated by individual and by year. Continuous variables were presented as median with lower and upper quartiles, and binary variables as percentages in descriptive analyses. Repeated measurements linear and logistic regression models with a banded autoregressive covariance (Toeplitz) structure [24] were used to study outcomes associated with diabetes care practices. All models were adjusted for sex, age (categorized as <6, 6-<12, 12-<18, ≥18 years), diabetes duration (categorized as <2, 2-<5, 5-<10, and ≥10 years), treatment modality (with or without insulin pump, with or without CGM) use (per each year) and the SWEET regions [25]. In addition, an interaction term between the year and the respective diabetes care practice was included. Regression results were presented as adjusted least square means or frequencies with 95%-confidence intervals. SAS version 9.4 (build TS1M7, SAS Institute Inc, Cary, NC) was used for statistical analyses, and a two-sided p-value <0.05 was considered statistically significant. 3 3. Results 3.1 3.1. Registry data from 2018 to 2021 for centers that shared information on changes in pediatric diabetes care provision A summary of the data cumulatively and by year throughout the 4-year observation period between 2018 and 2021 is presented in Table 1 . Median HbA1c was lower in 2021 by 0.2% (1 mmol/mol) compared to 2018, p<0.001. The insulin pump and CGM use significantly increased by 7.8% and 22.3 % during the observational period, respectively (p<0.001). The gender distribution and the proportions of participants with at least one SH or DKA were stable. The median number of data uploads per person with type 1 diabetes sent to the SWEET database by centers decreased from 3 to 2 annually during the pandemic.Table 1 Cumulative and annual data through the 4-year observation period for all participating centers with respective p values for the overall change between 2018 and 2021. Data presented as (%) for binary variables and median [IQR] for continuous variables. N – number of documented individuals in SWEET. BMI SDS – Body mass index standard deviation score, SH – the proportion of individuals with at least 1 episode of severe hypoglycemia, DKA – the proportion of individuals with at least 1 episode of diabetic ketoacidosis, CGM – continuous glucose monitoring 2018 2019 2020 2021 All p value for 2018-2021 overall Variable N N N N N Age in years 26853 13.8 [10.4-16.6] 28108 13.9 [10.6-16.7] 27297 14.0 [10.7-16.7] 27978 14.0 [10.8-16.8] 42978 14.8 [11.2-17.7] <0.001 Age at diabetes onset in years 26853 7.4 [4.2-10.7] 28108 7.5 [4.2-10.7] 27297 7.6 [4.3-10.8] 27978 7.6 [4.3-10.8] 42978 7.9 [4.5-11.2] 0.005 Diabetes duration in years 26853 4.9 [2.4-8.2] 28108 5.0 [2.5-8.3] 27297 5.0 [2.5-8.3] 27978 5.0 [2.4-8.5] 42978 5.4 [2.7-8.9] 0.002 HbA1c % 26064 7.8 [7.0-8.8] 27396 7.7 [6.9-8.8] 25273 7.7 [6.9-8.7] 26018 7.6 [6.9-8.7] 39579 7.7 [6.9-8.8] <0.001 HbA1c mmol/mol 26064 61 [53-73] 27396 61 [52-73] 25273 60 [52-72] 26018 60 [52-71] 39579 60 [52-73] <0.001 BMI-SDS 25170 0.55 [-0.13-1.27] 27109 0.55 [-0.14-1.28] 24631 0.59 [-0.11-1.31] 25896 0.61 [-0.11-1.35] 39341 0.57 [-0.15-1.32] <0.001 Total daily insulin dose unit/kg 22190 0.81 [0.65-0.99] 24547 0.81 [0.65-0.98] 22755 0.81 [0.64-0.99] 22978 0.80 [0.63-0.98] 34848 0.81 [0.64-1] 0.004 Percent male 26853 (51.6) 28108 (51.8) 27297 (51.5) 27978 (51.8) 42978 (52.0) 0.858 SH 26853 (1.9) 28108 (1.8) 27297 (1.8) 27978 (1.6) 42978 (1.7) 0.087 DKA 26853 (1.4) 28108 (1.4) 27297 (1.2) 27978 (1.4) 42978 (1.4) 0.3 Insulin pump use % 26853 (44.0) 28108 (47.5) 27297 (48.6) 27978 (51.8) 42978 (46.2) <0.001 CGM use % 22632 (48.4) 23766 (58.7) 24553 (64.9) 25671 (70.7) 38197 (60.5) <0.001 Insulin pump/CGM use % 26853 (59.9) 28108 (66.9) 27297 (72.1) 27978 (76.6) 42978 (67.8) <0.001 Number of datasets per individual 26853 3 [2], [3], [4] 28108 3 [2], [3], [4] 27297 2 [1], [2], [3] 27978 2 [1], [2], [3], [4] 42978 2 [1], [2], [3] <0.001 3.2 Changes in the provision of pediatric diabetes care by centers Out of the 82 (85%) centers that responded to the survey, data for the whole studied period (2018-2021) were available for 70 (72% of all SWEET centers). Only these 70 centers were included in the analysis. Proportions of centers and individuals in the centers were divided into four groups (Figure 1 ): telemedicine offered before the pandemic, additional psychological care provided due to the COVID-19 outbreak, changes in ambulatory care, and changes in inpatient care due to the pandemic.Figure 1 Proportions of centers (A) and young people with type 1 diabetes (PwD) (B) treated in the centers in relation to the questionnaire responses regarding: telemedicine offered before the pandemic, additional psychological care offered due to COVID-19 outbreak, changes in ambulatory care and changes in in-patient care due to the pandemic. Approximately half of the centers naïve to the use of telemedicine before the pandemic provided remote visits during COVID-19, but this was less frequent than face-to-face visits. Thirteen centers reported equal or more telemedicine than face-to-face visits before December 2019. Only one of the five centers that did not change their ambulatory care model (Europe -1, South America -2, Asia/Middle East/Africa -2) used telemedicine before the pandemic. The shift towards telemedicine was not consistent with the reimbursement of telemedicine, which was covered equally (n=23, 33%) or less compared to in-person visits (n=20, 28.5%) in two-thirds of all centers. Most (n=23, 85.2%) of the 27 centers for which telemedicine was not reimbursed provided it to their children with type 1 diabetes regardless of financial loss. More centers provided additional psychological support due to the pandemic if telemedicine was reimbursed equally to in-person visits (additional care in 19% vs. 14% if less or no reimbursement and no additional psychological service in 14% vs. 53% (full vs. partial/no telemedicine reimbursement). Almost all (n=67, 96%) centers were planning at least partially to continue to provide telemedicine. The remaining three centers used telemedicine partially or fully during the pandemic, although their healthcare systems did not refund the telemedicine visits equally to in-person visits. 3.3 3.3. Changes in pediatric diabetes care and outcomes of children with type 1 diabetes The centers that did not change ambulatory care practices were grouped with those with a partial switch to telemedicine for the regression analysis due to their small number. Overall, after adjusting for confounding factors (section 2.2.), the repeated measurements linear regression models showed that the changes in diabetes care during COVID-19 (offering or not additional psychological care p=0.011, partial/strong switch to telemedicine or no change in ambulatory practice p<0.001) were associated with fluctuations in glycemic control in the first and second year of the pandemic (Figures 2 A and 3A). The associations between different care practices and HbA1c, DKA, and SH were diverse. Interestingly, offering telemedicine before COVID-19 was significantly associated with the participant’s outcomes (linear regression models for HbA1c and DKA – p<0.001 and SH – p=0.028). Specific differences between centers using or not telemedicine before the pandemic in the years 2018-2021 are shown in supplementary material Figures S1 A to C.Figure 2 The specific changes of HbA1c (A) and the proportion of children with type 1 diabetes with at least 1 DKA (B) regarding additional psychological care offered due to the pandemic; *p<0.05. 3.3.1 3.3.1. Offering telemedicine before the pandemic Centers that offered telemedicine care before the pandemic had lower HbA1c at all time points (2018 – 8.06 vs. 8.13% (64.6 vs. 65.4 mmol/mol), 2019 - 8.07 vs. 8.14% (64.7 vs. 65.5 mmol/mol), 2020 – 8.08 vs. 8.22% (64.8 vs. 66.3 mmol/mol), 2021 – 8.06 vs. 8.11% (64.6 vs. 65.1 mmol/mol), p<0.001 for differences in years 2018 to 2020 and p=0.004 for differences in 2021) compared to those that did not use telemedicine before December 2019. Changes in HbA1c in subsequent years were significantly associated with pre-pandemic use of telemedicine (p<0.001). This change was more remarkable in centers naive to telemedicine before the pandemic, with a substantial increase in HbA1c in 2020 and a greater decrease in 2021. There was a significant association between offering telemedicine before the pandemic and the changes in the proportion of children with type 1 diabetes with at least one DKA episode (p<0.001). The proportion of individuals with DKA was significantly higher in centers offering telemedicine before the pandemic (12% vs. 4%, p<0.001). Still, it decreased annually to reach in 2021 values similar to those from centers not offering telemedicine before the pandemic (1% vs. 1%, p=0.547). The proportion of children with type 1 diabetes with DKA in centers not using telemedicine before COVID-19 varied, with an overall significant increase in 2021. The proportion of children with type 1 diabetes with at least one SH episode was significantly lower in 2018 in centers offering telemedicine before the pandemic, and changes during the pandemic were also associated with telemedicine use before COVID-19 (p=0.028). Details are included in the supplementary online material. 3.3.2 3.3.2. Additional psychological care offered due to COVID-19 The association between changes in HbA1c, the proportion of children with type 1 diabetes with at least 1 DKA episode, the proportion with at least one episode of SH, and additional psychological care are illustrated in Figure 2 and the supplementary materials (Figure S2 A). Linear and logistic regression models with repeated measurements revealed that additional psychological care was significantly associated with changes in all glycemic outcomes (HbA1c – p=0.011, DKA – p<0.001, and SH – p<0.001). The proportion of children with type 1 diabetes with at least one DKA or one SH was consistently lower in centers with additional psychological support than those without additional support. Conversely, an increase in HbA1c in those not offering additional psychological support and a decrease in centers with such support (respectively 8.06 to 8.12% (64.6 to 65.2 mmol/mol) and 8.12 to 7.98% (65.2 to 63.7 mmol/mol), p<0.001 for both) were observed. 3.3.3 3.3.3. Changes in ambulatory care Changes in ambulatory care were significantly associated with changes in HbA1c during the pandemic (p<0.001). Centers that switched partially to telemedicine or did not change their ambulatory practices showed a steady increase in HbA1c during the four years of observation, while those that switched almost entirely to telemedicine demonstrated significantly improved HbA1c values in 2021 compared to 2018, despite a higher pre-pandemic HbA1c and an initial increase observed in 2020 (Figure 3 A.). Different approaches to ambulatory care during COVID-19 were not associated with a significant change in the proportion of children with type 1 diabetes with at least 1 DKA episode (Figure 3B). Still, they were associated with the proportion of individuals with at least 1 SH episode (p=0.001) (supplementary Figure S2 B).Figure 3 The specific changes of HbA1c (A) and the proportion of children with type 1 diabetes with at least 1 DKA (B) regarding the adoption of telemedicine in ambulatory care due to the pandemic; *p<0.05. 4 4. Discussion Overall, the presented data showed that changes to the delivery of diabetes care due to the pandemic were associated with HbA1c as well as acute diabetes complications - not only immediately following the outbreak, reflecting substantial changes during lockdowns, but also in the following 2 years. These over-arching, international insights driven by the COVID-19 pandemic provided data for future developments in pediatric diabetes care. In this 4-year observation period, glycemic control in centers that routinely adopted telemedicine improved, whereas moderate or no telemedicine visits were associated with increasing HbA1c. Contrary to a previous study showing increased rates of DKA [16] during the first wave in 2020, ambulatory practices did not seem to impact the proportion of children with type 1 diabetes with acute complications. Our 4-year, real-world, multinational study outcomes are consistent with single pediatric diabetes center reports demonstrating glycemic control improvement [13], [18] or preservation [14], [15] during the pandemic. Other authors associated their findings with increased technology usage [15], [17], [26]. However, we have shown that the association between change in ambulatory care practices and improvement in HbA1c was significant even after adjusting for treatment modality. This observation and previous evidence that diabetes technology use, especially CGM, independently positively impacts glycemic control [27], [28] identify management approaches associated with improved glycemic outcomes. Unfortunately, worldwide disparities in access to modern technologies hamper their universal use and detrimentally influence the outcomes [29], particularly during the pandemic. We also noted that pre-COVID-19 differences in HbA1c, DKA, and SH between centers offering telemedicine and those naive to telemedicine in 2018 decreased 2 years after the pandemic outbreak. As we adjusted for technology use, this improvement can be potentially accounted for by adopting new models of care and flexibility in introducing novelties in daily care, as demonstrated in adult populations with diabetes [30]. Additional psychological care showed an association with lower HbA1c, as well as with a lower proportion of DKA and SH events. Children with type 1 diabetes from centers that offered additional psychological support had lower HbA1c, less DKA, and SH before the pandemic as well as 2 years after the pandemic outbreak. Structured psychological support for young individuals with diabetes embedded into multidisciplinary care [31] offers measurable benefits beyond glycemic control [32]. Not only interventions but the general availability of psychological care in pediatric diabetes centers was shown to be associated with decreased rates of DKA [33]. Easy access may facilitate early referral, engagement with children and adolescents with deteriorating glycemic control, support in maintaining motivation, and timely diagnosis of mental health problems. The positive associations of additional psychological support offered by centers during the pandemic may suggest, that these diabetes teams tried to and had resources to provide more than just routine diabetes care in this extraordinary situation. Maybe this proactive attitude allowed the children, adolescents and their caregivers to focus more on glycemic control. Mental health specialists may have also support for other diabetes team members and help them better adjust the care and treatment to the individual needs of the family. In some of the graphs, a diversion in 2020 from the general trend for the 4 years can be noted. This phenomenon may reflect the initial reaction to the requirement of an immediate change in daily routines. Alternatively, it may correspond to a reporting bias in the data gathered in the first months of the pandemic confounded by greater attendance or hospitalizations of those who had more diabetes-related complications, had an acute complication, or were strictly compliant with their visits and laboratory test schedule. This might suggest that the interpretation of data from the initial pandemic year should be taken with caution. The limitations of a registry-based study and self-reporting apply to this study. It cannot be considered a population-based study as not all centers from each country participated in the international SWEET registry. Additionally, to provide a full 4-year dataset for analysis, the results were based on data from 72% of the SWEET centers. The study was also limited by self-reporting and self-evaluation of changes in care, as there was no other tool to capture this information at that time. Several visits were potentially lost during the pandemic; however, our sensitivity analysis adjusted for the number of datasets sent to the SWEET database and did not impact the results. It cannot be excluded that teamwork and a more flexible approach to challenging situations contributed to these results [34] – these factors were not investigated in this study. The strength of our study is the large multicentral, multinational, multicultural dataset, including 40,000 young people with type 1 diabetes from the well-structured and audited SWEET registry [35]. In summary Changes in pediatric type 1 diabetes care driven by the COVID-19 pandemic and reported to the international SWEET registry showed significant associations with HbA1c and the proportion of children with type 1 diabetes who experienced at least 1 DKA or 1 SH throughout the first two years of the pandemic. The impact of the transformation of pediatric diabetes care delivery, including broader adoption of telemedicine and offering additional psychological care during the pandemic crisis, occurred independently of the concomitant increase in technology use. It may be that a hybrid diabetes care model with telemedicine may be adopted in routine diabetes care in the future and may also play a role in reducing disparities. However, diabetes clinics showed some hesitancy to maintain the use of telemedicine when reimbursements were inadequate, and therefore, a hybrid model could depend on sufficient reimbursement from payers. Funding: SWEET is a registered non-profit charity in Hannover, Germany. It is financed through membership fees of the participating centers (based on the income of the country of residence, according to the World Bank) and corporate members. We acknowledge with gratitude the support from the following SWEET e.V. corporate members – in alphabetical order: Abbott, Boehringer Ingelheim, DexCom Inc., Insulet, Eli Lilly & Co., Medtronic Europe, Sanofi. The Sweet Project is an ongoing registered research collaboration (NCT04427189). Author Contributions: A.C. and K.D. designed the study. A. C. researched data, participated in data interpretation, and wrote the manuscript. S.L. helped design the study, performed the statistical analysis, participated in data interpretation, and reviewed and edited the manuscript. K.D. participated in data interpretation and reviewed and edited the manuscript. H.A., T.A., N.B., N.C., L.DS., D.M., B.S., C.S., M.C.T., and B.Z. contributed to the discussion, reviewed and edited the manuscript. All authors approved the final version of the manuscript. A.C., S.L., and K.D. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix List of contributing centers: Australia, Geelong: Granada Medical Centre; Australia, Newcastle: John Hunter Childrens Hospital; Australia, Perth: Princess Margaret Hospital for Children; Australia, Queensland: South East Queensland Private Practice Group; Australia, Woolloongabba: Queensland Paediatric Endocrinology; Austria, Vienna: Universitätskinderklinik Wien; Bangladesh, Dhaka: BIRDEM, Diabeic Association of Bangladesh; Belgium, Leuven: University Hospital Leuven (UZ Leuven); Brasil, Curitiba: Centro de Diabetes Curitiba; Bulgaria, Sofia: University Paediatric Hospital; Bulgaria, Varna: University Hospital St. Marina; Cameroon, Yaounde: University of Yaounde; Canada, Calgary: Alberta Health Services; Canada, Halifax: IWK Health Centre; Canada, Markham: Markham Stouffville Hospital; Canada, Sherbrooke: Sherbrooke University; Canada, Vancouver: British Columbia Childrens Hospital; Chile, San Felipe: Hospital San Camilo; Congo Brazzaville, Pointe-Noire: General Hospital Adolphe Sice; Costa Rica, San Jose: National Childrens Hospital, Hospital CIMA; Croatia, Zagreb: University Clinical Hospital Center Sestre milosrdnice; Croatia, Zagreb: University Hospital Zagreb; Czech Republic, Prague: University Hospital Motol Prague; Denmark, Aarhus: University of Aarhus; Denmark, Herlev: Herlev University Hospital; Ecuador, Quito: Fundación Diabetes Juvenil Ecuador; Egypt , Cairo: Ain Shams University; England, Birmingham: Birmingham Childrens hospital; England, Leeds: The Leeds Paediatric Diabetes at the Leeds Childrens Hospital; England, London: Barts and the London NHS Trust; England, Mansfield: Sherwood Forest Hospital; France, Angers: University Hospital Angers; France, Bordaux: Centre Hospitalier Universitaire de Bordeaux; France, Paris: Hopital Necker Enfants Malades; Germany, Hannover: Kinderkrankenhaus Auf der Bult; Germany, Leverkusen: Klinikum Leverkusen – Kinderklinik; Greece, Athens: Athens University; Greece, Athens: P&A Kyriakou Childrens Hospital; Greece, Thessaloniki: AHEPA University Hospital, 2nd Department of Paediatrics, Aristotle University of Thessaloniki; Greece, Thessaloniki: Hippokration Hospital of Thessaloniki; Haiti, Port-Au-Prince: Fondacion Hatienne de Diabete; Hungary, Budapest: Semmelweis University; India, Ahmedabad Bareja: Rudraksha Insitute of Medical Sciences; India, Ahmedabad: Arogyam Health Care; India, Ahmedabad: Diacare Clinic; India, Aurangabad: Sarda centre for diabetes and self care; India, Belgaum: KLE Universitys Jawaharlal Nehru Medical College; India, Chennai: MV Diabetes center; India, Coimbatore: PSG institute of medical sciences; India, Kanpur: Center for Diabetes & Endocrine Diseases;India, Kota Rajasthan: Ramchandani Diabetes Care and Research Centre; India, Trivandrum: Jothydevs Diabetes And Research Centre; Iran, Shiraz: Shiraz University of Medical Sciences; Ireland, Cork: Cork University Hospital; Ireland, Dublin: Our Ladys Childrens Hospital; Ireland, Limerick: University of Limerick; Israel, Petah: Schneider Childrens Medical Center of Israel,Endocrinology; Italy, Ancona: Salesi University Hospital; Italy, Florence: Meyer Children Hospital;Italy, Mailand: Ospedale San Raffaele; Italy, Rom: Bambino Gesu Childrens Hospital; Italy, Turin: Centro Diabetologia Pediatrica; Italy, Verona: Universita di Verona; Korea, Seongnam: Seoul National University Bundang Hospital; Kuwait, Kuwait City: Dasman Institute; Latvia, Riga: Children clinical University Hospital; Lithuania, Kaunas: Hospital of LUHS Kauno Klinikos; Luxembourg, Centre Hospitalier de Luxembourg; Maldives, Male: Diabetes Society of Maldives; Mali, Bamako: NGO Santé Diabète / Hopital du Mali; Mauritius, Vacoas: T1Diams; Mexico, Mexico City: Centro Médico ABC Santa Fe; Morocco, Rabat: Children’s Hospital – Unit Of Pediatric Diabetology; Nepal, Butwal: Siddharta Children & Women Hospital; Netherlands, Rotterdam: Diabeter Nederland; New Zealand, Auckland: Auckland Starship Hospital; New Zealand, Christchurch: University of Otago and Canterbury District Health Board; Norway, Haugesund: Helse Fonna; Poland, Katowicze: Medical University of Silesia; Poland, Opole: USK Opole; Poland, Rzeszow: University of Rzeszow, Pediatric Endocrinology and Diabetes; Poland, Warsaw: Medical University of Warsaw; Portugal, Coimbra: Hospital Pediatrico de Coimbra; Portugal, Lisbon, Estafania: Hospital Dona Estefania; Portugal, Lisbon: APDP-Portuguese Diabetes Association; Portugal, Porto: Centro Hospitalae S. Joao; Romania, Bucharest: Diabetes Nutrition and Metabolic Diseases Clinic DiabNutriMed; Romania, Bucharest: Elias University Emergency Hospital; Romania, Buzias: Clinical Center Cristian Serban; Serbia, Belgrade: Institute for Mother and Child Healthcare; Slovenia, Ljubljana: University Childrens Hospital; Spain, Barakaldo: Hospital Universitario Cruces; Spain, Barcelona: Hospital Sant Joan de Deu; Sweden, Gothenburg: The Queen Silvia Childrens Hospital; Sweden, Uddevalla: Uddevalla Childrens Hospital; Taiwan, Tienan: National Cheng Kung University Hospital; Tanzania, Daressalam: Muhimbili National Hospital; Turkey, Duzce: University of Duzce,Department of Pediatric Endocrinology; Turkey, Ege University Faculty of Medicine; USA, Boston: Boston Childrens Hospital; USA, Cincinnati: Cincinnati Childrens; USA, Denver Colorado: Barbara Davis Center; USA, Stanford: Lucile Packard Children’s Hospital Acknowledgements The authors thank the following individuals for supporting this work: prof. Reinhard Holl (Ulm University, Germany) and Prof. Profj Battelino (UMC Ljubljana, Slovenia) for their invaluable support; Sascha Tittel for the data management, Andreas Hungele and Ramona Ranz for the DPV software (all of Ulm University, Germany, at the time when the study was carried out); Michael Witsch (Centre Hospitalier de Luxembourg, Luxembourg); Thomas Danne and Olga Kordonouri (Kinder- und Jugendkrankenhaus AUF DER BULT, Hannover, Germany) for center integration; and Bärbel Aschemeier (Kinder- und Jugendkrankenhaus AUF DER BULT) for initiating the SWEET collaboration. Finally, the authors thank all participating centers of the SWEET network, especially the collaboration centers in this investigation (Appendix). ==== Refs References 1 Penwill N. De Angulo N.R. Elster M. Pathak P. Ja C. Hochreiter D. Changes in Pediatric Hospital Care during the COVID-19 Pandemic: A National Qualitative Study Health Serv Res 56 Suppl 2 2021 50 51 10.1111/1475-6773.13818 2 Kendzerska T. Zhu D.T. 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Impact of COVID-19 lockdown on glycemic control in patients with type 1 diabetes Diabetes Res Clin Pract 166 2020 108348 10.1016/j.diabres.2020.108348 20 Forde H. Choudhary P. Lumb A. Wilmot E. Hussain S. Current provision and HCP experiences of remote care delivery and diabetes technology training for people with type 1 diabetes in the UK during the COVID-19 pandemic Diabet Med 39 2022 e14755 34862815 21 World Health Organization. WHO child growth standards: length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: methods and development, https://www.who.int/publications/i/item/924154693X; 2006 [accessed 10 September 2022]. 22 Abraham M.B. Karges B. Dovc K. Naranjo D. Arbelaez A.M. Mbogo J. ISPAD Clinical Practice Consensus Guidelines 2022: Assessment and management of hypoglycemia in children and adolescents with diabetes Pediatr Diabetes 23 2022 1322 1340 10.1111/pedi.13443 36537534 23 Glaser N. Fritsch M. Priyambada L. Rewers A. 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The role and need for psychological support in the treatment of adolescents and young people suffering from type 1 diabetes Front Psychol 13 2023 945042 10.3389/fpsyg.2022.945042 32 Feldman M.A. Yardley H.L. Bulan A. Kamboj M.K. Role of Psychologists in Pediatric Endocrinology Pediatr Clin North Am 69 2022 905 916 10.1016/j.pcl.2022.05.005 36207101 33 Chobot A. Eckert A.J. Biester T. Corathers S. Covinhas A. de Beaufort C. Psychological Care for Children and Adolescents with Diabetes and Patient Outcomes: Results from the International Pediatric Registry SWEET Pediatr Diabetes 2023 2023 1 9 34 de Beaufort C.E. Lange K. Swift P.G. Aman J. Cameron F. Castano L. Metabolic outcomes in young children with type 1 diabetes differ between treatment centers: the Hvidoere Study in Young Children 2009 Pediatr Diabetes 14 2013 422 428 10.1111/j.1399-5448.2012.00922.x 22957743 35 Lanzinger S. Zimmermann A. Ranjan A.G. Gani O. Pons Perez S. Akesson K. 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==== Front Int J Biol Macromol Int J Biol Macromol International Journal of Biological Macromolecules 0141-8130 1879-0003 Elsevier B.V. S0141-8130(23)02338-3 10.1016/j.ijbiomac.2023.125444 125444 Article Targeting DPP4-RBD interactions by sitagliptin and linagliptin delivers a potential host-directed therapy against pan-SARS-CoV-2 infections Mani Shailendra a1 Kaur Anupamjeet b1 Jakhar Kamini a Kumari Geetika b Sonar Sudipta a Kumar Amit b Das Sudesna c Kumar Santosh b Kumar Vijay b Kundu Rakesh d Pandey Anil Kumar e Singh Umesh Prasad c Majumdar Tanmay b⁎ a Translational Health Science and Technology Institute, Faridabad, India b National Institute of Immunology, New Delhi, India c CSIR-Indian Institute of Chemical Biology, Kolkata, India d Department of Zoology, Visva-Bharati University, Santiniketan, West Bengal, India e Department of Physiology, ESIC Medical College & Hospital, Faridabad, India ⁎ Corresponding author. 1 First author. 27 6 2023 1 8 2023 27 6 2023 245 125444125444 15 2 2023 13 6 2023 14 6 2023 © 2023 Elsevier B.V. All rights reserved. 2023 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Highly mutated SARS-CoV-2 is known aetiological factor for COVID-19. Here, we have demonstrated that the receptor binding domain (RBD) of the spike protein can interact with human dipeptidyl peptidase 4 (DPP4) to facilitate virus entry, in addition to the usual route of ACE2-RBD binding. Significant number of residues of RBD makes hydrogen bonds and hydrophobic interactions with α/β-hydrolase domain of DPP4. With this observation, we created a strategy to combat COVID-19 by circumventing the catalytic activity of DPP4 using its inhibitors. Sitagliptin, linagliptin or in combination disavowed RBD to establish a heterodimer complex with both DPP4 and ACE2 which is requisite strategy for virus entry into the cells. Both gliptins not only impede DPP4 activity, but also prevent ACE2-RBD interaction, crucial for virus growth. Sitagliptin, and linagliptin alone or in combination have avidity to impede the growth of pan-SARS-CoV-2 variants including original SARS-CoV-2, alpha, beta, delta, and kappa in a dose dependent manner. However, these drugs were unable to alter enzymatic activity of PLpro and Mpro. We conclude that viruses hijack DPP4 for cell invasion via RBD binding. Impeding RBD interaction with both DPP4 and ACE2 selectively by sitagliptin and linagliptin is an potential strategy for efficiently preventing viral replication. Graphical abstract Pleotropic efficacy of sitagliptin, and linagliptin to impede pan-variants of SARS-CoV-2 infections. Strategy I: Establishing RBD of pan-variants of SARS-CoV-2 can interact with DPP4 in addition to ACE2 for the viral entry. Strategy II: Sitagliptin, and linagliptin, alone or in combination can suppress the growth of SARS-CoV-2 variants including most common VOCs like original SARS-CoV-2, alpha, beta, delta and kappa clade by impeding the DPP4 and RBD interaction. Strategy III: Both sitagliptin and linagliptin exhibit pleotropic effects as a result of gliptins' capacity to target ACE2-RBD interaction.Unlabelled Image Keywords Pan-SARS-COV-2 DPP4-RBD complex ACE2-RBD interaction, repurposed therapy ==== Body pmc1 Introduction The RBD of the spike protein interacts with the ACE2 receptor [1,2], which is a recognised method of human-to-human transmission of the SARS-CoV-2 virus [[3], [4], [5]]. However, current paradox suggests that SARS-CoV-2 infection causes lymphopenia, where RNA and antigens are found in peripheral blood cells including lymphocytes (T, B, and NK cells) which do not express ACE2 or TMPRSS2 [[6], [7], [8]]. Even RBD can interact with a wide range of human cell types that lack ACE2 [6]. It implies that RBD can engage in interactions with host cells via alternative receptors other than ACE2. As an illustration, the major receptor for middle east respiratory syndrome coronavirus (MERS-CoV) is transmembrane protein, DPP4 (also known as CD26) [[9], [10], [11]]. It's noteworthy to mention that, in addition to the main receptor, several transmembrane proteins are usually necessary for virus entry into the target cell. It implies that the interaction between the RBD-spike and the human DPP4 is a crucial factor in aggravating the pathogenicity of SARS-CoV-2 [[12], [13], [14], [15], [16], [17], [18]]. A variety of epithelial, endothelial, and lymphocyte membranes express this plasma membrane glycoprotein [19,20]. For the treatment of type 2 diabetes (T2DM), it is regarded as a crucial therapeutic target. DPP4 inhibitors include sitagliptin, saxagliptin, vildagliptin, metformin, and linagliptin as examples of prospective therapies that could be investigated for COVID-19 treatment [21]. Several DPP4 inhibitors have shown conflicting results when used against SARS-CoV-2 [22]. Several genetic variants from the initial wild-type SARS-CoV-2 strain (Wt-SARS-CoV-2) have emerged throughout the pandemic since December 2019. Specific mutations within the Wt-RBD domain of Wt-SARS-CoV-2 culminate in the generation of various variants of concern (VOCs) defined by the WHO [23,24]. The most common VOCs are like B.1.1.7 (α-variant, N501Y substitution), B.1.351 (β-variant, combination of K417N, E484K, and N501Y substitution), B.1.617.2 (δ-variant, combination of L452R and T478K substitution), and B.1.617.1 (κ-variant, combination of L452R/E484Q). The major protease (Mpro/3CLpro; nsp5) and the papain-like protease (PLpro; nsp3) of the viral cysteine proteases constitute significant targets for mitigating viral infection. The Mpro, a class of highly conserved cysteine hydrolases are able to cleave polyproteins at different locations and create multiple functional proteins throughout the course of virus multiplication. Viral multiplication also requires PLpro. Therefore, inhibiting these enzymes is a highly effective antiviral treatment strategy [25,26]. In this study, we established RBD can interact with DPP4 other than ACE2 for the viral entry. Our vision is to develop a therapeutic strategy that can prevent the entry of all variants by eliminating the interactions of RBD with both DPP4 and ACE2. In search of potent inhibitors, structure assisted drug design and cellular research with live pan-virus variants demonstrated that particular gliptin, alone or in combination, can suppress the growth of SARS-CoV-2 variants. This will accelerate the discovery of clinically efficient therapeutic strategies for curtailing SARS-CoV-2 infection. 2 Materials and methods 2.1 Ethics statement We obtained ethical approval from the Institutional Bio-Safety Committee of National Institute of Immunology prior to conducting all studies: IBSC/448/2021 and IBKP-TAI No. C100833. The Institutional Bio-Safety Committee of THSTI also approved ethical permission for experiments involving live viruses: IBSC/200/2020, THS/298/2021 and IBKP-TAI No: C100458. All cellular experiments were carried out atleast three times independently in different cell lines. 2.2 Lineage of virus and its culture condition B.6 lineage isolation of SARS-CoV-2 has been described earlier [28]. In this study, 5 variants of SARS-CoV-2 were used. The original Wt-SARS-CoV-2 isolate was designated as USA-WA1/2020, NR-52281, and its accession no is MN985325, GISAID: EPI_ISL_404895, GenBank: MT020880. The B.1.1.7 (α-variant) from the United Kingdom denoted as USA/CA_CDC_5574/2020, NR-54011, and genebank: GISAID is EPI_ISL_751801. South African origin B.1.351 (β-variant) isolate comprises of hCoV-19/South Africa/KRISPK005325/2020, NR-54009, and its gene accession no is GISAID: EPI_ISL_678615. The Indian origin B.1.617.2 (δ-variant) isolate is known as THSTI_287, and its gene accession no is GenBank: MZ356566.1 [29]. The USA origin B.1.617 (κ-variant) is named as USA/CA-Stanford-15_S02/2021, and its gene accession no is GenBank: GISAID: EPI_ISL_1675223. Vero E6 (CRL1586) and Calu-3 (HTB55) cell lines were cultured in Dulbecco's modified Eagle medium (DMEM; Lonza) supplemented with 10 % fetal bovine serum (FBS; Himedia), 100 U/ml of penicillin, 100 μg/ml of streptomycin. All SARS-CoV-2 variants were propagated in Vero E6 cells or Calu-3 cells and virus passaging was restricted to 4–8 passages only. All the virus stocks were authenticated by whole genome sequencing as described in earlier information [27,30]. 2.3 Molecular docking analysis Direct binding to DPP4 and entry into the targeted cells are both made possible by the RBD of the spike protein of the SARS-CoV-2 virus. In the present study, molecular docking was performed to identify critical interactions and binding sites of DPP4 with Wt-RBD of the SARS-CoV-2 virus. The Wt-RBD crystal structure (PDB: 6LZG, Sec. S1) [31] of the original SARS-CoV-2 will be used in our study. For our docking analysis with several variants, we have selected crystal structures of RBD from the Protein Data Bank (PDB), such as PDB:7EKF for B.1.1.7 (α-RBD) [32], PDB:7WCR for B.1.351 (β-RBD) [33], PDB:7W9F for B.1.617.2 (δ-RBD) [34] and PDB:7SOC for B.1.617 (k-RBD) [35]. The structure of human DPP4 (PDB:4L72, Sec. S1) was retrieved from PDB [36]. All the water molecules, metal ions, and other residues like NAG or SO4 were removed from the crystal structures for molecular docking analysis. The blind protein:protein docking simulations between the RBD of various variants with human DPP4 molecule was performed using ClusPro 2.0 [37]. During the docking calculation, all the default parameters were used. PyMol was used to find the interacting residues within the binding pocket of the DPP4:RBD docked complex [38]. The molecular interactions between protein-protein complexes including hydrogen bonds and the bond lengths were analysed and depicted by using Ligplot+2.2.5. software [39]. PRODIGY server with a default 25 °C temperature setting was used for the binding affinity calculations of docked complexes [40]. The 3D structures of FDA approved DPP4 inhibitors [41,42], sitagliptin (PubChem CID: 4369359), linagliptin (PubChem CID: 10096344), vildagliptin (PubChem CID: 6918537), saxagliptin (PubChem CID: 11243969) and metformin (PubChem CID: 4091) were downloaded from the PubChem database. The molecular docking was performed using AutoDock Vina software [43]. The dimension of the grid box was set to 84 × 94 × 118 Å3 with grid centre at −14.658, −54.126, and −17.185 using ADT tools. In addition, the molecular interaction between ACE2-RBD complex (PDB: 6LZG, Sec. S1) [44] with gliptin (linagliptin and sitagliptin) was studied. All the water molecules, metal ions, and other residues were removed from the crystal structure of ACE2-RBD for docking analysis. The dimension of the grid box was set to 126 × 108 × 126 Å3 with grid centre at −31.113, 18.322, and −6.901 using ADT tools. The docked poses of ligand were clustered at 0.2 nm tolerance for RMSD and ranked based on binding energy. Out of 9 poses obtained in AutoDockVina, the best pose which lies within the binding pocket of protein with low free B.E. was selected for further analysis. 2.4 Human DPP4 and ACE2 gene targeting The DPP4 CRISPR/Cas9 KO plasmid (Santacruz, sc-400862) was transfected into Calu-3 cells to disrupt gene expression by causing a double-strand break (DSB) in a 5′ constitutive exon of the DPP4 (human) gene to create DPP4 knockout cells. Similarly, ACE2 CRISPR/Cas9 KO Plasmid (Santa Cruz, sc-401131-KO-2) was used to develop ACE2 knockout cells. The deletion of gene in Calu-3 cells was confirmed by immunoblotting (Fig. S1). 2.5 Microscopic study ACE2 knockout and DPP4 knockout Calu-3 cells (2.0 × 105/well) were plated separately onto cover glasses in a 9 cm2 well of the plates, grown overnight (to ∼5 × 105 cells), and transfected with Wt-RBD-Fc-IgG1 (Addgene # 141183) construct using lipofectamine 3000 (Invitrogen) following manufacturer's instructions. 0.8 μg plasmid was used for transfection per 5 × 105 cells in each 4 cm2 well of the plate. After 8 h post-transfection, cells were fixed in formaldehyde for 5 min and permeabilized with PBS containing 0.1 % Triton X-100. Fixed cells were blocked in PBS containing 1 % BSA for 1 h. Afterward, cells were incubated with either a primary antibody specific to DPP4 or ACE2 (Abcam, ab15348) or an anti-IgG1-Fc-AF488-labelled antibody (Invitrogen, A10631), followed by an alexa-fluor 647 tagged anti-rabbit secondary antibody (Abcam, ab150079) specific to primary antibody of DPP4 or ACE2. Mounting was done using fluoroshield (Sigma), and cells were visualized under a confocal laser scanning inverted microscope, Carl-Zeiss LSM980 (63×)2. 2.6 Immunoprecipitation, and immunoblot Calu-3 cells (2.0 × 105/well) were plated onto cover glasses in 9 cm2 well of the plates, grown overnight (to ∼5 × 105 cells), and was transfected with pLEX307-DPP4-puro (Addgene # 158451) alone or in combination with pCDNA3.1-RBD construct using lipofectamine 3000 (Invitrogen) following manufacturer's instructions. DPP4 knockout cells were utilised in one set to transfect the pCDNA3.1-RBD construct in order to perform the DPP4-RBD immunoprecipitation assay, which would verify the absence of any experimental artefact. In a separate experiment, pcEP4-myc-ACE2 was transfected with pCDNA3.1-RBD construct. 0.8 μg plasmid was used for transfection per 5 × 105 cells in each 4 cm2 well of the plate. After 8 h post-transfection, cells were treated with sitagliptin or linagliptin or their combination for 24 h. Then cells were lysed in lysis buffer containing 20 mM HEPES (pH 7.4), 50 mM NaCl, 1.5 mM MgCl2, 2 mM DTT, 2 mM EGTA, 10 mM NaF, 12.5 mM β-glycerophosphate, 1 mM Na3VO4, 5 mM Na4P2O7, 0.2 % (v/v) Triton X-100, and protease inhibitors (Himedia). Cellular lysates, containing 400 μg of total protein were precleared with mouse immunoglobulin G (IgG) agarose (Sigma) in IP buffer for 1 h, and then incubated overnight with IP-specific DPP4 antibody (cell signalling, 67,138), or myc-specific antibody and then with protein A/G beads for 1 h with rotation. After incubation, the beads were washed with 1× PBS, and protein complexes were eluted by adding 40 μl of 2× sample buffer to each IP reaction and heating at 50 °C for 15 min. For immunoblot, cells were lysed in the 1.5× Laemmli sample buffer containing protease inhibitor cocktails (Himedia) and for phospho-antibody, phosphatase inhibitor cocktails (Cell Signalling Technology) were additionally used. After sonication, samples were heated at 95 °C and equal amounts of proteins were analysed on denaturing SDS-polyacrylamide gels after estimation of protein. The proteins were transferred to the PVDF blotting membrane (Amersham Hybond) and probed with primary-antibody specific for RBD (Abcam, ab283946), DPP4, and ACE2 individually. Secondary antibody conjugated to horseradish peroxidase (HRP) were then used and bands were visualized by a chemiluminescence-based detection system (Amersham) using SuperSignal West Femto/Pico Plus substrate (Thermo-Fischer Scientific). β-actin was used as a control for normalization [45]. 2.7 Molecular dynamics (MD) simulation studies In the current investigation, 9 systems were established for MD simulation. The nine systems that were tested for 100 ns each were DPP4, DPP4 + linagliptin, DPP4 + sitagliptin, WtRBD-DPP4, WtRBD-DPP4 + linagliptin, WtRBD-DPP4 + sitagliptin, WtRBD-ACE2, WtRBD-ACE2 + sitagliptin, and WtRBD-ACE2 + linagliptin. All the simulations were carried out using a GROMACS-2022 software package [46]. The protein alone and protein-ligand complex were solvated in a cubic box using simple point charge (SPC) water molecules. The additional (0.15 M NaCl) Na and Cl ions were used to neutralize the system. The force field parameters for the ligands were obtained from the Automated Topology Builder (ATB) [47]. The CHARMM27 force field was used to generate the topology of all protein systems in explicit solvent [48]. We have performed 50,000 steps for the steepest descent energy minimization of all systems. A Verlet cutoff scheme was used with long-range electrostatics calculated by the particle-mesh Ewald (PME) method [49]. The pressure and temperature were kept at 1.0 bar and 310 K using the Parrinello−Rahman barostat [50] and modified Berendsen thermostat [51], respectively, for the MD simulation. Lincs constraint algorithm was used for constraining the atoms. The graphs were generated using OriginPro 9.0 software and molecular structures were visualized by PyMOL software. The MD trajectories were analysed using GROMACS tools. The conformational stability of proteins in the absence or presence of ligands was analysed using gmx rms, gmx rmsf, gmx gyrate and gmx sasa tools. 2.8 Calculation of binding free energy (∆Gbinding) of gliptins Poisson–Boltzmann surface area (MM–PBSA) approach of the molecular mechanics was used to calculate the ∆Gbinding between protein-ligand systems [52]. The ∆Gbinding was evaluated for the following systems: (i) WtRBD-DPP4 with linagliptin, (ii) WtRBD-DPP4 with sitagliptin, (iii) WtRBD-ACE2 + linagliptin, (iv) WtRBD-ACE2 + sitagliptin using g_mmpbsa tool. The impact of conformational entropy was ignored while calculating the relative binding free energy because of its small impact on the total binding free energy and high computational expense. The B.E. was calculated as the average over the last 300 frames in the aforementioned MD simulation systems. 2.9 Cells viability assay Only cells and virus infected cells were seeded in 96-well plates (10,000 cells/well in 150 μl medium) and treated with several gliptins, and remdesivir. Cells were analysed for proliferation by a colorimetric method for determining the number of viable cells, using the CellTiter 96® Aqueous One Solution Cell Proliferation Assay (Promega) as recommended by the manufacturer. The absorbance values were recorded at 490 nm after incubation at 37 °C for 4 h and corrected by subtracting the background absorbance (culture media alone). All samples were run twice in triplicate. Cell viability percentages were calculated as follows: cell viability % = [absorbance of treated cultures/absorbance of control cultures] × 100. 2.10 Determination of the efficacy of gliptins in limiting SARS-CoV-2 variants infection in vitro Vero E6 cells were cultured overnight in 48-well plates, infected them with 400 TCID 50/well (200 ul/well) of each SARS-CoV-2 variant, and then incubated for 1 h at 37 °C CO₂ incubators with intermittent shaking. Then cells were washed with DMEM (without FBS) and then 500 μl of DMEM supplemented with 2 % FBS was added to each of the wells. The plates were incubated for 48 h at 37 °C CO2 incubator in presence or absence of remdesivir and gliptins at different concentrations. Remdesivir was also used as a standard antiviral compound against SARS-CoV-2 infection [53]. The supernatant was collected at 48 h post-infection and qPCR was done to do absolute quantification of viral RNA copies using primers of nucleocapsid gene of SARS-CoV-2 based on CDC guidelines [53,54]. Two different set of primers (N1, and N2) of nucleocapsid gene were used and the absolute quantification of each gene was done to determine viral copy numbers/ml. For this, synthetic single stranded RNA with known copy number (Merck, EURM019) was used as a reference to generate the standard curve, with each run. All the experiments were done in triplicates with six biological repeats. Only cells and cells infected with various SARS-CoV-2 variants were kept to compare how much gliptins inhibited viral growth. Changes among treatment groups of cells or between sets of experiments were analysed by one-way ANOVA and Students t-test. Results were expressed as mean of virus log copy numbers/ml ± SD (standard deviation bars in graphs) of viral RNA copies, and presented in the log10 scale. p-values <0.01; and < 0.001 were considered statistically significant. Statistical analyses and data visualization were performed with the GraphPad Prism software Version 9.4.1(458). 2.11 DPP4 activity Only cells, DPP4 KO cells, virally infected cells, or infected cells treated with gliptins were tested for the presence of enzymatically active DPP4 using DPP4-GloTM Protease Assay (G8350,Promega). Luminescence was recorded as relative light units (RLU) on a Dynex MLX luminometer 30 min after adding the DPPIV-Glo™ Reagent. 2.12 PLpro expression and purification pK27Sumo_His-SUMO-PLpro (nsp3) (SARS-CoV-2) was a gift from John Diffley (Addgene plasmid # 169193; http://n2t.net/addgene:169193; RRID:Addgene_169193) [55]. This construct was expressed in Escherichia coli BL21. The induction for expression was done at O·D 0.8 with 0.5 mM IPTG and incubated at 18 °C overnight. The cell pellet was lysed by sonication in a lysis buffer (50 mM Tris-HCl, pH 7.5, 10 % glycerol, 1 mM DTT, 0.02 % Triton X-100, 500 mM NaCl, and 30 mM imidazole). Before lysis, the cell suspension was incubated with 100 μg/ml lysozyme for 20 min and finally sonicated 25 times for 30s each with an off period of 30 s in between. The lysate was centrifuged to collect the supernatant from it. The His-SUMO-tagged PL-pro was purified by affinity chromatography using Ni-NTA. The supernatant was loaded in a pre-equilibrated Ni-NTA purification column (Column volume (cv) of 3 ml). The column was equilibrated with fresh lysis buffer (10 cv) and washed with the same buffer (20 cv). The protein was eluted with an elution buffer containing 50 mM Tris-HCl, pH 7.5, 10 % glycerol, 1 mM DTT, 0.02 % Triton X-100, 500 mM NaCl, and 400 mM imidazole. Eluted protein was dialyzed using a buffer containing 50 mM Tris-HCl, pH 7.5, 10 % glycerol, 1 mM DTT, 0.02 % TritonX-100 and 50 mM NaCl to reduce the salt and imidazole. The His-SUMO tag was cleaved with 0.02 mg/ml His-Ulp1. The cleaved protein was further purified using a Ni-NTA column to remove the His-SUMO tag and His-Ulp1. The protein was further purified using size-exclusion chromatography. The protein was loaded in a pre-equilibrated Sephacryl S-100 HR-16/60 column (Cytiva). The column was equilibrated with a buffer containing 25 mM HEPES-KOH, pH 7.6, 10 % glycerol, 0.02 % Triton X-100, 150 mM NaCl, and 2 mM DTT. The peak fractions, of 1 ml each, were collected and stored at 4 °C for PLpro inhibition assay. 2.13 Ulp1 expression and purification pFGET19_Ulp1 was a gift from Hideo Iwai (Addgene plasmid # 64697; http://n2t.net/addgene:64697; RRID:Addgene_64697) [56]. The construct was expressed in E. coli BL21. The induction for expression was done at O·D 0.8 with 1 mM IPTG and incubated at 37 °C for 4 h. The cell pellet was lysed with a lysis buffer (50 mM Tris-HCl, pH 8, 5 mM magnesium acetate, 10 % glycerol, 1 mM DTT, 0.02 % Triton X-100, 500 mM NaCl, 20 mM imidazole). Before sonication-lysis, the cell suspension was incubated with 100 μg/ml lysozyme for 20 min and finally sonicated 25 times for 30 S each with 30 S off time in between. The cell lysate was centrifuged to collect the supernatant from it. The supernatant was loaded in a pre-equilibrated Ni-NTA purification column (cv - 3 ml). Ni-NTA was equilibrated with fresh lysis buffer (10 cv) and washed with the same buffer (20 cv). Finally, the protein was eluted with an elution buffer containing 250 mM imidazole (50 mM Tris-HCl, pH 8, 5 mM magnesium acetate, 10 % glycerol, 1 mM DTT, 0.02 % Triton X-100, 500 mM NaCl, 250 mM imidazole). In the final step of purification, the protein was loaded in a pre-equilibrated Sephacryl S-100 HR-16/60 column for size-exclusion chromatography. Before loading the protein on the column, the column was pre-equilibrated with the buffer (50 mM Tris-HCl, pH 8, 5 mM magnesium acetate, 10 % glycerol, 0.5 mM TCEP, 0.01 % Triton X-100, 500 mM NaCl). The eluted protein's peak fractions, each of 1 ml, were collected and stored at 4 °C for using it for the His-SUMO tag cleavage of PLpro. 2.14 Expression and purification of substrate for PLpro gel-based assay pGEX4T1_GST-PLproCS-MBP was a gift from John Diffley (Addgene plasmid # 169195; http://n2t.net/addgene:169195; RRID: Addgene_169195) [55]. PLpro gel-based assay substrate (GST-PLproCS-MBP) was constructed such as it has a GST tag at the N terminus, MBP tag at the C terminus, and in between the two tags there is a PLpro recognition site or cleavage site (TLKGG//APTKV) (nsp2/3 junction). PLpro is expected to cleave this substrate at G//A which leads to two fragments (25 kDa, 42 kDa). The expression induction was done at O·D 0.8 with 1 mM IPTG at 37 °C for 4 h. The cell pellet was lysed with buffer (50 mM Tris-HCl, pH 7.5, 10 % glycerol, 1 mM DTT, 0.02 % Triton X-100, 300 mM NaCl). Before sonication-lysis, the cell suspension was incubated with 100 μg/ml lysozyme for 20 min and finally sonicated 25 times for 30 s with an off time of 30 s in between. The lysate was centrifuged to collect the supernatant from it. The supernatant was loaded in a pre-equilibrated GST purification column (cv-3 ml). GST was equilibrated with fresh lysis buffer (10 cv), and the supernatant was incubated with GST beads for 3 h at 4 °C and washed with the same buffer (20 cv). Finally, the protein was eluted with additional 50 mM reduced Glutathione containing lysis buffer. In the final step of purification, the protein was loaded in a pre-equilibrated Sephacryl S-100 HR-16/60 column for size-exclusion chromatography. The column was run with elution with buffer containing 25 mM HEPES-KOH, pH 7.6, 10 % glycerol, 0.02 % Triton X-100, 150 mM NaCl, and 2 mM DTT. The peak fractions were collected and stored at 4 °C for using it in PLpro Gel-based assay. 2.15 PLpro gel-based assay with Sitagliptin and linagliptin 5 μM of the enzyme (PLpro) was incubated with sitagliptin and linagliptin (each at 10 μM, 100 μM, and 500 μM concentrations) for 10 or 20 min (in case of 500 μM concentration of inhibitors). Then 5 μM of gel-based assay substrate GST-PLproCS-MBP was added to the pre-incubated enzyme with compounds. The whole reaction was done at RT (25 °C) for 5 h. The assay was done in buffer containing 50 mM HEPES-KOH, pH 8, 5 mM magnesium acetate, 10 % glycerol, 0.5 mM TCEP, 0.01 % Triton X-100 and 500 mM NaCl. The reaction products were visualized through 12 % SDS-PAGE gel, which was stained with Coomassie brilliant blue stain (VWR Life Sciences). Image-based densitometric analysis was done using ImageLab software (6.1 version). 2.16 Expression and purification of Mpro of SARS-CoV-2 Expression and purification of the Mpro protein was done according to the method described by Iketani, S., et al., 2022 [57]. 2.17 Protease inhibition assay The followed assay protocol was similar to protocol mentioned by Zhang et al. 2020 with slight modifications in it [58]. The protease inhibition assay was carried out using Dabcyl-KTSAVLQ↓SGFRKM-E(Edans)-NH2 fluorescent peptide substrate at 15 μM concentration. The inhibitors at 100 μM concentrations were incubated with 180 nM of protein for 20 min at room temperature. Total reaction volume was 50 μM. The reaction was initiated by adding 15 μM of substrate. Reactions were done in duplicate. Fluoroscence intensity observed by excitation at 340 nm and emission at 460 nm. Relative Fluorescence Unit (RFU) was measured at 20 min (due to better signal to noise ratio). Buffer used in assay consist of 20 mM Tris, 100 mM NaCl, 1 mM EDTA, 1 mM DTT, pH 7.3. For positive control Mpro-13b inhibitor has been used at 1 μM and 5 μM concentration while in case of negative control no inhibitor has been used. Inhibition assay has been done at final 5 % DMSO in reaction. 3 Results and discussions 3.1 Binding prediction of human DPP4 with Wt-RBD of SARS-CoV-2 spike protein Experimental and computational studies have been performed to determine whether the Wt-RBD domain could interact with DPP4. Therefore, a blind molecular docking study was carried out again using the original crystallographic structure of the Wt-RBD domain of SARS-CoV-2 with human DPP4 protein (Fig. 1a; Fig. S2). In the best-docked position, Wt-RBD could interact with DPP4 with the favourable binding energy (B.E. = −14.9 kcal/mol) and dissociation constant (Kd) of 1.10E-11 M (Table 1). Five residues (L517, A520, G381, R357 and R355) of Wt-RBD domain form six hydrogen bonds with five residues (T736, T746, Q731, D243 and E244) present in the α/β-hydrolase domain of DPP4 (Fig. 1a). One salt-bridge was formed between the D243 (C=O) residue of DPP4 and R357 (NH2) of Wt-RBD. Significant number of residues of both Wt-RBD (17 amino acids from Y380-Y396, D428, T436, H519-A520 domain) and DPP4 (18 amino acids from F240-S242, F713-H754 domain) were involved in hydrophobic interactions, displayed the strong interaction between Wt-RBD and DPP4 (Fig. 1a and Fig. S2a; Table 1). Overall, hydrogen bonding and hydrophobic contacts were the major intermolecular forces that contribute to the favourable binding between Wt-RBD and DPP4 complex. 3.2 MD simulation of DPP4 and Wt-RBD complex Further, we have performed the classical MD simulation to investigate the dynamic behaviour of Wt-RBD and DPP4 binding in an explicit solvent. According to the representative conformation of MD simulation of WtRBD-DPP4 complex, RBD interacted with the C-terminus region (721–754) of the α/β-hydrolase domain and D244 residue of the β-propeller domain of DPP4 (Fig. 1b). Intriguingly, RBD failed to engage with the adenosine deaminase (ADA) and fibronectin binding site of DPP4. It suggested that RBD didn't impede the DPP4's other essential catalytic function. According to the results of molecular docking, the LigPlot depiction of the intermolecular hydrogen bonds and hydrophobic interactions were the main interaction involved in the binding of RBD and DPP4 (Fig. 1c). The RBD-DPP4 complex movie amply demonstrated that Wt-RBD remained bound to the DPP4 during the simulation (Movie S1). Majorly, Q731, T746, D737 and E244 of DPP4 showed hydrogen bonding with L517, H519 and R357 of Wt-RBD during the simulation. According to the aforementioned findings, DPP4 could be a promising receptor for SARS-CoV-2 entry in host cells through its robust interaction with RBD. 3.3 Cell-surface binding of RBD to DPP4 To elucidate the interaction between the Wt-RBD and DPP4, we performed a cell-surface-binding assay using ACE2 knockout cells. Transfected RBD-Fc was detected by anti-IgG1-Fc-AF488-labelled antibody. The RBD-Fc was fused with alexa-fluor 647 labelled-secondary antibody specific to human DPP4 in ACE2 knockout cells. Confocal microscopic images subsequently showed that both RBD-Fc and DPP4 were located mainly at the surface of the cell membrane (Fig. 1d). The overlay images showed the co-localization of RBD and DPP4 on the cell surface, validating their interaction, which is independent of ACE2 receptor (Fig. 1d).Fig. 1 Binding affinity of RBD with DPP4. a, The original crystallographic structure of the RBD domain (333–527 aa) of SARS-CoV-2 with human DPP4 protein (39–766 aa) was used for blind molecular docking. ‘A’ depicted residues of DPP4 and ‘B’ represented RBD residues. Hydrogen bonds were shown in yellow dotted lines, red dotted line represented salt bridge. b, Most populated conformation of MD simulation of Wt-RBD with DPP4 in which RBD interacted with the both C-terminus region (721–754) of the α/β hydrolase domain and D244 residue of the β-propeller domain of DPP4. c, 2D representation of most populated conformation of Wt-RBD-DPP4 simulation. The intermolecular hydrophobic contacts were shown in semi-circles, and hydrogen bonds were shown in green dashed lines. d, Representative confocal image of RBD-Fc (green) in ACE2 knockout cells stained with alexa-fluor 488 labelled-secondary antibody specific to IgG1-Fc and alexa-fluor 647 labelled-secondary antibody specific to DPP4 (red). Scale bar = 10 μm. e, Calu-3 cells were transfected with DPP4 expressing pLEX307-DPP4 alone or together with pCDNA3.1-RBD and both samples were immunoprecipitated with DPP4 antibody, and immunoblotted with RBD antibody. β-actin was used as an internal control. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Fig. 1 Table 1 Molecular docking analysis of DPP4 with RBD variants of SARS-CoV-2 Spike protein. Table 1Protein complex Binding affinity ΔG (kcal/mol) Dissociation constant Kd (M) at 25.0 °C Residue involved in intermolecular hydrogen bonding Residues involved in intermolecular hydrophobic contacts RBD domain (T333-P576) DPP4 (S39-P766) RBD domain (T333-P576) DPP4 (S39-P766) WtRBD+DPP4 −14.9 1.10E-11 L517, A520, G381, R357, R355 T736, T746, Q731, D243, E244 A520, P521, A522, H519, L518, E516, C391, F392, L390, D427, L387, V382, Y380, D428, T430, F429, Y396 F240, D737, S744, E738, S242, Y735, A747, H750, T753, K721, H757, H754, A732, M733, F713, W734 αRBD+DPP4 −17.3 2.0E-13 H519, T385, L517, D428, P412, D427, P463, K378, C379, G381, S383, R357 D737, S242, D729, T736, Y241, S239, Q714, E237, T251, D725, S720, F730, Q247 A520, P521, T393, L518, F392, L390, E516, Y396, T430, P426, F464, G413, K462, Y380, P384, Y369 E244, W734, P249, T706, Y238, F713, I236, R253, A717, K721, A732, V724 βRBD+DPP4 −20.0 2.2E-15 S477, K462, K484, L452, S349, G446, Q498, S469, T470, D467, Y489, R346, R355, N487, R466, Y421, N460, Y473, K356 F559, R560, E73, Y120, S101, D243, D104, Y105, N103, K71, H748, H100, G99, D96, Y48, N92, R54, N51, E97 G482, V483, G476, F486, F490, A352, Y351, I468, L492, V445, G485, N354, W353, A475, L455, F456, K458, R457 L55, L561, Y752, G741, W629, I102, S245, A743, N74, S93, S745, L49 δRBD+DPP4 −15.9 2.2E-12 Y453, A475, R403, Y505, N487, Y489, R408, D405, R452, T415, K417, Q498 T736, K50, M733, L702, S745, Q731, A732, E244, H757, H750, H754, S239 N501, F497, Y495, G496, Q409, Y473, F456, F490, L492, Y449, G446, G502, T500, L455, G416, G504, V503, Y421 F713, W734, S744, T746, Q749, S720, Y241, P249, K721, A717, F730, V724, T753 κRBD+DPP4 −16.3 1.1E-12 T500, Q498, G446, N501, G496, N450, Y449, S494, K444, F486, T478, K417, Q493, Y505, D405, R403, Y453, Q484 S690, R691, N685, R597, H682, E604, R317, P676, E677, R596, R684, D681, T350 Y489, L455, F456, V445, G485, G482, V483, S477 N694, S686, T600, I319, D678, N679, S642, N321, M348, S349, I295 In the present study, the PDBs used for RBD variants of SARS-CoV-2: wild-type (Wt) = 6LZG (chain B), alpha (α) = 7EKF(chain B), beta (β) = 7WCR(chain A), delta (δ) = 7W9F(chain E) and kappa (κ) = 7SOC(chain B). The PDB used for DPP4 is 4 L72 (chain A). We overexpressed DPP4 with and without a RBD expressing plasmid by transfection and immunoprecipitated it using DPP4 antibody to support the idea that RBD-DPP4 binding exists. The immunoprecipitated samples were subjected to individual immunoblotting using RBD antibody. Immunoprecipitated sample co-transfected with DPP4 and RBD showed the RBD band following immunoblotting, but the sample expressing only DPP4 without RBD did not display any RBD band (Fig. 1e). Together, our findings showed that DPP4 and RBD created a heterodimer for their stable interaction. 3.4 Binding prediction of WtRBD-DPP4 complex with gliptins Using molecular docking approach, we have examined some DPP4 inhibitors including sitagliptin, linagliptin, saxagliptin, vildagliptin, and metformin as prospective therapies for SARS-CoV-2 infection in an attempt to prevent viral entry through the DPP4-RBD interaction (Fig. 2a–e; Table 2). Among the tested gliptins, sitagliptin had the lowest dissociation constant (Kd = 1.07E-7 M) and the most favourable B.E. (ΔG = −9.5 kcal/mol) with the WtRBD-DPP4 complex. Linagliptin (ΔG = −8.1 kcal/mol, Kd = 1.1E-6 M) had the second highest binding energy, followed by saxagliptin (ΔG = −7.7 kcal/mol, Kd = 2.24E-6 M), vildagliptin (ΔG = −7.3 kcal/mol, Kd = 4.40E-6 M) and metformin (ΔG = −5.3 kcal/mol, Kd = 1.29E-4 M). In the best-docked pose of sitagliptin with WtRBD-DPP4 complex, sitagliptin bound in between the DPP4 and Wt-RBD complex, in which Y241(A), E244(A), P249(A) residues of DPP4; P463(B) and E516(B) residues of RBD showed hydrophobic interaction with sitagliptin. The R355(B) of RBD formed a hydrogen bond with the fluorine atom of sitagliptin. The favourable binding of WtRBD-DPP4 and sitagliptin (G = −9.5 kcal/mol) was facilitated by their hydrophobic interactions and hydrogen bonding (Fig. 2a). However, in the second most docked pose, sitagliptin interacted with D709(A), H740(A), N710(A), Y666(A), S630(A), W629(A), S552(A), L554(A) and Y547(A) residues of the catalytic site of DPP4 in WtRBD-DPP4 complex with ΔG = −8.6 kcal/mol (Fig. S3) which is consistent with earlier reports [59]. Other gliptins like linagliptin, saxagliptin, vildagliptin and metformin showed binding interaction with only DPP4 protein in the best-docked pose. Linagliptin formed hydrophobic contacts with G741(A), Y752(A), Y547(A), W629(A), W662(A), W666(A), N710(A), G741(A) residues and hydrogen bonding with S630(A) residue of DPP4, which belongs to the active site of DPP4 (Fig. 2b). Similarly, saxagliptin also showed hydrophobic interaction in the active site with E205(A), E206(A), S209(A), F357(A), Y631(A), Y666(A), Y662(A), N710(A) residues and hydrogen bonding with Y547(A) and S630(A) of DPP4, but with lower B.E. than sitagliptin and linagliptin (Fig. 2c). The vildagliptin showed binding at the middle of α/β-hydrolase domain and β-propeller of DPP4, exhibiting hydrophobic interaction with Y381(A), T401(A), G424(A), P426(A), F516(A), W525(A) and Q586(A) residues and hydrogen bonding with K523(A) residue of DPP4 (Fig. 2d). Among tested gliptins, metformin had the lowest B.E. (−5.3 kcal/mol) with WtRBD-DPP4 due to its smaller size and no aromatic/cyclic groups were present. It binds near to the entrance of DPP4 cavity, at β-propeller site of DPP4 protein, displayed hydrophobic contacts with W124(A), S127(A), Y128(A), T129(A), Y211(A), Y195(A), I198(A) residues and three hydrogen bonds with Q153(A), N170(A) and D192(A) residues of DPP4 (Fig. 2e). According to the docking analysis, sitagliptin and linagliptin, two of the five gliptins, may be effective in reducing SARS-CoV-2 infection. 3.5 Gliptins maintain the viability of cells We assessed the cytotoxicity of cultured cells incubated for 48 h in the presence or absence of each ligand in order to examine the impact of the gliptins on the viability of the Vero E6 cell lines. The results demonstrated that, at various doses, none of the inhibitors caused any considerable cell cytotoxicity. We have chosen a range of drug concentrations to investigate the effect of inhibitors on viral multiplication, based on a cell viability of 90 % or more in each well (Fig. 2f–j). The CC50 values of sitagliptin, linagliptin, vildagliptin, saxagliptin, and metformin were 67.36 μM, 21.28 μM, 130.81 μM, 21.06 μM, and 46.29 μM, respectively (Table 4).Fig. 2 Wt-RBD-DPP4 complex binding prediction with gliptins. The predicted binding mode of WtRBD-DPP4 complex with a, sitagliptin (orange), b, linagliptin (magenta), c, saxagliptin (red), d, vildagliptin (green), e, metformin (wheat) in best docked pose. The residues were forming the binding pocket shown in dark blue stick representation. The intermolecular hydrophobic contacts were shown in semi-circles, and hydrogen bonds were shown in green dashed lines. f-j, The effect of gliptins on cellular cytotoxicity was measured at different concentration. Data showed average values ± S.D. of three repeated experiments and six biological replicates ± S.D. were displayed here. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Fig. 2 Table 2 Molecular docking analysis of gliptins with RBD(variants)-DPP4 complex, WtRBD-ACE2 complex and PLpro using AutoDock Vina. Table 2Compound Protein Binding affinity ΔG (kcal/mol) Dissociation constant Kd (M) at 25.0 °C Residues involved in the intermolecular H–bonding interactions Residues involved in intermolecular hydrophobic contacts Residue Atom⁎ Distance (Å) Linagliptin WtRBD-DPP4 −8.1 1.14E-6 S630(A) NH: O 3.3 G741(A), Y752(A), Y547(A), W629(A), Y662(A), Y666(A), N710(A), G741(A) Sitagliptin WtRBD-DPP4 −9.5 1.07E-7 R355(B) NH:F 3.2 Y241(A), E244(A), P249(A), P463(B), E516(B) Vildagliptin WtRBD-DPP4 −7.3 4.40E-6 K523(A) NH:O 2.9 Y381(A), T401(A), G424(A), P426(A), F516(A), W525(A), Q586(A) Saxagliptin WtRBD-DPP4 −7.7 2.24E-6 Y547(A) S630(A) OH:O OH:O 3.0 2.7 E205(A), E206(A), S209(A), F357(A), Y631(A), Y666(A), Y662(A), N710(A) Metformin WtRBD-DPP4 −5.3 1.29E-4 Q153(A) N170(A) D192(A) NH:N CO:HN CO:HN CO:HN 2.9 3.2 3.1 3.0 W124(A), S127(A), Y128(A), T129(A), Y211(A), Y195(A), I198(A) Linagliptin αRBD-DPP4 −8.2 9.61E-7 T251(A) R253(A) NH:N NH:OH NH:N 3.0 2.9 3.2 Y248(A), P249(A), K250(A), W353(B), P463(B), F464(B), E465(B), R466(B) Sitagliptin αRBD-DPP4 −8.2 9.61E-7 E205(A) E206(A) Y547(A) S630(A) H740(A) CO:HN CO:HN OH:F OH:N NH:N 3.3 3.3 3.1 2.7 3.2 S209(A), F357(A), Y662(A) Linagliptin βRBD-DPP4 −8.7 4.13E-7 E205(A) CO:HN 3.0 E206(A), F357(A), Y547(A), K554(A), W629(A), S630(A), Y662(A), Y666(A), T478(B), P479(B), F486(B) Sitagliptin βRBD-DPP4 −9.4 1.27E-7 T478(B) OH:N 3.0 Y547(A), K554(A), N562(A), W563(A), W629(A), Y752(A), P479(B) N487(B) Linagliptin δRBD-DPP4 −7.8 1.89E-6 – – – Y48, R560(A), L561(A), N562(A), W627(A), W629(A), V653(A), G741(A), I742(A), I751(A), Y752(A) Sitagliptin δRBD-DPP4 −9.2 1.77E-7 Q527(A) NH:N 3.1 D545(A), K554(A), R560(A), N562(A), W627(A), W629(A), G741(A), Y752(A) Linagliptin κRBD-DPP4 −8.7 4.13E-7 Q553(A) CO: HN 2.9 Y547(A), W627(A), W629(A), V653(A), G741(A), I751(A), Y752(A) Sitagliptin κRBD-DPP4 −8.8 3.49E-7 G355(A) S376(A) NH:N NH:F 2.9 3.3 E347(A), M348(A), S349(A), T351(A), V354(A), E378(A), G380(A), D588(A) Linagliptin WtRBD-ACE2 −6.0 3.95E-5 – – – P321(A), N322(A), T324(A), G354(A), M383(A), A386(A), A387(A), R403(B), D405(B), Y505(B) Sitagliptin WtRBD-ACE2 −7.2 5.21E-6 N103(A) NH:F 3.1 Q98(A), Q101(A), Q102(A), N194(A), Y196(A), E208(A) Here, the PDB used for variants of RBD: wild-type = 6LZG (chain B), alpha = 7EKF(chain B), beta = 7WCR(chain A), delta = 7W9F(chain E) and kappa = 7SOC(chain B). The PDB used for DPP4 and WtRBD-ACE2 are 4 L72 (chain A) and 6LZG, respectively. ⁎ The atoms on right represent ligand atoms and on the left represent protein residue atoms. 3.6 Selected gliptins can encumber the Wt-SARS-CoV-2 infection The preceding sections predicted that impeding DPP4 may be therapeutically advantageous for the treatment of SARS-CoV2 infection. Therefore, we tested DPP4 inhibitors to determine whether they could encumber the SARS-CoV-2 infection in vitro. We infected Vero E6 cells with Wt-SARS-CoV-2 and treated them with sitagliptin, linagliptin, vildagliptin, saxagliptin, and metformin alone. We have found that sitagliptin and linagliptin, two of the DPP4 inhibitors, have demonstrated the greatest potential in terms of lowering viral replication with IC50 value of 1.46 μM and 2.21 μM, respectively (Fig. 3a and Table 4). Sitagliptin at the concentration of 1.98 μM can significantly abrogated the virus growth, whereas linagliptin had a similar inhibitory efficacy at a concentration of 1.06 μM. We were unable to identify any inhibitory efficacy of metformin, vildagliptin, or saxagliptin, even at higher concentrations (Fig. 3a). We also demonstrated that sitagliptin, at a concentration of 0.99 μM, partially encumbered the viral growth; however, at a concentration of 1.98 μM, it can significantly inhibited viral multiplication by almost six folds compared to cells with virus alone. This is very similar to the inhibitory efficacy of remdesivir (Fig. 3a,b). Linagliptin, likewise remdesivir, halted the multiplication of viruses at a minimum concentration of 1.06 μM (Fig. 3a,b). However, we have found that sitagliptin and linagliptin in combination had an inhibitory efficacy better than the remdesivir; even at lower concentration levels of 0.99 μM and 1.06 μM, respectively, sitagliptin and linagliptin suppressed viral proliferation significantly by 10 folds (Fig. 3b).Fig. 3 Efficacy of gliptins against SARS-CoV-2 infection. a, Effect of selected gliptins at different concentrations on Wt-SARS-CoV-2 infection was studied. b, Effect of sitagliptin and linagliptin alone or in their combination was checked to impede the SARS-CoV-2 infection in vitro. In both figure a and b, remdesivir was used as a reference anti-viral compound. The viral load was represented as an average of triplicate data, and six biological replicates ± S.D. were displayed here. The significance of viral load reduction was measured, p-values ** < 0.01; and *** < 0.001 were considered statistically significant. c, Confocal imaging was performed to evaluate the co-localization of Wt-RBD-Fc with DPP4 in the absence and presence of sitagliptin, linagliptin, or their combination in ACE2 knockout cells. d, Calu-3 cells were transfected with DPP4 expressing pLEX307-DPP4 except lane 6 where DPP4-KO cells were used. Similarly, pCDNA3.1-RBD was co-transfected in all lanes except lane 1 in the presence or absence of sitagliptin or linagliptin. All samples were immunoprecipitated with DPP4 antibody, and immunoblotted with RBD antibody. β-actin was used as an internal control. e, The effect of gliptins on viability of infected cells was measured at different concentration. Data showed average values ± S.D. of three repeated experiments and six biological replicates ± S.D. were displayed here. f, Only cells, DPP4 knockout cells (KO), virus infected cells, infected cells treated with remdesivir or gliptins were tested for DPP4 enzymatic activity. Throughout the simulation, the stability of the DPP4 (39–766) structure was evaluated by calculating its g, Root-mean-square deviation (RMSD), h, Radius of gyration (Rg), i, Solvent-accessible surface area (SASA), and j, Root-mean-square fluctuation (RMSF) for the following systems: DPP4, DPP4 + linagliptin, DPP4 + sitagliptin, WtRBD-DPP4, WtRBD-DPP4 + linagliptin and WtRBD-DPP4 + sitagliptin. k, RMSD, l, Rg, m, SASA and n, RMSF of Wt-RBD (333–527) structure were calculated for the following systems: WtRBD-DPP4, WtRBD-DPP4 + linagliptin, and WtRBD-DPP4 + sitagliptin. Fig. 3 3.7 Selected gliptins refuted the cell surface interaction of DPP4-RBD We used confocal microscopy to carry out a cell-surface binding assay to clarify the impact of gliptins on the interaction between the Wt-RBD-Fc and DPP4 in ACE2 knockout cells. We have already shown the ACE2 independent DPP4 interaction with RBD-Fc (Fig. 1d, 3c). However, sitagliptin treatment prevented the co-localization of RBD-Fc with DPP4 (Fig. 3c). Similarly, linagliptin treatment hampered the formation of a DPP4-RBD heterodimer complex. The interaction between RBD and DPP4 at the cell membrane, which is necessary for viral entry into cells, was prevented when both the gliptins were provided together in ACE2 knockout cells (Fig. 3c). We overexpressed DPP4 except panel 6 with RBD expressing plasmid by transfection and immunoprecipitated it using DPP4 antibody to support the idea that RBD-DPP4 binding exists (Fig. 3d). In panel 6, we expressed only RBD expressing plasmid in DPP4 knockout cells. The immunoprecipitated samples using DPP4 antibody were subjected to individual immunoblotting using RBD antibody. Immunoprecipitated sample co-transfected with DPP4 and RBD showed the RBD band following immunoblotting, but the sample expressing only DPP4 without RBD did not display any RBD band. Together, our findings showed that DPP4 and RBD created a heterodimer for their stable interaction (Fig. 3d). In presence of gliptins, we didn't observe any RBD band, which clearly indicated that gliptins inhibited DPP4 activity and disrupts DPP4-RBD interaction. 3.8 Effect of gliptins on cells viability and DPP4 enzymatic activity The cells infected with Wt-SARS-CoV-2 caused >40 % cell cytotoxicity, however, the effect of gliptins on cytotoxicity of infected cells reduced to 10 % was measured at different concentration. Around 90 % virus infected cells were remained viable after sitagliptin or linagliptin or its combinatorial treatment. The viability of infected cells after gliptins treatment even better than infected cells treated with remdesivir (Fig. 3e). We further observed that virus infection augmented cellular DPP4 enzymatic activity which was significantly reduced in treatment with sitagliptin and linagliptin or their combination (Fig. 3f). The abrogation of DPP4 enzymatic in infected cells by gliptins were dose dependent as higher concentration of any of gliptins inhibited more DPP4 activity than their respective lower concentration. Remdesivir treatment in infected cells did not affect the DPP4 activity. DPP4 knockout cells (KO) cells also did not show any DPP4 activity due to absence DPP4 gene (Fig. 3f). 3.9 MD simulation of WtRBD-DPP4 in presence of linagliptin or sitagliptin Using the live Wt-SARS-CoV-2 virus in our experiments and microscopy study, we established that sitagliptin and linagliptin were able to diminish SARS-CoV-2 infection. Additionally, we were interested in learning how specific gliptins prevented SARS-CoV-2 infection. In this context, we have performed the MD simulation of sitagliptin and linagliptin with the WtRBD-DPP4 complex. To study the impact of inhibitors on the DPP4 structure without Wt-RBD, we performed MD simulations of DPP4 alone in the absence and presence of linagliptin and sitagliptin (Fig. 3d–g). Initially, the RMSD, SASA, R g, and RMSF in DPP4 were analysed for three systems: DPP4, DPP4 + linagliptin and DPP4 + sitagliptin, which depicted that linagliptin and sitagliptin didn't induce any conformational instability to DPP4 in the absence of WtRBD, as there were no significant changes in the positions of atoms (RMSD and RMSF), the R g as well as SASA of DPP4 in presence of linagliptin and sitagliptin during the simulation (Fig. 3d–g, Fig. S4a–c). Whereas, with the addition of Wt-RBD to the DPP4 system, the RMSD of DPP4 significantly elevated from 0.25 ± 0.02 nm to 0.35 ± 0.07 nm (Fig. 3d). When Wt-RBD was present, DPP4's RMSD was increase to alter the interactions between the two large proteins and established a stable Wt-RBD-DPP4 complex. After 70 ns, the RMSD was stabilised, indicating that Wt-RBD and DPP4 have formed a stable complex (Fig. 3d). The RMSD of DPP4 complexed with Wt-RBD was considerably increased to 0.49 ± 0.16 nm in the presence of sitagliptin, although the RMSD of DPP4 alone with sitagliptin was only 0.12 ± 0.02 nm (Fig. 3d, Fig. S4a). Furthermore, sitagliptin impaired the structure of Wt-RBD in the WtRBD-DPP4 complex by increasing its RMSD to 1.33-fold, which was considerably higher (Fig. 3h, Fig. S4d). The RMSD results thus indicated that sitagliptin substantially destabilized the WtRBD-DPP4 complex. The RMSD of DPP4 attached with Wt-RBD increased to 2.17 folds in the presence of linagliptin, which was considerably greater than DPP4 alone with linagliptin (Fig. 3d, Fig. S4a). The fact that the RMSD of the Wt-RBD of the heterodimer complex increased by nearly two folds when linagliptin was present, suggested that the RBD became unstable (Fig. 3h). In Movie S2, simulation of WtRBD-DPP4 in the presence of sitagliptin illustrated how the drug interfered with the binding of DPP4 and WtRBD. The two residues of DPP4 (Q731 and E244) were involved in hydrogen bonding with two residues of WtRBD (G381, R355) having bond distance 1.9 and 1.7 Å, respectively, at the initial time of the simulation. These hydrogen bonds were also observed in the molecular docking study of DPP4 with WtRBD. In the simulation, sitagliptin pushed the Wt-RBD away from DPP4 by interacting with the Wt-RBD, which broke the hydrogen bonds between them. At the end of the simulation, the distance of hydrogen bonds between two residues had increased to > > 3.5 Å. Likewise, in the simulation of WtRBD-DPP4 with linagliptin, the hydrogen bond between DPP4 residues (E244 and T736) and Wt-RBD residues (R355 and L517) was disrupted by linagliptin (Movie S3). Throughout the simulation, linagliptin interacted strongly with DPP4, which enabled the separation of DPP4 from Wt-RBD. Further, we have studied the compactness of WtRBD-DPP4 in the presence and absence of gliptins by calculating the radius of the gyration (R g) parameter. In the absence of Wt-RBD, the average R g value of DPP4 alone is 2.74 ± 0.01 nm, whereas the average R g value of DPP4 in presence of Wt-RBD was increased to 3.45 ± 0.03 nm (Fig. 3e, Fig. S4b) indicated the instability of DPP4 in presence of Wt-RBD. In the presence of sitagliptin and linagliptin, the average R g value of DPP4 in WtRBD-DPP4 complex is 3.44 ± 0.04 nm and 3.45 ± 0.05 nm, respectively, which implies that neither sitagliptin nor linagliptin altered the radius of gyration of DPP4 in the presence of Wt-RBD (Fig. 3e, Fig. S4b). We assessed the SASA of WtRBD-DPP4 to advance our understanding of the compactness of the complex after gliptin treatment. The average SASA value of only DPP4 was 412.31 ± 4.99 nm2 which was significantly increased to 458.15 ± 5.44 nm2 in presence of Wt-RBD (Fig. 3f, Fig. S4c). The significantly higher SASA value of DPP4 in the presence of Wt-RBD demonstrated that the structure of DPP4 lost its compactness and stability in the presence of Wt-RBD, enhancing the structure's accessibility to solvents. Intriguingly, sitagliptin and linagliptin caused the average SASA value of DPP4 in WtRBD-DPP4 to increase to 464 ± 6.02 nm2 and 461.88 ± 7.75 nm2, respectively. Additionally, we noticed a little rise in the SASA of Wt-RBD when sitagliptin and linagliptin were present. The aforementioned finding indicates that gliptins weakened the structural stability the WtRBD-DPP4 complex (Fig. 3f). To examine the conformational fluctuations that occurred in DPP4 during simulation, the RMSF of each residue of DPP4 in the presence of Wt-RBD was evaluated. In the WtRBD-DPP4 complex, the average RMSF value of DPP4 fluctuated by ∼0.13 ± 0.05 nm, while the DPP4 alone system was found to fluctuate at an average of ∼0.09 ± 0.06 nm (Fig. 3g). Higher fluctuations were observed in DPP4 in the presence of Wt-RBD specifically at the N-terminus (87–235 aa) and C-terminus region (564–766 aa) of DPP4 which is the binding domain of Wt-RBD. In presence of sitagliptin and linagliptin, the RMSF value had increased to 0.27 ± 0.09 nm and 0.16 ± 0.08 nm, respectively (Fig. 3g). Likewise, the average RMSF of Wt-RBD increased significantly from 0.26 ± 0.10 to 0.55 ± 0.12 nm when sitagliptin was present. Sitagliptin significantly affected the destabilization of DPP4 and WtRBD, as evidenced by the 2.5-fold rise in RMSF that was seen in each of the residues of DPP4 and Wt-RBD (Fig. 3k). In addition, linagliptin enhanced the average RMSF value of Wt-RBD from 0.26 ± 0.10 nm to 0.35 ± 0.13 nm (Fig. 3k). According to the RMSF results, both sitagliptin and linagliptin caused more conformational instability to both DPP4 and Wt-RBD. Moreover, we have calculated the binding free energy (∆Gbinding) of sitagliptin and linagliptin with WtRBD-DPP4 complex by using MM–PBSA analysis (Table 3). The ∆Gbinding for WtRBD-DPP4 with sitagliptin and linagliptin was calculated to be −65.16 ± 0.86 kcal/mol and − 60.76 ± 0.73 kcal/mol, respectively. The MM-PBSA data highlighted that van der Waal energy and non-polar solvation energy contributes maximum to the favourable ∆Gbinding of sitagliptin and linagliptin with WtRBD-DPP4 complex. It can be speculated that the strong binding affinity of sitagliptin and linagliptin with WtRBD-DPP4 complex results into the escalation of RMSD, RMSF and SASA value of DPP4 and Wt-RBD proteins, leading to the destabilization of WtRBD-DPP4 complex. 3.10 Binding prediction of DPP4 with the RBD of VOCs Since pandemic started, different variants of SARS-CoV-2 have been rapidly evolving throughout the world. Since DPP4 has emerged as a promising therapeutic target to prevent SARS-CoV-2 infection, we have also examined DPP4's ability to bind to other SARS-CoV-2 variants, including VOCs: α-RBD (N501Y), β-RBD (K417N, E484K and N501Y), δ-RBD (T478K and L452R) and κ-RBD (E484Q and L452R) (Fig. S5). The B.E. of DPP4 with alpha, beta, delta and kappa RBD was −17.3, −20.0, −15.9 and −16.3 kcal/mol, respectively, in the best docked pose. The major residues VOCs involved in binding with DPP4 are summarised in Table 1. Among VOCs, specifically α-RBD, δ-RBD and κ-RBD preferentially bound to the external side of the α/β-hydrolase domain (506–766) of DPP4 (Fig. S5). The binding site of α-RBD with DPP4 was almost similar to Wt-RBD i.e. α/β-hydrolase domain of DPP4. However, the B.E. of αRBD-DPP4 complex (−17.3 kcal/mol) was higher than WtRBD-DPP4 complex (−14.9 kcal/mol) due to increased dipole–dipole interaction between α-RBD (28 residues) and DPP4 (25 residues). Interestingly, only β-RBD entered inside the cavity of DPP4, in which 37 residues spanning 357–521 domain of β-RBD interacted with 32 residues majorly from the β-propeller domain of DPP4, through hydrophobic interaction and hydrogen bonding, that leads to highest binding affinity (−20 kcal/mol) and lowest Kd value of 2.0E-13 M among VOCs. Notably, the mutated residue K484 of β-RBD also participated in hydrogen bonding with Y120 residue of DPP4 (Fig. S5b). The δ-RBD is double mutated derivative of Wt-RBD comprising T478K and L452R mutations. Interestingly, R452 residue of δ-RBD interacted with E244 residue of DPP4. Along with that, total 30 residues spanning 403–505 domain of δ-RBD showed dipole-dipole and hydrophobic interactions with 25 residues spanning 50, 702–757 and 241–244 domain of DPP4 (Fig. S5c). Similarly, 26 residues spanning 403–505 of κ-RBD interacted with 24 residues spanning 295–350, a 597 residue and 600–694 domain of DPP4. The mutated residue, Q484 in κ-RBD formed a hydrogen bond with T350 of DPP4 (Fig. S5d). The findings indicated that DPP4 has a remarkable ability to attach to each VOCs of the SARS-CoV-2 spike protein. 3.11 Binding prediction of sitagliptin/linagliptin with the RBD(VOCs)-DPP4 complex We next studied the binding potential of sitagliptin and linagliptin with VOCs of SARS-CoV-2 spike protein by molecular docking method (Fig. 4a–h; Table 2). Surprisingly, sitagliptin and linagliptin's binding sites with αRBD-DPP4 differed from those we found with WtRBD-DPP4, but both gliptins exhibited similar binding energies (−8.2 kcal/mol) and Kd value (9.61E-7 M) with αRBD-DPP4. Sitagliptin displayed hydrophobic interaction with S209(A), F357(A), and Y662(A) residues as well as hydrogen bonds with E205(A), E206(A), Y547(A), S630(A) and H740(A) residues situated at the centre of β-propeller and α/β-hydrolase domain of DPP4 (Fig. 4a). Linagliptin, in fact, had hydrophobic interaction with Y248(A), P249(A), Lys250(A), W353(B), P463(B), F464(B), E465(B), R466(B) residues and hydrogen bonding with T251(A), R253(A) residues near to the binding site αRBD and DPP4 (Fig. 4e). The main intermolecular forces between sitagliptin/linagliptin and the RBD-DPP4 complex were the hydrophobic interaction and hydrogen bonding.Table 3 The calculated binding free energy (∆Gbinding) for sitagliptin and linagliptin with WtRBD-DPP4, WtRBD-ACE2 and PLpro using the molecular mechanics Poisson Boltzmann surface area (MM–PBSA). Table 3Energy components (kcal/mol) WtRBD-DPP4 + sitagliptin WtRBD-DPP4 + linagliptin WtRBD-ACE2 + sitagliptin WtRBD-ACE2 + linagliptin ∆Evdw −42.02 ± 1.27 −43.26 ± 1.34 −33.85 ± 3.14 −53.96 ± 0.32 ∆Eelec −14.91 ± 1.06 −2.66 ± 0.82 −6.37 ± 3.33 −3.05 ± 0.15 ∆EMMa −56.93 ± 1.16 −45.92 ± 1.08 −40.22 ± 6.47 −57.01 ± 0.47 ∆Gps 26.25 ± 1.18 17.87 ± 1.41 18.24 ± 2.70 23.34 ± 0.19 ∆Gnps −34.48 ± 3.45 −32.71 ± 1.66 −28.45 ± 3.65 −44.31 ± 0.72 ∆Gsolvb −8.23 ± 2.27 −14.84 ± 0.33 −10.21 ± 0.95 −33.67 ± 0.53 ∆Gbindingc −65.16 ± 0.86 −60.76 ± 0.73 −50.43 ± 4.88 −77.97 ± 0.84 a ∆EMM = ∆Evdw + ∆Eelec. b ∆Gsolv = ∆Gps + ∆Gnps. c ∆Gbinding = ∆EMM + ∆Gsolv. Fig. 4 The binding and molecular interactions of the gliptins with RBD-DPP4 complex of VOCs. a–h, The best-docked poses of sitagliptin and linagliptin with αRBD-DPP4, βRBD-DPP4, δRBD-DPP4 and κRBD-DPP4 complexes illustrated here. The sitagliptin and linagliptin were represented by orange and magenta stick respectively. The residues forming the binding pocket were shown in dark blue stick. i–l, The effect of sitagliptin and linagliptin alone or in combination on numerous VOCs was investigated here. Remdesivir was used as a reference anti-viral compound. The viral load was represented as an average of triplicate data, and six biological replicates ± S.D. were displayed here. The significance of viral load reduction was measured, p-values ** < 0.01; and *** < 0.001. Two different set of primers (N1, and N2) of nucleocapsid gene of SARS-CoV-2 were used for RT-qPCR. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Fig. 4 In the case of the βRBD-DPP4 complex (Fig. 4b, f), sitagliptin and linagliptin bound at the 487–752 domain and 205–666 domain of DPP4 with the B.E. of −9.4 kcal/mol; −8.7 kcal/mol and Kd value of 1.27E-7 M; 4.13E-7 M, respectively (Table 2). With δRBD-DPP4 complex, sitagliptin (B.E. = −9.2 kcal/mol, Kd = 1.77E-7 M) and linagliptin (B.E. = −7.8 kcal/mol, Kd = 1.89E-6 M) showed interaction with 527–752 aa and 48, 560–629 aa of the α/β-hydrolase domain of DPP4, respectively, were mainly driven by van der Waal interactions. Remarkably, sitagliptin showed more binding interaction with δRBD-DPP4 than linagliptin (Fig. 4c,g, Table 2). Furthermore, in the case of the κRBD-DPP4 complex, sitagliptin displayed binding in the β-propeller domain spanning residues 347–380 and 588 of α/β-hydrolase domain of DPP4 with B.E. = −8.8 kcal/mol and Kd = 3.49E-7 M (Fig. 4d). Contrarily, linagliptin had shown its binding in the α/β-hydrolase domain of DPP4, spanning residues 547–751, with B.E. = −8.7 kcal/mol and Kd = 4.13E-7 M (Fig. 4h; Table 2). Sitagliptin and linagliptin had significantly strong binding interactions with all RBD (variants)-DPP4 complexes, with B.E. ranging from −7.8 to −9.4 kcal/mol. Based on their RBD-DPP4 complex binding profiles, sitagliptin and linagliptin may be effective therapeutic options for mitigating various SARS-CoV2 variants. 3.12 Gliptins inhibits SARS-CoV-2 VOCs infection The preceding sections decisively established how inhibiting DPP4 significantly reduce Wt-SARS-CoV-2 infection in an in vitro system (Fig. 3a, b). These findings inspired us to check the therapeutic benefits of gliptins against SARS-CoV-2 VOCs. Therefore, we infected Vero E6 cells with B1.1.7, B.1.351, B.1.617.2, and B.1.617.1 variants separately and treated with sitagliptin, or linagliptin alone or in their combination. Sitagliptin and linagliptin had demonstrated to be extremely promising in preventing the proliferation of the virus among variants we tested. Sitagliptin at the concentration 0.99 μM could partially inhibit α-SARS-CoV-2, β-SARS-CoV-2, κ-SARS-CoV-2 (Fig. 4i,j,l) but was unable to prevent even the partial growth of δ-SARS-CoV-2 variant (Fig. 4k). Linagliptin showed a dose-dependent inhibitory effectiveness against all VOCs (Fig. 4i–l). At a concentration of 1.98 μM, sitagliptin could significantly impede viral growth by nearly 6–7 folds among all variants (Fig. 4i–l). The sitagliptin showed IC50 value against variants of SARS-CoV-2 (B1.1.7, B.1.351, B.1.617.2, and B.1.617.1) ranges from 1.64 to 1.89 μM. Similarly, linagliptin showed IC50 value in range 1.92 to 2.32 μM (Table 4 ).Table 4 The CC50 and IC50 values of gliptins obtained from cell viability assay and SARS-CoV-2 inhibition assay, respectively. Table 4S·No Drug CC50 value (μM) IC50 value (μM) Wt-SARS-CoV-2 B.1.1.7 B.1.351 B.1.617.2 B.1.617 1 Sitagliptin 67.36 1.46 1.64 1.65 1.84 1.89 2 Linagliptin 21.28 2.21 1.95 1.92 2.32 2.27 3 Vildagliptin 130.81 3.83 – – – – 4 Saxagliptin 21.06 6.68 – – – – 5 Metformin 46.29 10.49 – – – – We further checked in combination of these two drugs and found that sitagliptin and linagliptin even at lower concertation of 0.99 μM and 1.06 μM respectively abrogated the viral growth (Fig. 4i–l). Nevertheless, at the lowest dose in our study, the combinatorial treatment was able to significantly halt the proliferation of all variants. Combinatorial sitagliptin and linagliptin at the lowest dose or sitagliptin and linagliptin alone at their highest doses were observed to entirely prevent the infection of all clades. This inhibitory efficacy of gliptins was even more effective than that of remdesivir among all VOCs. 3.13 Binding prediction of sitagliptin/linagliptin with ACE2 receptor Since the N53, N90, N103, N322, N432, N546, and N690 glycosylation of ACE2 is essential for viral entry, we explored the compatibility of sitagliptin and linagliptin for binding with the WtRBD-ACE2 complex [60,61]. In the molecular docking analysis, linagliptin with B.E. = −6.0 kcal/mol and Kd = 3.95E-5 M, displayed hydrophobic contacts with residues spanning 321–505 present at the extracellular domain of ACE2 (Fig. 5a,b; Table 2). The binding location of linagliptin was close to the binding site of Wt-RBD with ACE2. However, the sitagliptin showed interaction in 98–208 domain of ACE2 with B.E. = −7.2 kcal/mol and Kd = 5.21E-6 M (Fig. 5a,b; Table 2). Moreover, sitagliptin and the N103 residue of the ACE2 receptor established a hydrogen bond (3.1 Å), and linagliptin formed hydrophobic contact with N322 of ACE2. It's worth noting that these two drugs, linagliptin and sitagliptin, were coupled to the ACE2 receptor's N322 and N103 glycosylation sites, respectively, might be blocking ACE2-RBD binding and SARS-CoV-2 entry.Fig. 5 Binding affinity of RBD with ACE2. a, The best docked pose of sitagliptin (orange) and linagliptin (magenta) with WtRBD-ACE2 complex. The residues N103, and N322 were shown in red sphere. b, The inset depicted interactions of sitagliptin and linagliptin with WtRBD-ACE2 complex residues (blue colour stick). c, Simulations of WtRBD-ACE2 with sitagliptin and linagliptin were illustrated using snapshots of at different timepoints. d, Representative confocal image of RBD-GFP (green) in Calu-3 cells stained with an lexaFluor647-labelled secondary antibody specific to ACE2 (red). Scale bar = 5 μm. e, Calu-3 cells were transfected with myc-tagged ACE2 alone or in combination with pCDNA3.1-RBD in the presence or absence of sitagliptin or linagliptin. All samples were pulled down using myc antibody, and immunoblotted with RBD and ACE2 antibody separately. β-actin was used as an internal control. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Fig. 5 3.14 MD simulation of gliptins with ACE2 receptor Moreover, the detailed binding mechanism of sitagliptin and linagliptin with WtRBD-ACE2 complex was studied by using molecular dynamics simulation. In the WtRBD-ACE2 + sitagliptin system, the bonding between sitagliptin and N103 (glycosylation site of ACE2) remained uninterrupted throughout the simulation, which was driven by weak van der Waal interactions (Fig. 5c). Similarly, linagliptin was seen in contact with a glycosylation site, N322 of ACE2 (Fig. 5c). We have evaluated the ∆Gbinding of linagliptin and sitagliptin with WtRBD-ACE2 using the MM-PBSA method, which was found to be −77.97 ± 0.84 kcal/mol and − 50.43 ± 4.88 kcal/mol respectively (Table 3). Both sitagliptin and linagliptin was bound to WtRBD-DPP4 and WtRBD-ACE2 with a significantly higher binding energy. The van der Waal interaction and non-polar solvation energy were the two primary forces that strongly influenced the favourable binding energy. The results demonstrated that both linagliptin and sitagliptin may prevent the ACE2 receptor from getting glycosylated by blocking glycosylation sites, which might be a beneficial strategy to prevent ACE2-spike binding. 3.15 Cell-surface binding of RBD to ACE2 To elucidate the role of gliptins in impeding the interaction between the Wt-RBD and ACE2, we performed RBD-ACE2 binding assay. Transfected GFP-RBD was fused with alexa-fluor 647 labelled-secondary antibody specific to ACE2 in Calu-3 cells. Confocal microscopic images subsequently showed that ACE2 was located mainly at the cell membrane (Fig. 5d). GFP-RBD-positive cells were stained with alexa-fluor 647 labelled-ACE2, and the overlay images showed the co-localization of RBD and ACE2 on the cell surface, validating their interaction (Fig. 5d). However, in the presence of sitagliptin, linagliptin or their combination hampered the interaction of ACE2 with RBD (Fig. 5d). We overexpressed myc-ACE2 with and without a RBD expressing plasmid by transfection and immunoprecipitated it using myc antibody to support the idea that RBD-ACE2 binding exists. The immunoprecipitated samples were immunoblotted separately using RBD and ACE2 antibodies. Immunoprecipitated samples co-transfected with myc-ACE2 and RBD showed the RBD band following immunoblotting with RBD antibody, but the sample expressing only ACE2 without RBD did not display any RBD band (Fig. 5e). Together, our findings showed that ACE2 and RBD created a heterodimer for their stable interaction. However, in the presence of gliptins individually or in combination, disavowed to form a heterodimer complex of RBD and ACE2 (Fig. 5e). 3.16 PLpro, Ulp1, PLpro gel-based assay substrate expression, and purification The induced protein expression for His-SUMO-PLpro was checked by running SDS-PAGE gel (12 %) and the expressed protein (mol. wt. ≈ 49.7 kDa) appeared as the 49 kDa band (Fig. S6b). The N-terminal His-SUMO-tagged SARS-Cov2 PLpro protein purified through the Ni-NTA column protein is shown in Fig. S6c. N terminus His-SUMO tag of PLpro was cleaved by Ulp1 protease as shown in Fig. S6d. The appearance of two bands corresponding to PLpro and His-SUMO tag and disappearance original uncut protein suggested that the reaction was complete. After removing the His-SUMo tag by Ni-NTA purification (Fig. S6e, left panel), the FPLC purification was done where the PLpro protease peak was observed at 62 mL of elution volume (Fig. 5f). To check the protein purity, the protein was run on SDS PAGE (12 % gel) (Fig. S6e). PLpro tagless protein appeared around 35.6 KDa. This protein with authentic N and C terminus has been used for Gel-based PLpro assay. Similarly, Ulp1 protease and PLpro substrate were induced and confirmed in 12 % SDS-PAGE gel. Induction bands of Ulp1 protease and PLpro substrate were observed at 27.4 KDa and 67 KDa respectively (Fig. S5 g,k). Ulp1 & PLpro protein were purified by Ni-NTA purification column and GST purification column respectively (Fig. S6h,l). Finally, both proteins were purified by the FPLC purification column. The peak for Ulp1 and PLpro substrate were observed at 66.85 mL and 47.5 mL of elution volume during FPLC purification (Fig. S6j,m). PLpro substrate protein eluted in the void volume of the column as dimer (134 kDa) due to the presence of both GST and MBP tags at N and C terminals respectively (Fig. S6m). 3.17 PLpro inhibition assay Gel-based PLpro assay is a validation assay, as it can wipe out the artefacts which can be seen in the fluorescent-based assay. Thus, we have performed only the gel-based PLpro assay to test whether sitagliptin and linagliptin are as effective as were reported in a few other publications [25,26]. In our study, we were unable to see any significant inhibitory effect of sitagliptin and linagliptin on SARS-CoV-2 PLpro even when tested at high concentration of 500 μM. This contradictory result may be due to the difference in the assay system used. These two compounds have been previously assayed in fluorescence-based assay while we performed gel-based assay. Fluorescence-based assays are highly sensitive and high-throughput in nature, but they occasionally read artefacts, whereas gel-based assays are less sensitive and low-throughput but provide more authentic results, and thus hits filtered in the fluorescence-based assay were validated in Gel-based assay in various publications. As shown in Fig. 6 a–d (lanes 4–5), in the absence of any inhibitor, PLpro cleaved its substrate into two fragments (25 KDa and 42 KDa) and showed additional two bands along with enzyme (35.6 KDa) and substrate (67 KDa) in SDS-PAGE gel as expected. In the presence of inhibitors, the intensity of cleavage product bands should get reduced and the uncleaved substrate band is expected to get intensified at a significant level. But that is not observed in our experiment in the presence of Sitagliptin and Linagliptin at concentrations of 10 μM, 100 μM, and 500 μM (Fig. 6 a–c). Increasing the pre-incubation time of the enzyme with an inhibitor (from 10 min to 20 min) also produced similar results (Fig. 6d). These negative results were further confirmed by densitometric analysis of all four SDS-PAGE gel images to quantify the relative change of uncleaved substrate band and the cleaved product band (42 KDa) in the presence and the absence of inhibitor. 25 KDa cleaved band was not considered in the present analysis as it was too faint and produced large relative error in calculations. Our analysis suggested no significant inhibitory activity of Sitagliptin and Linagliptin on PLpro activity.Fig. 6 Gel-based protease assay. (a) lane 1- ladder, lane 2- only enzyme, lane 3- only substrate, lane 4 & 5- Substrate+enzyme, lane 6 & 7- Substrate+enzyme+10 μM Sitagliptin (pre-incubation of enzyme with inhibitor for 10 min before starting the reaction with substrate), lane 8 & 9- Substrate+enzyme+100 μM Sitagliptin (pre-incubation of enzyme with inhibitor for 10 min before starting the reaction with substrate); (b) lane 1- only enzyme, lane 2- ladder, lane 3- only substrate, lane 4 & 5- Substrate+enzyme, lane 6 & 7- Substrate+enzyme+10 μM Linagliptin (pre-incubation of enzyme with inhibitor for 10 min before starting the reaction with substrate), lane 8 & 9- Substrate+enzyme+100 μM Linagliptin (pre-incubation of enzyme with inhibitor for 10 min before starting the reaction with substrate); (c) lane 1- only enzyme, lane 2- ladder, lane 3- only substrate, lane 4 & 5- Substrate+enzyme, lane 6 & 7- Substrate+enzyme+500 μM Sitagliptin (pre-incubation of enzyme with inhibitor for 10 min before starting the reaction with substrate), lane 8 & 9- Substrate+enzyme+500 μM linagliptin (pre-incubation of enzyme with inhibitor for 10 min before starting the reaction with substrate); (d) lane 1- ladder, lane 2- only enzyme, lane 3- only substrate, lane 4 & 5- Substrate+enzyme, lane 6 & 7- Substrate+enzyme+500 μM sitagliptin (pre-incubation of enzyme with inhibitor for 20 min before starting the reaction with substrate), lane 8 & 9- Substrate+enzyme+500 μM linagliptin (pre-incubation of enzyme with inhibitor for 20 min before starting the reaction with substrate). * labelled band is of some impurity which was present along with substrate as it is also evident in lanes having only substrate in it. Fig. 6 3.18 Main protease (Mpro) inhibition assay Sitagliptin and linagliptin showed an inhibition of 21.45 ± 0.13 % and 31.46 ± 9.97 % at 100 μM concentration (Fig. 7 ). However, positive control (Mpro-13b) showed an inhibiton of >80 % at 5uM concentration. It implies that both the gliptins showed minimal effect on Mpro of SARS-CoV-2 inhibition.Fig. 7 Inhibition of Mpro enzymatic activity by gliptins. Negative control (no inhibitor), positive control (13b inhibitor) at 5 μM and 1 μM, sitagliptin (100 μM) and linagliptin (100 μM) were tested for Mpro enzymatic activity. Fig. 7 4 Conclusions By using an experimental and computational approach, we have established that DPP4 is an alternative host receptor for the entry of SARS-CoV-2 into cells. Both cell-surface-binding assay and molecular dynamics simulation study determined that Wt-RBD is interacted with DPP4 receptor and binds at the α/β-hydrolase domain of DPP4. Employing a drug repurposing methodology, we have tested gliptins against the SARS-CoV-2 infection. A molecular docking study of the aforementioned gliptins indicated the strong binding affinity of these gliptins with RBD of pan-variants. Among them, sitagliptin and linagliptin, either alone or in combination, have been shown to circumvent the proliferation of pan-VOCs of SARS-CoV-2 infection. Sitagliptin and linagliptin treatment hindered the interaction of RBD with DPP4 at the cell membrane. In this context, a comprehensive and detailed mechanistic study of sitagliptin and linagliptin's inhibitory activity against Wt-RBD-DPP4 complex was conducted using MD simulation. It is found that the two main forces: hydrophobic interaction and hydrogen bonding play a significant role in the strong binding affinity of gliptins with the WtRBD-DPP4 complex. Both sitagliptin and linagliptin were able to inhibit the interaction between Wt-RBD and DPP4 proteins in ACE2 independent manner, which leads to the inhibition of viral growth. Remarkably, it was shown that sitagliptin (IC50 value =1.46 μM) and linagliptin (IC50 value =2.21 μM), when administered alone or in combination, diminished SARS-CoV-2 infection in all lineages by 6–10 folds at the lowest given concentration. Furthermore, sitagliptin and linagliptin also inhibited the interaction between WtRBD and ACE2, a known cause for SARS-CoV-2 virus entry. The cell-surface binding assay disclosed that the interaction between ACE2 and Wt-RBD was interrupted in the presence of sitagliptin and linagliptin. MD simulation study reveals that sitagliptin and linagliptin were able to block two of the ACE2 receptor's glycosylation sites, N103 and N322, respectively, by interacting with them, which may account for the weak connection between ACE2 and Wt-RBD of SARS-CoV-2. However, the mechanism of glycosylated ACE2 inhibition requires additional wet lab validation, which will be investigated in the future. This data confirms past research that claimed preventing N103 and N322 glycosylation could stop viral entry into the host. In addition, the inhibitory potency of sitagliptin and linagliptin was checked against the enzymatic activity of PLpro and Mpro (responsible for SARS-CoV-2 virus replication) by using in vitro methods. It was discovered that these gliptins had a minimal impact on the inhibition of Mpro and no impact on PLpro inhibition. Overall, sitagliptin and linagliptin alone or in combination are suitable and efficient in preventing all SARS-CoV2 clades since they disrupt the interaction of RBD with both DPP4 and ACE2. Therefore, our discovery confirmed repurposed sitagliptin and linagliptin as a therapeutic strategy has the clinical potential to cure pan-SARS-CoV-2 infections including newly emerging variants. The following are the supplementary data related to this article.Fig. S1 Generation of CRISPR/Cas9 mediated knockout cells. The DPP4 CRISPR/Cas9 KO plasmid was used to generate DPP4 knockout cells. ACE2 CRISPR/Cas9 KO Plasmid was used to develop ACE2 knockout cells. The deletion of targeted gene was confirmed by western blot using selective antibody. Fig. S1 Fig. S2 2D illustration of the best dock pose of Wt-RBD with DPP4 complex. The intermolecular hydrophobic contacts were shown in semi-circles, and hydrogen bonds were shown in green dashed lines. Fig. S2 Fig. S3 Second binding pose of sitagliptin with WtRBD-DPP4 complex. Sitagliptin was shown in orange sticks and binding pocket residues were shown in blue sticks. Fig. S3 Fig. S4 MD simulation data of Wt-RBD-DPP4 with gliptins. The average a, RMSD, b, Rg, and c, SASA of DPP4 in the following systems: DPP4, DPP4 + linagliptin, DPP4 + sitagliptin, WtRBD-DPP4, WtRBD-DPP4 + linagliptin and WtRBD-DPP4 + sitagliptin. The average d, RMSD, e, Rg, and f, SASA of Wt-RBD in the following systems: WtRBD-DPP4, WtRBD-DPP4 + linagliptin, and WtRBD-DPP4 + sitagliptin. Fig. S4 Fig. S5 Interactions of human DPP4 with RBD of several variants. The original crystallographic structure of a, α-RBD, b, β-RBD, c, δ-RBD, and d, κ-RBD domain of SARS-CoV-2 with human DPP4 protein (39-766 aa) was used for blind molecular docking. ‘A’ depicts residues of DPP4 and ‘B’ represented RBD residues. Mutated residues in these RBDs were shown in red color. 2D representation of most populated confirmation of mutated-RBD-DPP4 complex were shown where intermolecular hydrophobic contacts were represented in semi-circles, and hydrogen bonds were in green dashed lines. Fig. S5 Fig. S6 PLpro, Ulp1, PLpro gel-based-assay-substrate expression and purification visualized through SDS-PAGE gel. (a)Protein ladder which has been used in all SDS-PAGE gels; (b) Expression of His-SUMO-PLpro in BL21: lane1- ladder, lane 2- Uninduced sample & lane 3- Induced sample; (c) Ni-NTA purification of His-SUMO-PLpro: lane 1-Ni-NTA purified His-SUMO-PLpro , lane 2- ladder; (d) Ulp1 digestion for His-SUMO tag removal: lane 1- ladder, lane 2,3- Ulp1 digested products of Ni-NTA purified His-SUMO-PLpro, lane 4- Before Ulp1 digestion, His-SUMO-PLpro protein; (e) Ni-NTA purification of Ulp1 digested products for removal of tag and FPLC purification: lane 1- Flow-through of re-Ni-NTA column purification containing only tag free Plro protein, lane 2- ladder, lane 3-FPLC purified PLpro protein; (f) FPLC chromatogram of tag-free PLpro protein; (g) Expressing Ulp1 protease in BL21: lane1- ladder, lane 2- Induced sample & lane 3- Uninduced sample; (h) Ni-NTA purification of Ulp1 protease: lane1- ladder, lane 2,3- Elutions1 and 2 of Ni-NTA column ; (i) FPLC purification of Ulp1: lane 1- FPLC purified Ulp1 protease, lane2- ladder ; (j) FPLC chromatogram of Ulp1 protease; (k) Expression of PLpro substrate in BL21 : lane 1- ladder, lane 2- Induced sample & lane 3- Uninduced sample; (l) Ni-NTA purification and FPLC purification of PLpro substrate: lane 1- Ni-NTA purified PLpro substrate, lane2- ladder, lane3- FPLC purified PLpro substrate; (m) FPLC chromatogram of PLpro substrate. Fig. S6 Movie S1 Representative MD simulation of the WtRBD (cyan)–DPP4 (yellow) complex. During the simulation, the Wt-RBD and DPP4 were united by hydrogen bonding interactions (distance <3.5 Å). Movie S1 Movie S2 Representative MD simulation of the WtRBD (cyan)–DPP4 (yellow) + sitagliptin (orange) complex. Sitagliptin interfered with the binding of DPP4 and WtRBD in the simulation, causing the disintegration of hydrogen bonds between Wt-RBD and DPP4. Movie S2 Movie S3 Representative MD simulation of the WtRBD (cyan)–DPP4 (yellow) + linagliptin (magenta) complex. In the simulation of WtRBD-DPP4 with linagliptin, the hydrogen bonding between DPP4 residues and Wt-RBD residues was disrupted by linagliptin. Movie S3 Sec. S1 The detail of amino acid sequence of RBD, DPP4, and ACE2 used in modelling study. Sec. S1 Declaration of competing interest Authors declare that they have no competing interests. Data availability Data will be made available on request. Acknowledgements We acknowledge the Department of Biotechnology Consortium for COVID-19 Research and all the consortium partners for making this study possible. We also thank Dr. Sagar Sengupta, and Dr. Neerja Wadhwa from NII for providing microscopic facility, and valuable inputs. The following reagents were deposited by the Center for Disease control and Prevention, and obtained through BEI resources, NIAID, NIH: SARS-related coronavirus-2, Isolate USA-WA1/2020, NR-52281, Isolate USA/CA_CDC_5574/2020, NR-54011, and Isolate hCoV-19/South Africa/KRISP-K005325/2020, (NR-54009, contributed by Alex Sigal and Tulio de Oliveira). SM is thankful to Translational Research Program-THSTI intramural grant. We thank all staff members of BSL3, THSTI for live virus experiments. We are very grateful to Dr. Debasisa Mohanty, Director, NII for providing full support for computational studies, valuable inputs and guidance. We would like to convey our sincere gratitude Dr. Pramod Garg, Executive director THSTI for his guidance and valuable suggestions. This study was supported by the grants from the CRG-SERB, Govt. of India (CRG/2021/000135) to A.K., and T.M. and by DBT-NII intramural core grant to T.M. Contributions S.M., A.K., and T.M. performed all the experiments, formal analysis, investigation, and methodology. Data curation, formal analysis, and reviewing the paper were done by S.M., A.K., K.J., G.K., S.S., A.K., S·K, R.K., and A.K.P. The expression, purification and enzymatic inhibition study of Mpro and PLpro was conducted by S.D., U.P.S. Computational studies, software analysis conceived by A.K., V.B., and T.M. Conceptualization of the project, data curation, formal analysis, funding, investigation, methodology, project validation, writing original draft, review and editing of the manuscript were performed by T.M. ==== Refs References 1 Lan J. Ge J. Yu J. Shan S. Zhou H. Fan S. Zhang Q. Shi X. 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==== Front Curr Opin Psychol Curr Opin Psychol Current Opinion in Psychology 2352-250X 2352-2518 Elsevier Ltd. S2352-250X(23)00071-4 10.1016/j.copsyc.2023.101626 101626 Review The compounded effect of the dual pandemic on ethnic-racial minority adolescents' mental health and psychosocial well-being Eboigbe Loretta I. Simon Carlisa B. Wang Yuqi S. Tyrell Fanita A. ∗ Department of Psychology, University of Maryland, College Park, United States ∗ Corresponding author: Fanita A. Tyrell 27 6 2023 8 2023 27 6 2023 52 101626101626 © 2023 Elsevier Ltd. All rights reserved. 2023 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. During the COVID-19 pandemic, U.S. youth faced various stressors that affected their schooling experiences, social relationships, family dynamics, and communities. These stressors negatively impacted youths' mental health. Compared to White youths, ethnic-racial minority youths were disproportionately affected by COVID-19-related health disparities and experienced elevated worry and stress. In particular, Black and Asian American youths faced the compounded effects of a dual pandemic due to their navigation of both COVID-19-related stressors and increased exposure to racial discrimination and racial injustice, which worsened their mental health outcomes. However, protective processes such as social support, ethnic-racial identity, and ethnic-racial socialization emerged as mechanisms that attenuated the effects of COVID-related stressors on ethnic-racial youths’ mental health and promoted their positive adaptation and psychosocial well-being. Keywords Dual pandemic Mental health Ethnic-racial minority Adolescents Racial discrimination This review comes from a themed issue on Coming of Age Amid the Pandemic (2024) Edited by Gabriel Velez and Camelia Hostinar ==== Body pmcBackground The COVID-19 pandemic had devastating effects on children and families, particularly those from ethnic-racial minority backgrounds. Non-Hispanic Black and Hispanic communities experienced disproportionately higher rates of COVID-19 infection, hospitalization, and mortality compared to non-Hispanic White communities in the United States [1]. These disparities were evident in pediatric populations, with non-Hispanic Black and Hispanic children experiencing higher rates of COVID-19 infections and exposure compared to White children [2]. Several factors contributed to these disparities, including poorer access to health care, limited community resources, overrepresentation of ethnic-racial minorities in essential service industries, higher reliance on public transportation, higher likelihood of living in crowded neighborhoods, and higher rates of living in multigenerational households [2]. Other historical factors such as the long-term mistrust of the healthcare system by ethnic-racial minority children and families exacerbate these health disparities, and illuminate how systemic racism and structural inequities continue to affect their lives, health, and well-being. Despite exposure to multiple health and race-related stressors, ethnic-racial minority youths relied on family resources and cultural strengths to successfully navigate the pandemic. This article highlights the stressors that ethnic-racial minority youths faced during the COVID-19 pandemic as well as the protective processes that promoted these youths’ resilience in the U.S (see Figure 1 ).Figure 1 Model of the dual pandemic and its effects on ethnic-racial minority youths. Figure 1 Stressors associated with the COVID-19 pandemic The COVID-19 pandemic and shelter-in-place policies caused significant disruption to youths' academic progress and milestones, mental health, physical health, and social relationships [3, 4, 5]. Family dynamics were affected by financial stress, loss of loved ones, and caregiver job loss [7]. Ethnic-racial minority families experienced more health and financial hardships compared to White families [6]. These hardships were associated with higher levels of parental psychological distress, parenting stress, and adolescent loneliness [7]. In fact, youths reported declines of open family communication, parental support, and family satisfaction that coincided with caregivers' perceptions of pandemic-related stress [8]. Consequently, strained family functioning negatively affected youths’ mental health [9]. COVID-19 stressors related to school, financial stress, and home-confinement influenced youths’ mental health [10,11]. For example, in a longitudinal study assessing the impact of family behaviors on psychopathology in a sample of children and adolescents living in the U.S., Rosen et al. [11] found that youths reported a substantial increase in internalizing and externalizing problems over the course of the COVID-19 pandemic. Relative to youths who were exposed to fewer pandemic-related stressors, youths who were exposed to more pandemic-related stressors reported higher rates of internalizing and externalizing symptoms during the period of the stay-at-home orders and six months later [11]. Many youths reported experiencing persistent feelings of sadness or hopelessness [12]. Empirical evidence suggests that some youths considered attempting suicide and a minority of youths attempted suicide during the pandemic [12]. Female and gender non-binary youths were especially vulnerable to experiencing negative mental health outcomes [13, 14, 15, 16]. All youths reported similar mental health concerns and challenges during the pandemic, but ethnic-racial minority youths experienced elevated COVID-19–related worry and stress that impacted their well-being [17,18]. Black youths reported that school was a major stressor and source of emotional toll, with many experiencing decreased concentration, reduced motivation, and a lack of focus relative to their academic tasks [19]. Disparities in access to virtual learning resources (e.g., limited internet access, lack of technological resources, lack of quiet spaces to do homework, less adult supervision due to caregivers being essential workers, and attending poorly resourced schools) among Black youths made it difficult for them to adjust academically [19,20]. A qualitative study assessing Black adolescents' experiences with COVID-19 challenges found that these youths also experienced behavioral challenges, such as difficulties with sleep and a lack of interest engaging in daily activities [19]. Youths reported worries with health hygiene (e.g., interacting with large crowds, being around sick people, worries about schools being unsanitary), which were more pronounced among youths who experienced personal losses in their family units [19]. In addition, youths were deeply affected by the lack of interaction with their teachers, friends at school, family members who lived outside of their homes, and their religious/spiritual community [19]. These challenges were further exacerbated by barriers to mental healthcare utilization, which decreased Black youths’ likelihood of receiving treatment for stressors and trauma related to the COVID-19 pandemic [21]. Racial injustice, discrimination, and media during COVID-19 Although ethnic-racial minority youths face higher levels of COVID-19 related stress, racism against Asian communities and the national spike in Black Lives Matter protests in the summer of 2020 against police brutality prompted youths’ awareness toward racism and discrimination, which compounded together to influence their mental health [22]. Adolescents who perceived higher levels of racism and discrimination during the COVID-19 pandemic, especially Asian, Black, and multiracial youths, reported poorer mental health outcomes and more difficulty concentrating, remembering, or making decisions [22]. Anti-Asian sentiments surrounding the origins of the virus and the highly publicized recordings of police brutality against Black individuals heightened youths’ anxiety, promoted feelings of othering, and fostered a reduced sense of belonging in these communities [19,23∗, 24∗∗, 25∗, 26∗]. Ethnic-racial minority youths reported anger, frustration, and hopelessness because they lacked resources to enact change against the cyclical nature of racial violence [27]. While adolescents used digital communication to maintain connection among family, friends, and school-related responsibilities, higher screen time increased exposure to racism- and discrimination-related messaging on social media platforms, both personally and vicariously [28]. The murder of George Floyd by police officers was a critical point during the COVID-19 pandemic that led to racial unrest and prompted action to hold police and other perpetrators accountable. Recordings of racial mistreatment, killings, and racial violence inflicted on Black people were shared rapidly across social media platforms. As a result, many adolescents engaged in social media activism to spread solidarity and share resources [27,28]. Although civic engagement has been linked to positive well-being, online racial justice activism also exposes youths to vicarious racial trauma and to an onslaught of racial discrimination via social media. In a study conducted by Tao and Fisher [29], the researchers highlighted how social media operated as both a harmful space for racism to occur and a safe space where youths could seek stress relief against racism and mental health distress. Tao and Fisher also found that adolescents who posted information about racial issues were more likely to report depressive symptoms and alcohol use disorder due to instances of racial discrimination experienced on social media platforms. COVID-19 discrimination towards Black youths The COVID-19 pandemic was viewed as a time of racial reckoning, especially surrounding anti-Blackness, police brutality, and COVID-19 disparities [30]. Although research on Black youths' experiences is limited, existing literature suggests that Black youths showed a strong awareness of this racial division and experienced high levels of racism-based stress [19]. A qualitative study by Crooks et al. [24] found that Black youths reported increased media coverage surrounding these injustices, which caused racial tension, anxiety, and distress. Experiencing online racial discrimination affected Black youths’ mental health [31]. Black youths who reported higher exposure to online racial discrimination and online traumatic events reported higher trauma symptoms of discrimination, such as, difficulty relaxing, feeling numb, worrying, discomfort, physiological symptoms, and worries about safety and the future [28]. The link between online racial discrimination and mental health was not fully explained by time spent online or by general cyber victimization experiences [31], suggesting other mechanistic pathways from exposure to online racial discrimination to mental health outcomes. Some Black youths watched and participated in protests to address systemic and interpersonal injustices, citing their experience of both fear and empowerment [24]. Additionally, Black youths reported having conversations about the racial divide with their family members, which were often emotionally draining, especially for youths from mixed race families [24]. Given that Black youths experienced separations from their social networks, they lost opportunities to utilize their communities to increase understanding about racial tensions [24]. Some youths received opposing messages about Black culture, which increased internalized stigma, negative public regard and private regard and led them to reject parts of their ethnic-racial identities [24]. For other youths, this moment created learning opportunities about culture and history, which increased their confidence and promoted empowerment [24]. This is consistent with a study by Rogers et al. [32], which found that the sociopolitical influence of Black Lives Matter changed youths’ ecological contexts and affected their ethnic-racial identity narratives. COVID-19 discrimination towards Asian American youths The COVID-19 pandemic was laced with misinformation and xenophobic sentiments that targeted Asian community members and led to discrimination, which negatively affected Asian American (AA) youths’ mental health. Survey data from a national representative sample of middle and high school students in the U.S. suggest that 82% of AA adolescents experienced COVID-19 related discrimination [33]. Another study found that 25% of AA youths reported higher frequency in the direct experiences of anti-Asian harassment since the beginning of the pandemic [26]. In a study examining cyberbullying before and during the COVID-19 pandemic, AA youths were most likely to report increased victimization during the pandemic. Specifically, 23.5% of AA youths reported being cyberbullied for their race in 2021 compared to 7.4% in 2019 [34]. Furthermore, AA adolescents and young adults who indicated feeling less safe after the pandemic started (76%) were more likely to report increased depression severity and experiences of discrimination since the start of the pandemic [26]. Among Eastern and Southeast Asian American (ESEAA) adolescents, experiences of online and in-person COVID-19 related discrimination predicted higher levels of anxiety, depression, and PTSD symptoms above and beyond lifetime discrimination and previous traumatic events [25,33]. A few studies with Chinese American youths revealed that about half of these youth were directly targeted by COVID-19 racial discrimination online (45.7%) and/or in-person (50.2%); [23,35]. Additionally, youths reported at least 1 incident of COVID-19 vicarious racial discrimination online (76.5%) and/or in-person (91.9%). Experiences of online direct discrimination, online vicarious discrimination, in-person direct discrimination, and sinophobia were negatively associated with youths' psychological well-being and positively associated with anxiety symptoms. Finally, adolescent's experiences of COVID-19 related racial discrimination were associated with more internalizing problems [23]. Resilience and protective processes for ethnic-racial minority youth during the COVID-19 pandemic Despite experiencing significant challenges and stressors during the COVID-19 pandemic, Black, AA, and other ethnic-racial minority youths received familial and communal support that buffered against the harmful effects of racism, discrimination, and xenophobia. A large body of research points to protective processes such as the promotion of a strong ethnic/racial identity [23,36], parental sharing of ethnic-racial socialization messages [37] and social support from trusted adults, parents, and peers [12,15,38, 39, 40] as buffers against the aversive effects of discrimination on youths’ mental health. For Black youths, research suggests that social support from family, school personnel, and their religious community and reliance on religious/spiritual coping (i.e., prayer and reading scripture) were pivotal in adjusting to COVID-19 pandemic challenges [19]. Helpful support included one-on-one time with parents, receiving lunch and computers to help students adjust to remote learning, and receiving emotional check-ins from religious youth leaders [19]. Social support and religious/spiritual coping are known adaptive coping mechanisms for the Black American community [41] and played a crucial role in helping adolescents process racial tensions and the widespread protests of the Black Lives Matter movement across the world [19,42]. However, future research should investigate contextual and cultural factors that protect Black youths from racial tension and discrimination during the COVID-19 era. Empirical evidence found that AA youths relied on their bicultural identity and heritage culture socialization from parents to protect them from internalizing problems and racial discrimination experienced during the COVID-19 pandemic [23,37]. Heritage culture socialization refers to the transmission of messages from AA parents to their children about being proud of their heritage culture, whereas bicultural identity is a phenomenon that speaks to the complex task of integrating values from two different cultures (e.g., Chinese heritage culture and American culture) [23,26]. These cultural assets were central to AA youths’ identities, resilience, and their ability to deal with COVID-19 race-related stress. Overall, cultural factors such as ethnic-racial socialization, ethnic-racial identity, and social support were effective at protecting both Black and AA youths from the harmful effects of discrimination during the COVID-19 pandemic. Conclusion During the COVID-19 pandemic, ethnic-racial minority youths, families, and communities experienced health disparities and psychosocial stressors at a disproportionate rate. Black and AA youths were uniquely vulnerable due to their experience of COVID-related stressors, vicarious trauma, and racial discrimination. Although COVID-19 challenges were associated with worsened mental health, ethnic-racial minority youths leaned on social support and cultural assets to navigate their circumstances. However, additional research is needed to investigate the structural and social level factors that promote positive health and adaptation among these youths. Health policies should be implemented to provide the institutional support and resources that are needed to combat persistent health disparities and mitigate the impact of structural racism and discrimination on ethnic-racial minority youths’ mental health. Additionally, public policies should provide regulations and protections to shield youths from experiencing online racism and to increase their sense of safety. 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Outstanding interest: This qualitative research study provides a social-ecological perspective on the impact of the COVID-19 pandemic on 25 Black girls (ages 9–18 years old), highlighting themes related to changes they experienced on an individual, interpersonal, institutional, and community level and the resilience and strength used in navigating these challenges. Ermis-Demirtas H. Luo Y. Huang Y.J. The impact of COVID-19-associated discrimination on anxiety and depression symptoms in Asian American Adolescents International Perspectives in Psychol 11 2022 153 160 10.1027/2157-3891/a000049 a. Special interest: This article examines the relationship between COVID-19-associated discrimination (in online and offline settings) and mental health symptoms, and addresses how coping strategies can mitigate the negative effects of discrimination on mental health. Huynh J. Chien J. Nguyen A.T. Honda D. Cho E.E. Xiong M. Doan T.T. Ngo T.D. The mental health of Asian American adolescents and young adults amid the rise of anti-Asian racism Front Public Health 10 2023 10.3389/fpubh.2022.958517 a. Special interest: This empirical article examines the impact of anti-Asian racism due to the COVID-19 pandemic on the health and social issues of 176 Asian American adolescents and young adults (ages 13–17 and 18–29). 27 Yeh C.J. Stanley S. Ramirez C.A. Borrero N.E. Navigating the “Dual pandemics”: the cumulative impact of the COVID-19 pandemic and rise in awareness of racial injustices among high school students of color in urban schools 2022 Urban Education 004208592210978 10.1177/00420859221097884 28 Maxie-Moreman A.D. Tynes B.M. Exposure to online racial discrimination and traumatic events online in Black adolescents and emerging adults J Res Adolesc 32 2022 254 269 10.1111/jora.12732 35122458 Tao X. Fisher C.B. Exposure to social media racial discrimination and mental health among adolescents of color J Youth Adolesc 51 2022 30 44 10.1007/s10964-021-01514-z 34686952 a. Special interest: This work investigates the association between exposure to racial discrimination on social media and mental health among adolescents of color (ages 15–18), and suggests that the negative impact of social media racial discrimination on mental health can be mitigated by social support and coping strategies. 30 Kishi Jones September 3). Demonstrations and political violence in America: new data for summer 2020 Armed Conflict Location & Event Data Project 2020 https://acleddata.com/2020/09/03/demonstrations-political-violence-in-america-new-data-for-summer-2020/ 31 Del Toro J. Wang M.-T. Online racism and mental health among Black American adolescents in 2020 J Am Acad Child Adolesc Psychiatry 62 2023 25 36.e8 10.1016/j.jaac.2022.07.004 35868431 32 Rogers L.O. Rosario R.J. Padilla D. Foo C. “[I]t's hard because it's the cops that are killing us for stupid stuff”: racial identity in the sociopolitical context of Black Lives Matter Dev Psychol 57 2021 87 101 10.1037/dev0001130 33271030 33 Ermis-Demirtas H. Luo Y. Huang Y.J. The trauma of COVID-19–fueled discrimination: posttraumatic stress in Asian American adolescents Prof Sch Counsel 26 2022 2156759X2211068 10.1177/2156759X221106814 34 Patchin J.W. Hinduja S. Cyberbullying among Asian American youth before and during the COVID-19 pandemic J Sch Health 93 2023 82 87 10.1111/josh.13249 36221854 35 Cheah C.S.L. Wang C. Ren H. Zong X. Cho H.S. Xue X. COVID-19 racism and mental health in Chinese American families Pediatrics 146 2020 10.1542/peds.2020-021816 36 Zong X. Cheah C.S.L. Ren H. Chinese American adolescents' experiences of COVID-19-related racial discrimination and anxiety: person-centered and intersectional approaches J Res Adolesc 32 2022 451 469 10.1111/jora.12696 34850993 Ren H. Cheah C.S.L. Zong X. Wang S. Cho H.S. Wang C. Xue X. Age-varying associations between Chinese American parents' racial–ethnic socialization and children's difficulties during the COVID-19 pandemic Asian American J of Psychol 13 2022 351 363 10.1037/aap0000278 a. Outstanding interest: This article examines the age-varying associations between Chinese American parents' racial-ethnic socialization and their children's (ages 4–18) difficulties during the COVID-19 pandemic. The authors highlight the importance of cultural socialization in promoting children's resilience. 38 Cohodes E.M. McCauley S. Gee D.G. Parental buffering of stress in the time of COVID-19: family-level factors may moderate the association between pandemic-related stress and youth symptomatology Research on Child and Adolescent Psychopathology 49 2021 935 948 10.1007/s10802-020-00732-6 33591457 39 Rodman A.M. Rosen M.L. Kasparek S.W. Mayes M. Lengua L. Meltzoff A.N. McLaughlin K.A. Social experiences and youth psychopathology during the COVID-19 pandemic: a longitudinal study Dev Psychopathol 2022 1 13 10.1017/S0954579422001250 40 Wang M.-T. Toro J. Del Scanlon C L. Schall J.D. Zhang A.L. Belmont A.M. Voltin S.E. Plevniak K.A. The roles of stress, coping, and parental support in adolescent psychological well-being in the context of COVID-19: a daily-diary study J Affect Disord 294 2021 245 253 10.1016/j.jad.2021.06.082 34303304 41 Kim D.H. Harty J. Takahashi L. Voisin D.R. The protective effects of religious beliefs on behavioral health factors among low income African American adolescents in Chicago J Child Fam Stud 27 2018 355 364 10.1007/s10826-017-0891-5 42 Butler-Barnes S.T. “What's going on?” Racism, COVID-19, and centering the voices of Black youth Am J Community Psychol 71 2023 101 113 10.1002/ajcp.12646 36661477
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==== Front Angiology Angiology spang ANG Angiology 0003-3197 1940-1574 SAGE Publications Sage CA: Los Angeles, CA 37358839 10.1177_00033197231186092 10.1177/00033197231186092 Editorial Reversibility of Aortic Stiffening During the First Waves of COVID-19 https://orcid.org/0000-0003-1678-3778 Zanoli Luca MD, PhD, FASN 1 Gaudio Agostino MD, PhD 1 https://orcid.org/0000-0002-9112-5540 Lo Cicero Lorenzo MD 1 1 Department of Clinical and Experimental Medicine, 60279 University of Catania , Catania, Italy Luca Zanoli, Nephrology, Department of Clinical and Experimental Medicine, Policlinico Universitario, University of Catania, Via Santa Sofia 78, Catania 95123, Italy. Email: luca.zanoli@unict.it 26 6 2023 26 6 2023 00033197231186092© The Author(s) 2023 2023 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. edited-statecorrected-proof typesetterts10 ==== Body pmcAcute and chronic inflammation can alter arterial physiology and lead to micro- and macro-vascular dysfunction (i.e., endothelial dysfunction, glomerular injury, elastic and muscular arteries stiffening) that, in turn, increase the risk for acute myocardial infarction and stroke.1,2 The first waves of Coronavirus Disease 2019 (COVID)-19 were characterized by an acute peak of severe inflammation, a rise in cardiovascular (CV) risk and the persistence of several symptoms for >12 weeks after the resolution of the acute phase of the disease. 3 In this setting, increased arterial stiffness may be a mediator between severe inflammation and increased CV risk. To date, only a few studies have evaluated the arterial stiffness in subjects with COVID-19. In an article published in this issue of Angiology, 4 Can et al enrolled 70 subjects (age 38 ± 10 years, female sex 59%) and measured, 1 and 7 months after the acute phase of COVID-19, the cardio‐ankle vascular index (CAVI), a measure of the overall stiffness of muscular and elastic arteries. 5 This study 4 is of interest for several reasons. First, most of the previously published articles that have reported a vascular dysfunction in COVID-19 patients were cross-sectional and can only hypothesize a causal relationship between COVID-19 infection and arterial stiffening. The longitudinal design of the Can et al 4 study allows to reveal a worsening of the vascular function (i.e., an increase in CAVI) 7 months after the resolution of the acute phase of COVID-19. This finding should be considered together with the results of the Methuselah Study, a multicenter longitudinal study that has evaluated the vascular function in patients with COVID-19. 6 In the latter study we reported that aortic stiffness was increased 12-24 weeks after the onset of the disease and was partially improved after a follow-up of 48 ± 6 weeks. Moreover, the vascular disease associated with COVID-19 involved several parts of the arterial bed and was linked with autonomic dysfunction. Accordingly, a trend for an improvement of aortic stiffness 12 months after COVID-19 was also reported in another independent longitudinal study. 7 Considered together, these studies suggest that the vascular function of patients with COVID-19 could progressively get worse for several months after the resolution of the acute phase of the disease and could be partly improved, despite not fully reverted, 12 months after COVID-19. This trajectory is more compatible with functional arterial stiffening and improvement of endothelial dysfunction rather than structural, and less reversible in the short-term, arterial stiffening.1,2 Longitudinal studies are needed to test whether residual vascular dysfunction of subjects with COVID-19 reported 1 year after the resolution of the acute phase of the disease could be fully reverted with longer follow-up or represent a sign of structural arterial stiffening and early vascular aging. Second, the arterial stiffening process during the first waves of COVID-19 seems to be influenced by the age of the patients. Recent studies reported that arterial stiffness was increased in young adults (age 21 ± 1 years) 3-5 weeks but not 12-14 weeks after the acute phase of COVID-19 (age 22-24 years)8,9 whereas, in older adults, Can et al, 4 and other independent groups,6,7 reported that arterial stiffness remained increased up to 7-12 months after the onset of COVID-19. This finding is of interest considering that subjects enrolled in these studies4,6,7 were free of known factors associated with increased arterial stiffness before COVID-19, including hypertension, diabetes, chronic kidney disease, dyslipidemia, stroke, ischemic heart disease, and current or former smoking. Third, the CAVI is supposed to be less dependent on blood pressure than pulse wave velocity, 5 the current reference method to measure regional arterial stiffness. 10 Consequently, the results of Can et al 4 suggest that the arterial stiffening process reported 7 months after COVID-19 should be blood pressure independent. The disadvantage of the use of CAVI as a measure of arterial stiffness is that Can et al 4 cannot study elastic and muscular arteries separately. Finally, Can et al 4 enrolled subjects with SARS-CoV-2 infection between December 2020 and June 2021, during the first waves of COVID-19. Considering that the arterial stiffening process seems to be influenced by the severity of the disease and high-sensitivity C-reactive protein levels at hospitalization for COVID-19,6,11 the effect of newer variants of SARS-CoV-2 and vaccination remains to be clarified. We could expect a higher incidence of subjects with increased arterial stiffness after the first waves of COVID-19 (characterized by a more severe inflammation during the acute phase of the disease than that observed in subjects infected by newer variants of SARS-CoV-2). The ongoing COVID-19 effects on arterial stiffness and vascular aging (CARTESIAN) study of the Association for research into arterial structure and physiology (ARTERY) Society could help to answer this question. 12 ORCID iDs Luca Zanoli https://orcid.org/0000-0003-1678-3778 Lorenzo Lo Cicero https://orcid.org/0000-0002-9112-5540 Author contribution: All authors have contributed equally to the submitted work. ==== Refs References 1 Zanoli L Briet M Empana JP , et al. Vascular consequences of inflammation: a position statement from the ESH working group on vascular structure and function and the ARTERY society. J Hypertens 2020;38 :1682-98.32649623 2 Zanoli L Lentini P Briet M , et al. Arterial stiffness in the heart disease of CKD. J Am Soc Nephrol 2019;30 :918-28.31040188 3 Nalbandian A Sehgal K Gupta A , et al. Post-acute COVID-19 syndrome. Nat Med 2021;27 :601-15.33753937 4 Can Y Kocayigit I Kocayigit H Çakmak BS Şahinöz M Akdemir R Ongoing effects of SARS-CoV-2 infection on arterial stiffness in healthy adults. Angiology 2023. In press. 5 Shirai K Utino J Otsuka K Takata M A novel blood pressure-independent arterial wall stiffness parameter; cardio-ankle vascular index (CAVI). J Atheroscler Thromb 2006;13 :101-7.16733298 6 Zanoli L Gaudio A Mikhailidis DP , et al. Vascular dysfunction of COVID-19 is partially reverted in the long-term. Circ Res 2022;130 :1276-85.35345906 7 Ikonomidis I Lambadiari V Mitrakou A , et al. Myocardial work and vascular dysfunction are partially improved at 12 months after COVID‐19 infection. Eur J Heart Fail 2022;24 :727-9.35138689 8 Ratchford SM Stickford JL Province VM , et al. Vascular alterations among young adults with SARS-CoV-2. Am J Physiol Heart Circ Physiol 2021;320 :H404-10.33306450 9 Nandadeva D Young BE Stephenset BY , et al. Blunted peripheral but not cerebral vasodilator function in young otherwise healthy adults with persistent symptoms following COVID-19. Am J Physiol Heart Circ Physiol 2021;321 :H479-84.34296966 10 Laurent S Cockcroft J Van Bortel L , et al. Expert consensus document on arterial stiffness: methodological issues and clinical applications. Eur Heart J 2006;27 :2588-605.17000623 11 Kumar N Kumar S Kumar A , et al. The COSEVAST study outcome: evidence of COVID-19 severity proportionate to surge in arterial stiffness. Indian J Crit Care Med 2021;25 :1113-9.34916742 12 Bruno RM Spronck B Hametner B , et al. Covid-19 effects on ARTErial stiffness and vascular ageing: CARTESIAN study rationale and protocol. Artery Res 2020;27 :59.35414837
PMC010xxxxxx/PMC10293862.txt
==== Front Am J Rhinol Allergy Am J Rhinol Allergy AJR spajr American Journal of Rhinology & Allergy 1945-8924 1945-8932 SAGE Publications Sage CA: Los Angeles, CA 10.1177/19458924231184055 10.1177_19458924231184055 Original Article Intra Nasal Use of Ethylene Diamine Tetra Acetic Acid for Improving Olfactory Dysfunction Post COVID-19 Abdelazim Mohamed H. MD 1 Mandour Zeyad MD 2 https://orcid.org/0000-0002-8907-3497 Abdelazim Ahmed H. PhD 3 Ismaiel Wael F. MD 1 Gamal Mohammed PhD 4 Abourehab Mohammed A.S. PhD 56 Alghamdi Saleh PhD 7 Alghamdi Mohamed A. PhD 8 Alrugi Rehab R. Pharm D Intern Student 9 Alharthi Rawan R. Pharm D 10 1 Department of Otolaryngology, Faculty of Medicine, 68820 Al-Azhar University , Damietta, Egypt 2 Department of Otolaryngology, 54562 Alexandria University , Alexandria, Egypt 3 Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, 68820 Al-Azhar University , Cairo, Egypt 4 Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, 158406 Beni-Suef University , Beni-Suef, Egypt 5 Department of Pharmaceutics, College of Pharmacy, 48058 Umm Al-Qura University , Makkah, Saudi Arabia 6 Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Minia University, Minia, Egypt 7 Department of Clinical Pharmacy, Faculty of Clinical Pharmacy, 158203 Al Baha University , Al Baha, Saudi Arabia 8 Department of Surgery, Division of Otolaryngology, Faculty of Medicine, 158203 Al Baha University , Al Baha City, Saudi Arabia 9 Pharm D student, College of Pharmacy, 204570 Shaqra University , Shaqra, Saudi Arabia 10 Department of Pharmacy, Dawadmi General Hospital, Dawadmi, Saudi Arabia Ahmed H. Abdelazim, Nasr City, 11751, Cairo, Egypt. Email: ahmed.hussienabdelazim@hotmail.com 25 6 2023 25 6 2023 19458924231184055© The Author(s) 2023 2023 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Background COVID-19 has been associated with olfactory dysfunction in many infected patients. The rise of calcium levels in the nasal secretions plays an essential role in the olfaction process with a desensitization effect on the olfactory receptor neurons and a negative impact on the olfaction transmission. Ethylene diamine tetra acetic acid (EDTA) is a chelating agent that can bind free calcium in the nasal secretions, thereby reducing the adverse effects of calcium on olfactory function. Objectives The objective of this work is to demonstrate the effect of intranasal EDTA on improving olfactory dysfunction following COVID-19. Methods Fifty patients with a history of COVID-19 and olfactory dysfunction that persisted for more than 6 months were enrolled in the current prospective randomized clinical trial. Participants were randomized into 2 equal groups. Twenty-five patients were treated with olfactory training only, while the remaining 25 patients received treatment with olfactory training and a topical nasal spray of ethylene diamine tetra acetic acid. The olfactory function was assessed before treatment and 3 months later using the Sniffin’ Sticks test. Additionally, the determination of calcium level in the nasal secretions was performed using an ion-selective electrode before treatment and 3 months later. Results Eighty-eight percent of the patients treated with olfactory training in addition to EDTA exhibited clinical improvement, while 60% showed improvement in patients treated with olfactory training only. Furthermore, a significant decrease in the measured calcium level in the nasal secretions was demonstrated after the use of ethylene diamine tetra compared to patients treated with olfactory training only. Conclusion Ethylene diamine tetra acetic acid may be associated with an improvement of the olfactory function post-COVID-19. COVID-19 chelating agent olfactory dysfunction EDTA sodium chloride randomized calcium Sniffin’ sticks anosmia hyposmia Deanship of scientific research at Umm Al-Qura University 23UQU4290565DSR57 edited-statecorrected-proof typesetterts19 ==== Body pmcIntroduction Olfactory dysfunction is one of the most commonly identified disorders, affecting approximately 20% of adults. 1 Although olfactory dysfunction is universally present, the exact mechanism has not been fully confirmed. Additionally, the limited availability of an effective treatment protocol for olfactory dysfunction could indeed be attributed to the simultaneous presence of neurological and biochemical factors. 2 The new global coronavirus disease 2019, (COVID-19) is primarily caused by the severe acute respiratory syndrome coronavirus. 2 Coronaviruses have the capability to enter the brain through the cribriform plate, which is located near the olfactory bulb and epithelium. 3 The neurons in the olfactory region are at high risk of injury due to the significant viral load in the nasal cavity. 4 Sudden and complete loss of olfactory function has been reported in many COVID-19 patients. 5 Although ions comprise only 1% of nasal secretions, 6 the ionic microenvironment in the olfactory cleft plays a crucial role in the chemical-electrical transduction pathway, facilitating the transmitting of olfactory information from the nasal lumen to the central processing system. 7 The disturbance of ionic levels is associated with the presence or progression of many diseases. 8 Previous reports suggest the major role of calcium in olfactory receptor neurons and the mechanism of olfactory transmission. Calcium, in conjunction with calmodulin, regulates sensitivity to cyclic adenosine monophosphate (cAMP) by entering the cilium during the olfactory response. This leads to a decreased channel sensitivity of cyclic nucleotide-gated channels to cAMP. When the olfactory receptor cells are exposed to different odorants, it stimulates the influx of calcium through cyclic nucleotide-gated channels into the small volume within the cilia. Consequently, there is an increase in intra-ciliary calcium, which establishes negative feedback on various stages of the olfaction transmission mechanism.9,10 More specifically, an increase in nasal calcium levels may lead to desensitization of the olfactory receptor neurons. It is hypothesized that the changes in the olfaction sensitivity resulting from alterations in the nasal calcium levels can significantly affect the sensitivity of cyclic nucleotide-gated channels and, consequently, the excitability of receptor neurons in vivo. This, in turn, can contribute to an improvement in the olfaction process.11–13 Ethylene diamine tetra acetic acid (EDTA) was introduced as a chelating agent used for the removal of toxic heavy metal ions. The disodium salt of EDTA is a common ingredient in the formulation of various pharmaceutical preparations. 14 EDTA possesses the ability to bind divalent or trivalent metal ions, such as calcium and magnesium cations, through 4 carboxylate groups and 2 amines groups. 15 EDTA is capable of sequestering free calcium from nasal secretions, forming a stable complex product. This property of EDTA may have implications for the treatment of olfactory disorders. Olfactory training is considered a common therapeutic alternative for postviral olfactory loss and has a strong scientific foundation.16–18 This prospective study was conducted to test the use of intranasal EDTA in addition to olfactory training in order to decrease the rise of calcium in the nasal secretions and improve olfactory dysfunction following COVID-19. Furthermore, a comprehensive description of the comparison between patients treated with olfactory training alone with patients treated with olfactory training along with topical nasal spray EDTA was provided. This study represents the first published clinical trial evaluating the use of intranasal EDTA use as a topical treatment to improve olfactory dysfunction post-COVID-19 infection. Materials and Methods Study Design A prospective randomized double clinical trial was conducted in the ENT Department of Damietta Faculty of Medicine, Al-Azhar University, Egypt. The study received approval from the Ethical Committee of Damietta Faculty of Medicine, Al Azhar University, Egypt (IRB,00012367-21-01-010). All methods were performed in accordance with the relevant guidelines and regulations. Sample Size Currently, there is no research available on the effects of EDTA on olfactory dysfunction related to COVID-19. Therefore, due to the lack of preliminary data or estimates of the effect size, the sample size for this study was determined based on feasibility. It is worth noting that reports have indicated that 79.5% of individuals with post-COVID-19 olfactory dysfunction may experience complete recovery within the first two months. 19 Additionally, a sample rejection rate of up to 10% was anticipated, and certain exclusion criteria were applied. In total, 300 patients were screened for eligibility, out of which 50 patients (31 females and 19 males) were randomly assigned to 2 groups. The enrolment period of all patients took place between March 2021 and December 2021, during which comprehensive characterization and examination were conducted. The study flowchart is depicted in Figure 1. Figure 1. The flow diagram of the proposed study. Inclusion Criteria To be enrolled in the study, patients had to meet the following inclusion criteria: being adults over 18 years of age, having a previous COVID-19 infection confirmed with a documented nasopharyngeal swab test, showing recovery from infection confirmed by a documented negative nasopharyngeal swab test, and exhibiting clinically confirmed signs of olfactory dysfunction persisting for more than six months. Exclusion criteria included: (1) patients younger than 18 years; (2) patients with olfactory dysfunction less than 6 months after testing negative for SARS-CoV-2; (3) patients with a history of previous olfactory dysfunction related to trauma or surgery; (4) patients with congenital olfactory loss and neurodegenerative diseases; (5) patients with psychiatric diseases; (6) patients who were currently taking medication for olfactory dysfunction; (7) pregnant patients and (8) patients currently participating in other COVID-19 trials. Entry into the Study Before participating in the study, the patients were approached by one of the study team members who explained the study objective, potential benefits, and possible adverse effects. Subsequently, all patients signed an informed consent form. After obtaining informed consent, the patients received the proposed treatment. Randomization Process Patients were assigned to 2 equal groups using a randomization method known as unratified block randomization. The randomization was achieved using a computer-generated randomization plan, which provides an additional layer of objectivity and eliminates any potential for human bias. A block size of 4 was chosen, where for every 4 consecutive patients enrolled in the study, 2 would be assigned to each group. The block size was selected to maintain a balance between the groups and prevent any systematic imbalances in treatment assignment. The randomization plan was kept confidential and securely stored to maintain blinding and prevent any potential interference or manipulation. The study investigators followed the predetermined plan strictly, adhering to the randomization sequence as provided by the computer-generated list. This approach adds rigor and reliability to the study design, enhancing the validity of the results obtained. Treatment Regimen Procedures Twenty-five patients were treated with olfactory training only using 4 standard odorants (phenyl ethyl alcohol [rose], eucalyptol [eucalyptus], citronellal [lemon], and eugenol [cloves]) for 3 months. 16 On the other hand, 25 patients were treated with olfactory training and 1% topical nasal spray of EDTA. The Department of Pharmaceutical Analytical Chemistry, Cairo Faculty of Pharmacy, Al-Azhar University, Egypt provided the appropriate standard procedures for formulating EDTA nasal spray solutions. Reports demonstrated that EDTA should be prepared less than 2% in pharmaceutical cosmetic preparations. Thus 1% EDTA was appropriate and safe to be used as a topical nasal solution. 19 Topical nasal spray EDTA, 1%, was prepared in phosphate buffer, pH 7.5. The medications were provided in nasal spray bottles which deliver a standardized volume of 0.1 ml. Patients were instructed and trained to instill the topical EDTA using a lying position with the head tilted back, which has been suggested to improve access to the upper nasal cavity. Variations in patient technique utilizing nasal spray delivery were controlled for in the study by providing detailed instructions to patients on the correct administration technique. Additionally, patients were observed and provided with feedback to ensure that they were complying with the recommended technique. Patients were also required to demonstrate their technique to the study staff and sign a compliance form to confirm their understanding of the correct technique. Study Outcomes Olfactory Function Assessment The “Sniffin’ Sticks” test is a validated assessment tool for evaluating olfactory function. Odorants are administrated using felt-tip pens that carry a tampon soaked with different concentrations of liquid odorant (phenyl ethanol dissolved in propylene glycol). Sixteen odorant concentrations were created through a stepwise diluting process. The pen's tip is positioned in front of the patient nose and carefully moved from left to right nostril and backward. 20 The threshold score (T) was evaluated using three alternative forced choice paradigms where patients were repeatedly presented with triplets of pens and had to assign one pen containing an odorous solution from 2 blanks filled with the solvent. 21 A staircase paradigm was employed, starting with the lowest odor concentration. In this paradigm, correctly identifying the odorous pen twice in a row or providing one incorrect answer marked a turning point. A turning point led to a decrease or increase in concentration in the subsequent triplet of odors. The threshold score (T) was the mean of the last four turning points in the staircase, with the final score ranging between 1 and 16 points. The discrimination (D) score was evaluated by introducing three alternative forced choice paradigms where patients were repeatedly presented with 2 pens containing the same odorant, while the third pen smelled differently. Patients were asked to discriminate the single pen with a different smell. The score was the sum of correctly identified odors, between 0 and 16 points. The identification score (I) was evaluated by introducing single pens where patients were asked to identify and label the smell, using four alternative descriptors for each pen. The total score was the sum of correctly identified pens, thus subjects could score between 0 and 16 points. 22 The final TDI score was the sum of scores for threshold, discrimination, and identification scores, with a range between 1 and 48 points. A TDI score below 16.75 points was considered to represent anosmia, and a TDI score between 16.75 and 30.50 points was considered to represent hyposmia. A TDI score of 30.75 points or more signified normosmia.23,24 In the current study, the same tester of olfaction was used for all patients to ensure consistency and accuracy of the olfactory assessments. The tester was trained and experienced in conducting “Sniffin’ Sticks” test and was blinded to the treatment allocation of the patients. In addition, the time of day for smell tests was kept consistent among the patient population to control for any confounding effects or diurnal variation in olfactory function. All smell tests were administered in the morning between 9:00 am and 12:00 pm. Determination of Calcium in the Nasal Secretions Nasal secretions were collected from the patients, and the most accurate representation of the nasal fluid composition was obtained immediately after a sneeze due to the relatively large amount of secretion produced. Nasal fluid was usually collected using a small stainless steel (approximately 10 mm × 5 mm × 2 mm). The stainless steel was clamped onto the septum between the nostrils enhancing the fluid to drain into 1.5-ml tube. 25 The volume of nasal secretion obtained was transferred to a series of centrifugation tubes after adding 0.5 ml of phosphate buffer solution. To denature proteins, 3 ml of acetonitrile was added to the centrifugation tubes. The tubes were shaken for 1 min and centrifuged at 4000 r/min for 30 min. The protein-free supernatant was evaporated to dryness, and the residues were diluted with phosphate buffer solution in 10-ml volumetric flasks. The standard procedure for developing a screen-printed selective electrode 26 was developed by the Department of Pharmaceutical Analytical Chemistry, Cairo Faculty of Pharmacy, Al-Azhar University. It was used for the determination of calcium level in the nasal secretion samples from all patients before treatment and 3 months later. Statistical Analysis All statistical analyses were conducted using SPSS v23 statistical software (SPSS, Inc, Chicago, Illinois). The obtained results were tested for normality and parametric or nonparametric tests were applied accordingly. Unless specified otherwise, results were presented as mean ± standard deviation. Differences in frequencies were evaluated using Fisher's exact probability test. ANOVA test was employed for the assessment the repeated measures within subject factor and between subject factor. Unpaired t-test was utilized to compare and test the significance of the results of the two groups. Chi-squared test was employed wherever appropriate. Statistical significance was considered when p < .05. Results Fifty patients, who had documented previous COVID-19 and olfactory dysfunction persisted more than 6 months’ post-SARS-CoV-2 negative testing, were enrolled in this study. The patients ranged in age from 20 to 60 years. There were 31 females and 19 males. In total, 25 patients received only olfactory training, while the remaining 25 received olfactory training in addition to topical EDTA treatment. The complete characteristics of the patients were described in Table 1. Differences in frequencies for total comorbidities were assessed using Fisher's exact probability test. There was a nonsignificant difference in the frequency of smoking between the group of patients treated with olfactory training alone and the group treated with olfactory training in addition to topical EDTA (3/25 vs 5/25; p = .70). Also, there were no significance between the two groups for other comorbidities, as shown in Table 1. EDTA exhibited the ability to chelate calcium in the nasal secretions forming soluble complex products. The reaction pathway for the interaction between EDTA and calcium was proposed and illustrated in Figure 2. Figure 2. The schematic reaction pathway of EDTA with calcium. EDTA, ethylene diamine tetra acetic acid. Table 1. Patient Demographics and Clinical Data. Character Olfactory training Olfactory training + EDTA p (Fisher exact probability test) Sample size, n 25 25 Age (years), mean ± SD 40.37 ± 8.58 41.25 ± 7.23 Days since symptoms to enrollment, mean ± SD 180 ± 5.28 180 ± 6.32 Gender (male/female), n 10/15 9/16 Smokers (current/never), n 3/22 5/20 0.70 Comorbidities, n Asthma 1 2 1.00 Diabetes 5 6 1.00 Hypertension 3 5 0.70 Migraine 0 1 1.00 Current medication Anti-histamine Metformin B-blocker ACE inhibitor Paracetamol Anti-histamine Glipizide Amlodipine ACE inhibitor Paracetamol The Sniffin’ Sticks test was used to assess olfactory function in all patients before treatment and 3 months later. Table 2 presents the mean pre- and post-treatment olfactory scores based on the treatment regimen. The change in the olfactory scores following treatment with olfactory training or olfactory training in addition to EDTA is depicted in Figure 3. In general, the change in TDI score was significantly greater in patients who received the olfactory training in addition to the EDTA group compared to the patients who received olfactory training (2.33 points, p = .008). More specifically in the patients who received olfactory training only, there was a significant improvement in the change of TDI scores but it did not reach clinical significance (5.2 points, p = .0004). As the patient was considered to be improved when the TDI score increased by 6 points. 27 On the other hand, in the patients who received the olfactory training in addition to EDTA, there was a significant improvement in the change of TDI scores which reached clinical significance (7.2 points, p = .00001). Furthermore, there was a trend toward improved T, D, and I scores (1.4 points, p = .091, 1.7 points, p = .005 and 2 points, p = .005, respectively) for the patients treated with olfactory training only. For the patients treated with olfactory training in addition to EDTA, there was a clinically significantly improved T, D, and I scores (2.4 points, p = .005, 2.9 points, p = .007, and 2.4 points, p = .004, respectively). Figure 3. Box and whisker plots showing the change of measured olfactory scores and the change of measured calcium concentration levels for group received olfactory training and group received olfactory training in addition to EDTA. EDTA, ethylene diamine tetra acetic acid. Table 2. Results of the Measured Olfactory Scores and the Measured Nasal Calcium Concentration Levels Pre and Post Treatment. Olfactory training Olfactory training + EDTA Pre administration Post administration Pre administration Post administration T score, mean ± SD 2.30 ± 0.21 3.75 ± 1.04 1.99 ± 0.14 4.12 ± 0.92 D score, mean ± SD 6.23 ± 0.22 7.95 ± 1.23 6.26 ± 0.27 9.25 ± 1.09 I score, mean ± SD 5.22 ± 0.21 7.27 ± 1.36 5.28 ± 0.25 7.69 ± 1.27 TDI score, mean ± SD 13.76 ± 0.36 18.95 ± 3.58 13.54 ± 0.35 21.06 ± 3.05 Nasal calcium concentration (mM), mean ± SD 38.12 ± 1.64 28.88 ± 5.90 37.96 ± 1.51 23.88 ± 5.64 When examining individual patient scores, it was observed that olfactory function clinically improved in 60% of patients who received olfactory training alone. However, in those who received EDTA in addition to olfactory training, 88% of patients showed improvement. The proportion of patients who improved with the additional EDTA was statistically significantly higher than the training alone group (x2 = 7.06, df = 2, p = .03). The concentration of calcium in the nasal secretions was assessed in all patients using screen-printed ion-selective electrodes. Electromotive force values were determined over a calcium concentration range of 100 to 0.001 mM. A standard calibration plot was constructed relating the electromotive force values to the calcium concentration. The designed electrode exhibited a near Nernstian slope of 29.23 mV/decade with a detection limit of 0.0001 mM in the dynamic range of 100 to 0.001 mM. The concentration of calcium in the nasal secretions was successfully determined using the developed electrode. The changes in the measured calcium level following treatment with olfactory training or olfactory training in addition to EDTA are depicted in Figure 3. The mean values of calcium concentration before treatment and 3 months later are presented in Table 2. An ANOVA test was conducted to statistically test the repeated measures of the measured olfactory scores and the repeated measures of the measured calcium concentration values before and after treatment with olfactory training or olfactory training in addition to EDTA. A significant difference was obtained for the measures of the whole measures (Table 3). To assess whether the results of patients treated with olfactory training in addition to EDTA showed a statistical significance compared to patients treated with olfactory training only, the change of the olfactory scores and the change of the measured nasal calcium concentration were compared using an unpaired t-test. Results were presented in Table 4. Based on the findings, it can be concluded that patients who received olfactory training in addition to EDTA exhibited a statistically significant difference and a relevant clinical improvement in olfactory function. EDTA was generally well tolerated, with nasal discharge being the most commonly reported side effect. However, mild burning sensations in the nose or throat were also reported. Table 3. ANOVA Statistical Testing for the Measured Olfactory Score Values and the Measured Values of the Nasal Calcium Concentrations. Group Threshold (T) statistical assessment results Source of variation df Sum of squares Mean square p Olfactory training Between group 1 26.06 26.06 1.40 × 10−8 Within group 48 27.05 0.56 Olfactory training + EDTA Between group 1 57.03 57.03 2.66 × 10−15 Within group 48 20.94 0.44   Discrimination (D) statistical assessment results Olfactory training Between group 1 36.98 36.98 1.20 × 10−8 Within group 48 37.59 0.78 Olfactory training + EDTA Between group 1 111.60 111.60 1.16 × 10−17 Within group 48 30.48 0.635   Identification (I) statistical assessment results Olfactory training Between group 1 51.61 51.61 2.1 × 10−9 Within group 48 45.66 0.95 Olfactory training + EDTA Between group 1 72.24 72.24 2.49 × 10−12 Within group 48 40 0.83   Nasal calcium concentration statistical assessment results Olfactory training Between group 1 1067.22 1076.22 1.39 × 10−9 Within group 48 917.28 19.11 Olfactory training + EDTA Between group 1 2487 2487 4.05 × 10−16 Within group 48 819 17.07 Statistical significance is indicated by bold. Table 4. Unpaired t Results for Statistical Comparing the Change of the Measured Olfactory Scores and the Change of Nasal Calcium Concentrations Between the Two Groups. Parameter  Change of T score Change of D score Change of I score Change of TDI score Change of calcium level t 1.17 1.52 1.58 1.61 1.67 p 0.014 0.001 0.002 0.008 0.002 Discussion Olfactory dysfunction has become a common symptom associated with many cases of coronavirus.28,29 Several reports have indicated an increase in calcium levels in the nasal secretions which can have negative effects on the olfactory mechanism. The shift in calcium concentration holds physiological significance and correlates with the olfaction management process.11–13 The current study was specifically designed to investigate the effectiveness of EDTA in improving olfactory dysfunction post-COVID-19. As olfactory training is commonly used as therapy for postviral olfactory loss, a prospective randomized clinical trial was conducted to assess the effect of intranasal EDTA in addition to olfactory training for olfactory dysfunction treatment. EDTA is a widely used chelating agent that has the ability to form strong water-soluble metal complexes with divalent and trivalent cations. These cations are incorporated into a ring-like structure resulting in the formation of complex products between the selected ion and EDTA. The chelation process is primarily influenced by pH and the presence of other competing metal ions. 30 Specifically, at a pH of 7.5, EDTA selectively forms a calcium–EDTA complex even in the presence of sodium, potassium, or magnesium cations. As a result of this mechanism, the use of EDTA leads to a decrease in calcium levels in nasal secretions. The findings of the current study suggest that reducing calcium levels in nasal secretions through EDTA may be associated with an improvement in olfactory function. It is very important to use precise methods to assess the olfactory function. The Sniffin’ Sticks test is a common assessment tool for evaluating human olfactory function. This test consists of three subtests, odor threshold (T), odor discrimination (D), and odor identification (I), each with a potential score of up to 16 points. A composite “TDI” score is calculated by summing the scores from the three subtests. The obtained results demonstrated that patients who received EDTA in addition to olfactory training exhibited significant improvement in the olfactory function, as evidenced by improved overall olfactory test scores. It is also recommended to quantify the calcium level in the nasal secretions. Potentiometric determination of the calcium using a screen-printed selective electrode offers the advantage of being suitable for small sample volumes, which is ideal for the current study on nasal secretions. 31 The electrode was designed and utilized to determine the calcium concentration before and after the described treatment. The observed sharp decrease in calcium levels can be attributed to the chelation of the calcium and the formation of the calcium EDTA complex product. While our findings suggest the potential benefits of EDTA in improving olfactory dysfunction, it is essential to expand this research to larger and more diverse populations considering the limitations posed by our small sample size. This study had several limitations. The primary limitation is the small sample size, which resulted in an underpowered analysis. Further studies are needed to validate the association between changes in the nasal calcium concentration and the olfactory function. It is important to expand this study to include larger and more diverse populations. Additionally, investigating the use of EDTA for olfactory dysfunction caused by factors other than COVID-19 infection should be considered. Conclusion This article demonstrated the effect of intranasal EDTA in reducing the elevated nasal calcium level and improving dysfunction post-COVID-19. Following the use of EDTA in addition to olfactory training, a significant improvement from anosmia to hyposmia was observed, accompanied by a notable decrease in nasal secretions calcium level. Further research is recommended to validate the efficacy of EDTA in treating olfactory dysfunction and its association with decreased calcium levels in nasal secretions. Acknowledgments The author(s) would like to thank the Deanship of scientific research at Umm Al-Qura University for supporting this work by grant code (23UQU4290565DSR57). The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article. This work was supported by the Deanship of scientific research at Umm Al-Qura University (grant number 23UQU4290565DSR57). ORCID iDs: Ahmed H. Abdelazim https://orcid.org/0000-0002-8907-3497 ==== Refs References 1 Brämerson A Johansson L Ek L , et al. Prevalence of olfactory dysfunction: the Skövde population-based study. Laryngoscope. 2004;114 (4):733–737.15064632 2 Kurahashi T Menini A . Mechanism of odorant adaptation in the olfactory receptor cell. Nature. 1997;385 (6618):725–729.9034189 3 Felsenstein S Herbert JA McNamara PS , et al. COVID-19: immunology and treatment options. Clin Immunol. 2020;215 :108448.32353634 4 Han AY Mukdad L Long JL , et al. Anosmia in COVID-19: mechanisms and significance. 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PMC010xxxxxx/PMC10293864.txt
==== Front J Intensive Care Med J Intensive Care Med JIC spjic Journal of Intensive Care Medicine 0885-0666 1525-1489 SAGE Publications Sage CA: Los Angeles, CA 37357595 10.1177/08850666231183398 10.1177_08850666231183398 Original Research Timing of Transport of Patients with COVID-19 Hayes Jane M. MD, MPH 12 https://orcid.org/0000-0002-8922-1955 Richards Jeremy B. MD, MA, FACP, ATSF 3 Frakes Michael A. APRN, FCCM 4 https://orcid.org/0000-0002-5541-5209 Cocchi Michael N. MD 5 Cohen Ari MD, FAAP 2 Cohen Jason E. DO, FACEP, FCCM 36 Dargin James MD 7 Friedman Franklin D. MS, MD, FACEP, FAAEM 8 https://orcid.org/0000-0001-7477-7531 Wilcox Susan R. MD, FACEP, FCCM, FAAEM 24 1 Department of Medicine, Washington University School of Medicine, St Louis, MO, USA 2 2348 Department of Emergency Medicine, Massachusetts General Hospital , Boston, MA, USA 3 14319 Division of Pulmonary and Critical Care Medicine, Mount Auburn Hospital , Cambridge, MA, USA 4 485798 Boston MedFlight , Bedford, MA, USA 5 1859 Department of Emergency Medicine, Beth Israel Deaconess Medical Center , Boston, MA, USA 6 1861 Department of Emergency Medicine, Brigham and Women's Hospital , Boston, MA, USA 7 Department of Emergency Medicine, Lahey Hospital & Medical Center, in memoriam, Burlington, MA, USA 8 1867 Department of Emergency Medicine, Tufts Medical Center , Boston, MA, USA Jeremy B. Richards, MD, MA, FACP, ATSF, 330 Mount Auburn Street, Cambridge, MA. Email: jeremy.richards@mah.harvard.edu 26 6 2023 26 6 2023 0885066623118339822 12 2022 05 6 2023 © The Author(s) 2023 2023 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Rationale The objective of this study was to evaluate the risk of mortality or ECMO cannulation for patients with confirmed or suspected COVID-19 transferred from sending hospitals to receiving tertiary care centers as a function of the duration of time at the sending hospital. Objective To determine outcomes of critically ill patients with COVID-19 who were transferred to tertiary or quarternary care medical centers. Materials and Methods Retrospective cohort study of critical care transports of patients to one of seven consortium tertiary care centers from March 1, 2020, through September 4, 2020. Age 14 years and older with confirmed or suspected COVID-19 transported from a sending hospital to a receiving tertiary care center by the critical care transport organization. Results Patients transported with confirmed or suspected COVID-19 to tertiary care centers had a mortality rate of 38.0%. Neither the number of days admitted, nor the number of days intubated at the sending hospital correlated with mortality (correlation coefficient 0.051 and −0.007, respectively). Similarly, neither the number of days admitted, nor number of days intubated at the sending hospital correlated with ECMO cannulation (correlation coefficient 0.008 and −0.036, respectively). Conclusion It may be reasonable to transfer a critically ill COVID-19 patient to a tertiary care center even if they have been admitted at the sending hospital for several days. mortality extracorporeal membrane oxygenation COVID-19 mechanical ventilation patient transfer tertiary care edited-statecorrected-proof typesetterts19 ==== Body pmcIntroduction More than 5.4 million patients have been admitted to a hospital in the United States with COVID-19, the disease caused by SARS-CoV-2 infection. 1 Many COVID-19 patients require transfer to a tertiary care center for critical care resources not available at sending hospitals, including intensive care unit (ICU) beds and extracorporeal membrane oxygenation (ECMO). Prior studies have shown that critically ill patients, especially those with severe acute hypoxemic respiratory failure, benefit from transport to tertiary care centers.2–4 Prior studies have also established that early initiation of lung protective ventilation may be important to decrease lung injury and the development or worsening of acute respiratory distress syndrome (ARDS).5–9 Clinicians at sending hospitals are responsible for identifying the patients who would benefit from transfer to a higher level of care. While ECMO centers improve outcomes for patients with ARDS, 4 clinicians must balance the need for a higher level of care with the challenges of transporting a patient. Interfacility transport of critically ill patients has been associated with risk of complications including hemodynamic instability, hypoxemia, and death.2,10,11 There is uncertainty in the literature and among health care providers about the risks associated with and outcomes for transport of critically ill patients with acute respiratory failure due to COVID-19, and data that address this knowledge gap could support the decision-making of clinicians at both sending and receiving hospitals. The objective of this study was to evaluate the risk of mortality or ECMO cannulation for patients with confirmed or suspected COVID-19 transferred from sending hospitals to receiving tertiary care centers as a function of the duration of time at the sending hospital. Our primary outcome was in-hospital mortality, and our secondary outcome was cannulation for ECMO at the receiving tertiary care center. Materials and Methods We performed a retrospective cohort study of Boston MedFlight transports of patients with confirmed or suspected COVID-19 from March 1, 2020, through September 9, 2020, from sending hospitals to tertiary care centers in Boston, Massachusetts. All decisions to transfer a patient were initiated by the physicians at sending hospitals.   This study was a quality improvement project to monitor performance during the COVID-19 pandemic, and as such, was deemed exempt from Institutional Review Board review. Boston MedFlight is a state-licensed and internationally accredited critical care transport organization operated by a consortium of tertiary care medical centers in the Boston metropolitan area and serving all southern New England. The program annually cares for more than 5000 patients and offers helicopter, fixed-wing, and ground transportation of neonatal, pediatric, and adult patients using dedicated nurse/paramedic teams who provide care under standing orders and with access to dedicated on-line medical direction. Providers undergo extensive training in management of severe hypoxemic respiratory failure through didactics, case reviews, and simulation scenarios. As part of ongoing quality improvement in 2020, the electronic medical record database for the critical care transport organization was queried daily for records of patients with acute respiratory failure requiring intubation and initiation of mechanical ventilation who were transported with a concern for COVID-19, and all charts were reviewed by the organization's Chief Quality Officer to ensure adherence to COVID protocols. 12 Post-transport outcomes are queried routinely by the organization for all transports. Charts were reviewed for inclusion in this project by two authors (JMH, SRW). Discrepancies were reviewed and resolved by consensus. Patients were included if they had confirmed or suspected COVID-19 at the time of transport by Boston MedFlight. At the time of the study, the available COVID-19 tests were unreliable giving rise to concern about false negative and false positive tests. 13 Given this uncertainty, there were many patients who were considered COVID-19 positive despite a negative test. Excluding patients who decompensated and died before a test could return positive could result in selection bias in our study. Patients were excluded if they were transported for an obvious non-COVID, non-respiratory failure indication. Patients were also excluded if they were transported to a non-ECMO center or to a non-consortium hospital. All consortium hospitals had the capacity to initiate patient on ECMO, and all consortium hospitals had similar criteria for initiation of VV-ECMO for acute hypoxic and/or hypercapnic respiratory failure. Transport records were reviewed for demographic characteristics such as age, sex, weight, and pertinent comorbidities including chronic kidney disease (CKD), congestive heart failure (CHF), hypertension, and stroke. Information regarding patient location at the sending hospital prior to transfer (emergency department [ED], ICU, floor) and mode of transportation (ground or helicopter) were recorded. The COVID-19 diagnosis, as known to the critical care transport team, was recorded. Transport records were also queried for the number of days admitted at the sending hospital before transfer and the number of days intubated at the sending hospital before transfer. Outcomes data extracted from receiving tertiary care center records included in-hospital mortality and ECMO cannulation. The primary outcome of interest was in-hospital mortality. The secondary outcome of interest was cannulation for ECMO at the receiving tertiary care center. Descriptive statistics were determined for clinical data with median and interquartile ranges (IQR) reported for continuous variables, and number and percentages for categorical variables. Point-biserial correlation was used to determine the correlation between the number of days admitted at the sending hospital and mortality, the number of days intubated at the sending hospital and mortality, the number of days admitted at the sending hospital and ECMO cannulation, and the number of days intubated at the sending hospital and ECMO cannulation. Univariate logistic analyses were performed using chi-squared testing to determine which factors were associated with mortality and ECMO cannulation. Multivariable regression was performed on variables that were significant on univariate analyses. After tabulating results in Microsoft Excel (Microsoft, Redmon, Washington), data were exported to JMP Pro version 15.0 (SAS Institute Inc, Cary, North Carolina). Results We identified 676 charts for review. After screening for inclusion and exclusion criteria, 321 charts remained for inclusion in this study (Figure 1). Patients were transferred from 48 sending hospitals to 6 receiving tertiary care centers. The majority of patients were male (65.7%, n = 211), with a median age of 61 years (IQR 51–69) (Table 1). Hypertension (43.3%, n = 139), diabetes (30.5%, n = 98), and obesity (22.7%, n = 73) were the most common reported comorbidities. COVID-19 diagnosis at the time of transport was confirmed in 57.3% (n = 184) of cases. Most patients were transferred from the ICU at the sending hospital (73.8%, n = 237). In general, patients were transferred for care not available at the sending hospital, such as access to trials of remdesivir, inhaled pulmonary vasodilators, or ECMO. Due to the disparate impact of COVID-19 on certain communities, some patients were transferred to match volumes with available resources. The specific reason for transport to a tertiary care center was not consistently available in the record and therefore could not be quantified. Patients were admitted at the sending hospital for a median of 2 days (IQR 0–7, range 0–61 days) before transport. Patients were intubated for a median of 1 day (IQR 0–3, range 0–29 days) before transport. Figure 1. Flowchart of patients screened, excluded, and enrolled in this study. Table 1. Patient Characteristics. Patient characteristics Value Age (median, IQR) 61 years (51–69) Sex (n, % male) 211 (65.7) Actual weight (kg) (median, IQR) 87 (76.5–105) Presence of a comorbidity (n, %) Asthma 24 (7.5) Chronic obstructive pulmonary disease 32 (10.0) Congestive heart failure 20 (6.2) Diabetes 98 (30.5) Hypertension 139 (43.3) Obesity 73 (22.7) Remote myocardial infarction/coronary artery disease 27 (8.4) Stroke 14 (4.4) None 20 (6.2) Location in hospital prior to transport (n, %) ED 74 (23.1) ICU 237 (73.8) Other 10 (3.1) Mode of transportation (n, %) Ground 281 (87.5) Helicopter 40 (12.5) COVID-19 status prior to transport (n, %) Confirmed 184 (57.3) Suspected 137 (42.7) Timing of transport Days at sending hospital before transport (median, IQR) 2, (0–7) Days intubated at sending hospital before transport (median, IQR) 1, (0–3) Mortality (n, %) Alive 199 (62.0) Dead 122 (38.0) ECMO cannulation (n, %) Cannulated 30 (9.3) Not cannulated 291 (90.7) In-hospital mortality was 38.0% (n = 122) and 6.9% (n = 22) of patients were cannulated for ECMO at the receiving tertiary care centers. Correlative analyses revealed that there was no significant correlation between the number of days admitted at the sending hospital nor the number of days intubated at the sending hospital and mortality (correlation coefficient 0.051 and −0.007, respectively) (Table 2). There was also no significant correlation between the number of days admitted at the sending hospital nor the number of days intubated at the sending hospital and ECMO cannulation (correlation coefficient 0.008 and −0.036, respectively). Table 2. Correlative Analyses for Mortality and ECMO Cannulation. Variable 1 Variable 2 Point-Biserial Correlation Coefficient Days admitted at sending hospital Death 0.051 Days intubated at sending hospital Death −0.007 Days admitted at sending hospital ECMO cannulation 0.008 Days intubated at sending hospital ECMO cannulation −0.036 ECMO cannulation Death 0.144 Univariate logistic analyses revealed that age (P = 0.002) and a history of hypertension (P = 0.034), CHF (P < 0.001), CKD (P = 0.002), and stroke (P = 0.010) were associated with increased mortality (Table 3). If a patient did not have any comorbidities, they had a significantly decreased risk of death (P = 0.020). Univariate logistic analyses revealed that patients were significantly less likely to be cannulated for ECMO with increasing age (P = 0.001). On multivariable regression analyses, no parameter significantly predicted mortality (Table 4). Table 3. Univariate Logistic Analyses for Mortality and ECMO Cannulation. Variable P Value Death ECMO Cannulation Sex 0.963 0.492 Age 0 . 002 0.001 Actual weight 0.831 0.245 Comorbidities Asthma 0.161 0.239 Cancer 0.702 0.317 Chronic kidney disease 0.002 0.796 Chronic obstructive pulmonary disease 0.146 0.149 Cirrhosis 0.927 0.320 Congestive heart failure <0.001 0.455 Diabetes 0.093 0.947 End stage renal disease 0.927 0.320 Human immunodeficiency virus 0.313 0.442 Hypertension 0.034 0.438 Intravenous drug use 0.381 0.998 Obesity 0.301 0.703 Other pulmonary diseases 0.078 0.998 Peripheral vascular disease 0.317 0.320 Pregnancy 0.166 0.139 Remote myocardial infarction/coronary artery disease 0.054 0.998 Smoking 0.976 0.207 Stroke 0.010 0.998 Other 0.133 0.804 None 0.020 0.132 Table 4. Multivariable Regression Analyses for Mortality. Parameter Odds Ratio (95% Confidence Interval) Age 1.01 (0.99–1.03) Chronic kidney disease 2.73 (0.89–8.37) Congestive heart failure 3.88 (1.31–11.46) Hypertension 1.39 (0.86–2.26) Stroke 3.00 (0.87–10.26) Discussion In this retrospective study of patients transferred to a tertiary care center for COVID-19, the number of days admitted at the sending hospital was not associated with in-hospital mortality at the receiving institution. The overall mortality for this group was 38%, consistent with the reported mortality rates for COVID patients requiring intubation.14–16 Our prior descriptive work assessing outcomes after transporting 254 patients transported with suspected or confirmed COVID respiratory failure found a mortality rate of 25%, 17 but many patients were still hospitalized at the time of analysis, leading to an underestimate of the in-hospital mortality rate. The current study was designed to have complete outcomes reported, thereby providing a more accurate reflection of in-hospital mortality. A large meta-analysis found that the mortality rate of COVID patients admitted to the ICU (n = 1526) was 40.9%. 16 Considering that the patients included in the current study were those transferred to tertiary care ECMO centers, often for services not available at the sending hospital, the acuity of this cohort was very high. Therefore, a mortality rate comparable to all intubated patients is impressive, indicating comparable outcomes despite the higher acuity. The CESAR trial from 2009 found improved outcomes for severe ARDS patients transferred to ECMO centers, even if they did not receive ECMO. 4 We have previously shown that patients with hypoxemic respiratory failure transferred to ECMO centers have a lower mortality rate than would be predicted. 3 Thus, in many instances, the benefit of transfer for patients with respiratory failure who may require ECMO outweighs the risk of transport. Studies of patients transferred with COVID respiratory failure have not shown a difference in outcomes as compared to patients primarily admitted to the tertiary care ICU 18 indicating that the benefits of tertiary care can be acquired even for patients later in their hospital course. Our current investigation supports these findings in COVID patients by demonstrating no correlation between days at the sending and mortality at the receiving hospital. These findings suggest that patients can be safely transported to receive tertiary care when they clinically require it, without specific concern for the duration of their prior hospitalization. The number of days intubated at the sending hospital was also not associated with in-hospital mortality at the receiving facility. Prior work has attempted to find correlations between the timing of intubation and outcomes in COVID. A recent prospective study of 205 patients found an adjusted hazard ratio (HR) of 2.45 (95% CI 1.29–4.65) for patients with a delay in intubation after the first respiratory support 19 ; however, these findings are not universal. A meta-analysis of 12 studies involving 8944 critically ill patients with COVID found no difference in mortality between patients undergoing early versus late intubation (with mortality rates of 45.4% vs 39.1%; RR 1.07, 95% CI 0.99–1.15, p = 0.08). 20 A prospective multicenter study of patients intubated for hypoxemic respiratory failure after a failed attempt at noninvasive ventilation had a mortality rate of 43%, comparable to the mortality rate for all intubated COVID patients. The meta-analysis also showed no differences in mortality with or without a prior trial of high-flow nasal cannula or noninvasive mechanical ventilation (48.9% vs 42.5%; RR 1.11, 95% CI 0.99–1.25, p = 0.08). 20 We found that 6.9% of our cohort were cannulated for ECMO at the receiving tertiary care center. Neither the number of days admitted at the sending hospital, nor the number of days intubated at the sending hospital were associated with ECMO cannulation. As anticipated, increasing age was found to significantly decrease the rate of cannulation for ECMO at receiving tertiary care centers. Prior studies have illustrated that increased age is a predictor of mortality for patients on ECMO 21 as well as COVID critical illness. 22 In this retrospective analysis of patients transferred by a critical care transport organization, we could not compare outcomes of our cohort with patients who were not transferred out of the sending hospitals. The number of days patients were admitted at the sending hospital prior to the initiation of transfer to a higher level of care is dependent on many factors that are not possible to quantify in this analysis. Nuanced changes in clinical status or lack of clinical improvement despite treatment are likely to have caused the request for transfer from clinicians at sending hospitals. Perhaps earlier transfer of less sick patients to a higher level of care could have resulted in decreased mortality. However, it is not always possible to predict which patients will require a high level of care, and critical care resources are limited. Therefore, determining the appropriate time to transfer a patient to a higher level of care is an ongoing challenge. During the unprecedented stress of the COVID-19 pandemic on the healthcare system, 23 clinicians have had to be even more judicious about utilization of critical care services. Our study suggests that even if a patient has been admitted and intubated for many days at a sending hospital, it is still reasonable to transfer a critically ill COVID-19 patient to a tertiary care center. Limitations This study has several limitations. First, patients in this study were selected by the sending hospitals’ clinicians for transfer and are at a high risk of selection bias. The sending clinicians’ decisions to transfer patients to a higher level of care may be influenced by numerous unmeasured variables. Second, patients who were sicker may have been transferred sooner, which represents another form of selection bias. Third, the patients’ comorbidities and severity of illness scores (eg, APACHE or SOFA scores) are not universally known and are limited to what is relayed to the critical care transport team and included in the transport record. However, these limitations reflect the realities of practice and critical care transport. An additional limitation reflects the QI nature of this work. Due to some practical constraints in following up patients during the height of the pandemic, follow-up information was limited at one of the receiving hospitals. We do not believe that this loss of mortality data significantly impacted the results, but this cannot be substantiated. Conclusion The risk of mortality or ECMO cannulation for patients with confirmed or suspected COVID-19 transferred from sending hospitals to tertiary care hospitals was found to be independent of the duration of time the patient was admitted at the sending hospital. Similarly, the risk of mortality or ECMO cannulation was also found to be independent of the duration of time a patient was intubated at the sending hospital. Therefore, it may still be reasonable to transfer a critically ill COVID-19 patient to a tertiary care center even if they have been admitted at the sending hospital for several days. Acknowledgements We would like to acknowledge the Boston MedFlight clinical teams. Their dedication to patient care is unparalleled, and these results could not have been accomplished without them. We would also like to acknowledge Dr James Dargin, who tragically passed away during the completion of this manuscript. His impact on patient care, education, and research cannot be overstated. The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. ORCID iDs: Jeremy B. Richards, MD, MA, FACP, ATSF https://orcid.org/0000-0002-8922-1955 Michael N. Cocchi, MD https://orcid.org/0000-0002-5541-5209 Susan R. Wilcox, MD, FACEP, FCCM, FAAEM https://orcid.org/0000-0001-7477-7531 ==== Refs References 1 Centers for Disease Control and Prevention. COVID Data Tracker. 2022. https://covid.cdc.gov/covid-data-tracker/#new-hospital-admissions. Accessed October 6, 2022. 2 Wilcox SR Saia MS Waden H , et al. Improved oxygenation after transport in patients with hypoxemic respiratory failure. Air Med J. 2015;34 (6):369-376. 10.1016/j.amj.2015.07.006. 3 Wilcox SR Richards JB Genthon A , et al. 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==== Front J Manag Inq J Manag Inq JMI spjmi Journal of Management Inquiry 1056-4926 1552-6542 SAGE Publications Sage CA: Los Angeles, CA 10.1177/10564926231182566 10.1177_10564926231182566 Empirical Research ‘Running Towards the Bullets’: Moral Injury in Critical Care Nursing in the COVID-19 Pandemic https://orcid.org/0000-0002-8574-7917 Griffin Martyn 1 Hamilton Peter 2 Harness Oonagh 3 Credland Nicki 4 McMurray Robert 5 1 7315 Sheffield University , Sheffield, UK 2 3057 Durham University , Durham, UK 3 5995 Northumbria University , Newcastle upon Tyne, UK 4 4019 Hull University , Hull, UK 5 RSCI, Dublin, Ireland Martyn Griffin, Sheffield University Management School, Conduit Rd, Sheffield, S10 1FL, UK. Email: m.a.griffin@sheffield.ac.uk 26 6 2023 26 6 2023 10564926231182566© The Author(s) 2023 2023 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. The COVID-19 pandemic placed unprecedented strain on healthcare professionals around the globe, particularly those working in intensive care units. It was reported that instances of moral injury – a betrayal of what is ethically right by those in positions of power – were widespread in these organizational settings. In this paper, we explore these emerging findings to ask: What are the experiences and implications of moral injury in critical care nursing during the pandemic? Drawing on 103 interviews with 54 critical care nurses, we offer insights into the experience of moral injury in a workplace experiencing crisis, focusing on (i) unsafe staffing levels, (ii) inadequate equipment, and (iii) inability to provide patients with a dignified death. We provide accounts of the implications of moral injury ranging from debilitating anxiety to post-traumatic stress disorder and sectioning, as well as widespread feelings of anger and guilt leading to an intention to leave the profession. healthcare qualitative research well-being edited-statecorrected-proof typesetterts19 ==== Body pmcI mean we’re all broken, we’re all beyond broken. There's not a staff member that isn’t. (Critical Care Nurse (CCN) 14) The COVID-19 pandemic has left indelible marks on us all – psychological and physiological scars that run deep, some that might never heal. For those working on the frontline, particularly in healthcare, life has been especially challenging (Maben & Bridges, 2020). Witnessing suffering during crisis in an organizational setting can be a traumatic experience (Powley, 2009), and we all owe a massive debt to those that, when called to do so, ran ‘towards the bullets’ and risked not only their own lives but their psychological well-being so that those infected with the virus might have a better chance at survival. During this period, the pandemic has put unprecedented strain on healthcare professionals around the globe (WHO, 2020) – in particular, in intensive care units (ICUs), where the very sickest patients were sedated, intubated, and often ventilated as doctors and nurses fought to wrestle each individual from the grip of a deadly virus with a high mortality rate, no known cure, and no known reliable course of treatment (Harris et al., 2021). The working environment in an ICU ward was made especially challenging due to a variety of factors, including a lack of adequate personal protection equipment (PPE), a shortage of medicines, the reduction of the staff-to-patient ratio from 1:1 to as low as 1:6, and the subsequent need to redeploy staff from other parts of the hospital often with no experience of ICU care (Arnetz et al., 2020). Patients died without loved ones at their bedsides, and nurses struggled to communicate in restrictive PPE and toiled for 12-h shifts in unbearable heat, fearing infection as hospitals were ravaged by a surging virus. In these circumstances, it was often impossible to deliver a normal standard of care, and ICU nurses were faced with the overwhelming responsibilities of care but often without the time, resources, or skill mix to do so in a way that felt adequate or safe (Crowe et al., 2021). The toll on ICU staff was extensive. A 2021 survey in the UK found ‘substantial rates of probable mental health disorders, and thoughts of self-harm, amongst ICU staff’ with ‘nearly one in five nurses … working in ICU report[ing] thoughts of self-harm or suicide’ (Greenberg et al., 2021, p. 1). It was also found that ‘these difficulties were especially prevalent in nurses’ (Greenberg et al., 2021), and for reasons yet unknown, they were substantially more likely to suffer (than doctors or other health workers) from serious mental health problems during the pandemic. Indeed, high levels of anxiety, depression, and post-traumatic stress disorder (PTSD) in nurses on the frontline have also been found within similar studies in China (Pan et al., 2021), Italy (Di Tella et al., 2021), France (Caillet et al., 2020), Turkey (Şanlıtürk, 2021), and Canada (Crowe et al., 2021). Efforts are now underway to understand the deleterious psychological effects on ICU nurses, starting with the root cause but extending to the implications and the potential ways of helping nurses both now and in the future (Williamson et al., 2020). One way of understanding ICU nurses’ experiences is through the concept of moral injury. Originally used within clinical psychiatry to understand the experience of war veterans (Shay, 1995), moral injury is described by Williamson et al. (2021, p. 453) as ‘the strong cognitive and emotional response that can occur following events that violate a person’s moral or ethical code’ that occurs due to ‘a betrayal by a trusted person in a high-stakes situation’. It leaves ‘a deep emotional wound’ (Čartolovni et al., 2021, p. 590) that can profoundly affect an individual. Concerns about moral injury in ICU nursing during the pandemic have been highlighted in the media (Alexander, 2021), professional bodies (RCN, 2021), and academic commentaries (Borges et al., 2020) as an urgent emerging problem requiring investigation. A lack of PPE, low staffing levels, and poor planning by those in positions of power are all potential sources of moral injury. And yet, very little is known about how nurses have made sense of experiences associated with moral injury in an organizational setting or how they have attempted to work towards moral repair (Goodstein et al., 2016). Indeed, there are ‘limited empirical studies on moral injury’, including in the area of healthcare, meaning that now is a ‘timely moment to speak about moral injury amongst healthcare professionals’ (Čartolovni et al., 2021, p. 590). With this in mind, and building upon the recent introduction of moral injury to organizational studies in this journal (Kalkman & Molendijk, 2021), we ask: What are the experiences and implications of moral injury in critical care nursing during the pandemic? In our paper, we argue that the cumulative and repetitive nature of moral injury over this period has been devastating for critical care nurses (CCNs). To show this, we reflect upon how they made sense of their experiences during the pandemic, including their assessment of general levels of support and how this challenged their own internalized standards of professional ideals and values. To do so, we adopt a ‘writing differently’ approach within our work, endorsing the claim that too often ‘scientific writing excises much of what it is to be human’ (Gilmore et al., 2019, p. 4), sacrificing a deeper understanding of issues on the altar of feigned disinterestedness and objectivity. We, instead, embrace an emotional writing style that invites the reader into the world of critical care nursing, exploring experiences of intense suffering in an organizational context without foregoing the centrality of academic rigour. Our article has three core theoretical and corresponding practical contributions. First, we provide a novel insight into the repetitive and cumulative nature of moral injury in an extreme, underexplored organizational setting, the ICU. We do so by providing a voice to an essential but largely unheard (and in some cases, silenced) group of workers during the pandemic: ICU nurses – amplifying their experiences of cumulative moral injury in an organizational context, focusing on the emotional and psychological consequences in the workplace. In doing so, we respond to calls within this journal to speak out against social injustices that the pandemic has brought into focus, telling the stories of an overworked and underpaid profession within the modern workforce (Peredo et al., 2022). Second, we highlight the tendency to individualize responses to moral injury by pushing the burden of moral repair back on to workers, here the ICU nurses. In doing so, we emphasize the collective and intersubjective dimensions of moral injury and the need to take these more seriously to address them in the workplace. Finally, our exploration of nurses’ experiences in the ICU reveals the centrality of power relations to the concept of moral injury and shows how essential it is for institutions to take a systemic approach to tackling moral injury. That is, they must also consider both political and structural dimensions if they are going to minimize occurrences of moral injury in the workplace and collectively help workers to repair and recover from it over time. In the remainder of the article, we first discuss the context of ICU nursing and explore how moral injury is a productive theoretical frame to understand their experiences. We then outline the methodological approach, focusing on 103 interviews with 54 CCNs over a 12-month period at the height of the pandemic in the UK. We then present our findings, focusing on experiences, implications, coping mechanisms, and required action for reducing moral injury, and then discuss the broader relevance of these. Finally, we conclude by suggesting that the experiences of CCNs must be listened to and learnt from by senior healthcare managers and government officials if we are to avoid the horrors of moral injury and its implications in the future.ICU nursing in context: It takes critical thinking, almost a bit of perfectionism. A strong work ethic, attention to detail, and that drive to keep pushing. Even when you’re exhausted, to keep pushing. (CCN25) In 2009, approximately 30 million people were treated worldwide within ICUs (Vincent et al., 2009). Despite this, public knowledge of what goes on within an ICU ward is still limited (García-Labattut, 2006), and as a workplace, it remains somewhat shrouded in mystery. According to Adam and Osborne (2001, p. 1), the purpose of the ICU is to ‘to provide care for severely ill patients with potentially reversible conditions’ usually ‘with potential or established organ failure’ with the intention of ‘reduc[ing] avoidable mortality in critically ill patients’. The ICU nurse – and the principle of one-to-one patient care – is central to ICU provision. Indeed, for the patient and their families, the ICU nurse is their primary contact, providing a depth and duration of interaction that far exceeds that of many other occupations attending ICU. ICU nursing involves not only high levels of skill development, qualifications, and enhancement (Leiter et al., 1994), which has contributed to its increasing professionalization (McMurray, 2011), but also an emotional component of communicating with dying patients and supporting scared and often grieving families. The need to make complex decisions and choices within ambiguous care scenarios around holding or withdrawing treatment and securing a ‘good’ death for their patient when required places a huge burden on these nurses (Hoy et al., 2007). The ICU can, as a result, be an extremely stressful working environment. The emotional and psychological strain of being an ICU nurse requires of them constant self-regulation (Hayward & Tuckey, 2011) and resilience (Powley, 2009). The regularity and close proximity of grief and a range of intense emotional experiences have been shown to lead to compassion fatigue and nurses wanting to leave the profession (Dashtipour et al., 2021). Not surprisingly, studies have found that a significant number of CCNs experience severe burnout, including the main symptoms of exhaustion, depersonalization, and reduced personal accomplishment (Moss et al., 2016). It is an extremely difficult profession, and the ICU nurse is susceptible to a range of negative outcomes – a speciality in which nurses have a high degree of commitment but in which psychological and emotional pressures were already at extremely high levels leading to calls for action within the critical care community (Poncet et al., 2007). And then, in March 2020, the pandemic arrived.The pandemic: It was like this alien thing that was coming to attack us and we were on the frontline. (CCN1) As COVID-19 cases rose in the initial months of the pandemic, the UK was considered especially vulnerable. The country possessed a low number of critical care beds relative to its population, ‘with just 7.3 critical care beds per 100,000 people, more than half the average in OECD EU nations’, compared to 29.2 beds per 100,000 in Germany (BMA, 2022). The immediate reaction was to postpone non-urgent planned operations, increase the number of beds in ICU (often through expansion into areas conventionally used for other purposes), and redeploy staff from elsewhere in the National Health Service (NHS) to boost staff numbers. Despite these measures, numerous studies point towards problems within hospitals during this period. In a survey of frontline doctors, Harris et al. (2021, p. 6) highlight an ‘overwhelmed system’ reflected by the reduced nurse-to-patient ratio in which medics felt like ‘cannon fodder … exposed and unprotected’ not least by a lack of PPE. Meanwhile, one Canadian survey of ICU nurses in the pandemic highlighted a fear of ‘staying safe’ within the hospital (Crowe et al., 2021, p. 7) due to worries of contracting the virus and passing it to family. Finally, a Turkish survey of ICU nurses identified ‘high working hours … and failure in patient treatment’ leading to abnormally high death rates as additionally difficult for staff in trying to adjust in the first year of the pandemic (Şanlıtürk, 2021, p. 1). The psychological implications on ICU nurses were significant. Multiple studies conducted during the pandemic have reflected this. One UK survey of 709 staff in ICU suggests that ‘45% [of surveyed staff] met the threshold for probable clinical significance on at least one of the following measures: severe depression (6%), PTSD (40%), severe anxiety (11%), or problem drinking (7%)’. They go on to add that ‘more than one in seven clinicians (and nearly one in five nurses) in our sample working in ICU reported thoughts of self-harm or suicide [which] is also highly concerning’ (Greenberg et al., 2021, p. 4). This has been supported by surveys conducted in other health systems, which show, for example, that 14% of healthcare workers in a study conducted in Wuhan, China, were suffering from PTSD (Pan et al., 2021); ICU nurses in Italy were showing signs of post-traumatic stress (Di Tella et al., 2021); and in French ICUs, ‘the incidence of anxiety and depression were 48% and 16%, respectively … [with] … PTSD symptoms … present in 27% of respondents’ (Caillet et al., 2020). The current state of psychological distress in ICUs has led to calls by World Health Organization (WHO) (2022) and others (Greenberg & Tracy, 2020) to provide a deeper and more layered understanding of ICU nurses’ experiences during the pandemic in order to protect them in the future. At present, however, whilst multiple quantitative surveys exist, there is a lack of a deeper qualitative and longitudinal understanding of the experiences of ICU nurses over this period. It is important, we argue, to try to explore more deeply the ways that their expectations about care have been challenged in the workplace and the consequences of doing so. Moral Injury: The Theoretical Frame Conventionally, there are a variety of organizational approaches that could explore the psychological experiences of nurses during a time of crisis, encountering the competing demands of their organization and their own values and ethical beliefs. We might focus, for instance, on breaches of trust (Sama & Shoaf, 2008) or of the psychological contract (Zacher & Rudolph, 2021). Alternatively, we might try to capture the deleterious effect on stress and well-being of staff in morally taxing situations (Guest, 2017), including implications of psychological injury (De Rond & Lok, 2016) or of resistance and deviance that staff might engage in to push back against the imposition of what they feel is wrong or unsound (Pelly, 2017). Whilst accepting the benefits of potential studies through these perspectives, we believe that another much less explored concept in organizational studies can offer more in this context. A concept that captures not only the moral and ethical component of the organizational situation but also the affective internal experience of the worker: moral injury. Moral injury involves a ‘deep emotional wound and is unique to those who bear witness to intense human suffering and cruelty’ (Čartolovni et al., 2021, p. 590). It involves experiencing what Litz et al. (2009, p. 700) call a potentially morally injurious event (PMIE), that is, ‘perpetrating, failing to prevent, or bearing witness to acts that transgress deeply held moral beliefs and expectations’. Importantly, according to Shay (2014, p. 182), moral injury is felt as ‘a betrayal of what's right, by someone who holds legitimate authority … in a high-stakes situation’. All three components must be present. Originally, and until quite recently, moral injury was used to explain the lingering psychological injuries suffered by military personnel (Richardson et al., 2020; Shay, 1995), such as being sent into warzones without adequate weaponry or support. Over the past few years, however, moral injury has been applied to a wider range of professions, including police officers (Komarovskaya et al., 2011) and child protection officers (Haight et al., 2017). More recently, in this journal, moral injury was introduced to the organizational studies literature for the first time (Kalkman & Molendijk, 2021, p. 221), exploring how border guards as ‘lower level organizational members face moral challenges because their personal values conflict with organizational directions’. The authors suggest that strategic ambiguity within an organization (encouraged by senior leaders) can contribute to the experiences of moral injury, particularly for those who are more vulnerable (not in leadership positions) within an organization. Carucci and Praslova (2022) meanwhile suggest that the experience of moral injury – being asked to make moral decisions within jobs that contravene deeply held values and beliefs – might be a more acute reason for employee distress and resignation than burnout and other commonly used concepts in organizational studies. As they argue, drawing directly on the concept of moral injury, ‘the mass exodus from our workplaces is, in part, a proclamation that people can’t – and won’t – tolerate mistreatment, injustice, and incompetence from their leaders anymore, particularly at the expense of their dignity and values’ (Carucci & Praslova, 2022). We know from previous studies on health professionals that an increase in moral injury is linked directly to an increase in serious mental health problems ranging from depression (Nash et al., 2013) to PTSD (Gibbons et al., 2013), although it is distinct from the latter insofar as its deeper engagement with our moral and ethical core. In a recent systematic review, Williamson et al. (2021, p. 453) found that moral injury leads to ‘profound feelings of shame and guilt, and alterations in cognitions and beliefs (e.g. “I am a failure”, “colleagues don’t care about me”), as well as maladaptive coping responses (e.g. substance misuse, social withdrawal, or self-destructive acts)’. The experience of moral injury in healthcare settings has also been linked to suicidal thoughts that ‘include social withdrawal, self-depreciating emotions, and a loss of meaning’ (Williamson et al., 2018, p. 344). Other studies show feelings of alienation by healthcare workers following experiences of moral injury in the workplace (Gibbons et al., 2013) and even of physical pain (Koenig et al., 2018). There have been indications during the pandemic that healthcare workers might have been suffering from moral injury. In the USA, Mantri et al. (2021) show through a large survey of health professionals that moral injury was prevalent within the workforce, particularly for those who had direct experience of caring for COVID-19 patients. In addition to this, Borges et al. (2020) and Williamson et al. (2020, p. 318) both wrote short commentaries with the latter warning of the dangers of an ‘exposure to traumatic events’ warning of an ‘increased risk of moral injury if staff feel unaware or unprepared for emotional/psychological consequences of decisions’, especially ‘if leaders are perceived to not take responsibility for the event(s) and are unsupportive of staff’. In a US survey of healthcare workers during the pandemic, Litam and Balkin (2021, p. 3) also found a link between the experience of moral injury and negative outcomes such as ‘difficulty sleeping, feeling on edge, and avoidance of activities that remind healthcare workers of frightening experiences of their patients’. There have, nevertheless, been a limited number of qualitative studies into moral injury of NHS staff during the pandemic. In their study, French et al. (2021) found that ‘abandonment as betrayal … dishonesty and lack of accountability … and fractured relationship to management or the NHS’ were all central experiences for staff during this period. The 16 staff from across the NHS that were interviewed ‘voiced feelings that their death would be meaningless to the leadership … and that … [n]ot only had leaders failed to live up to the trust placed in them, they had failed to place well-earned trust in their employees by not acknowledging the reality of the situation’ (French et al., 2021, p. 517, 520). The study, whilst limited in size, duration, and focus on ICU nurses, calls for greater moral repair – attempting to heal the emotional wounds of the pandemic – whilst also asking for greater accountability. It is upon this work that we wish to build, providing a deeper understanding of ICU nurses’ experiences on the frontline during the pandemic. It is increasingly recognized that the concept of moral injury has relevance to healthcare and a ‘multidisciplinary appeal’ (Griffin et al., 2019, p. 350) that can integrate perspectives from healthcare to psychology and sociology (see also Molendijk et al., 2022). Yet it is also understood that it is thus far underresearched as an emerging concept (Williamson et al., 2018, p. 345). Until very recently, organizational scholars, in particular, have failed to engage with this term to understand its relevance to people within their working lives. Instead, there has been a tendency either to rely upon legalistic approaches to understanding the breaches of expectations in the workplace or to portray these situations as failures of a leader–follower relationship. Whilst these concepts have been productive elsewhere, we argue that the concept of moral injury can help retrain a focus on the lived experience of the ethically and morally transgressed, helping us as organizational scholars to see and understand the emotional wounds of those that work in difficult circumstances. We argue that it is important, therefore, to focus on exploring these issues of moral injury with a wider disciplinary lens but with a qualitative focus on ICU nurses in order to understand the experiences and the implications of moral injury in depth. Consequently, this leads us to ask within our study: What are the experiences and implications of moral injury in critical care nursing during the pandemic? It is to answer this question that we now turn. Methodology Data Collection Early in the pandemic, the research team decided to conduct longitudinal semi-structured interviews to gain insight into the experiences of CCNs (Hermanowicz, 2013). After a recruitment campaign with the support of the British Association of Critical Care Nurses (BACCN), we recruited 54 CCNs who worked in ICU prior to and during the pandemic. All recruits were band five or six nurses (i.e. not in management roles), reflecting our intention to focus primarily on the experiences of those at the coalface during the pandemic. This purposive selection strategy was not to ignore or downplay the complex difficulties that faced leaders within the NHS throughout the pandemic; they indeed have their own story to tell. With a focus on moral injury, though, this study chose to concentrate on non-managerial staff and the experiences that they faced on the frontline during this period. The experience of the nurses ranged from just under 2 years to over 30 years, and all but two of the participants worked in ICU wards in UK hospitals (the other two worked in Ireland). There were 38 different hospitals/ICU wards represented within the study, providing geographical diversity. In terms of gender, whilst NMC (2023) reports that 11% of nurses are male, only three participants were male representing approximately 6% of the participants. Likewise, in relation to ethnicity, there was a lack of representation as it proved difficult to recruit from minority groups, and here again, the sample was unrepresentative as only one ethnic minority CCN was interviewed. The recruitment of ethnic minorities has been identified as a wider problem for studies of this kind (Brown et al., 2014). There were three rounds of interviews in September/October 2020, January/February 2021, and May/June 2021. The value of interviewing over time was allowing us to ‘capture critical moments of change and transitions’, enabling participants to describe how feelings and situations changed during a highly disruptive and fluid time in their working lives (Vogl et al., 2018, p. 178). There was an expected attrition in nurses from phase one (54 nurses) to phase three (24) due to the emotional and difficult nature of the interviews and the complexities of timetabling meetings with exhausted nurses. A total of 103 interviews were ultimately conducted. These lasted on average 75 min in phase one (where we asked 54 nurses more broadly about ICU nursing and experiences during the pandemic), 55 min in phase 2 (reinterviewing 29 nurses), and 30 min in phase 3 (follow-up interviews with 24 nurses). Whilst there was an interview guide (decided deliberatively by the research team in between phases), the overall intention of the interviews was to enable the CCNs to voice whatever issues they were facing in ICUs during the pandemic. Many of the interviews, conducted by all members of the research team, were emotional for both participants and interviewers. We met weekly to debrief from what were (at times) disturbing and uncomfortable interviews that stayed with many of us long after they had ended. Given the subject matter, interviews unintentionally but somewhat unavoidably took on a therapeutic component, with nurses able to offload experiences and memories to interviewers (Birch & Miller, 2000). This had unintended consequences on the interviewers who used their debrief sessions to vocalize their own emotional reactions to stories that they heard. Free counselling sessions were offered to the nurses participating through a trained psychotherapist who was also part of the wider research team, with a number of them taking up this opportunity in between phases and after the study had been completed. Data Analysis We sought to avoid an overtly ‘mechanistic’ analysis of the data, seeking instead a more organic approach in which we sought to bring the voices and stories of the CCNs to life. That is, to embrace an analytical approach that enabled us to amplify the voices of the nurses to explore the experience of moral injury by this group of people in the pandemic. The interviews were transcribed by a professional transcription service and analysed through NVivo. Inspired by the principle of developing analytic themes from template analysis (King, 2012), the data were coded following Locke et al.'s (2020) ‘delimiting the field’ approach. In what was a highly iterative and team approach to coding (Giesen & Roeser, 2020), we began with the calls from multiple sources that moral injury is a problem requiring urgent investigation in critical care organizational settings (Alexander, 2021; Borges et al., 2020; RCN, 2021). We therefore used the existing literature on moral injury to discuss and produce four template questions to answer whilst analysing the data (which are displayed in Figures 1 to 3). Following Pratt (2009), Figures 1 to 3 also include ‘proof quotes’ not included in the main analysis to show the depth and generalizability of the data whilst clearly indicating how we reached our codes and categories of analysis. Figure 1. Coding architecture (experiences). Figure 2. Coding architecture (implications). Figure 3. Coding architecture (coping mechanisms and required actions). The analysis therefore asked: (a) ‘In what ways are critical care nurses experiencing moral injury during the pandemic?’ Following the literature on moral injury, specifically Litz et al. (2009, p. 700), we looked for PMIEs that involved ‘perpetrating, failing to prevent, or bearing witness to acts that transgress deeply held beliefs and expectations’ in the critical care working environment. Following Čartolovni et al. (2021, p. 590), we also looked for instances of ‘deep emotional wounds’ that (as with Shay's (2014) definition of moral injury) included a ‘betrayal of what's right by someone who holds legitimate authority … in a high-stakes situation’. Author One's analysis of the data led to the creation of 16 codes that, following discussion and cross-checking within the team, were then grouped into 4 categories that best reflect the sources of moral injury during this period. In keeping with this pattern, during the analysis, we also asked: (b) ‘What are the implications of moral injury of critical care nurses during the pandemic?’, reflecting the personal consequences of these experiences of CCNs who endured moral injury. This led to the creation of 16 codes and then 3 categories. In addition to this, we explored the following: (c) ‘What coping mechanisms did ICU nurses employ to address and/or repair moral injury during the pandemic?’ – an effort to understand how CCNs had tried to work through these experiences of moral injury, which led to the creation of 11 codes and 3 categories – and, finally, (d) ‘What organizational actions are required to avoid and/or repair moral injury?’, which reflected on the collective organizational response to moral injury, leading to the creation of 4 codes and 1 category. This analysis enabled us to construct a narrative around the experience of moral injury by CCNs during the pandemic whilst drawing out lessons learnt about how moral injury might be better avoided and repaired. Findings Our findings focus on the experience of moral injury by ICU nurses during the pandemic. We concentrate initially on exploring the kinds of situations that nurses experienced on the frontline (Litam & Balkin, 2021; Williamson et al., 2020), which challenged their deeply held moral beliefs and expectations about care (Litz et al., 2009). There are four broad kinds of experiences we consider in turn, reflecting the repetitive and thereby cumulative nature of moral injury when working in the ICU. We then consider the implications of these experiences of moral injury and the extent to which coping mechanisms and institutional responses were used to pursue moral repair (Paul et al., 2014). Experiences of Moral Injury in the ICU Unsafe Staffing Levels and Skill Mix As the pandemic began and the influx of patients requiring critical care increased exponentially, the exceptional nature of the crisis became more apparent. CCN2 recalls, ‘it was like some kind of warzone, it was incredibly dangerous and incredibly stressful.’ One source of danger was the sudden shift in the ratio of nurses to patients away from 1-to-1 nursing (Şanlıtürk, 2021). Two nurses recalled:We were taking one to five, one to six patients per nurse. So obviously having six patients on a ventilator … when we’re only supposed to have one, just completely throws you off guard … you feel like ‘how could I possibly provide the same level of care to one patient that I normally do to six people?’ To do that … every day. You were constantly worried that you were making mistakes. (CCN30) We were looking after far too many patients. We were doubling up, tripling up … it was just absolute, absolute madness. (CCN38) These exceptional circumstances were the primary breeding ground for the experience of moral injury in the ICU (Čartolovni et al., 2021). They led to instances where basic care was, at times, impossible to deliver (Harris et al., 2021): ‘I mean honestly, just getting through the day and everyone still being alive; that was like all we could ask for. Like we couldn’t do a lot of the things we normally do’ (CCN28). There was a widespread feeling that basics around care were being compromised and that the ethical standards of care were being transgressed, leading to an unsafe working environment (French et al., 2021): ‘It was just unsafe, that place was unsafe, so working there was actually quite stressful’ (CCN42). The redeployment of non-ICU nurses to the ICU during the early months of the pandemic was intended to help with rebalancing the nurse-to-patient ratio. However, the vast majority of the redeployed nurses had little-to-no experience of ICU nursing, leading to reduced skill mix on wards and, with this, added pressure. CCN2 explained: ‘It was correcting all of their mistakes, basically, and also looking after them in the sense that some of them were really, really helpful and really got on with it, and some of them were outright dangerous. They wouldn’t listen to you and just kind of did their own thing. That was incredibly stressful.’ Whilst there was a widespread appreciation by the ICU nurses of those who had offered (or been forced) to come and support, there was also a feeling that this amounted to a danger within the ward and the creation of an unsafe working environment: ‘I’m just like, this is dangerous – this is my PIN number [unique nurse registration code] and my career, this is someone's life more, than that, this is somebody's relative and you’re asking me to supervise [an untrained member of staff], I can’t do that when I’ve got nine other patients to supervise with two other untrained members of staff at the same time’ (CCN11). Lack of Support from Senior Staff This imposition of untrained staff during a period of unsafe staffing levels was felt by ICU nurses as a moral injury that they carried with them throughout the pandemic. These effects were, indeed, cumulative as nurses (particularly in the third phase of interviews) expressed anger towards those in positions of power who were deemed responsible for these ethical transgressions. Many ICU nurses blamed management within the NHS, highlighting the lack of senior nurses in visible patient-facing roles. CCN47 stated: ‘I think one of the band sevens, I think I saw her on there once, and that was because she was showing the chief exec. Around. It caused a bit of bitterness really.’ Other nurses highlighted institutional powers: ‘So people are pissed off and they don’t know if they’re pissed off with COVID or the actual Trust. Because we feel let down by the Trust, just as we feel let down by the government. The Trust is doing exactly the same thing as the government to be honest. So, it's a massive shortage of staff. They knew this was going to happen, nothing was in place to prevent that, they just said get on with it’ (CCN42). Each of these contributions to staff shortage (repeated by nurses across the interviews) – and subsequent inability to get the right assistance with patients – was felt as a moral injury insofar that it was fundamentally ‘a betrayal of what's right, by someone who holds legitimate authority’ (Shay, 2014, p. 182). There was a sense of betrayal by ICU nurses that a systemic weakness in staffing and historical lack of preparedness had placed them in morally painful situations in which delivering normal levels of safe and effective care was at times impossible. Inadequate Resources to Provide Good Care The widespread reports of a lack of PPE in hospitals also played a significant role in engendering a sense of moral injury for the CCNs. It echoes findings from studies on doctors during the pandemic who felt the lack of appropriate equipment left them ‘exposed’ and was ‘a kick in the teeth’ (Harris et al., 2021, p. 1367). CCN3 explained: ‘We started to get really upset because … they couldn’t get [PPE] fast enough. We hadn’t got enough masks, or if you’d got your mask, you hadn’t got enough suits.’ When PPE was being delivered, it was sometimes substandard or unsuitable:We normally wear the big surgical gowns, but we ran out of those. So, you were literally being taped into ten different aprons. It was like wearing ten bin bags. It was a joke. You literally went in, and this is supposed to be your protective equipment, and then someone with a bit of tape was literally taping you together. (CCN37) Nurses also reported being asked to reuse PPE between breaks and ‘to double our risk, pretty much, by putting contaminated gowns back on’ (CCN1). All of these (and similar) instances left ICU nurses in a constant state of vulnerability to infection and the fear of taking the virus home to their families (Crowe et al., 2021). One nurse (CCN18) recalled: ‘There were quite a few shifts after that, that I would just sit my car and cry before going home. It was just out of sheer exhaustion and fear. I was worried that I would bring it home.’ Other nurses directly evoked the sense of a warzone in which they and their colleagues were poorly equipped and vulnerable to attack: ‘It was sort of that horrible feeling of going, oh actually could I die … a lot of my friends are nurses and doctors and I think that was the sadness … we’re fighting this war and not all of us may not be here at the end of it’ (CCN32). Where PPE was available, and nurses felt more secure from infection, it was an incredibly uncomfortable physical experience to endure wearing it for 12-h shifts (Koenig, 2018). The nurses explained: ‘We’ve got nurses that have had scarring from just pressure damage’ (CCN22) and ‘A lot of the girls … have still got scars on their noses’ (CCN10). Others described ‘wearing PPE for 13 h is like, it's like being in a desert with no water’ (CCN39), an issue that was made worse by the fact that ICU nurses could not in any case drink too much fluid (as they could not afford to lose PPE for regular toilet breaks). The resulting moral dilemmas, influenced by what was felt to be a lack of political planning and institutional care, all contributed to the experience of moral injury. One nurse explained: ‘A human right to be able to go and get a drink or go and have a wee isn’t it? Yeah. But nurses hold their bladders until they can’t do it anymore’ (CCN41). In the midst of this daily endurance, the shortage of medical equipment and medicines due to the sheer volume of patients and workload was particularly distressing to nurses in their efforts to maintain patient care. CCN23 described a situation in which ‘we didn’t even have piped in oxygen at some of the bed spaces [within the ICU]. So, we were having to use portables. Four months we were down there, and we still didn’t know where anything was. It was ridiculously dangerous and everybody was out of their comfort zone and everything we’d known was normal, was gone.’ Many nurses reported running out of medication so that they were having to make choices that felt unethical insofar that they contravened what they had been taught about the best care:We were running out of sedation … so basically like everything we’ve been taught [went out of the window] I feel like, it went back like 20 years where people were giving benzos because they didn’t realize … [negative patient outcomes] … like delirium and PTSD. So, I felt like I was going against what I should be doing by doing that. Kind of, it goes against best practice doesn’t it? All the things that you know you should be doing, but all of a sudden you can’t do anymore. (CCN16) This situation experienced by ICU nurses was reflective of an overwhelmed system exacerbated by COVID-19 (Harris et al., 2021). It led to feelings of moral injury in which nurses knowing what care was required were not able to give it due to a lack of basic resources. The sense of betrayal by their workplace and the wider mis-management of a government response created emotional wounds that were felt deeply. One nurse (CCN31) stated: ‘So I think for me that was really challenging, because like, how can you give care when you haven’t even got the basic stuff to keep your patients sedated and safe, let alone anything else.’ The resulting damage to professional standards and expectations, as well as nurses’ professional commitment to offer care and prevent harm, had a knock-on effect of causing feelings of ethical transgression, which manifested as a major source of moral injury. Unable to Give Patients a Dignified Treatment/Death The novel nature of the virus during the pandemic also contributed to the experience of moral injury. As medical staff sought ways of slowing down the progress of the virus and reversing its effects, it led to situations where every viable treatment was being applied to a patient in an effort to save them despite the improbability of survival – a term that many nurses referred to as ‘flogging’:You flog people and then you’re ultimately looking after them, thinking, we all know they’re not going to manage, they’re not going to survive this but they’re not quite at that stage yet, that on paper we can withdraw [let them die] but we know that we are going to ultimately do it. There's no real satisfaction. You know, it's not a very satisfying job to just know that you’re keeping somebody going and that ultimately, you’ll be withdrawing treatment on them in a few days. (CCN47) Many nurses felt that this came at the expense of the patient, leading to situations in which care might be considered inhumane (Harris et al., 2021). CCN39 explains:I think a lot of people that aren’t nurses or doctors maybe don’t realize there's a huge dignity and a privilege in giving a good death. That's part of nursing, certainly, that you give dignity and compassion and empathy, and you can give somebody a good death. Even attached to a ventilator, on a filter machine, it's that ability to go, we’ve got as far as we can, this patient isn’t going to survive, let's give them a good death. What's happening is, I think we’ve lost the ability to give a good death. On a daily (or even hourly) basis therefore, ICU nurses were being asked to be complicit with what they often considered an inhumane death – professionally speaking perhaps the most serious of ethical transgressions – that was for all intents and purposes avoidable through different choices by those in positions of power. This contributed further to feelings of moral injury that as we shall see stayed with these nurses in a variety of different ways. One of the most distressing aspects of patient care during this period was the added indignity of patients dying alone without families at the bedsides. Normally, patients – sometimes sedated and intubated but other times not – would be allowed close family members in with them to say goodbye, with nurses playing a central role in facilitating this process, which enabled what they might consider a ‘good’ death. In this situation, however, given the lack of time available, the requirement for families to use already scarce PPE, and hospital and government guidelines blocking them from doing so in any case, end-of-life care became a traumatic experience. CCN14 explained:Not having the families at the bedside for end of life, that was really difficult. So, it was, for all intents and purposes, it was the nurse holding the hand and stroking the forehead … we facilitated a virtual end of life if the family wanted that, or we would have rang the family, ‘what do you want us to tell your loved one? What is the most important message that you want to give them?’ And we would be whispering that into the patient's ears. Other nurses such as CCN16 spoke about a patient's wife: ‘She just gave me a load of, like [audio] WhatsApp messages for me to play to his ear, which I did for like 3 days before he died. I feel like that really affected me.’ It was this somewhat unnatural situation with patient care that CCNs most regularly stated as the most upsetting, with nurses describing it as ‘heart-wrenching’ (CCN30), ‘horrific’ (CCN39), ‘horrendous’ (CNN47), and ‘morally wrong’ (CCN40), which reflected the ethical transgressions they felt complicit with, completely in opposition to the usual provision of a good death. CCN6 explained her frustrations and sympathies with the families: ‘I could imagine if it was me being kept in a room away from my person, I got really annoyed. Because where did they think that this was a good decision? Bring that woman in, let her say goodbye to her husband with the dignity that he deserves and the love that he deserves. So, I was really annoyed.’ The situation in the ICU, overall, was one in which nurses felt like they were never able to do enough with the resources that they had. CCN27 described: ‘It was almost like a battlefield. I personally don’t know what that feels like, so I don’t want to be flippant with that term at all, but it felt like a warzone and it felt like we were running to stand still.’ As another nurse described, despite all of these instances of moral injury and emotional wounds being inflicted, they were still ‘running towards the bullets’ (CCN18) and taking them on (in the form of emotional wounds) again and again. The overarching theme in the experience of moral injury in the ICU during this period was having to drop expectations in medical care across multiple areas, which severely impacted nurses’ core ethical codes and values (Čartolovni et al., 2021). There were situations where nurses felt the following: ‘We couldn’t give the same standard of care that we normally could, [and] that it wasn’t enough and that people deserved more than we were giving them’ (CCN28) and, another, ‘It changed the way I view nursing … so many things went on that just you would never, ever, you would just never, ever see or do or accept that went on, you know’ (CCN39). Whilst entirely blameless for the situations they found themselves within then, it was these memories of disappointment in the medical care provided that haunted many of these nurses due to the high professional and ethical standards they held themselves and their colleagues up to. The cumulative nature of moral injury in a relatively short period of time was perhaps the most damaging aspect to ICU nurses. It became difficult for them to pinpoint that one moment that inflicted an emotional wound. CCN44 explains: ‘My stresses seem to be an accumulation of many things that what's caused it, you can’t pinpoint one thing. And it feels like lots of little things, but actually when I’ve spoken to the psychologist, they’re not lots of little things; they’re lots of very big things.’ The lack of rest and capacity to recover during the peaks and troughs within the waves of the pandemic made it extremely difficult to process what was going on or to begin to heal. There has been little chance for rest and recuperation: ‘I think it's a culmination of everything. It's from not recovering from the first wave, not resting from it, and then the expectation that they have on you is incredible’ (CCN2). There was a sense of an ongoing collective experience of moral injury as a profession in which, according to one nurse, ‘feels like the rest of the world's moving on and they’re all happy, and then the poor ICU nurses are just stuck in this horrible never-ending kind of nightmare’ (CCN33). Implications of Moral Injury on CCNs Emotional Burden of Moral Injury The cumulative nature of moral injury during the pandemic had a variety of implications on CCNs. The emotional impact of working on COVID wards during the pandemic was the most immediate of these, with guilt being one of the foremost outcomes mentioned in the interviews (Williamson et al., 2021) – guilt, that is, in the face of powerful forces being unable to make different moral and ethical decisions in a time of crisis. One nurse, CCN34, explained her own experiences as follows:I went and had a chat with [my therapist] … and he talked about the fact that, there was a lot of cases during the Afghan and Iraq war where there were a lot of military nurses having the same issue, that they were feeling guilty about the fact that they survived … survivor's guilt. He called it a moral injury … that health professionals can go on to experience through the fact that they haven’t been able to save somebody, but they’re getting to carry on and live their life. Many nurses spoke about their ‘anger’ (CCN8) or ‘rage’ (CCN23) emanating from their own powerlessness in the face of moral injury in the ICU, culminating in instances of being unable to regulate their emotions (Hayward & Tuckey, 2011). This surfaced through nurses either snapping at colleagues in the workplace or (as one of the most commonly coded instances) breaking down in tears in the ICU. CCN3 remarked: ‘You’re seeing [nurses], you’re seeing them falling to bits, you know, people walked off the unit … people just sat and cried.’ And CCN9 remarked: ‘I basically spent the whole nightshift walking round, crying. I just did all my work, fortunately my patients weren’t really conscious enough to see me, but I just spent the whole night walking around doing my jobs, crying.’ Another nurse (CCN14), in a managerial role, noted the broader effect on nurses in her hospital between the second and third waves:A lot of our ICU staff have just collapsed. We have them breaking down during shifts, you know crying, you know at home, crying on shifts, it's, you know, it's almost like the cavalry have arrived, albeit late, and we’ve sort of let our guard down a little bit. It's an emotional rollercoaster at the moment; with no highs, all lows … the hardcore ICU team, you know [are] broken, you know, and we just go off, have a cry, have a cup of tea, have a walk outside, come back in and get at it again. Other nurses, such as CCN42, spoke about emotional outbursts on days off from the ICU:I went for a run to the park and I was running and I started to feel like a rising pressure in my head, like something was rising, and it was mainly anger, but I don’t know what I was angry of. I felt something very intense rising over a few minutes and I had to stop the run and I went into absolute hysterical mode. I was hysterical. And I don’t even know at the time what the hell is going on with my brain. I thought that I’d lost it, I thought I’d lost my mind. I had to stop running and it was just hysterical crying in the middle of a park and I couldn’t calm it down. It felt like the brain was on fire. It was kind of on fire. And there was like a million thoughts at the time, I don’t even know what was the main theme. I think it was anger. Interestingly, this type of reaction is consistent with those of many war veterans suffering from moral injury who blame leaders and those in positions of power for placing them in situations where ethical transgressions were impossible to avoid. Shay (2014, p. 185) argues that ‘where leadership malpractice inflicts moral injury, the body codes it as physical attack … and lastingly imprints the physiology every bit as much as if it had been a physical attack’. The reaction of CCN42 to the cumulative experiences in the ICU clearly reflects the trauma of moral injury being felt physically within the body, the effects of an invisible emotional wound made visible. Conversely, another response to the immense emotional burden of moral injury was to withdraw and feel very little at all. Nurses spoke of ‘really shutting down and not thinking or feeling or anything. You just felt numb’ (CCN22), whilst others spoke of detachment and withdrawal from everyday life and engagements so that they did not have to feign being emotionally in control. CCN44 explained: ‘I didn’t want to speak to anyone and I fell out with a couple of friends, because they kept messaging me and asking me how I was, and I was like, please just don’t talk to me, I don’t want to talk to anyone.’ Psychological Burden of Moral Injury The psychological burden of cumulative and repetitive moral injury on CCNs was also significant (Litam & Balkin, 2021). Reminiscent of war veterans’ experiences of moral injury, ICU nurses regularly spoke of flashbacks that invaded their working lives. One nurse, CCN25, remarked: ‘You have these visions you can’t even get rid of. I said to my colleagues, how many ghosts can there be in your head? These people are dead, I’m still carrying them around.’ These ghosts manifested in a number of different ways in the home lives of many of the nurses in the form of sleepless nights, ‘being afraid to sleep’ (CCN7) due to nightmares (Stolt et al., 2021), heart palpitations, and debilitating panic attacks resulting from the trauma of repeated moral injury. One nurse described such an instance: ‘A physical reaction of the trauma … [a] situation that I could not leave behind, and it lasted for 3 days, constantly. I was actually shaking for 3 days, constantly having the flashes [of my dying patients]’ (CCN42.) CCN9 added another very common experience during this period: ‘I was waking up in the night, between four and eight times, and I was having nightmares and waking my boyfriend up with shouting. He said it was about work. He could tell. He said I’d cry in my sleep a lot and shout work-related stuff out.’ More broadly, ICU nurses reported widespread experiences of anxiety, stress, and panic attacks as a result of the emotional wounds of moral injury (Williamson et al., 2018). CCN20 explained: ‘Mental health has had a massive, massive impact because of the pressure, because of the amount of people that we’ve seen die. The fact that we were just helpless.’ This was not limited to a few nurses in the ICU either or those that had suffered previously with mental health problems: ‘It definitely feels like, across the board, not just on a personal point of view but my colleagues, everybody's really sort of, at the end of their rope really in terms of being able to cope with the stress’ (CCN38). In addition to this, the effects of the psychological impact on nurses due to moral injury were widely considered to be something that would be long-lasting (Caillet et al., 2020). One nurse (CCN3) put it quite bluntly, signalling the detrimental impact of these experiences:It upset me to see some of the [nurses]. One girl kept tapping her head so strongly. It was so bad. She was in a state with herself, and to watch her, she was looking at me eye-to-eye, just trying to stop crying. You could actually taste the atmosphere. You could slice it. You could just slice it. It was awful. It was awful. Anyway, yeah, the end of my career, love (laughs). A number of nurses spoke directly about experiencing symptoms of mental breakdown and PTSD, another outcome attributed to the experience of moral injury in war veterans (Currier et al., 2015) and increasingly being found in healthcare settings (Bartzak, 2015), especially since the start of the pandemic (Williamson et al., 2021). Some nurses spoke of being diagnosed through visits to psychotherapists: ‘He diagnosed me with complex trauma PTSD, just because of the events in the past and events at work’ (CCN16), whilst others spoke about nurses on their wards who had been affected in this way: ‘I know lots of my colleagues have been off. They’ve all gone, you know, they’ve all got PTSD’ (CCN37) and ‘I mean we’re all broken, we’re all beyond broken. There's not a staff member that isn’t’ (CCN14). For some nurses, this situation had got so bad that they had suicidal thoughts: ‘I phoned the GP … and I’d said to them, look, I’ve had thoughts that I didn’t want to get up in the morning, didn’t want to exist anymore. Didn’t want to do my job. I’ve had enough. I feel like a number’ (CCN6), whilst another nurse (CCN16), with no previous mental health problems prior to the pandemic, spoke about the severe implications of her own encounters with moral injury: ‘[I experienced] more nightmares and flashbacks about my patients; I was sectioned twice over the summer under a Section 2.’ Practical Implications of Moral Injury Previous studies on those experiencing moral injury in war settings have shown that individuals often subsequently undergo existential crises in which they question who they are and what they are doing with their lives (Shay, 2014). Similarly, the prevalence of moral injury during the pandemic fostered a sense of alienation (Gibbons et al., 2013) and led to lowering of morale and led many nurses to question the extent to which their employer has their best interests at heart: ‘The NHS would see me in a bloody coffin before, you know, they would stop taking from us, so I have to address myself and be me, look after [my]self’ (CCN41). Indeed, given the extreme nature of the implications of moral injury for ICU nurses during this period, it is perhaps unsurprising that the most commonly coded term in the study was ‘intention to leave’ (Carucci & Praslova, 2022). There is a palpable fear of a ‘mass exodus’ of talented and experienced staff occurring and of traumatized nurses ‘leaving in droves’ (CCN37). For many nurses, the pandemic has shortened their careers dramatically, forcing them to question why they were working for an institution that failed to shield them from cumulative moral injury: ‘I’ve actually handed my notice in. Whereas I probably would have gone on until I was 60, I just think no I’m going to do it now’ (CCN18). And another remarked: ‘I’d say this pandemic alone has probably taken about 5 years off our nursing careers, just (laughs) looking ahead into the future. I was just thinking the other day that, technically, I could still work for 42 years or something. I was like, there's not a hope in hell I’d be able to do that’ (CCN35). For many, the emotional wounds inflicted by the pandemic have meant moving out of nursing altogether:Seven people handed their notice [to my ICU] in within the week. Some people left without an alternative job. Half of them left and didn’t go for nursing positions; people have started their own businesses, like dog-walking businesses and something else. So, one of them is working in a supermarket. (CCN44) There is a widespread feeling of having had enough with the ongoing nature of moral injury on a daily basis: ‘I’ve seen so many dead people. I’ve seen so many people die and I don’t want to do it anymore. I don’t want to – it's so depressing. There's no light in it at the moment’ (CCN39). However, despite the cumulative experiences of moral injury, many nurses still feel that critical care is their ‘calling’ and that they could not imagine themselves doing anything else: ‘I have been looking for other jobs, but the thing is, if I weren’t in critical care, I don’t know what I would do. It's hard, because what I feel like, I feel like the job that I love has been taken away from me at the moment’ (CCN23). The important thing to ask, therefore, is what is being done in the ICU to help CCNs cope with these experiences of moral injury and their implications and, perhaps even more urgently, what could be done to improve the working lives of CCNs in the future. Coping Mechanisms and Organizational Action to Avoid and/or Repair Moral Injury Avoidance of Memories of Moral Injury Consistent with previous studies in other settings, CCNs relied on ‘cognitive avoidance as a maladaptive coping strategy’ (Williamson et al., 2018, p. 344). This might involve compartmentalizing and trying to forget what happened: ‘It was horrible. Like I’ve got – I feel like I’ve actually blocked parts of it out. There's stuff I can’t remember. I can’t remember all of the patients. I feel like I just blocked it out’ (CCN9). Other nurses spoke about ‘sucking it up’ (CCN1) or ‘burying your head in the sand’ (CCN38) and not having the ‘psychological safety … to say I’m not managing’ (CCN21). Much like a soldier's mentality, it was considered part of being an ICU nurse to be tough enough to cope with moral injury and show resilience in crisis (Powley, 2009). Whilst some nurses spoke about reducing hours to cope, the majority reflected this mindset: ‘It's a pandemic, get on with it’ (CCN39). Many nurses suggested that away from the ICU, they avoided feelings associated with moral injury (anger, guilt, etc.) through drink. Some were light-hearted about this: ‘Thank God for alcohol’ (CCN29), whilst others were more concerned: ‘Alcohol played a big part and then I realized, this is becoming a bit of an issue’ (CCN20). Individual Pursuit of Well-Being to Repair Moral Injury Nurses also undertook a variety of restorative activities to try and go through a process of moral repair or healing from the wounds of moral injury (Litz et al., 2009). This ranged from communicating with colleagues and friends and gaining support through family (cf. Mantri et al., 2021) to actively seeking psychological support. CCN12 explained: ‘Towards the end of the first wave, I had to get myself some private counselling because it was just too much.’ Another (CCN16) suggested that they had found an external agency to help with psychological issues she was experiencing: ‘There's a charity called Frontline19 that I found on Facebook. They gave me three counselling sessions for free. I was supposed to only have 12 sessions but he says it's not going to stop until like it needs stopping.’ Nurses also outlined a broad range of hobbies they undertook during the pandemic to either distract from the implications of moral injury endured (painting, walking, and photography, for example) or attempt to heal through self-care (meditation, yoga, and hypnotherapy). In each of these broader coping mechanisms above, however, the solution to moral injury seems to be both one of individualization – the nurse was expected to confront their moral injury alone. It was clear, from the widespread sense of abandonment, that many nurses felt that there was an ongoing abdication of responsibility by those in positions of power in terms of helping them to repair and recover following experiences of moral injury, further compounding its effects. Collective Organizational Support to Avoid/Repair Moral Injury (and Actions Required) Our analysis demonstrates the paucity and inconsistency of collective organizational support for ICU nurses. Whilst, for many, there was existing support through internally organized well-being schemes and limited counselling, this was in some cases (especially during the first two phases of interviews) missing entirely and/or slow to arrive. CCN11 suggested: ‘The psychologists? I never saw a psychologist, never met one, never had a phone call, never was given a phone number.’ And CCN43 remarked: ‘It sounds really harsh, but nothing really. Nothing like set-up officially … there was no sort of support for anyone psychologically.’ Most worryingly, nurses like CCN16, who had the most serious signs of moral injury and had, as noted above, been sectioned due to their experiences in the ICU, had found it impossible to receive anything beyond basic counselling: ‘The first time I was sectioned I was put on their waiting list [for Cognitive Behavioural Therapy] and I was took off the second time I was sectioned, so I’m still on the waiting list. I’ve contacted work and I’ve contacted the head of nursing as well who I know quite well. And they can’t like push me up the waiting list at all.’ One might speculate that this further betrayal by those in positions of power could only act to prolong and worsen this nurse's existing wounds of moral injury, conjuring yet more ethical transgressions upon those already committed. Other nurses suggested that whilst there were a number of nurse-led initiatives and people trained in psychological first aid, it was often a case of a lack of availability of this psychological help: ‘There's this misconception that there's nothing out there, but the stuff that's out there, it's not easily accessible’ (CCN21). It was also reported regularly that nurses were either unable to take breaks to visit psychologists during their shifts or expected to come in at other times, which were inconvenient:They’re only half an hour or 45 min but we aren’t allowed to leave the ward to go to them. You have to be on a roster day off, and in reality, working through a pandemic, no one's going to come into the hospital on a day off for a session. (CCN35) In many cases, then, rather than being addressed directly and rapidly (Greenberg et al., 2021), the emotional wounds of moral injury were being left to fester, and as seen in the case of war veterans, prolonged exposure to feelings of shame, guilt, and anger can make these wounds all the more difficult to repair (Litz et al., 2009). The ongoing lack of care by those in positions of power could be considered further acts of ethical transgressions that, again, deepen emotional wounds and make attempts at repair (when eventually received) even more difficult. Whilst there were accounts of support from the hierarchy in terms of dealing with the outcomes of moral injury and the associated emotional and psychological distress, there were many more voices of disappointment and anger at senior staff within the NHS. This was not only for the aforementioned lack of staffing but also for failing to come to the nurses’ aid once moral injury had occurred. CCN9 explained: ‘I’ve been extremely disappointed in … the people higher up within the hospital hierarchy. I’ve felt really, really disappointed in the support or their lack of support that has been given to me.’ And CCN23: ‘I don’t feel that the establishment or the management – I don’t think our management have got any real understanding of what the intensive care nurses have been through, and I don’t feel like we’ve been offered any real support.’ These findings calling for improved support from the hierarchy in regard to moral injury experienced within the ICU echo Kalkman and Molendijk's (2021) suggestion that leaders can be purposefully ambiguous in their strategy, thus contributing to feelings of moral injury and, indeed, compounding the mistakes that they have made. It also points towards the need for a more systemic approach to tackling moral injury in which those in positions of power realize their roles in a healthcare system that allows those who put their bodies and minds at risk to go without the psychological and emotional support that they need. Finally, in terms of collective organizational responses to moral injury, nurses pointed towards support through improved communication as central to aspects of moral repair. Surprisingly, in addition to obvious calls for increased staffing levels, one of the most called-for activities across the whole study was increased and/or improved debriefing at the end of shifts. On many wards, it simply was not happening: ‘We never debrief in my unit, which I think is not great’ (CCN2), which was seen as detrimental to learning and contributing to either avoiding future moral injury or assisting in moral repair: ‘At the end of each shift, I think we should have a debrief, definitely. I don’t think we should leave it too late to have ‘let's talk sessions’ because I’ve got some powerful feedback, but I do think we left it a bit too late’ (CCN46). In other ICUs where this did occur, it often happened informally. CCN1 stated: ‘Staff took it upon themselves to debrief. But there were no senior members of staff that said, this is what we need to be doing, we need to be having a debriefing session after our shifts. That is what we needed at that time. We needed to all sit there together and chat about our day and our feelings before we went off home. But we didn’t get any of that, really.’ This seems to support Griffin et al.'s (2019, p. 357) suggestion that ‘recovery from moral injury likely involves an affirmative community effort to understand and reintegrate the morally injured, as well as to accept shared responsibility for that injury’. Discussion and Conclusion We set out in this article to respond to recent calls to explore the experience of ICU nurses during the pandemic (Greenberg & Tracy, 2020; WHO, 2020). We took up the challenge to do so through the lens of moral injury (Čartolovni et al., 2021) and to do so empirically by giving a voice to the nurses who have given so much in recent years but have, for the most part, been unheard victims of a health system that takes advantage of people's willingness to do difficult and traumatizing labour for relatively low wages (Peredo et al., 2022) – exploiting nurses as ‘Willing Slaves’ (Bunting, 2004). We now offer theoretical and practical contributions of our study that we believe further the debate and deepen understanding of moral injury in frontline healthcare specifically and relation to work that traumatizes or injures more generally. In sum, our study offers a deepened theoretical understanding of moral injury as cumulative, intersubjective, and systemic in such a way that it invites a more collectivist approach to moral injury. The practical implication of this broader understanding of moral injury is that in professional contexts such as critical care, organizations can and should better understand how, where possible, to minimize instances of moral injury and when necessary speed up the process of moral repair. Theoretical Implications: Moral Injury Reformulated This study set out to investigate moral injury beyond its traditional psychological context, considering it through an interdisciplinary approach – by also embracing health, nursing, and organizational studies – which has, in turn, helped shift the theoretical understanding of the concept (Molendijk, 2018). First, our study has shown that whilst moral injury is often portrayed as a singular experience (‘a betrayal of what's right, by someone who holds legitimate authority’ (Shay, 2014, p. 182)), it can become multifarious and repetitive. Within the context of ICU, ethical transgressions were experienced by nurses on a daily basis in a multiplicity of different ways that deeply challenged their expectations about what constituted good and safe care. The moral injury was cumulative in nature (spanning more than a year) and expands our understanding of the ways in which moral injury can materialize and embed itself as an outcome of both previously routine activities in the workplace and the disruption of those routines. Our study shows that in extreme organizational settings or crises such as the pandemic, the emotional wounds of moral injury can be layered and experienced concurrently rather than just intermittently. The vast array of mental health conditions experienced in our study ranging from debilitating panic attacks to sectioning, coupled with the widespread intention to leave the profession, reflects how particularly devastating cumulative moral injuries can be. Second, our study shifts the conceptual understanding of moral injury as a phenomenon experienced solely by an individual. One of the central reasons for the lack of collective action when it comes to repairing moral injury is that it tends to be approached as what Griffin et al. (2019, p. 357) call a ‘product of intrapsychic conflict’ – something experienced by the individual and thereby to be given meaning and solved by the individual alone (Currier et al., 2015). This has the effect of passing much of the responsibility for repair (and even the moral injury itself) onto the individual. Our data show that individualization of meaning-making and responsibility for moral injury inhibited the process of moral repair. Indeed, it served to heighten a sense of isolation and despair. In contrast, we emphasize the intersubjective dimensions of moral injury in organizational settings and the desire of nurses to communicate, share, and work through the emotional wounds that have occurred as part of shared occupational experiences. This is also supported through emerging research on repairing moral injury. Shay (2014, p. 189), for instance, argues that ‘you don’t get recovery without social connection … recovery happens only in the community’. Third, our consideration of moral injury in the context of the ICU has also enabled us to show the systemic nature of moral injury. Building on previous research, our study showed ethical transgressions and omissions by those in positions of power, whether in government or hospitals, translated into a sense of powerlessness and betrayal by ICU nurses (French et al., 2021; Kalkman & Molendijk, 2021). This powerlessness was a result not only of the lack of support and planning in the moment of crisis but also of the systemic failure to plan for and resource in the years preceding the pandemic (e.g. understaffing and paucity of critical care beds resulting from a programme welfare state retrenchment and underinvestment). Our conceptualization of moral injury thus highlights its systemic and political components that go beyond but nonetheless impact the individual (Wiinikka-Lydon, 2017, p. 221). In this respect, moral injury, as a theoretical concept, should be understood as part of organizational systems and processes that sustain and fuel its very continuation, often stymying change from occurring or making it more difficult to contest and challenge established orders on a daily basis. Practical Contributions: Moral Injury as a Catalyst for Organizational Change The practical contributions of our study involve spreading awareness and deeper understanding of the experiences of moral injury as they relate to CCNs specifically whilst also encouraging consideration of the implications for workers vulnerable to moral injury more broadly. The point is to call on those in power, from government ministers to organizational leaders, to acknowledge the extent to which the tasks they require may emotionally wound workers and, just as importantly, to take steps to promote repair. It points to the need to give voice to the experiences – the moral injuries – of those working on our frontlines so that pain can be addressed and repair enacted through individual attention, collective care, and organizational change. This paper is one mechanism for voicing the moral injury experienced by CCNs. The first step is to give voice to injuries. Perhaps one of the most important impacts of this paper has been to help ICU nurses understand they are not alone. On reading early drafts of the paper, much of the feedback from nurses was one of relief that they were not alone in feeling the emotions and psychological distress that they did during the pandemic. There was relief at being heard individually and collectively. Such relief points to the absence of voice within their own workplaces, communities, and national health systems. Bearing isolated witness to a clap for carers is no substitute for informal support over coffee, formal offloading at the shift's end, leader presence, or systemic emotional support from crisis beginning to end. We would argue that employers, leaders, and organizations they represent have a responsibility to give voice – to seek out, actively listen to, and respond to – the moral injury that arises in the course of work, whether that be the labour of the CCNs, public servants, private employees, or organized volunteers. The study also provides a deeper awareness of the kinds of moral and ethical transgressions that can take place (Williamson et al., 2021). This is especially important, as Litz et al. (2009) identify, in the first steps towards moral repair. In their work with war veterans, the first steps of learning to deal with moral injury was ‘to have patients verbalize what they did or saw, how it has affected them … to disclose the transgression, articulate their attributions and how they have been feeling about themselves since the experience’ (Litz et al., 2009, p. 704). This study is valuable for the critical care community, therefore, insofar as it begins to collectively name the sources of moral injury so that they can be repaired. It is also an important precursor to considering the organizational change required to minimize recurrence. Our exploration of the implications of moral injury on ICU nurses provided deeper evidence of its deleterious effects on mental health and well-being (Borges et al., 2020; Williamson et al., 2020). Our study provides a qualitative exploration of the emotional and psychological effects of moral injury that show widespread instances of resulting anxiety, PTSD, self-harming, and even sectioning. The practical contribution here is that the widespread experience of moral injury requires drastically improved psychological support for ICU nurses to work through feelings of guilt, shame, and anger. Whilst forms of cognitive therapies have been successfully used on war veterans suffering from moral injury that might also help ICU nurses, other studies suggest that ‘no validated treatment for moral injury currently exists … and [instead] approaches that focus on self-forgiveness, acceptance, self-compassion, and (if possible) making amends might hold more promise’ (Williamson et al., 2021, p. 454). This will, in any case, require vastly improved access to emotional and psychological help for ICU nurses and, indeed, other occupations on the frontline of the pandemic and other extreme settings. Whilst by the final phase of our study this was improving, too many nurses had been left to deal with the emotional wounds of moral injury for far too long and many remain in situations where they simply are not getting the support that they require. Beyond the pandemic, these findings invite us to consider how and when the work of other occupations might invite injury, the nature of that injury, and its effects and treatment (Litz et al., 2009). Treatment is likely to vary according to the specifics of the industry, occupation, individual, task, and context in question such that one-size-fits-all remedies are unlikely to be viable. Giving greater voice to the type and impact of moral injury faced by different occupations will however afford a sharing of experiences and a greater range of repair approaches (Goodstein et al., 2016). Finally, our study provides a more practical understanding of what needs to occur organizationally to begin to minimize instances of moral injury in extreme workplaces such as critical care. Our findings suggest that significant improvement in organizational communication and learning will need to be central to this process. Prior to the pandemic, interventions showed the power of nurses talking within their communities about the emotional impact of healthcare work (‘coping together collectively through dialogue’) to reduce the psychological distress of healthcare (Taylor et al., 2018). This desire for a more collectivist approach here was confirmed further through the very high number of ICU nurses in the study who spoke about their desire for much more regular debriefs as part of a team that might, in turn, be able to apply early pressure to any emotional wounds of moral injury that had occurred on shift. In this sense, employers might usefully learn from the debriefing, sharing, and similar offload practices of voluntary organizations such as Samaritans (see McMurray, 2022). In practical terms, stopping the occurrence of moral injury in an ICU ward entirely is, more or less, impossible. Nevertheless, through proper organizational responses to the causes of moral injury, such as (according to our data) tackling chronic understaffing in ICU wards, providing better support to staff, and resourcing the ward with appropriate medicines and equipment, the worst excesses of moral injury would be reduced and the impact ameliorated. Studies also point towards better training for senior staff in which ‘leadership at all levels should be trained to be aware of betrayal-based moral injury and to engage in moral repair to reduce staff intent to leave and encourage mutual trust’ (French et al., 2021, p. 516). Beyond this, we argue that it is also important to understand the practical experience and implications of moral injury as a societal responsibility and the result of decisions taken on a political level as to how we decide to fund and support our healthcare systems and those that work within them. Our study suggests that more must be done to support ICU nurses (and healthcare professionals more broadly) from the emotional and psychological implications of moral injury in their working lives. This paper calls into question the levels of support offered to those who were caught ‘running towards the bullets’ and encourages a deeper reflection on what we owe to those who suffer deep emotional wounds so we, our families, and members of our community might receive care when we need it most. The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. ORCID iD: Martyn Griffin https://orcid.org/0000-0002-8574-7917 ==== Refs References Adam S. Osborne S. (2001). Critical care nursing: Science and practice. Oxford University Press. Alexander M. (2021, April). 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==== Front Community Coll Rev Community Coll Rev CRW spcrw Community College Review 0091-5521 1940-2325 SAGE Publications Sage CA: Los Angeles, CA 10.1177/00915521231182116 10.1177_00915521231182116 Research Report Reimagining Community College Math Reform Amid COVID-19 https://orcid.org/0000-0003-2322-5416 Wickersham Kelly 1 Zheng Peiwen 1 Wang Xueli 1 Prevost Amy 1 1 University of Wisconsin-Madison, Madison, WI, USA Kelly Wickersham, University of Wisconsin-Madison, 677 Educational Sciences, 1025 West Johnson Street, Madison, WI 53706, USA. Email: krconrad@wisc.edu 26 6 2023 26 6 2023 00915521231182116© The Author(s) 2023 2023 North Carolina State University This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Objective: In Spring 2020 when COVID-19 hit, community colleges moved almost all classes online. This disruption impacts recent math reforms, including contextualization, raising concerns about sustained faculty and institutional leadership commitment. This study investigated how community college faculty teaching contextualized math courses adapted their instruction in response to COVID-19-related disruptions and how community college and instructional leadership addressed math contextualization efforts in response to COVID-19. Methods: Using multiple case studies, we conducted interviews with faculty and institutional leaders from two large community colleges in a Midwestern state. We also integrated field notes, observations, lesson plans, project documentation, and other contextual information as complementary data. Results: Three themes revealed how faculty and institutional leaders navigated the process of adapting contextualization efforts throughout the pandemic: reaching out to create community remotely, reimagining contextualization or pushing the pause button, and skilling up to persist through and toward change. Contribution: This study provides insight into the unique challenges and innovations due to sudden yet enduring disruptions that impact instruction, faculty development, and institutional support around instructional reform in the community college. This research informs faculty and institutional leaders navigating sustained efforts around math reform to identify actions to help institutions and their faculty continue advancing high-impact approaches and initiatives to math instruction in any environment. community college instructional reform contextualized math faculty COVID-19 crisis case study National Science Foundation https://doi.org/10.13039/100000001 DUE-1700625 National Science Foundation https://doi.org/10.13039/100000001 DUE-210029 edited-statecorrected-proof typesetterts1 ==== Body pmcIn Spring 2020, as the world was hit by the COVID-19 pandemic, like other postsecondary institutions, community colleges moved almost all their classes to an online environment. This disruption impacts recent math reforms at many community colleges (Bickerstaff et al., 2021) and raises concerns about sustained faculty and institutional leadership commitment. These math reforms often center a contextualized approach to designing the curriculum and facilitating lessons, also referred to as contextualization. This approach relies on hands-on activities, experiential learning, group interaction, and labs or workshops (Perin, 2011; Wang et al., 2017; Yamada et al., 2018), which have faced several barriers introduced by remote instruction, including lack of access to fieldwork, workshops, and labs, among other in-person activities (Day et al., 2021). Understanding how instructional reform efforts such as math contextualization unfold across shifting circumstances and contexts during the pandemic is critical and timely. Notably, math contextualization has gained traction and demonstrated promise for resolving the longstanding challenges associated with math learning and success at community colleges (Parker et al., 2018; Wang et al., 2022). Yet, any instructional reform entails high levels of effort by faculty in terms of professional development, curriculum revision, and adjustments to instructional delivery (Borda et al., 2020; Wickersham & Wang, 2022). COVID-19 compounded these efforts through the transition to online learning environments, thrusting faculty into the landscape of educational technology, online platforms, and relevant training, some of which may be uncharted waters. This study provides insight into the unique challenges and resulting innovations that arose due to sudden yet enduring disruptions that impact instruction, faculty development, and institutional supports around instructional reform in the community college. This research informs faculty and institutional leaders navigating sustained efforts around math reform to identify actions to help institutions and their faculty continue advancing high-impact approaches and initiatives to math instruction in any environment. Drawing upon multiple case studies that focus on two community colleges, our study examines how their faculty and institutional leaders navigated the process of adapting contextualization efforts amid the pandemic. The following questions guide our inquiry: How do community college faculty teaching contextualized math courses adapt their instruction in response to COVID-19-related disruptions? How does community college and instructional leadership address math contextualization efforts by faculty in response to COVID-19? Relevant Literature Two strands of higher education literature inform our study: instructional reform and crisis management and adaptation. Instructional Reform in Higher Education Instructional reform has been a prominent issue in higher education and especially community colleges, where teaching and learning is a core function (Crespín-Trujillo & Hora, 2021). These institutions enroll large numbers of historically underserved students (Cohen et al., 2014), making enhanced structural support for student learning a high priority. This sector has engaged in significant instructional reform spanning developmental and college-level areas (e.g., Brown & Bickerstaff, 2021; Eddy et al., 2021; Logue et al., 2019; Nagy & Gilbert, 2021; Rutschow et al., 2019). Improving instruction has shown to have positive impacts on student outcomes (Lancaster & Lundberg, 2019; Umbach & Wawrzynski, 2005). A particular area that has prompted instructional reform is math. One growing reform strategy includes contextualization, which centers meaning making of math content and concepts; drawing connections across knowledge, skills, and concepts; and collaborative discussion and problem-solving, all grounded in hands-on, real-world, and workforce contexts (Perin, 2011; Wang et al., 2017; Yamada et al., 2018). Contextualization entails both the design of lessons or curriculum and delivery of instruction that “intentionally prioritize activities and content that require students to work on realistic, relevant concepts they would encounter in authentic career and life contexts” (Wang et al., 2022, p. 314). Contextualization has shown to alleviate math anxiety or fear and improve motivation and self-efficacy (Wang et al., 2017; Wickersham & Nachman, 2023). In addition, students exposed to contextualization tend to have higher performance, persistence, and completion in comparison to students not enrolled in contextualized courses (Jenkins et al., 2009; Parker et al., 2018; Quarles & Davis, 2017; Skuratowicz et al., 2020; Wang et al., 2022; Yamada et al., 2018). A key focus and ingredient of math reform, contextualization involves intensive professional development, curriculum revision, and instructional delivery (Borda et al., 2020; Wickersham & Wang, 2022). To sustain this reform, institutional efforts should be ongoing, collaborative, and meaningful to bolster faculty motivation and support in the face of instructional, structural, and other challenges along the way (National Academies of Sciences, Engineering, and Medicine, 2019; Quarles & Davis, 2017). Emergent challenges brought by the COVID-19 crisis and remote instruction can disrupt community colleges’ daily functions and contextualization efforts. Higher Education Crisis Management and Adaptation Various crises impact higher education, ranging from violence on campus to social unrest and natural disasters among others (Jackson & Terrell, 2007; Lindell et al., 2007), giving rise to crisis management, relevant training, and change (Catullo et al., 2009; Holzweiss & Walker, 2018). One facet of crisis management is learning continuity (Rayburn et al., 2020), as crises cause major shifts in the mode and consistency of teaching and learning (SchWeber, 2013). A recent crisis affecting learning continuity is the COVID-19 pandemic, which led community colleges and other postsecondary institutions to move many of their courses to an online environment. To maintain learning continuity amid the COVID-19 crisis, faculty harnessed unfamiliar or new technology for remote instruction (Pokhrel & Chhetri, 2021). The added layer of remote instruction competes with community college faculty math reform efforts, including contextualization, making technological adaptions difficult to pursue (Pape & Prosser, 2018). While many institutions have specialists or centers to support faculty with online and remote instruction, the pandemic pushed the limits of these resources (Hodges et al., 2020). Consequently, faculty may only receive basic training and guidance instead of specific supports for contextualization. Further, these adaptations presented challenges for instructors relying on labs, group work, and other hands-on experiences (Day et al., 2021), all of which are hallmarks of contextualization (Kalchik & Oertle, 2010). With the sudden shift to remote instruction, faculty were no longer able to conduct in-person contextualized activities as originally designed. Instead, they resorted to more traditional approaches such as heavily relying on reading assignments and instructor demonstrations (Barton, 2020). These approaches can result in less engagement in courses and connection with instructors, lack of hands-on experiences or equipment, and technology challenges (Barton, 2020). Despite these challenges, the pandemic presents opportunities to rethink the future of instruction (Prokes & Housel, 2021), allowing instructors to explore various platforms, instructional tools, and collaboration with other faculty to improve remote learning (Pokhrel & Chhetri, 2021) throughout and beyond the COVID-19 crisis. This is especially important for contextualization and other innovative approaches in adapting to new, remote educational needs (Pokhrel & Chhetri, 2021). Yet, little is known about the specific changes and adaptations made to contextualized instruction and related math reform in community colleges due to COVID-19. Empirical work is needed to understand the unique challenges and better support and sustain math reform and instructional change in the face of unprecedented disruptions. Conceptual Framework Our study relies on a framework of change, drawing upon Kezar’s (2018) multifaceted framework for understanding change (See Figure 1). Kezar argues that those seeking change tend to focus on the content of the change, rather than the change process itself and how multiple factors and contexts shape the success or failure of the change. Taking a macro approach and drawing upon extant theories of change, Kezar’s framework lays out three areas that guide change: type of change, context for change, and agency and leadership. Figure 1. Conceptual framework for the study adapted from Kezar (2018). The first area, type of change, refers to content (e.g., setting up a new support center or incorporating new technology into a program), forces and/or sources (i.e., where the change originates and the logic behind it), scope (i.e., minor adjustments or transformative change), focus (i.e., process, structure, etc.), level (i.e., institutional, departmental, individual), and intentionality (i.e., planned or unplanned change; Kezar, 2018). Second, context for change involves economic/political/social factors impacting the change, institutional culture, higher education context (e.g., shared governance, power structures, loose coupling, etc.), and external stakeholders (e.g., government agencies, professional societies, etc.; Kezar, 2018). Third, agency and leadership relate to types of change agents and leadership, including top-to-bottom, bottom-up or grassroots, collective, and shared (Kezar, 2018). These three areas are analyzed and feed into one another toward the final element, which is approach to change, including developing strategies to make change and relevant theories that inform the strategies. In our study, adaptations to math contextualization represent the type and content of change, the focus being processes such as instructional approaches and curricula, the level being individual and organizational. COVID-19 embodies the context for change as an external social factor impacting the change, and community colleges as agile adaptive higher education institutions. Community college faculty and leaders are the change agents as we explore their leadership strategies and approaches to change. We situate this framework within math contextualization efforts as a form of ongoing instructional reform, and how community colleges make changes due to the pandemic. This lens will assist in unraveling the multiple dimensions and complexities of the change process in higher education in light of unexpected disruptions. Methods Study Context and Cases The current study is part of a 3-year mixed methods research project funded by the National Science Foundation (NSF). The project is a collaboration among a university and two large, comprehensive community colleges in the Midwest (referred to as Two Lakes College and West Shore College) and follows faculty professional development and math contextualization efforts across developmental and college-level math courses offered within technical education and math departments at the community colleges. Each institution adopted their own approach to professional development and implementation of math contextualization as appropriate for their individual institutional contexts. Faculty from college math, technical education, and developmental math areas were encouraged to attend several professional development workshops and support activities on math contextualization at each institution from 2017 through 2020. Participation was voluntary and faculty implemented contextualization at their discretion. Over the course of the project, 44 faculty at Two Lakes College (TLC) and 46 at West Shore College (WSC) participated in at least one activity (such as workshops) of the professional development opportunities. Although it is difficult to quantify the level of faculty engagement both individually and institutionally, insights from our practitioner colleagues at the participating colleges suggest that this initiative was among one of the most robust in recent years. The faculty participants came from a variety of disciplinary areas spanning math, developmental education, and technical education. Faculty who opted to use contextualization implemented a range of strategies that center intentional connections between math concepts and disciplinary/workforce contexts, often supported by hands-on exercises using classroom tools (e.g., circuits, sheet metal, tape measures). These approaches were new and differed from those in non-contextualized courses. Research Design Case study examines a phenomenon, process, or organization (Merriam & Tisdell, 2016). We adopted multiple case studies to explore how faculty and institutional leadership adapt instructional reform amid COVID-19 at the two colleges within their respective contexts and structures. Multiple cases allow for variance and comparison, as well as further-reaching results, robustness, and greater validity (Miles et al., 2014). Case study entails a bounded system and unit of analysis (Merriam & Tisdell, 2016). For our study, the bounded systems were the math contextualization professional development and related efforts at each college. The units of analysis represented faculty professional development participants teaching contextualized math courses, along with instructional and institutional leadership who provided various levels of support for math contextualization efforts at each college. Data Collection Case studies integrate several data sources, including interviews, artifacts, documents, observations, and records (Merriam & Tisdell, 2016). Interviews with faculty and institutional leaders from each college represented our primary data source given their central role in case studies (Merriam, 1988). In addition, we utilized field notes, observations, lesson plans developed throughout the project up until COVID-19, project documentation, and other contextual information (e.g., institutional communication and websites) as complementary data to support the interview data and enrich the cases. We collected some of this complementary data (e.g., lesson plans) prior to the interviews, while others throughout and after interview data collection (e.g., field notes). Upon completion of the interviews, we conducted a comprehensive review of all project documentation, as well as institutional communication and website updates during the time of our research to lend further context. To recruit interview participants in light of our research questions, we reached out to the 33 faculty from TLC and WSC who consistently attended professional development throughout the past 4 years and taught contextualized courses in Spring 2020 when COVID-19 hit. This allowed us to focus on faculty who were fully engaged in and substantially carried out contextualization in their courses at the onset of the pandemic to address our research focus. Nine faculty members from TLC and nine from WSC agreed to take part in this research, resulting in a total of 18 faculty participants. To answer our second research question, we also invited 11 leaders from both colleges who were involved in supporting instruction and contextualization efforts throughout the pandemic. Of these leaders, eight consented to participate in our study. See Table 1 for interview participant details. Table 1. Interview Participant Characteristics. Study name Gender Employment status Position Program area Two Lakes Liz Female Full-time Faculty College math Brian Male Full-time Faculty College math Robin Female Full-time Faculty lead College math Greg Male Full-time Faculty Technical education Carpenter Male Full-time Faculty Technical education Charley Female Full-time Faculty Technical education Terry Male Full-time Faculty Technical education Anna Female Full-time Faculty Technical education Sandy Female Full-time Faculty Developmental education Steven Male Full-time Dean Arts and sciences Alice Female Full-time Provost James Male Full-time Associate Dean Applied sciences Marcus Male Full-time Dean Academic achievement West Shore Daniel Male Full-time Faculty Lead College math Marlene Female Full-time Faculty College math Hannah Female Full-time Faculty lead College math Beth Female Full-time Faculty College math Aaron Male Full-time Faculty Technical education Walter Male Full-time Faculty lead Technical education William Male Full-time Faculty lead Technical education Iris Female Full-time Faculty Technical education Jeff Male Full-time Faculty lead Technical education Maya Female Full-time Director Teaching excellence Alan Male Full-time Director Scheduling/retention Khadim Male Full-time Dean General education Mary Female Full-time Director Institutional research We conducted most of the faculty virtual interviews during Summer 2020 and completed leadership virtual interviews during Fall 2020. Interviews were semi-structured and lasted about 60 minutes. The faculty interview protocol consisted of questions about instructional adjustments, experiences with contextualization, professional development, building community around contextualization, and plans or supports needed for contextualization in light of COVID-19. The institutional leadership protocol contained questions about experiences, reactions, and support for faculty and students in general and related to math contextualization throughout and beyond the pandemic. For the interviews, we took field notes, observations, and reflective memos to document contextual information and emergent findings. Data Analysis Aligning with case study methodology, we constructed in-depth descriptions of the cases using the various data sources. For each case, we applied a combination of initial, in vivo, and evaluation coding (Saldaña, 2013) to the interview data to develop codes, which we used to form initial categories. We refined the categories toward themes, followed by further analysis and integration of field notes, observations, faculty lesson plans, and other contextual data such as institutional communication and websites to triangulate, reinforce, and add richness to the themes, interview data, and cases. We also conducted within-case and cross-case theme analyses to reach broader explanations and conceptualizations (Merriam & Tisdell, 2016). We conducted several rounds of this process, revisiting and distilling the categories and themes to ensure they were reflective of the data and the research questions. Trustworthiness We engaged various strategies to establish the trustworthiness of our study. First, there were multiple researchers involved in the research process who performed several rounds of cross-checking and calibration from interview protocol development and data collection to data analysis and formulation of findings. Second, data collection was consistent to ensure uniformity and robustness across cases. Third, triangulation was integral to our study due to the multiple case studies and the use of various data sources (e.g., interviews, observations, field notes). Finally, we performed member checks (Lincoln & Guba, 1985) by presenting emergent results (Thomas, 2017) to faculty and scholars as an opportunity for them to discuss, reflect, and question findings. Findings We begin with a description of our cases situated within our conceptual framework, followed by the themes that emerged from our analysis. The findings are grounded in the interviews, along with faculty lesson plans created throughout the project up until COVID-19; contextual data such as institutional communication, documents, and websites (including college level more broadly and specific college areas like teaching excellence centers); field notes; and observations. Description of Cases At TLC and WSC, contextualization was the initial type of change planned through the 3-year mixed methods research project supported by NSF and launched in Summer 2017. TLC’s context for change was tied to their college already having several contextualized math courses through Carnegie Math Pathways and their desire to expand contextualization within and beyond the math department to improve student success in math. WSC’s context for change was a recent transition to a guided pathways approach to better support student success in math and college, with contextualization dovetailing those efforts. There were a few technical education programs that used contextualization based on previous efforts, but the approach was not widespread. WSC leveraged the project to broaden contextualization across math and technical areas. Both colleges achieved this change through top-down and shared leadership by way of college, school, and departmental leaders and lead faculty, with their strategies for change rooted in scientific management and social cognition theories, such as supporting and coordinating ongoing professional development and communities of practice for faculty from 2017 to 2020. Faculty participated in two workshops per year for 2 years, along with summer webinars, a math contextualization community of practice, and ongoing support from lead faculty throughout the duration of the project. These activities and supports led to curricular and instructional changes among individual faculty members. Although a labor-intensive, iterative process, contextualization efforts continued to grow, spreading across pre-college, math, and technical areas. Pockets of faculty collaboration within and across departments drove improvements and expansion, along with institutional researchers and leaders tracking and supporting contextualization and related impacts. At WSC, to complement faculty efforts and guided pathways initiatives, the college adopted Carnegie Math Pathways (n.d.) contextualized courses in their math department. This action solidified contextualization as a more permanent approach at WSC, in addition to the existing reform across math and technical education area. In Spring 2020, as TLC and WSC faculty were well into the semester using their contextualized lessons and courses, COVID-19 hit. The pandemic was an external social factor that served as one context for change. The emergent information on the severity of the COVID-19 virus and social distancing guidelines made in-person instruction nearly impossible. However, true to the spirit of community colleges as agile and adaptive institutions, another context for change, institutional leaders and faculty changed course in response to the pandemic, prompting a wave of unexpected changes. Many instructors shifted to remote instruction and adapted their contextualization approaches, which we describe in later sections. In addition, some instructors modified course structures for students to return in the summer to complete lab or workshop requirements, with social distancing and sanitizing guidelines in place. These adjustments to contextualization represented the content and type of change. Situated within agency and leadership, institutional leaders and faculty were change agents who engaged in a combination of top-down, collective, and shared leadership to facilitate the evolving changes. From top-down, TLC and WSC leadership drew upon and reallocated resources (e.g., funding for technological equipment and tools; teaching and learning centers for faculty; financial, academic, and other support services for students; additional professional development and training). Shared leadership entailed institutional leaders empowering faculty to support one another during the transition to remote instruction and faculty leads to adapt and maintain contextualization efforts as they saw fit. Collective leadership emerged through building deeper relationships and trust between faculty and institutional leaders to keep all motivated and supported through the planned and unplanned changes. As the colleges contemplated and contended with these elements of change, they grounded their approach to change in three areas consisting of a blend of cultural, evolutionary, and scientific management theories. First, faculty and institutional leaders were relationship oriented and emphasized community in remote environments. Second, faculty adapted (e.g., integrating new software applications, videos, online platforms) or temporarily suspended their contextualization efforts (e.g., eliminating group work or hands-on experiences), with the specific approaches detailed in the following sections on the themes. Third, faculty and institutional leaders relied on further training to sustain contextualization throughout and beyond remote instruction. Here we segue into the three broader themes that reflected faculty and institutional leadership approaches at both colleges to bolster instructional reform amid COVID-19: reaching out to create community remotely, reimagining contextualization or pushing the pause button, and skilling up to persist through and toward change. Reaching Out to Create Community Remotely In the first theme, community represented an important context that informed the colleges’ cultural approach to change. Specifically, faculty and institutional leaders at both colleges highlighted challenges and approaches to cultivating a sense of community in a remote environment. Community included connecting with students, as well as keeping faculty communities alive. Staying connected with students Prior to COVID-19, WSC and TLC faculty pointed out the ease with which they interacted with students, building a strong sense of community within and beyond the colleges. When the pandemic struck, the colleges limited capacity or completely shut down areas of their campuses, which caused regular interactions to dissipate. Faculty responded by employing supportive, regular messaging via email, text, phone calls, and applications or online platforms. In addition to phone calls as a primary way to keep in touch with students throughout the pandemic, faculty were creative in ensuring students were connected. Robin, a TLC math instructor, used a texting application before the pandemic, which she found helpful for remote instruction: “I already had this texting app that I used with my students all the time. . .I could reach out to them, and they knew to use it.” Jeff, a WSC technical faculty, used the college’s online platform to stay connected with students through videos: “They don’t miss school as much because they can always go on and check out my video.” Another way faculty maintained community was by establishing flexible availability anchored in students’ schedules. Because the pandemic disrupted students’ lives and work, faculty made themselves and their courses widely available. At TLC, flexibility reflected faculty being available to students whenever and however needed. For example, Greg, a TLC technical instructor, broadened his availability and responded to students promptly: “I would be available instantly between eight and one in the afternoon. . .And then after that. . .I told them. . .I’ll try to get back to you as quick as possible or maybe within an hour.” Terry, another technical instructor, set up several office hours and met students in person: “. . .we drove over to [TLC campus] and parked in the parking lot, and we went over stuff.” At WSC, flexible availability centered on class scheduling to help students stay connected to their courses. Although technical faculty member Jeff started his class meeting at the usual scheduled time, he recorded the sessions so students could access his course anytime: “But if for some reason they missed the class or they miss meeting at eight o’clock, I have recorded it so that they can watch it.” Marlene, a math instructor, also recorded her sessions to ensure flexibility around students’ schedules: “I would always have about three or four students who would be absent, but they knew that the sessions were recorded, and they could come in and at least watch the sessions.” Although faculty did what they could to create community remotely, they were limited in the connections they could facilitate. Institutional leaders at both colleges supplied technological equipment and internet access to keep students connected. TLC and WSC leadership coordinated the provision of computers and Wi-Fi. James, TLC Associate Dean of Applied Sciences, described this process: “We tried to identify every student who did not have internet access at home to provide them a laptop and a hotspot. . . .We’re going to keep you going.” However, WSC’s Wi-Fi resource was limited, so the institution extended their Wi-Fi. Alan, WSC Director of Scheduling/Retention, discussed this scenario: “We turned up our Wi-Fi. . .You can drive to campus. . .sit in the parking lot. . .if all else fails. . .just come to our campus.” Institutional leadership also mobilized virtual campus services and community resources for academic, financial, and mental health support to amplify faculty efforts and keep students connected. Alice, Provost of TLC, described a system implemented during the pandemic: “. . .we just launched. . .a new IT system called Navigate. . .a platform that connects the entire student experience. . .it builds kind of communities of care. . .based on student needs, the resources come to them.” Alan, Director of Scheduling/Retention, gave an example at WSC: “I think one of the good things that we did was moving our support structures online and virtual, you know for example tutoring support.” Mary, WSC Director of Institutional Research, developed a survey for students in need of emergency aid: “. . .there’s kind of a whole process developed to make sure the students receive the funds to support what they need, like either personal needs or technology needs.” COVID-19 made it difficult to stay connected with students, but faculty and institutional leaders came together to create a close community to keep students going and supported in a remote environment. Keeping the faculty community alive The pandemic also posed challenges to maintaining relationships among faculty. To overcome this, TLC and WSC faculty leaned into faculty communities, including their contextualization communities of practice established within their institutions at the onset of the project. This specific community helped faculty persist with contextualization and other teaching challenges. TLC math instructor Robin mentioned the value of the contextualization community of practice during the pandemic: “And one of the things that’s been really hard with the pandemic is you don’t just run into people. . .But so just how great that’s been to have this community.” For Hannah, a WSC math instructor, the community of practice was the safety net and support system she tapped into to navigate COVID-19 challenges and remote instruction: “When we come to community of practice, when COVID happened, my community of practice, my community did not fail.” In addition to the existing contextualization community of practice, faculty cultivated and connected with new community members. Iris, a WSC technical instructor, drew in new faculty, staying connected, sharing contextualized lessons on blueprint reading for welding and others developed through the project, and adjusting them as needed:We met as our small groups and then shared the [contextualization lesson] files with everybody else. . .go over what we had created, and how to use it. . .have different people assess it to see, “Oh, maybe you should add this or that to it.” For Anna, a TLC technical faculty member newer to contextualization, the contextualization community of practice allowed her to connect with more instructors than in the past: “It was always just Robin and I working together. And so, I guess kudos to COVID, it kind of made this group of instructors [using contextualization] sort of coalesce and meet virtually.” Institutional leadership and faculty also facilitated and supported departmental and cross-departmental community. At TLC, the faculty community tended to be interdisciplinary, as described by Sandy, a developmental instructor: “I got together with my fellow math teacher. . .health math teacher too. . .we got together. . .talking about how are things going, are there any changes we need to make, you know, things like that.” Steven, Dean of Arts and Sciences, reached out to faculty to build community: “I’ve made more than one call in the last few months to our phenomenal online faculty to have them reach out to a particular faculty member who’s really struggling and work with them, reassure them.” Faculty community at WSC mostly centered on disciplinary communities. Beth, a math instructor, communicated regularly with other math faculty colleagues: “. . .we text back and forth, or we call. And we just had a math department meeting. . .so that was really good to see everybody.” Jeff, a technical faculty member, experienced increased connection with colleagues in the technical areas: “I think we’re interacting more, way more than we ever did before. . .I get questions, emails and conversations and Zoom meetings and Google, I’m meeting more people than I ever met before in my life.” Maya, Director of Teaching Excellence, encouraged faculty community through additional positions and finances to support them: “Some of the best things we’ve been able to do is just pay our faculty to be supportive of each other. For example, these faculty development coach positions that we have. . .we need to continue doing that. . .with contextualization.” The contextualization community of practice helped maintain and grow faculty community, along with institutional leadership reaching out to further ensure that faculty stayed connected with one another. However, faculty were still confronted with the challenge of how to approach contextualization in remote instruction. Reimagining Contextualization or Pushing the Pause Button The second theme reveals that the pandemic forced faculty to make difficult choices regarding their contextualization efforts, resulting in a more or less evolutionary approach between modifying contextualization for remote instruction or pushing the pause button. Maintaining central tenets of contextualization Employing contextualized practices (i.e., in-person interactions and hands-on activities, such as group work, experiential learning, labs, and workshops) remotely was a significant hurdle. Several faculty members at both colleges adjusted their approaches to maintain the broader principles of contextualization in a remote environment. For example, to promote student interaction and collaboration that was present in many faculty lesson plans prior to the pandemic, several faculty leveraged various online platforms. Sandy, a TLC developmental education instructor, mentioned a few options to promote collaborative experiences: “You can use Blackboard Collaborate or WebEx. I used Blackboard Collaborate where you can set up groups quite easily.” Khadim, WSC Dean of General Education, identified Zoom as another venue “that was contextualized as well for the math project so that students can still go out into breakout sessions during the learning process.” Albeit not fully comparable replacements for working together and building relationships in person, faculty attempted to preserve this facet of contextualization. Faculty at both colleges, especially from technical areas, maintained applied and hands-on activities central to their contextualized lessons created during the project and prior to the pandemic through various programs, simulation, and other innovative approaches. In an interview, Carpenter, a TLC technical instructor, detailed a new web application he used to go about contextualization in a remote environment for construction and remodeling:. . .we started using something called Rise 360. . .it creates great interactive lessons. It’s really cool. . .[when] you go to online. . .you lose opportunities to connect the material and to get them excited about it. So, math is one of things like when you show them ‘This is a tool. Check this out.’ . . .Rise [360] is a great interactive [web app]. . .[we] use the Rise interactivity for them to learn the basic concepts of the terms. . .what’s the joist, what’s a rafter, what’s a limelight. WSC technical faculty Iris also explained in an interview how she used creativity to facilitate hands-on and experiential activities remotely. This was important, as her contextualized lessons for blueprint reading before the pandemic centered on students exploring structural shapes and metal and creating bills of materials for welding:They couldn’t come in the lab to weld something, I gave them the option to make it and grade them on problem solving and critical thinking by making something in their home. . .with whatever materials. . .they still have to create a print and a material list. Other faculty leveraged contextualization experiences that occurred in person prior to remote instruction to introduce materials and approaches that, although less contextualized, still resonated with students. These strategies included articles, case studies, and videos focusing on concepts and problems students would encounter in the real world and workforce. Aaron, WSC technical faculty member, described his adjustments:I had to come up with assignments that were actually hands-on lab. . .I had a series of really good articles that. . .talked about problems with automation. And so fortunately, we had enough time in already [before COVID] that students knew exactly what these problems were and. . .how common these problems are. WSC math instructor Marlene used videos to help students understand and apply math concepts, compared to the more hands-on and group activities evidenced in her contextualized lessons she developed before COVID-19:We watched what happens when a chill comes across your skin, and the students were able to identify the input and the output, and they were able to say that not every input has the same output under unique conditions. And they were able to transfer that idea into mathematical functions. . . they were able to make some meaning that you can’t make in a lecture by watching the video. While instructors carried out activities more or less aligned with the bigger picture of contextualization, there remained challenges that impacted their efforts, described next. Drifting back to the traditional Some TLC and WSC faculty resorted to a de-evolutionary approach and suspended contextualization efforts in the shift to remote instruction. One reason they did this was to prioritize student needs, as highlighted by WSC math instructor Hannah: “But COVID took out my contextualization. . .but there’s nothing we can do about that. I mean that’s actually all moot, compared to other things that had happened. . .like none of that actually mattered, we are in a pandemic.” Robin, a TLC math faculty member, expressed her disappointment in being unable to fully carry out contextualization. Her lessons, which she created before the pandemic, contained material that was contextualized, but she could not engage in the pedagogical aspects that comprised contextualized teaching. Yet, her primary concern was for her students, who had their own challenges attending college amid COVID-19:That was really hard for me. . .I put a lot of effort into it and so then just—really just worried about my students. . .I had students that just sort of disappeared for a couple of weeks and then come back and like they were helping their aunt. . .I’m not happy with any of it. . .that’s been very hard to manage. At WSC, administrative streamlining of course structures and adjusting student attendance requirements also caused faculty to pause contextualization. The streamlining of courses resulted in less time for faculty to implement and further develop contextualization in a remote environment. Marlene, a math faculty member, discussed this scenario:And they did what they could, I think, to bring consistency to what was happening online. But in doing that, they implemented a lot of new requirements and standards that had to be met by the teachers which then, I think, took away a lot of time for teachers to explore different lessons and different ways of teaching, including contextualization. Beth, another math instructor, echoed how faculty were preoccupied “trying to meet these administrative criteria for what our Blackboard shell is supposed to look like. And we have spent no time trying to figure out the best way to put the lesson online.” Further, the available course catalog illustrated the number of courses that were no longer offered, often putting the onus for developmental math on instructors of courses that would have otherwise been ripe for contextualization. Regarding course attendance requirements, flexible attendance and synchronous versus asynchronous sessions, complicated contextualization, especially group work. Daniel, a WSC math instructor, explained:. . .after we went to the online setting. . .students were not mandated to be at class. . .And it’s really hard to do group work when there’s only two or three people that come, and then the rest of them are doing it at whatever time. . .so unfortunately the lessons that I would’ve normally done that were contextualized kind of got put to the side. Remote instruction prompted faculty to reimagine contextualization in ways that were different from the lessons they initially developed. In some instances, they maintained central tenets of this approach, and in others, instructors drew on less contextualized options that were still compelling. Despite their best efforts, there were some faculty who had to push the pause button, as student needs and administrative adjustments took priority. Skilling Up to Persist Through and Toward Change The final theme reflects a scientific management approach to how faculty engaged in and institutional leaders supported training and ongoing professional development around contextualization and remote instruction to keep moving forward with math instructional reform. Preparing students for a remote contextualization experience While faculty aimed to sustain contextualization, they also trained their students for remote instruction. Liz, a TLC math instructor, recreated her contextualized classroom experience and prepared her students for the skills they would need, like submitting assignments, working together in the platform, and so on to make the change as smooth as possible: “. . .that Friday, we went to class and demonstrated what we were gonna do. . .I did it like in this online live way where I just did what I would have done in class, but I did it on Blackboard Collaborate.” Khadim, WSC Dean of General Education, also described how faculty maintained reform efforts by preparing students to move into a remote contextualized environment:[Faculty] constructed. . .learning activities and videos. . .embedded in some of the course shells, so students would go there to learn how to navigate from the main classroom to the workshops. . .how to collaborate in the workshop environment and so on and so forth. Besides gearing students up for remote contextualization, faculty at both colleges bridged technological shifts and ongoing reform efforts toward an evolving, post-COVID-19 workforce. Greg, a TLC technical faculty, explained how he brought contextualization and technology together to train students to work with local clients in alternative modes: “. . .we worked on a housing development. . .That was almost entirely all done online and the finished product at the end was a PowerPoint presentation that would be done for the client.” Although students conducted the work remotely, they were able to leverage their growing technological skills to complete the contextualized project and deliver it to clients. Jeff, WSC technical instructor, illuminated the challenges and promises of merging contextualization and technology in training students for the ever-changing job market:And I gotta teach them the benefits of doing [Blackboard] Collaborate how. . .this will help you in your future too. . .when you become a manager or when you’re running the plant or when you’re working in sales, you may have to go on and use Google and Zoom. So not only are we learning about welding now, we’re also learning about other skills that make you better, well-rounded when you get a job. Faculty not only prepared students for contextualization in a remote environment, but they equipped students with important tools they may likely need in a post-COVID-19 job market. Sustaining faculty contextualization efforts TLC and WSC faculty were also engaged in various activities to persist in their contextualization efforts amid the pandemic. This included ongoing professional development and support related to contextualization. WSC math instructor Daniel continued to work on and assist other faculty in developing contextualized lessons throughout the pandemic: “. . .we’ve got about four teachers. . .writing some contextualized lessons in the COVID experience. And I am gonna try and help them with that. . .that part of it hasn’t really gone away for me. . .I’m still active with that.” Sandy, a TLC developmental education instructor, participated in an online teaching course that she found particularly helpful with her contextualization practices. The colleges’ centers for excellence in teaching and learning offered virtual teaching and learning workshops, which they advertised through social media along with intra-college communication channels and newsletters:I took a. . .preparing to teach online that our college offered to us. . .For those of us [using contextualization]. . .we had written the curriculum to be face-to-face, ‘cause it was a lot of group work. . .But fortunately, like I said the training, we really focused on: Oh well, how can we do group work in an online environment? How can we do breakout sessions or? So, the training from our college really made the difference. Another way faculty sought to sustain and improve contextualization was through student feedback and reactions. Iris, a WSC technical faculty member, pointed out that she collects regular feedback to enhance and refine her contextualized lessons: “I look for their feedback, it’s really important because whatever their feedback is, the next group will have similar feelings. . .curriculum is always evolving.” Similarly, TLC technical instructor Carpenter encouraged his students to share their honest take on his contextualized course to make it better: “And at the very end, I sat them down. . .I’m like ‘Ok, be frank. . .what worked, what didn’t.’” Faculty found student input to be a valuable part of sustaining and advancing contextualization efforts at the colleges. TLC and WSC leaders highlighted their commitment to sustaining contextualization beyond the pandemic by collecting and supplying data to inform and guide faculty efforts. Steven, TLC Dean of Arts and Sciences, highlighted how sharing data with faculty made a difference: “The developmental faculty, particularly in math. . .seemed to appreciate the contextualized opportunity and the data that we’re showing them about student success through contextualized opportunities.” Mary, WSC Director of Institutional Research, described her involvement in collecting course data to help faculty persist in their work:The faculty come together to discuss [contextualization]. . .each term is where they’re supposed to look at their data. . .they ask IR for data to prepare for those days, so then the teachers can look at their data together and discuss what’s the best practice they could implement in their classroom. Although the pandemic could pose challenges to ongoing contextualization, faculty participated in further professional development and gathered student feedback to boost their efforts, along with data from college and administrative leadership. Discussion Math reform continues to be essential to improving teaching and learning in community colleges. Yet disruptions, namely the pandemic, can complicate fruitful efforts. Here, we reflect on our findings in light of our framework and relevant empirical work. The Push and Pull of Unplanned Change Change—planned or unplanned—is not without its challenges and complexity as evidenced in our study. Based on the multifaceted framework for understanding change, any issues that emerge in the change process may stem from the analysis and interpretation of the type, context, and/or approach to change, along with structures, policies, and other factors based on various change theories (Kezar, 2018). As a result, the dimensions of change need to be regularly revisited for unanticipated challenges or circumstances, which are especially likely to occur with long-term initiatives (Kezar, 2018), such as contextualization efforts at the colleges in our study. Yet, our findings demonstrate some unforeseen contexts and types of change, like COVID-19 and the need to shift to remote instruction, that even with the best understanding and planning, cannot be anticipated. While Kezar’s (2018) framework offers insight into the challenges of change processes, we also see how unplanned change creates a push and pull scenario for institutional leaders and faculty. For example, despite the disruptions of COVID-19, some faculty persisted and reimagined contextualization to maintain its broader principles. However, there were institutional adaptations at one of the colleges that complicated reform efforts, specifically flexible student attendance and uniform online course shell structures. These changes shifted several faculty away from innovation and toward the operational and structural tasks of remote instruction. Taken together, individuals and institutions may adapt and change in ways that both inspire and impede innovation. Multi-Faceted Leadership and Agency During Crisis Our study revealed a confluence of leadership strategies based on our conceptual framework, ranging from mobilizing resources for faculty and students (top-down) to the provision of supports, connections, and data without interfering with faculty efforts and adaptations (collective and shared). These findings speak to the delicate balance of employing leadership and promoting agency, and reinforce the complex, multiple approaches to change (Kezar, 2018), even amid crisis. These strategies also go beyond the framework and point to the evolving leadership process from one that has been historically hierarchical toward one that is relational, contextual, collective, and emboldens others to lead and make change (Eddy & Kirby, 2020; Kezar & Carducci, 2007). Agency and leadership emerged in a nuanced way for faculty in our study. On the one hand, they took initiative and created community, modified contextualization as they saw fit, and persisted with their math reform efforts. On the other hand, the pandemic and related institutional responses constrained faculty agency and leadership, as their instructional approaches and adaptations were limited within the confines of a remote environment and administrative requirements. These findings align with Kezar’s (2018) framework in the sense that there are circumstances and contexts that shape change agents’ capacity to enact change, no matter where they are situated within an organization. We extend the framework by illuminating how agency and teaching fluctuate within certain contexts (Griffiths, 2015), such as crisis situations. Crisis as Opportunities for Change Although crisis is disruptive, it can also present opportunities for change. Many of the adaptations and changes that were made amid COVID-19 were much needed or already being implemented. TLC’s Navigate platform, geared toward student experience and care, was an important tool that was a positive change during crisis. At WSC, moving tutoring and support services online helped broaden reach for students in need. While these changes center student support more broadly, they were complementary to instruction and contextualization. Our study not only reveals how contexts inform and impact change (Kezar, 2018), but how disruptive contexts, like the pandemic, can create environments ripe for change. As a result, these online tools and services may intersect with and amplify faculty’s instructional efforts by providing more holistic support for students. The transition to remote instruction also forced contextualization to take new shape. Teaching and learning will continue to evolve as remote instruction becomes widespread in colleges’ course options. The pandemic sparked a new level of creativity and innovation in instruction, including simulation, new online platforms, software programs that center hands-on learning, among others. These developments make a culture of innovation more crucial than ever when it comes to change, leadership, and higher education (Eddy & Kirby, 2020). Our study underscores the importance of an institutional culture that values innovation to guide and sustain change (Kezar, 2018). Community colleges are uniquely poised to innovate amid crisis considering their longstanding flexibility and adaptability (Cohen et al., 2014). There is immense promise to push the boundaries of and approaches to contextualization long after the pandemic. Implications for Research and Practice Our findings point to several implications for policy, practice, and research. First, the community of practice, such as the contextualization one from our study, serves as a vital anchor for ongoing support and reform. This community proves to be especially critical during disruptive circumstances. Communities of practice entail professional development and learning as a social, interactive process (Wenger, 1998), keeping faculty connected, even amid crisis. Such communities can offset the isolation and remote environments caused by the pandemic. Institutional leaders also play a critical role, as they can encourage and support the formation of communities before, during, and well beyond disruptions. Second, institutions should revisit and reduce potential stressors and regulations that may hamper instructional reform and innovation. Faculty workloads and time constraints already limit instructional improvement (Bailey et al., 2015; Brown & Bickerstaff, 2021; Wickersham & Wang, 2022). Our findings demonstrate how the pandemic compounded existing challenges to instructional reform. Although additional structures implemented during a crisis are well-intended, they may counterintuitively impact faculty efforts to make effective, continued instructional reform. Institutions and leadership need to commit to and support the reallocation of tasks and duties to free up space for faculty to engage with teaching innovations. Another lesson from the pandemic and the study’s findings is the importance of a disruption preparedness mindset—in other words, being ready for future disruptions and quick transitions among both faculty and institutional leaders. Despite efforts to integrate crisis planning and management in higher education (Catullo et al., 2009), institutions still find themselves blindsided by unexpected events and circumstances. While colleges cannot practically plan for every potential crisis, cultivating a mindset that is flexible and prepared for unexpected changes will empower faculty and institutional leaders to be open to change moving forward. For research, there are several potential avenues for future inquiry. The pandemic spurred a surge in online instruction that will continue to grow in coming years. While several faculty in our study adapted contextualization, empirical work should explore which contextualization strategies translate into and are optimal for virtual environments, including what can be contextualized for online courses and what cannot. Also, with the focus of our research on faculty and institutional leaders, future inquiry should center students’ voices and experiences with disruptions in contextualized courses to offer a fuller picture of instructional reform and innovation amid crisis. Finally, additional research is needed to investigate long-term changes and impacts after the pandemic. Work toward this end would include further theory building to capture the complexities of and approaches to change, especially those that are unplanned in relation to various crisis situations and how they unravel to shape and transform change over time. Conclusion As the effects of the pandemic continue to fuel unplanned change in higher education, colleges are compelled to rethink teaching and learning. Instructional reform, such as contextualization, is being reimagined in new environments and modes to facilitate meaningful learning and success in math. A heightened level of adaptation and nimbleness will be crucial to sustain such efforts. Although COVID-19 has posed significant challenges, community college faculty and leaders have risen to the occasion. Crisis has become an opportunity for change and innovation to expand how institutional leaders, faculty, and other institutional staff approach student success and reform in math and college. The authors would like to thank Xiwei Zhu and Brit Wagner for their research assistance. They are also grateful to the editor and anonymous reviewers for providing insightful comments on earlier drafts of this work. Author Biographies Kelly Wickersham is a researcher in the Wisconsin Center for Education Research at the University of Wisconsin-Madison. Her research focuses on student pathways, mobility, and success in higher education, as well as faculty development and community college STEM education, experiences, transfer, and completion. Peiwen Zheng is a doctoral candidate in the Department of Educational Leadership and Policy Analysis at the University of Wisconsin-Madison. Her research interests revolve around international community college student well-being and success. Xueli Wang is the Barbara and Glenn Thompson Endowed Professor in Educational Leadership at the University of Wisconsin-Madison. Her research encompasses community colleges and STEM education, aiming to identify practices, structures, and policies toward transformative change for equitable student outcomes. Amy Prevost is a researcher in the Wisconsin Center for Education Research at the University of Wisconsin-Madison. Her areas of research interest include educational pathways in STEM, improving student outcomes at the postsecondary level, as well as access to career and experiences that contribute to students’ abilities and transfer knowledge. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was conducted as part of larger research projects supported by the National Science Foundation (Grant Nos. DUE-1700625 and DUE-210029). 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==== Front J Telemed Telecare J Telemed Telecare JTT spjtt Journal of Telemedicine and Telecare 1357-633X 1758-1109 SAGE Publications Sage UK: London, England 37357745 10.1177/1357633X231181714 10.1177_1357633X231181714 RESEARCH/Original Article Telemedicine for follow-up of systemic lupus erythematosus during the 2019 coronavirus pandemic: A pragmatic randomized controlled trial https://orcid.org/0000-0001-7113-9390 So Ho 1 Chow Evelyn 1 Cheng Isaac T 1 Lau Sze-Lok 1 Li Tena K 1 Szeto Cheuk-Chun 1 Tam Lai-Shan 1 1 Department of Medicine & Therapeutics, 26451 The Chinese University of Hong Kong , Shatin, Hong Kong Lai-Shan Tam, Department of Medicine and Therapeutics, The Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong. Email: lstam@cuhk.edu.hk 26 6 2023 26 6 2023 1357633X2311817143 4 2023 28 5 2023 © The Author(s) 2023 2023 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Introduction Patients with systemic lupus erythematous were vulnerable to severe coronavirus disease 2019 infection and the negative impact of disrupted healthcare delivery. Telemedicine has been a popular alternative to standard in-person care during the pandemic despite the lack of evidence. Methods This was a 1-year pragmatic randomized-controlled trial. Patients followed at the lupus nephritis clinic were randomized to either telemedicine or standard follow-up in a 1:1 ratio. Patients in the telemedicine group were followed up via videoconferencing. Standard follow-up group patients continued conventional in-person outpatient care. The primary outcome of the study was the proportion of patients in low disease activity after 1 year. Secondary outcomes included cost-of-illness, safety, and various patient-reported outcomes. Results From 6/2020 to 12/2021, 144 patients were randomized and 141 patients (telemedicine: 70, standard follow-up: 71) completed the study. At 1 year, 80.0% and 80.2% of the patients in the telemedicine group and standard follow-up group were in lupus low disease activity state or complete remission, respectively (p = 0.967). Systemic lupus erythematous disease activity indices, number of flares and frequency of follow-ups were also similar. There were no differences in the cost-of-illness, quality of life or mental health scores. However, significantly more patients in the telemedicine group (41.4% vs 5.6%; p < 0.001) required switch of mode of follow-up and higher proportion of them had hospitalization during the study period (32.9% vs 15.5%; p = 0.016). Being in the telemedicine group or not in low disease activity at baseline were the independent predictors of hospitalization (odds ratio: 2.6; 95% confidence interval: 1.1–6.1, odds ratio: 2.7, 95% confidence interval: 1.1–6.7, respectively) in the post hoc analysis. Conclusions In patients with systemic lupus erythematous, telemedicine predominant follow-up resulted in similar 1-year disease control compared to standard care. However, it needed to be complemented by in-person visits, especially in patients with unstable disease. Coronavirus disease 2019 systemic lupus erythematosus telehealth telemedicine Hong Kong College of Physicians Young Investigator Research Grant 2020 edited-statecorrected-proof typesetterts19 ==== Body pmcIntroduction Patients with systemic lupus erythematosus (SLE) are at increased risk of severe coronavirus disease 2019 (COVID-19) infection due to the underlying immune dysregulation, comorbidities, and use of immunosuppressive medications.1,2 During the outbreak, these vulnerable patients faced the difficult choice between COVID-19 infection risk during a clinic visit and postponing the needed care. The management of their disease might also be affected by the disruption in healthcare delivery. An apparent alternative would be to adopt telemedicine (TM) or telehealth, the use of telecommunication technologies to provide medical information and services, to maintain medical care while minimizing exposure.3,4 Despite being widely implemented during the pandemic, evidence supporting the use of TM in rheumatology has been limited. 5 A systematic review in 2017 concluded that there was no good evidence supporting the use of TM for managing autoimmune rheumatic diseases due to the high risk of bias of the published studies. 6 In a subsequent randomized controlled trial (RCT) in 2018, TM follow-up (FU) could achieve similar disease control as conventional care in rheumatoid arthritis patients with low disease activity or in remission. 7 Two studies conducted during COVID-19 outbreak reported favorable acceptance of TM as the mode of care in patients with connective tissue diseases.8,9 However, there is no RCT-evaluating TM FU in patients with SLE. We hypothesize that TM is an effective and safe mode of healthcare delivery for maintaining stable SLE disease control during the COVID-19 pandemic. Thus, we conducted a one-year RCT comparing Telemedicine and standard in-person FU in patients with SLE (Tele-SLE). We primarily aimed to evaluate the effectiveness of TM delivered care in maintaining disease activity control compared to conventional in-person outpatient FU in patients with SLE via a pragmatic trial. The secondary objectives were to compare the patient-reported outcomes (PROs), cost-of-illness, and safety between the two modes of healthcare delivery. The 6-month results of the study focusing on patient satisfaction has been previously reported. 10 Patients and methods Study design and patients This was a 1-year, single-center, open-label, pragmatic RCT conducted at a regional hospital in Hong Kong. From May to December 2020, consecutive patients ≥ 18-year old with SLE according to the 2019 EULAR/ACR classification criteria followed up at the lupus nephritis clinic were invited by the investigators to participate in the study. 11 Patients or their carers needed to possess the technology for conducting a TM consultation (a smartphone, tablet or computer with audio and video capabilities and internet connection). Patients were excluded if they were pregnant, incapable of answering a questionnaire, or unwilling to attend blood and urine tests. Participants were randomized 1:1 to either TM (TM group) or standard FU (SF group) using a computer-generated random number sequence. Baseline clinical characteristics were collected before randomization and allocation concealment was ensured by the use of sequentially numbered, opaque, sealed envelopes. The study was approved by the local ethical committee and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. Interventions Patients randomized to receive TM FU were scheduled for a real-time video consultation via video-conferencing software ZOOM (Zoom Video Communications Inc, California, US). Patients in the SF group received standard in-person outpatient care. In both groups, medication titration was based on a shared decision between the patients and treating clinicians aiming at achieving complete remission or Lupus Low Disease Activity State (LLDAS) according to the international management guideline. 12 Complete remission was defined as absence of clinical activity with no use of systemic glucocorticoids (GC), and immunosuppressants (IS); and LLDAS as systemic lupus erythematosus disease activity index 2000 (SLEDAI-2k) ≤ 4, no activity in any major organ, no new disease activity features, physician global assessment (PGA) ≤ 1 with GC ≤7.5 mg of prednisone daily and well tolerated IS.13,14 In-person clinic consultations could be arranged as requested by the clinicians or patients in the TM group. Similarly, TM consultations could be arranged as required in the SF group. The frequency of visits was based on clinical judgements, as well as mutual decisions of the attending clinicians and patients. Prior to each consultation, all patients needed to have pre-ordered blood (complete blood count, liver and renal function tests, c3, c4 and anti-dsDNA) and 24-hour urine total protein checked. Blood pressure and body weight measurements were done prior to each consultation at the clinic for the SF group or at home for the TM group. All consultations, either virtual or physical, were performed by clinicians with more than 5 years of experience (HS, CCS, and LST). Both the rheumatologists and patients had no previous experience with virtual consultations. Prescribed medications could be collected in person by the patients or their designated representatives at the hospital or community pharmacies. Study outcomes SLE disease activity at each consultation was assessed by SLEDAI-2k and PGA. Disease flares were captured with the SELENA flare index. 15 All participants were asked to complete the LupusQoL, Health Assessment Questionnaire (HAQ) and Hospital Anxiety and Depression Scale (HADS). LupusQoL is a disease-targeted PRO measure that is developed for SLE patients. HAQ covers various common daily activities to assess disability. HADS was developed to assess anxiety and depression in medical patients. The Chinese versions of the above tools have all been studied in patients with rheumatic diseases.16–18 The participants were also asked to complete a questionnaire on employment outcomes and out-of-pocket expenses at the end of the study. 19 Both direct and indirect costs of illness were assessed. Direct costs collected consisted of all costs of private hospital/clinic facilities (including costs of visits, medications, investigations, and hospitalizations) as public health care services were largely free-of-charge; and patients’ out-of-pocket expenses for health products, non-traditional therapies (hydrotherapy, acupuncture, and massage), aid devices, transportation fee to the health care providers, private household helper, and adaptation to houses. Indirect costs represented the productivity loss due to SLE, which included sick leave, unemployment, and days off from household work or daily activities. Participants were asked to rate post-consultation satisfaction. The responses were assigned a value of 0–4 (strongly disagree to strongly agree), with a higher score indicating that the respondent was satisfied with the FU and a 2 indicating a neutral response (Supplemental Figure 1). The primary endpoint of the study was the percentage of patients in complete remission or LLDAS at the end of the study. Secondary endpoints included: (a) the lupusQoL, HAQ and HADS scores; (b) direct and indirect costs of illness; (c) number of FU visits and hospitalization during the study period; (d) incidence of COVID-19 infection in one year; and (e) patient overall satisfaction score. Statistical analysis To test for equality, considering a difference less than 20% was of no clinical importance, and the percentage of patients in remission or LLDAS was 65% after one year in both groups, the required sample size in each group to achieve an 80% power at 95% confidence was 70. For each group, baseline demographic and clinical characteristics were reported as mean values with 95%CIs for continuous variables and as numbers and percentages for categorical variables. The groups were be compared by chi-square test and student t-test where appropriate. We used an intention-to-treat approach to analyze the outcomes of the randomized patients. The outcomes in the TM and SF groups were compared by chi-squared test or Fisher's exact test and Student's t-test where appropriate. Within-group changes from baseline to the last FU were analyzed by paired Student's t-test. Multivariate regression models were used to adjust for the baseline differences between the two groups if any. Results with p-value of less than 0.05 were regarded as significant. Statistical analyses were performed using the Statistics Package for Social Sciences (IBM SPSS Statistics Version 26 [IBM, Armonk, New York, USA]). Results From June 2020, a total of 144 patients with SLE were randomized (TM: 72, SF: 72) and 3 patients self-withdrew from the study. At the end of the study (December 2021), 70 patients in the TM group and 71 patients in the SF completed one-year FU (Figure 1). All patients were Chinese. The mean age was 44.5 ± 11.4 years and there was a female predominance of 90.8%. The median time from initial SLE diagnosis to randomization was 168 (range: 1–528) months. The majority of the patients had biopsy-proven class III, IV or V lupus nephritis (87.2%) and were on prednisolone (89.4%) with a median daily dose of 5 (range: 0–35) mg. A significant proportion of the patients (73.8%) were on antimalarials. Many of them (68.1%) were on additional IS with the commonest being mycophenolate mofetil (43.2%). At the beginning of the study, while 66.0% of the patients were in LLDAS, none were in complete disease remission. There were no baseline differences, including demographics, SLEDAI-2k (TM: 3.8 ± 2.3, SF: 3.2 ± 2.2, p = 0.13), PGA (TM: 0.62 ± 0.65, SF: 0.46 ± 0.59, p = 0.13) and SLE damage index (TM: 1.1 ± 1.3, SF: 0.8 ± 1.1, p = 0.10), between the two groups (Table 1). Figure 1. Trial profile. TM: telemedicine; SF: standard follow-up. Table 1. Baseline demographic and clinical characteristics of the patients in the intention-to-treat population. All participants (N = 141) Standard group (N = 71) Telemedicine group (N = 70) Age, years 44.5 ± 11.4 44.5 ± 11.3 44.4 + 11.6 Female sex 128 (90.8) 64 (90.1) 64 (91.4) Chinese 141 (100) 71 (100) 70 (100) Median time from diagnosis to randomization, months 168 (range: 1–528) 144 (range: 1–432) 198 (range: 6–528) Biopsy-proven LN class III/IV/V 123 (87.2%) 60 (84.5) 63 (90.0) SLEDAI-2k 3.5 ± 2.3 3.2 ± 2.2 3.8 ± 2.3 PGA score 0.54 ± 0.62 0.46 ± 0.59 0.62 ± 0.65 SLE damage index 1.0 ± 1.2 0.8 ± 1.1 1.1 ± 1.3 LLDAS 93 (66.0) 49 (69.0) 44 (62.9) Complete remission 0 (0) 0 (0) 0 (0) Prednisolone use 126 (89.4) 60 (84.5) 66 (94.3) Median prednisolone dosage, mg/day 5.0 (range: 0–35.0) 5.0 (range: 0–35.0) 5.0 (range: 0–35.0) Immunosuppressive agent use 97 (68.1) 48 (67.6) 49 (70.0) Antimalarial use 104 (73.8) 55 (77.5) 49 (70.0) Data are reported as mean ± SD or number (%) unless stated otherwise. SLEDAI-2K: Systemic Lupus Erythematosus Disease Activity Index 2000; SLE: systemic lupus erythematous; PGA: physician global assessment; LLDAS: lupus low disease activity state; SDI: Systemic Lupus International Collaborating Clinics/American College of Rheumatology (SLICC/ACR) Damage Index. The results with respect to the primary and major secondary outcomes are provided in Table 2. At one year, 80.0% and 80.2% of the patients in the TM group and SF group were in LLDAS or complete remission, respectively (p = 0.967). SLE disease activity indices including SLEDAI-2k, PGA, proteinuria amount and serum anti-ds-DNA level remained similar between the two groups. There was also no difference in the mean daily prednisolone dose (TM: 4.82 ± 2.83 mg, SF: 4.55 ± 3.16 mg, p = 0.601) at the end of the study comparing the two groups. Within the study period, 28 (40%) patients in the TM group and 21 (29.6%) patients in the SF group had disease flare (p = 0.20). There were no differences in the lupusQoL and HADS scores between the two groups at the end of the study. A slightly higher HAQ score was noted in the TM group (TM: 0.28 ± 0.49, SF: 0.14 ± 0.31, p = 0.035). The PGA significantly improved in the TM group (baseline: 0.62 ± 0.65, end of FU: 0.40 ± 0.50, p = 0.014), whereas the HAQ score dropped in the SF group (baseline: 0.20 ± 0.44, end of FU: 0.14 ± 0.31, p = 0.049). Otherwise, there was no other significant change in the disease activity or PRO parameters within the two groups from baseline to the end of the study. There was also no significant change in the mean daily prednisolone dose in both groups. Table 2. Primary and key secondary outcomes in the intention-to-treat population. Standard group (N = 71) Telemedicine group (N = 70) P-value Primary endpoint Proportion of patients in complete remission or LLDAS 80.0% 80.2% 0.967 Proportion of patients in complete remission 2.8% 0% 0.496 Key secondary outcomes SLEDAI-2k 2.8 ± 2.6 3.3 ± 2.3 0.243 PGA 0.36 ± 0.46 0.40 ± 0.50 0.639 Proteinuria, g/day 0.37 ± 0.46 0.44 ± 0.67 0.431 Serum anti-ds-DNA 174.5 ± 201.7 221.2 ± 227.0 0.199 Prednisolone dosage, mg/day 4.55 ± 3.16 4.82 ± 2.83 0.601 Proportion of patients with flare 29.6% 40.0% 0.200 HADS Anxiety scale 5.8 ± 4.0 5.6 ± 4.3 0.770 Depression scale 5.3 ± 3.9 5.3 ± 4.2 0.989 Lupus QoL score for Physical health Pain Planning Intimate relationship Burden to others Emotional health Body image Fatigue 82.4 ± 18.2 83.1 ± 20.1 86.5 ± 18.2 76.1 ± 28.9 79.7 ± 19.0 81.8 ± 17.3 79.5 ± 23.8 77.1 ± 20.4 77.6 ± 21.3 80.9 ± 17.9 82.7 ± 21.3 74.0 ± 25.4 73.6 ± 22.9 81.5 ± 18.9 79.6 ± 21.4 75.1 ± 20.7 0.160 0.500 0.261 0.727 0.086 0.910 0.993 0.562 HAQ 0.14 ± 0.31 0.28 ± 0.49 0.035 Overall patient satisfaction 3.2 ± 0.8 3.4 ± 0.6 0.030 Mean indirect costs of illness, HKD 26,681 12,016 0.200 Out-of-pocket costs for health care services, HKD 12,297 13,547 0.830 Total number of FU (physical or TM) 5.7 ± 1.7 6.0 ± 2.0 0.400 Proportion of patients with switch of FU mode 5.6% 41.4% <0.001 Proportion of patients requiring hospitalization 15.5% 32.9% 0.016 Incidence of COVID-19 0% 0% n/a Data are reported as mean ± SD or number (%). LLDAS: lupus low disease activity state; SLEDAI-2K: Systemic Lupus Erythematosus Disease Activity Index 2000; PGA: physician global assessment; HADS: Hospital Anxiety and Depression Scale; HAQ: Health Assessment Questionnaire Disability Index; FU: follow-up. Although the mean indirect costs of illness were numerically higher in the SF group (HKD26,681 vs HKD12,016, p = 0.20), the out-of-pocket costs for health care services other than those provided by the public sector were similar between the two groups (TM: HKD13,547 vs SF: HKD12,297, p = 0.83) in the study period. The total number of FU (physical or TM) was similar in the two groups (TM: 6.0 ± 2.0, SF: 5.7 ± 1.7, p = 0.40). However, significantly more patients in the TM group (29/70, 41.4% vs 4/71, 5.6%; p < 0.001) required a switch of mode of FU (reasons listed in Supplemental Table 1); and 18 (24.7%) patients in the TM group were followed-up physically in the last consultation. The proportion of patients requiring hospitalization during the study period was also higher in the TM group (TM: 23/70, 32.9% vs 11/71, 15.5%; p = 0.016) (causes listed in Supplemental Table 2). In the post hoc analysis, patients who were hospitalized were more likely to be in the TM group, had more active disease with higher damage scores, and tended to be on higher dose of GC (Table 3). After adjusting for damage score, prednisolone dosage and age, being in the TM group (OR 2.6, 95% CI 1.1–6.1) or not being in LLDAS at baseline (OR 2.8, 95%CI 1.2–9.7) were the independent predictors of hospitalization (dependent variable) in the multivariate logistic regression model. None of the participants had COVID-19 infection throughout the study. Table 3. Baseline characteristics of the patients with and without hospitalization in one year. Patients without hospitalization (N = 107) Patients with hospitalization (N = 34) P-value Age, years 44.0 ± 11.4 46.1 ± 11.3 0.345 Female sex 95 (88.8) 33 (97.1) 0.189 Time from diagnosis to randomization, months 180 ± 107 180 ± 117 0.99 Biopsy-proven LN class III/IV/V 92 (86.0) 31 (91.2) 1.000 SLE damage index 0.8 ± 1.2 1.4 ± 1.3 0.029 SLEDAI-2k 3.2 ± 2.2 4.3 ± 2.3 0.014 PGA score 0.54 ± 0.49 0.91 ± 0.82 0.002 Proteinuria, g/day 0.36 ± 0.53 0.82 ± 0.82 0.004 Serum anti-ds-DNA 194 ± 197 219 ± 231 0.529 Mean prednisolone dosage, mg/day 4.8 ± 3.6 7.2 ± 7.6 0.083 Immunosuppressive agent use 69 (64.5) 27 (79.4) 0.139 Randomized to TM group 47 (43.9) 23 (67.6) 0.016 Data are reported as mean ± SD or number (%). LN: lupus nephritis; SLEDAI-2K: systemic lupus erythematosus disease activity index 2000; PGA: physician global assessment; TM: telemedicine. The waiting time from entering the clinic waiting room (virtual or real) to seeing the doctors was much shorter in the TM group (16.2 ± 22.8 vs 63.0 ± 35.6 min, p < 0.001) (Figure 2A). The overall patient satisfaction score about the FU process was significantly higher in the TM group (3.4 ± 0.6 vs 3.2 ± 0.8, p = 0.03) (Figure 2B). At the end of the study, 67.9% of the overall participants agreed (versus 15.0% who did not agree) to use TM as a mode of future FU. Figure 2. (A) Mean waiting time between entering the clinic waiting room (virtual or real) and seeing a rheumatologist. Data for each group are presented as box plots: The line within boxes correspond to median; the range between the lower (Q1) and upper (Q3) bounds of the boxes is the IQR. Whiskers represent scores outside IQR and ends in maximum and minimum. Data were analyzed using Mann-Whitney-U test. (B) Satisfaction scores of patients who used telemedicine compared to standard follow-up. The response is shown as a percentage with positive responses on the right. The neutral category was removed when calculating percentages. TM, telemedicine, SF, standard follow-up; IQR: interquartile range. Discussion Due to obvious logistic advantages, remote care by TM has been widely used during the pandemic. A survey conducted in 35 European countries from May to June 2020 showed that the majority of face-to-face rheumatology consultations were converted into TM consultations. 20 Evidence supporting the efficacious, safe, and cost-effective use of TM in autoimmune rheumatic diseases is urgently needed. 21 Tele-SLE was the first RCT comparing TM versus standard in-person FU in patients with SLE. We found that in a pragmatic clinical setting, the disease activity control at one year in patients randomized to TM care was similar to those in the conventional physical consultation group. There were also no differences in quality of life, psychological outcomes, and patient-reported cost-of-illness between the two groups. However, patients in the TM group or with unstable baseline disease had an increased risk of hospitalization during the one-year study period. The clinical care of patients with SLE has been heavily jeopardized by the COVID-19 pandemic. In a questionnaire-based study from the SMILE Lupus Cohort in Italy, about two-third of patients had missed appointments secondary to healthcare delivery disruption. 22 An online survey from the INSPIRE Lupus Cohort in India reported worsening of disease in 25% of patients, likely contributed by difficulty in scheduling hospital visits as a result of the lockdown restrictions. 23 We demonstrated in the current study that TM could be a feasible alternative to physical FU in patients with SLE. An observational study done in Singapore during the pandemic also showed the disease activity at the next visit and the corticosteroid dosages prescribed were similar between teleconsultations and physical FUs in SLE patients. 24 In a large American survey done in 2021 of over 6000 patients with autoimmune rheumatic diseases (15% with SLE), patients who had TM visits were at lower risk of stopping medications than patients who had neither an office nor a TM visit (OR 0.67, 95%CI: 0.51–0.88). 25 The fear of infection and interruption of medical care had negative repercussion on lupus patients’ mental as well as general health. A questionnaire-based study in 405 patients with SLE using Depression, Anxiety and Stress Scales (DASS-21) found that stress, anxiety, and depression were moderate to severe in 12.3%, 38.7%, and 27.7% of the respondents in May 2020. 26 In another study of 63 patients with SLE done during the pandemic, 47.5% and 48.3% of the questionnaire respondents reported an increase in anxiety and depression respectively, and over 40% scored worse in measures of pain interference, fatigue and cognitive abilities. 27 A French nation-wide survey with 536 SLE respondents revealed that the main reported difficulties during the pandemic were issues regarding access to medical care (25.4%), loss of employment (24.4%) and financial difficulties (11%). 28 The comparable psycho-social outcomes of SLE patients in the TM and SF groups shown in our study are reassuring that virtual care could address the major concerns of the patients during the pandemic without increasing costs. It should however be noted that patients in the TM group had more hospitalizations and a significant proportion of them required conversion to in-person consultation during the study period. These could off-set the conceived benefits of TM. Although many of the hospitalizations did not appear to be related to SLE diseases, the major reasons for switching to physical visits were perceived unstable disease and occurrence of new symptoms. It may reflect the lack of confidence of either patients or clinicians in virtual assessment accuracy. In fact, a survey in 2021 revealed that 93% of the clinicians and 86% of the patients with autoimmune rheumatic diseases (32% with SLE) rated TM as worse than physical consultations in terms of assessment accuracy. 29 It was found in a recent prospective study in patients with inflammatory rheumatic diseases that the most significant treatment decision discordance between virtual and physical consultations was GC tapering in patients with SLE. 30 A retrospective study done during the pandemic also reported a higher likelihood of additional face-to-face appointment after TM consultation in patients with lupus in a rheumatology unit. 31 It appeared that lupus patients with unstable disease control could not be optimally managed by TM alone, as not being in LLDAS was a predictor of hospitalization in our study. During the COVID-19 outbreak or when the health facilities are overstretched, a hybrid mode of FU with TM complemented by in-person visits when necessary may be helpful. TM can also be integrated into the conventional system for managing patients with stable SLE to cope with the increasing disease prevalence and workforce limitations beyond the pandemic.32,33 Further research on validation of disease-specific PRO, adaptation of the physical examination to virtual consultations, and development of innovative technologies allowing remote monitoring of clinical conditions is encouraged. Appropriate training for healthcare providers is also vital to improve the efficacy and sustainability of telehealth.34,35 There are several limitations. First, the enrollment of patients accepting TM care only and the lack of blinding may introduce bias. In fact, a significant proportion of patients refused participating in the trial at screening, although we found no difference in the baseline clinical characteristics between the study subjects and those who refused TM (data not shown). About 10% of screened patients were excluded due to a lack of equipment. The issue of digital literacy potentially exacerbating the health care disparity should be addressed. 36 Second, the predominance of patients with nephritis may limit the generalizability of the results. Monitoring of proteinuria is the fundamental element of disease activity assessment in nephritis patients, whereas physical examination may be more essential in patients with mainly cutaneous and musculoskeletal manifestations. On the other hand, the lack of urinary sediment examination might lead to under-estimation of disease activity in the TM group. Third, as a significant proportion of the sampled patients already experienced one of the defining components of the study outcome at baseline, i.e., LLDAS, the power to detect a difference between the groups was reduced. The cross-sectional endpoints also might not represent the lupus disease control over time, and the more realistic definition of remission was published only after the commencement of the study. 37 Lastly, due to the low transmission rate in Hong Kong during the study period, the hypothesis of reducing the risk of COVID-19 infection by remote care could not be properly tested. Our results should be interpreted in the context of the local outbreak situation and anti-endemic measures which could affect the patient's preference for TM. Conclusion In the first trial comparing remote and in-person care during the COVID-19 pandemic, SLE patients in the TM group had similar 1-year disease activity control and better satisfaction versus those in the standard FU group. There were no significant differences in PROs and cost-of-illness. However, a significant proportion of patients cared for by TM required in-person visits or were hospitalized during the study period. The results of the study suggest that TM-delivered care could help minimize exposure to SARS-CoV-2, while maintaining disease control and psychosocial well-being during the pandemic, but should be supplemented by physical visits as required, particularly in SLE patients with unstable disease. Supplemental Material sj-docx-1-jtt-10.1177_1357633X231181714 - Supplemental material for Telemedicine for follow-up of systemic lupus erythematosus during the 2019 coronavirus pandemic: A pragmatic randomized controlled trial Click here for additional data file. Supplemental material, sj-docx-1-jtt-10.1177_1357633X231181714 for Telemedicine for follow-up of systemic lupus erythematosus during the 2019 coronavirus pandemic: A pragmatic randomized controlled trial by Ho So, Evelyn Chow, Isaac T Cheng, Sze-Lok Lau, Tena K Li, Cheuk-Chun Szeto and Lai-Shan Tam in Journal of Telemedicine and Telecare Acknowledgements The authors would like to express our gratitude to all medical staff, research assistants and participating patients. We would also like to thank the University of Central Lancashire & East Lancashire Hospitals NHS Trust for granting us permission to use the LupusQoL questionnaire. Authors’ contributions: HS, CCS, and LST designed the trial. HS, EC, ITC, SLL, and TKL collected the study data. HS, EC, ITC, SLL, and TKL performed the data analysis. HS, CCS, and LST wrote the manuscript. All authors critically reviewed the manuscript for important intellectual content. Availability of data and material: Data are available upon request. The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Ethics approval: This study was approved by the Joint Chinese University of Hong Kong – New Territories East Cluster Clinical Research Ethics Committee, No. 2020-0254. Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Hong Kong College of Physicians Young Investigator Research Grant (grant number 2020). Informed consent: Written informed consent was obtained from all study participants. Trial registration number: NCT04368299 ORCID iD: Ho So https://orcid.org/0000-0001-7113-9390 Supplemental material: Supplemental material for this article is available online. ==== Refs References 1 Cordtz R Kristensen S Dalgaard LPH , et al. Incidence of COVID-19 hospitalisation in patients with systemic lupus erythematosus: A nationwide cohort study from Denmark. J Clin Med 2021; 10 : 3842.34501290 2 Ugarte-Gil MF Alarcón GS Izadi Z , et al. Characteristics associated with poor COVID-19 outcomes in individuals with systemic lupus erythematosus: Data from the COVID-19 global rheumatology alliance. Ann Rheum Dis 2022; 81 : 970–978.35172961 3 Landewé RB Machado PM Kroon F , et al. EULAR provisional recommendations for the management of rheumatic and musculoskeletal diseases in the context of SARS-CoV-2. Ann Rheum Dis 2020; 79 : 851–858.32503854 4 Mikuls TR Johnson SR Fraenkel L , et al. American college of rheumatology guidance for the management of rheumatic disease in adult patients during the COVID-19 pandemic: Version 1. Arthritis Rheumatol 2020; 72 : 1241–1251.32349183 5 Ahmed S Grainger R Santosa A , et al. APLAR recommendations on the practice of telemedicine in rheumatology. Int J Rheum Dis 2022; 25 : 247–258.35043576 6 McDougall JA Ferucci ED Glover J , et al. Telerheumatology: A systematic review. Arthritis Care Res (Hoboken) 2017; 69 : 1546–1557.27863164 7 de Thurah A Stengaard-Pedersen K Axelsen M , et al. Tele-health followup strategy for tight control of disease activity in rheumatoid arthritis: Results of a randomized controlled trial. Arthritis Care Res (Hoboken) 2018; 70 : 353–360.28511288 8 Cavagna L Zanframundo G Codullo V , et al. Telemedicine in rheumatology: A reliable approach beyond the pandemic. Rheumatology (Oxford) 2021; 60 : 366–370.32893293 9 So H Szeto CC Tam LS . Patient acceptance of using telemedicine for follow-up of lupus nephritis in the COVID-19 outbreak. Ann Rheum Dis 2021; 80 : e97.32581085 10 So H Chow E Cheng IT , et al. Use of telemedicine for follow-up of lupus nephritis in the COVID-19 outbreak: The 6-month results of a randomized controlled trial. Lupus 2022; 31 : 488–494.35254169 11 Aringer M Costenbader K Daikh D , et al. 2019 European league against rheumatism/American college of rheumatology classification criteria for systemic lupus erythematosus. Arthritis Rheumatol 2019; 71 : 1400–1412.31385462 12 Fanouriakis A Kostopoulou M Alunno A , et al. 2019 Update of the EULAR recommendations for the management of systemic lupus erythematosus. Ann Rheum Dis 2019; 78 : 736–745.30926722 13 Franklyn K Lau CS Navarra SV , et al. Definition and initial validation of a lupus low disease activity state (LLDAS). Ann Rheum Dis 2016; 75 : 1615–1621.26458737 14 Drenkard C Villa AR García-Padilla C , et al. Remission of systemic lupus erythematosus. Medicine-Baltimore 1996; 75 : 88–98.8606630 15 Petri M Buyon J Kim M . Classification and definition of major flares in SLE clinical trials. Lupus 1999; 8 : 685–691.10568907 16 Wang SL Wu B Leng L , et al. Validity of LupusQoL-China for the assessment of health related quality of life in Chinese patients with systemic lupus erythematosus. PLoS One 2013; 8 : e63795.23717486 17 Hu H Luan L Yang K , et al. Psychometric validation of Chinese health assessment questionnaire for use in rheumatoid arthritis patients in China. Int J Rheum Dis 2017; 20 : 1987–1992.26929002 18 Mok CC Chan KL Ho LY . Association of depressive/anxiety symptoms with quality of life and work ability in patients with systemic lupus erythematosus. Clin Exp Rheumatol 2016; 34 : 389–395.27049836 19 Zhu TY Tam LS Leung YY , et al. Socioeconomic burden of psoriatic arthritis in Hong Kong: Direct and indirect costs and the influence of disease pattern. J Rheumatol 2010; 37 : 1214–1220.20360186 20 Dejaco C Alunno A Bijlsma JW , et al. Influence of COVID-19 pandemic on decisions for the management of people with inflammatory rheumatic and musculoskeletal diseases: A survey among EULAR countries. Ann Rheum Dis 2021; 80 : 518–526.33158877 21 de Thurah A Bosch P Marques A , et al. 2022 EULAR points to consider for remote care in rheumatic and musculoskeletal diseases. Ann Rheum Dis 2022; 81 : 1065–1071.35470160 22 Ramirez GA Argolini LM Bellocchi C , et al. Impact of the COVID-19 pandemic in patients with systemic lupus erythematosus throughout one year. Clin Immunol 2021; 231 : 108845.34478882 23 Rathi M Singh P Bi HP , et al. Impact of the COVID-19 pandemic on patients with systemic lupus erythematosus: Observations from an Indian inception cohort. Lupus 2021; 30 : 158–164.33019877 24 Au Eong JTW Lateef A Liang S , et al. Impact of teleconsultation on subsequent disease activity and flares in patients with systemic lupus erythematosus. Rheumatology (Oxford) 2022; 61 : 1911–1918.34554232 25 George MD Baker JF Banerjee S , et al. Social distancing, health care disruptions, telemedicine use, and treatment interruption during the COVID-19 pandemic in patients with or without autoimmune rheumatic disease. ACR Open Rheumatology 2021; 3 : 381–389.33934576 26 Tee CA Salido EO Reyes PWC , et al. Psychological state and associated factors during the 2019 coronavirus disease (COVID-19) pandemic among Filipinos with rheumatoid arthritis or systemic lupus erythematosus. Open Access Rheumatol 2020; 12 : 215–222.33061689 27 Kasturi S Price LL Paushkin V , et al. Impact of the first wave of the COVID-19 pandemic on systemic lupus erythematosus patients: Results from a multi-center prospective cohort. Lupus 2021; 30 : 1747–1755.34284676 28 Scherlinger M Zein N Gottenberg J-E , et al. Difficulties and psychological impact of the SARS-CoV-2 pandemic in patients with systemic lupus erythematosus: A nationwide patient association study. Healthcare 2022; 10 : 330.35206945 29 Sloan M Lever E Harwood R , et al. 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Workforce requirements in rheumatology: A systematic literature review informing the development of a workforce prediction risk of bias tool and the EULAR points to consider. RMD Open 2018; 4 : e000756.30714580 34 Smith AC Thomas E Snoswell CL , et al. Telehealth for global emergencies: Implications for coronavirus disease 2019 (COVID-19). J Telemed Telecare 2020; 26 : 309–313.32196391 35 Thomas EE Haydon HM Mehrotra A , et al. Building on the momentum: Sustaining telehealth beyond COVID-19. J Telemed Telecare 2022; 28 : 301–308.32985380 36 Gallegos-Rejas VM Thomas EE Kelly JT , et al. A multi-stakeholder approach is needed to reduce the digital divide and encourage equitable access to telehealth. J Telemed Telecare 2023; 29 : 73–78.35733379 37 van Vollenhoven RF Bertsias G Doria A , et al. 2021 DORIS definition of remission in SLE: Final recommendations from an international task force. Lupus Sci Med 2021; 8 : e000538.34819388
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==== Front RAE sprae Review & Expositor 0034-6373 2052-9449 SAGE Publications Sage UK: London, England 10.1177/00346373231159924 10.1177_00346373231159924 Words about (Re-)Embracing Creation . . . Lessons learned from a pandemic vegetable patch Jackson Melissa A. Sophia Theological Seminary, Dinwiddie, VA, USA Melissa A. Jackson, Sophia Theological Seminary, PO Box 266 Dinwiddie, VA, 23833 USA. Email: mjackson@sophiasem.org 26 6 2023 11 2022 26 6 2023 119 3-4 283292 © The Author(s) 2023 2023 Review & Expositor, Inc This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Late in the pre-pandemic winter of 2019, a small group began meeting around the idea of establishing a small seminary supported by a small produce farm. The onset of the pandemic meant the end of in-person work to advance the seminary. The group turned itself to COVID-safe farming work instead. Ps 103 and Rom 8:18-25 aid in reflecting on lessons about farming, faith, and community learned from the midst of a vegetable patch during a global pandemic. creation farming pandemic Psalm 103 Romans 8:18-25 tomatoes typesetterts1 ==== Body pmcIntroduction 1 Shall I begin with a short version of the still-unfolding story that began in the winter of 2019 and brings me here today? Late in that pre-pandemic winter, a group of five folks began meeting around the idea of establishing a small seminary that would be supported by a small produce farm. Students at this seminary, alongside faculty and community volunteers, would provide labor for the farm. The proceeds from sale of the farm’s produce, primarily through a CSA model, would provide the seminary’s operating costs. 2 Students would thus be able to attend seminary without tuition and housing expenses. From cheese to eggs to beer and even locally to fruitcake, this basic premise is a re-imagining of a centuries-old practice: religious communities supporting themselves through some type of small-scale industry. The initial idea grew, became an endeavor, and got a name, Sophia Theological Seminary and Sophia Farms, as the group of five began moving small-step-by-small-step toward bringing the idea into reality. And then, we arrived, with the rest of the global community, at March 2020. The onset of the pandemic meant the end of the work we had been doing to advance the seminary, as the work was nearly all meeting individuals, groups, and congregations in person. Like so much else in our lives, all those efforts came to a halt. The group of five had no inkling of how to proceed. We thought the project was going to end before it had an opportunity to begin. Unexpectedly (to wildly understate the situation), into that bewildering void stepped one of the endeavor’s trustees, a pastor-farmer, who offered that our group could run a pilot farming project on her central Virginia farm; a project we could safely undertake within the constraints of COVID. And so, in the spring of 2020, our project executed a pandemic-induced swerve, and the group of five (now six) shifted focus from advancing the seminary to piloting the farming operations. We planted a 10th of an acre of Roma tomatoes and a smaller plot of cayenne and serrano peppers. (If you are not familiar, those are hot ones: small, red, and mighty!) Then and now, I think of that pilot plot as nothing less than miraculous. At a time when our leadership had no sense of how to move forward, through this trustee’s inspiration and generosity, led, as she tells it, by the prompting of God’s spirit, we had an experience that was amazing, life-giving, and hope-filled, smack dab in the midst of the life-draining difficulties facing our world, our community, and our fledgling project. Thus, I stand here now as the testimony to how a biblical scholar, working in a group organized around a commitment to theological education, came to learn more than she ever imagined possible while covered in sweat and bug spray, standing in the middle of a pandemic vegetable patch. To be sure, some of the lessons I learned were fairly basic and straightforward. For example, Sometimes you absolutely must put your head down and get to the end of the row. And then sometimes you absolutely must take a break. Having rotten and rotting tomato flesh squashed firmly into the tread of your shoes makes for a pretty unpleasant 45-minute car ride home. The end of a good, hard day is always bittersweet. The most perfect fruit is rarely in plain view. The tomato you grow yourself tastes better than all other tomatoes. Without exception. While a port-a-potty is essential, genuine hospitality requires little else. Eventually, despite any obstacle, even a global pandemic, church people will figure out a way to eat together when they gather together. In the hands of inspired and committed (and infinitely stubborn) people, a brilliant idea will never be thwarted . . . not even by a global pandemic. Along with these basic lessons, however, I certainly learned many more complex, challenging, and important lessons, lessons that confronted us during the pandemic and because of it, but lessons that also remain significant in the pandemic’s aftermath, as the global community continues to move forward, to recover, and to heal. Allow me to share four of those lessons. Lesson 1: 90% of farming is about the dirt. The other 10%, pretty much one way or another, is also about the dirt In our farming, we are applying the practices of sustainable agriculture. In general terms, sustainable agriculture is a focus on developing and maintaining practices intentionally designed for the long-term and holistic thriving of the land, of those who work on it, and of those who benefit from its produce. The Sophia Farms website has a glossary page so visitors can learn a bit about sustainable agriculture. The glossary begins with an explanation of the term “sustainable agriculture” and then proceeds with definitions of 11 terms, all key principles and practices for a sustainable agriculture system. Of those 11 terms, no fewer than eight, relate directly to the soil, and two of the remaining three terms also relate fairly closely to the soil. 3 The origin, the source, the beginning of a successfully sustainable farming venture is what lies at the very bottom of it: the dirt. But, of course, Christians should already hear and understand the truth echoing in this discovery, because we already know well the sacred story of our own origins, of our source. The beginning for all humanity is also there in the dirt. “For God knows how we were formed; God remembers that we are dust” (Ps 103:14). We humans began and remain dust, soil, earth, dirt. And I can testify here. My experience on the pandemic vegetable patch has definitely revealed this truth, because most days on the farm I look down at myself at the day’s end, and I sincerely wonder where the dirt ends and where I begin. Humans come from the dirt; humans return to the dirt; in between, humans survive on the produce of the dirt. Furthermore, anyone who works on a pilot vegetable patch in the summer of a global pandemic also digs the dirt, tromps around in the dirt, wears the dirt, quite possibly lies down in the dirt, inadvertently usually eats some dirt, and, to sum up, exists pretty much at one with the dirt. But again, is this observation not what we already know from our origin story? The Lord God formed the human (the adam), dust from the ground (the adamah), and breathed into the creature’s nostrils breath of life, and the human (the adam) became a living being (Gen 2:7, author’s translation). Human life, as Gen 2 tells the story, begins with God digging in the dirt, then shaping it into a breath-filled, living being. Human life begins as do our tomatoes, crafted from the same earth that gives life to the fruit hanging on the vine. Furthermore, as the Genesis story continues, these earth-creatures are called to care for what God has created: to work it, to serve it (עבד) and to watch over it, to preserve it (שׁמר, Gen 2:15). To tend that garden, indeed to tend any garden, is to tend its dirt. And in tending the dirt, any dirt, humans tend the source of their own being. This circle is one of being and of care. It is an interwoven, inextricable existence: God, humanity, and the ground. First, the ground is indispensable for God’s creative work. God depends on the ground, using it as a medium for God’s work, material shaped and breathed into life. Second, humans depend on the ground for their sustenance and livelihood. The ground is humanity’s source: for work, directly or indirectly, and for life, for its very existence. Without the dirt, humanity cannot live. Third, the ground depends on humans for its proper development. As humans cannot thrive without the dirt, neither can the dirt thrive without human care and effort. 4 God relies on the ground to shape life and relies on humankind to care for that life. Humankind relies on the ground for its food and on God for its sustenance. The ground relies on God for its creation and on humankind for its care. A three-way interconnection and interdependence. The pandemic, however, challenged all understanding of connectedness. Closeness to one another became dangerous, and creating boundaries, barriers to that closeness, became the best way to express love and care for each other. This idea, put in such plain terms, sounds an absurd oxymoron. How can separation possibly be the best expression of closeness? Yet, when we understand our world, both the people in it and the spaces we inhabit, as interconnected, we can begin to accept the fundamental reality that my neighbor’s well-being (shalom in the Hebrew understanding) is my well-being and my well-being is my neighbor’s well-being. In perceiving how everything is connected in God, woven together at its genesis by God, maybe the Christian community can begin to better understand and endure situations in which the call to care for all or some part of God’s creation requires such acts of perplexing, nearly unbearable sacrifice. We are from the dirt and, as such, we are as beautiful and also as fragile as that perfect tomato ripening on the vine. If we are willing to exhaust ourselves caring for that tomato through sweltering heat, pounding rain, bug-filled days, and pest-filled nights, how can we do any less in caring for our neighbor, for those other dirt-shaped, divine-breathed creations of the Almighty? When enumerating the lessons, large and small, taught to me by Roma tomatoes, hot peppers, and the soil out of which they grew, one of the more personal lessons has been: you can have a doctorate in Hebrew Bible from an elite university and still be just about the most ignorant person in the proverbial room. I naively thought that growing vegetables was about water and sunlight and, I dunno, probably the plants. As it turns out, if I had been quicker to connect that Hebrew Bible I so diligently study with the ground on which I was standing, I would have remembered, as God remembers in Ps 103:14, that the cultivation of life began with God digging in the dirt, followed by a call from that God for all humankind to get its hands dirty as well, to dig deeply and to tend fully God’s creation, understanding that to love and serve creation is to love and serve God, one another, and even ourselves. Lesson 2: The plants must become ugly before the fruit can become beautiful For the first couple of months, the unbroken rows of perfectly green, lush tomato plants were beautiful. First, they were small and beautiful. Then they were a bit taller and beautiful. Then they were full of blossoms, and they were beautiful. Then they began to produce these tiny, perfect green tomatoes, and they were beautiful. But, after that, something else began happening. The plants began to grow brown and scraggly, and they continued to become more brown and more scraggly. Then, after a hurricane remnant rolled through central Virginia, a bunch of the plants gave up trying to stand and just fell over. The plants were not so beautiful anymore. To be honest, in my eyes, they had become pretty ugly. As the wise farmers and gardeners among you already know, however, this description of the plants’ outward appearance is not the full picture. In this metamorphosis from green and lush to brown and scraggly, what was actually happening was that the leaves and stems were yielding their own beauty for the good of the fruit. As the plants became ugly, the tomatoes were becoming beautiful. And what beauties they were, all shades of red and orange, no two exactly the same, hanging thick and enticing on the now-decidedly brown, definitely ugly, plants. To describe the vegetable patch in these terms of visual attractiveness, of aesthetics, of how the plants looked to my eyes, is to describe the patch as a work of art meant to be beautiful to behold. Yet, the scene that evolved over the months can be described another way. As each plant matured, it transferred its energy, its resources, its effort away from growing branches and leaves and into growing fruit. This process happened over a scientifically determined number of weeks and included attending to the proper preparation of the soil beforehand and the proper monitoring for pests and sufficient hydration during their growth cycle. With these plant requirements satisfied, the plant could be expected to produce X pounds of tomatoes per plant, and each tomato specimen would grow to the approximate size, shape, and hue of its particular variety. The time to harvest the Romas eventually arrived. We put our heads down, and we worked hard, very hard. We worked as instructed, ruthlessly exiling to the compost pile any tomato that did not meet the necessarily high standard for storing and processing. We picked and culled and washed and sorted, all with precision and according to the process set down for us. Then, every so often, someone would lift their head, look around, and observe of the surroundings, “Wow, this place sure is beautiful!” Every so often, someone would stop, hold up a near-perfect tomato and declare, “Wow, this tomato is beautiful.” We worked in cadences dictated by measurement, numbers, precision, and process, and we worked in rhythms carried by pause, reflection, admiration, and enjoyment. Our leadership is blessed with two experienced agricultural and farming experts. One of them is spreadsheets and charts and drawings and equations and calculations. He pores over topographical maps and analyzes soil samples. He speaks the elemental language of the periodic table. If he has two tools with him, one will be a tape measure. The other keeps records with a pencil and a wire-bound notebook, all the pages wavy and crinkled from the water that has dribbled onto them over time. She supervises plantings largely by feel and uses a combination of coaxing and finessing to get the aged tractor to do her bidding. She knows every rise and fall of the land and, looking out over the farm, sees each year of the life she has shared with it. These two gifted individuals are the dovetailing of the science and the art of farming. To tend God’s earth is to stand in deepest awe of the beauty of God’s art, to experience the full palate of the landscape and the arresting colors and shapes of the fruit. To tend God’s earth is also to dig deeply into the agricultural science to understand the processes necessary for the thriving of the crop and to nurture the plants to maturity gently and persistently according to those processes so they can bear fruit that will provide for humankind’s bodily needs. Christians are fairly well attuned to appreciating the art, the beauty of the earth. Indeed, congregations and choirs regularly sing a hymn about it. But these two farmers working in tandem in this vegetable patch are a reminder that to tend God’s creation is also to appreciate the beauty of the science, that understanding the mechanisms and processes at work in the ripening of tomatoes is as deeply theological as standing in awe at the sight of a field full of the brilliantly red fruit. Yet, Christians through the centuries have argued, sometimes to the death, over the place of science in theology and in the life of faith. This conflict continues to rage. Despite recognizing that the tomato on the vine is both a miracle of God’s handiwork and a convergence of countless scientific processes, some Christians are still insisting that the Bible is the only book necessary to understanding the complexity of creation. Despite affirming scriptural “truth” that all human life has a common source, Christians are still clashing fiercely over precisely which of those lives matter. Despite claiming to be obedient to God, some Christians are still dogmatically refuting compelling data that would enable humankind to better obey the command to serve and to preserve God’s good creation. This ongoing discord reveals the extent to which people of faith have become disconnected from creation. Yet, in Rom 8:18-25,the focus is on the solidarity of humans and their world in both the present and the future. Paul could no more think of persons apart from their environment than he could conceive of them apart from their bodies. 5 Humanity does not exist apart from the created order. No human exists apart from other humans or from the world surrounding them. To argue otherwise is to deny the fullness and beauty of the intricately connected creation, an interconnectedness woven together not just in nature, but also in the nature of God. The psalmist certainly does not allow for argument over God’s “creating” nature: The Lord is a maker of righteousness and justice for all those being oppressed (103:6, author’s translation). The maker of all creation, who pulled humanity forth from the ground and breathed into its nostrils, is the one who makes righteousness and justice for, in, among, and with that creation. In an act to be appreciated both aesthetically and botanically, a tomato plant yields one part of itself for another, as the plant’s transition to brown, scraggly vine enables the formation of nutritious, life-giving fruit. Neither the beauty of a plant, nor that of any human, resides in its transitory outward appearance. The material world is impermanent, the psalmist says. The faithful and steadfast love of God (hesed, חסד) is eternal. 6 As objects of such love, humankind is called to participate with God as co-makers of a righteous and just creation. In a cycle of life that manifests as both art and science, the abundance of beauty poured into us is the beauty we are to offer abundantly for the flourishing of others. God calls us to put the beauty of our being, dust and breath, into serving both the creator and the created, whether knee-deep in the dirt or shoulder-to-shoulder with our neighbor. Lesson 3: Community is backs bent together over shared work for a common purpose The first conversations about the formation of this entity included discussions of identity: who we were and who we were striving to be. We spoke in those discussions about denominational identity, how we “saw” the seminary, how we thought others might see it, and how we understood this community to be in relationship to others. In looking around our table, we had to honestly say that denominationally we were baptist. Our leadership group grew up in churches and served churches and attended churches and taught those who would serve churches. In the majority of instances, “churches” meant baptist churches. Yet we also knew with conviction that, while the seminary would naturally reflect our collective baptist background, we did not want it to be limited by our collective baptist background. Our aspiration was and is to be open, to be inclusive, to value the difference that would certainly make us stronger when we came together. The language, then, that we adopted for ourselves is captured in one of our core values: The community claims its heritage as little “b” baptist, understanding this heritage as historical, transcending specific denominational confines, and equally commits itself to ecumenical and interfaith work, locally and globally. Little “b” baptist is a concept borrowed from the late theologian James McClendon, who discusses baptists as having “distinguishing marks.” The following two such distinguishing marks are (1) understanding the Bible as authoritative for a life of faith lived in the world and (2) understanding the church as having freedom given solely by God, as expressed, for example, in the principle of the separation of church and state. 7 The articulation in this core value above is thus to hold together being little “b” baptist with a commitment to working in ecumenical and interfaith partnerships. Fast forward several months from these initial conversations, and we were able to bring onto the board of trustees a recently retired Episcopal priest. Incredibly quickly he became, and remains, one of the community’s most zealous advocates, telling any and all about the endeavor with excitement and enthusiasm. When the call went out for volunteers to help on the pilot vegetable plot, he issued the call in his circles, and from week 1, a handful of those people responded, and, just like that, volunteer work days in the vegetable patch became a sort of bapto-palian coalition. While our pilot vegetable plots featured Roma tomatoes primarily, a smaller plot was dedicated to cayenne and serrano peppers. Upon completion of the tomato harvest, the time arrived for the pepper harvest. The plan devised was to pull the pepper plants up completely, gather them in a central area known as The Grove, and pick/wash/sort the peppers in a single session. So, there we are, our bapto-palian coalition all seated in a wide circle under a group of shade trees, our backs bent over the pepper plants and a few huge rinsing/sorting buckets. The scene looked like it could have been a grainy and faded sepia photograph from a great-grandparent’s photo album. As we work, we chat away. I answer a barrage of questions about plans for the seminary and farm. A number of ordinated folk in the circle recount their stories of calls to ministry. Then the conversation gently meanders through various other topics. Eventually, the conversation turns to what was in front of us, namely the hot peppers. Someone mentions that we are still trying to choose a name for the hot sauce we would make from the peppers, a comment that sends us down a whole new line of conversation. One suggestion is “Jesus loves you, but this sauce is really hot.” Multiple options are floated relating to the sauce’s relative hotness in comparison to another infamously hot place (I am speaking, of course, of Florida). Dante’s Inferno gets a mention. 8 On and on the conversation continues as we labor away, trying to work through the enormous pile of hot pepper plants. At some point in this wandering conversation, one of our Episcopalians relates a particularly interesting and humorous story. Without warning, one of our baptist minister volunteers blurts out, disarmingly and simply, “I love Episcopalians. They are so much fun.” True, this statement sounds somewhat cheesy repeated out of its original context, but this enthusiastic outburst captured the genuine camaraderie of that pepper-picking experience. And the honesty of the statement caused me to pause in the moment, look around the circle, and realize that we were there in that circle due, in no small part, to our Episcopalians. People who were strangers 2 months ago were now completely indispensable to the thriving of this young endeavor and its community. Backs bent together over shared work for a common purpose always form community. Out in that pandemic vegetable patch, honoring a commitment to reaching beyond our known circles, those new relationships and the community formed through them helped give this endeavor new life. Relationships and partnerships are not limited to the places where we expect them. Jesus’s whole life story, as the Gospels tell it, could be titled A Series of Unplanned Encounters and Unexpected Relationships with Unlikely People. When we open our lives to unknown possibility or, in the language of Rom 8, await in hope for what we cannot see, God can weave us together in wonderfully unexpected ways and fill the vegetable patch with the most lovely and wonderful Episcopalians. Lesson 4: We will want things we cannot have, but the thing we get might be exactly the thing we need In early conversations, our leadership imagined a number of positive aspects to the pandemic tomato and pepper pilot plots: we would learn lots; we could begin to farm now, so we did not, in the near future, have to get both a farm and a seminary up and running simultaneously; the farming would provide an opportunity to regain the momentum and inspiration that the pandemic had stifled; we could begin making some income through sale of the produce. The question then arose of how to make the endeavor work. We were only six people at that time, so we would definitely require help. We will approach supportive church congregations and our trustees to see if we can find some folk who would be willing to come out early on the weekend to this unknown, untested project about a 50-minute drive from our base in Richmond (Virginia) and get hot and sweaty and filthy and bug-bitten and sunburned and sore. I mean, really, how could this plan not work? Yet, a handful of people we asked said yes. The next time we asked, they again said yes. And they continued saying yes. Then they began saying strange things such as “this is fun” and “this is highlight of my week” and “when are we all getting out here again.” And one morning, I looked up and looked around. People were scattered here and there. They were hot and sweaty and filthy and bug-bitten and well on their way to being sunburned and sore. And they were smiling and laughing and enjoying themselves. This sudden feeling of hopefulness washed over me, a feeling that had been elusive during those long pandemic months. In our planning, the leadership imagined the pilot plots could achieve a number of things for our nascent endeavor. We had not been able to yet see the impact on us of that place and that experience and on the way it would knit us together as a community. We certainly wanted many things we simply could not have. What we got was unexpected and hope-filled and life-giving. It was precisely what we needed. To be sure, this lesson is not one to be learned only during a global pandemic. Individuals and communities often confront this challenge, and these “things” for which we long but cannot have are often easy to name. The more difficult part comes in the challenge of Rom 8:24-25: to give up our yearning for what we can see and place our hope in what we cannot see. Corrie and Betsie ten Boom were sisters in a Dutch family of Christians who, during the Second World War, hid Jewish people in a secret room in the family home above the family shop. The family’s actions were discovered, and they were arrested. Eventually, Corrie and Betsie were imprisoned in the Ravensbrück concentration camp about 50 miles north of Berlin. Corrie narrates the following incident in her memoir The Hiding Place. A group of women including Corrie and Betsie have just been moved to a different barracks in the camp. The first night they discover that the sleeping quarters are infested with fleas. Betsie remembers a passage they had read that morning from their Bible, which they had managed to conceal throughout their imprisonment: “Give thanks in all circumstances” (1 Thess 5:18). Betsie excitedly declares that this verse is the answer “for every single thing about this new barracks!” She urges Corrie to give thanks for their being assigned in the barracks together, for the Bible they had managed to keep in their possession, for the crowded conditions that meant more women could meet God in the Bible’s pages, and for the fleas. Corrie replies, “Betsie, there’s no way even God can make me grateful for a flea.” Betsie quotes the Thessalonians verse again with emphasis, “Give thanks in all circumstances.” “And so,” Corrie remembers, “we stood between piers of bunks and gave thanks for fleas. But this time I was sure Betsie was wrong.” 9 Over the next weeks and months, Corrie and Betsie hold Bible studies back in the barracks in the evenings. The meetings grow in size and frequency. All the while, one thing they can never understand is why no guard presence impedes them in holding their Bible studies. Then one evening, Corrie returns to a waiting Betsie who announces that she has discovered the reason. That afternoon, she said, there had been confusion in her knitting group about sock sizes and they had asked the supervisor to come and settle it:“But she wouldn’t. She wouldn’t step through the door and neither would the guards. And you know why?” Betsie could not keep the triumph from her voice: “Because of the fleas! That’s what she said, ‘That place is crawling with fleas!’” My mind rush back to our first hour in this place. I remembered Betsie’s bowed head, remembered her thanks to God for creatures I could see no use for. 10 Betsie had insisted on giving thanks for creatures Corrie could find no use for. Facing circumstances nearly unimaginable, Betsie, and a more reluctant Corrie, turned themselves and their prayers to a God they believed could and would be present in their midst in ways that were tangible, but were also beyond their present imagining. In being inspired to give thanks even for the fleas, Betsie had placed her hope in what she did not see. The Rom 8 text attests that deep suffering can co-exist with unremitting hope. Not only humankind, but all creation, can be in the midst of an experience so excruciating that the only possible utterances are groans, and yet still look beyond the present crushing reality to a promised, but as-yet-unseen, redemption from that suffering. Furthermore, as Paul reckons it, the intensity of this present suffering recedes into insignificance when compared to the magnificence that is on its way (v. 18). And so, in times of suffering and of hoping for the unseen, we wait. In Rom 8:23 and 25, the verb translated in the NRSV as “wait” (α’πεκδέχομαι, apekdechomai) has a particular sense, not of waiting passively, twiddling one’s thumbs, gazing off into the distance, mindlessly staring at one’s phone, but to wait with expectation, in a state of watchfulness and anticipation, to be on the lookout, to await. My little nephew is taken with anything that has a motor, from a toy car to a garage door opener to a delivery van to a backhoe. When he hears even the faintest sound of something that could prove interesting, he goes tearing off to find a spot where he can vigilantly watch the street. He is completely certain that something amazing is coming his way, and he puts himself in the best position possible to meet that amazing thing. Often, as he is tearing off, the rest of us are left asking him, “What is it?” He has heard something we have not. A prosaic truth is that his young hearing is probably better than ours. What is more deeply true, however, is that he hears these faintest of rumblings because he has been expecting to hear them all along. Like my nephew, who is certain that something awesome is already on its way, Christians are enjoined in Rom 8 to wait—not passively, not in despair, but in expectation for what we do not yet see, for what we cannot yet have. I know I have never given thanks for a flea, either literal or metaphorical. Yet, Betsie ten Boom’s faith required her to give thanks even for the fleas. Betsie had an expectation; she was, in all circumstances, awaiting something she could not see. All creation groaned under the weight of the pandemic. It revealed in newly painful ways the elusive nature of hope and the challenge of imagining how restoration can emerge from a present, pressing, and seemingly impossible situation. An inevitability of life is living through times of longing for what we cannot have. And yet, woven through the words of Rom 8:18-25 of how painfully full of groans life can be is a persistent thread of hope, hope in God’s liberation and redemption. God, who in Christ brought life from death, calls forth new life from out of the struggle and the longing. And for those who suffer, the means of liberation is not constrained by the scope of their limited vision. Quite the opposite, our deepest hope should reside in a coming revelation of what we have not yet been able to glimpse. All creation awaits with unrelenting hope, forever persisting and forever expecting a coming revelation of God’s salvation. Conclusion Standing in the pandemic vegetable patch, I learned many lessons about farming the land and about growing tomatoes and hot peppers. The most significant lessons, however, taught me about God’s good creation, about our young community of faith, about who we are and who we desire to be, and about our life and witness as those pulled forth from the dust of the ground, animated by our creator, and sent forth to serve the creator and to preserve the created. Author biography Melissa A. Jackson is on faculty of the newly founded Sophia Theological Seminary, based in central Virginia. She was previously on the faculty of Baptist Theological Seminary at Richmond. Jackson earned her doctoral degree from the University of Oxford. She has written extensively on comedy in the Hebrew Bible, particularly its intersection with feminist-critical interpretation. Her work includes the monograph, Comedy and Feminist Interpretation of the Hebrew Bible: A Subversive Collaboration (Oxford University Press). Jackson is the Managing Editor of Review & Expositor. She enjoys eating tomatoes, especially the ones she helps grow. 1. This article began as a July 2021 sermon series for a Baptist congregation in central Virginia. The sermon texts were Ps 103 and Rom 8:18-25. 2. In this context, CSA is an abbreviation for Community Supported Agriculture. In a CSA, people pre-purchase a share of a local farm’s produce. The subscriptions fund the costs of farming. Then, as the produce is harvested, the shareholders receive a regular, usually weekly, portion of the produce. This partnership between a farm and its local community is one of shared costs and shared returns. For information and links to resources, see “Community Supported Agriculture,” USDA National Agriculture Library, https://www.nal.usda.gov/farms-and-agricultural-production-systems/community-supported-agriculture. 3. The eight terms are compost, cover crops, drip irrigation, no-till, nutrient management, organic matter, soil health, and soil structure. The two closely related terms are cultural control methods and organic production. The 11th term is community supported agriculture. See “Glossary,” Sophia Farms, https://www.sophiafarms.org/glossary/. 4. The articulation of this three-way interdependent relationship originated with Rev. Neil Zahradka, resident expert on all matters farming. See “Why a Farm? Farming as the Way of Sophia,” Sophia Farms, https://www.sophiafarms.org/why-a-farm/. 5. Charles H. Talbert, Romans, SHBC (Macon, GA: Smyth & Helwys, 2002), 213–14. 6. For discussion on the use of hesed (חסד) in Ps 103, see Nancy L. deClaissé-Walford, Rolf A. Jacobson, and Beth LaNeel Tanner, The Book of Psalms, NICOT (Grand Rapids: Eerdmans, 2014), 759–68. 7. James W. McClendon, Jr., Ethics: Systematic Theology, vol. 1, 2nd ed. (Nashville: Abingdon, 2002), 26–34. The five “distinguishing marks” are biblicism, liberty, discipleship, community, and mission. McClendon discusses each of the five en route to his postulation of “the baptist vision,” a unifying description of little “b” baptists, which is in “motto” form: “The church now is the primitive church and the church on judgment day” (p. 30). See also James W. McClendon, Doctrine: Systematic Theology, vol. 2 (Nashville: Abingdon, 1994), 44–46. 8. The sauce was eventually named Use Wisely Hot Sauce. Sophia (σοφία) translates from Greek as “wisdom,” thus the sauce name is a serious instruction delivered with a humorous wink. 9. Corrie ten Boom with John and Elizabeth Sherrill, The Hiding Place (New York: Bantam, 1971), 198–99. 10. ten Boom, Hiding Place, 208–209.
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==== Front Topics Early Child Spec Educ Topics Early Child Spec Educ TEC sptec Topics in Early Childhood Special Education 0271-1214 1538-4845 SAGE Publications Sage CA: Los Angeles, CA 10.1177/02711214231182023 10.1177_02711214231182023 Reports of Original Research Families’ Experiences With Online Instruction and Behavior Support During COVID-19 https://orcid.org/0000-0002-8587-5767 Kelly Elizabeth M. 1 https://orcid.org/0000-0001-7985-3505 Harbin Shawna G. 1 Schwartz Ilene S. 1 1 University of Washington, Seattle, USA Elizabeth M. Kelly, College of Education, University of Washington, Haring Center, Box 357925, 1981 N.E. Columbia St., Seattle, WA 98195, USA. Email: empowers@uw.edu 26 6 2023 26 6 2023 02711214231182023© Hammill Institute on Disabilities 2023 2023 Hammill Institute on Disabilities This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. In the Spring of 2020, COVID-19 forced school buildings to close across the United States. As a result, many early learning programs and elementary schools moved their services online. Families of young children with challenging behaviors receiving complex educational and behavioral services in traditional brick-and-mortar classrooms were suddenly required to work closely with educators to support their children’s academic, social-emotional, and behavioral progress. This study used a qualitative approach to examine families’ experiences with children’s challenging behavior, online instruction, and behavior support during COVID-19 school building closures. Findings underscore important themes related to families’ perceptions of child challenging behavior at home, challenges with children’s meaningful participation in online instruction, families’ perceived responsibilities and priorities, and future recommendations. Implications for educators are discussed. COVID-19 early learning families qualitative challenging behavior family-professional partnership nstitute of Education Sciences https://doi.org/10.13039/100005246 R324A180061 Office of Special Education Programs, Office of Special Education and Rehabilitative Services https://doi.org/10.13039/100007296 A99031 edited-statecorrected-proof typesetterts1 ==== Body pmcChallenging behavior is a common concern among families with young children of all abilities (Hemmeter et al., 2021). Historically, researchers defined young children’s behavior as “challenging” when it interfered with learning or social interactions, or when it was dangerous to the child or others (Fox et al., 2002). The concept of challenging behavior in children, however, has evolved and should be considered through an intersectional lens recognizing multiple contextual factors. When young children engage in challenging behaviors such aggression, tantrums, or self-injury, their social identities (e.g., gender, race, and (dis)ability) all likely intersect to influence how it is identified and reported by adults in their environment (Dever et al., 2016). For instance, young children in minoritized racial groups have been rated as having higher levels of challenging behaviors by their teachers (Kulkarni & Sullivan, 2022). Both children’s and adults’ intersectional identities likely contribute to the conceptualization of young children’s challenging behavior in early learning and home environments. Studies aimed at understanding the family impact of parenting a child with challenging behavior consistently describe feelings of stress, isolation, and parental incompetence (Doubet & Ostrosky, 2015). Families that have young children with challenging behavior face increased caregiving demands that may contribute to negative family interactions (Lucyshyn et al., 2018). These families often report negative impacts on their routines, family roles, and emotional well-being. For example, families may avoid community outings that are important to their family’s overall quality of life, like going to church or visiting friends, due to concerns about their child’s behavior (Hayes & Watson, 2013). Family-Professional Partnerships Effective family-professional partnerships promote important benefits for families. Partnerships are correlated with parental self-efficacy, parental well-being, and positive parent-child interactions (Trivette et al., 2010). For example, successful partnerships increase parental confidence and competence (Dunst & Dempsey, 2007) and decrease parental stress (Burke & Hodapp, 2014). Family-professional partnerships occur when families and professionals work together to build upon each other’s’ expertise and experiences for the purpose of making and implementing decisions that will directly benefit a child. Successful family-professional partnerships are characterized by commitment, effective communication, mutual respect, trust, equality, and professional competence (Blue-Banning et al., 2004; Turnbull et al., 2015). Given the added stress and lost learning opportunities when children engage in challenging behavior, family-professional partnerships are crucial for supporting these children and their families (Doubet & Ostrosky, 2015; Trivette et al., 2010). Family members are the experts of their own family values, beliefs, and cultural practices. Family interactions can either promote challenging behaviors or reduce them; therefore, family members play a critical role in the trajectory of their child’s behavioral repertoire and learning experiences (Dunlap & Fox, 2008). Professionals should build trusting relationships and communicate with families about the behavior support process in ways that are family-centered, practical, and grounded in neutral, objective data (Kelly et al., 2022). They should also communicate and respond appropriately to families when cultural differences arise in the identification and definition of challenging behavior (Beneke & Cheatham, 2016). Professionals who work with families marginalized by education systems (e.g., families from Black or Indigenous communities) may need to engage in additional trust- and relationship-building activities before families can safely and effectively collaborate with them (Ishimaru, 2020). Finally, professionals must collaborate with families to develop behavior strategies that are culturally and contextually fit to the social and physical environment in which they are implemented (Dunlap & Fox, 2008). COVID-19 Disruptions and Online Instruction In the Spring of 2020, COVID-19 disrupted education for 55.1 million students across the United States (Peele & Riser-Kositsky, 2020). The Pacific Northwest was the first region to be significantly impacted by the virus (Baker & Fink, 2020). In response, school buildings were closed, largely resulting in a model of online learning across an extended period (Office of the Superintendent of Public Instruction, 2020). Many families with young children receiving individualized educational and behavioral services in traditional brick-and-mortar classrooms were suddenly required to take on the role of their child’s primary service provider and many felt unprepared to do so without significant support from their child’s teacher and educational team (e.g., Hill, 2020). Although schools were mandated to continue providing specially designed instruction and services from related service specialists to student with disabilities, there is a lack of research examining the behavior supports and instruction families received from educators, or how families and educators partnered with them to support children’s behavior and learning at home. Prior to COVID-19, few, if any, early learning programs were delivered online; therefore, there was no model of remote learning for educators and families to follow. The literature suggests that early learning programs took varied approaches to educating young children and supporting their families during the pandemic, when in-person learning was unavailable. For example, educators relied on technology to communicate with families (Tarrant & Nagasawa, 2020), set up online classes through synchronous Zoom meetings or asynchronous videos (Steed & Leech, 2021; Szente, 2020), and/or delivered learning materials to families’ homes (Dayal & Tiko, 2020). These strategies, however, relied on family members or caregivers to significantly support or directly implement children’s learning at home, placing a burden on families who were already likely experiencing other COVID-19 stressors (Luna et al., 2023). Conceptual Framework This study draws upon two conceptual frameworks to describe families’ perceptions of challenging behavior and their experiences with online learning and behavior support during the first several months of online instruction due to COVID-19 school building closures. First, Dunst and Trivette’s (2009) family capacity-building framework was used to frame family roles, understand the impact of COVID-19 school closures on the whole family unit, and center families’ experiences throughout the analysis process. The family capacity-building framework is a strengths-based model of early intervention focused on promoting family competence and confidence. It centers the family role in family-professional partnerships and promotes family capacity-building strategies based on six guiding principles (Dunst & Trivette, 2009). Dual capacity-building was the second framework used to guide this work (Mapp & Bergman, 2019). It was used to guide interpretation of families’ educational recommendations and connect findings to implications for educators, program administrators, and policy makers. The dual capacity-building framework asserts that strong family-professional partnerships are established when both families and educators are provided with the requisite knowledge and ability to support one another and meaningfully collaborate. In turn, these family-professional partnerships lead to positive child, family, educator, and program outcomes (Mapp & Bergman, 2019; Mapp & Kuttner, 2013). This framework suggests that when certain conditions are met and shared goals are identified, families and school staff are well-positioned to equitably collaborate and sustain long-lasting partnerships. Taken together, these two frameworks provide a foundation for understanding families’ perspectives and experiences during COVID-19. The purpose of this research study was to investigate families’ experiences supporting their young children with online instruction and behavior during the initial months of COVID-19 school building closures. The following research questions guided this study: RQ1. How did families describe their young children’s challenging behaviors following school building closures? RQ2. What were families’ experiences with online learning during school building closures? RQ3. What recommendations do families with young children with challenging behaviors have regarding online instruction during school building closures? Methods Our study used qualitative, in-depth interviewing to understand families’ experiences with early learning and behavior support during initial COVID-19 school building closures. A qualitative approach allowed us to understand and describe families’ complex experiences and interactions with education settings during early stages of the COVID-19 crisis. Our study was conducted over 6 months, beginning in June 2020. Researcher Positionality Qualitative researchers are the primary instrument of data collection and analysis; therefore, it is important to acknowledge the role that the researcher plays in influencing the topic under study (Merriam & Tisdell, 2016). All three authors are white women who are Board Certified Behavior Analysts (BCBA). We have years of experience working with young children with challenging behaviors and their families. The first two authors also acknowledge how our personal experiences as parents of young children during COVID-19 school closures influenced our positionality. To understand our positionality in relation to this topic, we engaged in a process of reflexivity throughout the research process by documenting and discussing our experiences as parents, our preconceived assumptions about other parents’ experiences, and the similarities and differences between our participants’ experiences and our own. Sampling We used purposeful sampling to obtain diverse participants who could provide in-depth knowledge about their experiences (Merriam & Tisdell, 2016). We identified families for recruitment from an existing recruitment database within a university-based research center. Their research participant experience signaled they were likely to be comfortable with, and capable of, reflecting on and sharing information about their families’ early learning experiences. Participants Recruitment Recruitment began after research approval was obtained from a university institutional review board. We recruited family members by sending a short email in their preferred language (English or Spanish), inviting them to participate in the study. Five family members responded to the initial recruitment email via phone or email and were screened for eligibility. Family members were included as participants in the study if they reported having (a) at least one child enrolled in an early learning program or elementary school between March and June 2020, (b) their child had an individualized education plan (IEP) with at least one goal related to social-emotional learning or behavior, (c) their child engaged in challenging behavior at home between March and June 2020, and (d) their child was between 3 and 8 years old at the time of the study. All five families who were screened met inclusion criteria. Family members We identified one member of each family as a primary participant: Anna, Paul, Louisa, Piper, and Katrina. Each family member had between 1 and 5 children living in the home. Four family members identified as biological parents to children that qualified them for this study. One family member was the biological aunt and adoptive parent to two qualifying children and biological mother to one qualifying child (Anna). Four participants identified as women, and one identified as a man. Their annual incomes ranged from $30,000 to over $150,000. Participant education levels ranged from a ninth-grade education to a master’s degree. Four participants identified as white, and one identified as Black. One participant also identified as Latina. See Table 1 for a summary of family member demographic information. Table 1. Family Member Demographic Characteristics. Family member Ethnicity/race Gender Age Level of education Household income Primary occupation Number of children living in the home Number of children with IEPs Anna White Woman 35 Less than high school degree $30,000–39,000 Stay-at-home mom 5 4a Louisa White, Latina Woman 37 Master’s degree $70,000–79,999 Research technician 1 1 Katrina White Woman 34 Master’s degree $70,000–79,999 Dance teacher 2 1 Piper White Woman 35 Bachelor’s degree $150,000+ N/A 1 1 Paul Black Man 44 Some college, but no degree $40,000–49,999 Chef 3 2 Note. IEP = individualized education plan. a IEP status reported by family member. Children Each family member had one or more children who attended a publicly funded elementary school or early learning program, qualified for special education services, and demonstrated challenging behaviors. Anna had three qualifying children and Paul had two. Louisa, Piper, and Katrina each had one qualifying child. Family members reported a range of child behaviors including lack of cooperation, tantrums, aggression, property destruction, taking items from others without permission, feces smearing, and elopement. Three children had documented functional behavior assessments (FBAs) and individualized behavior intervention plans (BIPs). Children’s racial and ethnic identities were similar to their family members. See Table 2 for a summary of child demographic information. Table 2. Child Demographic Characteristics. Family member Child Child grade level Disability a Race/ethnicity BSP Anna Aston ELP OCD, EBD White No Shannon ELP ADD, ADHD, NAS White No Sarah ELP FASD, ODD White No Louisa Alice Kindergarten Unspecified White, Latina Yes Katrina Cailean Kindergarten Unspecified White No Piper Parkes First grade 16p11.2 microdeletion, ASD, ADHD, SPD, language delay, hypotonia White Yes Paul Makayla ELP ASD Black Yes Trinity ELP ASD Back No Note. BSP = behavior support plan; ELP = early learning program; OCD = obsessive-compulsive disorder; EBD = emotional-behavioral disorder; ADD = attention deficit disorder; ADHD = attention deficit hyperactive disorder; NAS = neonatal abstinence syndrome; FASD = fetal alcohol spectrum disorders; ODD = oppositional-defiant disorder; SPD = sensory processing disorder; ASD = autism spectrum disorder. a Reported by family member participant. Data Collection We collected data from multiple data sources, specifically participant interviews and documents shared by the family. We collected basic demographic information from four of five participants via an online survey using Qualtrics prior to conducting interviews. Demographic data was collected from the fifth participant at the beginning of the interview via Zoom video conferencing software in response to the participant’s request. Following interviews, each participant received a $100 gift card as compensation. Interviews We used semi-structured interview questions to elicit focused information about families’ educational and behavior support experiences during COVID-19 school building closures (see Supplemental Material for interview questions). At the beginning of each interview, each family member identified their qualifying children and described their strengths. Anna, Paul, and Katrina also identified children that did not qualify them for this study. While Anna described experiences related to her three qualifying children throughout the interview, she also discussed experiences with her oldest son (i.e., non-qualifying child). Paul and Katrina rarely mentioned non-qualifying children during their interviews. During each interview we asked families about their children’s challenging behavior at home and at school prior to and during school building closures, their experiences with instruction and behavior supports prior to school building closures, and their experiences with instruction and behavior supports during COVID-19 school building closures. Our semi structured interview protocol was developed by the first two authors based on the conceptual framework, previous literature, and research questions. Interviews were conducted using FERPA-compliant Zoom video conferencing software and then video- and audio-recorded and transcribed via Zoom. All interviews were conducted in family members’ preferred language. Four were conducted in English by the first author and one was conducted in Spanish by a doctoral student and native Spanish speaker. The Spanish language interview was transcribed in Spanish and then translated to English. All transcripts were reviewed for accuracy prior to coding. Family members participated in one interview each and interviews ranged from 1.40 to 1.68 hours, with an average of 1.55 hours. Artifacts We asked families to share any documents or email communications collected between March and June of 2020 that may contribute to our understanding of the information shared during interviews. We provided suggestions about what types of artifacts would be useful to share (e.g., email correspondence between family members and educators at their child’s school or early learning program, IEPs, FBAs, BIPs, academic or social-emotional materials provided by school personnel). Most document artifacts were email communications between families and programs, however, artifacts also included IEPs, FBAs, BIPs, and visual supports. Family members provided 68 pages of artifacts; however, the range of artifact submission from each family varied between 2 to 30 artifacts. Family members who reported less access to resources (e.g., technology, income, literacy) tended to share fewer artifacts while those who reported greater access to resources shared the most artifacts. Artifacts were used to validate or invalidate data gathered during interviews. Data Analysis We used thematic analysis to identify common themes among our participants’ experiences (Braun & Clarke, 2006). Data sources yielded a total of 150 pages of transcripts, 7.73 hours of video files, 68 pages of artifacts, and 23 pages of researcher notes and memos. Transcripts and artifacts were uploaded into Dedoose, a software program to facilitate qualitative analysis. Two members of the research team independently coded the data over 4 months, meeting biweekly to discuss progress and make analysis decisions. Data analysis occurred in several steps. First, we developed a preliminary codebook to use during first-round coding that consisted of a priori codes (i.e., codes developed prior to analysis) based on our conceptual framework and research questions. The preliminary codebook included 17 codes with detailed descriptions. Examples of codes included “Closures affecting caregivers,” “Family empowerment,” “Child behavior.” Following codebook development and initial research team meetings, we began first round coding by independently coding transcripts and artifacts using a structural coding process (Saldaña, 2016). During first-round coding, we applied a priori structural codes while allowing for additional, inductive codes. This resulted in a combination of deductive and inductive coding using structural, descriptive, and values codes (Saldaña, 2016). As we coded, we generated research notes and analytic memos which we shared at biweekly meetings to further develop the coding scheme and reorganize codes. For example, following first round coding of the first three transcripts, we collapsed the codes “Families as educators,” “Family empowerment,” “Family-identified needs,” and “Family-identified strengths” under a single code called “Family expertise.” This iterative analysis process helped us compare codes and check for consistency and accuracy of code application, while simultaneously identifying any disconfirming evidence (Patton, 2015). Members of the research team wrote analytic memos once following interviews and twice during coding. We prepared for second round coding by independently code mapping to visually reorganize, restructure, and make meaning of the data (Saldaña, 2016). We used the free online platform, Padlet, to visually display, organize, and restructure the data throughout two rounds of code mapping. Following code mapping, we developed a preliminary operational model diagram to visually display analytic categories and evidence to make meaning of families’ experiences. The combination of multiple rounds of versus code mapping and operational model diagramming resulted in the development of broad themes and claims. These broad themes and claims were then shared with colleagues and “shop talked” (Patton, 2015). Finally, we grouped codes from the first-round coding and code-mapping processes to conduct second round coding (Saldaña, 2016). This coding process allowed us to further develop themes, confirm initial claims, and locate disconfirming evidence. For a visual of the data coding process, see Figure 1. We continued this iterative process of coding, theme development, and shop-talking until we reached analysis satiation (Patton, 2015). Figure 1. Coding process diagram. Trustworthiness and Credibility We used several methods for establishing the trustworthiness and credibility of our findings (Brantlinger et al., 2005). First, we collected evidence from multiple sources (e.g., interviews, artifacts) and cross-analyzed the data. Second, we worked closely with trusted colleagues familiar with the topic under study. Members of the research team met and debriefed bi-weekly during coding cycles, theme development, and the initial writing process. Finally, we developed a member check survey based on Birt et al.’s (2016) modified five-step synthesized member check (SMC) process to enhance the credibility of our findings. SMC steps include: (a) preparing a synthesized summary of major research themes, (b) checking on participant availability to complete the SMC, (c) distributing the SMC with an explanation of the overall purpose, (d) gathering responses and analyzing any new data, and (e) integrating new evidence into the findings. Four family members completed the survey online using Qualtrics. One family member completed the survey over the phone. Based on data collected during the SMC, we modified one theme and removed another. Findings All family members reported COVID-19 school building closures impacted the academic, social-emotional, and behavioral supports their children received from school personnel. In turn, these changes in services impacted the whole family. We identified themes related to family members’ perceptions of children’s challenging behavior following school building closures (RQ1), family experiences with online learning (RQ2), and family member recommendations for online instruction (RQ3). Families’ Perceptions of Challenging Behavior Initially, when asked what challenging behaviors their children engaged in, many family members described their child’s difficulty with communication, an academic skill, or an important home-based routine. For example, Paul’s first response to this question was “[My daughter] had very limited communication. She also had . . . she wasn’t able to write or read.” He went on to describe the difficulties his family encountered with both his daughters’ communication and academic behaviors. Katrina mentioned a lack of self-advocacy as her son’s biggest challenging behavior and Anna initially described her daughter, Shannon’s, difficulty during a morning routine. After additional probing, however, most family members revealed further behaviors traditionally seen as “problematic” by school staff like aggression, elopement, feces smearing, and lack of cooperation with adult instructions. Family members did not appear to consider challenging behavior a primary identifying characteristic of their child. Though most families reported their children engaged in challenging behavior at home following closures, it did not seem to be their primary concern. Rather, most families described their children holistically. They suggested challenging behaviors were manageable given sufficient resources, time, and support. In general, families seemed more interested in discussing skill delays, experiences and concerns with online instruction, competing responsibilities, and maintaining their families’ emotional well-being. Families’ Experiences With Online Learning In response to RQ2, each family participant described a unique and complex experience with online education during the initial months of school building closures. While the initial study purpose was to discover how children with behaviors of concern and their families experienced online learning, we found family members mostly described their experiences and recommendations irrespective of child behavior. Four major sub-themes related to families’ experiences emerged from the data: difficulty participating in meaningful online instruction, increased family responsibilities, impact on the whole family unit, and a focus on prioritizing emotional family well-being. Difficulty participating in meaningful online instruction Family members found it challenging for their children to participate in meaningful online instruction. Due to a timely response by district staff, program administrators, and educators, all families received the technology necessary to access online learning (e.g., laptops, tablets). Access, however, does not guarantee participation. Their children were often unable to participate in all instruction offered to them without significant assistance. One family member, Piper, said it took “considerably more time and effort” than she expected to support her son’s meaningful online participation. Katrina’s family was able to sustain momentum for online learning during the month of April, but quickly burned out. While her son was able to participate in online classes with her support, it eventually exhausted her to the point that they dropped out of all but one, 30-minute class per week by the end of the year. She described it this way:It took us about maybe two weeks to kind of get into a rhythm, and then I think April was great. I think we got about halfway through May, and then it was like, we were lucky if we got online learning done, if we followed the whole schedule. Anna had multiple children participating in online instruction ranging from preschool through middle school. Online engagement was a challenge for all her children. While she worked hard to support her children’s meaningful participation in online instruction, it was overwhelming for her. In describing her families’ overall experience, she said, “This online distance learning is too much for our family. It’s a fight every day.” Anna reported her family members frequently argued about the participation expectations in online instruction. Some family members described concerns their child’s skill delays prevented them from participating in online learning. They were also concerned that their children’s lack of participation in academic instruction would increase the learning gap between them and their peers and possibly result in additional social-emotional or behavioral challenges over time. This concern was evident across all family members. Paul described this concern for his two daughters, both diagnosed with autism spectrum disorder (ASD) by saying “We will fall behind because of those behaviors . . . We have to find a way to recover those and to help them to keep going, to keep learning.” Family members reported an overall lack of attention to their children’s social-emotional and behavior support needs during the school building closure. Four of the five families reported that their children’s educators delivered general, but not individualized, social-emotional instruction online. Only one of the four family members, however, believed online social-emotional instruction was beneficial for her child (Katrina). Family member, Louisa, felt her daughter, Alice, was learning the social-emotional skills she needed to be successful in social situations at school prior to COVID-19. When school buildings closed, the opportunities that Alice needed to practice social skills with peers in person was no longer available. This led Louisa to believe “. . . I feel like nothing virtual has helped [Alice] with the emotions, because Alice already knew what she had to do when she left school. But she wasn’t able to practice it. And the virtual stuff doesn’t teach her that.” Increased family responsibility Family members felt increased responsibility for their children’s academic and social-emotional learning during online instruction. All family members reported spending increased time and energy helping their children access online instruction, understanding the learning schedule, navigating technology issues, participating in online learning activities, troubleshooting barriers to engagement, and supplementing online instruction with home-based activities. While some embraced their role as primary educator, others expressed concern that the demands of their new role were beyond their capability and negatively affected family dynamics. Piper’s child initially received approximately 2 hours of online, teacher-led instruction per day. To supplement, she provided an additional 3 hours of home-based instruction per day. When Piper’s son had difficulty engaging in class online, she reduced the number of hours he spent online, but increased home-based instruction. Additionally, Piper created and implemented her own behavior support strategies at home (e.g., visual supports, offering choices, embedding her son’s preferred interests in learning activities) to prevent challenging behavior and support her son’s engagement in home-based instruction. Paul worked closely with his children’s early learning educators to support their learning and positive behaviors at home. Paul communicated frequently with his daughters’ educators to modify and supplement the online learning opportunities that were being provided with social-emotional and academic learning activities based on his family’s needs, values, and priorities. Paul’s educators honored his families’ preferences and supported their learning goals by providing additional resources (e.g., written instructions, video models, visual supports) and hands-on materials delivered directly to their home. Paul felt grateful for the increased responsibility supporting his children at home, saying, “I don’t say it’s a good thing that this happened . . . but [my wife and I] got to know [our children] more and we got to help them to try and enforce some of the things that they already knew.” Louisa, Anna, and Katrina also felt increased responsibility supporting their child at home but found it to be more difficult than Paul or Piper. All three were committed to helping their children learn but unsure of how to fulfill the role of primary educator. All of them reported a lack of competence and confidence in supporting their children’s learning. Katrina reported that for the first several weeks, she received a large packet of printed materials weekly from the school for her son, Cailean, but did not receive instructions on how to use them and quickly felt overwhelmed. Cailean’s teachers emailed worksheets and other printable materials that Katrina could use at home, but she did not have a printer, so she was unable to use them. In sum, most parents felt overwhelmed by their increased responsibility as educators in the home during online instruction for a variety of reasons. Perhaps Anna described this best when she said, “I’m not a teacher. I can’t do what they do . . . You’re gonna have to educate me before I can educate them.” Impact on the whole family unit Family members felt as though the shift to online education, and the increased responsibility they held for supporting their children in this new learning environment, impacted their whole family. Some family participants described this impact negatively, reporting an increase in family stress and marital tension. Anna expressed concern about the change in dynamic between her husband and older son, saying “It’s put fire between my husband and my son because my husband is not a learner like [Anthony], whereas I am.” She also described negative interactions between her and her older son when he was unable to complete online learning activities due to his disability. All five families expressed strong emotions about their experiences. Most described ongoing frustration and exhaustion with supporting their child’s online instruction. Louisa described it as “having a battery with no charge.” Louisa suggested the exhaustion her and her husband felt supporting their daughter led to increased fighting between them by saying “. . . it’s much more difficult to keep the peace of mind as a couple when we’re so tired . . .” Katrina also felt exhausted trying to maintain a full day schedule for her son at home, while working part time and caring for her younger daughter, saying “We got weary . . . I got tired of managing all of it every day . . . There’s a reason I don’t homeschool my kids.” Unlike the other family participants, Piper suggested that the move to online learning had a positive impact on her family. Her son, Parkes, was in first grade and engaged in significant challenging behaviors at school prior to COVID-19. While he had a BIP in place at school, Piper questioned whether it was being implemented with fidelity. Parkes often came home from school feeling deflated and described himself as a “bad kid.” When the school building closed, Piper was able to provide home-based behavior support and academic instruction in addition to the online instruction that Parkes received from his teachers. She described how the shift impacted her family by saying “. . . it’s been really good for him. And for me, it’s been really good. . . . [When Parkes was in school] it was a very difficult time and has been for three years . . .” Focus on prioritizing family emotional well-being While families expressed concern about their child’s ability to participate in learning during school building closures, all prioritized the emotional well-being of the family unit over child instruction. Families demonstrated this priority in different ways. For example, Katrina’s son only attended one, 30-minute social-emotional lesson online per week by the end of the school year and no academic instruction. Anna also allowed her children to stop attending online instruction based on the guidance of individuals within her formal and informal support networks to prioritize the emotional well-being of her family unit. She summarized her commitment to family well-being by saying, “Coming from a family that has depression, anxiety, social-emotional delays, we’ve had to prioritize mental health over education in our home.” Unlike Anna and Katrina, Louisa maintained her child’s attendance in online instruction through the end of the school year. However, when asked about her continued participation in online instruction into the following school year (2020–2021), Louisa shared the importance of her family’s emotional well-being by saying: “I’ll just try to have peace at home to help my daughter . . . I’ll keep moving forward with the academics, but that won’t be my priority.” Family Member Recommendations for Online Instruction To answer RQ3, all five family members provided recommendations for educators and administrators, and district leaders regarding online learning and behavior support should school building closures persist long term or occur again in the future. Recommendations related to the following two categories: “hands-on” materials and family-professional partnerships. “Hands-on” materials Family members recommended schools provide more “hands-on” (i.e., physical) materials for them to supplement online learning at home. Two families had access to physical materials throughout their experience and spoke positively about the benefits of these materials. Educators for Paul’s daughter, Makayla, delivered physical learning and behavior support materials to her home immediately following school building closures. They also frequently emailed individualized visual supports to Paul aimed at helping him increase Makayla’s understanding of online learning expectations and facilitate engagement in online social-emotional activities with peers. Despite his positive experience, he suggested that should online instruction persist long into the future, “it’s going to be challenging for us as parents, we would want more materials” during school building closures. Piper felt that Parkes’s teachers did not provide sufficient physical materials necessary for supporting his individualized learning and recommended that schools spend more time gathering and delivering learning materials to students. Anna, Katrina, and Louisa agreed that young children, especially those with disabilities or challenging behavior that interfere with online learning, needed access to supplemental “hands-on” materials. They added that along with materials, parents should be provided with instructions on how to help their children use the materials to maximize benefit. Katrina described these materials as “learning kits” that could be assembled and distributed to families. Even though she received some printed materials from her school district to supplement Cailean’s online instruction, she described the materials as “thick” and “indecipherable” packets. In sum, all families wanted access to materials with adequate instructions to help them support their children’s learning at home. Family-professional partnerships Family members recommended school personnel spend more time building positive, supportive partnerships with families when children attend school online. Family members described the importance of family capacity building as central to the concept of family-professional partnerships. Paul reported he received a lot of support from his daughter’s educators and school family support team following school building closures. While reflecting on recommendations he would make to school leaders though, he said:I would ask them to come up with a support system that helps parents with challenged kids. Besides helping [children] to learn at home, a way to help [parents] feel comfortable or absorb all that pressure. There is going to be a lot of pressure because there’s a lot of . . . we use a lot of energy with these kids. Katrina suggested that schools could increase family capacity by arranging family member introductions and encouraging them to form family support groups. She did not “think that the school can figure it out on their own,” meaning that schools cannot and should not be solely responsible for shouldering a family’s increased responsibility related to supporting young online learners with challenging behavior, but suggested that schools could facilitate social connections between families to establish informal support networks to “. . . just [be] able to talk to other parents and figure out how we can ease the burden for one another . . .” Several family members suggested their lack of expertise prevented them from offering their children all the support they needed to be successful learners online. They emphasized the importance of partnering with educators to increase their capacity to implement instruction at home and recommended that educators and administrators spend more time developing plans to increase family skills. Anna, Piper, and Louisa all explicitly stated that educators and specialists had important training and skills that they did not. However, they followed these statements by saying that when their young children were learning from home, parents fulfilled the primary educator role; therefore, it was crucial for them to learn how to effectively educate their children. Anna described this by saying: “. . . I really learned that for me and my kids I learn so much from professionals . . . I pick up on what they’re doing, and I try to copy them.” Family members described the importance of effective and meaningful collaboration and communication with school staff to support their children. Several family members prefaced their family-school communication recommendation with descriptions of the challenges they had receiving timely, useful communication from educators and administrators about online learning schedules, expectations, and child progress. Anna felt responsible for initiating nearly all communication with her children’s educators and administrators. She reported that she would often send emails or leave phone messages for educators that went unanswered. When she was able to reach them, the interaction was often brief and tense. Piper also described insufficient communication interactions between her and her child’s educators. She emphasized the importance of meaningful communication between family members and educators beyond the standard, “one-off, 20-minute, check the boxes” IEP meeting. When asked what that might look like, she said:. . . talk with families in an in-depth conversation. Call the parents. If they don’t answer the phone, go to their house . . . Make an individual plan. Look, this is the irony of it all, right? Make an individual education plan. That’s what you should be doing. Discussion The current study examined families’ experiences with children’s online instruction and behavior support during the initial months of COVID-19 school building closures. Findings revealed important themes related to family members’ perceptions of challenging behavior, family members’ experiences with online learning, and family members’ recommendations. This study provides new information about early COVID-related, online learning impacts for young children with complex educational and behavioral needs and their families. Defining Challenging Behavior Family members described their children’s challenging behavior differently than expected based on commonly reported behaviors in the literature (e.g., Matson & Nebel-Schwalm, 2007). Their descriptions indicated differences between how family members and educators may define challenging behavior. When asked to describe their children’s challenging behavior, family members initially identified communication and academic skill delays. Only after multiple probes did they identify the types of behavior often labeled “challenging” by educators. Though somewhat surprising, this finding is consistent with a small body of literature examining family and professional definitions of challenging behavior (e.g., Kulkarni & Sullivan, 2022). It is likely that families and professionals are influenced by context and culture (their own and those of others) when identifying behaviors of concern. Adult expectations may also influence the different kinds of behaviors parents and professionals identify and prioritize for intervention. Professionals who have a history of working with young children with disabilities may expect their students to have developmental delays, but not challenging behavior (Banks & Obiakor, 2015). On the other hand, family members may expect their young children to have challenging behavior (e.g., tantrums), but not developmental delays. Future research should broadly focus on differences between family members’ and educators’ definitions of challenging behavior, and how definitional differences impact behavior interventions across school and home environments. Future research should also explore how changes in instructional environments (i.e., changes from in-person to online instruction) influence the identification and prioritization of behavior for intervention and the types of instruction that are delivered to them. Following focused questioning about challenging behavior, most family members generally avoided describing their children’s challenging behavior as central to their experiences during COVID-19 school building closures. Rather, they tended to focus discussions on the impact that the shift from in-person to online instruction had on their families, the subsequent increased responsibility they felt for supporting their children’s academic and social-emotional learning, and the effort they made maintaining their families’ emotional well-being. There are several reasons for why families may have described their experiences in this way. First, it is possible that the shift family members made from caregiver to caregiver and educator was more stressful than their child’s challenging behavior; therefore, more salient of a discussion issue. Similarly, it is possible that other stressors associated with COVID-19, but unrelated to child challenging behavior, increased simultaneously with family members’ new educator roles, resulting in cumulative stressors that impacted overall family functioning and well-being. For example, several participants reported that they or other family members living in the home started telecommuting, were furloughed, or lost their jobs entirely during the pandemic. This is consistent with some family stress models that suggest one major event (i.e., COVID-19) can result in a pile up of stressors that affect the whole family social system (Perry, 2005). Families may have perceived COVID-19 stressors unrelated to their child’s behavior to be more impactful on their daily functioning at the time of the study, and thus more important to emphasize during interviews, than their child’s behavior. Family Responsibilities and Well-Being School building closures had a profound impact on families’ lives by shifting the balance of their roles and responsibilities. Like other emerging studies exploring family participant experiences during COVID-19 (e.g., Dong et al., 2020; Garbe et al., 2020), family members in our study felt unprepared for their new roles and responsibilities during school building closures. In response, families tended toward a “survive and then thrive” approach to managing their new demands by prioritizing family well-being over their children’s academic learning (Garbe et al., 2020, p. 57). Families’ responses are consistent with well-known psychological models that suggest people tend to concentrate on meeting basic needs and building secure relationships before prioritizing their own individualized learning (Bloom, 1956; Maslow, 1943). Implications for Online Learning During School Building Closures Similar to our findings, recent literature on the impacts of COVID-19 school closures demonstrates home-based implementation of young children’s instruction and behavior support heavily relied on families, leading to increased opportunity gaps for children whose family members did not have the capacity to serve as their children’s primary educator (e.g., Steed & Leech, 2021). In the future, educators can take measured steps to narrow opportunity gaps for children with disabilities and challenging behavior by increasing effective family-school communication and culturally responsive, family-capacity building strategies (Garbe et al., 2020; Luna et al., 2023). Educators can prepare for online learning by meeting with family members to gather information about their priorities for their young children’s behaviors and learning. Such information may include how SDI and behavior support priorities shift from in-person to online learning environments. Educators may use this information to increase family capacity by providing family members with knowledge about how to use everyday family routines and experiences to support their educational and behavioral priorities now. In the event of future building closures, they may also use the information to inform online instruction by combining teacher-directed instruction with family capacity building strategies (Dunst & Trivette, 2009). These strategies should be revisited as often as educators and families need. Limitations The findings of this study should be interpreted with respect to the following limitations. First, this study was conducted immediately following COVID-19 school building closures. While temporal context was intentionally considered throughout the study’s design and implementation, family members’ limited experiences with online learning at the time of the study likely impacted their responses to interview questions. At the time of this study, there were still many uncertainties about COVID-19 (e.g., pandemic duration) and school closures (e.g., when and how school buildings would open again). It is possible that given a longer time with similar circumstances, families would respond differently to questions about their child’s challenging behavior, online instruction, and recommendations for school supports. Second, family members provided varying amounts of artifact data. This prevented us from cross-analyzing transcript and artifact data equally across participants, possibly limiting the scope of data triangulation. Additional data collection and analysis of artifacts or similar data sources would likely increase the trustworthiness and credibility of the findings. Conclusion This study describes families’ experiences helping their young children with challenging behaviors participate in online education during initial COVID-19 school building closures. The findings offer promising information about what families need to effectively support young children’s learning at home should school building close again. Understanding families’ experiences and responding to their recommendations may help policymakers, administrators, and educators prepare for future disruptions to traditional education systems for vulnerable student populations (i.e., young children with disabilities, young children with behaviors that interfere with learning) by designing policies and procedures that center family needs and build family capacity. Supplemental Material sj-docx-1-tec-10.1177_02711214231182023 – Supplemental material for Families’ Experiences With Online Instruction and Behavior Support During COVID-19 Click here for additional data file. Supplemental material, sj-docx-1-tec-10.1177_02711214231182023 for Families’ Experiences With Online Instruction and Behavior Support During COVID-19 by Elizabeth M. Kelly, Shawna G. Harbin and Ilene S. Schwartz in Topics in Early Childhood Special Education The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R324A180061, and the Office of Special Education Programs through Grant A99031, to the University of Washington. 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==== Front spteu TEU Tourism Economics 1354-8166 2044-0375 SAGE Publications Sage UK: London, England 10.1177_13548166231185422 10.1177/13548166231185422 Empirical Article The impact of the COVID-19 outbreak on intra- and inter-regional domestic travel: Evidence from Spain https://orcid.org/0000-0003-2372-1102 Álvarez-Díaz Marcos Chamorro-Rivas José María González-Gómez Manuel Otero-Giráldez María Soledad 16784 University Of Vigo , Ourense, Spain Marcos Álvarez-Díaz, University of Vigo, As Lagoas, Campus Universitario, Ourense 32004, Spain. Email: marcos.alvarez@uvigo.es 26 6 2023 26 6 2023 13548166231185422© The Author(s) 2023 2023 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. This study empirically compares domestic tourists’ behavior before and after the Covid-19 outbreak. Specifically, official data are used to characterize the travel behavior of residents in Spain who traveled through this country for reasons of leisure, recreation, and vacations in 2019 and 2020. A discrete choice model is employed to unravel the main variables that influence the decision of being an inter-regional traveler. The bootstrap p-value method is used to detect significant changes in the marginal effect of the different variables after the Covid-19 outbreak. The estimation results demonstrate the following: (i) domestic inter- and intra-regional travelers have significant differences and, therefore, policy-makers should not design and implement “one size fits all” policies for domestic tourism; (ii) in addition to socio-economic factors, the regional push-pull factors (e.g., weather) and trip-related characteristics (e.g., type of accommodation) are also important in explaining inter-regional travel decisions; (iii) a high number of Covid-19 infections in the region of origin increases the probability of traveling inter-regionally; and (iv) the Covid-19 outbreak has caused an abrupt shift in domestic travelers’ profiles. Domestic tourism traveler behavior bootstrapping Covid-19 Spain edited-statecorrected-proof typesetterts10 ==== Body pmcIntroduction The COVID-19 pandemic implies an unprecedented crisis in the tourism economy. The tourism sector has never experienced a crisis like this before. The strong impact has led to significant changes not just in tourism industry figures, but also in the intrinsic nature of tourism activities. There is a broad consensus that tourism will not be the same as it used to be (Zenker and Kock, 2020). One of the main changes has been a mental shift in tourists’ travel behavior. The pandemic has reshaped and significantly impacted tourists’ profiles and preferences. One of the most evident shifts has been that tourists are now more prone to taking domestic trips than before the pandemic. International tourism has been the focal point for decision-makers and researchers at the expense of domestic tourism (Bayih and Singh, 2020). International tourism analysis is considered to be of great importance for the economies of many countries as it is a source of foreign exchange earnings used to pay for imports and maintain the level of international reserves (Brida et al., 2016). However, in times of economic, socio-political, or health crises, tourists usually opt for domestic destinations over foreign ones because of various reasons. First, international tourists are generally more risk-sensitive and vulnerable (Cheng, 2012), and the risk-aversion is even greater and more evident in the case of a deadly pandemic such as Covid-19 (Im et al., 2021). The pandemic has caused an unprecedented level of fear and people are avoiding traveling long distances, preferring to travel to nearby destinations instead (Zheng et al., 2021). Second, limitations to mobility have led to a greater predisposition for domestic tourism, which is less affected by such restrictions (Duro et al., 2022). Finally, tourists opt for domestic trips to support the national economy, a behavior that was recently coined in the literature as “ethnocentrism tourism.” (Kock et al., 2019; Zenker and Kock, 2020). For all these reasons, domestic tourism will play a crucial role during the recovery phase of the hospitality and tourism sectors, and hence its study has become more important since the outbreak of the pandemic (OECD, 2020; Arbulú et al., 2021; Miao et al., 2021). Since the Covid-19 outbreak, many firms have sought refuge in the domestic tourism market, and many national and regional policy-makers have adopted policies to encourage domestic tourism. Policy-makers are implementing institutional promotional campaigns and income tax deductions for domestic tourism, as well as issuing tourist vouchers to spend domestically to help reactivate the sector. All these policies are being applied assuming that domestic tourism is homogeneous. However, the motivations of domestic tourists who travel from one region to another (inter-regional tourists) may be significantly different from those who move only within their own region (intra-regional tourists). Although inter- and intra-regional tourism may be two sides of the same coin, they could have specific characteristics that make them different. Therefore, implementing “one size fits all” policies for domestic tourism may not be the most efficient way to revive the hospitality and tourism sectors. Compared to intra-regional domestic tourism, inter-regional tourism is more valuable from a social point of view as it allows the redistribution of national income from richer to poorer regions (Canavan, 2013). Hence, promoting this kind of domestic tourism can represent a beneficial policy to not only reactivate the tourism sector during a pandemic but can also be an opportunity to better redistribute wealth among the different regions of a country. Additionally, inter-regional travelers generate more wealth for the destination region as they are characterized by a higher level of expenditure. Thus, the promotion of inter-regional tourism seems an optimal measure to revitalize the industry. For that reason, understanding the main factors that drive the decision of making inter- or intra-regional trips is an important informational requirement to design and implement efficient policies. However, no studies have investigated these types of domestic tourism before or after the Covid-19 outbreak. This study aims to address this gap in the literature for the specific case of Spain. As explained in Duro et al. (2021), the choice of Spain is justified because its economy is highly dependent on international tourism, and it has been one of the economies most affected by the pandemic. Given this context, this study makes three main contributions to the existing literature. First, we demonstrate that domestic tourism is not homogeneous: The drivers that lead travelers to make an inter- or intra-regional trip are different. Most studies were conducted to identify and characterize specific niches within the domestic tourism markets such as “sun and sand” tourism (Priego et al., 2015), religious tourism (Bandyopadhyay et al., 2008), or the cultural tourism (Álvarez-Díaz et al., 2022), but there is a lack of studies that have explored the main factors that drive the decisions to travel inter- or intra-regionally. Second, we identify the most significant variables that explain the individual´s decision to make an inter-regional domestic trip in 2019 and 2020. We specifically estimate the impact of these variables on the probability of being an inter-regional traveler. At this point, we also analyze the impact of Covid-19 on the decisions of traveling domestically. Third, we contribute to the emerging strand of research that investigates how tourists’ profiles may have changed owing to the pandemic. Specifically, we statistically test the null hypothesis of whether the pandemic significantly modified tourists’ characteristics and preferences. We perform hypothesis testing using a non-parametric method based on the calculation of bootstrap p-values (Racine and MacKinnon, 2007). The paper is organized as follows. After this introductory section, Section 2 reviews the main literature on domestic tourism and Covid-19. Section 3 briefly describes the model, and the variables included in the analysis. Section 4 presents and discusses the main results derived from the estimated model. Section 5 summarizes the main results and concludes. Literature review and motivations of the study Despite most worldwide tourism being domestic in nature, tourism research has mainly focused on the international market. This is reflected in different studies that report the scarce number of investigations devoted to understanding domestic tourism (Eugenio-Martín and Campos-Soria, 2010; Canavan, 2013; Bayih and Singh, 2020; Boto-García and Mayor, 2022). The lack of a common definition of domestic trips and the scarcity of data are the main reasons provided in the literature to explain why domestic tourism is an under-researched topic (Cortés-Jiménez, 2008; Canavan, 2013; De la Mata and Llano-Verdura, 2012; Priego et al., 2015; Llorca-Rodríguez et al., 2020; Lee, 2021). However, researchers are now beginning to pay much more attention to the domestic market as the concept of domestic tourism is now clearly defined (UN, 2010), and many countries have made advances in travel and tourism data collection. Additionally, the outbreak of the Covid-19 pandemic has increased interest in the domestic market as this market is expected to be the engine of the sector’s recovery (Arbulú et al., 2021). Nevertheless, tourism researchers must make even greater efforts to better understand the causes and consequences of domestic tourism. Delving into the specialized literature, the analysis of the main determinants of domestic tourism has been scarcely explored. Most existing studies assume the principles of economic theory, that is, they give importance to economic variables such as prices and income. Some examples of studies that examined the impact of economic variables can be found in Salman et al. (2007), Athanasopoulos and Hyndman (2008), Allen et al. (2009), and Garín-Muñoz (2009). Although economic variables are important, other non-economic variables should not be disregarded to explain domestic travel behavior. Marrocu and Paci (2013) concluded that income level and relative prices are not the only factors that affect domestic tourism flows. They found that Italian regions with a large number of natural amenities and recreational activities received a significantly greater flow of domestic visits. They also found that the higher the accessibility of the destination region, the higher the regional tourism inflows. Yang and Wong (2012) also concluded that natural amenities, recreational activities, and accessibility of the destination are important determinants to explain domestic inflows in the case of China. Additionally, some studies explore the importance of weather conditions at the destination as a determinant of domestic tourism (Taylor and Arigoni-Ortiz, 2009; Otero-Giráldez et al., 2012; Bujosa and Rossello, 2013). Álvarez-Díaz et al. (2020) confirm the relevance of both economic variables (relative prices and income) and non-economic variables (natural amenities, recreational activities, accessibility, and meteorological factors) to explain domestic demand for tourism in Spain. Although less studied traditionally, a strand of the literature investigates the effects of natural and man-made crises on international tourism demand. However, these effects are hardly explored in the study of domestic tourism (Papatheodorou et al., 2010). The sparse existing research mainly focuses on the effect of economic and financial crises (Cafiso et al., 2018), natural disasters (Barbhuiya and Chatterjee, 2020), terrorist attacks (Voltes-Dorta et al., 2016), political instabilities (Álvarez-Díaz et al., 2019) and dangerous infectious diseases such as the SARS, the Avian Flu, and the Ebola outbreak (Cahyanto et al., 2016). Regarding this latter point, Richter (2003) had already warned that one of globalization’s greatest challenges would be the negative effect of tourism through the transmission of infectious disease and other health-related crises. However, very few studies analyzed the effect of dangerous infectious diseases or pandemics on tourism before the COVID-19 outbreak (Novelli et al., 2018). The outbreak of the Covid-19 pandemic led to an immediate and unprecedented response from tourism researchers. In fact, the Covid-19 pandemic has had more negative consequences than any prior crises that have affected the tourism sector. It has not only implied a radical change in the tourism activity, but can also be considered a turning point in the literature on tourism. No previous pandemic had similar implications and consequences for the global economy as the Covid-19 pandemic (Gössling et al., 2020). Since the outbreak of the pandemic, there is evidence of a growing interest in analyzing its consequences on tourism and, specifically, on domestic tourism given that this market is supposed to play an important role in the recovery of the tourism sector (Arbulú et al., 2021; Boto-García and Mayor, 2022). Based on their research purpose, we have detected five big areas of research in the recent literature on the link between Covid-19 and domestic tourism:(i) Analyses of the potential transformation of the tourism system due to the Covid-19 pandemic. In this sense, Hall et al., (2020) consider that changes in tourism caused by Covid-19 will be irregular in space and time since some destinations will reconsider the nature of their tourism industry focusing more on local, and seeking more sustainable ways of tourism. In this line, Gössling et al., (2020) explore how Covid-19 may transform the tourism system as part of the society and economy. (ii) Assessment of the vulnerability and strengths of domestic tourism in the face of the pandemic. Authors argue that the domestic market will recover in a relatively short period of time (Provenzano and Volo, 2021; Boto-García and Mayor, 2022), as well as those regions with low-density population (Falk et al., 2022), and with a better position to offer rural accommodation (Marques et al., 2022). Tan et al. (2021) affirm that short-distance market will show a stronger resilience than medium- and long-distance markets. On the other hand, it seems that the main tourist destinations present higher levels of vulnerability (Duro et al., 2021); although vulnerability will depend on the traditional orientation of a region’s portfolio on the domestic market, the volatility of its domestic demand and the capacity to attract new segments of domestic tourists (Arbulú et al., 2021). (iii) Evaluation of the recovery measures focused on the domestic tourism market. Political measures to mitigate the negative effects of Covid-19 are assessed by different studies. For instance, Volgger et al. (2021) find that measures of banning international tourists and flexible cancellation policies showed strong effects to revive domestic tourists’ booking intentions; but other measures such as advertising were less promising. Foo et al. (2021) discuss stimulus package based on tax incentives, restructuring of loans and postponement of repayments to banks and a wage-subsidy program implemented by the Malaysian government. Matsuura and Saito (2022) support the implementation of domestic travel subsidies based on price-discount strategy because it is a cost-effective tool for sustainable tourism businesses. (iv) Determination of the role of domestic tourism as a spreading factor of the virus.Nunkoo et al., (2021) conclude that domestic tourism positively contributes to the spread of Covid-19 cases and deaths during the early phases of the pandemic. However, in a later period of the pandemic, they find that domestic tourism is not associated with a greater spread of the virus. (v) Research on domestic tourists’ behavior and risk perceptions after the outbreak. An emerging strand of the literature focuses on the changes in the domestic tourists’ behavior and risks perception after the Covid-19 outbreak. Most of the studies conclude that traveling decision-making is influenced by the travel risk perception (Zeng et al., 2021; Neuburger and Egger, 2021), demographic factors such as age, gender, education and social status (Boto-García and Leoni, 2021; Ivanova, 2021), and safety and security (Moya-Calderon et al., 2021). These studies also reveal that individuals have a higher preference for seeking more information about the risks of the pandemic (Meng et al., 2021), visiting destinations with a reliable health system, by car and with the family (Ivanova, 2021), and safe and secure places such as protected natural areas (Moya-Calderón, 2021). Our study lies within this area of research as our purpose is to investigate the motivations behind tourists’ decision to travel before and after the COVID-19 outbreak. In light of this general review, we can identify three significant gaps in the literature on domestic tourism. One of the main motivations of our research is to demonstrate that there are significant differences in the decisions of inter or intra-regional travel. To the best of our knowledge, this is the first study that explores such differences. This research question is relevant from a practical and theoretical point of view since it allows us to understand better the different factors that drive domestic tourism demand, where the domestic tourism market is going; and where and how the decision-makers should intervene. To carry out our study, we assume a micro-approach to explore the influence of a set of explanatory variables on the decisions of traveling. Additionally, we explicitly estimate the impact of the level of incidence of Covid-19 at the destination region and the region of origin on the probability of traveling inter-regionally. Additionally, our study also responds to the recent calls for more research on how the Covid-19 pandemic has changed travelers’ profiles (Peluso and Pichierri, 2021). Specifically, we fulfill this gap by analyzing the determinants that have experienced a significant change in 2020 (after the outbreak), compared to 2019 (before the outbreak). In this sense, our study makes a significant contribution to the evolving literature on the impact of the Covid-19 pandemic on travelers’ profiles. Model and variables Discrete choice micro-economic models have been widely used in many areas to determine the probability that an individual chooses a specific alternative. McFadden (1974) established the theoretical foundations of the discrete choice micro-econometric models; and Morley (1994) explained the theoretical model for the specific case of tourism decisions. The empirical use of these models in tourism has been significant (see, for example, the explanation and references given in Alegre and Pou, 2004). In our study, we consider the most commonly used discrete choice model: the logit model. 1 Following the econometric indications given in Wooldridge (2012), we assume that the individual’s response probability to travel inter-regionally can be expressed by the model (1) P(Y=1|X,R,T,S)=G(X·β+R·γ+T·δ+S·φ) where Y is a binary random variable that verifies Y=1 if we observe that the individual has made an inter-regional trip, and Y=0 if the trip is intra-regional. P(Y=1|X,R,T,S) corresponds to the probability of taking an inter-regional trip ( Y=1 ) subject to a set of explanatory variables that are grouped in the matrices X, R, T, and S. G(·) is the logistic function. The vectors β , γ , δ , and φ are the parameters of the model. Tables 1 and 2 list and describe the explanatory variables included in the matrices X, R, and T. In these tables, we also specify the sources from which the data were collected. It is important to highlight that all these data are public and come from official institutions. Most of the data are sourced from the Resident Travel Survey 2019 and 2020, which is updated monthly by the Spanish Statistical Office (INE). This survey offered socio-economic and travel-related information from 44,054 interviewees resident in Spain who traveled through this country for reasons of leisure, recreation, and vacations in 2019 and 2020. 2 It is important to underline that interviewees are representative of the Spanish population and provide information about real trips (that is, the survey is not based on travel intentions). The other sources used in this study are the Spanish State Meteorological Agency - AEMET, the Spanish Ministry of Economic Affairs and Digital Transition, and the Foundation for Environmental Education (FEE).Table 1. Socio-economic variables used in the analysis to explain the main factors that drive the decision of taking a domestic interregional trip in Spain during 2019 (pre-pandemic year) and 2020 (pandemic year). Characteristic Variable Description Source Socio-economic characteristics (Matrix X) Age Age Age of the traveler (in years). Resident travel survey 2019 and 2020. Spanish statistical Office-INE (www.ine.es) Gender Gender Value one if the individual is a man, and zero if a woman. Marital status Married Value one if the individual is single, and zero otherwise (base category). Widow Value one if the individual is widow, and zero otherwise. Divorced Value one if the individual is divorced or separated, and zero otherwise. Single Value one if the individual is single, and zero otherwise. Household income High_Income Value one if the individual earns more than 3500€ per month Middle_Income Value one if the individual earns from 1500€ to 3500€ per month Low_Income Value one if the individual earns less than 1500€ per month (base category). Level of education Higher_Studies Value one if the individual has higher studies, and zero otherwise. Secondary_Studies Value one if the individual has secondary studies, and zero otherwise. Primary_Studies Value one if the individual has primary studies, and zero otherwise (base category). Labor market status Employed Value one if the individual is employed, and zero otherwise (base category) Unemployed Value one if the individual is unemployed, and zero otherwise. Inactive Value one if the individual belongs to the inactive population (retired people, students,…), and zero otherwise Table 2. Regional and Trip-related variables used in the analysis to explain the main factors that drive the decision of taking a domestic interregional trip in Spain during 2019 (pre-pandemic year) and 2020 (pandemic year). Characteristic Variable Description Data source Regional characteristics (Matrix R) Weather conditions Rainfalld Total monthly regional rainfall, in milliliters of rain. Indicator of the weather characteristics of the region during the month of the trip. It approximates the capacity of the regions to attract (Rainfalld) or push visitors (Rainfallo). Spanish State Meteorological Agency - AEMETwww.aemet.es Rainfallo Natural amenities BlueFlagd Number of blue flags per 100 km of coast of the region. Indicator of the capacity to attract (BlueFlagd) or push (BlueFlago) “sun-and-beach” visitors. Foundation for environmental education (FEE), blue flag programme www.blueflag.global. BlueFlago Relative prices GCPI Relative general consumer price Index (CPI) between the region of destination and of origin. Indicator of the relative cost of living between the region of origin and the region of destination. Spanish statistical Office-INE www.ine.es Insularity Island Value one if the region of destination is an island, and zero otherwise. COVID impact Covidd Confirmed COVID-19 cases per 100,000 inhabitants one month before the trip at the destination region (Covidd) or at the origin region (Covido). Indicator of the level of incidence of the disease. Spanish Ministry of health www.datos.gob.es Covido Trip-related characteristics (Matrix T) Minors on the trip Memb15 Number of members of the trip less than 15 years old. Resident Travel Survey 2019 and 2020. Spanish Statistical Office-INE www.ine.es Main mode of transport Car Value one if the main mean of transport is the car, and zero otherwise. Accomodation Hotel Value one if the main accommodation establishments is a hotel. Matrix X includes variables that collect the socio-economic characteristics of the travelers. These individual characteristics are considered to play a determining role in the decisions to consume tourism services (Alegre and Pou, 2004). Thus, we include variables such as the traveler’s age, gender, marital status, household income, level of education, and labor market status in matrix X. Matrix R contains the regional push and pull factors. The pull factors are those characteristics that attract tourists to a given destination (e.g., good weather in the region of destination), while the push factors refer to specific characteristics that affect the person’s decision to take a vacation (e.g., bad weather in the region of origin). Kim et al. (2003) affirm that a push-pull framework is a helpful approach for studying the motivations underlying travelers’ decisions. For that reason, Matrix R groups variables such as the weather conditions (rainfall), the quality of natural amenities (Blue Flag beaches), and the incidence of Covid-19 (confirmed cases) both in the region of origin and in the destination region. This matrix also includes a variable that approximates the relative cost of living between the region of origin and the destination region, and a dichotomous variable to point out whether the region is an island. Few studies have incorporated trip-related characteristics into the analysis, which has created an immediate need for research in this area (Sung et al., 2001). To perform a complete modeling procedure, matrix T includes variables that collect specific characteristics of the trip variables such as the main mode of transport used on the trip, the number of minors (aged less than 15 years), and the type of accommodation during the trip. Finally, matrix S has monthly dummy variables to control for the seasonal patterns strongly observed on leisure trips (Bernini and Craccolici, 2015). Due to space limitations, the estimates of these dummy variables are not reported, but they are available from the authors upon request. Table 3 summarizes the descriptive statistics for each explanatory variable.Table 3. Main descriptive statistics of the variables. Year 2019 Year 2020 Continuous variables Mean SD Max. Min. Mean SD Max. Min. Age 50.43 15.21 85 15 49.61 15.27 85 15 Rainfalld 40.00 52.86 424.70 0 37.33 50.62 461.50 0 Rainfallo 41.47 52.99 424.70 0 37.23 48.58 461.50 0 BlueFalgd 7.54 8.38 26.06 0 7.98 8.48 25.87 0 Blueflago 5.82 7.56 26.06 0 6.15 7.89 25.87 0 GCPI −0.01 0.39 1.93 −1.89 −0.03 0.34 1.72 −1.60 Covidd — — — — 115.15 261.46 2281.3 0 Covido — — — — 215.18 343.42 2281.3 0 Memb15 0.38 0.74 5 0 0.37 0.74 5 0 Dummy variables Frequency Y 54.03% 45.90% Gender 49.05% 48.29% Married 59.83% 58.25% Widowed 4.43% 3.85% Divorced 7.95% 7.33% Single 27.79% 30.57% High_Income 20.38% 24.22% Middle_Income 57.43% 55.44% Low_Income 22.19% 20.34% Higher_Studies 53.49% 57.79% Secondary_Studies 41.70% 38.60% Primary_Studies 4.81% 3.61% Employed 62.53% 63.33% Unemployed 6.16% 7.02% Inactive 31.31% 29.65% Island 7.49% 9.20% Car 84.17% 90.06% Hotel 27.20% 20.20% Total number of observations 29,234 14,820 Source: Own elaboration. Empirical findings Three models were developed and estimated according to the logit specification given in equation (1). For comparison purposes, Models 1 and 2 include all explanatory variables except those variables that collect the incidence of Covid-19 (COVIDO and COVIDD). The difference is that Model 1 is estimated with 2019 data, while Model 2 is estimated using 2020 data. Model 3 mirrors Model 2, but includes the Covid-19 variables. The reason for including Model 3 in our analysis is that it allows us to explicitly investigate the effect of Covid-19 on travelers’ profiles. Additionally, Model 3 allows us to check the robustness of the differences between 2019 and 2020. The results of the estimated models are presented in Table 4. The robustness and accuracy of the estimated models are verified through different ways. First, most estimates show the expected signs are statistically significant and agree with previous findings on domestic demand for tourism. Moreover, the Ordinary Least Square model was fitted, and the model was tested for multicollinearity using the variance inflation factor (VIF). The variance inflation factors for all variables are less than 10, which indicate that multicollinearity is not a serious problem for any of the estimated models (Gujarati, 2003). Second, the models present an acceptable level of adjustment (McFadden R-squared equal to 0.19 for Model 1, and 0.21 for Model 2 and Model 3), and more than 71.6% of the cases were correctly classified. Finally, the Likelihood-Ratio statistic indicates that the model is statistically significant (p-value = 0.00).Table 4. Estimated logistic regression model. Year 2019 Year 2020 Model 1 Model 2 Model 3 Intercept 1.2105*** 2.0454*** 2.0158*** Age 0.0298*** 0.0069 0.0090 Age2 −0.0004*** −0.0002* −0.0002** Gender 0.0428 0.0537 0.0547 Widowed 0.0940 0.2486** 0.2677** Divorced −0.0250 0.2664*** 0.2735*** Single 0.1486*** 0.0724 0.0771 High_Income 0.3666*** 0.4246*** 0.4210*** Middle_Income 0.2038*** 0.3320*** 0.3281*** Higher_Studies 0.2823*** −0.2313** −0.2419** Secondary_Studies 0.1560** −0.2732** −0.2906** Unemployed −0.1368** −0.1663** −0.1622** Inactive 0.0355 0.0251 0.0447 Rainfalld −0.0032*** −0.0023*** −0.0024*** Rainfallo 0.0026*** 0.0001 0.0004 BlueFlagd 0.0058*** 0.0216*** 0.0181*** BlueFlago −0.0987*** −0.1175*** −0.1124*** GCPI −0.0644* −0.3842*** −0.3350*** Island −2.1116*** −1.9861*** −1.9993*** Covidd — — −0.0015*** Covido — — 0.0009*** Memb15 0.0272 −0.0013 0.0031 Car −1.7779*** −1.6453*** −1.6296*** Hotel 1.2483*** 1.0117*** 1.0100*** McFadden R-squared 0.18 0.21 0.21 Cases correctly classified 71.60% (c = 0.540) 74.43% (c = 0.490) 74.94% (c = 0.482) Note: The symbols ***, **, and * mean statistically significant at 1%, 5%, and 10%, respectively. The estimates for the seasonal dummies are omitted for the sake of brevity. They are available upon request. Main determinants of domestic tourism demand Socio-economic characteristics From Table 4, we can see that the socio-economic characteristics of travelers have a significant effect on inter-regional domestic traveling. For 2019, we observe in Model 1 that the variable AGE is significant and has a positive sign, and its squared term is also significant, but it presents a negative sign. This means that there is a concave relationship between the traveler’s age and the propensity to travel outside the region of residence. This result follows the inverted U-shaped lifecycle profile already detected in previous studies on tourism decisions (see Eugenio-Martín and Campos-Soria (2010)). Specifically, it indicates that the oldest people are less likely to take interregional trips. The worsening health and the mobility difficulties associated with old age could explain this lower predisposition to travel longer distances, and a higher preference for traveling within the region of residence. Conversely, this effect is not so strong in 2020. The variable AGE and its squared term are not statistically significant in Model 2, and only the variable’s squared term is significant in Model 3. This finding is in line with Boto-García and Leoni (2021) who found that travelers’ age was not statistically significant to explain traveling intentions after the Covid-19 outbreak. A traveler’s gender is not a determinant variable in the decision to travel inter-regionally. This finding is based on the observation that the GENDER variable is not statically significant either before or after the Covid-19 outbreak and confirms previous research that found no gender differences in tourist decision-making. For instance, Carr (1999) found no gender differences in tourist behaviors. Lin et al. (2014) conclude that no gender differences exist in tourist hesitation and the justifiability of destination decisions. These authors argue that, regardless of gender, most tourists have similar motivations for going on vacation, identical travel risks and have access to the same quantity of tourism relevant information. For the specific case of the Covid-19 pandemic, Ivanova et al. (2021) found that travelers’ gender did not influence the frequency to travel. On the other hand, Boto-García and Leoni (2021) found that women were more risk-averse and reluctant to travel during the Covid-19 outbreak. Some authors assume that travelers’ marital status may be a relevant factor in travel decisions (Eugenio-Martin and Campos-Soria, 2010; Wang et al., 2006). In our case, for 2019, the sign and statistical significance of the variable SINGLE in Model 1 indicates that single people are more prone to traveling inter-regionally than married people (MARRIED is the base category). In contrast, being divorced or widowed does not seem to be a determinant factor. The variables DIVORCED and WIDOWED have a positive sign, but they are not statistically significant compared to the base category. At this point, it is important to underline that this picture is quite different for 2020. Models 2 and 3 show that the sign of SINGLE continues to be positive, but the variable is not significant. This finding reveals that single people are not significantly more prone to traveling than married people. We also observe that the variables DIVORCED and WIDOWED maintain the positive sign, but now they exert a significant influence on the probability of taking an interregional trip. Therefore, this implies that divorced and widowed people have been traveling outside the region of residence significantly more than married people after the Covid-19 outbreak. This result could be due to the phenomenon that divorced and widowed people tend to be less risk-averse than married people (Light and Ahn, 2010). Household income has been widely acknowledged as a fundamental factor to explain the individual´s decision to make a trip (Eugenio-Martín and Campos-Soria, 2011). Our findings corroborate this both for the pre-pandemic year of 2019 and for the pandemic year of 2020. The variables MIDDLE INCOME and HIGH INCOME are positive and highly statistically significant for all the estimated models. Thus, regardless of the models, we observe that the higher the income, the greater propensity to travel inter-regionally. Previous studies have already confirmed a positive relationship between income and travel intentions both in risky (Voltes-Dorta et al., 2016) and riskless times (Alegre and Pou, 2004). Travelers’ educational attainment has been frequently considered in travel and tourism studies as a determinant of domestic tourism (Sung et al., 2001). In our study, the variables HIGHER STUDIES and SECONDARY STUDIES are statistically significant to explain the decision to travel inter-regionally both for 2019 and 2020. Since LOW STUDIES is the base category, we can affirm that the higher the education, the higher the propensity to travel inter-regionally. However, we observe an important change in travelers’ profiles because of the pandemic. While the effect of these variables is positive for the pre-pandemic year (Model 1), the contrary is observed for 2020 when the pandemic started (Model 2 and Model 3). This result implies that the most educated people were more likely to travel inter-regionally in 2019, and more averse to doing it in 2020. The most educated people better assimilate and process information about the risks and consequences of the pandemic, and strictly adhere to the public health guidelines on social distancing and quarantine restrictions (Carlucci et al., 2020). These reasons may explain why they are more reluctant to travel inter-regionally and prefer traveling within their region of residence. In contrast, Boto-García and Leoni (2021) found that vocational training and high education had a positive and significant influence on travel intention in Spain for 2020. Golets et al. (2021) had similar findings in the case of Brazilian travelers. Nevertheless, these studies addressed domestic tourism in a homogeneous way, without distinguishing between inter- or intra-regional tourism. Moreover, they focused on travel intentions, and not on real travel decisions. Finally, the findings in Golets et al. (2021) must be treated with caution as the authors admit that their study was based on a self-selection online survey that was not representative of the population. Travelers’ labor market status is also a factor that may determine interregional domestic trips. We find a negative and significant effect of UNEMPLOYED, indicating that unemployed people are statistically less predisposed to take an interregional trip compared to employed people (EMPLOYED is the base category). This result is in line with the theoretical micro-economic concept of precautionary saving: there is a reduction in current consumption and an increase in savings when there is uncertainty about future income (Deaton, 1997). Other authors have also empirically found that, in general, unemployment negatively affects the probability of participating in domestic tourism (Bernini and Cracolici, 2015; Alegre and Pou, 2004). Characteristics of the destination region and the region of origin: Push and pull factors Pull and push factors are useful for examining the motivations underlying tourist and visitation behavior: tourists in deciding “where to go” take into account the particular characteristics of the destination region, but also characteristics of the region of residence (Kim et al., 2003). Research on pull and push factors is well established in the literature and has been widely used to examine the motivations behind tourists’ decisions (Uysal et al., 2008). Our analysis adopts this approach and assumes that the attractiveness or repulsiveness of the destination regions and the regions of origin may be influential factors in the individual’s decision to travel for tourism. Table 4 displays the estimates of the pull and push factors. According to the applied academic literature, climatic and meteorological conditions are important factors that explain domestic tourism (Álvarez-Díaz et al., 2020). Agnew and Palutikof (2006) and Falk (2014) affirm that domestic travelers usually react more to bad weather at the destination than international travelers. This is because domestic trips do not need to be planned too far in advance, and it is easier to cancel a domestic trip than an international one (Taylor and Arigoni-Ortiz, 2009). In our study, we opt for including Rainfall as indicator of the weather characteristics of the region because is a clear deterrent factor of domestic tourism demand. 3 Regardless of the models presented in Table 4, we notice that RAINFALLD is statistically significant and shows a negative sign. This means that higher levels of rainfall in the destination region reduce the probability of traveling inter-regionally. Previous literature reported that bad weather at the destination was a deterrent for domestic tourism (Taylor and Arigoni-Ortiz, 2009; Eugenio-Martín and Campos-Soria, 2011; Bujosa and Rosselló, 2013; Falk, 2014; Álvarez-Díaz et al., 2020). However, our analysis of bad weather in the region of origin is original. For 2019, Table 4 shows that RAINFALLO in Model 1 presents a significant positive effect, suggesting that rainfall in the region of residence is a push factor: the higher the rainfall at the destination of origin, the higher the probability of traveling inter-regionally. In the case of 2020, RAINFALLO in Model 2 and Model 3 preserves the positive sign but is statistically insignificant. Thus, the pandemic seems to have reduced the significance of the push effect of bad weather on the region of residence. Given that the main Spanish domestic tourism segment is “sun and sand,” we also include in our analysis the total number of beaches awarded Blue Flag status as pull and push factors. This variable is assumed to be a good proxy of the capacity of a region to attract or retain “sun-and-sand” visitors since destinations with Blue Flags are full of natural, cultural, and recreational amenities (Castillo-Manzano et al., 2020). For 2019, Model 1 points out that the presence of Blue Flag beaches is a pull factor that attracts both inter- and intra-regional visitors. The positive sign and statistical significance of the number of Blue Flag beaches at the destination (BLUEFLAGD) indicate that individuals are more predisposed to visit regions with beaches of high quality. In contrast, a negative sign and the statistical significance of the number of Blue Flags at the region of origin (BLUEFLAGO) is a deterrent factor of interregional traveling: The greater the number of beaches at the origin with blue flags, the less likely it is that a traveler will travel outside the region of origin. Comparing with the estimates of the models for 2020 (Model 2 and Model 3), we observe that these variables maintain the sign and their high statistical significance. The relative cost of living in the destination region compared to the region of origin is widely considered an influencing determinant of domestic traveling decisions (Massidda and Etzo, 2012). Although there is no good proxy for this determinant, the Consumer Price Index (CPI) at the destination over the CPI in the region of origin is a variable commonly used in tourism demand modeling to approximate the relative regional cost of living (see the discussion on this problem in Álvarez-Díaz et al. (2020), and the references included in it). In Table 4, we can observe that the variable GCPI presents the expected negative sign for all models. As prices increase in the destination region with respect to the region of origin, travelers show a lower preference for taking interregional trips. This finding is in agreement with previous studies that found a significant and negative influence of relative prices on domestic tourism (see Garín-Muñoz, 2009; Massidda and Etzo, 2012; Álvarez-Díaz et al., 2017). It is interesting to note that the variable is marginally significant for 2019 (Model 1) but gains explanatory significance for 2020 (Model 2 and Model 3). Given that insularity may have specific characteristics, we include the dummy variable ISLAND in the models. The variable is highly significant and has a negative sign for both 2019 (Model 1) and 2020 (Models 2 and 3). This strongly suggests that being an insular region harms the likelihood of traveling inter-regionally. Therefore, the larger distances and the more expensive transport modes necessary for the trip (airplane or ship) are a strong impediment to visiting the islands. Travelers respond negatively to the prevalence of Covid-19 disease and its transmission rates in the population (Engle et al., 2020). To corroborate this, Model 3 includes the variables COVIDD and COVIDO to account for the epidemiological situation in the region of origin and the destination region, respectively. Specifically, they compute the accumulated regional number of confirmed Covid-19 cases through a PCR test in the previous month of the trip. To allow for comparison across regions, we express these variables in confirmed cases per 100,000 inhabitants. Model 3 in Table 4 shows that COVIDD has a negative sign and is highly statistically significant. Travelers are statistically less predisposed to visit regions with a high number of confirmed Covid-19 cases. This finding is obvious and expected. Previous empirical literature has already reported a reduction in the domestic tourism demand for those regions with a poor epidemiological situation (Boto-García and Mayor, 2022; Matsuura and Saito, 2022). However, a much more interesting and novel finding in this study is that COVIDO is significant and exerts a positive effect on the probability of traveling inter-regionally. This result is remarkable as it suggests that a high number of confirmed cases in the region of origin encouraged individuals to travel outside their own region. Boto-García and Leoni (2021) provided a similar result. They found that higher Covid-19 exposure increases travel intentions as tourism can act as a stress reliever. Trip-related characteristics As for trip-related characteristics, our estimates reveal that inter-regional travelers are statistically less inclined to use a car as the main means of transport. This finding is based on the observation that the variable CAR is negative and statistically significant, both for 2019 (Model 1) and for 2020 (Model 2 and Model 3). Thus, inter-regional travelers make significantly less use of cars as the main means of transport for the trip. Additionally, inter-regional travelers tend to stay in hotels as HOTEL shows a positive and statistically significant sign for 2019 (Model 1) and 2020 (Model 2 and Model 3). In contrast, we find that the number of children participating in the trip has no statistical influence on the probability to travel inter-regionally (i.e., MEMB15 was not significant in any of the models shown in Table 4), for any of the analyzed years. Analysis of the marginal effects and statistical comparison We discussed the significance and signs of the explanatory variables in the previous section. This first analysis allowed us (i) to know that domestic inter- and intra-regional travelers have different behavioral patterns, (ii) to determine the most significant variables to explain the likelihood of making an inter-regional trip, and (iii) to confirm that not only economic variables are important, but so are the personal variables (e.g., age or marital status), push and pull factors of the regions (e.g., weather), and trip-related characteristics (e.g., means of transport or accommodation). To complete our analysis, we need to go one step forward and investigate how the Covid-19 pandemic has changed travelers’ profiles. To do so, we estimate the average marginal effects of each one of the explanatory variables of the models shown in Table 5. The marginal effect of an explanatory variable is defined as the change in the probability of being an inter-regional traveler because that explanatory variable changes by one unit, whereas all the other variables remain constant. To be as accurate as possible, we follow the instructions given in Wooldridge (2012) to estimate the marginal effects depending on whether the variables are continuous, discrete, binary, or non-linear. All calculations were programmed in Matlab 2020b.Table 5. Estimated marginal effects and bootstrap comparison. Year 2019 Year 2020 Marginal effect differences 2019–2020 Model 1 Model 2 Model 3 Model 1 vs Model 2 Model 1 vs Model 3 Age −0.0013*** −0.0019*** −0.002*** 0.0006 0.0007 Gender 0.0083 0.0099 0.0101 0.0016 0.0018 Widowed 0.0181 0.0460** 0.0492** 0.0279 0.0311 Divorced −0.0048 0.0494*** 0.0503*** 0.0542*** 0.0551*** Single 0.0287*** 0.0134 0.0142 0.0153 0.0145 High_Income 0.0704*** 0.0786*** 0.0774*** 0.0082 0.0070 Middle_Income 0.0394*** 0.0614*** 0.0602*** 0.022* 0.0208* Higher_Studies 0.0548*** −0.0426* −0.0442** 0.0974*** 0.0990*** Secondary_Studies 0.0300** −0.0506** −0.0535** 0.0806*** 0.0835*** Unemployed −0.0265** −0.0308** −0.0298** 0.0043 0.0033 Inactive 0.0069 0.0046 0.0082 0.0023 0.0013 Rainfalld −0.0006*** −0.0004*** −0.0004*** 0.0002 0.0002 Rainfallo 0.0005*** 0.0001 0.0001 0.0005** 0.0004** BlueFlagd 0.0011*** 0.0040*** 0.0033*** 0.0029*** 0.0022*** BlueFlago −0.0191*** −0.0218*** −0.0207*** 0.0027** 0.0016* GCPI −0.0124* −0.0712*** −0.0616*** 0.0588*** 0.0492*** Island −0.3621*** −0.3196*** −0.3201*** 0.0425*** 0.0420*** Covidd — — −0.0003*** — — Covido — — 0.0002*** — — Memb15 0.0053 −0.0002 0.0006 0.0055 0.0047 Car −0.3111*** −0.2881*** −0.2840*** 0.0230 0.0271* Hotel 0.2400*** 0.1892*** 0.1874*** 0.0508*** 0.0526*** Note: The calculation of the p-value is based upon a kernel estimate of the CDF of the bootstrap statistics following the indications of Racine and MacKinnon (2007). The choice of the bandwidth is theoretically optimal for estimating densities for the normal distribution (Bowman and Azzalini, 1997). In bold are the characteristics for which the annual differences are significantly different at a level of significance of 10% (p-value<0.1). The proposed null hypothesis to be tested is (2) H0:δi=γi,2019−γi,2020=0 where γi,2019 and γi,2020 are the marginal effects of the explanatory variable i on the probability of making an interregional trip in 2019 and 2020, respectively. δi is the difference between effects. The rejection of the null hypothesis implies that the pandemic caused a statistically significant change in the marginal effect of variable i (that is, there is a change in travelers’ profiles). The acceptance or rejection of the null hypothesis is performed through a non-parametric approach that computes a p-value based on a kernel estimate of the cumulative density function of the bootstrap estimators. The main advantage of this approach is that it ensures a feasible, reliable, and consistent inference (Racine and MacKinnon, 2007). An empirical implementation of this approach in the field of Tourism Economics is explained in Álvarez-Díaz et al. (2022). The estimated marginal effects, their differences between 2019 and 2020, and bootstrapped p-values are reported in Table 5. The main finding is that the pandemic has changed the nature of domestic tourism in Spain. There are statistical differences in the magnitude of the marginal effects of some variables when values in 2019 and 2020 are compared. Regarding travelers’ socio-economic profiles, we observe that divorce and middle-income earnings significantly increased their impact on the probability of making an interregional domestic trip in 2020 compared to 2019. The impact of DIVORCED was negative and not significant for 2019 (−0.005 in Model 1), but this impact on the likelihood of making an inter-regional domestic trip changed to positive and significant for 2020 (0.045 in Model 2 and 0.050 in Model 3). Middle-income earners also became more likely to take inter-regional trips after the pandemic outbreak. The impact of the variable MIDDLE_INCOME was estimated to be positive and statistically significant in 2019 (0.039 in Model 1), and this positive impact increased significantly in 2020 (0.061 and 0.060 for Model 2 and Model 3, respectively). A salient socio-economic change in the profile caused by the pandemic is that the most educated people became statistically less predisposed to travel inter-regionally. While the estimated marginal effects of SECONDARY_STUDIES and HIGHER_STUDIES showed positive impacts on the likelihood in 2019 (Model 1), these impacts turned negative in 2020 (Model 2 and Model 3). The bootstrapped p-value rejects the null hypothesis suggesting an evident change in the inter-regional traveler’s profile. As for the marginal effects of the regional characteristics, we also identify some significant differences in the inter-regional traveler profile between 2019 and 2020. First, we detect that the impact of bad weather at origin is significantly lower after the pandemic outbreak. The difference in the marginal effect associated with RAINFALLO is statistically lower in 2020 (0.0001 both in Model 2 and in Model 3) than in 2019 (0.0005 in Model 1). Second, travelers are now more sensitive to higher prices in the destination region. A higher regional price differential in 2020 caused a bigger reduction in the probability of traveling outside the region of residence compared to 2019. The effect of the pandemic on household incomes may explain that travelers are less keen to visit regions relatively more expensive in 2020 than in 2019. Third, having high-quality beaches in the destination region significantly increases the likelihood of traveling inter-regionally in 2020 compared to 2019. The marginal effect of BLUEFLAGD is significant and positive for 2019 (0.001 in Model 1), but it is even more so for 2020 (0.004 in Model 2 and 0.003 in Model 3). The positive impact of this variable on the likelihood is statistically more positive after the pandemic outbreak. In contrast, having good beaches in the region of origin discourages visiting other regions. The impact of BLUEFLAGO on the likelihood of visiting other regions is statistically more negative for 2020 (−0.022 in Model 2 and −0.021 in Model 3), as compared to 2019 (−0.019 in Model 1). Fourth, people are less reluctant to visit island regions in comparison with 2019. The impact of visiting an island is less negative than before. The economy of the two Spanish archipelagos is fundamentally based on tourism. They were the first territories to implement pandemic control measures to reduce the risk of contagion in all activities related to the tourism sector. Finally, with respect to the trip-related characteristics, it is noteworthy to mention that interregional travelers in 2020 stayed significantly less in hotels than in 2019. The impact of HOTEL on the likelihood of traveling inter-regionally is high, positive, and significant in 2019 (0.24 in Model 1); but we observe a significant reduction for 2020 (0.189 in Model 2 and 0.187 in Model 3). This reduction (0.05 less in Model 2 and Model 3) is statistically significant suggesting that inter-regional travelers showed a lower preference for staying in hotels in 2020. This finding corroborates the growing interest in other types of less crowded accommodations such as vacation rental apartments, cottages, and camping sites (Uglis et al., 2022). Conclusion Analyzing the main changes caused by the pandemic on the tourists’ profile is an important topic to understand where the domestic tourism market is going, and where decision-makers should intervene. This study helps develop a profile of domestic tourism participants and underscores the changes in this market because of the pandemic. This information is crucial both from a practical and from a theoretical point of view. From a theoretical point of view, it allows us to understand better the different factors that drive domestic tourism demand after the pandemic outbreak. From a practical point of view, it is relevant for the design and implementation of policies that promote the recovery of the hospitality and tourism sector. The recovery to pre-pandemic levels is not expected in the coming years because the Covid-19 outbreak has reshaped and left profound marks on tourists’ preferences and travel decisions. Despite its importance, few studies have addressed the changes in the profiles and preferences of tourists (Peluso and Pichierri, 2021). This study attempts to fill this gap by examining the motivations behind tourists’ decisions of traveling domestically before and after the Covid-19 outbreak. The empirical evidence reported in this study can offer valuable information for the successful recovery of the tourist sector. A better understanding of the main determinants of being an inter-regional traveler, and a deep knowledge of the changes caused by the pandemic on the tourists’ preferences will lead to a more efficient implementation of managerial and political measures to reactivate the tourism sector. In conclusion, we can summarize the main findings of our study in the following points:Inter and intra-regional travelers have specific characteristics that set them apart. To the best of our knowledge, this study is the first one that stresses that domestic tourism is heterogeneous in the sense that inter- and intra-travelers show different profiles, and that they have reacted differently to the pandemic. This finding is relevant from a practical point of view as policy-makers and entrepreneurs usually consider domestic tourism as a homogeneous market segment. Thus, the design and implementation of “one size fits all” policies or strategies may not be optimal to revive the hospitality and tourism sectors without accounting for the differences between interregional and intraregional traveling reported in domestic tourism recovery (Matsuura and Saito, 2022). Our study contributes to fulfill this gap by identifying explanatory factors that led people to take interregional travels. Socio-economic factors are not the only important factors that explain the probability of traveling inter-regionally. Our findings show that socio-economic variables such as individual’s income, age, marital, and labor market status are relevant to explaining the decision of making an inter-regional trip. Moreover, price increases in the destination region with respect to the region of origin reduce the probability of making an interregional trip. All these results are in accordance with previous studies (Boto-García and Leoni, 2021). However, regional push and pull factors are also significant determinants. We find that higher levels of rainfall in the destination region reduce the probability of traveling inter-regionally. This finding is not new as previous research reached similar conclusions (see, for instance, Álvarez-Díaz et al., 2020). By contrast, a much more novel finding of our study is that more rainfall in the region of origin increases the probability of traveling to visit another region. The presence of high-quality beaches in the region of origin discourages traveling outside the region of residence, but it is a pull factor in the case of destination regions. In addition, our results show that trip-related characteristics are also influential factors. A high number of Covid-19 infections at the region of origin increases the probability of traveling inter-regionally. This is a novel finding. Previous studies have reported expected findings such as the higher the incidence of the disease at the destination, the lower the probability of traveling inter-regionally (see Boto-García and Mayor, 2022; for instance). However, our finding shows that a high incidence of Covid-19 in the region of origin acts as a push factor. Individuals that live in a region with a high incidence of Covid-19 are more prone to travel inter-regionally. Covid-19 has shaped travelers’ profiles and preferences. The estimation of marginal effects of each one of the explanatory variables allows us to compare the results before and after the Covid-19 outbreak. We find that the traveler profile has significantly changed: divorced, less educated people, and middle-income earners showed a significantly higher probability of making an inter-regional trip in 2020 compared to 2019. Another significant change is that inter-regional travelers are more sensitive to relative price increases and are less likely to stay in hotels in 2020. The effect of weather in the region of origin loses explanatory capacity as a push factor, whereas the presence of high-quality beaches in the region of origin increases the number of intra-regional travelers. Finally, we note that this research is not exempt from some limitations. First, the Covid-19 outbreak brought not only the rapid expansion of the tourism and hospitality industry during the last decades to a halt but also shaped the traveler profile as shown in this study. Unfortunately, the pandemic is not over yet, and further research will be needed to continue monitoring the effects of this health crisis on travelers’ profiles. Second, this study included the variable “Blue Flag,” which is usually used as a proxy of the capacity of a region to attract or retain tourism. However, other types of attractions such as natural parks, theme parks, or museums could have been also considered. In turn, the variable “Gender” only uses the biological classification between men and women because the INES’s survey employed in this study has not yet adopted a broader classification for this variable that includes socially constructed characteristics (as described by The World Health Organization regional office for Europe, for example). A new definition would allow us to include it in the analysis of tourists’ decisions. Third, this study focuses on Spain, but different stages of market maturity and cultural differences mean that traveler behavior may differ across regions and countries. Future research can focus on other countries where data on actual travel behavior is available. Author biographies Marcos Álvarez-Díaz is Full Professor in the Faculty of Management Sciences and Tourism at the University of Vigo. He received his M.A., M.Phil. and Ph.D. from Columbia University. He previously worked as professor at the University of the Balearic Islands, and as researcher at the Joint Research Center of the European Commission. His research interests include econometrics and time series modeling and forecasting. José María Chamorro-Rivas is Associate Professor in the Department of Applied Economics at the University of Vigo. He has a degree in Mathematics from the University of Barcelona and a Ph.D. in Economic and Business Sciences from the Universitat Autònoma Barcelona. His current lines of research focus on the study of the effects of Covid-19 on tourism, the geography of innovation and the taxonomy of firms' names. Manuel González-Gómez is Associate Professor in the Department of Applied Economics at the University of Vigo. He holds a Ph.D. in Economics from the University of Vigo. He has participated in several scientific projects and projects of knowledge transfer and has co-authored and authored several scientific articles dealing with the topics of environmental economics, tourism economics and air transportation. María Soledad Otero-Giráldez is Associate Professor in the Department of Applied Economics at the University of Vigo. She obtained her Ph.D. in Economics from the University of Vigo. Her main lines of research focus on tourism economics, forest economics and labor economics. She has published several scientific articles on these and other topics. ORCID iD Marcos Alvarez-Diaz https://orcid.org/0000-0003-2372-1102 Notes The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. 1. Alternatively, we estimated a probit model. The estimates of the probit model were basically the same as those of the logit model shown in Table 4. Given the statistical equivalence of the two models, we center on the estimates of the logit model because it had a slightly lower value in the Akaike Information Criterium (AIC). The results of the probit model are available upon request. 2. To be more specific, the INE’s survey considers the following reasons for traveling for leisure, recreation, and vacations: sun-and-beach tourism, cultural tourism, sport tourism, gastronomic tourism, and health and wellness tourism. Sun-and-beach tourism is the main reason for traveling both in 2019 (23.8% of the trips) and in 2020 (28.8% of the trips). 3. Other meteorological variables could have been included instead of Rainfall such as Temperature; however, its effect is not so clear because of the presence of a non-linear relationship between temperature and tourism (Rossello-Nadal, 2014). Moreover, Rainfall could have a more influence than Temperature when explaining tourism decisions (see, for instance, Liu, 2016). ==== Refs References Agnew MD Palutikof JP (2006) Impacts of short-term climate variability in the UK on demand for domestic and international tourism. Climate Research 31 (1 ): 109–120. 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==== Front Nutr Health Nutr Health NAH spnah Nutrition and Health 0260-1060 2047-945X SAGE Publications Sage UK: London, England 37365874 10.1177/02601060231183592 10.1177_02601060231183592 Short Communication Food price and availability in Solomon Islands during COVID-19: A food environment survey https://orcid.org/0000-0002-1567-381X Farrell Penny 1 Bogard Jessica 2 Thow Anne Marie 1 Boylan Sinead 3 Johnson Ellen 1 Tutuo Jillian 4 1 Menzies Centre for Health Policy and Economics, Charles Perkins Centre (D17), Sydney School of Public Health, 4334 The University of Sydney , Sydney, NSW, Australia 2 Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia 3 Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation, Eveleigh, NSW, Australia 4 WorldFish, Honiara, Solomon Islands Penny Farrell, Menzies Centre for Health Policy and Economics, Charles Perkins Centre (D17), Sydney School of Public Health, The University of Sydney, Sydney, NSW 2006, Australia. Email: penny.farrell@sydney.edu.au 26 6 2023 26 6 2023 02601060231183592© The Author(s) 2023 2023 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Background: In Solomon Islands, the retail food environment is an important food source, for instance, the dominant source of fresh fruit and vegetables for urban consumers is open markets. The effects of COVID-19 mitigation measures (such as restriction of human movement and border closures) in early 2020 placed food security at risk in many parts of the community. Of particular concern was the risk of price gouging in an already price-sensitive market. Aims: The study aimed to provide rapid and policy-relevant information on the pricing of foods in the urban food environment in Solomon Islands in the context of the unfolding COVID-19 pandemic. Methods: A vendor survey was conducted in July to August 2020 and repeated in July 2021 using a survey tool that collected information on type, quantity, and price of food on offer. Findings: We found price reductions among the majority of fresh fruit and non-starchy vegetables available. A trend of rising prices was reported for some other commodities, such as fresh locally caught fish. Conclusion: Our findings highlight the impact of 'schocks to the food system' on food prices as a potential barrier or enabler to consumption of fresh foods purchased from urban areas – an important finding in a price sensitive market. The survey design was successful in collecting pricing data from the retail food environment during a time of external ‘shock to the system’. Our approach is applicable to other settings needing a rapid survey of the external food environment. Food prices availability food security food environment COVID-19 Solomon Islands Pacific Island Countries and Territories Food and Agriculture Organization of the United Nations TCP/SOI/3801 Australian Centre for International Agricultural Research https://doi.org/10.13039/501100000974 FIS/2018/155 edited-statecorrected-proof typesetterts19 ==== Body pmcIntroduction Solomon Islands, an archipelago of around 1000 islands with a population of just over 700,000 people (The World Bank, 2022), is faced with a triple burden of malnutrition and high rates of food insecurity (Albert et al., 2020; Troubat et al., 2021). Over recent decades increased consumption of highly processed, energy-dense yet nutrient-poor foods have accelerated rates of non-communicable diseases (NCDs), while stunting, undernourishment, and micronutrient deficiencies persist at high rates (Andersen et al., 2013; DiBello et al., 2009; Global Panel on Agriculture and Food Systems for Nutrition, 2016; Jones and Charlton, 2015; Seiden et al., 2012; Sharp and Andrew, 2021; Snowdon et al., 2010; Vogliano et al., 2021). The availability and relative affordability of imported processed foods have also contributed to the triple burden of malnutrition, exacerbated by a decline in local food production resulting from loss of soil fertility, pests, and diseases, cash cropping for export, limited arable land available, and climate change (Farrell et al., 2021; Iese et al., 2021). The burden of malnutrition in Solomon Islands reflects the wider experience in the Pacific region and globally. It is clear that successfully tackling it will require strengthening policies to promote healthy food environments (Reeve et al., 2022). In order to develop policies and programs to address these complex issues, there is a need for contemporary and context-specific information relating to where food comes from and how it is sourced. This is especially important at times of shock to the food system, including the COVID-19 pandemic, where external drivers can cause significant disruptions to healthy food access (Farrell et al., 2020; Iese et al., 2021). In Solomon Islands, the retail food environment is an important food source, for instance, the dominant source of fresh fruit and vegetables for urban consumers is open markets (Bogard et al., 2021). Although Solomon Islands had no recorded COVID-19 cases until December 2021, the effects of COVID-19 mitigation measures (such as restriction of human movement and border closures) in early 2020 placed food security at risk in many parts of the community. Of particular concern was the risk of price gouging in an already price-sensitive market, and potential impacts of closures or restricted operation of venues such as open markets and eat-in venues (FAO, 2023; Farrell et al., 2020; Iese et al., 2021). The aim of this article is to present and analyse pricing data from a food retail survey which was conducted in three provinces in Solomon Islands in mid-2020 and mid-2021, to provide policy makers and other stakeholders with critical information on the pricing of locally produced and imported foods in the external (market/store focussed) food environment, with implications for food and nutrition security during the COVID-19 pandemic and beyond. A secondary aim is to contribute to global knowledge on methods and metrics for rapid and scalable food environment assessments, as there are limited tools available for rapid food environment and pricing assessment (Turner, 2017), particularly in low- and middle-income settings. Methods Study design A vendor survey was conducted in July to August 2020 and repeated in July 2021 using a survey tool that collected information on type, quantity, and price of food on offer. In this survey, we defined ‘vendor’ as a food retail outlet (see Figure 1 for types of outlets included). The survey included a predetermined list of 82 different food commodities based on a validated list of most frequently acquired foods from the 2012 to 2013 Solomon Islands Household Income and Expenditure Survey, and informed by previous research in Solomon Islands (Eriksson et al., 2020; Farrell et al., 2021). The survey design drew on published INFORMAS (Mackay et al., 2017) methods for food retail environment research and procedures for weighing fresh produce was adapted from a World Food Program (Caccavale and Flämig, 2017) protocol. Food vendors were classified using a typology of food environments for the Pacific region (Bogard et al., 2021). Figure 1. Vendor-reported perceived price changes between 2020 and 2021 by vendor type. Study survey, recruitment, and data collection The survey was conducted in Gizo, Auki, and Honiara. The study recruited food vendors within the open-air markets situated in the urban centre, and shops or canteens in the immediate vicinity of the open-air markets. These areas were specifically selected as they represent the three main formal markets in Solomon Islands, and constitute the main source of food for the majority of the local population. Vendors were selected using convenience sampling. The survey measured vendor perceived and market food pricing. First, information on vendor-perceived price changes was collected by asking the question ‘How has the way you price your products been affected over the last few months?’ The survey then measured directly measured price-per-volume information on the predetermined list of food commodities (see Appendix for full list). Non-packaged, fresh food commodities were each weighed three times per commodity using digital scales. This article presents the pricing information only. Additional information collected in the survey (published elsewhere; FAO, 2023; Farrell et al., 2023) included vendor opening hours and days, food availability, and information on where vendors sourced their products. Participants’ responses were recorded by the survey enumerators on tablets programmed with the data collection template using the program KoBoToolbox (KoboToolBox, 2023). Data analysis Thematic analysis was used to examine and derive the key points in the interview data on the effects of COVID-19 on food price. Analysis of pricing data was performed in Microsoft Excel to derive descriptive statistics (mean and standard deviation). For non-packaged commodities, the average of the three weights was used to determine price in Solomon Islands Dollar (SBD) per kg. For packaged commodities, we compared package size of commonly used packages, to account for variations in per-kilo pricing for different package sizes sold. Foods were grouped using the Pacific Guidelines for Healthy Living (PGHL) (Pacific Community, 2018). Results The total number of participants (vendors) surveyed was 556 in 2020 (412 in Honiara, 103 in Auki, 41 in Gizo) and 652 in 2021 (442 in Honiara, 128 in Auki, and 82 in Gizo). Interview and produce weight data entry took 30–60 min per vendor. In the 2020 survey, four vendors did not consent to participate, and in 2021, one vendor did not consent to participate. All vendors who consented were included to participate. Almost two-thirds of vendors (61%) surveyed reported pricing changes since the start of the pandemic. This was especially prevalent in Honiara (Figure 2). It was more likely for canteens (20%), shops (26%), and supermarkets (33%) to have price rises than market vendors (4.6%) (Figure 1). Figure 2. Vendor-reported perceived price changes between 2020 and 2021 by vendor location. Between 2020 and 2021, the most commonly purchased ‘protective foods’ (fruit and non-starchy vegetables) decreased in price (Table 1). The pricing of ‘energy foods’ (carbohydrate-based foods) between 2020 and 2021 showed a mixed picture: starchy vegetable prices dropped consistently in Auki, and casava and sweet potato prices also dropped in Gizo. There was not a notable change in the price of white bread. The price of instant noodles was relatively stable. There was no consistent change in the price of sugar-sweetened beverages. Table 1. Price in SBD per kg or per specified package. Food group and specific commodity Honiara Auki Gizo 2020 2021 2020 2021 2020 2021 Protective foods: non-starchy vegetables Slippery cabbage Mean 9.95 9.06 8.98 5.39 9.07 8.59 SD 3.9 2.2 2.7 2.8 3.3 0.7 n 57 26 5 3 2 2 Bok choy Mean 17.03 12.48 10.79 9.32 14.74 8.47 SD 6.0 4.9 2.7 1.1 0.0 n 21 28 11 5 2 1 Fern (Diplazium esculentum) Mean 6.01 4.63 5.63 10.04 6.70 7.56 SD 2.4 2.1 3.0 6.7 2.1 0.4 n 15 17 2 3 2 2 Tomato Mean 15.23 12.04 16.61 15.67 31.35 58.14 SD 5.5 3.8 2.4 4.6 8.9 n 38 45 4 5 5 1 Watercress Mean 22.72 14.44 13.76 14.93 11.44 18.02 SD 3.5 3.7 0.2 1.9 4.8 n 11 9 2 4 3 1 Protective foods: fruit Banana Mean 10.92 4.15 8.24 6.05 6.92 5.13 SD 4.6 1.3 2.1 0.4 1.9 1.0 n 23 29 3 3 3 3 Papaya Mean 9.29 4.39 4.72 5.83 9.53 5.55 SD 6.8 1.6 0.9 3.2 7.9 1.6 n 46 38 4 4 2 2 Watermelon Mean 6.16 6.55 5.72 6.55 5.34 13.45 SD 3.3 2.1 0.7 0.5 1.6 1.4 n 21 23 2 3 2 2 Pineapple Mean 9.09 8.97 10.45 7.43 4.19 SD 2.1 2.7 3.4 0.0 n 5 16 5 2 0 1 Energy food to choose Cassava Mean 2.59 3.11 4.35 2.58 5.57 3.63 SD 1.0 0.8 1.8 0.5 0.5 1.0 n 20 21 8 4 2 4 Green cooking banana Mean 8.90 7.18 6.62 6.17 6.27 6.99 SD 2.5 2.1 1.1 0.3 1.5 4.6 n 16 24 2 2 3 2 Sweet potato Mean 5.31 5.55 6.40 6.03 10.19 8.39 SD 2.3 1.3 1.5 0.7 4.5 1.9 n 30 25 9 4 4 4 Energy food to limit White rice (5 kg bag) Mean 40 52.5 41.4 36 48.8 SD 0.7 5.4 8.5 11.7 n 0 1 2 10 2 5 Instant noodles (85 g packet) Mean 2.4 2 2.6 2.3 2.1 2.1 SD 0.3 0 0.4 0.3 0.2 0.2 n 15 22 9 12 5 27 White sliced bread – 1 loaf Mean 11.0 11.7 7.5 9.7 10 SD 1.4 0.8 3.5 0.6 n 2 6 0 2 3 1 Energy foods to avoid: sugar sweetened beverages Soft drink (325 ml), canned Mean 4.9 4.9 5.7 5.3 5.0 5.0 SD 0.8 0.3 0.70 0.6 0 0 n 13 17 16 34 5 11 Cola drink (325 ml), canned Mean 5.6 6.5 8 7.6 7.2 6.8 SD 0.5 0.8 0.4 1.2 1.4 0.8 n 7 19 11 30 5 9 Milk tea (3 in 1) 20 g sachet Mean 1.3 1.2 1.9 1.4 1.7 1.1 SD 0.27 0.2 0.65 0.2 0.48 0.2 n 6 21 16 36 4 12 Body-building foods Fresh reef fish Mean 18.54 61.58 37.72 44.19 27.13 SD 17.5 33.9 12.6 20.3 7.9 n 19 11 4 7 0 6 Canned tuna (170 g) Mean 6.8 5.9 6.7 6.5 12.0 5.7 SD 2.8 0.6 0.6 0.6 0.7 n 6 33 19 44 1 18 Eggs: pack of 10 Mean 32.3 34 38.3 38.6 35.7 35 SD 2.6 0 1.5 1.5 2.1 0 n 4 2 3 7 3 4 Imported, chicken (wings) (2 kg) Mean 73.1 78.1 75.2 84.3 73.0 79.1 SD 8.3 8.0 27.8 5.0 8.1 3.7 n 7 7 5 21 4 11 Milk: anchor liquid (1 l) Mean 10.3 19.5 13.8 20 15 17.2 SD 0.3 3.3 1 0 5.0 n 3 4 4 2 1 4 Green shading indicates price increase; red shading indicates price decrease; yellow shadow indicates no change or missing data. There was a notable increase in the market price of fresh reef fish amongst surveyed vendors, especially in Honiara – although we note a high standard deviation (variation from the mean). The per-litre price of milk appears to have also increased amongst surveyed vendors since the start of the pandemic, as has the price of imported chicken wings. The price of canned tuna and eggs remained relatively constant between the 2 years. Discussion This study has contributed to urban food environment monitoring relevant to food and nutrition security in a food system that is vulnerable to external and often concurrent shocks, which include the COVID-19 pandemic and the effects of mitigation measures (Farrell et al., 2020; Iese et al., 2021), extreme weather events, worldwide economic recession, global fuel price fluctuations, and civil unrest. The price changes seen in our survey reflect a complex situation, with drivers associated with government pandemic mitigation policy including an urban-to-rural migration trend (Iese et al., 2021), and the effects of decreased tourism and economic downturn (Farrell et al., 2020). The implications of price fluctuations are also likely to have a mixed effect, for instance, a reduction in market price of fruit and non-starchy vegetables would increase accessibility to this healthy food group for households reliant on purchasing them, but have a negative effect for the incomes of households dependent on fruit and vegetable market sales. For urban households in Solomon Islands, markets are an important source of locally produced food including fruit, vegetables, and fish. For instance, national survey data from prior to the COVID-19 pandemic show that the dominant source of fruit and vegetables for households in urban areas was open-air markets, as opposed to rural areas where they are usually home-grown (Bogard et al., 2021; Farrell et al., 2023). However, since 2020, there has been an increase in household production of fruit and vegetables, which appears to be linked with a downturn in market demand (Iese et al., 2021). The increase in home production (in many ways a return to traditional food systems) has bolstered many households against acute food insecurity amidst the COVID-19 mitigation response, however, this has not been the case for all households (Iese et al., 2021). The inevitable loss of livelihood for vendors and those economically linked with the fruit and vegetable value chain is important for policy makers to address. We note that women make up 80–90% of fresh fruit and vegetable market vendors (FAO, 2023; International Finance Corporation, 2010; Reeve et al., 2019). The price increase of fresh locally caught reef fish, especially in Honiara, was likely due to a combination of reduced supply following movement restriction and market closure. In addition, freight price has increased as fuel price also increased. We also observed and surveyed a higher number of market vendors in 2021 compared with 2020, and this higher number of vendors in the market might have added competition, which may have affected market price. Pricing of key imported commodities presented a mixed picture – prices appeared relatively stable for tinned tuna and rice, and prices increased for imported chicken, although these results should be interpreted with the low number of vendors selling these commodities in our survey in mind. Price and availability of imported food is important to monitor as the diversity of national food supply (i.e., combining locally produced with imported food) has important implications for national food security. Reliance on local food systems alone does not necessarily meet the capacity for complete self-sufficiency in Solomon Islands (Andrew et al., 2022; Vogliano et al., 2021). Market vendors reported fewer price rises compared with shop and supermarket retailers which may reflect the shift to home gardening of fresh produce. In addition, the effects of shipping costs and delays on some packaged imported foods may have led to price increases in supermarkets and shops. The vendor reported trends which showed market vendors reporting fewer price rises compared with shop and supermarket retailers were consistent with the objective pricing data presented in Table 1, which showed price drops for the majority of fresh fruit and vegetables – the predominant foods sold by market vendors. The findings of this study suggest that policy action to protect prices, especially during times of acute ‘shock to the system’, would be stronger if tailored to specific vendor type (e.g., those in fresh produce markets compared with those in supermarkets). The importance of fresh local produce for nutrition in Solomon Islands (Pacific Community, 2018) and the price fluctuations observed in our study, suggest that financial support for fresh food vendors and other small and medium enterprises involved in the supply chain (Farrell et al., 2020) would enable them to sell produce at a price that is affordable to the population while also staying in business. In this survey population, there is some cross-over between ‘formal’ (licenced) and ‘informal’ (non-licenced) market vendors (Bogard et al., 2021). Regulation of market fees and fair and consistent licencing measures so that they are affordable for market vendors can also contribute to both vendor livelihoods and the supply of healthy food to the population (International Finance Corporation, 2010). Finally, the apparent impact of transport costs on prices suggests that strengthening inter- and intra-island transport will also ensure availability of food supply during external shocks to the food system (Farrell et al., 2020). This article has several strengths and limitations. The study used a novel survey tool which was developed based on validated approaches to measure and map the external food environment and as such also offers much-needed methodological advancement in this space. The survey is scalable, with the main resources required being enumerators, weighing scales, data entry equipment or software, and transport. Context-appropriate and ideally validated commodity lists would be required to use the survey other countries. Accuracy of pricing data is likely to be greater for fruit and vegetable prices, which had far more vendors surveyed, than for other commodities with a lower sample size such as bread. This study reported descriptive statistics only. The survey was also limited to only surveying urban food retail-based environments, however, we note that cultivated food environments (home-grown food) are the main source of fruit and vegetables in rural areas (Bogard et al., 2021). We also note that price is just one aspect of the food retail environment – other important ones include availability, and vendor properties such as opening hours (Turner et al., 2018). However, in Solomon Islands and elsewhere, food price is an important predictor of food acquisition and affordability, especially in urban areas (Andersen et al., 2013; Farrell et al., 2021; Horsey et al., 2019). Conclusion This study offers policy-relevant information about food price in Solomon Islands over the first two years of the COVID-19 pandemic, and our findings highlight price changes as a potential barrier or enabler to consumption of fresh foods purchased from urban areas – an important finding in a price-sensitive market. A key finding of this study was the decrease in prices of many fresh locally grown foods – which could increase the amount of protective foods consumed by those who rely on the retail food environment to acquire them. However, the price drops could have serious livelihood implications for vendors. Monitoring food price regularly and especially during times of crisis is important in order to protect food and nutrition security. The survey design presented in this article was successful in rapidly collecting pricing data from the retail food environment during a time of external ‘shock to the system’ in Solomon Islands. Moving forward, the methodology presented in this article offers a straightforward, low-resource, and rapid way to monitor the food retail environment in a mixed formal/informal setting, that could be applied in other low- and middle-income country contexts. Acknowledgments We would like to extend our sincere thanks to the survey participants, survey enumerators, and Dr Anouk Ride for her assistance with the survey analysis. Appendix: List of commodities in survey Category Food item Fruit Watermelon Pineapple (small) Mango Banana Rambutan Guava Orange Mandarin Avocado Banana (yellow) Papaya (Pawpaw) Non-starchy fresh vegetables Slippery cabbage Fern (Diplazium esculentum) Watercress Pumpkin tips Bok choy Beans, green Tomato Capsicum – green/red Green peas Pumpkin Starchy fresh vegetables Cassava/tapioca/manioc Potato, sweet Yam Taro, giant swamp Green cooking banana/plantain Breadfruit Canned Best choice or edgell, mixed vegetables (420 g) Best choice or edgell, green peas (420 g) Best choice or edgell, corn (420 g) Frozen Best choice, mixed vegetables (250 g) Nuts Ngali nut Peanut Cutnut (Barringtonia procera) Alite nut (Terminalia kaernbacchi) Legumes Bean Split peas Lentils Cowpeas Wingbean Coconut Brown Green Fish and other seafood Fresh reef fish Fresh tuna (any tuna species except Island Bonito) Skipjack tuna (Island Bonito) Salted fish (fish stored in brine, e.g., tuna, mackerel, sardines) Solomon blue, canned tuna/taiyo (170 g) Solomon blue, canned tuna/taiyo (300 g) Waioka, canned tuna (180 g) Coral Sea, canned tuna (170 g) Energy foods Bread White, sliced/loaf Cake, slice Rice, white Solrais (5 kg) Solrais (10 kg) Solrais (20 kg) Noodles, instant, dry Mamei (85 g) Reava (85 g) Baking staples Delite Nambawan flour, white (5 kg) Delite Nambawan flour, white (10 kg) Delite Nambawan flour, white (20 kg) Sugar, white (1 kg) Cereals Sanitarium, Weetbix (375 g) Kellogg's cornflakes (450 g) Diary and milk products Anchor milk, liquid (1.5 l) Anchor milk, powder, full cream (250 g) Anchor butter 1/4 Ib Meadow lea, 250 g Cooking oil Ezy cook/letizia/filma, vegetable oil (250 ml) KoKonaut Pacific, coconut oil (250 ml) Meat, eggs Eggs (pack of 10) Local, chicken (whole) Imported, chicken (wings) (2 kg) Nambawan meat, sausage (chicken/beef) (400 g) Sugar sweetened beverages COCA COLA (330 ml), canned SPRITE (330 ml), canned CHEERS (330 ml), canned Soft drink (SOLBREW mango/pineapple: 285 ml bottle) SZEBA (orange/pineapple/lime/raspberry:300 ml bottle) JUICE (Fresho, apple, orange, mango, etc.:250 ml pack) Cordial drink, e.g., Tang 25 g sachet Milk tea (3 in 1) 20 g per sachet Coffee, mix (e.g., 3 in 1) 20 g per sachet Author contributions: The study was conceptualized by JT, PF, and JB. Data collection was undertaken by JT. Data analysis was undertaken by JT, PF, JB, and SB. PF, EJ, and AT drafted the manuscript. Availability of data and materials: Data availability De-identified data available upon reasonable request to the authors. Consent for publication: All authors have read and agreed to the published version of the manuscript. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Ethical statement: Ethical approval for this study was given by the University of Wollongong Human Research Ethics Committee (2020/246) and the CSIRO Social and Interdisciplinary Science Human Research Ethics Committee (187/21). Each vendor was given a Participant Information Sheet and consent was given by vendors before participating. Permission to conduct the survey was also granted by local authorities in each province. Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Australian Centre for International Agricultural Research (ACIAR) (Pacific Food Systems Project FIS/2018/155) and the Food and Agriculture Organization of the United Nations (FAO) (FAO Project TCP/SOI/3801. ORCID iD: Penny Farrell https://orcid.org/0000-0002-1567-381X ==== Refs References Albert J Bogard J Siota F , et al. (2020) Malnutrition in rural Solomon Islands: An analysis of the problem and its drivers. Maternal & Child Nutrition 16 (2 ): e12921. DOI: 10.1111/mcn.1292132004423 Andersen AB Thilsted SH Schwarz A-M (2013) Food and nutrition security in Solomon Islands. Report, WorldFish, Malaysia. Andrew NL Allison EH Brewer T , et al. (2022) Continuity and change in the contemporary Pacific food system. Global Food Security 32(9765) : 100608. DOI: 10.1016/j.gfs.2021.100608 Bogard JR Andrew NL Farrell P , et al. 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==== Front J Public Policy Mark J Public Policy Mark PPO spppo Journal of Public Policy & Marketing 0743-9156 1547-7207 SAGE Publications Sage CA: Los Angeles, CA 10.1177/07439156231183001 10.1177_07439156231183001 Article Biohacking COVID-19: Sharing Is Not Always Caring https://orcid.org/0000-0001-6750-1641 Lima Vitor Belk Russell Vitor Lima is Assistant Professor of Marketing, ESCP Business School, Spain (email: vlima@escp.eu). Russell Belk is Kraft Foods Canada Research Chair, Schulich School of Business, York University, Canada (email: rbelk@schulich.yorku.ca). 26 6 2023 26 6 2023 07439156231183001© American Marketing Association 2023 2023 American Marketing Association This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. This netnographic study investigates how and why people engage with citizen science initiatives and share insights from them in the context of the COVID-19 pandemic. Specifically, this research focuses on biohacking, a form of citizen science in which individuals conduct innovative but controversial self-experiments. In a context of ideological, behavioral, and emotional tensions, biohackers seek to do what they consider to be “the right thing” for themselves and others. Some biohackers believed that governmental “solutions” for the pandemic were not “correct” or “the best” and shared scientifically unproven protocols to develop, for example, homemade vaccines. However, in many cases, biohackers may unintentionally create harm while intending to do good by sharing such “solutions.” In this vein, this research shows that sharing is not always caring, as biohacking related to COVID-19 exemplifies. Although sharing is a form of prosocial behavior, it has different motivations that may invert its epistemic prosocial orientation to an antisocial one. This orientation results in new challenges, as well as strengthening old challenges, for policy makers facing public crises, such as pandemics. The prescriptions for policy makers offered in this article aim to help reduce such an impact on governmental efforts to tackle collective crises. biohacking sharing prosocial behavior antisocial behavior public sector innovation lab citizen science COVID-19 edited-statecorrected-proof typesetterts19 ==== Body pmcHuman history is full of challenging situations in which we, as citizens, are placed in delicate positions in which we are obliged “to do the right thing” and to make tough but ultimately beneficial choices. For example, in the 1950s, American virologist Dr. Jonas Salk worked toward a vaccine against the virus that causes polio, a terrible disease also known as poliomyelitis or infantile paralysis (Oshinsky 2005). Salk's approach to solving the polio crisis ignored traditional scientific protocols; he used controversial experiments that involved giving injections to himself and his family. After years of challenging prevailing scientific orthodoxy, on March 26, 1953, Salk announced himself a hero on a national radio show and declared his success in developing a vaccine against polio. For some, he was irresponsible and criminal. However, by breaking the rules, Salk prevented death or paralysis for thousands of children. Scholars have devoted considerable attention to investigating prosocial behaviors, which have been described as a broad range of actions intended to benefit one or more people other than oneself (e.g., Batson 2022; Pfattheicher, Nielsen, and Thielmann 2022; Septianto, Seo, and Paramita 2021). The complex nature of prosociality as a social phenomenon means that it can take the form of behaviors such as helping (Price, Feick, and Audrey 1995), comforting (Turley and O’Donohoe 2012), sharing (Baker and Baker 2016), cooperating (Ozanne and Ozanne 2021), celebrating (Brick et al. 2023), exchanging (Blau 1964), and accessing (Bardhi and Eckhardt 2012). Key among marketing studies is Belk's theory on sharing (Belk 2007, 2010, 2014a, 2014b, 2016), which broadly refers to the “act and process of distributing what is ours to others for their use and/or the act or process of receiving or taking something from others for our use” (Belk 2007, p. 127). For all its significant contributions, most research to date seems to presume the adage “sharing is caring” and to suggest that altruism is the primary motivation prompting such behavior. However, contrary to the overwhelmingly positive association in the literature between sharing and altruism, Batson (2019) argues that a one-to-one correspondence between prosocial actions and altruism does not exist when it comes to the motivation for sharing. In ideal terms, sharing may be an inclusionary act of caring, but in practice, it is often an exclusionary act of selfishness (Belk 2016; Ozanne and Ruvio 2016). Thus, despite being a form of prosocial behavior, sharing has a fringe side, with a pro-ego component that leads to unintended consequences such as excluding others. The notion that sharing is more nuanced than researchers have assumed and that it does not necessarily equate to an act of caring for others is central to our article. The COVID-19 pandemic provides a unique context for investigating different motives for acting prosocially and sharing, especially when considering the social phenomenon of biohacking, or do-it-yourself biology (DIY biology). Biohacking is a type of citizen science, which is noninstitutionalized scientific research conducted by nonprofessional scientists (Irwin 1995). Biohacking enthusiasts, or biohackers, work on a broad range of biotechnological self-experiments, or hacks, to increase human well-being (Vaage 2017). In the case of the pandemic and the pursuit of solutions for tackling it, biohackers share advice based on bold but potentially dangerous DIY experiments. They purchase ingredients on the internet (e.g., intravenous [IV] supplementation kits), follow biohacking protocols to mix materials and methods (e.g., combining an IV with taking vitamin D), test their hacks while hoping for the best, and then share the results on social media. Biohacking is a manifestation of the institutional tensions among diverging scientific communities regarding the conditions of producing knowledge to serve society's needs. To an extent, biohacking can be compared to Salk's self-experiments with the poliomyelitis vaccine, whose declared purpose was to increase others’ well-being. An issue arising from unpredictable, spontaneously precipitated, deviant, and occasionally illegal procedures (Delfanti 2013) is that these behaviors may unintentionally create harm though they arise out of care. In the pandemic's context of panic-driven governmental and political actions, general uncertainty, and low health literacy, some biohacking ideas may gain popularity and contribute to the current “infodemic,” the plethora of false or misleading information shared online about the pandemic (Mende, Vallen, and Berry 2021). The increased spread of fake news, fake facts, and conspiracy theories about COVID-19 may have led people to distrust traditional scientific institutions and resist governmental efforts. People's compliance with public policies and certain prosocial behaviors are conducive to solving public crises (Guerrini et al. 2018; Scott et al. 2020). Thus, we ask: How and why do people engage with citizen science initiatives and share insights from them? We conducted a netnographic study (Kozinets 2020) on social media by following hashtags and discussions about biohacking related to COVID-19. Relying on abductive reasoning (Timmermans and Tavory 2022), we contribute, first, to the sharing theory (Belk 2007, 2010, 2014a, 2014b, 2016) by theorizing about possible motivations to act prosocially. We do so by showing that the pursuit of biohacking ideas and their consequent sharing can be altruistic, collectivist, and principlist, but an egoistic element may transform sharing into another prosocial or antisocial behavior. This insight is of high relevance because citizen scientists, like biohackers, may play a relevant role in shaping policy making throughout the entire policy process to solve the public crisis. Second, we recommend that policy makers create a public sector innovation (PSI) lab (Blomkamp 2022; Cole 2022; McGann, Blomkamp, and Lewis 2018), a multidisciplinary team focused on designing public policies collaboratively with biohackers. To expand on our ideas, the next section presents an overview of the literature about biohacking as a rebellious manifestation of citizen science, followed by a look at sharing theory. Then, we introduce the research context, data collection, and analysis procedures. Finally, we present our discussion, recommendations for policy makers, and conclusions. Theoretical Background Citizen Science and Rebellious Biohacking Citizen science is a form of scientific research conducted by members of the general public who may or may not collaborate with scientific institutions (Irwin 1995). Notwithstanding its nonprofessional status, it aims at furthering humanity's well-being through scientific endeavors. That is, it is simultaneously science for the people and science by the people (Strasser et al. 2019). Despite its conflictual history, citizen science has achieved legitimacy through continued funding from the National Science Foundation since the early 1990s (Bonney 2016). From one point of view, then, citizen science is consistent with traditional science because of the professionalization of those involved and the following of prevalent scientific methods. In contrast, for some, citizen science's dependence on amateurs without formal credentials makes it antithetical to and a threat to scientific institutions. More than simply a participatory style of research, citizen science is a locus of power. It is sustained by ongoing ideological discourses and by emotional and behavioral tensions that shape disagreements about how, where, and why scientific knowledge should be produced (Strasser et al. 2019). Some radical citizen scientists consider citizen science projects as serving a capitalist agenda rather than truly serving people's needs, owing to the scientific establishment's lack of recognition accorded to citizen scientists. In response to this charge, Wolff and Muñoz (2021) advocate for a model of “political participation” in which citizen scientists should be recognized as equal experts in producing knowledge and aiding decision making rather than as external contributors who simply collect data for formal authorities. This is similar to the significant impact that political participation now has on the policy-making process, but in a citizen science context. Schade et al. (2021) argue that citizen science has educational benefits, as it promotes scientific literacy, individual learning, and the development of scientific skills. Through active citizen participation, there is an increased sense of ownership of research results, which can influence the entire policy decision-making process, from preparation to implementation to evaluation (Chapman and Hodges 2017). Here, a relevant example is the AIDS Coalition to Unleash Power (ACT UP), formed in the 1980s (Mahr and Strasser 2021). The coalition actively defined research and federal drug approval agendas, developed clinical protocols, and assessed outcomes. As citizen scientists, these patients and their relatives had a distinctive advantage in contributing experiential knowledge that professional researchers might have overlooked. Consequently, the U.S. government expedited its drug approval process, enabling quicker availability of potentially life-sustaining medications. Another exemplary policy-related outcome of ACT UP was the approval of the needle exchange program. After being influenced by visits to babies of HIV-positive mothers in U.S. hospitals and by the lobbying of needle exchange program supporters, New Haven's mayor, John Daniels, revaluated his initial resistance and approved the initiative (Keefe, Lane, and Swarts 2006). One extreme manifestation of the tensions between citizen scientists and scientific institutions is biohacking. Since the 1980s, early biohacking movements, also called “noninstitutional biology,” were crowdfunded and had the support of the famous Counter Culture Labs, a microbiology makerspace that promoted biohackers’ ideas. Biohackers have an ethos rooted in rebellious hacker culture (Delfanti 2013); they question the proprietary structure of scientific information and urge ultimate freedom when it comes to sharing knowledge and material resources. This stance can be seen in the many cases of biohackers’ resistance to private (e.g., YouTube) and governmental (e.g., Food and Drug Administration) regulatory efforts (Zettler 2022). For biohackers, there is a commitment to promote open science as it goes against freedom of thought to prohibit individuals from engaging in scientific practices. To somehow “fight the system,” biohackers produce creative workarounds that oppose the high and well-defended thresholds for producing and sharing information characteristic of capitalist systems (Yetisen 2018). Biohackers and others interested in DIY biology are a part of the same ecosystem (Vaage 2017) and have at least three common characteristics that are relevant to the policy-making process (Guerrini et al. 2018): (1) they bring biotechnology to the greater public and increase society's welfare; (2) they use research methods for purposes other than formal science, and research is performed by people without the same formal training as professional scientists, which makes them suspect and subject to greater ethical scrutiny; and (3) they tackle various issues related to biotechnology, from patenting rights to the proper applications of hacks. Further, biohacking as a social practice is broad; it can range from simple DIY self-experiments like consuming caffeine for a cognitive boost to extreme practices like developing chemical compounds to produce human night vision (Lima, Grubits de Paula Pessôa, and Belk 2022). Although biohacking may involve self-experiments to benefit oneself, it has a prosocial and collective dimension for the practice of sharing information and material resources. Some projects are crowdsourced and driven by the idea of “do-it-with-others,” an ethos aimed at hacking objects mainly for biomedical purposes. “Do-it-with-others” biohacking comprises the redesigning of technical equipment to make biology accessible; an example is the use of OpenPCR, an open-source machine to sequence DNA, instead of a conventional polymerase chain reaction device. Sharing as a Form of Prosocial Behavior Possibly, many societies would not exist as we know them without prosocial behavior. Individuals donate their time and possessions to charitable organizations (Nardini et al. 2022), give to their friends and family (Rapert, Thyroff, and Grace 2021), join forces with policy makers to solve systemic vulnerabilities (Bublitz et al. 2021), share information to alleviate structural tensions and strengthen group cohesion (Brouard et al. 2022), and so forth. In line with such pervasiveness, extant work on prosociality often defines it as a desirable and positive social behavior toward another individual (Pfattheicher, Nielsen, and Thielmann 2022). However, an issue emerging from this overly selfless outlining is the confusion about the close relationship between prosociality and altruism. While altruism aims to increase others’ welfare, prosocial behaviors reflect a broader category of welfare-promoting actions (Batson 2019). Such a fuzzy connection results in an overwhelming amount of work neglecting nuances of prosociality with its intended and unintended consequences (Batson 2022). In marketing and consumer research, such a complex connection appears noticeably in studies drawing on Belk's (2010) work on sharing as a form of prosocial behavior. In its original conceptualization, sharing is defined as a socially inclusive, selfless, communal behavior reflecting altruistic motivations to benefit others with no expectations of reciprocity (Belk 2007, 2010, 2014a, 2014b). Past work thus seems to theorize sharing mostly as an act of agapic love, a taken-for-granted and well-regarded prosocial behavior (e.g., Dholakia, Jung, and Chowdhry 2018). Recently, however, scholars have questioned the adequacy of existing theorizations for their narrowed view of sharing as an altruistic prosocial phenomenon (e.g., Albinsson, Wolf, and Kopf 2010; Appau, Ozanne, and Klein 2015; Ozanne and Ruvio 2016). The same questioning is an ongoing discussion in other fields, such as psychology and philosophy (e.g., Pfattheicher, Nielsen, and Thielmann 2022), and has been largely influenced by Batson, Ahmad, and Stocks’s (2011) typology of motivations for prosocial behavior. As they propose, prosocial behavior is motivated by altruism, egoism, principlism, and collectivism. Here, in the case of principlism, sharing may be a way to uphold major moral principles, such as justice (Batson, Ahmad, and Stocks 2011). As an example, Ozanne and Ozanne's (2011) work shows that sharing of toys at public toy libraries may be motivated by a sense of fairness and may help parents raise their children as moral citizens. As for collectivism, communal identity and a sense of duty toward a certain community may also motivate sharing (Batson, Ahmad, and Stocks 2011). Baker and Baker's (2016) study is an exemplary case. They highlight the meaningful role of shared material resources in reshaping a community's collective identity during recovery from a disaster. The last motivation proposed by Batson, Ahmad, and Stocks (2011) is egoism. As they propose, actions may have a prosocial orientation but are simultaneously egoistically motivated because of the use of others for self-benefits. This is what Belk (2016) theorizes as selfish sharing. As an example, consider the case of injectable drug users, as in some of Bathje et al.’s (2020) interviews; these users mentioned knowing about the dangers of sharing syringes but still did so in order to avoid being isolated from their community of users. Although egoistic sharing may not necessarily intend to harm others, it is hardly inclusionary. To further contemporary discussions on the topic (e.g., Batson 2022; Brick et al. 2023), rather than considering egoism as an “independent” motivation, we posit that a pro-ego component exists in each of Batson, Ahmad, and Stocks’s (2011) motivations for acting prosocially, or sharing in this case. To develop our proposition, Andreoni's (1989, 1990) concept of impure altruism is a relevant starting point. For Andreoni, other people, ideas, and social causes serve as proxies for the actualization of self-benefits. This then suggests that altruism, principlism, and collectivism have an egoistic component though they are profoundly rooted in prosocial logic. In essence, such an “impure,” pro-ego component is a case of “helping me by helping you.” Here, the result of acting prosocially is a myriad of emotions including joy, happiness, excitement, satisfaction, and so forth. This emotional reward for “doing good” is referred to as having a “warm glow” (Andreoni 1990). As Pfattheicher, Nielsen, and Thielmann (2022) explain, individual and collective emotions are meaningful components of prosocial behaviors, be they to increase the welfare of others or the self (Batson 2010). In this vein, by combining the sharing theory (Belk 2007, 2010, 2014a, 2014b, 2016) and motivations for acting prosocially (Batson, Ahmad, and Stocks 2011) with Andreoni's (1989, 1990) notion of impure altruism, we find answers to our research question. That is, we believe that such a combination is crucial to provide insights about how and why people engage with citizen science initiatives and share insights from them. Research Context: Navigating the COVID-19 Pandemic As of May 2023, over six million people worldwide had died after contracting COVID-19 (Our World in Data 2023). Despite the genetic complexity of the coronavirus and its unique potential for mutations, the challenges faced in the COVID-19 pandemic go beyond its biological dimensions. Shortages of oxygen in many health care units have led people to die of suffocation, already-overwhelmed health professionals have faced harassment, and rich countries are implementing booster shots, despite the unequal distribution of vaccines to poor countries. These are just a few examples of the social aspects of COVID-19 and offer additional reasons for curbing the disease as quickly as possible. Instead of working with the World Health Organization (WHO) and the scientific community to pursue this aim, some policy makers adopted controversial measures, such as avoiding lockdowns purportedly for economic reasons. Indeed, the pandemic has a political dimension that has perhaps been much more influential in the decision-making processes of policy makers and in shaping public beliefs than has the health care system (Mahr and Strasser 2021). In many cases, people follow instructions and take actions based on personal beliefs and political affiliations rather than science (Mende, Vallen, and Berry 2021). Such responses then foster tensions between scientific communities and the public because of differing ideological discourses, emotions, and behaviors evidenced on a daily basis. With the global rise of the extreme right, those with divergent opinions can be segregated through new forms of organization and new tools (e.g., social media). These avenues for ideological isolation can nurture messages from social movements that may be antigovernment, antipolitical, or antiregulation, similar to biohacker sentiments. Consequently, the biohacking pursuit of an alternative health care solution has become somewhat entwined with fake facts, fake news, and conspiracy theories (Baumgaertner 2021). Methods We rigorously followed Kozinets’s (2020) netnographic guidelines and precisely adhered to his investigative data operations protocol. This protocol presupposes following informational traces created in communications among people, social media platforms, and other market actors (Kozinets 2020). Investigative data are not generated by the researcher but by others, generally unknown, and are selected for theoretical and contextual relevance to compose the data set. From December 2019, when the first tweets mentioning the virus emerged (e.g., https://twitter.com/kRiZcPEc/status/1212010091399921667), to April 2022, we monitored keyword combinations and hashtags (see Table 1), paying attention to narratives about the pandemic on Facebook, Twitter, and Instagram. The natural flow of messages led us to two specialized Google Groups: DIYbio and Humanity+ Members. On these platforms, conversations about practices for tackling the crisis other than those proposed by the World Health Organization (2021) were observed and purposively selected, following the principle of interdiscursivity or orders of discourse (Greimas 1987). That is, we focused on keywords and hashtags and considered the semantic linkages among different messages over time and across social media platforms. Netnographic sampling is generally purposive or theoretically driven rather than representative. This means that the number of interactions or utterances is less relevant to the analytical process (Kozinets 2020). Although most of our data are textual, additional data include our netnographic immersion journal, screenshots, photos, and videos downloaded from social media that serve as cultural material. The combination of multiple data types enabled a greater immersion in the sociocultural dimension of the pandemic and allowed a reflexivity process (Wallendorf and Belk 1989). Data collection and analysis stopped when theoretical saturation was reached (Denzin and Lincoln 2018). Table 1. Data Set. Source Example of Keywords and Hashtags Total Facebook Biohacking; biohacker; hack; hacking; covid-19; covid; quarantine; DIYbio 1,650 posts with comments Twitter Biohacking AND covid; biohacking AND covid-19; hack AND covid; hacking AND coronavirus; Biohackers AND coronavirus; biohackers AND quarantine; #DIYbio; #biohacking; #covid 4,683 tweets Instagram #DIYbio; #biohacking; #hack; #covid; #biohacker; #quarantine; #self-isolation; #coronavirus; #covid-19 2,903 posts with comments DIYbio Vaccine; homemade; covid-19; covid; DIYbio; DIY; biohacking; alternative; immunization; CRISPR 222 messages Humanity+ Members Vaccine; homemade; covid-19; covid; DIYbio; DIY; biohacking; alternative; immunization; CRISPR 133 messages Our ethical protocol strictly followed Kozinets’s (2020) recommendations. First, this study received approval from an Ethics Review Board from the first author's university at the time. Second, the lead author fully disclosed his identity as a researcher on all social media profiles and presented himself as such in the case of Google Groups. Third, we assigned pseudonyms to respect the anonymity of those involved with biohacking because it is a sensitive topic. Lastly, although the data presented in this study come from the public portion of social media and groups, we carefully paraphrased quotes to avoid the traceability of profiles. The final data set consists of 9,591 manually retrieved textual entries (see Table 1). Regarding our coding, our abductive cycles followed Timmermans and Tavory's (2022) principles of constantly revisiting the phenomenon, defamiliarization, and alternative casing. In abiding by these principles, our study revealed findings that partly reinforced inferences and conjectures about biohacking while introducing new insights. To ensure trustworthiness (Wallendorf and Belk 1989), we sustained our arguments through (1) prolonged engagement within the field, as the first author established himself as a member of biohacking communities; (2) purposive sampling; and (3) reflexive journaling. Findings Motivations for acting—or sharing—can be seen as social acts, and not necessarily as a direct expression of some inner psychological state (Mills 1940). In different situations, people rely on specific words, expressions, and statements that are socially accepted to explain, justify, and make sense of their motivations and behaviors (Gilman 2021). On social media, accounts of what, how, and why to conduct biohacking to tackle COVID-19 can be understood as linguistic cues of biohackers’ motivations. In answering our research question on how and why people engage with citizen science initiatives and share insights from these initiatives, we identified two main themes. In the case of the first theme, “Hacking to Share,” biohackers try to “do the right thing” by conducting self-experiments and sharing resources and information about them. In the second theme, “Sharing to Hack,” biohackers share resources and information to create hacks for others. These two main themes are split into six subthemes that are organized based on Batson, Ahmad, and Stocks’s (2011) typology of motivations for acting prosocially (i.e., sharing) combined with Andreoni's (1989, 1990) impure, egoistic element as a common characteristic. In this vein, the subtheme “altruism” includes accounts suggesting that a person hacks to benefit others out of care. Counterindications of such altruistic acts are categorized as “impure altruism” because of the depiction of others as a proxy for self-benefit, such as receiving payment. References to sharing hacks to support moral values, such as justice, are grouped as “principlism.” Suggestions of the reinforcement of personal moral values are grouped under the subtheme “impure principlism.” Accounts of hacks for the purpose of benefiting a group, which may enhance one's sense of belonging, are categorized as “collectivism.” Finally, within this collective perspective, accounts of people sharing biohacks to achieve social recognition and rewards are grouped as “impure collectivism.” We also point to the permeability of sharing with other behaviors, prosocial or not, which are further detailed as shown in Table 2. This is followed by several unintended consequences that belie the assumption that “sharing is caring,” especially in turbulent contexts. Table 2. Behaviors Coupled with Sharing. Behaviors Descriptions Donating Act of donating money, goods, blood, or organs that are given to help a person or organizations (e.g., Bradford and Boyd 2020) Helping Giving aid or support to another person (e.g., Dunfield 2014) Gift-giving The transference of ownership via giving a gift; reciprocal exchange is nonobligatory in appearance but obligatory in practice (e.g., Belk 2010) Cooperating Acting with another person to reach a common end or to achieve an effect jointly (e.g., Dunfield 2014) Recruiting The processes of identifying, attracting, interviewing, selecting, and hiring personnel for a business, social cause, or other purposes (e.g., Bardhi and Eckhardt 2015) Resisting An activity that interferes with changes of a social practice that is required by a social group (e.g., Albinsson, Wolf, and Kopf 2010) Promoting Marketing communication tool that communicates a sponsored message to promote or sell a product (e.g., Bardhi and Eckhardt 2015) Selling Interactions between actors with the goal of creating and maintaining value through the alignment of institutional arrangements and commercial relationships (e.g., Scaraboto 2015) Hacking to Share The “Hacking to Share” theme encompasses circumstances in which biohackers first try treatments, measure them, and only then share information about and material resources for the action they believe should be pursued (or not). In most instances, these cases are examples of individual “DIY” biohacking projects aimed at tackling the COVID-19 pandemic. Altruism and impure altruism In the case of sharing personal health care experiences on social media, Mahr and Strasser (2021) argue that such behavior indicates prosociality, which reflects the altruistic aspect of sharing (Belk 2010). Indications of the adage “sharing is caring” in biohackers’ posts can be seen in their avowed care and use of expressions such as “I just wanna help,” “glad to help,” and “love.” For example, Dom posted some hacks that were shared with his loved ones:With the coronavirus at the top of everyone's mind, protecting yourself against it should be too. That's doing whatever it takes to support your immune system [and] natural processes. In my friendship group (colleagues, speakers, and close friends), it always is the priority. One of the best ways to strengthen your immune system is through sauna sessions. This [is] because of its ability to raise the body's temperature, like creating a false fever. Producing a false fever by raising your temperature helps because when your body spikes a fever, it stimulates immune function. Summary: Sauna helps kill viruses. Be healthier. Dom's recommendations depart from experiences with his inner social circle of Instagram followers, who may not have the same emotional connections as his beloved people. Such sharing, as Belk (2010) proposes, seems to be for the sake of caring for others without necessarily creating intimate bonds. The tricky aspect, however, is that, despite scientific speculations about the therapeutic benefit of saunas against COVID-19 (Kunutsor and Laukkanen 2021), there is no official recommendation for saunas as a preventive measure. Rather than benefiting others through altruistic but fringe sharing, then, cases like Dom's may lead others to experience adverse consequences in trying to replicate the hack. In the middle ground between altruism and egoism, people may have egoistic motivations for sharing, such as shame, pride, and even selfish concerns like getting paid for something, but may avow altruistic reasons for sharing (Andreoni 1989, 1990). Dash's Instagram post exemplifies a case of impure altruism deploying a manipulative discursive strategy:I’ve just created a back-to-basics, fundamental guide to biohacking [and] health optimization … and in ordinary times, I’d be excited [and] overcome with joy at the thought of welcoming so many people into the biohacking world. But instead, I’m worried. Worried for so many people locked inside, the pressure makes them think they’re healthy at home or occasionally at a supermarket with a mask. When we need to take control and focus on our health, we’ve had that power taken away. Today, the fightback [sic] begins. I want to empower everyone who wants to be happier, healthier, and the BEST version of themselves. There's something extraordinary in the link to the bio. Visit: ***.com Dash's post begins altruistically but concludes by asking readers to click on a link for a paid course based on her hacks. Despite the avowed care she expresses, the fact that she uses others as a means to an end challenges the conceptualization of sharing as a nonreciprocal, generous, and purely altruistic act (Belk 2007). In this “Hacking to Share” example, sharing becomes a gateway for selling (Scaraboto 2015). Moreover, Dash's negative mentions of masks, mandates, and lockdowns highlight the unhelpfully political aspect of the pandemic. This ideological volatility impacts policy makers’ efforts to secure consumers’ understanding of how and why they should comply with public norms (Stewart 2021). Collectivism and impure collectivism Baker and Baker (2016) show that individuals collaborate by sharing material and immaterial resources to overcome challenges and encourage recovery in collective catastrophes. As an example of such collectivism (Batson, Ahmad, and Stocks 2011), a group of biohackers created a DIY project to develop a homemade vaccine. As declared by Jeff on Facebook, they intended to help biohackers in doing the same for fellow biohackers, as well as biohacking enthusiasts wishing for an alternative solution to the pandemic:I have been silent on my social media profiles for a few weeks because I have been working furiously on a new project. Science, in my opinion, is free. I get a lot of criticism because of that and the theatrics. But I want scientific discovery to be spectacular. I want it to be beautiful. My goal is to make you laugh, cry, and hate. This will be a story for the ages. I have read a scientific paper that inspired me to create a DIY Covid-19 vaccine project with my besties, Derek and Martha. We will test a Covid-19 DNA vaccine based on that paper. And this isn’t the wild part. We want to teach you how to create your own vaccine. We will give a free online course over the next months on YouTube. It’ll be exciting and tense, but I wanna share all of that because this is what science should be. By using the words “laugh,” “cry,” “hate,” “wild,” “exciting,” and “tense,” for instance, Jeff portrays not only his emotional predisposition but also a connection between possible personal motivations for doing good through sharing and the desired collective outcomes of such sharing. In this case, sharing and helping, which are different types of prosocial behaviors, are intermingled because they may have the same motivation: to alleviate a negative state of stress (Dunfield 2014). In practice, while sharing happens through the distribution of resources, helping occurs by correcting unintended negative outcomes. However, the message's somewhat rebellious and defiant tone may have the unintended consequence of nurturing filter bubbles (algorithmically driven networks of agreeable content; Rhodes 2021). With a similar sense-defying tone at the end, Mary's message on Instagram shows traces of impure collectivism:BREAKING NEWS: We are now requiring Vitamin D Passports at my Canadian shop for COVID-19 reasons. When we have a stronger immune system and are healthier, we can protect all [those] around us. If your vitamin D levels are at least 60, like mine, all of you are welcome to enter and enjoy a gift card with a purchase of a biohacking service. Do you have yours already? We can all get biohacked together safely. Maybe the #canadiantruckers can help us with a protest against the government and make it official. Although scientifically debated (Pereira et al. 2022), higher vitamin D levels may not protect people from the coronavirus. They will, however, allow those interested in Mary's biohacking business to receive some perks. Despite avowing a sense of “we-ness,” in this example of “Hacking to Share,” Mary's message disguises an egoistic motivation and is more about promoting her business than about sharing as an act of caring. Regarding the potential consequences of recommendations like Mary's, Greene and Murphy (2021, p. 781) argue that “even a single exposure to health misinformation may ‘nudge’ behavior,” leading people to self-harm. Principlism and impure principlism Grounded in the notion of freedom and an emphasis on open access to knowledge, biohackers sometimes share their hacks in support of moral principles. Timothy's post has some cues about principlism as a motivation for sharing (Batson, Ahmad, and Stocks 2011):Can you imagine having one of these cards with your vitamin D levels, recent exercise and food intake details? The Federation of Censored Knowledge United (FCKU) is not real and records like these are unlikely to become mandatory. So it's up to you to learn about and consistently apply time-tested habits to naturally support your health. … I think everyone should have a choice regarding their health, including access to as many sources as possible to make an informed decision. “Choice” and “informed” are the underlying terms here. I’m not for or against vaccines; some have proven to help, and some haven’t, and apparently at least 51% of the USA's population does not want one. I will have it one day, but I will not be a “crash test dummy.” I’ll wait for the data and longitudinal studies that the medical system always claims [are] required to validate. Timothy's morally loaded suggestion to rely on supplements, exercise, and nutrition as possible alternatives for vaccines is marked by expressions like “censored,” “mandatory,” “choice,” “decision,” and “crash test dummy.” Timothy's account points to the tensions between traditional scientific communities and citizen scientists, which enable biohacking to emerge as a social phenomenon (Delfanti 2013). In Timothy's case, acting prosocially by sharing becomes a way to resist the ideological structures of supposedly oppressive systems and institutions. Although his avowed motivation may be to do good by questioning the morality of some public health care guidelines, a complex issue in Timothy's post is its semantic association with conspiracy theories around COVID-19. Such shared self-experiments, which are supposedly for the common good, are not without moral criticism from biohackers, as found in David's post on a Google Group:Just because some famous biohackers are using it, sharing it, and fiercely saying that it is “the right thing to do,” like you’re doing here, doesn’t mean that this hack is morally okay and scientifically unquestionable. I agree with Thomas. Biohacking a virus like a coronavirus this way is one example of [how] anything that is potentially good is also potentially very dangerous. By injecting these chemicals into your body, you’re adding something to your immune system's blacklist. For example, the Pandemrix Influenza, the flu vaccine, gave permanent narcolepsy to some people because it targets a neuroreceptor very similar to the one you do in your hack. So, there is history showing that this approach is not good. The consequence was the patient's immune system wiping everything out. This hack can leave your and others’ immune systems eating away chunks of your brains. Despite the hype about biohacking created by some news media in the COVID-19 pandemic context (Baumgaertner 2021), this message displays nonconformity, worry, and condemnation. As the last example of the “Hacking to Share” theme, David's post highlights the questionable moral notions underlying both hacking and sharing when seen as “the right thing to do.” His message critiques what seems to be impure principlism on the part of his fellow biohackers. This critique, in turn, leaves us to question: According to whom is biohacking the “right thing to do”? Sharing to Hack In the “Sharing to Hack” theme, the collective of biohackers shares information and supplies to work on new hacks to be first tested on other people and objects. This theme also involves cases of biohackers sharing their own bodies to be hacked in trials of homemade vaccines. The following examples can be framed as a “do-it-with-others” type of biohacking. Altruism and impure altruism In an example of altruism as an avowed motivation (Batson, Ahmad, and Stocks 2011), some biohackers share their knowledge and projects in the hope of helping others without necessarily considering the risk of unintended consequences. This seems to be the case with Brian. In a Google Group, he shared the process needed to create the RaDVaC, an intranasal vaccine:I became fascinated with RaDVaC (https://radvac.org/vaccine/) as soon as I heard about it in July. I read the paper carefully, researched the references and decided to take it and produce this intranasal vaccine. I got so happy that I went further. I’ve now created a mobile lab that fits in a suitcase and have been traveling around with it to help people make their own vaccines. Once they understand the process, I leave all supplies with them for free and return to buy more. It's just a few hundred dollars for this small lab. It's brilliant. Brian's fascination and avowed willingness to help others are materialized by his itinerant lab and his free distribution of supplies along the way. This altruistic account also shows that his prosocial act of sharing is enmeshed with some degree of personal sacrifice (e.g., financial, emotional, temporal), which is characteristic of donations (Bradford and Boyd 2020). Here, Brian shares information but donates tangible and intangible resources, blurring the epistemic boundary of different prosocial behaviors. Despite his generous act, however, there is no mention of the results, side effects, or peculiarities arising from his N-of-1 experiment. The somewhat altruistically motivated development of alternative vaccines has continued alongside the emergence of new variants, as Alex mentioned on Twitter in early 2022: “RaDVaC team will release an open-source RNA vaccine candidate soon.” Despite ongoing institutional efforts to combat the pandemic, then, biohacking ideas are still bubbling away. We found more egoistic cases of “helping me by helping you” through sharing hacks such as Monna's tweets: Monna: Is anyone on Twitter working on an open-source prophylactic solution using SARS-Coronavirus-2 counteracting monoclonal antibodies? I would love to participate. QQ: I’m not an expert, but I’ve heard of it, but it seems to be unlikely because people I trust were not excited [about it]. A friend sent me [this message]: “I had completely forgotten about the homemade intranasal vaccine. It seems easy to do it, but no one has yet revealed the results of it.” Monna: I have a selfish reason to see this solution coming to reality: I have no measurable immune response to all doses of Moderna's vaccine. Probably because of my immune suppression post-transplant. I am motivated by the RaDVaC project. Dom: Monoclonals are kinda complicated, I wouldn’t be surprised at all if anyone is doing them in a sort of biohacker way. Monna: One small thing I already know is that povidone-iodine swabbed up in your nose probably helps protect you from COVID. You can also gargle in your throat. Despite skepticism from others in the thread, Monna's accounts illustrate impure altruism in her pursuit of an alternative solution. She even shares a “small thing” to supposedly “help” others. Although Mahr and Strasser (2021) note that sharing health care information on social media may be altruistic, messages like these may backfire and foster the infodemic (Mende, Vallen, and Berry 2021). Collectivism and impure collectivism As an example of collectivism (Batson, Ahmad, and Stocks 2011) in the “Sharing to Hack” theme, biohackers worldwide worked on the Coronavirus Tech Handbook, an open-source document containing shared knowledge and hacks for consumers, companies, experts, and policy makers, among others. On Twitter, several biohackers praised the relevance of this one-of-a-kind project: DNT: Crowdsourced library of tools for tackling COVID-19. Let's put our minds together. Jessy: This will be helpful for everyone: from presidents, policymakers, and residents to community leaders. I’m so impressed and relieved. James: During #COVID-19 it's not easy to know how to be useful, especially as a programmer. Luckily, @n**, who ran the Handbook to help governments [and] citizens get engaged, has set up the Handbook. As part of that, I’m helping them. Sharing, in this case, may be motivated by collectivism, but it seems to be considerably shaped by the outcomes of recruitment and participation. As James emphasizes, in the COVID-19 pandemic, it is hard to know how to contribute effectively toward solutions. This ambiguity reiterates Irwin's (2001) arguments advocating for citizen science initiatives such as biohacking when objective and appropriate institutional or governmental responses to public crises are lacking. When it comes to institutionalized biohacking endeavors, sharing information to hack the virus is most effectively driven by hackathons, short-term events in which programmers and other technology enthusiasts create functioning hardware, software, and chemical solutions. In most cases, these events take the common good as their underlying moral principle, as in the example of the WHO's Regional Office for Africa, which hosted the continent's first COVID-19 virtual hackathon (see World Health Organization 2020). However, as the saying goes, “There is no such thing as a free lunch.” These gatherings can eventually become examples of impure collectivism through the emergence of business opportunities that suggest financial benefits for those involved rather than just communal generosity (Andreoni 1989, 1990; Batson, Ahmad, and Stocks 2011). An example of such impure collectivism is the Hack for Wuhan hackathon, sponsored by Run the World, a company in Silicon Valley: Haaboli: Support uniting mission-driven innovators, Hack for Wuhan by Wuhan2020 connects developers, designers, and creators all over the world to use technology to come up with something to help fight COVID-19. Zablon: Let us hack together against it. We are seeking novel designs, prototypes, and business or social impact projects. This effort will make a profound impact. Kaiyuanshe: Join Hack for Wuhan to fight against coronavirus with us!!! Hack for Wuhan is a public welfare endeavor to contribute to anti-epidemic activities. The apparent sense of belonging characteristic of communities is sustained by sharing information and material resources with peers “to do the right thing,” which, in this case, is to hack the coronavirus. Nevertheless, expressions like “support uniting mission-driven innovators” and “let us hack together,” which are semiotically connected to brands and companies, indicate proximity with economic exchanges rather than sharing (Belk 2010). Given the pandemic's ideological and political nature, a serious issue that may arise in some contexts is businesses turning to political lobbying to secure their agenda. In such cases, people may ignore governmental guidelines and adhere to guidelines proposed by companies or preferred brands. Sharing thus may turn into pseudo-sharing because of the association with marketing efforts (Belk 2014a). Principlism and impure principlism Biohacking as a righteous endeavor is fundamentally shaped by the major virtue of sharing to collectively gather knowledge through making (Lima, Grubits de Paula Pessôa, and Belk 2022). As an example of principlism in the “Sharing to Hack” theme, in a Google Group, Andreas and Brian discussed the ethical considerations to be taken into account before sharing and conducting biohacking: Andreas: There are people already trying it, and even GC [George Church] snorted it (not that this necessarily means something, I know). Their solution only uses materials and methods that are already validated and have scientific acceptance. The main idea is to take synthesized peptides and after mixing them with chitosan, you snort the solution. To be honest, after reading some reports of unpredictable long-term organ damage because of coronavirus, their homemade vaccine doesn’t look so crazy anymore. In the worst case, you snort some chitosan that will decompose and peptides that will turn into amino acids. Brian: Since 2011, we have been spending significant energy creating ethics codes for working with DIYbio. That year, people and representatives from regional groups of DIY biologists from Europe came to BIOS Centre to craft our code of ethics for our emerging movement. Your proposal is guerilla science, something that should only be used in “end of life” situations. Remember Steve Jobs and his experimental therapies for cancer. Brian's message highlights the fuzzy limits between individual freedom and social dangers in sharing and developing DIY experiments that are too extreme. Yetisen (2018) argues that biohackers’ individual autonomy, accessibility, and voluntarism go hand in hand with collective dependence, inaccessibility, and social obligations. Behaving ethically, in this case, means sharing ideas and resources to collectively create hacks as long as they can be ethically justified. However, the impure, or egoistic, element of principlism as an avowed motivation has been found in some messages regarding human challenge trials, a type of medical research in which volunteers share their bodies to be infected (or hacked) to accelerate the development of therapeutics (Zettler 2022). Alastair's post illustrates this element:I got infected with COVID-19 as part of a trial and am donating the €5000 I received for participating. You get paid a lot for participating in these human trials. It's a good thing that volunteers receive [compensation] for being in trials, and it's a good thing that everyone got paid for being in this one too. However, some have said that this means that volunteers are financially motivated to take on risks that they otherwise wouldn’t. Bradford and Boyd (2020) suggest that the sacrifice of the physical self (i.e., the body) may be essential in some contexts of prosocial behavior. However, by sharing one's body to be hacked by others, people may experience an egoistic “warm glow” (Andreoni 1990), as Kevin describes:To @o**, who I have been working [with] for a while now, what you do, your work, a lot of which had been done before I ever even know of your existence, you’ve done astoundingly—it was the best form to participate in a health project I’ve ever filled out! To the volunteers I have never met: you did something remarkable, I sincerely hope you realize just how wonderful it is. Like Kevin, many other biohackers and participants in COVID-19 human challenge trials were praised for their virtuous behavior, as in the following Twitter thread: Marc: These are brave people. I salute them all. Seriously, do these altruistic people fit this selfish world? CNN: Thousands of people want to be exposed to Covid-19 for science. Dan: The difference between altruistic people like them is that they consider other people to be as worthy as their loved ones. Strangers are no less deserving of care. Sinz54: They’re heroes for taking a huge risk. According to the site recruiting volunteers, the risk of death can be around 2%. These messages have linguistic traces of morality, virtuousness, voluntarism, generosity, bravery, sympathy, and kindness. Nevertheless, volunteering to be hacked could be an egoistic means to an end for the sake of “getting my share” (Belk 2016, p. 37). Here, impure principlism may take the form of financial benefits or social appreciation (Andreoni 1990). Discussion This study examines how and why people engage with citizen science initiatives and share insights from them. To do so, we explore distinct nuances of prosocial behavior (i.e., sharing) in the COVID-19 pandemic. We demonstrate that, in a context of ideological, behavioral, and emotional tensions, biohackers seek to do what they believe is “the right thing” for themselves and others. There is the perception that governments did not offer the “correct or best solution” to the problem, as seen in Dash's and Timothy's posts. They do so by sharing information, material resources, and their bodies for biohacking. Nevertheless, such sharing is not necessarily an act of caring for others because it entails some impure or egoistic elements that are symptomatic of Andreoni's (1989, 1990) impure altruism. As the biohackers’ accounts reveal, it is true that in some cases, avowed motivations may indicate generosity (altruism), morality (principlism), and a sense of togetherness (collectivism). However, some of these accounts reveal self-benefits, such as financial rewards (impure altruism), the reinforcement of their sense of morality (impure principlism), and enhanced social appraisal (impure collectivism). These self-benefits, in turn, blur the epistemic boundaries of sharing as prosocial behavior, especially when unintended consequences, such as harming and not caring, occur. Figure 1, which is based on our findings but is also consistent with prior literature, illustrates our insights. Figure 1. Insights from the Context. The result of this is the formation of new challenges or the strengthening of old challenges for policy makers in facing public crises, such as the COVID-19 pandemic. In this vein, the following section presents theoretical implications for the sharing theory (Belk 2007, 2010, 2014a, 2014b, 2016) and recommendations for policy makers. Theoretical Contributions This study makes two theoretical contributions to the sharing theory (Belk 2007, 2010, 2014a, 2014b, 2016). First, it issues a corrective to the assumed view of sharing as mostly an uncontested and unequivocally altruistic type of prosocial behavior. Indeed, prior work has identified different possible motivations for sharing (e.g., Albinsson, Wolf, and Kopf 2010; Baker and Baker 2016; Belk 2016; Ozanne and Ruvio 2016; Scaraboto 2015). However, so far, none of this work has theorized about a pro-ego component (e.g., impure altruism), blurring the epistemic boundaries between sharing and other prosocial and even antisocial behaviors. As our findings demonstrate, sharing occasionally occurs in tandem with another behavior constitutive of social relations, be it motivated by altruism, principlism, or collectivism and their impure variants. This coupling can, in turn, lead to various intended and unintended outcomes, such as caring and harming, respectively. On this issue, Belk (2010, p. 728) questions whether consumers truly differentiate sharing from gift-giving, helping, donating, borrowing, selling, and so on. Given the confusion in distinguishing these constructs and the discussions of the similarities between them, Table 2 shows a list of behaviors found in our data that were sometimes coupled with sharing. There is no sharp criterion for forming these couples, such as mandatory mutual exclusion. Instead, they are to be seen as part of a continuum of prosocial or antisocial behaviors. Furthermore, it should be borne in mind that other coupled behaviors can be identified in different sociocultural contexts. Sharing is sometimes coupled with helping, for example, as seen in Dom's message on Instagram, while Brian's account comprises not only sharing but also donating. In another case, however, we can see Dash sharing information while simultaneously asking for subscriptions and payments, which is far from the conceptualization of sharing as a selfless act. Because sharing is “socially constructed, embedded in values, cultural norms, relations, and human emotions” (Price and Belk 2016, p. 193), a key element of understanding such coupling behaviors is recognizing the presence of different emotions. Here, Andreoni's (1990) concept of warm glow is useful to unfold this insight. Indeed, as acknowledged in the literature, many factors influence the pursuit of this emotional reward for “doing good” (Llamas and Thomsen 2016). It is safe to say, however, that over the course of the COVID-19 outbreak, many of us have experienced the loss of personal and collective notions about what emotion is “acceptable” to feel. Here, we may wonder: When does happiness become guilty? When is the warm glow transformed into “cold glow,” or feeling bad for supposedly “doing good”? The mixture of sadness, happiness, fear, love, care, hate, anger, and disgust energizing different ideologies and social practices has made the meanings of “behaving prosocially” unclear during the pandemic. One extreme but clear example of this lack of clarity is found in pathological altruism. This form of altruism manifests in an attempt to promote the welfare of others at the cost of pernicious long-term consequences to the self (Oakley et al. 2012). The pandemic has accentuated such behaviors because of the different forms of vulnerability (e.g., emotional, social) that it has triggered. Pathological altruism becomes especially complicated when money or other forms of reward are involved (Oakley et al. 2012). As seen in the “Hacking to Share” theme, some biohackers may enter a circuit of dangerous social performances and consequently detrimental rewards. They do so by engaging in a sharing–promoting coupling that results in the antisocial outcome of harming, as opposed to the desired, prosocial caring outcome. In these cases, the more rebellious, bold, and potentially controversial the shared hacks are, the more likes, comments, and shares biohackers receive. Likewise, the more recognition, interactions, and followers biohackers receive, the more money and social appraisals arise from these hacks. Thus, warm glow may come at the expense of the biohacker's well-being. This study's second theoretical contribution relates to individual or collective “acceptable” emotions energizing possible sharing couplings. To begin with, Belk (2010) argues that sharing outside the immediate social circle usually creates no intimate bonds. In contrast, we show that a sharing–cooperating coupling, for example, may lead the giver to experience a sense of belonging that could be emotionally charged. As seen in some biohackers’ accounts of volunteering in human challenge trials, the “we-ness” may relate to an imagined sense of “being part of the solution and not the problem.” Collectivism and principlism may forge such a feeling and nurture the aggregate extended self beyond intimate others (Belk 1988, 2010). This insight is aligned with other recent findings on experiences of self-extension when sharing with people who are not kin, and its benign societal impacts (e.g., Llamas and Thomsen 2016). However, unlike Belk's (2010) sharing prototype in which emotional bonds are created through love and caring, some biohackers are united by disgust, rage, and skepticism. These emotions are found in the case of biohackers who teach others how to create homemade vaccines because of their lack of trust in traditional scientific institutions. In such contexts, we argue that the permeability of personal boundaries might be more related to the “acceptable” emotions and behaviors emerging from the sociocultural contexts than to a real desire for sharing with others outside the immediate family. That is, if I offer my homemade vaccine formula without any clinical trials or other proofs of efficacy, I may be knowingly or unknowingly seeking online likes, kudos, and positive comments more than actually seeking to benefit my followers. This desire to be a hero by sharing “the cure” may result in a premature rush for glory with or without careless or pernicious intent. Monetizing the sharing inclination is commonly criticized for exploiting the ethical high ground and warm feel of sharing (Bardhi and Eckhardt 2015), but the theoretical monkey wrench, in this case, is instead a desire to be a microcelebrity among biohackers. Recommendations for Policy Makers As our data show, citizen science projects involving biohacking have the potential to offer valuable insights that can inform various public policy decisions. In turbulent contexts of panic-driven governmental and political actions, such as the COVID-19 pandemic, it is essential to foster civil–government partnerships to benefit the community as a whole. In this vein, we recommend establishing a PSI lab, a permanent, multidisciplinary team focused on creating public policies using human-centric design-thinking techniques (Cole 2022; McGann, Blomkamp, and Lewis 2018). A PSI lab is likely to be adequate for ameliorating acrimonious relationships among biohackers, other citizen scientists, policy makers, and traditional scientists. The rationale for our recommendation is that a PSI lab prompts active participation from all stakeholders to foster society's well-being. In doing so, it tackles different societal problems (e.g., fake news, access to public services, endemics, environmental issues, and poverty) from a unique perspective (Tõnurist, Kattel, and Lember 2017). Because of the PSI lab's variety of methodological expertise that is not usually the purview of policy makers, it has the potential to generate highly innovative ideas (Blomkamp 2022). Following the global trend of using citizen science by governments and multilateral organizations to tackle societal challenges (Shanley et al. 2019), we thus advocate for policy makers to encourage people to engage in citizen science but within certain boundaries provided by PSI labs. In this vein, like the Barcelona Urban Lab in Spain, the PSI lab should be a government-controlled unit to avoid serving interests other than those of citizens. Although such a lab can operate at different levels (e.g., state, national), we recommend a more local focus for three reasons: First, each level may have different policies, which makes collaboration across levels difficult. Second, it is easier to begin a small project, test it, and then scale it. Third, citizen science initiatives like biohacking do not usually rely on large laboratory infrastructure and big budgets. At this point, we acknowledge that citizen science initiatives emerge differently worldwide, and because of that, a PSI lab might be subjected to local legislation (Guerrini et al. 2018). However, our proposal focuses on two policy processes relevant to most citizen science projects and thus potential transference to different countries. Thus, the PSI lab should concentrate on (1) problem recognition and (2) proposal of solutions (Howlett, Ramesh, and Perl 2020). The problem recognition stage begins with identifying key actors connecting policy makers and biohackers: the local maker communities. These communities comprise techno-enthusiasts who work on DIY creative projects building on open-source hardware to solve problems (Delfanti 2013). Many biohackers work at makerspaces and innovation labs and participate in makerspace hackathons. The makers community shares the biohackers’ bold approach to innovation, but the community works within normative frameworks. Many of these communities receive public funding, making them a strategic ally. Once partnerships are established, the PSI lab must rely on educational strategies that comprise, for instance, multidisciplinary forums focused on designing public policies collaboratively with biohackers. Because biohackers seek to do “the right thing” for themselves and others, invitations to form partnerships should rely on linguistic cues of principlism and collectivism (Batson, Ahmad, and Stocks 2011). Policy makers could establish joint sessions such as those held at the American Marketing Association’s annual Marketing and Public Policy Conference with the aim of mutual learning. On the biohackers’ side, their participation has the potential to create legitimacy for their practices, which could influence the policy process by working more closely together. This would be similar to the result achieved by ACT UP members who were appointed to pharmaceutical company advisory boards and government committees (Keefe, Lane, and Swarts 2006). As seen in our data, it is relatively easy to purchase biohacking kits online, access “how-to-do-it” tutorials on YouTube, and follow online discussions on the chosen topic. This ease of access is apparent in the availability of direct-to-consumer genetic tests that consumers purchase to make health care decisions, despite their lack of genetic literacy for interpreting the results (Pearson and Liu-Thompkins 2012). In some cases of a sharing–helping type of coupling, biohackers use genetic tests from companies like 23andMe as blueprints to conceive, test, evaluate, and share hacks on social media. Such initiatives, however, may be followed by others replicating the self-experiment or consuming the homemade substance in question without considering the biological uniqueness of such experiments. Moreover, although online platforms tag some messages as misleading information, chances are high that the biohacking suggestions under discussion may nurture conspiracy theories and filter bubbles (Mende, Vallen, and Berry 2021). This complex scenario can be a focal topic for policy makers and medical authorities when sharing their ethical concerns and explaining the liability of biohacking. Such debates would constitute part of the empathizing process of the design-thinking method, in which the goal is to obtain insights into people's experiences with different problems (Brown 2019; Lewis, McGann, and Blomkamp 2020). In other words, the results of educational strategies must increase the PSI lab's knowledge about how biohackers see the world and validate (or not) assumptions about them. The knowledge gathered from this stage would then inform the subsequent policy creation process, namely proposal of solutions. Here, the PSI lab must rely on engagement strategies premised on the cocreation of solutions. After defining problems (e.g., fake facts about hydroxychloroquine), the PSI lab team must promote iterative cycles of the design-thinking method's ideation process, pursuing innovative ways to solve issues (Brown 2019; Lewis, McGann, and Blomkamp 2020). Accordingly, “the options on what to do about a publicly recognized problem” (Howlett, Ramesh, and Perl 2020, p. 132) must take into consideration the fact that, first, biohacking is a manifestation of the tensions among diverging scientific communities about the conditions of producing knowledge. Second, however, biohacking, like other citizen science initiatives, also has a prosocial orientation, and biohackers have distinct motivations for different societal challenges. In response to these factors, the PSI lab could, for example, replicate the WHO's Regional Office for Africa (World Health Organization 2020), relying on local maker communities’ social capital to create partnerships with biohackers and to promote hackathons in makerspaces. Doing so could enable pioneer creative solutions such as low-cost COVID-19 tests (Baumgaertner 2021) and develop collaborative guidelines for biohacking activities. As our findings show, many hacks framed as apparent sharing–cooperating couplings were initiated by a series of altruistic, benevolent acts. These acts were then recognized, celebrated, and followed by others. In an unsuccessful example of such collaborations, however, and despite the avowed collectivist motivation for its creation, the crowdsourced Coronavirus Tech Handbook may be a case in which such cooperation backfired. Crowdsourced initiatives like this one can unintentionally contribute to panic-driven governmental and political actions brought on by the pandemic and increase resistance against scientific institutions. In cases like this, the PSI lab's formal participation could help prevent unintended consequences arising from the intention to do good. Therefore, the proposed outcome of engagement strategies is a set of cocreated proposals based on scientific evidence and social accountability aligned with citizens’ needs. Crucial to informing both educational and engagement strategies are the data that are collected and analyzed through the processes of tracking and understanding. In this sense, our suggestion is aligned with Guerrini et al.’s (2018) recommendations for outlining protocols for using data from citizen science projects in designing public policies. Figure 2 illustrates our proposition, highlighting iterative processes (dashed arrows) and subsequential connections. Figure 2. PSI Lab. The PSI lab team must track keywords and hashtags to help identify trends and patterns from information shared over the internet. Tracking can be achieved through netnographic digital research methods (Kozinets 2020), as deployed in the current study. From the data collected, both quantitative and qualitative insights can be gained from shared (mis)information, such as Monna's suggestion for gargling povidone-iodine to supposedly avoid COVID-19. These insights would then facilitate the interlocution between policy makers and biohackers. Given that the PSI lab could aid governments at levels other than the municipal level (Cole 2022; Kronsell and Mukhtar-Landgren 2018), these proposals have the potential to provide different stakeholders with a better understanding of what constitutes freedom of action and freedom of speech, of what motivates people to pursue risky alternatives to solving issues, and, potentially, how to anticipate similar crises. Additionally, the PSI lab's insights could inform government communication strategies, since well-defined messages from public institutions and their successful uptake are essential for controlling the pandemic (Stewart 2021). Conclusion Biohacking and other forms of self-experimentation, such as those carried out by Dr. Salk to produce the polio vaccine, are nothing new, but they are likely to remain fringe endeavors. However, there are no mentions of self-experiments either in the Declaration of Helsinki, which is a set of bioethical principles and rules for human experimentation proposed by the World Medical Association (1964), nor in the Nuremberg Code (1947), a set of research ethics established after the atrocities committed during World War II. Even the U.S. Food and Drug Administration faces challenges in clearly defining, following, evaluating, and regulating biohacking and self-experimentation (Yetisen 2018). Some biohacking initiatives, such as the one created by the biohacker Will Canine, can indeed be of genuine help to humanity. Together with other biohackers, they launched a Kickstarter campaign in 2020 to create an automated, low-cost robot platform, called OT-2, capable of bypassing elite labs and diagnosing COVID-19. In New York, for example, as a result of this innovation, diagnostic times shrank from two weeks to 12 hours, which undoubtedly contributed to saving lives (Baumgaertner 2021). In such cases, policy makers and other public actors should recognize that an approach other than that proposed in this research could fail if it is top-down and paternalistic. They also should recognize that, like other citizen science initiatives, many biohacking projects seek to create innovative and fast solutions to tackle significant problems, such as the pandemic. Much like policy makers, some biohackers genuinely believe that their decisions and actions are “the right thing to do” for themselves and for others. Despite biohackers’ various motivations for sharing their bodies, ideas, and material resources, the resulting hacks may have a considerable societal impact, whether positive and intended or negative and unintended. We hope our research sparks greater interest in understanding the phenomenon of biohacking and inspires future studies that investigate the many aspects not examined here. Future research can explore, for example, the limits of individual freedom in consuming and conducting self-experiments, the role of governments in composing research ethics boards for biohacking projects, the ethical issues involved in selling biohacking products, and possible influences of health care literacy and socioeconomic contexts on the acceptance of biohacking in tackling societal challenges. Additionally, researchers can investigate specific challenges regarding policy domains that are pertinent to most citizen science projects, such as intellectual property, research integrity, participant protections, and the process of incorporating citizen science findings into practical policy decisions. Editor Kelly D. Martin Associate Editor Hope Jensen Schau The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. ORCID iD: Vitor Lima https://orcid.org/0000-0001-6750-1641 ==== Refs References Albinsson Pia A. Wolf Marco Kopf Dennis A. (2010), “Anti-Consumption in East Germany: Consumer Resistance to Hyperconsumption,” Journal of Consumer Behaviour, 9 (6 ), 412–25. Andreoni James (1989), “Giving with Impure Altruism: Applications to Charity and Ricardian Equivalence,” Journal of Political Economy, 97 (6 ), 1447–58. Andreoni James (1990), “Impure Altruism and Donations to Public Goods: A Theory of Warm-Glow Giving,” Economic Journal, 100 (401 ), 464–77. Appau Samuelson Ozanne Julie L. 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==== Front J Am Med Dir Assoc J Am Med Dir Assoc Journal of the American Medical Directors Association 1525-8610 1538-9375 Published by Elsevier Inc. on behalf of AMDA -- The Society for Post-Acute and Long-Term Care Medicine. S1525-8610(23)00570-4 10.1016/j.jamda.2023.06.010 Original Studies Perceptions of palliative and end-of-life care capacity among frontline staff and administrators in long-term care homes during the COVID-19 pandemic in Ontario, Canada: a mixed-methods evaluation Sun Annie H. MPH 12 Ménard Alixe MSc 1 Farrell Emily MSc 2 Filip Angelina MD MHA 2 Katz Andrea BA 2 Orosz Zsofia MHA 2 Hsu Amy T. PhD 134∗ 1 Bruyère Research Institute, Ottawa, Ontario, Canada 2 Ontario Centres for Learning, Research and Innovation in Long-Term Care at Bruyère, Ottawa, Ontario, Canada 3 Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada 4 Department of Family Medicine, University of Ottawa, Ottawa, Ontario, Canada ∗ Corresponding author: Amy T. Hsu, PhD 43 Bruyère Street Office 566J Ottawa, ON K1N 5C8 T: 613-562-6262, ext. 1753 E: 27 6 2023 27 6 2023 9 12 2022 18 5 2023 12 6 2023 © 2023 Published by Elsevier Inc. on behalf of AMDA -- The Society for Post-Acute and Long-Term Care Medicine. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objectives The COVID-19 pandemic has greatly affected the morbidity and mortality of residents in long-term care (LTC) homes. However, not much is known about its impact on staff’s perception of capacity to provide palliative and end-of-life (EOL) care for LTC residents over the course of the pandemic. We investigated changes in self-reported confidence among LTC workers and their experience in providing palliative and EOL care to residents before and during the COVID-19 pandemic. Design Mixed-methods evaluation using surveys (n=19) and semi-structured interviews (n=28). Setting and Participants Frontline workers from 9 LTC homes who participated in Communication at End-of-Life Program in Ontario, Canada, between August 2019 and March 2020. Methods The survey captured LTC staff’s confidence level, including attitudes towards death and dying; relationships with residents and families; and participation in palliative and EOL care. The interviews identified facilitators and barriers to providing palliative and EOL care during the pandemic. Results The COVID-19 pandemic negatively impacted frontline LTC staff’s confidence in their role as palliative care providers. Participants also reported notable challenges to providing resident-centered palliative and EOL care. Specifically, visitation restriction has led to increased loneliness and isolation of residents and impeded staff’s ability to build supportive relationships with families. Furthermore, staffing shortages due to the single-site work restriction and illness increased workload. Psychological stress caused by a fear of COVID-19 infection and transmission also hindered staff’s capacity to provide good palliative and EOL care. Conclusions and Implications Frontline LTC staff—even those who felt competent in their knowledge and skills in providing palliative and EOL care after receiving training—reported notable difficulties in providing resident-centered palliative and EOL care during the COVID-19 pandemic. Key words palliative care long-term care COVID-19 survey personal narrative patient-centered care ==== Body pmcFundingsources: This study was supported by the Government of Ontario through the Ontario Centres for Learning, Research, and Innovation in Long-Term Care at Bruyère. Author contributions: AHS and AM contributed to data acquisition, analysis and interpretation, as well as manuscript writing. EF, AF, AK and ZO contributed to the acquisition of funding, study design, data acquisition and critical revisions of the manuscript. ATH was responsible for the study design, and contributed to the analysis, interpretation, as well as manuscript writing. All authors approved the final version to be published and agreed to be accountable for all aspects of the work. Declaration of conflicting interests: The authors have no conflicts of interest to declare. Brief summary: Frontline staff in long-term care homes in Ontario, Canada have been negatively impacted by the COVID-19 pandemic in their role to providing resident-centred palliative and end-of-life care. Notable challenges impeding their confidence and competency in care delivery include: visitation restriction, staffing shortage, and psychological stress.
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==== Front Public Health Public Health Public Health 0033-3506 1476-5616 The Royal Society for Public Health. Published by Elsevier Ltd. S0033-3506(23)00221-4 10.1016/j.puhe.2023.06.031 Original Research Associations between Delirium and SARS-CoV-2 Pandemic Visitor Restrictions Among Hospitalized Patients Thilges Sarah PhD a∗ Egbert Jamie BS b Jakuboski Samantha BA c Qeadan Fares PhD b a Loyola University Medical Center, Department of Psychiatry and Behavioral Neurosciences, 2160 South First Avenue, Maywood, IL United States b Loyola University Chicago, Parkinson School of Health Sciences and Public Health, 2160 South First Avenue, Maywood, IL United States c Loyola University Chicago, Stritch School of Medicine, 2160 South First Avenue, Maywood, IL United States ∗ Corresponding author. ; Loyola University Medical Center; 2160 South First Ave, Maywood, IL 60153. 27 6 2023 27 6 2023 3 3 2023 14 6 2023 23 6 2023 © 2023 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objectives Delirium is associated with increased morbidity and mortality, but environmental and behavioral factors may decrease risk of developing delirium and thus must be considered. To investigate trends in delirium prevalence and examine associations of visitor restrictions with delirium diagnoses among all patients hospitalized during and prior to the novel coronavirus SARS-CoV-2 (COVID-19) pandemic. Study Design Retrospective epidemiological assessment. Methods The medical records of all patients (n=33,141) hospitalized within a three-hospital academic medical center system in a large Midwestern metropolitan area from March 20, 2019 through March 19, 2021 were analyzed. Results The overall prevalence of delirium during COVID-19 was 11.26% (CI: 10.79%, 11.73%) compared to 9.28% (CI: 8.82%, 9.73%) before COVID-19. From our adjusted logistic regression analyses, we observed that the odds of delirium among non-isolated patients were significantly higher during COVID-19 visitor restrictions (aOR: 1.354; 95% CI: 1,233, 1.488; p<0.0001) than before. The odds of delirium among isolated patients were not significantly higher during COVID-19 visitor restrictions (aOR: 1.145; 95% CI: 0.974, 1.346; p=0.1006) than before. Conclusions Medically isolated patients remained at high risk of developing delirium both prior to and during COVID-19 era visitor restrictions. However, non-medically isolated patients had significantly increased risk of delirium during the social isolation of visitor restrictions compared to prior to visitor restrictions. Keywords SARS-CoV-2 delirium patient isolation ==== Body pmc
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==== Front Res Dev Disabil Res Dev Disabil Research in Developmental Disabilities 0891-4222 1873-3379 Elsevier Ltd. S0891-4222(23)00141-5 10.1016/j.ridd.2023.104563 104563 Article Implementing a scientifically based educational intervention in mainstream primary schools before and during COVID-19 era: Evidence from Greek-speaking children Styliani Tsesmeli N. ⁎ Ioanna Skarmoutsou Department of Educational Studies and Social Work, University of Patras, Greece ⁎ Corresponding author. 27 6 2023 27 6 2023 10456329 1 2023 20 6 2023 24 6 2023 © 2023 Elsevier Ltd. All rights reserved. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background While morphological awareness has been recognized as a fundamental skill in students’ literacy acquisition, experimental evidence is still scarce, especially regarding studies during pandemic. Aim The aim of the study was to present a scientifically based educational intervention of morphological awareness which was implemented in two mainstream primary schools in Greece during COVID-19 era (2020-2021). Methods Participants were 72 primary school student (3rd/4th Grades) who were divided into an intervention and a control group per class. All students were assessed via tests of intelligence, literacy and language before pandemic. The intervention took place during pandemic in the school classroom of the experimental groups and included a pre-test, a training program and a post-test. The experimental material comprised of compounds which pose particular difficulties to children in terms of spelling and meaning. Results The results showed that the systematic exercise of the morphological structure of words increased significantly students’ spelling and semantic performance, including those with low literacy performance. Conclusions These findings underline the importance and feasibility of implementing scientifically based educational interventions in mainstream education during COVID-19 era. Theoretical and practical issues concerning the implementation of hybrid models of educational interventions and scientific research in education are discussed. Keywords Education during COVID-19 Literacy Morphological Intervention Spelling ==== Body pmc What this paper adds? Morphological awareness training has been acknowledged to play a crucial role in children’s literacy and language development and subsequently in their academic performance (Casalis et al., 2018, Goodwin and Ahn, 2013, Levesque et al., 2021, Lyster et al., 2016). While various educational intervention programs have been developed over the last decades, experimental evidence of morphological training studies are still sparse, especially in a phonologically transparent orthographic system, such as Greek (Seymour et al., 2003). Meanwhile, little is known about the feasibility of morphological interventions during COVID-19 era, due to the abrupt disruption of school functioning. This study is one of the first to examine the implementation of a morphologically educational intervention via an hybrid model including e-learning in a mainstream school classroom with spelling-disabled children during the pandemic in Greece. Findings provide evidence that the implementation of a scientifically based intervention program including e-learning is feasible and beneficial for both typically developing children and those with literacy difficulties. 1 Introduction Given the outbreak of COVID-19 pandemic, implementation of extreme measures, such as social distancing and quarantine, were necessitated to prevent the spread of the disease. As a sequence, more than 1.5 billion students worldwide (UNESCO, 2020) had to receive remote instruction being at home (Georgiou & Parrila, 2023). Greece has been one of the first countries in Europe to implement early measures against COVID-19. During the period March 2020 / May 2021 (with exceptions during summer periods) severe restrictions on the mobility of all citizens were imposed with some exceptions for basic necessities (Anagnostopoulou et al., 2022). As a sequence, primary schools in Greece should have lessons via remote learning for about 1 and a half school years while most teachers and students were first-time users of the technology-based learning process. Current research shows that extensive lockdowns in schools during pandemic and the move to remote learning environments in numerous countries could have significant and negative educational effects on children’s academic learning and their cognitive development (Soriano-Ferrer et al., 2021). In fact, recent systematic review and meta-analytic studies (Betthäuser et al., 2022, Georgiou and Parrila, 2023, König and Frey, 2022), showed that COVID-19 pandemic has impacted children’s reading performance, particularly in early school grades. It is also indicated that students with reading disabilities seem to be affected more than students without reading disabilities (Baschenis et al., 2021, Relyea et al., 2023) as well as children from lower socioeconomic backgrounds in comparison with those from higher socioeconomic environments (Segers et al., 2023). Therefore, relevant studies suggest that interventions targeting to diminish these negative effects should be developed in order to avoid the risk of poor school performance (Baschenis et al., 2021). These studies also emphasize the need to provide immediate support for reading-disabled children highlighting the importance of developing prevention programs to moderate any future negative impacts of COVID-19 on these children (Soriano-Ferrer et al., 2021). Under this line, a number of literacy intervention studies developed during pandemic. For instance, Cancer et al. (2021), investigated during pandemic in Italy the effectiveness of a music-based reading intervention (syllabic blending/reading, word/sublexical decoding) on 30 children with dyslexia aged 8-13 which were equally divided into a remote and an in-presence group. The findings showed that both remote and in-presence training were capable in improving reading skills on dyslexic children, indicating the potential effects of remote training programs for reading-disabled children. Cadime et al. (2022) evaluated intervention effects on word reading, oral reading fluency and listening comprehension on a sample of 81 Portuguese 2nd graders (aged 7-8 years) being at-risk for reading disabilities. The intervention was implemented in 27 sessions both in school class and remotely during the COVID-19 pandemic, through an online platform which includes educational activities for reading-disabled children. Results showed that the program was effective in improving all targeted skills, indicating that an intervention implemented remotely through a digital program could be helpful in enhancing literacy skills for struggling readers. Sucena et al. (2022) studied the effectiveness of a 5-week reading intervention program (letter-sound, phonemic awareness, decoding & spelling) on a sample of 446 Portuguese 2nd graders (aged 7-8 years) after school closure due to COVID-19. Findings showed that all students enhanced their reading performance following the intervention, indicating the positive effect of an early training program in reading acquisition. The above studies underline the importance of preventive educational programs on a variety of literacy skills via remote/in presence mode to improve the literacy level of both early school graders as well as reading-disabled children during pandemic (Georgiou & Parrila, 2023). Over the last decades, morphology has been receiving increasing attention among studies concerning children’s literacy acquisition (Kirby et al., 2012, Levesque et al., 2021, Nunes and Bryant, 2006, Sénéchal and Kearnan, 2007). Morphological awareness has been acknowledged as an essential skill in language development for both typical and non-typical children, while morphological instruction is recognized to be beneficial for the enhancement of reading, spelling, vocabulary, and reading comprehension of students, especially those with literacy difficulties (Bowers et al., 2010, Gilbert et al., 2013, Goodwin and Ahn, 2013). Despite current evidence on reading interventions, there is a scarcity of intervention studies on spelling and writing (Georgiou & Parrila, 2023), especially in relation to children's morphological skills. The present study aimed to assess a scientifically based educational intervention of spelling and semantic understanding of complex words designed to support children with spelling difficulties within a mainstream classroom. The study was implemented in two mainstream primary schools in Greece before and during the COVID-19 pandemic. The intervention aimed to target the connections between morphological awareness and spelling of morphologically complex words which was found to be particularly effective for primary school readers. At a recent meta-analysis by Galuschka et al. (2020) on 34 spelling intervention studies during the last decade found that orthographic and morphological interventions showed high to moderate effect sizes. Authors argue that orthographic spelling rules, as well as morphological instruction, should be provided as soon as children master the basic phoneme-grapheme correspondences which are not sufficient on their own for skilled spelling. Intervention studies on morphological awareness carried out on early readers, even preschoolers, illustrate that young learners are also responsive to morphological instruction (Lyster et al., 2016, Ramirez et al., 2014). Galuschka et al. (2020) meta-analysis showed that morphological training is profitable on school students across grades. In a recent study by Casalis et al. (2018) on a sample of 70 French children of typical development (Grade 3) divided into a morphology-intervention group (N = 35) and a spelling-control group (N = 35), training was conducted in school by their teachers aiming to connect morphological analysis to spelling via a variety of classroom activities (i.e., segmentation and production of derivations/inflections). Results showed that improving children's morphological awareness can improve their spelling achievement, while gains on morphologically complex words were durable over time after 5 months of the intervention. In addition, Kargl and Landerl (2018) on another study on a large sample of 796 German students following Grades 4–7 in Austria showed that morphological awareness predicted children's spelling skills above and beyond intelligence and phonological spelling skills. They also showed that a morphology- based spelling intervention via a digital educational program of 6 weeks was found to be efficient to improve students' spelling abilities, beyond phonological skills. The above studies indicated that morphological facilitation is feasible on a variety of orthographic systems of differing phonological transparency presenting the evidence that morphological training could be of an essential advantage to students of typical and non-typical development of various ages. However, intervention studies in this field are still sparse during the COVID-19 pandemic especially on spelling and writing, due to the fact that conducting intervention studies during the pandemic, a time of abrupt disruption of school functioning, when most children were receiving instruction remotely was particularly challenging (Georgiou & Parrila, 2023). The present study aimed to investigate the effects of an educational intervention of morphological awareness implemented before and during COVID-19 era on spelling and semantic understanding of compounds by Greek primary school students (7-9 years old), since they pose particular difficulties for them, while their frequency is steadily increasing in academic school texts of higher school grades (AUTHOR, 2017). Training effects will be evaluated by comparing two groups of children in terms of participating in the training program of the study and in terms of their differing levels of spelling ability, since children with literacy difficulties in Greece attend the same regular classroom of a mainstream school1 like their typically developing classmates. The training program, which was administered via an hybrid model (e-learning and in-class teaching) during pandemic extends earlier studies on morphological awareness implemented by AUTHORS (2009) and AUTHOR (2017) on situ-settings. 1.1 Research hypotheses of the study The main hypotheses for the study are formulated as follows: (i) Each experimental group would enhance considerably their spelling performance of compounds after training, as an effect of the intervention (Casalis et al., 2018, Nunes and Bryant, 2006, AUTHORS, 2009, AUTHOR, 2017, Wolter and Dilworth, 2014), in comparison with the control groups which would not present any improvement between the pre- and post-test of the intervention. (ii) Each experimental group would increase substantially their semantic performance of compounds after training, due to intervention (Bowers and Kirby, 2010, Ramirez et al., 2014), if compared with the control groups which would not present any gains between the pre- and post-tests of the intervention. (iii) Children of low and high spelling ability of the experimental group would improve considerably their spelling and semantic performance after training in comparison with relevant children of the control groups which would not present any improvement after the post-test of the intervention (Baschenis et al., 2021, Cadime et al., 2022, Sucena et al., 2022). (iv) Finally, it will be examined whether semantic knowledge of compounds would contribute to children's spelling improvement via hierarchical regression analyses on the experimental and control groups of the study (AUTHOR, 2017, Lyster et al., 2016). 2 Method 2.1 Participants Participants were 72 Greek students (43 males, 29 females) following the third and the fourth Grade of two mainstream primary schools in the Prefecture of Achaia in the western region of Greece. Participants were divided into one intervention and one control group per class in order to evaluate training effects, as follows:(i) Grade 3-Experimental group (N=20): They were 20 children with a mean chronological age of 7.91 years (SD = 0.47) who took part in the training program of the intervention study. (ii) Grade 3-Control group (N=19): They were 19 children with a mean chronological age of 7.94 years (SD = 0.43) who did not participate in the training program but attended only their mainstream classroom teaching. (iii) Grade 4-Experimental group (N=17): They were 17 children with a mean chronological age of 8.71 years (SD = 0.46) who participated in the training program of the intervention study. (iv) Grade 4-Control group (N=16): They were 16 children with a mean chronological age of 8.91 years (SD = 0.41) who did not take part in the training program but attended only their mainstream classroom teaching. Children were further divided into two ability groups depending on their performance on the standardized literacy measures to evaluate training effects in terms of their differing levels of spelling ability:(i) a Low Ability group (Experimental: 13, Control: 12, N=25), which comprised of children whose performance were either at or below the 10th percentile on standardized spelling task or a combination of a standardized spelling score of 25th percentile along with a standardized reading score of at or below the 30th percentile, and (ii) a High Ability group (Experimental: 24, Control: 23, N=47), which comprised of children whose performance were at or above the 25th percentile on the standardized spelling task. All participants were Greek monolinguals of various socioeconomic backgrounds while none of them had any mental, hearing, visual or serious health problems based on their teachers' statements. Written consent was given by children's parents, teachers and school headmaster for their participation in the study along with an official permission by the Regional Directorate of Primary Education for conducting research in schools. 2.2 Children's Preliminary assessment All children were preliminary assessed prior pandemic via standardized tests of non-verbal intelligence, literacy and language by the second author. Children were firstly evaluated while attending Grades 1 and 2 (see, tasks i-v) and then when they moved to Grades 2 and 3 respectively (see, task vi). Tasks were given either individually to children by the second author (tasks i, ii, iii) or were administered to children by their classroom teachers (tasks iv, v, vi). These tests are presented in detail as follows:(i) Children's non-verbal intelligence was estimated by Raven’s Coloured Progressive Matrices (Raven et al., 2008, Sideridis et al., 2015), an assessment of general cognitive ability requiring inductive reasoning (N=36 items). Items are listed in order of increasing difficulty and consist of visual geometric design with a missing piece. In each item, children were asked to identify the missing element that completes a pattern choosing from a multiple-choice scheme of six choices. (ii) Phonological awareness was assessed by the standardized tasks of Phonemic Distinction and Non-word Segmentation (Porpodas, 2007). The Phonemic Distinction task consisted of 24 pairs of non-words and children had to distinguish whether each pair comprised of the same or different phonemes (e.g., ψάο-ράο /psao-rao/). The Non-word Segmentation task entailed 24 non-words where children should segment into their phonemes (e.g., τρέο /treo/: /t/, /r/, /e/, /o/). (iii) Reading ability of words/non-words was evaluated by the standardized Test of Reading Performance (TORP; Padeliadu & Sideridis, 2002). Every child had to read 40 words which were ordered by word frequency and increasing phonological difficulty (e.g., από /apo/ from, ξύλα /xila/ woods). Subsequently, they had to read 19 non-words of ascending phonological difficulty (e.g., δαλές /ðales/, μπεσίκο /besiko/). (iv) Spelling performance of words was assessed by the standardized Test of Spelling Ability (Mouzaki et al., 2007). The tasks entail 60 words derived from school books, structured by word length and ascending phonographic difficulty (e.g., έλα /ela/ come, άλογο /aloϪo/ horse). All words were dictated to children by their classroom teachers. (v) Written vocabulary was estimated by the standardized Test of Screening of Reading Ability (Tafa, 1995), which consists of 42 sentences with an open section in the end. Children had to complete the sentences by choosing among four words the one that suits the sentence both semantically and syntactically (e.g., Το τρένο που πηγαίνει από την Αθήνα στη Θεσσαλονίκη έχει πολλά [βαπόρια, βουνά, αμάξια, βαγόνια] /To treno pu piϪeni apo tin Athina sti Thessaloniki echi pola [vaporia, vuna, amaxia, vaϪonia]. The train that goes from Athens to Thessaloniki has many [ships, mountains, cars, vagons]). (vi) Reading comprehension was evaluated by the relevant subtest of the Test of Reading Performance (Padeliadu & Sideridis, 2002). Children were asked to read silently four passages of increasing difficulty and answer the four multiple choice questions per passage. 2.2.1 Performance on preliminary assessments Table 1 presents accuracy rates for the preliminary assessments of the experimental and control groups of Grades 3 and 4. Total correct score for each preliminary task equals to 1 point for every accurate response and 0 points for every inaccurate one. To evaluate the differences between the experimental and control group of each grade independent samples t-tests were performed, which verified that the experimental group of Grade 1 did not differ statistically from the control group of the same grade on Non-verbal Intelligence, t(37) =.223, p =.825, Phonemic Distinction, t(37) =.632, p =.531, Non-word Segmentation, t(37) =.742, p =.463, Word Reading, t(37) = 1.051, p =.300, Non-word Reading, t(37) = 1.254, p =.218, Spelling, t(37) = 1.195, p =.240, Vocabulary, t(37) = 1.328, p =.192, and Reading Comprehension, t(37) = 1.700, p =.098. Accordingly, the experimental group of Grade 2 did not present any statistical differences from the control group of the same grade on Non-verbal Intelligence, t(31) =.549, p=.587, Phonemic Distinction, t(31) = 1.006, p =.322, Non-word Segmentation, t(31) = 1.159, p=.064, Word Reading, t(31) = 1.919, p=.064, Non-word Reading, t(31) =.899, p=.375, Spelling, t(31) = 1.256, p=.219, and Reading Comprehension, t(31) =.283, p=.779, except for Vocabulary, t(31) = 2.802, p=.009, verifying that the experimental and control group of each grade had the same baseline of performance on cognitive, literacy and language abilities before the intervention.Table 1 Preliminary assessments for Experimental and Control groups (% accuracy means, standard deviations in parentheses). Table 1 Grade 1 Grade 2 Test Experimental Control Experimental Control ANOVAs** Non-verbal Intelligence 52.50 (4.31) 52.19 (4.30) 62.41 (3.81) 61.80 (2.37) F(3, 71) = 38.595; p<.001 Phonemic Distinction 75.00 (7.52) 73.46 (7.63) 87.50 (6.90) 89.84 (6.44) F(3, 71) = 24.302; p<.001 Non-word Segmentation 66.45 (6.82) 64.69 (8.02) 82.59 (8.23) 85.93 (8.31) F(3, 71) = 34.388; p<.001 Word Reading 84.37 (4.92) 82.63 (5.43) 92.79 (4.13) 89.53 (5.56) F(3, 71) = 15.369; p<.01 Non-Word Reading 78.15 (8.75) 74.79 (1.82) 82.04 (8.53) 84.53 (7.31) F(3, 71) = 4.830; p<.01 Word Spelling 29.16 (11.67) 25.52 (6.48) 62.45 (24.81) 72.29 (19.72) F(3, 71) = 34.868; p<.001 Expressive Vocabulary 50.47 (16.93) 56.51 (10.55) 63.44 (13.80) 75.89 (11.52) F(3, 71) = 11.325; p<.001 Reading Comprehension* 75.00 (9.41) 69.54 (10.60) 76.47 (10.34) 75.44 (10.42) F(3, 71) = 1.716; p=.172 Note: *Reading Comprehension was given to all students when attending Grades 2 & 3, respectively. Note: ** One-way ANOVAs for each test in which Group (Experimental- Grade 3, Experimental -Grade 4, Control-Grade 3, Control-Grade 4) was the between-participants' factor. 2.3 The Intervention study 2.3.1 General procedure of the study The experimental design of the study based on the pre-/post-test designs where measures are taken before and after the intervention. Typically, measures used in the pre- and post-test are the same, and changes from pre- to post-test are interpreted to reflect the effectiveness of the intervention (Frey, 2018). The study extends earlier studies by AUTHORS (2009) and AUTHOR (2017). The general procedure of the intervention included a pre-test, a training program and a post-test (see, Table 2). The intervention was implemented in 12 sessions over a period of two months during COVID-19 era.Table 2 General Procedure of the Intervention Study. Table 2 SESSIONS GROUPS Pre-tests Spelling (N=60 items) 2 ×45΄ Experimental/ Control Groups Grades 3 & 4 Meaning (N=60 items) 2 ×45΄ Experimental/ Control Groups Grades 3 & 4 Training Program (Ν=20 compounds) 4 ×45΄ Experimental Groups Grades 3 & 4 Post-Tests Spelling (N=60 items) 2 ×45΄ Experimental/ Control Groups Grades 3 & 4 Meaning (N=60 items) 2 ×45΄ Experimental/ Control Groups Grades 3 & 4 2.3.2 Experimental Stimuli The experimental stimuli entailed 60 morphologically complex words (40 compounds +20 derivations). All words selected from school books so as to be consistent with students' vocabulary acquisition and developmental stage of literacy. For the purpose of the study, all compounds were semantically transparent and divided in terms of their morphological transparency into: (i) Morphologically transparent (N=20) which were items whose constituents remain intact during word formation process (e.g., door + bell > doorbell) and therefore, they are visible to young readers, e.g., βουνοκορφή /vunokorfi/ mountain-top, and (ii) Morphologically opaque (N=20), which were items whose constituents undergo morphophonological or orthographic changes (e.g., happy + ness > happiness) and thus they are not easily identified by young readers, e.g., νεροπότηρο /neropotiro/ water-glass. Experimental items were also categorized in terms of their inclusion in the Training Program of the study: (a) Trained items (N=20): They were included in the training program; children received instruction on these items, (b) Non-trained compounds (N=20) were not used in the training program but had a similar word structure (consisting of two stems and a suffix) to the trained ones so as to facilitate generalization from trained to non-trained items and (c) Non-trained derivations (N=20) were also not used in the training program. They had a non-similar word structure to the trained items (derivations consist of a stem and a suffix whereas compounds consist of two stems and a suffix) so as to control for both training and generalization effects to trained and non-trained compounds, namely, to examine whether the training effect was limited only to trained items or generalized to non-trained ones (Bowers and Kirby, 2010, Nunes and Bryant, 2006). 2.3.3 Assessments before the training program All items were randomized to constitute the pre- and post-tests (see, Appendix I). Pre-tests included a Spelling and a Meaning Task (N=60 items per task). The Spelling task was administered to experimental and control groups in two sessions of about 45 minutes. All items were dictated to children by their teachers in their classroom. Likewise, the Meaning task was also given to both experimental and control groups by their teachers in two sessions of about 45 minutes. Children had to write down on a A4 sheet the definition of each compound. A participant’s score in each task was the total number of correct answers. 2.3.4 Training program The training program entailed 20 semantically transparent compounds, whose meaning was composed by their constituents, e.g., birdhouse, (Libben, 1998), selected from school books so as to be consistent with children's vocabulary acquisition and developmental stage of literacy. The training program was implemented by each class teacher in 4 sessions of about 45 minutes for each experimental group after detailed instructions and educational material which were given to the teachers by the second author. During every session feedback was provided to the second author via an online teleconference platform (Skype), due to constrained school access at the time of pandemic. Throughout each session students received instruction on 5 compounds. The intervention program had a sequential nature in terms of morphological complexity: sessions moved gradually from transparent compounds which could be easily decomposed by students to opaque items whose internal structure is obscure to children and therefore more challenging for them to distinguish (AUTHORS, 2023). The focus of the training program was to help students to gain insight progressively into the internal structure of compounds and how this understanding was related to their spelling and semantic understanding. All sessions involved instruction based on two main principles: (i) each compound consists of two stems and a suffix (e.g., πικρόγλυκος /pikroγlikos/ bittersweet) and (ii) similar stems of compounds are spelled identically and have the same meaning. Sessions entailed the following sections of educational activities: Segmentation, Formation, Constituents’ Reversal, Spelling practice, Meaning and Written Production of compounds (see, Appendix II). During the Segmentation section children were asked to decompose a compound into its constituents (e.g., νεροπότηρο /neropotiro/ water-glass < νερό /nero/ water + ποτήρι /potiri/ glass) whereas, in the Formation section children had to form a compound from two stems (e.g., μαύρο /mavro/ black + πίνακας /pinakas/ board > μαυροπίνακας /mavropinakas/ blackboard). Children should reverse the two constituents of the compound in order to create a new one in the Constituents’ Reversal (e.g., αλατοπίπερο /alatopipero/ salt-pepper < αλάτι /alati/ salt + πιπέρι /piperi/ pepper). During the Spelling practice compounds were dictated to all children, which had to write them down on a sheet of paper. Children should then provide the written meaning of compounds in the Meaning section (e.g., πρασινοκίτρινος /prasinokitrinos/: of green-yellow colour). Finally, in the Written Production section students had to produce compounds from a base word (e.g., χιόνι /chioni/ snow, χιονάνθρωπος /chionantropos/ snowman) or by completing open-ended sentences and small passages. The control groups did not take part in the training program but attended the formal curriculum taught in schools involving a great range of activities for enhancing literacy (e.g., reading passages, learning to write essays, grammar exercises, vocabulary, etc.). These activities, however, did not focus on compound's training. 2.3.5 Assessments after the training program Post-tests which included the same items and tasks (Spelling and Meaning; N=60 items per task) where given to all groups according to the administration procedure of the pre-tests. 2.3.6 Procedure The intervention took place during pandemic via e-learning in the school classrooms of the two experimental groups and implemented by their teacher with the participation of the second author via the digital tool Skype. Detailed instructions and educational material were given to the teachers by the second author. All sessions of the training program were delivered through group work in the classroom. Each experimental group was divided in three to four smaller groups of three to four children each, depending on the class size. Each group was trained in compounding via educational activities which were constructed by the second author. Educational activities entailed real objects such as cards, plastic bricks, puzzles, dices and board games which children had to use in order to segment, form or produce compounds. The procedure of the study lasted 12 sessions for every grade, and was implemented by the second author in the autumn semester of the school year over a period of 3 months (see, Table 2). 3 Results Each accurate response in the tasks was given 1 point and each inaccurate response 0 points. Mean percentage accuracy rates are used in all statistical analyses presented in the next section. Gain scores were calculated by subtracting the post-test accuracy scores from the pre-test's ones. 3.1 Spelling Training effects of the Intervention study Table 3 presents spelling accuracy rates for pre- and post-tests of the Intervention study for the groups along with their gain scores (N=60). The significance of the difference of the means among the groups was tested by a 2 × 2 × 2 ANOVA in which Grade (Grade 3, Grade 4) and Group (Experimental, Control) were between-participants factors and Training (Pre-test, Post-test) was a within-participants factor. This revealed significant effects for Training, F(1, 68) = 16.073; p <.001, partial η 2 =.191, Group, F(1, 68) = 9.962; p =.003, partial η 2 =.125, and Grade, F(1, 68) = 21.540; p <.001, partial η 2 =.241. The interactions Training x Group, F(1, 68) = 42.132; p <.001, partial η 2 =.383, and Grade x Group, F(1, 68) = 4.073; p =.048, partial η 2 =.057, were also significant, indicating that training effects were different across the experimental and the control groups. Training x Grade, F(1, 68) = 1.349; p =.249, partial η 2 =.019, and Training x Grade x Group, F(1, 68) =.069; p =.794, partial η 2 =.001, were not significant, suggesting that training effects were similar for both grades. To further evaluate these effects, 2 (Pre-, Post-) x 1 ANOVAs were pursued separately for each group.Table 3 Training effects for Spelling and Meaning tasks for the experimental and control groups (% of accuracy means, standard deviations in parentheses). Table 3 Grade 3 Grade 4 Experimental Control Experimental Control Spelling Pre-test 47.42 (8.74) 50.96 (11.48) 61.18 (11.36) 56.67 (6.75) Post-test 57.33 (5.78) 49.65 (9.60) 68.73 (7.33) 53.85 (6.40) Gains +9.91 -1,31 +7.55 -2,82 Meaning Pre-test 55.58 (7.54) 58.42 (6.00) 58.33 (6.69) 53.85 (14.55) Post-test 68.33 (3.71) 58.33 (6.24) 72.25 (3.48) 61.04 (2.91) Gains +12.75 -0,09 +13.92 +7.19 Specifically, separate ANOVAs for each experimental group of Grades 3 and 4 showed that the two experimental groups improved significantly on spelling as an effect of training (Grade 3: F(1, 19) = 26.433; p <.001, partial η 2 =.582, Grade 4: F(1, 16) = 19.095; p <.001, partial η 2 =.544). On the contrary, separate ANOVAs for each control group of Grades 3 and 4 showed that the two control groups did not present any significant improvement (Grade 3: F(1, 18) =.676; p =.422, partial η 2 =.036) or presenting a significant loss (Grade 4: F(1, 15) = 7.314; p =.016, partial η 2 =.328) on spelling after training. 3.2 Meaning Training effects of the Intervention study Table 3 also presents meaning accuracy rates for pre- and post-tests of the Intervention study for the groups along with their gain scores (N=60). The significance of the difference of the means among the groups was tested by a 2 (Grade: Grade 3, Grade 4) × 2 (Group: Experimental, Control) × 2 (Training: Pre-, Post-) ANOVA which showed significant effects for Training, F(1, 68) = 56.135; p <.001, partial η 2 =.452, and Group, F(1, 68) = 20.775; p <.001, partial η 2 =.234, whereas Grade was not significant, F(1, 68) =.921; p =.341, partial η 2 =.013. The interactions Grade x Group, F(1, 68) = 2.864; p =.093, partial η 2 =.041, Training x Grade, F(1, 68) = 3.512; p =.065, partial η 2 =.049, and Training x Grade x Group, F(1, 68) = 1.834; p =.180, partial η 2=.026, were not significant, implying that training effects were similar for both grades. However, significant effects were revealed for the interaction Training x Group, F(1, 68) = 18.854; p <.001, partial η 2 =.217, suggesting that training effects differed between the experimental and the control groups. Thus, separate ANOVAs for each group were further conducted to explore the above differences. Particularly, separate ANOVAs for each experimental group of Grades 3 and 4 indicated that the two experimental groups enhanced significantly its meaning performance as a result of the intervention (Grade 3: F(1, 19) = 59.693; p <.001, partial η 2 =.759, Grade 4: F(1, 16) = 76.560; p <.001, partial η 2 =.827). In contrast, the same analyses for the control groups of each grade showed that the two groups did not present any significant meaning improvement after training (Grade 3: F(1, 18) =.002; p =.966, partial η 2 =.0001, Grade 4: F(1, 15) = 4.135; p =.060, partial η 2 =.216). 3.3 Training Effects for Spelling across Ability Groups In order to further explore the effects of the intervention to children with differing ability levels, children of each training group were divided into two ability groups depending on their performance on the standardized measures of literacy tasks (see, Section 2.1.). Table 4 presents accuracy rates for the pre- and post-tests of the intervention study for the two spelling ability groups. The significance of the difference of the means among the groups was tested by a 2 × 2 × 2 ANOVA in which Group (Experimental, Control) and Ability (Low, High) were between-participants factors and Training (Pre-, Post-) was a within-participants factor. This showed significant effects for Training, F(1, 68) = 12.102; p <.01, partial η 2 =.151, Group, F(1, 68) = 5.992; p =.017, partial η 2 =.081, and Ability, F(1, 68) = 8.596; p =.005, partial η 2 =.112. The interactions Training x Group, F(1, 68) = 38.456; p <.001, partial η 2 =.361, was also significant, implying that training effects were different among the experimental and control groups. However, the interactions Training x Ability, F(1, 68) = 2.420; p =.124, partial η 2 =.034, Group x Ability, F(1, 68) =.176; p =.677, partial η 2 =.003, and Training x Group x Ability, F(1, 68) =.067; p =.796, partial η 2 =.001, were not significant, indicating similar training effects for both ability groups.Table 4 Spelling and Meaning tasks in the experimental and control groups of low/high spelling ability (% of accuracy means, standard deviations in parentheses). Table 4 SPELLING MEANING Pre-test Post-test Gains Pre-test Post-test Gains Experimental Low 50.00 (11.53) 56.79 (8.26) +6.79 57.69 (6.63) 65.26 (3.76) +7.57 Control Low 50.69 (8.60) 47.22 (5.79) -3.47 60.10 (6.20) 56.05 (5.01) -4.05 Experimental High 55.76 (12.13) 65.69 (7.22) +9.93 57.90 (5.61) 66.70 (4.20) +8.80 Control High 55.07 (10.39) 53.84 (8.82) -1,23 59.69 (4.58) 58.09 (2.31) -1,6 Independent samples t-tests per each ability group revealed no significant differences on the experimental and control groups of low performance, t(23) = -.170, p =.867, and high performance, t(44) = -.022, p =.982, before the intervention. The same analysis showed that the experimental group of low literacy skill presented a significant improvement on its spelling performance, t(23) = 3.328, p =.003, due to the intervention, contrasted to the control group of the same ability. Similarly, the experimental group of high ability enhanced significantly its spelling performance, t(44) = 4.863, p <.001, as an effect of the intervention, in comparison with the control group of the same ability. Differences were also significant between experimental group of low and high literacy skill, t(35) = -3.403, p =.002, indicating that although the experimental group of low ability achieved important gains as an effect of the training program, children of low ability did not reach the levels of high ability children of the same group. On the contrary, the control group of low ability presented a significant fall of their spelling performance compared to the control group of high ability, t(33) = -2.340, p =.025. 3.4 Training Effects for Meaning across Ability Groups Table 4 also presents accuracy rates for pre- and post-tests of the intervention study for the same ability groups (see, Section 3.4). The significance of the difference of the means among the groups was tested by a relevant 2 (Group: Experimental, Control) × 2 (Ability: Low, High) × 2 (Training; Pre-test, Post-test) ANOVA which verified significant effects for Training, F(1, 68) = 16.014; p <.001, partial η 2 =.191, and Group, F(1, 68) = 12.183; p =.001, partial η 2=.152, but not for Ability, F(1, 68) =.706; p =.404, partial η 2 =.010. The interactions Training x Group, F(1, 68) = 1.894; p =.173, partial η 2 =.027, was also significant, suggesting that training effects were different among the experimental and control groups, whereas Training x Ability, F(1, 68) = 2.420; p =.124, partial η 2 =.034, Group x Ability, F(1, 68) =.000; p =.997, partial η 2 =.000, and Training x Group x Ability, F(1, 68) =.210; p =.648, partial η 2 =.003, were not significant, implying similar training effects for both ability groups. Independent samples t-tests per each ability group revealed no significant differences on the semantic performance between the experimental and the control group of low literacy skill, t(23) = -.935, p =.359, and high literacy skill, t(44) = -1.328, p =.191, before the intervention. The same analysis showed that the experimental group of low ability presented a significant improvement on its semantic performance due to training compared to the control group of the same ability, t(23) = 5.225, p <.001. Similarly, the experimental group of high ability enhanced significantly its semantic performance as an effect of the intervention, in comparison with the control group of the same ability, t(33.782) = 8.462, p <.001. Differences were also significant between the experimental groups of low and high literacy skill, t(35) = -3.403, p =.002, suggesting that children from the experimental group of low ability not only improved their semantic performance but succeeded to diminish the distance between them and those of high ability. However, the control group of low and high ability showed no significant change of their semantic performance, t(13.502) = -1.342, p =.202, after intervention. 3.5 Semantic contribution to spelling development in experimental/control groups A final enquiry of the study was to examine whether semantic understanding of words would contribute to their spelling development. Table 5a,b presents Pearson correlations between Spelling and Meaning tasks of the intervention study for the experimental and control group2 . These showed that, only for the experimental group, the Meaning post-test correlated significantly with the Spelling pre- (r =.421, p <.05) and post-test (r =.421, p <.543, p <.001). There were no any significant correlations among any of the meaning and spelling tasks for the control group.Table 5 Pearson correlations between Spelling and Meaning tasks for the experimental and control groups. Table 5 Experimental group Control group Spelling pre- Spelling post- Meaning pre- Meaning post- Spelling pre- Spelling post- Meaning pre- Meaning post- Spelling pre- - .754*** .189 .421* - .810*** -.146 .109 Spelling post- .754*** - .176 .543*** .810*** - -.199 -.058 Meaning pre- .189 .176 - .403* -.146 -.199 - .334ª Meaning post- .421* .543*** .403* - .109 .058 .334* - *= p <.05, ***= p <.001, ª= p =.05 Therefore, separate hierarchical regression analyses of the semantic tasks for the experimental and the control groups were further conducted to examine their unique contribution to the spelling tasks. Tables 6a, 6b summarize the outcome of these analyses on the spelling pre- and post-tests for the experimental and control group accordingly where semantic pre- and post measures entered as predictors, each as a last step, after children's preliminary tasks (see, 5-6 Models). In terms of spelling post-test, entry of meaning post-test as the final predictor, after controlling for chronological age, intelligence, word reading and vocabulary indicated a significant and unique contribution to the spelling post-test (.075, p <.05) (Model 6) only for the experimental group. There were no any significant semantic contributionsTable 6a Multiple regression analyses contribution of preliminary and meaning tasks to the spelling task for the experimental group. Table 6aModel Predictors Spelling pre- Change R2 Beta coefficients Spelling post- Change R2 Beta coefficients 1 1. Chronological Age .179** .423** .259*** .509*** 2 2. Intelligence .007 .163 .041 .405 3 3. Word Reading .023 .190 .116* .425 4 4. Vocabulary .015 .150 .004 .079 5 5. Meaning task pre- .027 .177 .021 .157 6 6. Meaning task post- .063 .317 .075* .346* *= p <.05, **= p <.01, ***= p <.001 Table 6b Multiple regression analyses contribution of preliminary and meaning tasks to the spelling task for the control group. Table 6bModel Predictors Spelling pre- Change R2 Beta coefficients Spelling post- Change R2 Beta coefficients 1 1. Chronological Age .061 .247 .102 .319 2 2. Intelligence .019 .282 .011 -.211 3 3. Word Reading .004 -.075 .009 .115 4 4. Vocabulary .020 .220 .004 -.103 5 5. Meaning task pre- .011 -.108 .009 -.102 6 6. Meaning task post- .009 .115 .007 .098 *= p <.05, **= p <.01, ***= p <.001 to the spelling tasks for the control group3 . These results indicated that semantic knowledge contributed to children's spelling improvement as their participation in the intervention. 4 Discussion The study aimed to investigate the effects of an educational intervention of morphological awareness implemented before and during COVID-19 era on spelling and semantic understanding of compounds by Greek primary school students. The training program, which was administered via an hybrid model (e-learning and in- class teaching) during pandemic extends earlier studies on morphological awareness implemented by AUTHORS (2009) and AUTHOR (2017) on situ-settings. The findings confirmed the hypotheses of the study. Before the beginning of the intervention, results from the preliminary assessments conducted before the outbreak of pandemic when children were at their first and second grade of their school year, indicated that the experimental and control group of each grade did not present any significant differences on their non-verbal intelligence, phonological ability, word spelling, word and non-word reading and passage reading comprehension, except of vocabulary for 2nd graders. These findings verified that the experimental and control groups of each grade had the same baseline of performance on cognitive, literacy and language abilities before the intervention. In relation to the first hypothesis, results from the intervention study, implemented during COVID-19 pandemic, on spelling morphologically complex words showed that the two experimental groups attending Grades 3 and 4, despite their developmental differences, improved significantly their spelling performance after a short training program of 4 hourly sessions. On the contrary, the control groups of both grades which did not participate in the training program, did not present any significant improvement on their spelling performance after the intervention. According to the second hypothesis, training effects were also significant for the semantic performance, as both experimental groups presented a significant increase to their semantic understanding of compounds, after intervention. This was not the case for the control groups of both grades which exhibited no significant change on the semantic performance, confirming that the improvement of the experimental groups was due to the training. These findings are in accordance with experimental data from relevant intervention studies (Casalis et al., 2018, Gellert et al., 2020, Ramirez et al., 2014, AUTHOR, 2017, Wolter and Dilworth, 2014). They provide evidence that the systematic exercise of the structure of morphologically complex words could be beneficial for readers’ literacy skills in terms of both spelling and semantic understanding of a complex and difficult vocabulary which is important for their subsequent cognitive and academic development. Another notable issue is the training effects of the study regarding children of different spelling ability in the classroom, since current research shows that extensive lockdowns in schools during COVID-19 had significant and negative educational effects for young children, especially for those with reading difficulties (Baschenis et al., 2021, Soriano-Ferrer et al., 2021). These studies suggest that interventions targeting to diminish these negative effects should be developed in order to avoid the risk of poor school performance. In fact, according to the third hypothesis, findings from the present study revealed that Greek children of low spelling ability who participated in the intervention study during pandemic increased significantly its spelling and semantic performance of a complex vocabulary as well as children from the experimental group of high ability. In particular, the experimental groups of children of low and high literacy skills enhanced significantly their spelling performance as an effect of the intervention, although the spelling improvement of the experimental group of low ability was significantly lower than the experimental group of high ability. On the other hand, the control groups of both literacy skills did not improve significantly their performance on spelling these items. Accordingly, the experimental groups of high and low literacy skill enhanced significantly and almost at the same extent their semantic performance as an effect of the training program presenting important gains on the semantic understanding of compounds. At the same time, the relevant control groups did not present any improvement on these items, verifying that the enhancement of the experimental groups was due to their participation in the training program. These results offer empirical support that a morphological intervention of an hybrid model could be effective in addressing the needs of struggling and typical readers, while they corroborate with current experimental literature (Baschenis, et al., 2022; Cadime et al., 2022; Sucena et al., 2022) that emphasizes the need to provide immediate support for reading-disabled children highlighting the importance of developing prevention programs to moderate any future negative impacts of COVID-19 on these children (Soriano-Ferrer et al., 2021). A final enquiry of the study was to examine whether semantic knowledge of compounds would contribute to children's spelling improvement. Findings from hierarchical regression analyses verified that children's semantic knowledge contributed significantly their spelling performance for the experimental group only after intervention. This effect was absent for the control group. These results showed that semantic knowledge contributed to children's spelling improvement as a result of their participation in the intervention, and not the vice versa, indicating that children’s improvement on spelling depend on their semantic understanding of words (AUTHOR, 2017, Lyster et al., 2016). This is an important aspect of the study, since children's increased vocabulary knowledge would assist them to accelerate their text reading comprehension, leading to a better academic performance (AUTHOR, 2017, Lyster et al., 2016). The above findings underline the feasibility of implementing a scientifically based morphological intervention in mainstream education during COVID-19 era. They also indicate that an educational intervention administered through a blended or an hybrid model, where instruction is provided in classroom along e-learning, could be beneficial to typically as well as to children with spelling difficulties. Furthermore, it is evident that a systematic and intensive intervention targeting the morphemic structure of words could be helpful in enhancing students’ spelling and semantic understanding of words over a short period of time, particularly when it comes to children demonstrating low literacy skills prior the intervention. Thus, the application of an hybrid model in mainstream education might as well promote differentiation in instruction at an individual level, improving not only students exhibiting high literacy skills but also those with low literacy skills. In this case, the training program should be well-structured and employ a variety of playful educational activities, while involving the use of a digital tool in the classroom. The findings are quite encouraging as they suggest that children of low literacy skills not only make large gains but they attempt to diminish the gap that lies between them and the typical readers (Baschenis, et al., 2022; Cadime et al., 2022). The combination of on-line learning and in-classroom instruction seems to be helpful in promoting students’ progress while implementing a multimodal approach to learning process. Besides traditional teaching, on-line learning could be quite engaging for students as it offers the opportunity to support learning through different types of activities such as personalized learning tasks (e.g., educational games, problem solving tasks) and on-line activities for collaborative learning (e.g., use of roles during reading). In this way, instruction can be customized enhancing targeted skills for young readers (see, EARLI, 2023). Hence, the findings of the present study have several substantial implications for educational practice. The instruction of the morphological structure of words seems to be quite beneficial to typical and non-typical readers, especially when assisted by on-line learning in a school classroom setting (Galuschka et al., 2020). Students’ gains could be further promoted if the training program is of sequential and explicit structure, moving from simple to more complex items while employing several educational materials, such as board games (AUTHOR, 2017). Comparable interventions should employ digital tools alongside traditional classroom instruction as a way to support children meet their full learning potential and to overcome barriers caused by school disruption as in the case of COVID-19 era. For this reason, different types of learning strategies should be applied, providing multiple opportunities for learning. In particular, support could be provided to children through a combination of digital means (audio, video, text), while instruction should be differentiated to best meet the learning pace of typically and non-typically developing children through personalized and collaborative tasks (see, EARLI, 2023). Some of the limitations of this study would be the investigation of the acquisition of morphologically complex items in wider samples for the experimental and control group and in a broader range of chronological ages, so as to increase the generalization of the results to the pertinent populations. Another important aspect that should be taken into account is the short duration of the training, since it was not feasible during pandemic to implement a study without time restrictions where schools would close without any notice after a short period of time. Furthermore, it should be taken into account that a potential long-term follow-up of the experimental and control groups would provide stronger training effects for the experimental groups. 5 Conclusions However, this study is among the first attempts to examine the implementation of an educational intervention in Greek, involving an hybrid model of instruction during the COVID-19 pandemic (EARLI, 2023). Findings provide evidence that the administration of a scientifically based intervention program combining e-learning and in-classroom instruction is feasible and efficient in enhancing the spelling and semantic understanding of morphologically complex words for both typical and non-typical readers. These findings are in line with current experimental literature (Baschenis, et al., 2022; Cadime et al., 2022; Casalis et al., 2018; Gellert et al., 2020; Ramirez et al., 2014; AUTHOR, 2017; Wolter & Dilworth, 2014), and prominent for the development of supplementary approaches for typically developing children and children with literacy difficulties, integrating remote and on-site teaching strategies. APPENDIX I Table A1 Spelling & Meaning tasks: pre-/ post-test of the intervention study (Ν=60 items). Table A1ID Word Category Items F1 F2 No of Graph No of Phon ID Word Category Ιtems F1 F2 No of Graph No of Phon COMP ζωοτροφή /zootrofi/ animal food 13 0 8 8 COMP χιονοθύελλα /xjonoθiela/ snowstorm 24 3 10 10 COMP βουνοκορφή /vunokorfi/ mountain-top 10 4 10 9 COMP κουκλοθέατρο /kukloθeatro/ puppet theatre 36 27 12 11 COMP αετοφωλιά /aetofolia/ eagle-nest 1 0 9 9 COMP χαρτοπετσέτα /xartopetseta/ paper napkin 17 7 12 12 COMP πρασινοκίτρινος /prasinokitrinos/ green-yellow 0 0 15 15 COMP γαλαζοπράσινος /γalazoprasinos/ blue-green 1 0 14 14 COMP ηλιοκαμένος /iliokamenos/ sunburnt 10 0 11 11 COMP αλυσοδεμένος /alisoδemenos/ chain-tied 0 0 12 12 COMP καλοντυμένος /kalontimenos/ well-dressed 39 0 12 12 COMP δαφνοστεφανωμένος /δafnostefanomenos/ crowned with laurel 3 0 17 17 COMP ελαιοπαραγωγός /eleoparaγογοs/ olive oil producer 12 0 14 13 COMP οδοντογιατρός /oδontojatros/ dentist 5 1 13 12 COMP ανοιγοκλείνω /aniγoklino/ open-close 1 2 12 10 COMP ανεβοκατεβάζω /anevokatevazo/ go up and down 0 0 13 13 COMP δενδροφυτεύω /δenδrofitevo/ to plant trees 0 0 12 12 COMP σημαιοστολίζω /simeostolizo/ to decorate with flags 0 1 13 12 COMP αγριοκοιτάζω /aγriokitazo/ glare 0 3 12 11 COMP αφισοκολλώ /afisokolo/ to hang posters 0 0 10 10 COMP κοκκινόχωμα /kokinoxoma/ red-soil 15 12 11 10 DER απεραντοσύνη /aperantosini/ vastness 36 3 12 12 COMP πονόλαιμος /ponolemos/ sore throat 1 0 10 9 DER κουρείο /kurio/ barber shop 26 5 7 5 COMP χρυσόσκονη /xrisoskoni/ gold-dust 5 4 10 10 DER εργατικότητα /erγatikotita/ industriousness 46 2 12 12 COMP ασπρόμαυρος /aspromavros/ black and white 13 0 11 11 DER ελαιώνας /eleonas/ olive grove 28 7 8 7 COMP κοσμοξάκουστος /kosmoksakustos/ world-renowned 0 0 14 14 DER αφράτος /afratos/ fluffy 1 0 7 7 COMP αργοκίνητος /arγokinitos/ moving slowly 2 2 11 11 DER αγριωπός /aγriopos/ fierce-looking 2 1 8 8 COMP θεατρόφιλος /θeatrofilos/ theatre-lover 3 1 11 11 DER νησιώτης /nisiotis/ islander 24 1 8 8 COMP ολοκάθαρα /olokaθara/ clearly 25 3 9 9 DER κοντεύω /kontevo/ approach 17 4 7 7 COMP αξιοθαύμαστα /aksioθafmasta/ admirably 16 1 12 13 DER ζεσταίνω /zesteno/ get warm 2 2 8 7 COMP κακόγουστα /kakoγusta/ without elegance 7 0 10 9 DER ετοιμάζω /etimazo/ get ready 39 10 8 7 COMP αυγολέμονο /avγolemono/ sauce with egg and lemon 1 1 10 10 DER κατάβαση /katavasi/ descent 30 4 8 8 COMP νεροπότηρο /neropotiro/ water-glass 3 0 10 10 DER προσφορά /prosfora/ offer 4 64 8 8 COMP αγριογούρουνο /aγrioγuruno/ wild pig 15 12 13 11 DER διάθεση /δiaθesi/ mood 5 60 7 7 COMP στενόμακρος /stenomakros/ long-narrow 2 2 11 11 DER κατάδυση /kataδisi/ diving 51 2 8 8 COMP καγκελόφραχτος /kagkelofraxtos/ guard rail 1 0 14 14 DER αποδοτικός /apoδotikos/ efficient 33 0 10 10 COMP ηλιόλουστος /iliolustos/ sunlit 5 2 11 10 DER υποβοηθητικός /ipovoiθitikos/ facilitating 2 0 13 13 COMP αγρόσπιτο /aγrospito/ farmer’s house 0 0 9 9 DER επιβαρυντικός /epivarintikos/ damaging 5 1 13 13 COMP μεγαλόφωνα /meγalofona/ out-loud 23 5 10 10 DER αναζητώ /anazito/ search 66 11 7 7 COMP δεξιόστροφα /δeksiostrofa/ clockwise 14 0 11 12 DER προσκαλώ /proskalo/ invite 19 25 8 8 COMP καλότυχα /kalotixa/ fortunately 0 0 8 8 DER μετακινώ /metakino/ move 2 5 8 8 Note 1: COM for Compounds, DER for Derivations, F1 and F2 for word frequencies. Note 2: The frequencies (F1) are based on The Hellenic National Corpus (Institute of Language and Speech Processing, 2000) containing about 34,000,000 Greek words. The frequencies (F2) are based on HelexKids (http://www.helexkids.org/home) (see, Terzopoulos et al., 2017). APPENDIX II Table A2 Educational Activities for the Training Programme. Table A2COMPOUND SEGMENTATION Children had to analyze the compound word into its constituents, using cards. Below is an example: COMPOUND πρασινοκίτρινος /prasinokitrinos/ green-yellow 1ST CONSTITUENT πράσινος /prasinos/ green 2ND CONSTITUENT κίτρινος /kitrinos/ yellow COMPOUND SYNTHESIS Students had to blend two stems to form a compound word, using bricks, dominoes & cards. Below is an example: 1ST STEM νερό /nero/ water 2ND STEM ποτήρι /potiri/ glass COMPOUND νεροπότηρο /neropotiro/ water-glass CONSTITUENTS REVERSAL Children had to reverse the two constituents of a compound to form a new compound word using cards. Below is an example: ασπρόμαυρος /aspromavros/ white and black μαυρόασπρος /mavroaspros/ black and white SPELLING PRACTICE Students should write down the compound word 3 times. MEANING PRACTICE Children should explain the meaning of the compounds. ORAL/WRITTEN PRODUCTION Students should produce orally/by writing as many compounds as possible of the same word family. Data availability The authors do not have permission to share data. 1 They could also follow a Learning Support Unit (LSU) within the school a few times per week. However, not all schools have a LSU. Participants of the present study did not follow a LSU. 2 Spelling pre-test correlated significantly with Spelling post-test for both groups (experimental: r =.754, p <.001, control: r =.810, p <.001). Similarly, Meaning pre- and post-tests correlated significantly for the experimental (r =.403, p <.05) and marginally for the control group (r =.334, p =.05) (see, Table 5). 3 Relevant hierarchical analyses performed to investigate the vice versa, namely whether any of the spelling tasks would contribute to the meaning tasks, indicated that the Spelling pre-test contributed as a final step to the Meaning post-test marginally (Change R2 =.083, p =.056, beta coefficients:.328, p =.056), after controlling for the above preliminary tasks, only for the experimental group. No other relationship was significant either for the experimental or the control group. ==== Refs References AUTHOR (2017). AUTHORS (2009). AUTHORS (2023). Anagnostopoulou T. Siannis F. Kyriafinis D. Sela M. 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==== Front Vaccine Vaccine Vaccine 0264-410X 1873-2518 Published by Elsevier Ltd. S0264-410X(23)00780-6 10.1016/j.vaccine.2023.06.077 Short Communication Safety surveillance of the NVX-CoV2373 COVID-19 vaccine among Koreans aged 18 years and over Kim Seontae ⁎ Ko Mijeong Heo Yesul Lee Yeon-Kyeng Kwon Yunhyung Choi Seok-Kyoung Bahng Eunok COVID-19 Vaccination Task Force, Korea Disease Control and Prevention Agency, Cheongju, Korea ⁎ Corresponding author at:COVID-19 Vaccination Task Force, Korea Disease Control and Prevention Agency, 187 Osongsaengmyeong 2-ro, Osong-eup, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do 28159, Korea 27 6 2023 27 6 2023 10 5 2023 17 6 2023 26 6 2023 © 2023 Published by Elsevier Ltd. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background In the Republic of Korea (Korea), the NVX-CoV2373 (Novavax) coronavirus disease 2019 (COVID-19) vaccination was administered to 18-year-olds and over from February 14, 2022. This study sought to assess the frequency and severity of reported adverse events following the Novavax COVID-19 vaccination in Korea. Methods Adverse events based on two national vaccine safety data were analyzed; the COVID-19 vaccination management system (CVMS) and the text-message survey (TMS). Results CVMS identified that the reporting rate of adverse events per 100,000 doses were lower after booster doses (84.0) than after dose 1 (254.6) or dose 2 (272.9); and in 65-year-olds and over (83.4) than in 18- to 64-year-olds (168.1). The TMS found that local and systemic adverse events were lower in 65-year-olds and over than in 18- to 64-year-olds (p<0.001). Conclusions Overall, we identified no major safety issues and fewer adverse events following the Novavax COVID-19 vaccination among 65-year-olds and over in Korea. Keywords COVID-19 COVID-19 vaccines Vaccination Vaccines Safety ==== Body pmc1 Introduction In the Republic of Korea (Korea), the Ministry of Food and Drug Safety (MFDS) authorized the NVX-CoV2373 (Novavax) coronavirus disease 2019 (COVID-19) vaccine for primary immunization—two doses with three weeks apart—of persons aged 18 years and over on January 12, 2022 [1]. The Novavax COVID-19 vaccine is a recombinant spike protein nanoparticle vaccine with Matrix-M adjuvant and was shown to be effective to protect against infection with SARS-CoV-2 that causes COVID-19 based on clinical trials [2], [3], [4], [5]. Following the World Health Organization (WHO) global recommendations for use and a conclusion by the Korea Advisory Committee on Immunization Practices (KACIP) in 2022 [6], [7], [8], the primary series (dose 1 and dose 2) of Novavax COVID-19 vaccine was nationally distributed to persons aged 18 years and over from February 14, 2022, targeting unvaccinated individuals and high-risk groups for severe COVID-19 and death including hospitalized patients, the elderly and severely disabled [9]. Although dose 3 and dose 4 of the Novavax COVID-19 vaccine were initially recommended for the ages of 18 years and over from February 14, 2022, and for the ages of 50 years and over from July 18, 2022, respectively [9], [10], [11], booster doses (dose 3 or more) regardless of administration history of dose 3 or dose 4 have become available for persons who received the primary series of COVID-19 vaccines since December 17, 2022 in Korea [12]. Moreover, homologous booster doses of Novavax COVID-19 vaccination are recommended as a standard immunization and persons after the primary series of mRNA- or viral vector-based COVID-19 vaccine can receive booster doses of the Novavax COVID-19 vaccine if they request or due to medical reasons assessed by immunization doctors [9], [10] The Korea Disease Control and Prevention Agency (KDCA) operates the COVID-19 vaccination management system (CVMS, a web-based passive vaccine safety surveillance system) to detect safety signals, monitoring adverse events following immunization (AEFIs) for further evaluation. Doctors and forensic pathologists can report AEFIs to the CVMS regardless of causation between vaccines and events according to the Infectious Disease Control and Prevention Act. In addition, the text-message survey (TMS) is conducted to investigate adverse events and health conditions following COVID-19 vaccination for specific populations who consent to receive text messages through smartphones on the first day of vaccination [13]. This study aimed to assess the frequency and severity of reported adverse events following the Novavax COVID-19 vaccination among 18-year-olds and over in Korea. 2 Methods 2.1 COVID-19 Vaccination Management System We used data on adverse events after the primary series and booster doses of Novavax COVID-19 vaccination among persons aged 18 years and over reported to the CVMS from February 14 to December 31, 2022. Data on vaccines other than the Novavax COVID-19 vaccine, administered outside of Korea, and before authorization for use were excluded. Adverse events were classified into two types: non-serious and serious events according to the Guidelines for Adverse Events Following COVID-19 Immunization [13]. Non-serious adverse events include common reactions such as redness, pain, and swelling at the injection site, myalgia, fever, headache, chills, and others. Serious adverse events include death, suspected anaphylaxis, adverse events of special interest (AESIs), intensive care unit admission, life-threatening events, permanent disability or sequelae, and others. 2.2 Text-message survey Text messages were sent to persons aged 18 years and over who received the primary series of the Novavax COVID-19 vaccine to complete daily surveys on days 0 to 7 from February 21 to April 24, 2022. The survey items included questions about experiences of local adverse events (pain, redness, swelling, itching, and urticaria at the injection site) and systemic adverse events (fever or heat, chills, headache, joint pain, myalgia, fatigue or tiredness, nausea, vomiting, diarrhea, abdominal pain, rash, armpit tenderness, chest pain, and dizziness), limits to normal daily activities, and visits to healthcare facilities (emergency room, hospitalization, clinic) following vaccination. The participants could report multiple adverse events on each day after vaccination. If a participant reported an on-going adverse event for more than one day, this was counted as one adverse event. 2.3 Statistical analysis The number of non-serious and serious adverse events reported in the CVMS and their reporting rates per 100,000 doses administered were analyzed by sex, age group (18–64 and ≥65 years), and vaccine dose. The types of symptoms and signs were described with the reporting rate per 100,000 doses in decreasing order of the number of cases reported as adverse events. The events do not indicate medically confirmed diagnoses since all adverse events reported to the CVMS are suspected cases. The number of adverse events and health conditions reported through the TMS at least once during days 0 to 7 after vaccination were analyzed by vaccine dose and age group (18–64 and ≥65 years). All survey items were assessed by the chi-square or Fisher exact test as appropriate to compare differences between two age groups. A p-value less than 0.05 was considered statistically significant. As data on COVID-19 status were not collected in both the CVMS and TMS, adverse events were not analyzed considering the history of COVID-19 infection in this study. 2.4 Statistical software All analyses were conducted using SAS ver. 9.4 (SAS Institute, Cary, NC, USA). 2.5 Ethical considerations The surveillance activity based on the CVMS was conducted by the KDCA under the government regulations; the study was not subject to institutional review board approval. The study based on the TMS was exempted from review by the Public Institutional Review Board designated by the Ministry of Health and Welfare (No. P01-202206-01-033). 3 Results 3.1 Adverse events reported in the COVID-19 Vaccination Management System From February 14 to December 31, 2022, the CVMS received a total of 1,230 adverse events reports among persons aged 18 years and over after primary and booster doses of the Novavax COVID-19 vaccination (Table 1 ); 1,158 (94.1%) were non-serious and 72 (5.9%) were serious. Serious adverse events included death (16, 1.3%), suspected anaphylaxis (11, 0.9%), and others including major adverse events such as AESIs for COVID-19 vaccines (45, 3.7%). A total of 926,982 doses were administered during the study period, showing an overall reporting rate per 100,000 doses of 132.7. The reporting rate was lower after booster doses (84.0) than after dose 1 (254.6) or dose 2 (272.9); and in 65-year-olds and over (83.4) than in 18- to 64-year-olds (168.1). Among non-serious adverse events, the most commonly reported symptoms based on the reporting rate per 100,000 doses were myalgia (28.2), headache (26.2), dizziness (19.0), chest pain (18.0), and allergic reactions (17.5) (Table 2 ). Among serious adverse events, acute paralysis (1.4) showed the greatest reporting rate per 100,000 doses, followed by anaphylaxis including anaphylactoid reactions (1.2), and acute cardiovascular injury including myocarditis (0.8).Table 1 Characteristics of adverse events reported to the CVMS among persons aged 18 years and over after Novavax COVID-19 vaccination, Republic of Korea, February 14 to December 31, 2022 Variable Number of doses administered Adverse eventsa Total Non-serious adverse eventsb Serious adverse eventsc Sub-total Death Anaphylaxis Othersd Total 926,982 1,230 (132.7) 1,158 (124.9) 72 (7.8) 16 (1.7) 11 (1.2) 45 (4.9) Dose 1 138,264 352 (254.6) 328 (237.2) 24 (17.4) 6 (4.3) 5 (3.6) 13 (9.4) Dose 2 113,955 311 (272.9) 300 (263.3) 11 (9.7) 1 (0.9) 3 (2.6) 7 (6.1) Boosterse 674,763 567 (84.0) 530 (78.5) 37 (5.5) 9 (1.3) 3 (0.4) 25 (3.7) Sex Male 440,822 464 (105.3) 431 (97.8) 33 (7.5) 9 (2.0) 2 (05) 22 (5.0) Dose 1 60,733 131 (215.7) 124 (204.2) 7 (11.5) 2 (3.3) 1 (1.6) 4 (6.6) Dose 2 49,301 103 (208.9) 97 (196.8) 6 (12.2) 1 (2.0) 1 (2.0) 4 (8.1) Boosters 330,788 230 (69.5) 210 (63.5) 20 (6.0) 6 (1.8) 0 14 (4.2) Female 486,160 766 (157.6) 727 (149.5) 39 (8.0) 7 (1.4) 9 (1.9) 23 (4.7) Dose 1 77,531 221 (285.0) 204 (263.1) 17 (21.9) 4 (5.2) 4 (5.2) 9 (11.6) Dose 2 64,654 208 (321.7) 203 (314.0) 5 (7.7) 0 2 (3.1) 3 (4.6) Boosters 343,975 337 (98.0) 320 (93.0) 17 (4.9) 3 (0.9) 3 (0.9) 11 (3.2) Age (years) 18–64 539,698 907 (168.1) 868 (160.8) 39 (7.2) 2 (0.4) 11 (2.0) 26 (4.8) Dose 1 114,212 314 (274.9) 297 (260.0) 17 (14.9) 0 5 (4.4) 12 (10.5) Dose 2 93,001 272 (292.5) 264 (283.9) 8 (8.6) 1 (1.1) 3 (3.2) 4 (4.3) Boosters 332,485 321 (96.5) 307 (92.3) 14 (4.2) 1 (0.3) 3 (0.9) 10 (3.0) ≥65 387,284 323 (83.4) 290 (74.9) 33 (8.5) 14 (3.6) 0 19 (4.9) Dose 1 24,052 38 (158.0) 31 (128.9) 7 (29.1) 6 (24.9) 0 1 (4.2) Dose 2 20,954 39 (186.1) 36 (171.8) 3 (14.3) 0 0 3 (14.3) Boosters 342,278 246 (71.9) 223 (65.2) 23 (6.7) 8 (2.3) 0 15 (4.4) Data are presented as n (per 100,000): the reporting rate of adverse events per 100,000 doses administered. CVMS, COVID-19 vaccination management system; COVID-19, coronavirus disease 2019. a Data were based on suspected cases following COVID-19 vaccination reported by medical institutions or doctors; therefore, the results do not indicate medically confirmed diagnoses or causality between the events and the vaccines. b Non-serious adverse events include common symptoms such as redness, pain, and swelling at the injection site, myalgia, fever, headache, chills, and others. c Serious adverse events include death, suspected anaphylaxis, and others. d Others include adverse events of special interest (AESIs), intensive care unit admission, life-threatening events, permanent disability or sequelae, and others. e Boosters include dose 3 or more. Table 2 Types of symptoms and signs reported to the CVMS among persons aged 18 years and over after Novavax COVID-19 vaccination, Republic of Korea, February 14 to December 31, 2022 Symptoms and signsa Case (per 100,000) Non-serious adverse events (n=1,158) Myalgia 261 (28.2) Headache 243 (26.2) Dizziness 176 (19.0) Chest pain 167 (18.0) Allergic reactions 162 (17.5) Pain, redness, or swelling at the injection site within 3 days after 127 (13.7) Nausea 114 (12.3) Dyspneab 94 (10.1) Itchingb 86 (9.3) Chills 80 (8.6) Fever 73 (7.9) Vomiting 53 (5.7) Cellulitis 37 (4.0) Abdominal pain 35 (3.8) Diarrhea 30 (3.2) Lymphadenitis 29 (3.1) Arthritis 26 (2.8) Abnormal uterine bleeding 26 (2.8) Severe local adverse events 15 (1.6) Abscess at the injection site 4 (0.4) Severe adverse events (n=72) including reports of death Acute paralysisc 13 (1.4) Anaphylaxisd 11 (1.2) Acute cardiovascular injurye 7 (0.8) Vaccine-associated enhanced disease 6 (0.6) Thrombosisf 3 (0.3) Guillain-Barre syndrome 3 (0.3) Encephalopathy or encephalitis 3 (0.3) Acute liver injury 1 (0.1) Acute reactive arthritis 1 (0.1) Acute kidney injury 1 (0.1) Multisystem inflammatory syndrome 1 (0.1) Anosmia or ageusia 1 (0.1) Convulsions or seizures 1 (0.1) Data are presented as n (per 100,000): the reporting rate of adverse events per 100,000 doses administered. CVMS, COVID-19 vaccination management system; COVID-19, coronavirus disease 2019. a Data were based on suspected cases following COVID-19 vaccination reported by medical institutions or doctors; therefore, the results do not indicate medically confirmed diagnoses or causality between the events and the vaccines. b Dyspnea and itching were reported from March 10, 2022. c Acute paralysis includes facial paralysis and general paralysis d Anaphylaxis includes anaphylactoid reactions. e Acute cardiovascular injury includes myocarditis and others. f Thrombosis includes phlebothrombosis, cerebral venous sinus thrombosis, and thrombosis with thrombocytopenia syndrome. 3.2 Adverse events reported through the text-message survey From February 21 to April 24, 2022, a total number of participants aged 18 years and over enrolled in at least one survey on days 0 to 7 following Novavax COVID-19 vaccination was 5,020 after dose 1 and 2,885 after dose 2 (Table 3 ). Local and systemic adverse events after dose 1 were higher in 18- to 64-year-olds (local: 42.5%; systemic: 45.1%) than in 65-year-olds and over (local: 28.2%; systemic: 31.8%) (p<0.001). This trend was the same after dose 2, indicating a higher proportion of local and systemic adverse events in 18- to 64-year-olds (local: 70.6%; systemic: 68.3%) than in 65-year-olds and over (local: 43.6%; systemic: 41.7%) (p<0.001) (Figure 1 ; Table 3). Among local adverse events, injection site pain, itching, and swelling were the most frequently reported, and among systemic adverse events, fatigue or tiredness, myalgia, and headache were the most frequently reported in both age groups after either dose. More than one-tenth reported that they were limited to performing normal daily activities after dose 1 but there was no statistical difference between 18- to 64-year-olds (15.6%) and 65-year-olds and over (11.4%) (p=0.059). Meanwhile, this proportion after dose 2 was 36.1% in 18- to 64-year-olds and 15.2% in 65-year-olds and over (p<0.001) (Figure 1; Table 3). Approximately 2.5 to 3.8% of all participants visited healthcare facilities including emergency rooms, hospitalization, and clinics after either dose.Table 3 Adverse events and health conditions reported among persons aged 18 years and over following Novavax COVID-19 vaccination, Republic of Korea, February 21 to April 24, 2022 Eventsa) Dose 1 (n=5,020) Dose 2 (n=2,885) 18–64 years (n=4,740) ≥65 years (n=280) p-valueb 18–64 years (n=2,681) ≥65 years (n=204) p-valueb Local adverse events 2,013 (42.5) 79 (28.2) <0.001 1,894 (70.6) 89 (43.6) <0.001 Pain 1,559 (32.9) 49 (17.5) <0.001 1,560 (58.2) 66 (32.4) <0.001 Redness 73 (1.5) 2 (0.7) 0.441 487 (18.2) 23 (11.3) 0.013 Swelling 173 (3.6) 5 (1.8) 0.101 695 (25.9) 24 (11.8) <0.001 Itching 402 (8.5) 10 (3.6) 0.004 1,029 (38.4) 52 (25.5) <0.001 Urticaria 60 (1.3) 1 (0.4) 0.260 105 (3.9) 6 (2.9) 0.485 Others 809 (17.1) 46 (16.4) 0.782 442 (16.5) 31 (15.2) 0.631 Systemic adverse events 2,136 (45.1) 89 (31.8) <0.001 1,831 (68.3) 85 (41.7) <0.001 Fever 552 (11.6) 24 (8.6) 0.117 707 (26.4) 25 (12.3) <0.001 Chills 431 (9.1) 12 (4.3) 0.006 696 (26.0) 16 (7.8) <0.001 Headache 990 (20.9) 42 (15.0) 0.018 1,000 (37.3) 26 (12.7) <0.001 Joint pain 312 (6.6) 14 (5.0) 0.297 424 (15.8) 10 (4.9) <0.001 Myalgia 1,014 (21.4) 34 (12.1) <0.001 1,188 (44.3) 48 (23.5) <0.001 Fatigue or tiredness 1,253 (26.4) 48 (17.1) 0.001 1,291 (48.2) 43 (21.1) <0.001 Nausea 383 (8.1) 8 (2.9) 0.002 307 (11.5) 7 (3.4) <0.001 Vomiting 39 (0.8) 1 (0.4) 0.725 35 (1.3) 0 0.172 Diarrhea 198 (4.2) 9 (3.2) 0.431 132 (4.9) 3 (1.5) 0.024 Abdominal pain 128 (2.7) 5 (1.8) 0.354 101 (3.8) 1 (0.5) 0.015 Rash 10 (0.2) 0 0.442 14 (0.5) 0 0.618 Armpit tenderness 295 (6.2) 10 (3.6) 0.071 389 (14.5) 22 (10.8) 0.142 Chest pain 325 (6.9) 9 (3.2) 0.018 174 (6.5) 4 (2.0) 0.010 Dizziness 548 (11.6) 19 (6.8) 0.014 427 (15.9) 21 (10.3) 0.032 Others 489 (10.3) 23 (8.2) 0.259 304 (11.3) 19 (9.3) 0.377 Limits to normal daily activities 740 (15.6) 32 (11.4) 0.059 969 (36.1) 31 (15.2) <0.001 Visits to healthcare facilities 141 (3.0) 10 (3.6) 0.323 102 (3.8) 5 (2.5) 0.324 Emergency room 23 (0.5) 1 (0.4) 1.000 6 (0.2) 0 1.000 Hospitalization 2 (0.0) 0 1.000 2 (0.1) 0 1.000 Clinic 127 (2.7) 9 (3.2) 0.592 98 (3.7) 5 (2.5) 0.372 Data are presented as n (%): the percentage of respondents who reported adverse events and health conditions at least once during days 0 to 7 after vaccination. COVID-19, coronavirus disease 2019. a Participants could report multiple adverse events on each day; if a participant reported an on-going adverse event for more than one day, this was counted as one adverse event. b Chi-square or Fisher exact test was conducted as appropriate. Figure 1 Adverse events and health conditions reported among persons aged 18 years and over following Novavax coronavirus disease 2019 vaccination, Republic of Korea, February 21 to April 24, 2022. Values represent the percentage of respondents who reported adverse events and health conditions at least once during days 0 to 7 after vaccination. 4 Discussion The results of the TMS are consistent with the safety data reported in clinical trials [4], [5] and the United States (US) [14]; adverse events following Novavax COVID-19 vaccination were more frequently reported among 18- to 64-year-olds compared to 65-year-olds and over. However, when making comparisons with a clinical trial, caution should be exercised as the TMS was based on self-reporting without being medically verified. The most common adverse events after the primary series of Novavax COVID-19 vaccination among persons aged 18 years and over were injection site pain, fatigue, myalgia, or headache and these results are similar to the safety data assessed by the US and European Medicines Agency (EMA) [14], [15]. According to the CVMS, the overall reporting rate of adverse events after Novavax COVID-19 vaccination among persons aged 18 years and over was 132.7 per 100,000 doses administered. This rate was lowest compared to that reported following other COVID-19 vaccines in Korea: Janssen (588.0), AstraZeneca (543.0), Moderna (450.0), Pfizer-BioNTech (304.0) as of December 31, 2022 [16]. However, considering the difference in target populations (e.g., age) and immunization regimens (e.g., homologous or heterologous) for each vaccine administered in Korea, caution should be taken in interpreting the results. Moreover, the reporting rate per 100,000 doses of booster doses (84.0) was the lowest compared to that of dose 1 (254.6) or dose 2 (272.9); these results are similar to safety data from surveys in Australia, reporting the lowest proportion in booster doses (dose 1: 37%; dose 2: 57%; boosters: 31%) as of May 1, 2023 [17]. The total number of serious adverse events reported in the CVMS was 72 including 13 suspected cases of acute paralysis, seven suspected cases of acute cardiovascular injury, and 16 deaths. Until now, none of the death reports was evaluated to be associated with the Novavax COVID-19 vaccine based on epidemiological investigation results and medical records through an initial review performed by provincial rapid response teams. Reviewing seven suspected cases of acute cardiovascular injury, the number of cases was four in females and three in males with a median age of 44 (18 to 86) years. The number of cases after dose 1 (4; 57.1%) was higher than after booster doses (2; 28.6%) or dose 2 (1; 14.3%), and suspected cases of myocarditis were four and others were three. In clinical trials, four to five myocarditis and/or pericarditis cases with a temporal relationship to the vaccine were detected among 41,546 Novavax COVID-19 vaccine recipients aged 16 years and over, and global post-authorization surveillance identified 35 reports (males: 20; females: 15) of myocarditis and/or pericarditis among 744,235 Novavax COVID-19 vaccine recipients with a median age of 34 years. However, it was determined that myocarditis rates and vaccine effectiveness for the Novavax COVID-19 vaccine cannot be compared directly based on available data, highlighting the importance of post-authorization monitoring for both vaccine effectiveness and safety [18]. Reviewing 13 suspected cases of acute paralysis, the number of cases was seven in females and six in males with a median age of 52 (37 to 77) years. The number of cases after booster doses (7; 53.8%) was greater than after dose 1 (3; 23.1%) or dose 2 (3; 23.1%), and the majority of suspected cases were facial paralysis (10; 76.9%) followed by general paralysis (3; 23.1%). Although very few cases of facial paralysis including Bell’s palsy following mRNA-based or viral vector COVID-19 vaccination were reported in clinical trials [19], [20] and a systematic review [21], no clear evidence that these cases are associated with the COVID-19 vaccines has been identified. Furthermore, the disproportionality analysis using the pharmacovigilance database concluded that mRNA-based COVID-19 vaccines did not show an increased risk of facial paralysis compared to other viral vaccines [22]. In this respect, although the information on the recently authorized protein subunit-based COVID-19 vaccine is currently scarce, this study supports that the benefit of Novavax COVID-19 vaccination outweighs any potential risk and recommends continuing vaccine safety surveillance to provide additional information [14]; therefore, further study would be required for conclusive evidence on the Novavax COVID-19 vaccine-associated serious adverse events such as myocarditis and facial paralysis, providing medically confirmed diagnoses for suspected cases in Korea. We acknowledge that this study has some limitations. First, all adverse events were not medically confirmed for an accurate diagnosis as the data were based on suspected cases reported following COVID-19 vaccination; therefore, the results do not indicate causality. Second, the results might have been subject to underestimation since adverse events reported in the CVMS are based on individuals who visited medical institutions; and on-going events from the TMS during days 0 to 7 after vaccination were counted as one event respectively. Third, the analysis did not take into consideration the history of COVID-infection, which may have affected the frequency and severity of adverse events. Fourth, the findings cannot be generalized to the entire population in Korea as the text messages were sent to Novavax COVID-19 vaccine recipients with smartphones during a particular period. In conclusion, we demonstrated no major safety issues and fewer adverse events in 65-year-olds and over compared to 18- to 64-year-olds following the Novavax COVID-19 vaccination in Korea based on two national vaccine safety data. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability The authors do not have permission to share data. ==== Refs References 1 Ministry of Food and Drug Safety (MFDS). Press release: authorization for the Novavax COVID-19 vaccine (NVX-CoV2373) [Internet]. Cheongju: MFDS; 2022 [cited 2023 Mar 1]. 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Available from: 7 World Health Ogranization (WHO) Annexes to the interim recommendations for use of the Novavax NVX-CoV2373 vaccine against COVID-19 [Internet] 2022 WHO Geneva [cited 2023 Jun 16]. Available from: 8 Korea Disease Control and Prevention Agency (KDCA). National immunization program: Korea Advisory Committee on Immunization Practices (KACIP) [Internet]. Cheongju: KDCA; 2022 [cited 2023 Mar 1]. Available from: https://nip.kdca.go.kr/irhp/infm/goVcntInfo.do?menuLv=1&menuCd=1414. Korean. 9 Korea Disease Control and Prevention Agency (KDCA). Press release: initiation of Novavax COVID-19 vaccination starting from February 14 [Internet]. Cheongju: KDCA; 2022 [cited 2023 Mar 1]. Available from: https://www.kdca.go.kr/board/board.es?mid=a20501010000&bid=0015&list_no=718599&cg_code=&act=view&nPage=1. Korean. 10 Korea Disease Control and Prevention Agency (KDCA). Press release: initiation of the fourth dose of COVID-19 vaccination for persons aged 60 years and over [Internet]. 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Guideline for Adverse Events Following COVID-19 Immunization [Internet]. 2nd ed. Cheongju: KDCA; 2021 [cited 2023 Mar 1]. Available from: https://www.kdca.go.kr/filepath/boardSyview.es?bid=0019&list_no=717293&seq=1. Korean. 14 Twentyman E. Wallace M. Roper L.E. Interim Recommendation of the Advisory Committee on Immunization Practices for Use of the Novavax COVID-19 Vaccine in Persons Aged ≥18 years — United States, July 2022 MMWR Morb Mortal Wkly Rep 71 2022 988 992 35925807 15 European Medicines Agency (EMA) Nuvaxovid: EPAR — product information [Internet] 2023 Amsterdam [cited 2023 Mar 28]. Available from: 16 Korea Disease Control and Prevention Agency (KDCA). COVID-19 Vaccine Safety Report (week 96) [Internet]. Cheongju: KDCA; 2022 [cited 2023 Mar 28]. Available from: https://ncv.kdca.go.kr/upload_comm/syview/doc.html?fn=167288523723700.pdf&rs=/upload_comm/docu/0032. Korean. 17 AusVaxSafety. Novavax COVID-19 vaccine safety data — All participants [Internet] 2023 [cited 2023 May 5]. AusVaxSafety Westmead, NSW Available from: 18 Twentyman E. Evidence to Recommendation Framework: Novavax COVID-19 Vaccine, Adjuvanted in adults ages 18 years and older [Internet] 2021 CDC Atlanta, GA [cited 2023 May 5]. Available from: 19 Polack F.P. Thomas S.J. Kitchin N. Safety and efficacy of the BNT162b2 mRNA COVID-19 vaccine N Engl J Med 383 2020 2603 2615 33301246 20 Baden L.R. El Sahly H.M. Essink B. Safety and efficacy of the mRNA-1273 SARS-CoV-2 vaccine N Engl J Med 384 2021 403 416 33378609 21 Khurshid M. Ansari I. Ahmad H. Development of facial palsy following COVID-19 vaccination: A systematic review Ann Med Surg (Lond) 82 2022 104758 22 Renoud L. Khouri C. Revol B. Association of Facial Paralysis With mRNA COVID-19 Vaccines: A Disproportionality Analysis Using the World Health Organization Pharmacovigilance Database JAMA Intern Med. 181 9 2021 1243 1245 33904857
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==== Front Int J Educ Dev Int J Educ Dev International Journal of Educational Development 0738-0593 0738-0593 Published by Elsevier Ltd. S0738-0593(23)00117-7 10.1016/j.ijedudev.2023.102841 102841 Article Toward a holistic approach to EdTech effectiveness: Lessons from Covid-19 research in Bangladesh, Ghana, Kenya, Pakistan, and Sierra Leone Nicolai Susan ab⁎ Jordan Katy cb1 Adam Taskeen dbe Kaye Tom db2 Myers Christina ab a ODI, 203 Blackfriars Road, London, UK, SE1 8NJ b EdTech Hub c Faculty of Education, University of Cambridge, 184 Hills Rd, Cambridge CB2 8PQ, UK d Open Development & Education, e Faculty of Education, University of Johannesburg, PO Box 524, Auckland Park 2006, South Africa ⁎ Corresponding author at: ODI, 203 Blackfriars Road, London, UK, SE1 8NJ 1 edtechhub.org 2 https://opendeved.net/ 27 6 2023 27 6 2023 10284118 12 2022 9 5 2023 23 6 2023 © 2023 Published by Elsevier Ltd. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. During school closures prompted by the Covid-19 pandemic, educational technology (EdTech) was often used to continue educational provision. In this article, we consider EdTech effectiveness using a holistic framework, and synthesise findings from 10 primary research studies of EdTech interventions conducted in low- and middle-income countries during the pandemic. The framework includes five main lenses: learning outcomes, enhancing equity, implementation context, cost and affordability, and alignment and scale. While in-person schooling has largely resumed, there continues to be further integration of EdTech into education systems globally. This analysis provides evidence-based insights and highlights knowledge gaps to shape holistic analysis of both EdTech mainstreaming and future research into the effective use of EdTech to strengthen learning. Keywords International education Development Educational technology Covid-19 pandemic Research synthesis ==== Body pmc1 Introduction School closures were one of the most widespread policy responses as the Covid-19 pandemic unfolded across the globe (Hale et al., 2021). During this period, technology for education (EdTech) took on new prominence as a lifeline for learners, teachers, parents, and caregivers (Bozkurt et al., 2020, Dreesen et al., 2020, Haßler et al., 2020). With in-person schooling having now largely resumed, there is an opportunity to take stock of lessons from this ‘great EdTech experiment’ and contribute to a growing body of literature exploring how EdTech can be more effective in supporting learning going forward (Barron Rodriguez et al., 2021, Munoz-Najar et al., 2021, Williamson et al., 2021). 1.1 Covid-19 and questions on EdTech effectiveness The question of EdTech effectiveness is critical, particularly in low- and middle-income countries (LMICs) where evidence shows that the pandemic has only worsened the pre-existing learning crisis. In countries with available data, four out of five have reported learning losses (UNESCO et al., 2022). According to the Global Education Evidence Advisory Panel (GEEAP, 2022), on average, school closures in LMICs were longer than in high-income countries, students had less technology access for continuing education, and there was less adaptation in provision. This has meant that learning loss during the pandemic was often larger in LMICs compared to Organisation for Economic Cooperation and Development (OECD) countries (ibid.), with the World Bank (2021) estimating that learning poverty has risen to 70% in the former. Yet, during the pandemic, the quality and reach of different remote learning policies and approaches varied greatly (World Bank et al., 2021), with “evidence … mounting of the low effectiveness of remote learning efforts” (GEEAP, 2022, p.6). EdTech is now increasingly included in government education sector plans (Chuang et al., 2022, UNICEF, 2022), and while 2020 alone saw over USD 16 billion in venture capital in EdTech, there are predictions of a global market worth USD 404 billion by 2025 (HolonIQ, 2021, HolonIQ, 2022). Ensuring these investments are spent wisely is important, with critical reflection needed on the potential of EdTech interventions and under what conditions they appear to be effective. 1.2 EdTech Hub and Covid-19 primary research In 2020, as countries grappled with EdTech’s role in supporting remote learning, many actors pivoted to respond to their needs and challenges. As part of this, the EdTech Hub - a global research partnership focused on evidence and decision-making on technology in education - issued a call for primary research on EdTech use during the Covid-19 pandemic. Selected from a pool of more than 175 proposals, a group of 10 studies were commissioned. These were conducted over the course of 2021 in Bangladesh, Ghana, Kenya, Pakistan, and Sierra Leone, countries that were prioritised because of a perceived high level of interest in EdTech and the potential to share applicable lessons across contexts (EdTech Hub, 2022). The studies entailed research on remote learning using low-tech interventions aimed at providing education for students of different ages, training for teachers, and engaging parents and caregivers in their children’s learning, with one study unique in its focus on data architecture. Three main types of devices were used across the studies: radio, television, and mobiles/smartphones. The projects have now all concluded and published findings in their own right, offering an opportunity to reflect on the portfolio as a whole and identify broader messages for the field. 1.3 1.3 Research questions In this paper, we take a synoptic view across our portfolio of 10 studies and analyse findings through a lens of EdTech effectiveness. Our conceptualisation of effectiveness is based on a framework by Hollow and Jefferies (2022), which poses five critical questions for considering EdTech effectiveness ( Fig. 1). The framework acknowledges that there is a need for robust research evidence to be available to policymakers but also that evidence needs to be contextualised and nuanced. Applying this framework leads to an analysis of the impact of EdTech interventions in terms of learning outcomes; enhancing equity; cost and affordability; implementation issues and context; and alignment and scale. We examine what existing literature says in relation to EdTech effectiveness in each of these realms and situate related findings from our studies within this framework. This allows us to present a holistic understanding of the effectiveness of EdTech in relation to the experiences of the research portfolio, identifying evidence-based insights and knowledge gaps for future research.Fig. 1 Framework for EdTech effectiveness used in the analysis of 10 primary research studies conducted during the Covid-19 pandemic, after Hollow and Jefferies (2022). Fig. 1 As such, this study is guided by the following main research questions:1. What are the characteristics of the research designs used in the portfolio of EdTech and Covid-19 projects? 2. What are the emergent findings from across the portfolio in relation to EdTech effectiveness and implications for education as the pandemic subsides? 2 Methods The research questions are addressed using content and thematic analysis (Cohen, et al., 2007). The sample is defined as the final research reports produced by each of the 10 projects in the portfolio. Each research report is a comprehensive account of the research project, its rationale, design, and findings. Reports range from 59 to 100 pages in length, with an average length of 77 pages. The reports were published between mid-2021 and early 2022. As the sample consists of research reports which are publicly available documents published through the EdTech Hub website, there are no ethical concerns associated with this study. An overview of the 10 projects used in our sample is provided in Table 1.Table 1 Overview of the 10 projects which formed the EdTech Hub Covid-19 research projects portfolio. Table 1Study Location Research partner Project title Research design Data collected Adil et al. (2021) Pakistan Sustainable Development Policy Institute Investigating the Impact on Learning Outcomes Through the Use of EdTech During Covid-19: Evidence from an RCT in the Punjab province of Pakistan RCT Interviews; focus groups; survey; learning assessments Afoakwah et al. (2021) Ghana Rising Academy Network Dialling up Learning: Testing the Impact of Delivering Educational Content via Interactive Voice Response to Students and Teachers in Ghana RCT Survey; learning assessments Amenya et al. (2021) Kenya Education Development Trust The Power of Girls' Reading Camps: Exploring the impact of radio lessons, peer learning and targeted paper-based resources on girls' remote learning in Kenya Mixed methods Interviews; focus groups; learning asssessments Ananga et al. (2021) Ghana Transforming Teacher Education and Learning (T-TEL) T-TEL COVID-19 Impact Assessment Study Mixed methods Interviews; survey; virtual lesson observations Aurino et al. (2022) Ghana Innovations for Poverty Action Nudges to Improve Learning and Gender Parity: Preliminary findings on supporting parent–child educational engagement during Covid-19 using mobile phones RCT Survey; learning assessment Fab Inc. (2021) Sierra Leone Fab Inc. Learning from experience: A post-Covid-19 data architecture for a resilient education data ecosystem in Sierra Leone Data dashboard development Secondary data analysis Hodor et al. (2021) Ghana Participatory Development Associates Voices and Evidence from End-Users of the GLTV and GLRRP Remote Learning Programme in Ghana: Insights for inclusive policy and programming Qualitative and participatory approach Interviews; focus groups; secondary data analysis Islam et al. (2021) Bangladesh Beyond Peace Integration of Technology in Education for Marginalised Children in an Urban Slum of Dhaka City During the Covid-19 Pandemic Quantitative survey Survey Islam et al. (2022) Bangladesh Monash University Delivering Remote Learning Using a Low-Tech Solution: Evidence from an RCT during the Covid-19 Pandemic RCT Survey; learning assessment; secondary data analysis Tembey et al. (2021) Kenya Busara Understanding Barriers to Girls’ Access and Use of EdTech in Kenya During Covid-19 Mixed methods Interviews; survey Once collated, the reports were closely read and categorised according to a range of aspects of the project and research design, in order to address the first research question. Categories included the educational context, types of EdTech used, types of research questions addressed, research methods used, and outcomes measured. The second research question was addressed by coding findings in relation to different ways in which ‘effectiveness’ can be conceptualised. We used deductive thematic analysis (Xu & Zammit, 2020), adapting a framework drawn from Hollow and Jefferies (2022, p.4), which sets out five main questions for EdTech decision-makers:1. Will this use of technology lead to a sustained impact on learning outcomes? 2. Will this use of technology work for the most marginalised children and enhance equity? 3. Will this use of technology be feasible to scale in a cost-effective manner that is affordable for the context? 4. Will this use of technology be effective in the specific implementation context? 5. Will this use of technology align with government priorities and lead to the strengthening of national education systems? While the collection of studies funded under the EdTech Hub Covid-19 grants scheme provides a robust and diverse snapshot of studies undertaken in response to the pandemic, there are, of course, limitations. Focusing the sample on our portfolio means that other studies undertaken during the pandemic are not included, so the analysis is not exhaustive nor representative. Moreover, the sample reflects the priorities of EdTech Hub in terms of subject matter and geography; however, this collection can be viewed as a reliable source as it includes studies across a consistent and focused period, which have all been through EdTech Hub’s quality assurance process. In due course a wider range of peer-reviewed outputs will become available from other studies undertaken during the pandemic, however at the time of undertaking this analysis, few studies had been publicly reported as comprehensively as the portfolio of reports. While the synthesis is subjective to an extent, it builds on the work across the projects and consolidates what we have learned, what has been echoed between studies, and potential points of departure for further research. 3 Characteristics of the studies To illustrate the range of topics and approaches covered by the Covid-19 response, the studies were categorised according to several aspects of the inquiry and research design. An overview of the nature of the inquiry of the studies, including the types of EdTech used, the purpose of the studies, and the user groups involved, is shown in Table 2.Table 2 Types of EdTech used, for what purpose and with which user groups, by studies within the Covid-19 research projects portfolio. Table 2Study EdTech used Purpose and learning outcomes Location Participants and sample size Adil et al. (2021) Online learning using Teaching at the Right Level (TaRL), computers Promotion of maths, Urdu, and English; professional development in tech- assisted instruction Pakistan: Bahawalnagar district in the province of Punjab 258 students in Grade 8 and 15 teachers from 12 schools in a remote area Afoakwah et al. (2021) Interactive Voice Response (IVR) audio lessons Promotion of foundational numeracy & literacy skills; professional development on instruction Ghana: network of low-cost private primary schools 1,359 students in Grades 4, 5, 6 and 333 teachers from 30 schools Amenya et al. (2021) Radio and reading camps Promotion of reading and maths Kenya: ASAL (Arid and Semi-Arid Lands) areas (Kilifi, Turkana, Samburu, and Tana River) 1,230 girl students in Grades 6 and 7 Ananga et al. (2021) Online learning Assessment of teaching and learning improvement Ghana: colleges of education 356 pre-service student teachers from marginalised backgrounds from 30 public colleges of education Aurino et al. (2022) Text message “nudges” Behaviour change in children’s learning to promote gender parity Ghana: rural regions in Northern Ghana Parents and carers from 2,628 households with school-aged children with low levels of literacy/education, girl students Fab Inc. (2021) Data systems Development of data architecture and dashboard tool Sierra Leone: national data Secondary analysis of Annual School Census data and Multiple Indicator Cluster Surveys Hodor et al. (2021) Television and radio lessons Assessment of effective continuous learning in English, mathematics, science, and social studies Ghana: urban and rural districts across three regions, the northern zone (Northern Region), middle zone (Ashanti Region) and southern zone (Greater Accra Region) 285 participants across a range of stakeholders, including in-school and out-of-school learners; learners with disabilities, parents, teachers, headteachers and radio stations Islam et al. (2021) Television, radio, smartphone, computer Assessment of device access and usage Bangladesh: Korail urban slum in Dhaka 476 households with students in Grades 6 to 10, low-income backgrounds Islam et al. (2022) IVR audio lessons Promotion of English and Bangla language literacy, numeracy, non-cognitive skills Bangladesh: Khulna and Satkhira districts 1,763 children from 90 villages: students aged 5 to 10; low-income backgrounds; parents and caregivers Tembey et al. (2021) Television, radio, video (e.g. YouTube) Assessment of girls’ barriers, participatory product development Kenya: Nairobi and rural counties 494 caregivers of girl students aged 7–14, low-income backgrounds The study topics covered a range of different participants and perspectives within educational systems. Studies most frequently focused on students as participants (seven studies), spanning a range of ages and grades across primary and secondary education. Teachers were included in three studies, while parents and caregivers were a focus in two instances. One study was distinct in that it focused on how data is used to support decision-making at the level of the education ministry, with teachers as potential beneficiaries of the data use. In relation to the role of EdTech, the research studies focused on low-tech and low-connectivity technology in their interventions and used a range of technology-based approaches to reach their target groups. Three main types of devices were used: radio (six studies, often IVR applications), TV (three studies), and mobile devices and smartphones (two studies). Two studies focused on online learning, which could be accessed via computers or mobile devices with good connectivity. The form of content delivered through the devices included audio lessons, audio-visual lessons (e.g., YouTube), text messages, social media/digital applications (e.g., Zoom, WhatsApp), and digital platforms (for online learning). Multiple modalities were used in three cases– typically involving both television and radio – which may have reflected the broader practice of using a range of media to reach as wide a range of learners as possible (Dreesen et al., 2020). Most studies addressed research questions focused on evaluating the impacts of the various EdTech interventions, particularly in terms of learning outcomes and participation in education. Furthermore, the studies also sought to identify factors that could foster supportive conditions but also barriers to uptake. Most studies looked at factors of vulnerability and marginalisation, such as income level, location, and disability, to varying extents. More than half examined ways that EdTech could advance gender equality by promoting girls and women (i.e., through teacher professional development) in remote education. A variety of research methods were employed, with all studies collecting primary data ( Fig. 2) and undertaking literature reviews. Most studies were ‘mixed methods’ in the sense that a combination of qualitative and quantitative methods was used. Qualitative methods involved interviews, focus groups, and lesson observations. Quantitative methods included surveys, learning assessments, randomised control trials (RCT), and secondary data analysis.Fig. 2 Range of methods used to collect data across studies. Primarily qualitative methods are shown in blue, while quantitative methods are in orange. Fig. 2 Across the studies, five main types of measures were used to gauge the projects’ impact and outcomes. An aggregated categorisation of these measures includes changes to learning outcomes; engagement with content; levers to access and use; group-based variations; and perceptions and awareness. Additionally, while studies were asked to consider cost-effectiveness, the extent to which this was possible in practice was limited. More detail on specific measuresis provided in the discussion in Section 4. 4 Analysis related to effectiveness The question of how effective the use of EdTech is – and how this varies according to different forms of EdTech, or in comparison to non-EdTech educational initiatives – is a core concern for the field (Evans and Popova, 2015, McEwan, 2015, Rodriguez-Segura, 2020). The focus of our analysis here is on the different ways in which the studies conceptualised and evaluated the ‘effectiveness’ of the interventions at hand. As outlined above, five main areas are used, which together can provide a more holistic picture of effectiveness. In this section, each will be introduced and discussed in turn, drawing on examples from the projects for further insight. 4.1 Impact on learning outcomes That learning levels are low in many countries around the world, particularly LMICs, is well established (World Bank, 2022, World Bank, 2018, World Bank, 2019, UNESCO, 2017). In light of what has often been called a ‘learning crisis’, the measurement of learning outcomes has taken on a new importance, both at the macro level through large-scale learning assessments (Addey et al., 2017, Angrist et al., 2021, Appels et al., 2022;), as well as at the more micro level, focusing on schools and students (Andrade et al., 2021, Barrett, 2016). Evidence of learning gains has become a kind of ‘gold standard’ to which education systems and programmes are held in terms of showing their ‘effectiveness’. This is equally true in EdTech, where questions of effectiveness are often closely tied to whether or not learning gains have been achieved (Rodriguez-Segura, 2020, See et al., 2022). This became even more critical during the pandemic when remote learning became prominent. The issue of ‘learning losses’ as a result of school closures – conceptualised as a combination of lost educational opportunities and/or diminished attainment (Pier et al., 2021) – was an immediate concern. Measurement of learning outcomes has therefore been key in examining gains or losses in close to real-time (e.g. Angrist et al., 2020a). The issue of how to measure the impact on learning outcomes was a primary concern for nearly all of our studies, with most of them measuring learning gains directly (Adil et al., 2021, Afoakwah et al., 2021, Amenya et al., 2021, Aurino et al., 2022, Islam et al., 2022), with others also collecting data in relation to perceived impacts on learning outcomes reported by participants (e.g., Hodor et al., 2021). Assessment tools used were mainly focused on foundational learning outcomes, often using instruments which have been developed and validated independently and composed of test items for both literacy and numeracy. It is notable that the availability of existing validated tools, which could be rapidly deployed, allowed the collection of data on learning gains despite the relatively short timescale and crisis context. Student learning assessment tools used by the studies included:• Adil et al. (2021): Language and mathematics tests administered for the ASER (Annual Status of Education Report) Survey, and grade-appropriate tests based on the Punjab textbook board syllabus for maths, English, and Urdu for older children. • Afoakwah et al. (2021): Grade 4 TIMSS (Trends in International Mathematics and Science Study) items (TIMSS, 2013); • Amenya et al. (2021): SeGRA (Senior Grade Reading Assessment) and SeGMA (Senior Grade Mathematics Assessment) covering literacy and mathematics for older pupils; • Aurino et al. (2022): Literacy and numeracy were both assessed, using items from the IDELA (International Development and Early Learning Assessment; Pisani et al., 2018), EGRA (Early Grade Reading Assessment; RTI International, 2016), EGMA (Early Grade Math Assessment; Platas et al., 2014) and the Young Lives surveys (Barnett et al., 2013); • Islam et al., 2022: Assessment taken from the national curriculum of Bangladesh covering English literacy, Bangla literacy, numeracy, and general knowledge (Islam et al., 2022, p.60); plus, Renzulli scales to measure noncognitive outcomes (with mothers as respondents) (Renzulli et al., 2002). Assessment data collected across the studies showed mixed results in terms of learning outcomes, where perceptions of effectiveness also varied. Although not directly comparable, the findings do, in some instances, illustrate positive effects on learning outcomes and highlight the need for additional data to understand variation. For instance, in a Bangladesh study, IVR showed positive effects on learning outcomes – mainly English literacy and numeracy – for primary school children along with other areas, with evidence also demonstrating increases in students’ interest, attention span and time investment in education (Islam et al., 2022). However, positive effects were less pronounced in another IVR study in Ghana, only impacting one aspect of numeracy: place value knowledge (Afoakwah et al., 2021). Online TaRL (in Pakistan) had variable effects for Grade 8 students, causing language scores to go up but not affecting maths scores (Adil et al., 2021). As noted above, the availability of validated tools to measure learning outcomes allowed the studies to generate data rapidly, which will continue to be an important focus for research projects seeking to understand learners’ progress in the wake of the closures and identify the gaps in returning to standard educational provision. Furthermore, beyond the domains of literacy and numeracy, the range of types of learning outcomes for which robust tools are available is limited (Anderson, 2019). An observation which emerged from the analysis was that when used, assessment was found to contribute to improved learning, but there was also concern about a lack of transparency about what would be assessed and how it would be used (Adil et al., 2021), which is a key practical consideration for future research designs. While the measurement of learning outcomes is fundamental to evaluating educational efficacy, this alone does not shed light on the pedagogies that support these gains. Learners across our studies indicate a desire for increased opportunities for interactivity and tailoring interventions to the right level for the individual. For example, in a Ghana study, learners felt radio and television programmes should be taught at a slower pace, be more interactive and repeated (Hodor et al., 2021). In one study using IVR on phones, students reported that the quiz was the best part of the lesson and that they would like to see more interactive elements, such as choice in lesson content (Afoakwah et al., 2021). Similarly, students indicated a preference for using social media platforms for learning (such as Facebook and YouTube, also Zoom and Google Meet), mostly through smartphones, because of the heightened interaction compared to one-way modalities (Islam et al., 2021). In contrast, only a fifth of respondents reported watching the government’s televised educational programming (Islam et al., 2021). 4.2 Enhancing equity Equity has been a long-standing concern in the field of EdTech. In ‘Rethinking the Digital Divide’, Warschauer (2003) argues that equitable access to digital technology needs to go beyond merely providing devices and internet connections; it needs to factor in language and literacy variances, human and social relationships, communities, and institutional structures. Citing further factors of representation, contribution, and non-neutral networks/technologies, Graham et al. (2015) conclude that striving for equality is not just a technological challenge but a socio-technological one. Writing in the context of the pandemic, Unwin et al. (2020) argue that for EdTech to be equitable, it needs to intentionally focus on reaching the poorest and most marginalised and not just be a by-product of scale. Research on justice-oriented digitised education further argues that for EdTech to support marginalised groups effectively, teachers and learners need to be equipped in terms of accessing, utilising, and benefitting equitably from EdTech provisions (Adam, 2022). The equity lenses used in the research studies reviewed correlated with reflections which highlight that equity policies and research, including in EdTech, tend to focus more on girls and rural populations in comparison to other marginalised groups such as ethnic and religious minorities (Zubairi et al., 2021, Sarwar et al., 2021). In terms of equity, some studies explicitly focused on certain marginalised groups: girls and young women (Amenya et al., 2021, Aurino et al., 2022, Tembey et al., 2021), low-income groups (Afoakwah et al., 2021, Ananga et al., 2021, Islam et al., 2021, Islam et al., 2022), learners from urban slums (Islam et al., 2021), and remote or rural regions (Adil et al., 2021, Amenya et al., 2021, Aurino et al., 2022, Hodor et al., 2021, Islam et al., 2022). In other studies, unique findings on equitable effectiveness arose, such as for learners with visual or hearing impairments (Ananga et al., 2021, Hodor et al., 2021) and learners from tribal or religious minorities (Hodor et al., 2021). Access to, and effective use of, EdTech was found to be differentiated based on gender in eight studies. Studies evidenced varied gendered findings, and teachers and caregivers were often the ones exhibiting gender biases. In Pakistan, gender inequity was high, and 29% of caregivers did not allow girls to have access to EdTech for cultural, religious, and financial regions (Adil et al., 2021). In Bangladesh, gender differences were noted where 77% of male respondents had internet access (in comparison to 59% of female respondents) and 56% of male respondents used devices for education (in comparison to 44% of female respondents). By contrast, a study in Kenya found that no major differences were reported in their household surveys, although, at a community level, the education of males took precedence over that of females, implying that if a choice had to be made when resources are limited, boys would be prioritised. (Tembey et al., 2021). Building on existing literature that argues that girls engage and benefit more than boys when provided with the same level of access to technology (Webb et al., 2020, as cited in Tembey et al., 2021), Islam et al. (2021) noted similar evidence. A particularly thought-provoking, gender-specific finding in Ghana was that female tutors were harder on themselves in assessing their performance, yet they outperformed their male counterparts on more difficult teaching components such as student–teacher participation and critical thinking (Ananga et al., 2021). To provide access to educational offerings in rural and remote settings during the pandemic, radio, TV and IVR were used. Rural settings are often characterised by low connectivity, lower literacy and education levels, lower income or dependence on farming, longer distances to walk to school, and learners having less time due to supporting their parents’ work (e.g., farming or caregiving). In Ghana, the national TV programme had more coverage in comparison to the radio programme due to the unavailability or poor quality of radio stations (Hodor et al., 2021). In both programmes, learners felt they needed a slower pace, more interactivity and repeated programming (ibid.). To increase interactivity in hard-to-reach areas, IVR was used in studies in Ghana and Bangladesh (Afoakwah et al., 2021, Islam et al., 2022). The Ghana IVR intervention in rural areas had the highest completion rates, and this was attributed to highly invested School Leaders who embraced the intervention; this emphasises again that EdTech needs to be coupled with human support and motivation (Afoakwah et al., 2021). The same was found in the IVR study in Bangladesh, where improvements were attributed to motivated caregivers who were nudged to engage in the children’s learning (Islam et al., 2022). While the treatment effects of the low-tech IVR in lower-income quartiles were the largest in the Bangladesh study, it was noted that these solutions were less attractive to relatively well-off families who have more accessibility, illustrating the need for tailoring EdTech offerings to contextual factors and target-group preferences (ibid.). The need to tailor to contextual factors is also underscored by contrasting findings which emerged from a similar study also undertaken in Bangladesh. Beam et al. (2022) tested the impacts of SMS nudges to engage with online learning and educational TV, in addition to teacher outreach and reduction of internet costs. The SMS nudges were found to have positive effects on learning outcomes, higher socioeconomic tatus households benefitted to a greater extent (ibid.) In terms of accessibility for learners with special educational needs and disabilities, some studies showed that when executed well, EdTech can support such learners in ways that cannot be possible in traditional face-to-face settings. In a Ghana study on pre-service teacher training, the online learning offering improved participation through easier access to digital resources (e.g., through voice initialising), ability to study independently, less travel time, recorded lessons for replay, and less dependence on friends and family (Ananga et al., 2021). Similarly, the television and radio programmes in Ghana also supported younger students with disabilities, such as through sign language in telecasting, provided these learners had adequate support based on their disability (Hodor et al., 2021). While most of the studies reviewed the equitable nature of EdTech in the teaching and learning space, Fab Inc (2021) analysed this from a management and administration perspective in Sierra Leone. Through the analysis of Annual School Census datasets, national-level data on classroom sizes, enrolment, infrastructure, gender parity, and children with disabilities were able to be used to inform decision-making. The study concluded that the use of data to provide local analytics helps governments to develop tailored and localised solutions such that vulnerable and marginalised groups can be prioritised. Moving beyond access to equitable benefits, some studies explicitly focused on information sharing as a strategy to increase use. The use of information and behavioural nudges was also a notable focus for research in the wider field, such as the World Bank Strategic Innovation Evaluation Fund, as part of Covid-19 responses (Beam et al., 2022, Crawfurd et al., 2021; Geven et al., 2021). Volunteers from Amenya et al.’s (2021) study in Kenya worked with communities and parents to sensitise them on the benefits of attending the reading camps, and this was seen as one of the main factors that made the reading camps work. However, the findings from Aurino et al.’s (2022) study had mixed results: behavioural nudges that were given through text messages on average “decreased caregiver engagement, decreased self-reported school enrollment and attendance, decreased caregiver mental health, and decreased children’s academic skills” (Aurino et al., 2022, p. 9). Upon disaggregation of data, the negative findings correlated with lower-income families, while more positive findings were correlated with caregivers who have some level of education. Reporting on a related part of the same intervention, Wolf and Aurino (2021) note that while boys’ education tended to be prioritised over girls’ in terms of engaging with remote learning, boys were less likely to be as encouraged to return to formal schooling. This example highlights the importance of being attentive to gender and intersectionality. 4.3 Cost and affordability Cost is a critical issue not just in EdTech, but across all of education where prioritisation and investment choices need to be made (Beeharry, 2021). By collecting consistent information about the costs of interventions, and standardised measures of educational outcomes, comparable cost-effectiveness metrics can be produced (Bhula et al., 2020), such as Learning-Adjusted Years of Schooling (LAYS) (Angrist et al., 2020b). This may be of critical importance to educational decision-making in low-resource contexts, and initiatives such as the ‘Smart Buys’ seek to provide actionable advice based on cost-effectiveness metrics (GEEAP, 2020). However, there are also important debates about the extent to which cost-effectiveness can be transferable and practical challenges in relation to measuring the full costs associated with initiatives (Evans & Popova, 2016). Furthermore, in order to ensure educational progress is equitable and also reaches marginalised learners, additional costs may be necessary (Sabates et al., 2018, Zubairi et al., 2021). For EdTech, both capital and operational expenditure need to be considered to understand costs, affordability, and feasibility to scale (Mitchell and D’Rozario, 2022). Amongst our Covid-19 studies, only Islam et al. (2022) provided formal calculations of cost-effectiveness for the intervention being studied. However, other studies discussed practical issues and experiences which have implications for the cost and affordability of interventions (e.g. Adil et al., 2021; Afoakwah et al., 2021; Amenya et al., 2021; Ananga et al., 2021; Aurino et al., 2022; Fab Inc., 2021; Hodor et al., 2021; Islam et al., 2021; Tembey et al., 2021). For example, several highlighted significant capital costs in investments of time and resources in set-up, stating that operational recurring costs could be reduced (e.g. Afoakwah et al., 2021). The studies also provided insight into the less visible costs associated with interventions (e.g. Amenya et al., 2021; Hodor et al., 2021). Islam et al. (2022) provided a full cost-effectiveness analysis and made their data available. While the fixed cost of the IVR intervention per student was very high relative to the variable cost (total USD 27.5 per student over 15 weeks for 1,182 students in two districts). In terms of costs, fixed and variable costs associated with the programme were recorded, so an average cost per student could be estimated; "Variable costs were voice and SMS charges, household reach-out expenditure, etc., and fixed costs were IVR platform development, content development, programme management expenditure, etc." (Islam et al., 2022, p.44). The impact on learning outcomes followed the framework set out by Angrist et al. (2020b) to express learning gains in terms of LAYS. Hence, cost-effectiveness could then be calculated in terms of the number of LAYS gained per USD 100 spent. As part of this more detailed cost-effectiveness analysis, Islam et al. (2022) emphasised that scaling further could drive cost down. The use of LAYS as a cost-effectiveness metric allows comparisons to be drawn alongside a wide range of other EdTech and non-EdTech interventions. Islam et al. (2022) reported that both interventions tested resulted in learning gains equivalent to “2.21 SD (or 2.16 LAYS) per USD 100 of spending for the Standard group and 2.37 SD (or 2.31 LAYS) for the Extended group” (Islam et al., 2022, p.3). Note that the Standard group received literacy and numeracy modules, while the Extended group received a transferable skills module in addition to literacy and numeracy content. In comparison with the range of initiatives with impacts expressed as LAYS gained per USD 100 as reported by Angrist et al. (2020b; Figure B1, p.40), these figures would place the intervention approximately within the top third of interventions plotted. Although Islam et al. (2022) is the only example which provided cost-effectiveness calculations, cost more generally was discussed across the range of studies. A common issue was how to cover access to EdTech devices and data costs. Even where household ownership of mobile phones was high, including low-income households, internet and data costs remained a challenge (Adil et al., 2021, Islam et al., 2021, Islam et al., 2022). In low-resource settings, policymakers must consider costs relating to hardware alongside smaller costs that end-users face. This was also an issue for student teachers, who Ananga et al. (2021) recommend should be provided with a monetary amount to cover the high cost of data to access virtual learning, and those affected by the device gap should also be provided smartphones. With the return to schooling, it is also important to consider what cost effectiveness is being consider relative to, and whether there are any trade-offs in provision. For example, Schueler and Rodriguez-Segura (2021) report on the use of a telementoring intervention in Kenya, following a similar model to Angrist et al. (2020a), but undertaken as schools reopened. For students who returned to in-person education, telementoring was not beneficial, as “tutoring substituted away from more productive uses of time, at least among returning students” (Schueler & Rodriguez-Segura, 2021, p.1). Changes in household costs from EdTech participation varied, underscoring the need to consider context carefully and the risks of over-generalising. In one study, the intervention reduced household costs; having learners engaging with educational television and radio programmes helped to reduce the cost burden of hiring a private teacher, however, in rural areas, there were limited radio stations, making accessing a radio signal more challenging (Hodor et al., 2021). While diversifying language in the interventions was a suggestion to improve learning comprehension, there are many languages in Ghana, which would make this adaptation costly (ibid.) Conversely, the need to access devices incurred further costs to households in another study. Neighbours allowed girls to use their radios and other devices but charged a fee for it, limiting girls’ access (Amenya et al., 2021). The studies also noted the importance of carefully considering the validity of cost comparisons, and that the appropriate benchmarks need to be considered in context. Aurino et al. (2022) recommend further research on how one-directional text-based interventions compare with other interventions, such as in-person community dialogue with parents, for example. Tembey et al. (2021) note that EdTech can be more cost-effective for the user than buying textbooks but requires changing entrenched perceptions about what constitutes appropriate learning materials (Tembey et al., 2021). The example of developing an educational data dashboard in Sierra Leone provides unique reflections on technical development and ongoing costs, with a main choice at the outset being between coding a bespoke dashboard and utilising providers of business analytics software and the licences involved (Fab Inc., 2021). 4.4 Implementation context While school-effectiveness literature has over the years tended to emphasise context in explaining performance (Hopkins et al., 1997, Reynolds, 2010), more recently, there is a sense that this focus has largely disappeared. Accounts of high-performing education systems have advanced the idea that replication can lead to better educational outcomes and performance (Barber et al., 2010, Mourshed et al., 2010). This logic discounts the complexity of education systems as well as the contextual and cultural boundaries in which they operate (Harris, et al., 2015). Despite the powerful influence of contextual factors on learning, understanding which factors are more effective is scarce and inconclusive, partly because it is difficult to generalise ‘what works’ (Bates and Glennerster, 2017, Glewwe et al., 2020). For EdTech, this challenge of attribution, as well as the uptake of evidence and integration into practice equally, is mediated by a contextual and complex ecosystem of issues (Pellini et al., 2021). Ultimately, greater use of technology is only one approach to improving teaching and learning, and as such needs to be tackled from a systems perspective (Bapna, et al., 2021). While none of our studies were focused on the systems level or explicitly sought to address issues of political economy, the variation in learning outcomes noted in Section 4.1 underscores the importance of considering interventions holistically and being attentive to contextual factors. All our studies provide valuable insights into contextual factors which played a role; while acknowledging that examples are specific, they also help to shed light on the types of additional factors to be considered within the design of EdTech programmes in a different context. The difficulties in EdTech access were emphasised in all studies, with issues of devices, data, and connectivity – including hardware, end-user costs and electricity - seen as base determinants in relation to effectiveness. For example, in Ghana, Ananga et al. (2021) found that nine out of ten teachers had poor internet access, which limited their ability to attend teacher training synchronously. In Kenya, participant access to stable electricity and connectivity facilitated their engagement with digital content (Tembey et al., 2021). Some studies show that attempting to deliver EdTech programmes in areas lacking essential services may have a negative impact; Aurino et al. (2022) noted that receiving behavioural nudges to engage in interventions without having the relevant resources increased caregiver stress. Access challenges were identified for girls, linked to social norms and balancing other obligations (see Section 4.2). One of the key findings from Amenya et al. (2021) highlights the importance of considering how EdTech is being used alongside other educational activities and how that impacts learning outcomes. The intervention in Kenya examined the use of television and radio lessons for girls, either in conjunction with or without reading camps. Participation in reading camps was associated with significant gains in both numeracy and literacy; however, “The use of radio and television lessons was not significantly associated with higher learning outcomes, except where girls accessed media as a group outside of camps” (Amenya et al., 2021, p.8). The combination of the added radio component to the reading camp and the peer learning group was the only time that the radio lessons were found to improve performance (ibid.). Afoakwah et al. (2021), Hodor et al. (2021) and Islam et al. (2021) all noted the important role that parents play in facilitating students’ access to content. Given the central role of parents, an important aspect of content tailoring was to ensure messages and supporting materials were appropriate for the educational background of the caregivers (Aurino et al., 2022). Moreover, studies identified that parental traits – digital skills, language literacy, confidence, and motivation – all impact their intention and ability to support their children to engage with EdTech tools (Adil et al., 2021, Amenya et al., 2021, Aurino et al., 2022, Hodor et al., 2021, Islam et al., 2021, Tembey et al., 2021). This means that EdTech interventions will likely be more effective when they help build parental confidence and self-efficacy in supporting technology-enabled learning. Perceptions of the quality of educational materials and content delivered through EdTech were also found to be important contextual elements. The studies often revealed that non-academic design decisions – such as the language of instruction, speaking style and speed of delivery – were key perceived indicators of quality (Afoakwah et al., 2021). Tembey et al. (2021) found that making the links between EdTech tools and national curricula increased parental confidence in the quality and appropriateness of the resources provided. Once teachers and parents have access to high-quality content, they are able to ensure that it is accessible to students in a variety of innovative ways. In Ghana and Pakistan, teachers used WhatsApp to share content during school closures (Adil et al., 2021, Hodor et al., 2021). One study in Bangladesh used IVR to provide lessons by feature phone; in another study, students expressed a preference to receive content via online platforms including Facebook, YouTube, Zoom and Google Meet (Islam et al., 2022, Islam et al., 2021). Finally, in Kenya, educational experiences were provided via television (Tembey et al., 2021). The studies also demonstrate that EdTech tools can be used to establish and strengthen communication channels and chains of connections between teachers, parents and caregivers, and communities. Tembey et al. (2021) highlighted the importance of teacher-to-caregiver connections through easily accessible touch points – such as from teachers, resource packs, and lists of digital tools – to gain technical advice and answers to content questions for their children. Similarly, Aurino et al., 2022 suggest this could be achieved at low cost by targeting caregivers with informal check-ins, messaging and learning content (i.e., by phone, text message, IVR audio lessons) that are timed and tailored to their backgrounds and social contexts (e.g., language, education level). Nudges and incentives for parent and caregiver engagement, particularly female caregivers, were found to have an impact to an extent (e.g., Tembey et al., 2021). Examples within the studies also show that community involvement can be crucial in addition to links between teachers and caregivers (Amenya et al., 2021, Hodor et al., 2021). Co-design processes can help foster social factors, including awareness of technology as an educational tool and cultural openness to using EdTech to support learning (Adil et al., 2021, Islam et al., 2021, Tembey et al., 2021). The studies demonstrated that not only is co-design helpful to increase social alignment and support eventual uptake, but it also generates important feedback about how interventions can be made more effective. Listening to user needs to understand design features such as flexibility in the time of day and tailoring interventions to educational background, language, and reading level were emphasised. The importance of localisation of EdTech interventions was also highlighted, for instance, in terms of speaking style, speed, and repetition of lessons. Ananga et al. (2021) highlighted how a better understanding of gender dimensions helps tailor support to enable female pre-service teachers to engage more effectively. Similarly, Tembey et al. (2021) found that co-designing interventions helped to address parents’ concerns about online safety and inappropriate use of technology in parallel to learning. 4.5 Alignment and scale EdTech effectiveness at scale is likely to depend to some extent on alignment with education systems and sector plans (Hollow and Jefferies, 2022). However, even when aligned, a key challenge that the EdTech sector has faced is so-called ‘pilotitis’, where many pilot projects do not lead to scale (Principles for Digital Development, 2022). Other reasons why EdTech often does not scale include a lack of evidence-based designs, limited end-user involvement, limited funding, a focus on the product or tech as opposed to the problem, and a lack of a strategic approach from governments (Gove et al., 2017, Simpson et al., 2021). Drawing on EdTech scaling initiatives in Chile, China, Indonesia, and the United States of America, Omidyar Network (2019) developed a model for equitable EdTech scaling that outlines four categories: 1) demand-led EdTech supply and sustainable models; 2) enabling infrastructure; 3) education policy, strategy, action-planning, and financing, and 4) human capacity and multi-stakeholder collaboration to bring the vision to fruition. From an education system perspective, the Tusome project was a technology-enabled education intervention in Kenya successfully scaled using Crouch and DeStefano’s (2017) framework that outlines three core functions to enable large-scale education change (1) setting and communicating expectations, (2) monitoring and guaranteeing accountability for meeting those expectations, and (3) intervening to ensure the support needed to assist students and schools that are struggling (Piper et al., 2018). While the studies selected in our portfolio showed signs of alignment with national systems and potential to reach scale due to low-tech and/or cost-effective EdTech offerings, the short study period meant that interventions didn’t move to scale during these timeframes, with the exception of Hodor et al. (2021), which evaluated the national Ghanaian remote learning programmes, and Adil et al. (2021), which evaluated the impact of low-tech solutions in Bahawalnagar district in Pakistan. Still, the studies highlighted a variety of insights that speak to these issues. Several studies found that when the EdTech offering is embedded into existing systems and processes (as opposed to an add-on), it increases uptake (Adil et al., 2021, Hodor et al., 2021, Islam et al., 2022, Tembey et al., 2021). The systems could be education-related, for example, working with educational activities already used, or technological systems, for example, using instant messaging like WhatsApp that has already penetrated into a community (Hodor et al., 2021). When linked to school curricula, sustainable benefits observed included increased student engagement, increased use of EdTech offerings and more motivation (Adil et al., 2021, Afoakwah et al., 2021). To ensure sustainability at a national scale, interventions need to be aligned with national curricula, scheduling, strategies, and action plans (Ananga et al., 2021). This was illustrated in Kenya, where mapping resources to the national curricula was a key step toward parental acceptance and potential sustainability. In some cases, when well aligned, the intervention or research is then able to feed back into strategic policies to improve them (Fab Inc, 2021, Hodor et al., 2021). Building on guidelines from Omidyar Network (2019) that emphasise the importance of having a national information and communications technology (ICT) backbone (i.e., electricity, telecommunication infrastructure and internet access) in place to support scaling of EdTech use for all, studies identified similar leverage points. For example, in Ghana’s pre-service teaching colleges, the internet was provided by Ghana Tertiary Education Commission (GTEC) and Transforming Teacher Education and Learning (T-TEL) on campuses, enabling the intervention to reach more students, with ongoing work to tackle the issue of internet connectivity more broadly (Ananga et al., 2021). In unpacking barriers to scaling, some studies sought to uncover why EdTech use is limited, even when there is accessibility. Islam et al. (2021) outlined reasons such as lack of trust, lack of permission to use devices, limited technical knowledge and digital literacy, lack of interest and lack of a formal offering by an educational institution. Relatedly, parents’ literacy and digital literacy impacted use, either due to their reluctance to allow their children to use devices or their limited ability to support their children (Adil et al., 2021, Tembey et al., 2021). Hodor et al. (2021) and Tembey et al. (2021) similarly outlined a lack of awareness and advertising of educational offerings. In some cases, the educational benefits of technology are not apparent to users who associate devices with entertainment or socialising (Adil et al., 2021). As shifts back to in-person learning began, a common theme from our studies was that learners and teachers who had used EdTech had hopes that remote learning could shift to blended learning, combining in-person teaching and learning with technology. Some learners emphasised that EdTech-enabled distance learning was no replacement for in-person learning (Adil et al., 2021, Islam et al., 2021); others said they would like a hybrid approach with in-person learning supplemented by EdTech (Islam et al., 2021). In Ghana, as schools opened, 15% of teachers interviewed had transitioned to blended learning (Hodor et al., 2021). Some teachers encouraged learners to participate in the national TV and radio remote learning programmes, and others incorporated radio broadcasting into the school timetable, following it with support and activities (ibid). While most stakeholders in Ananga et al.’s (2021) study are confident to move forward with blended learning, they cautioned that demonstrations and exams are better done in person. This stresses the need for interventions to investigate the most effective mix of digital, remote, and face-to-face learning that suits the context and purpose, noting macroscopic factors such as political will and institutional capacity that also come into play (Poirier et al., 2019) 5 Conclusions While the issue of school closure — one of the most acute aspects of the educational crisis during the pandemic – has passed, EdTech has also played a role as part of the return to schooling. With the use of EdTech increasingly used to support teaching and learning, there is an ever-mounting need to consider the determinants of effectiveness in technology-based education interventions. This paper illustrates the use of a holistic framework to understand EdTech effectiveness, drawing on ten primary research studies conducted during Covid-19 in low-resource settings. The framework considers multiple perspectives, including impact on learning outcomes, enhancing equity, cost and affordability, implementation context, and alignment and scale. By applying this framework, our findings highlight the valuable insights that can be gained and uncovered through a holistic approach to effectiveness. Understanding EdTech effectiveness through the lens of learning outcomes is a starting point, but neither straightforward nor sufficient. Overall, mixed results from our studies underline a need for additional and more nuanced data to understand variations, underscoring the importance of mixed-methods studies. While studies largely used existing assessment tools focused on foundational literacy and numeracy, their experience demonstrates that standardised tools should be used alongside context-specific educational measures. A need for comparative work on the specific affordances of different forms of EdTech also emerged, for example, in relation to learning outcomes in different subject areas. Learner preference for interactive pedagogy of social media and group learning was a further insight important in supporting learning gains. The Covid-19 pandemic has shown how educational inequalities can be exacerbated for teachers and learners who do not have the ability to access and utilise online and digital modes of learning. Analysis of our studies in making EdTech more effective from an equity lens reveals lessons in relation to specific groups; this includes gender biases against girls and young women, the role of facilitators and caregivers in rural and hard-to-reach settings, and adaptive approaches and tech important for learners with disabilities. The role that data plays in advancing equity and the importance of moving from EdTech access to equitable benefit also emerged. Given that studies using seemingly similar EdTech approaches observed different impacts in different contexts, it is clear that there is no silver bullet approach to designing for equity. While much research has focused on equity in terms of access, more is needed on how to support the equitable benefit of EdTech. A better understanding of cost and affordability are similarly critical to the effectiveness of EdTech. With only one study in our sample fully calculating measures of cost-effectiveness, this is surely an area where more effort and research are needed, given the potential for comparison it offers. In addition, our studies highlight the need to view cost over time, as an early intervention at a smaller scale will have much higher costs, and to consider the stage at which cost was calculated, to ensure comparisons are like-for-like. With the potential to scale often being cited as a rationale for EdTech cost-effectiveness, it is important to consider other issues such as upfront investment alongside ongoing costs and develop a fuller picture of metrics, financing, household costs and alternative comparisons. Research gaps in this area include the need for guidance for comparable costing, as well as conducting and greater sharing of cost-effectiveness analyses and how these can be consistently applied. Additional insight on the effectiveness of EdTech can be found in considering the context. In the low-resource settings of our studies, barriers to EdTech access are prevalent and overcoming these remains central, but not sufficient, to effectiveness. Several of our studies showed that the use of EdTech alongside other group-based interventions can positively impact learning outcomes, whereas a technology-based intervention on its own is less effective. Moreover, with Covid-19-related school closures having forced a shift in parents’ and caregivers’ roles in their children’s learning, their perceptions of the quality of an EdTech offering and the importance of regular communication channels is critical. Many of the studies also emphasised that for EdTech offerings to be effective in context, co-design with relevant stakeholders is important. Thus, a critical research gap in relation to context centres around the rigour and pacing of co-design, ensuring this is better based on evidence rather than anecdote. An indication of effectiveness is also found in the degree to which EdTech aligns and scales. The presence of a national ICT backbone is an important piece, but it alone is not enough. Alignment with national curricula was highlighted in several of our studies as an important aspect of EdTech acceptance that could facilitate acceptance and scaling. Further practices that can support scaling in equitable ways were identified, such as improving the digital literacy of learners and teachers, involving parents and caregivers, and explicitly explaining the educational purposes of EdTech. Moving forward, increased adoption of blended learning is seen both as desirable and as a way toward further scale. With EdTech increasingly included in national education plans, further research is needed on how governments and other actors can foster an EdTech ecosystem that aligns and scales to their priorities. Together, greater insight into each of these five elements can paint a fuller picture of the effectiveness of EdTech. While the studies focused upon the continuation of learning during the pandemic, the findings also have practical implications for the return to schooling. In terms of learning outcomes, the use of assessment tools and demonstrated efficacy of short-term EdTech interventions are affordances which could be utilised to assist with identifying learning losses and providing targeted additional support for learners who have experienced losses to a greater extent. However, the discussions in relation to equity are a reminder that any EdTech intervention alongside a return to schooling needs to be carefully considered within its context. Cost-effectiveness raises questions about what EdTech interventions should be considered relative to, in the return to schooling (e.g. Schueler & Rodriguez-Segura, 2021). In relation to both implementation and scale, the discussion points to the role of teachers, as co-designers and facilitators of a more blended approach to education. Overall, our analysis underscores that having access to technology is only a very small part of effectiveness, with wider elements deserving as much, if not more, attention. It also suggests that EdTech works best with designs that explicitly incorporate co-creation (with users) and contextualisation and that address equity issues from the onset. Insights from further applications of this framework will be relevant for policymakers continuing to integrate technology into education systems during any ongoing closures and beyond. Uncited references (GEEAP. Global Education Evidence Advisory Panel, 2020, GEEAP. Global Education Evidence Advisory Panel, 2022, UNESCO, 2022, World Bank, 2021) CRediT authorship contribution statement SN: Conceptualization; Writing - Original Draft; Writing - Review & Editing; Visualization; Supervision. KJ: Conceptualization; Writing - Original Draft; Writing - Review & Editing; Visualization. CM: Conceptualization; Writing - Original Draft;TA: Writing - Original Draft; Writing - Review & Editing. TK: Writing - Original Draft. Acknowledgements This research was funded through the work of The EdTech Hub (http://www.edtechhub.org). Many thanks to all the authors of the Covid-19 grant reports and participants in the studies, as well as Kate Jefferies, Chris McBurnie and Asma Zubairi, who played a role along with this paper’s authors in supporting the research. Further thanks go to Kate Jefferies and Ashley Stepanek Lockart for their work on a linked paper and Mike Trucano for his feedback on an earlier version of our analysis. ==== Refs References Adam T. Digital literacy needs for online learning among peri-urban, marginalised youth in South Africa International Journal of Mobile and Blended Learning 14 3 2022 1 19 10.4018/IJMBL.310940 Addey C. Sellar S. Steiner-Khamsi G. Lingard B. Verger A. The rise of international large-scale assessments and rationales for participation Compare: A Journal of Comparative and International Education 47 3 2017 434 452 Adil F. Nazir R. Akhtar M. 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The state of the global education crisis: A path to recovery. World Bank Group. https://documents.worldbank.org/en/publication/documents-reports/documentdetail/416991638768297704/the-state-of-the-global-education-crisis-a-path-to-recovery Xu W. Zammit K. Applying thematic analysis to education: A hybrid approach to interpreting data in practitioner research International Journal of Qualitative Methods 19 2020 1 19 10.1177/1609406920918810 Zubairi A. Kreimeia A. Jefferies K. Nicolai S. EdTech to reach the most marginalised: A call to action EdTech Hub 2021 https://doi.org/10.53832/edtechhub.0045
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==== Front Poetics (Amst) Poetics (Amst) Poetics (Hague, Netherlands) 0304-422X 1872-7514 Elsevier B.V. S0304-422X(23)00048-7 10.1016/j.poetic.2023.101808 101808 Article The “Waves:” Conceptualizing Covid-19 as an Event Through One (Particularly) Contested Metaphor Rekenthaler Nick ⁎ New York University, Department of Sociology ⁎ Corresponding author:Nick Rekenthaler, New York University, Department of Sociology, 295 Lafayette Street 4th Floor, New York, NY, 10012 27 6 2023 27 6 2023 10180813 6 2022 26 5 2023 23 6 2023 © 2023 Elsevier B.V. All rights reserved. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. This paper bridges scholarship on events with that on metaphors, positing metaphors as a proxy for competing “forms of eventfulness.” Focusing specifically on the “wave” metaphor, I draw from 471 Governor's Covid-19 Briefing transcripts across ten governors—five Democratic, five Republican—from the year 2020 to identify two competing forms of eventfulness with respect to the Covid-19 pandemic. As I show, using both discourse analytic techniques and simple text counts, Democratic governors take up the “wave” metaphor to present what I call “cascading” eventfulness, defined by multiple conditional moments of rupture, or “waves.” In contrast, Republican governors largely avoid the “wave” metaphor to present what I call “calamitous” eventfulness, defined by a singular, decisive moment of rupture. I conclude with a discussion of how my findings contribute to scholarship on eventfulness and political ideology. Keywords Covid-19 Rupture Event Metaphor Discourse ==== Body pmc1 Introduction “There Isn't a Coronavirus ‘Second Wave,’” Mike Pence declared to headline his Wall Street Journal op-ed on June 16th, 2020. As he explained, “In recent days, the media has taken to sounding the alarm bells over a ‘second wave’ of coronavirus infections. Such panic is overblown. Thanks to the leadership of President Trump and the courage and compassion of the American people, our public health system is far stronger than it was four months ago, and we are winning the fight against the invisible enemy” (Pence, 2020). In short, Pence contrasts two conceptions of what we may call the “event” (see, e.g., Sewell, 1996; Clemens, 2007; Wagner-Pacifici, 2010) of Covid-19: a “[winning] fight” against an “invisible enemy” versus a series of “waves.” This dichotomy begs the central question of this paper: what is the significance of the “wave” metaphor to the Covid-19 pandemic? An event may be defined as the consolidation of a moment of rupture in a form (Wagner-Pacifici, 2010; 2017). The moment of rupture of the Covid-19 pandemic was clear when on March 13th, 2020, the Trump Administration declared a national emergency, and, less than a week later, states began issuing stay-at-home orders for their citizens as the national daily case count climbed exponentially (USAToday, 2020). The broad form of Covid-19 was also clear when, by the end of March, comparisons to the 1918 pandemic began to abound (e.g., Sanders, 2020). More contentious was the pandemic's “form of eventfulness” (Tavory & Wagner-Pacifici, 2021) or the specific way this pandemic was to be conceived. Scholars of events have analyzed many discursive devices to theorize events in their unfolding. One yet unexplored, though fruitful device, is metaphor. Beginning with Aristotle, metaphors have been touted for their ability to simplify complex information. More recently, Lakoff & Johnson (1980) have elaborated a “Conceptual Metaphor Theory” (CMT) to argue that metaphors are a central aspect of human cognition. Since then, sociologists (e.g., Levine, 1995; Tierney et al., 2006; Jerolmack, 2008) have increasingly analyzed actors’ use of conceptual metaphors, including the multifaceted “wave” metaphor (Santa Ana, 2002; Biggs, 2003; Jones & Helmreich, 2020). In this paper, I employ discourse analytic techniques in addition to simple text counts to analyze 471 Governor's Covid-19 Briefing transcripts1 across ten governors—five Democratic and five Republican—from the year 2020. Contending that metaphors are a yet undertheorized proxy for identifying competing forms of eventfulness, I show that governors’ adoption (or avoidance) of the “wave” metaphor provides insight into how they conceived of the pandemic's temporal form; these conceptualizations in turn influenced how governors acted upon the event. 2 Events and Their Metaphors 2.1 Events, Forms, and Forms of Eventfulness Sewell (1996: 100) famously defines historical events as “…that relatively rare subclass of happenings that significantly transforms structures.” One event that has received much scholarly attention—from both Sewell (ibid) and others (e.g., Wallerstein, 1989; Furet, 1981)—is the French Revolution. Other examples of historical events include Captain Cook's visits to the Hawaiian Islands (Sahlins, 1991), the French Miners’ Strike of 1906 (Tilly, 1985), the September 11th attacks (Wagner-Pacifici, 2010), and the 2008 financial crisis (Sewell, 2012). As Wagner-Pacfici (2017) points out, events are “paradoxical” in the sense of being at once “singular” and “general.” That is to say, every event is unique, sui generis, and yet at the same time belongs to a broader class of events that allow us to define it as such. As Boltanski (2014: x) observes, “The event as a singularity thus takes on full meaning only by being related to an entity credited with an identity, a certain stability across time…” More concretely, Wagner-Pacifici (2017) notes that scholars of events have been equally interested in the particulars that gave rise to “The French Revolution” as the way it represents “revolutionary events” in general. Of course, events take shape across time; they need to unfold (Griffin, 1993).2 As Sewell (1996:229) explains, “In spite of the punctualist connotations of the term, historical events are never instantaneous happenings: they always have a duration, a period that elapses between the initial rupture and the subsequent structural transformation.” And, as he shows through his favorite case study, our understanding of an event's singularity necessitates our understanding of its generality: the initial rupture of the “taking of the Bastille” could not be understood as the beginning of the structural transformation known as the “French Revolution” unless contemporaries understood it in its duration as a “taking”—that is, as a revolutionary act (235). Building on Sewell's work, Wagner-Pacifici's (2010; 2017) analytical framework of “political semiosis” seeks to trace events in their discursive unfolding. She conceptualizes an event's general structure as its “form” (2017: 84) and defines an event as the consolidation of a moment of rupture in a form. She explains, “Ruptures disarticulate. And in the space opened up by disarticulations, individuals and institutions craft events out of available forms” (ibid). For example, she traces how media accounts of the “accidents” of two airplanes flying into the World Trade Center progressively morphed into “incidents,” and, eventually, into their solidified form as “terrorist attacks” to become the “September 11 attacks” (2010: 1352). Establishing an event's form is important as it gives us a sense of how it will unfold. White (2008: 21) observes that myths, fictional stories, and histories share not only a common form—“the story, fable, tale, parable allegory”—but, in so doing, also share a common content. He calls the homology between an event's form and its content its “narrative substance” to indicate that events are, to some extent, narratable. As the logic goes, just as identifying a novel's form as a “mystery” gives us a sense of its broad structure—i.e., an initial crime followed by a prolonged investigation and ending with a surprising discovery—so, too, does identifying an event's form as, say, a “revolution.” Sewell (1996: 235) observes, “We are by now used to the notion that revolutions are radical transformations in political systems imposed by violent uprisings of the people.” Framed another way, establishing an event's form is an attempt to manage its ambiguity; it is an effort to re-articulate what was made uncertain in a moment of rupture—to capture the generality in the singular. And yet, as Tavory and Wagner-Pacifici (2021) show in a recent paper, there are events for which the exact meaning behind that broad form is contested. Extending Wagner-Pacifici's analytical framework of “political semiosis,” Tavory and Wagner-Pacifici (2021) consider three “forms of eventfulness” with respect to the same would-be event: climate change. In this case, rather than to theorize climate change within a general form (e.g., a “disaster”), they seek to theorize its specific formal iterations (2021: 1). Tavory and Wagner-Pacifici identify three forms of eventfulness across three sets of actors, which each “positions rupture, actors, and the trajectories, temporal landscapes and immediate contexts of action in distinctive ways” (2021: 9). They call these forms of eventfulness “scientific eventfulness,” “radical eventfulness,” and “sensible eventfulness.” A form of eventfulness thus identifies a particular orientation towards an event—a lens through which a set of actors mutually view it. Tavory and Wagner-Pacifici (2021: 9) summarize the three lenses with respect to climate change: “Scientists face the tension between their disciplinary forms of communicating, and the hesitant and conditional modalizations these imply, and the definition of climate change as a critical event; radical eventfulness is more direct, locating the event in the present, as well as in a generationally defined future; the sensible temporality is torn between constructing trajectories that would avoid the worst outcomes of climate crisis, and a temporal landscape within which non-eventful actors go on their merry way.” Taking the Covid-19 pandemic as my object of study, I contend that metaphors serve as a yet unexplored, though highly effective proxy, for identifying competing forms of eventfulness. 2.2 Metaphors as Connoting Forms of Eventfulness Aristotle offers one of the earliest accounts of metaphor in Poetics. 3 He writes, “Metaphor consists in giving the thing a name that belongs to something else; the transference being either from genus to species, or from species to genus, or from species to species, or on grounds of analogy” (1457b7-9). He provides the example of an actor stating, “Here stands my ship” to indicate a ship that is anchored (1457b10). In this case, the ship is not literally standing—as ships do not “stand”—but is effectively standing in the way a person might: in other words, it is upright and at rest. While Aristotle further contends that metaphors are “agreeable” to us in allowing us to “get hold of new ideas easily” (Rhetoric, 1410b3), cognitive linguists have gone a step further to argue that metaphors are central to everyday life. Presenting their Conceptual Metaphor Theory (CMT), Lakoff and Johnson (1980: 3) argue that our conceptual system is “fundamentally metaphorical in nature.” For example, they assert that the “argument is war” metaphor is not just a matter of speech—as in the expression, “I demolished his argument” (4)—but is also a matter of thought; we actually think of arguments in terms of war: in terms of “winning” against an “opponent” on a “battlefield.” According to Lakoff and Johnson (1980), there are a core set of conceptual metaphors like the “argument is war” metaphor which are shared not just by English speakers, but cross-culturally. Jimenez et al. (2021) suggest that the “immigration as inundation” metaphor is another of such metaphors. They explain, “…immigration discourse often relies on an immigration metaphor that compares immigrants to water and their perceived impact on one's country to dangerous flooding” (2021:159). Indeed, we often hear politicians speak of immigrants as “pouring in,” “flooding across borders,” and, of course, coming in “waves.” In this sense, the metaphor of a “wave” thus serves as a proxy for the conceptual linking of immigrants and inundation. The “wave” metaphor has received particular attention from scholars across a variety of fields. For example, in addition to immigration (Santa Ana, 2002; Jimenez et al., 2021), the metaphor has been studied in the context of “waves” of feminism (e.g., Nicholson, 2010) and labor strike “waves” (Biggs, 2003). The “wave” metaphor has also employed been employed by Sewell (2012) himself in describing the “waves within waves” of economic cycles. The “wave” metaphor clearly resonates (McDonnell et al., 2018) across a variety of domains. The question again is, what, exactly, does it signify? Consider again the quote from Woolf (1931) that frames this paper. As she (hypnotically) exemplifies, waves inundate in multiples; they repeat. Said a third way, the presence of one “wave” of feminism/immigration/strike suggests another just behind it. I argue it is for this reason that Pence so adamantly rejects the metaphor of a “second wave,” replacing it with that of a “winning fight” against an “invisible enemy:” while a fight can be a singular entity, a wave cannot. In this paper, I follow many others (e.g., Craig, 2020; Jones & Helmreich, 2020; Wicke & Bologensi, 2021) in observing the centrality of the “wave” metaphor to the Covid-19 pandemic. As Jones and Helmreich (2020) observe, “Regardless of how Pence and Fauci parse the phrases of the pandemic, though, the language of epidemic waves is here to stay, carrying a significance both symbolic and epistemic, existential as well as epidemiological.” It is this symbolic (and, at times, perhaps even existential) meaning I explore in the ensuing pages. 4 Yet, whereas the authors mentioned above merely hint at potential differences in its usage across party lines, I take the “wave” metaphor's political contestation as my central object of study. Given the rich body of sociological research that shows ideological liberals as perceiving more risk of contracting Covid than ideological conservatives (Shepherd et al., 2020), liberal states as being more amenable to public health recommendations than conservative states (Hill et al., 2021), and Democratic governors as more likely to treat recommendations as a “call to action” than Republican governors (Kirgil & Voyer, 2022), I expect Democratic governors to follow Fauci (Lovelace Jr., 2020) in taking up the “wave” metaphor and Republican governors to follow Pence in rejecting it. 3 Data and Analysis 3.1 Data This paper uses 471 Covid-19 Press Conference Briefings (n = 3 164 649 words) across ten governors from the year 2020 (March-December) to investigate two competing forms of eventfulness with respect to the Covid-19 pandemic. The ten governors are as follows: Andrew Cuomo (D- New York), Brian Kemp (R- Georgia), Gavin Newsom (D- California), Greg Abbott (R- Texas), Gretchen Whitmer (D- Michigan), J.B. Pritzker (D- Illinois), Mike DeWine (R- Ohio), Ron DeSantis (R- Florida), Roy Cooper (R- North Carolina), and Tom Wolf (D- Pennsylvania). I selected these ten governors as they represented the ten most populous states in 2020, and, conveniently, five are Democratic, and five are Republican.5 Of the total 471 transcripts, 292 (∼62%) were drawn from Democratic governors, and 179 (∼38%) were drawn from Republican governors. For a specific breakdown of the number of transcripts by governor, see Figure 1 below.Fig. 1 Number of Transcripts by Governor. Fig 1 These press conference briefings are, on average, about forty-five minutes in length—or ∼6,500 words—and include four distinct sections: (1) an initial statement from the governor, (2) supporting statements by local experts, (3) a question-and-answer session with the public, and (4) a closing statement from the governor. While the governors are rarely the sole speaker in the press conferences, they almost always speak the most. I collected all the press conference transcripts from Rev.com, a speech-to-text transcription service with an open library of thousands of full transcripts. Specifically, I gathered the transcripts from the “Covid-19 Briefing and Press Conference Transcripts” section of the library via web scraping.6 All transcripts that matched the study's inclusion criteria—that is, dated from March-December 2020 and hosted by one of the ten governors listed above—were downloaded and converted into .txt format for analysis. 3.2 Analysis The analysis proceeded in four phases and followed an abductive approach (Timmermans & Tavory, 2012) in the sense of double-fitting theory and findings. In an initial exploratory phase, I selected a random selection of transcripts (5 for each governor, so 50 in total; n = 339 763 words) to read, looking out for any metaphorical expressions that described the structure of the pandemic (see, e.g., Wicke & Bolognesi, 2021). Following Pence's op-ed, I was particularly interested in all instances of the “wave” metaphor, hypothesizing that Democratic governors might employ the term more frequently than Republican governors; this is indeed what I found. It was also during this phase that I identified my other metaphors of interest, which I defined as those metaphors that were used by at least separate three governors and in at least ten transcripts in total (see Appendix A for a full list of these metaphors).7 In the second phase, I established my quantitative trends. As the “wave” metaphor was my central object of inquiry, I first identified all transcripts that contained the string “wave” and then manually checked to ensure that they employed the term correctly—that is, by the governor themselves, as a metaphor, and in reference to the structure of the Covid-19 pandemic (alternatively, if someone other else used the metaphor, or if the governor used it to refer to “a wave to a friend,” it was discounted). A similar process was repeated for all other metaphors of interest (e.g., “battle, “curve,” “phase,” “uptick”). It was during this analysis that I calculated the “wave” metaphor to have by far the biggest effect size in terms of being used more frequently by one party than the other. 8 In the third phase, I qualitatively coded a subsample of 120/471 (25%) of the transcripts (n = 821 314 words) using the qualitative data analysis software, NVIVO. Of this subsample, 63 (n = 425 537 words) were drawn from Democratic governors and 57 (n = 395 777 words) were drawn from Republican governors. As with my overall analysis, I developed these codes abductively, beginning with a set of known codes—e.g., “Wave Metaphor,” “Surge Metaphor,” “Reference to the 1918 pandemic”—to which I recursively added supplementary codes when necessary—e.g., “Reference to Natural Disasters,” “Uncertain Language.” In total, I created 27 distinct codes (see Appendix A). My selection criteria for this process of coding was as follows: 1) where possible, I gathered one transcript at random from each month for each governor,9 and 2) I additionally gathered transcripts that had been identified as particularly interesting during the cleaning process in the second phase of analysis. In the final phase, I conducted discourse analysis on the subsample of transcripts collected during the previous phase. Briefly, discourse analysis may be defined as “the study of language in use” (Gee, 2014: 8). Given the focus of this study, attention was given to all metaphorical expressions, though paramount attention was given to any instantiation of the “wave” metaphor. 4 Findings Covid-19 was declared a national emergency by the Trump administration on March 13th, and, just six days later, state governors began issuing stay-at-home orders. The start date and length of these stay-at-home orders for each of the ten states included in this analysis is listed in Figure 2 below.Fig. 2 Stay-at-home Orders by State; table values calculated using the figures provided here: https://www.usatoday.com/storytelling/coronavirus-reopening-america-map/#restrictions Fig 2 As the figure shows, by April 3rd, all ten governors had issued a stay-at-home order. Yet, as the figure also shows, there was a difference in the length of those stay-at-home orders across parties. Whereas, on average, Democratic governors issued stay-at-home orders lasting 64 days, Republican governors issued stay-at-home orders lasting only 41 days. Such a discrepancy is likely partially the result of initial systematic differences in Covid-19 infection rates across states by political party (NYT, 2023). However, this is certainly not the only explanation—especially given that, later into the year when Republican-led states were experiencing higher daily cases counts compared to Democrat-led states, those Republican-led states were yet more likely to ease opening restrictions (Adolph et al., 2021). Instead, as I show, after an initial moment of uncertainty, Democratic and Republican governors came to conceptualize the pandemic through competing forms of eventfulness, each with their own strategies of action (Swidler, 1986). 4.1 March: Towards an Articulation of Rupture Daily national Covid-19 case counts rose steadily from March 13th to the end of the month (see Figure 3 ). Additionally, as noted above, by the end of the month, 7/10 governors had issued a stay-at home-order—and the three that had not would very soon do so.Fig. 3 Daily Trends in Number of Covid-19 Cases in the US in 2020 (as Reported to the CDC); source: https://www.statista.com/statistics/1102816/coronavirus-covid19-cases-number-us-americans-by-day/ Fig 3 Phrased another way, for these governors, March was marked by a moment of rupture or “the experience… that the ground has dramatically shifted” (Tavory and Wagner-Pacifici, 2021:1). In this way, it was marked by a consensual feeling of the “unknown” as it was clear that something was happening, and yet this something had not yet coalesced into a particular form. Whitmer describes the situation on March 18th: “So we are in unprecedented times, and we've got serious challenges ahead, but I am more confident than ever that when we all pull together and do our part, we will get through this.” Whitmer—among others10 —exemplifies this state of unknowing in her use of the phrase “unprecedented” and in her ambiguous indexical “this.” Indeed, looking back at her quote, it is not at all clear to what Whitmer's “this” refers: the “unprecedent times”? The “serious challenges?” Something else? Following Wagner-Pacifici (2017: 84), Whitmer's quote appears to be a clear example of how “ruptures disarticulate.” Faced with this disarticulation, this first month of the pandemic involved governors testing various metaphors to give the moment of rupture a form. One popular category of metaphors was to compare the pandemic to a natural disaster, as Pritzker does on March 4th. He explains, “…when we start looking at things like hurricanes and that, they strike a portion of the country and sometimes we can go pull from another state, pull capability. This is like a hurricane hit all 50 states at once.” Yet, for Pritzker, there is an ambivalence to this comparison, as the event is both like a hurricane and more than a hurricane; it is more like 50 hurricanes. However, in the same briefing, Pritzker elsewhere refers to it as a “battle,” and, later, an “outbreak.” Thus, while vibrantly imagined, Pritzker's comparisons remain inconclusive. Cooper displays similar equivocation when he describes the situation to the people of North Carolina on March 18th, employing both disaster and military metaphors side-by-side. He comments:I've stood at this podium on sunny days with a Tar Heel, blue sky outside and warned about a hurricane that may be on the way. It's hard to grasp that a hurricane is coming before the worst hits. We know that this situation will get worse before it gets better. And I know that people are shell shocked. One day you're at work and you're going about your business, and the next day your world has turned upside down… You worry about every cough. The change is dramatic. Like Pritzker, Cooper is reminded of past hurricanes, and yet he, too, recognizes that there is something different about “this situation,” also comparing it to the violent experience of “shell shock.” That is to say, the change is both “dramatic” and immediate, completing shifting one's daily routine from one day to the next. Indeed, there is perhaps no better description of rupture than Cooper's image of a world “turned upside down.” Other categories of metaphors were also evoked during this first month of the pandemic as governors attempted to give form to this event-(rapidly)-in-the-making. For example, Whitmer, Kemp, and DeWine respectively describe it as “a fluid situation,” (March 18th) “this storm” (March 23rd) and “this monster” (March 31st). But as with the natural disaster and military metaphors, there were not yet systemic differences in the use of these metaphors across parties. As Figure 4 below reveals, it was rather in April that a systematic difference solidified in terms of the metaphors Democratic and Republicans governors used to describe the structure of the pandemic: while Democratic governors increasingly took up the “wave” metaphor to suggest a pandemic with multiple, conditional moments of rupture, or “waves,” Republican governors largely avoided the metaphor to represent a “calamitous eventfulness” with a singular, decisive moment of rupture.Fig. 4 Comparison of the Proportion of Transcripts that Include the “Wave” Metaphor Across Political Party. Fig 4 4.2 April: A Divergence in Forms of Eventfulness At the end of March and early into April, governors across both sides of the political divide began to test out the “wave” metaphor with increasing frequency. However, unlike Pence's quote that frames this paper, these governors did not yet speak of a “second wave;” rather the “wave” to which they referred was a thoroughly singular entity. Consider the following quote by Cuomo from March 31st:This is going to be a rolling wave across the country. New York. Then it'll be Detroit, then it'll be New Orleans, then it will be California. If we were smart as a nation, come help us in New York… then let's all go help the next place. Then the next place. Then the next place. Compare that to DeWine's on April 1st:New York is much more densely populated. It certainly may travel quicker there. But what we're seeing in New York is what we work every single day to avoid here in the state of Ohio when this wave finally does really hit us. And so this is what we're trying to prepare for. For both Cuomo and DeWine, this “wave” is both inevitable and national in scope: it will begin in New York and then “roll” across the country, hitting every state along the way; it may “travel quicker” in some places, but, whether in New York, California, Ohio, etc. it will “really hit.” As such, in contrast to the ambiguous natural disaster/wartime metaphors of Pritzker and Cooper from early- and mid-March, there is now a clearer sense of the pandemic's temporal orientation: like a “wave” in a soccer stadium, the threat is immediate, though fleeting. Specifically, as Cuomo explains earlier in the same transcript, “If our apex is 14 to 21 days, that's our apex. You then have to come down the other side of the mountain once you hit the apex.” Adding the “14 to 21 days” of the apex plus the time it takes to “come down the other side of the mountain,” perhaps the temporal orientation is somewhere between one and two months.11 This notion of a metaphorical “wave” is not only shared across political party but is also interchangeable with other metaphors that connote the rough shape of a “mountain.” DeWine continues, “So when we get to this, the surge…” Thus, it appears that Cuomo and DeWine have arrived at one possible answer to the question as to what Whitmer's ambiguous “this” signifies: a “wave” or a “surge” of cases—that is, cases that quickly rise across the country, peak after a few weeks, and then, over the course of month or so, recede. However, this notion of a singular surge-like “wave” soon becomes contested as Covid-19 is increasingly compared to the “Spanish Flu” of 1918.12 Referencing the fact that the pandemic has been present in New York for 37 days, Cuomo remarks on April 7th, “The 1918 pandemic that we talk about peaked in New York for six months, it came through in three waves and it peaked for six months. 30,000 people died in New York during that pandemic.” Suddenly, Cuomo presents a very different shape of the pandemic: the apex of “14 to 21 days” is replaced by a peak of “six months;” a singular “rolling wave” of cases is replaced by “three waves” of them; and the roughly 5,500 recorded deaths in New York as of April 7th is replaced by a near six-fold increase of “30,000” deaths; all at once, the pandemic morphs into something much broader in scope. Following Cuomo's comparison to the 1918 pandemic, within a week, Newsom, Whitmer, Pritzker, and Cuomo himself all adopt the notion of multiple “waves” to refer to a future “second wave” of the pandemic. For example, as Whitmer explains on April 9th,Singapore was considered the gold standard for combating COVID-19, but then they stopped. And now they've got a second wave. That would be the most devastating thing for our state if we think that on April 30th, that we just flip a switch and life returns to how it was, it's not going to be how it was. We just all have to come to terms with that. And that's the harsh truth. Whitmer's statement may be taken as a warning—a declaration of the “harsh truth.” Note the narrative structure that underlies the beginning of her statement: “Singapore was considered the gold standard… then they stopped… and now they've got a second wave.” This narrative structure does two things: (1) it turns the inevitability of the “rolling wave” into a conditionality and (2) it expands the singular moment of rupture into a series. That is to say, for Whitmer, the issue is no longer that “stopping” one's combative efforts could result in a larger “wave;” it is that “stopping” could actually result in a future “second wave.” As such, Whitmer's statement positions a form of eventfulness that “cascades” into the future—one that cannot be reversed with the simple “flip [of] a switch.” Whitmer's statement can be directly contrasted to that by Abbott from April 27th. He explains:Phase 1 opens up on May 1st. We want to usher in Phase 2 as quickly as possible, most likely around the May 18th timetable, if there is no flare up of COVID-19 and let me explain what that means. We will be tracking data. Because there will be an increase in the amount of testing, it's only logical to see there would be an increase and the number of people that test positive. So just because there may be an increase in the number of people that test positive, that alone is not a decisive criteria. Abbott's statement is far more optimistic. The proverbial “switch” has already been “flipped:” phases 1 and 2 are quickly approaching. And while he also acknowledges a conditionality in the potential of a “flare up,” it is ultimately an inconsequential conditionality. Unlike a destructive “second wave,” this “flare up” is something that, according to Abbott, (1) can be explained away, (2) is “only logical,” and (3) is not a “decisive criteria.” In other words, like a “flare up” on the skin, it might require momentary attention, but it will not cause real rupture. As such, Abbott's statement rather positions a form of eventfulness defined by a singular “calamity” with a clearly delineable beginning, middle, and end, which, at this point, is already in its aftermath. 4.3 Republicans: Calamitous Eventfulness I illustrate calamitous eventfulness first. As noted above, calamitous eventfulness is marked by a singular and decisive moment of rupture. For the Republican governors, this moment of rupture occurred during the first month of the pandemic, from roughly mid-March to mid-April when that “rolling wave” was taking over the country. After that, the “calamitous” aspect of the event had subsided—the “emergency tragedy” as Abbott described it on March 19th, was over. The language of calamitous eventfulness is thus defined less by any particular combination of metaphors—e.g., “uptick,” “flare up,” “surge,”—than by its explicit avoidance of the “wave” metaphor. Consider the following quote from DeSantis on May 6th, which directly builds on that from Abbott above:I can tell you outside of these three counties, whenever we see an uptick in cases, it's either a reporting dump where cases had been backlogged, a prison that'll have 20, 30, or… a nursing home. So those are discrete problems. We've worked hard to obviously address the longterm care facilities, but all that is going to continue to go. And I think that there's sometimes people out there saying either force everyone to stay in their house infinitum or do nothing, and that's ridiculous. Obviously, we're still going to be fighting very hard. But I think we also have to look at the data and look at the facts. Although DeSantis uses the metaphor of an “uptick” instead of a “flare up,” his message is broadly the same as Abbott's: worry not. Where there is an “uptick,” he says, it is likely due to a “reporting dump,” a prison, or a nursing home. In other words, like Abbott, he downplays a possible rise in cases, attributing it to something extraneous like a data error or the unique environment of a “total institution” (Goffman, 1961). Moreover, again, the threat is an “uptick,” not a “wave.” That is to say, the threat is localized, operating at the county level, and singular: an “uptick.” Unlike a “wave,” one “uptick” does not portend multiple “upticks” any more than it portends a real change in the situation. Like a “reporting dump,” a prison, and a nursing home, it is a “discrete problem.” Of course, more than to merely campaign against worrying, the language of calamitous eventfulness advocates for moving forward. As DeSantis says, in the past, “we've worked hard;” in contrast, now is the time “to continue to go.” Without explicitly naming them, DeSantis thus positions himself in direct opposition to Democratic governors—“people out there”—who “force everyone to stay in their house infinitum” or, more succinctly, “do nothing.” In contrast, he is doing two things: (1) he is “still going to be fighting,” importantly, while (2) looking at the “data” and the “facts.” Meanwhile, in framing a possible rise in cases as an “uptick,” the conditionality is inconsequential, as, even if it does occur, it's unlikely to cause real rupture. Consider another quote from Cooper on November 23rd, which he makes in response to the following set of questions: “Are there plans at the state level to deploy staff from other hospitals, either in the state or out of state? Should the staffing needs become the really critical piece of this puzzle?”Good question… We learned a lot in March, April, May, June when hospitals surged, eliminated elective surgeries, and got ready for the spike that really never came. We've worked really hard to keep our virus spread more level, but we learned a lot, and these hospitals now know when they need to go into their surge mode, and Dr. Cohen has been talking with them on a frequent basis, and I have been talking with them as well. The state is prepared to help with a surge if necessary. Cooper's response—which largely evades the reporter's questions—is less straightforward than those provided by Abbott and DeSantis above. Unlike them, he does not immediately explain away the occurrence of a “surge” or “spike,” as they do with a “flare up” and “uptick,” respectively. At the same time, in line with a calamitous eventfulness that sees a singular, resolved moment of rupture, his statement similarly minimizes the severity of the situation: in the same breath as he acknowledges that “hospitals surged,” he contends that the “spike… never really came;” May/June was too late for that. Additionally, his statement is riddled with reassuring language: “we learned a lot” (twice), “we've worked really hard,” “Dr. Cohen has been talking with them,” “I have been talking with them.” Indeed, as if to lower the reporter's whistle once and for all regarding the state's disaster plans—hinting, perhaps, at a future “wave” of infections—he concludes with the almost performative declaration, “The state is prepared to help with a surge if necessary.” 4.4 The “Wave” Metaphor As Figure 4 shows, Republican governors do occasionally use the “wave” metaphor beyond that initial moment of rupture in March and April when experimenting with different articulations of the pandemic. However, even when they use the metaphor, they employ it differently than do Democratic governors. Consider the following quote from Mike DeWine on December 10th:The next three weeks will really be the most important three weeks for all of us in this pandemic. We're heading into the biggest holiday season of the year. So much bigger than Thanksgiving. We're doing this while riding the biggest wave of COVID-19 that we've had so far. As we saw with Whitmer's quote from April 9th—and will see in more detail in the following section—Democratic governors adopt the “wave” metaphor to refer to future, conditional moments of rupture; they speak of yet unexperienced “second waves,” “third waves,” etc. In contrast, in the few instances where Republican governors use the metaphor, they use it to refer to either a past situation, or, more commonly—as in this case—a current one. As DeWine says, “We're doing this while riding the biggest wave of Covid-19 that we've had so far.” As the metaphor merely intends to signify a general swell in cases over the “next three weeks,” DeWine could have replaced the word “wave” with “uptick,” “flare up,” “spike,” etc. to the same effect—and, in fact, in most cases, does do this. DeWine's metaphor of “riding” the “wave”—as opposed to say, “readying” for a “wave” or, alternatively, “getting pummeled” by a “wave”—also speaks to a calamitous eventfulness that takes heed of “upticks,” “flare ups,” and “spikes” all the while continuing to push forward. Thus, although there might be a “wave” of cases, it is not a cascading one. 4.5 Democrats: Cascading Eventfulness In contrast to the Republican governors, beginning in mid-April, Democratic governors increasingly take up the “wave” metaphor to represent what I call “cascading” eventfulness. This form of eventfulness is marked by language that is both more pessimistic and more uncertain that that of calamitous eventfulness as it sees the potential for multiple moments of rupture. As such, while the language of cascading eventfulness similarly employs the metaphors of an “uptick,” “flare up,” “spike,” etc., to refer to in-the-moment rises in case counts, it is distinguished by its warnings of future “waves.” To begin, consider the following quote from Newsom on May 7th:This emergency has not passed us by, and we need to maintain our vigilance, maintain our distance physically from one another and continue to do our best to meet the spirit of these guidelines and not get ahead of ourselves at peril that we see this disease come back in fury and a second wave that makes the first wave pale in comparison. We are not out of the woods yet. Let's continue to be vigilant, continue to do what we've done to get to this point, and I hope folks see some light at the end of this tunnel, and we look forward to more of that light being shone down on them. Newsom's statement comes the day after that provided by DeSantis above, and yet its message directly contrasts it: (1) whereas DeSantis situates danger in the past “where cases had been backlogged.” Newsom situates danger in the present of “this emergency;” (2) whereas DeSantis downplays the severity of an “uptick in cases,” Newsom warns of the destructive “fury” of a “second wave;” (3) whereas DeSantis attributes such an “uptick” to one of a few “discrete problems,” Newsom follows Whitmer in connecting the current pandemic to that of 1918 when he speaks of “a second wave that makes the first wave pale in comparison;” and (4) whereas DeSantis's statement leans forward, advocating for people of Florida to “continue to go,” Newsom's statement leans backwards, advocating for the people of California to rather “continue to……meet the spirit of these guidelines…be vigilant… [and] do what we've done to get to this point.” Indeed, whereas the language of calamitous eventfulness speaks to an event whose structure is well understood—i.e., marked by an initial calamity—the language of cascading eventfulness speaks to an event whose structure is, for the most part, yet unknown. It is for this same reason that DeSantis’ statement projects confidence—as he tells his audience, for example, “…that's ridiculous” and “Obviously we're still going to be…”—Newsom's relies on a series of metaphors that suggest uncertainty: in addition to the metaphor of multiple “waves” whose count and severity are yet to be determined, he speaks of being yet in the darkness of the “woods” and “this tunnel.”13 Compare Newsom's quote to one by Cuomo from August 19th:From my point of view… we're in the midst of this. Let's look at what we did right, let's look at what we did wrong, because we still have a lot more to do. And even if once we get past this COVID, we have to deal with it in the fall, then we have a possibility of a second wave, and then there's going to be something after COVID. MERS, SARS, Ebola, H1N1, swine flu. Oh, that'll never happen again. Oh, that'll never happen again. Oh, that'll never happen again. Yeah, sure. Look, I was even there for Ebola. I knew that we didn't know what we were doing for Ebola. So let's learn. When Newsom made his statement on May 7th, case counts in California were relatively low, but were slowly creeping up day-by-day. In contrast, Cuomo's quote comes at a time when case counts in New York were steady for almost two months following the initial “rolling wave” of cases in April and May (NYT, 2023). And yet, like Newsom—and unlike the Republican governors cited above—Cuomo's message is far from assured, warning that New York is still “in the midst of this” with the “possibility of a second wave” looming on the horizon. Such caution amidst a seemingly controlled situation is emblematic of a form of eventfulness that views future rupture as a continual threat, a conditionality. Framed in the conditional, cascading eventfulness thus differs from calamitous eventfulness in positing an event that may be acted upon. That is why it is far more common for Democratic governors to cite pandemics of the past compared to Republican governors, as Cuomo does here with “MERS, SARS, Ebola, H1N1, swine flu.” As Cuomo suggests, we may “learn” from our mistakes of the past and apply them to the present. As if replying to DeSantis who earlier cited the Democratic chorus of those who “force everyone to stay in their house infinitum,” Cuomo cites the Republican counterpart with their repeated mantra, “Oh, that'll never happen again.” And just as DeSantis replied, “that's ridiculous,” Cuomo quips, “Yeah, sure.” Indeed, it is here that we see the central tension of calamitous and cascading eventfulness writ large: whereas the former sees an event with a singular, decisive moment of rupture—one that will never happen again—the latter sees an event with the potential for multiple moments of rupture—ones that, if we act as if they will never happen again, then almost certainly will happen again. I present a summary table of the two forms of eventfulness in Figure 5 below.14 Fig. 5 Comparing Calamitous and Cascading Eventfulness. Fig 5 5 Discussion, Limitations, and Conclusion 5.1 Discussion This paper analyzed ten governors’ use (or avoidance) of the “wave” metaphor across 471 Covid-19 Briefing transcripts from the year 2020 to identify two competing forms of eventfulness. As the pandemic began to unfold in March 2020, both Democratic and Republican governors tested out a variety of metaphors to give the rupture of a rapidly expanding national case count a form; at this time, there were not yet systematic differences in the use of these metaphors nor in the framing of the pandemic as an event across parties. Then, in early April, a divergence occurred whereby Democratic governors increasingly took up the “wave” metaphor to refer to future “waves” of cases, while Republican governors largely avoided the metaphor. I called this Democratic conceptualization “cascading” eventfulness to suggest an event form that cascades into the future—one with multiple, conditional moments of rupture. In contrast, I called this Republican conceptualization “calamitous” eventfulness to suggest an event form defined by a discrete calamity—a singular, decisive moment of rupture. So, what is the significance of the “wave” metaphor to the Covid-19 pandemic? On one level, as I've shown, the “wave” metaphor is significant because it acquired a unique political bent (e.g., Lakoff, 1995; Voss et al., 1992): whereas governors of both parties spoke freely of an upcoming pandemic “surge,” “spike,” “curve,” etc., it was almost always Democratic governors who spoke of an upcoming “wave.” On a deeper level, the “wave” metaphor is significant as it came to symbolize a particular understanding of Covid-19 as an event: to speak of multiple “waves” became a proxy for a conceptualization of the pandemic that was not yet fixed; in this case, the entirety of the pandemic was the “event.” Inversely, to explicitly avoid the “wave” metaphor became a proxy for a conceptualization of the pandemic that was, in broad strokes, settled; in this case, only the onset of the pandemic was the “event.” These findings contribute to literature on eventfulness in two ways. First, they expand our understanding of event temporalities. Events have been theorized across a range of different time horizons: e.g., one-off (e.g., Tilly, 1985), proceeding along a “long durée” (Braudel & Wallerstein, 2009), cumulative (Pierson, 2003). Following Sewell (2012: 304) who observes an “uncanny… repetition compulsion” of the economic crisis, I show that thinking in terms of a rhythmic structure of events is important as, much like the structure of social life (Snyder, 2016; Tavory, 2018), events are often less linear than we assume. Moreover, their linearity can itself be a point of contestation, as in the case here, resulting in a national level “arrythmia” (LeFebvre, 2004). Second, I offer metaphors as an alternative avenue through which to identify competing forms of eventfulness; while Wagner-Pacifici (2010) hints at the importance of metaphors in her elaboration of “political semiosis,” I contend that metaphors are a core representational feature of events and deserve increased attention. Wagner-Pacifici (2010: 1363) notes that representational features of events such as images and graphs produce copies of an event and, in doing so, “...assume a world that has, at least provisionally, stabilized.” As I've shown, metaphors such as the “wave” work much like images and graphs, simplifying complex temporal, rhythmic, and geographic information into tangible, readily available forms.15 These findings also contribute to recent research that connects political ideology to Covid-19 policy decisions. Alexander and Smith (2020: 264) observe, “The first few weeks and months [of the pandemic] were a remarkable demonstration of a societal capacity for highspeed bricolage as familiar structures of meanings (narrative, iconography, genre, binary codes) and meaningful practices (collective rituals, interaction rituals, and performances) were bolted and glued together in new ways.” I show that a pandemic composed of multiple cascading “waves” was one such narrative that was selectively performed (or not performed) to “shape… reality in turn” (ibid)—and to be shaped by reality in turn.16 For Democratic governors, the “wave” metaphor became a sort of rallying cry for an ideological stance that emphasizes the importance of collective action (Kirgil & Voyer, 2022), expresses strong trust in science (Hamilton & Safford, 2021) and government institutions (Shepherd et al., 2020) and stresses the need to protect the “vulnerable” (Perry et al., 2021). Conversely, for Republican governors, the “wave” metaphor directly threatened an ideological stance that emphasizes the importance of work (Kirgil & Voyer, 2022), expresses little trust in science (Hamilton & Safford, 2021) and government institutions (Shepherd et al., 2020) and stresses the need to protect the economy and individual liberty (Perry et al., 2021). 5.2 Limitations My study has several limitations. For one, my corpus did not include all those transcripts from each governor from 2020, but only those transcripts made available to me through my data source at Rev.com; it is possible that those transcripts that employed the “wave” metaphor were relatively more popular than others, thus exaggerating my results. More importantly, my corpus did not include transcripts from all fifty governors, but only those from the ten most populous states; it's unclear how my results would generalize to the other forty states.17 I also did not qualitatively code my entire corpus but merely a subsample, placing parituclar emphasis on those transcripts that employed the “wave” metaphor; this may have led me to miss interesting patterns concerning other metaphors. I should additionally note that my analysis relied on a single coder. More conceptually, I should make clear that I do not know how the “wave” metaphor affected the decisions of Democratic and Republican governors; my argument is descriptive rather than causal. Following Winchester and Green (2019), I imagine, rather, the “wave” metaphor migrated from serving as a justification to a motivation for action (or inaction) at different points in time. All I can say is, by some course of events, the metaphor resonated (McDonnell et al., 2018; Zhou, 2022) with Democratic governors and explicitly did not resonate with Republican governors. 5.3 Conclusion Today's Covid-19 landscape looks quite different than that of 2020. While experts continue to warn about the pandemic's ongoing threat (e.g., The Lancet, 2023), Democratic and Republican governors’ language and policies look much more alike than different, advocating above all for “liv[ing] with” the virus—to quote DeSantis from June 25th, 2020. In fact, Newsom mirrored DeSantis's language almost exactly when, a year-and-a-half later, he adopted the country's first “endemic” virus policy on February 17th, 2022, announcing, “We are moving past the crisis phase into a phase where we will work to live with this virus” (NPR, 2022). Given this shift away from a cascading conceptualization of the pandemic, one might predict that talk of “waves” has likewise decreased among Democrats over the past two years. This is precisely what I found in a preliminary search of the liberal-minded New York Times’ Archive: from March 1st to December 31st, 2020, nearly three times as many articles included the terms “wave” and “Covid,” as compared to the same time period in 2022 (NYT Archive).18 As others have observed (e.g., Greene & Vargha, 2020), a pandemic rarely “ends” in the biological sense as becomes folded into the regular structure of daily life. Framed in this way, we may understand the difference between cascading and calamitous eventfulness as a divergence in where one draws the dividing line between “national emergency” (a cascading “wave”) and “life as normal” (a mere “surge”). It will be interesting to track which metaphors appear in the lexicon of future events—and even more interestingly, which metaphors are rejected. Indeed, as this paper has shown, the space between metaphors can itself be symbolic, suggestive of an underlying fissure in an event's conceptualized forms of eventfulness. Sources of Finance None Uncited References Adolph et al., 2022, Lakoff and Johnson, 2008, Lefebvre, 2013, McDonnell et al., 2017, McKeon, 2009, New York Times 2023, Rev 2022, Sahlins, 2009, Biggs, 2005, Tavory and Wagner-Pacifici, 2022, USA Today 2022, Woolf and Woolf, 1992 Declaration of Competing Interest None Nick Rekenthaler is a PhD candidate in Sociology at NYU. His research interests center around the areas of medical sociology and mental health, culture, morality, and social control. He is also pursuing a MFA in Fiction at NYU. Appendix Supplementary materials Image, application 1 Acknowledgements The author would like to thank Carly Knight, Lynne Haney, Robin Wagner-Pacifici, and Iddo Tavory for their helpful feedback and comments. The author would additionally like to thank the members of his Research & Writing class. “The waves broke and spread their waters swiftly over the shore. One after another they massed themselves and fell; the spray tossed itself back with the energy of their fall. The waves were steeped deep-blue save for a pattern of diamond-pointed light on their backs which rippled as the backs of great horses ripple with muscles as they move. The waves fell; withdrew and fell again, like the thud of a great beast stamping.” ∼ Virginia Woolf, The Waves 1 Covid-19 Briefing transcripts fall under the umbrella of “political press conferences,” which have been studied by many scholars (e.g., Fairclough, 2013; Bhatia, 2006) for their persuasive potential. Moreover, Covid-19 Briefing transcripts have themselves been fertile ground for studying the performance of politics (e.g., Villegas, 2020; Kirgil & Voyer, 2022). 2 Or, as Braudel (1960: 9), reminds us, “…in the language of history there can scarcely be perfect synchrony.” 3 Sontag (2001: 93) begins AIDS and Its Metaphors by noting, “By metaphor I meant nothing more or less than the earliest and most succinct definition I know, which is Aristotle's in his Poetics.” 4 A cautionary note: “wave” talk is not always metaphorical with a “symbolic” meaning. In the same article, Jones and Helmreich (2020) observe that mathematical modeling has allowed scientists to predict actual, material waves in cases, as with the seasonal flu; the “wave” remains metaphorical in the case of the Covid-19 pandemic, however, as, particularly in 2020, its structure was yet unknown. 5 At the time of data analysis (March-May 2022), they also represented the ten states with the most recorded cases of Covid-19 since the beginning of the pandemic (NYT, 2023) 6 An employee at Rev gave me permission to do this. 7 Of course, many more metaphors were identified than those included in Appendix A: “swell,” “ripple,” “invisible enemy,” “monster,” etc. However, these metaphors were used too infrequently to allow me to tease apart systematic differences in usage across parties. For example, the “monster” metaphor was used across only seven transcripts—all of which belonging to Governor DeWine. 8 I calculated this effect size using an odds ratio test. The odds value of the “wave” metaphor was 8.82; this is compared to, for example, .765 for the “battle” metaphor, 1.32 for the “curve” metaphor, 1.31 for the “phase” metaphor, and .571 for the “uptick” metaphor. 9 I say “where possible” as most governors did not have a transcript for every month. 10 For example, Kemp refers to “this unprecedented event” on March 27th. 11 Thinking on a similar time horizon, two days earlier, DeWine speaks of reevaluating the situation on May 1st. 12 For a detailed account of the 1918 pandemic, see Barry, 2020. 13 Note also that this uncertain language extends into the future: “I hope folks see some light at the end of this tunnel.” 14 The proxy metaphor(s) of “calamitous eventfulness” is an open question for future research. As I've described, while I found that Republican governors explicitly avoided the “wave” metaphor, I did not find any corresponding metaphor that Democratic governors explicitly avoided; a first place to look might be the “invisible enemy” metaphor that Pence employed in his op-ed. 15 It is perhaps no coincidence that the “wave” metaphor is often accompanied by a pictorial representation, and vice versa (see, e.g., Jones & Helmreich, 2020). 16 I'm wary of the lurking causal claim; see Limitations. 17 A caveat: at the very early stages of my analysis, I qualitatively analyzed a few transcripts from eight additional governors—Baker (R- Massachusetts), Ducey (R- Arizona), Hogan (R- Maryland), and Little (D- Idaho), as well as Beshear (D- Kentucky), Inslee (D- Washington), Kelly (D- Kansas), and Murphy (D- New Jersey)—and found very similar results regarding use of the “wave” metaphor. 18 Specifically, 1,753 articles included the terms in 2020 versus only 641 articles in 2022. 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==== Front Appl Math Comput Appl Math Comput Applied Mathematics and Computation 0096-3003 0096-3003 Elsevier Inc. S0096-3003(23)00379-X 10.1016/j.amc.2023.128210 128210 Article Stability analysis and optimal control of a fractional-order generalized SEIR model for the COVID-19 pandemic☆ Xu Conghui a Yu Yongguang ⁎b Ren Guojian b Sun Yuqin c Si Xinhui a a School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China b School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, China c Department of Mathematics and Computer Engineering, Ordos Institute of Technology, Ordos 017000, China ⁎ Corresponding author. 27 6 2023 27 6 2023 12821022 11 2022 22 6 2023 24 6 2023 © 2023 Elsevier Inc. All rights reserved. 2023 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. In view of the spread of corona virus disease 2019 (COVID-19), this paper proposes a fractional-order generalized SEIR model. The non-negativity of the solution of the model is discussed. Based on the established threshold R0, the existence of the disease-free equilibrium and endemic equilibrium is analyzed. Then, sufficient conditions are established to ensure the local asymptotic stability of the equilibria. The parameters of the model are identified based on the statistical data of COVID-19 cases. Furthermore, the validity of the model for describing the COVID-19 outbreak is verified. Meanwhile, the accuracy of the relevant theoretical results are also verified. Considering the relevant strategies of COVID-19 prevention and control, the fractional optimal control problem (FOCP) is proposed. Numerical schemes for Riemann-Liouville (R-L) fractional-order adjoint system with transversal conditions is presented. Based on the relevant statistical data, the corresponding FOCP is numerically solved, and the control effect of the COVID-19 outbreak under the optimal control strategy is discussed. Keywords Fractional-order Stability COVID-19 Optimal control Parameter identification ==== Body pmc1 Introduction Since the COVID-19 outbreak, it has rapidly spread around the world, seriously threatening public order of human society. In recent years, scholars have researched the spread of COVID-19 from various aspects, such as transmission characteristics, pathological characteristics, epidemic prevention and control, etc [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]. Among them, it is a hot topic to analyze the spread trend of COVID-19 from the perspective of biomathematics model. The construction of appropriate mathematical models is conducive to discussing the relevant dynamical behaviors of COVID-19. Moreover, formulating appropriate control strategies based on the COVID-19 model can also provide guidance for preventing the COVID-19 pandemic. At present, various mathematical models have been used to analyze the spread of COVID-19. Most studies focus on the effective modeling, dynamic analysis, prediction and optimal control of the COVID-19 pandemic. In [13], the COVID-19 model considering isolation measures is established, and the corresponding dynamic behavior is analyzed. In [14], the epidemic model is established to analyze the transmission characteristics and epidemic control of COVID-19. In [15], [16], [17], the optimal control problem under different control strategies is discussed. During the COVID-19 outbreak, the COVID-19 has undergone many mutations, leading to great differences in the transmission ability between different types of viruses. Meanwhile, considering the differences in prevention and control measures and geographical location among different countries, the transmission characteristic of the virus is also quite different. The incidence rate is an important part of the epidemic model used to describe the transmission capacity and characteristics of viruses. Due to the differences in the transmission mechanism caused by the above objective factors, the spread of viruses in different regions needs to be characterized by different incidence rates. Based on the above analysis, a COVID-19 model with generalized incidence rate is established, and most of the existing incidence rates are a special form of the generalized incidence rate, so that the model can effectively characterize the COVID-19 pandemic in different situations. Strong infectivity is one of the main reasons for the rapid spread of COVID-19 worldwide. This implies the trend of the COVID-19 outbreak dependent on historical case data. The fractional model can effectively describe the characteristics of heredity and memory during the transmission of infectious diseases. In addition, relevant researches show that fractional calculus can better describe some nonlinear phenomena in reality. There are some researches on COVID-19 model, which are analyzed by fractional calculus [18], [19], [20], [21], [22]. In general, fractional-order models can effectively overcome the shortcomings of integer-order models that cannot completely fit data. Moreover, the memory of fractional-order models is also conducive to describing the spread of infectious diseases. Compared to Riemann-Liouville fractional-order derivative, Caputo fractional-order derivative has a relatively clear physical meaning and is easy to measure. Caputo fractional-order derivative is more conducive to establishing and analyzing the epidemic models. Therefore, Caputo fractional-order derivative is selected to establish the corresponding epidemic model. In this paper, the COVID-19 pandemic is discussed by the relevant statistical data. Many effective measures have been implemented to control the spread of COVID-19, including restrictions on population movement, home-quarantine, nucleic acid testing, COVID-19 vaccine, trajectory screening, etc. The essence of these measures is to reduce the effective contacts rate between susceptible group and infected group. However, these measures consume substantial costs and resources. Achieving the optimal effect of epidemic control under limited costs and resources is the problem that the optimal control theory needs to solve. In 2002, Agrawal discussed Riemann-Liouville type fractional-order variational problem, which provided convenience for the research of fractional optimal control problem (FOCP) [23]. In 2004, Agrawal discussed the FOCP of Riemann-Liouville fractional-order system [24]. In 2012, Ding researched the FOCP of Caputo HIV model and discussed the corresponding numerical methods [25]. It is of great significance to study the optimal control of COVID-19 model for epidemic prevention and control. There have been some relevant researches on the optimal control of COVID-19 models. However, there are relatively few researches on optimal control of COVID-19 based on fractional-order model. This paper selects appropriate control factors to simulate the reduction of effective contact between susceptible individuals and virus carriers under various measures. Further, the corresponding FOCP of the COVID-19 pandemic is discussed based on the relevant statistical data. The main contributions are as follows: (1) According to the actual situation of the COVID-19 pandemic, the COVID-19 model with generalized incidence is established based on the Caputo fractional-order derivative. Further, some dynamic behaviors of the model are analyzed. (2) Identify the parameters of the model according to relevant statistical data, which verifies the effectiveness of the model for depicting the COVID-19 pandemic and the accuracy of the relevant theoretical results. (3) Based on the relevant statistical data, the corresponding FOCP of the COVID-19 pandemic is proposed. the numerical format of Riemann-Liouville adjoint system with transversality condition is given, and the FOCP of the COVID-19 model is solved numerically. The structure of this paper is designed as: the Caputo fractional-order COVID-19 model with generalized incidence rate is established in Section 2. Some dynamic behaviors of the model are analyzed in Section 3. The corresponding FOCP is proposed in Section 4. By the measurement data, the validity of the COVID-19 model in describing the COVID-19 pandemic and the accuracy of the relevant theoretical results are verified in Section 5. Meanwhile, the corresponding FOCP is solved numerically. Finally, the conclusion of this paper is given in Section 6. 2 Preliminaries and modeling This section introduces some basic theories and related lemmas that will be used in the subsequent analysis of this paper. In view of the relevant prevention and control measures implemented during the transmission of COVID-19, a fractional-order COVID-19 model is established. 2.1 Preliminaries There are three classical types of fractional calculus: Riemann-Liouville (R-L), Gru¨nwald-Letnikov (G-L) and Caputo. The Caputo fractional-order derivative can describe the relevant physical meaning in reality, which is also convenient to describe the biological dynamic system. Therefore, the Caputo fractional-order derivative is selected to characterize the COVID-19 pandemic.Definition 1 [26], [27] The Caputo fractional-order derivative is0CDtαf(t)=1Γ(n−α)∫0tdnf(ξ)dξn(t−ξ)n−1−αdξ, where n−1<α<n. In particular, 0CDtαf(t)=dnf(t)dtn, if α=n holds. Definition 2 [26], [27] The Riemann-Liouville fractional-order integration istIbαf(t)=1Γ(α)∫tbf(ξ)(ξ−t)1−αdξ, the Riemann-Liouville fractional-order derivative istRDbαf(t)=(−1)nΓ(n−α)dndtn∫tbf(ξ)(ξ−t)α+1−ndξ, where n−1≤α<n, n indicates a positive integer. Definition 3 [28,29] The Mittag-Leffler function isEα(z)=∑i=0∞ziΓ(1+αi), where α>0. The relevant stability theorems for Caputo fractional-order system are introduced below. Consider the following n-dimensional system.(1) {0CDtαm(t)=ψ(m(t)),m(0)∈Rn, m(t)=(m1(t),m2(t),⋯,mn(t))T, ψ(m(t))=(ψ1(m(t)),ψ2(m(t)),⋯,ψn(m(t)))T, α∈(0,1), ψ(m(t)) satisfies the local Lipschitz condition with respect to m.Lemma 1 [30] If all eigenvalues of the Jacobian matrix corresponding toψ(m(t))at the equilibrium satisfy|arg(λi)|>απ2, the equilibrium of system(1)is locally asymptotically stable. If there are eigenvalues which make the condition|arg(λi)|<απ2hold, then the equilibrium is unstable. 2.2 COVID-19 model The corresponding epidemic model is proposed to simulate the COVID-19 pandemic. The incubation period of the virus is an important factor in the widespread of COVID-19. In general, the traditional SEIR (Susceptible-Exposed-Infected-Removed) model is used to characterize the spread of viruses with incubation period. For the COVID-19 pandemic, the SEIR model is improved based on the following analysis:• Considering the enhancement of nucleic acid detection capability, E no longer represents the size of group carrying virus in incubation period, but the size of virus carriers with low virus concentration that cannot be detected. Similarly, I represents the size of virus carriers that can be detected. • Considering the isolation measures during the COVID-19 epidemic, a new class Q is added to describe the size of currently isolated group. • The size of temporarily removed individuals is represented by R. It refers to the size of the group that has temporary immunity or is not exposed to the environment due to self-protection measures. Such consideration can not only describe the secondary infection of recovered individuals caused by virus mutation, but also describe the self-protection measures of susceptible individuals. According to the previous analysis, the transmission capacity of COVID-19 in different environments is different. In order to make the established COVID-19 model more widely applicable, the generalized incidence rate f(S,I) is used to characterize the spread capacity of COVID-19, which meets the following conditions: (H1) f(S,0)=f(0,I)=0, for S,I>0, (H2) ∂f(S,0)∂S=0, for S>0, (H3) ∂f(S,I)∂S>0 and ∂f(S,I)∂I>0, for S,I>0, (H4) ∂f(S,I)∂I−f(S,I)I<0, for S,I>0. On the other hand, in order to describe the characteristics of heredity and memory in the process of disease transmission, the corresponding COVID-19 model is established through Caputo fractional-order derivative. According to [31], it is necessary to ensure that the dimensions of t on the left and right sides of the model are consistent. Therefore, the corresponding fractional-order COVID-19 model is expressed as:(2) {0CDtαS(t)=Λ−βcαf(S,I)+θαR(t)−(ρα+dα)S(t),0CDtαE(t)=βcαf(S,I)−(ϵα+dα)E(t),0CDtαI(t)=ϵαE(t)−(δα+γα+κ1α+dα)I(t),0CDtαQ(t)=δαI(t)−(ηα+κ2α+dα)Q(t),0CDtαR(t)=ραS(t)+γαI(t)+ηαQ(t)−(θα+dα)R(t), where α∈(0,1), (S(0),E(0),I(0),Q(0),R(0))∈(0,0,0,0,0)∪R+5, Λ is the growth of population caused by migration movement, birth and death, the other parameters are explained in Table 1 , the corresponding flow diagram is shown in Figure 1 , the above model divides all individuals into the following five compartments:• Susceptible S(t): the size of susceptible group at the time t, • Exposed E(t): the size of virus carriers with low virus concentration that cannot be detected at the time t, • Infected I(t): the size of virus carriers that can be detected at the time t, • Isolated Q(t): the size of group who have been isolated from the infected group at the time t, • Temporarily removed R(t): the size of group temporarily immunized or not exposed to virus carriers at time t. Table 1 Parameter Description (per day) Table 1Parameters Description β The transmission probabilities c Contact rate i.e., the average number of susceptible individuals contacted by per infected individual per day ρ Self protection rate θ Transmission rate from temporarily removed population to susceptible population caused by immune failure or removal of self-protection measures ϵ Rate of progression from exposed group to the infected group δ Isolation rate κ1 Death rate in infected group caused by COVID-19 κ2 Death rate in isolated group caused by COVID-19 η Recovery rate γ Self recovery rate d Natural death rate Fig. 1 Flow diagram for the model (2). Fig. 1 According to previous analysis, the COVID-19 model (2) has the following characteristics. The temporarily removed group is introduced to describe the self-protection mechanisms of susceptible group and the temporary immunity of cured individuals. The trend of isolated individuals is described in model (2), which facilitates the analysis of the COVID-19 pandemic in conjunction with statistical data. In addition, compared with most existing COVID-19 models with specific forms of incidence rate, the generalized incidence rate is considered in the COVID-19 model (2), which makes the model more general in describing the propagation ability of COVID-19. 3 Qualitative analysis Some qualitative properties of model (2) are discussed in this section, including the non-negativity of the solution and the stability of the equilibria. The model (2) obviously satisfies the local Lipschitz condition, which can guarantee the existence and uniqueness of the solution of model (2) [26], [28]. 3.1 Non-negativity Theorem 1 For any initial value (S(0),E(0),I(0),Q(0),R(0))∈(0,0,0,0,0)∪R+5 , the solution of model (2) is non-negative. Moreover, there exists a positive invariant set of model (2) , which is defined as Ω={(S,E,I,Q,R)∈R+5:0≤S+E+I+Q+R≤Λdα} . Proof Establishing the following comparison system(3) {0CDtαS=−βcαf(S,I)+θαR−(ρα+dα)S,0CDtαE=βcαf(S,I)−(ϵα+dα)E,0CDtαI=ϵαE−(δα+γα+κ1α+dα)I,0CDtαQ=δαI−(ηα+κ2α+dα)Q,0CDtαR=ραS+γαI+ηαQ−(θα+dα)R, where S(0)=E(0)=I(0)=Q(0)=R(0)=0. Obviously, (0,0,0,0,0) is the unique solution of comparison model (3). According to fractional-order comparison theorem, for initial value (S(0),E(0),I(0),Q(0),R(0))∈(0,0,0,0,0)∪R+5, the solution of model (2) is non-negative. By adding the left and right sides of the model (2), one has0CDtαN(t)≤Λ−dαN(t). Let(4) {0CDtαN1(t)=Λ−dαN1(t),N1(0)=N(0). Based on the fractional-order comparison theorem, we haveN(t)≤N1(t)=(−Λdα+N(0))Eα(−dαtα)+Λdα. It is further obtained thatN(t)≤Λdα, when N0≤Λdα holds. □ 3.2 Existence of the equilibria The basic reproduction number R0 corresponding to model (2) is established. The threshold R0 can be interpreted as the average number of newly infected individuals in the uninfected population caused by an infected individual. With reference to Ref. [34], [35], [36], the threshold R0 is established by the next generation matrix method. There exists a unique disease-free equilibrium P0=(S0,0,0,0,R0) in model (2), whereS0=Λ(θα+dα)dα(θα+ρα+dα),R0=ραΛdα(θα+ρα+dα). Further, we haveR0=ζ(FV−1), where ζ denotes spectral radius, F and V are expressed asF=[0βcα∂f(S0,0)∂I0000000],V=[ϵα+dα00−ϵαδα+γα+κ1α+dα00−δαηα+κ2α+dα], Therefore, we can deduce thatR0=βcαϵα(ϵα+dα)(δα+γα+κ1α+dα)∂f(S0,0)∂I. Theorem 2 There is a unique disease-free equilibriumP0=(S0,0,0,0,R0)in model(2). IfR0>1, model(2)has a unique endemic equilibriumP*=(S*,E*,I*,Q*,R*). Proof In the previous analysis, the existence of equilibrium P0 for model (2) has been discussed. Therefore, the following mainly analyzes the existence of endemic equilibrium P*=(S*,E*,I*,Q*,R*), it is calculated that(5) I*=m1E*,Q*=m2I*, wherem1=ϵαδα+γα+κ1α+dα,m2=δαηα+κ2α+dα. Similarly, it can be obtained that(6) R*=(ρα+dα)S*+(ϵα+dα)E*−Λθα, andS*=Λ(θα+dα)−(A1−A2)E*dα(θα+ρα+dα), whereA1=(θα+dα)(ϵα+dα),A2=θαγαm1+θαηαm1m2. It can be obtained from equations  (5) and (6) thatA1−A2=A3−A4(δα+γα+κ1α+dα)(ηα+κ2α+dα), whereA3=(θα+dα)(ϵα+dα)(δα+γα+κ1α+dα)(ηα+κ2α+dα),A4=θαγαϵα(ηα+κ2α+dα)+θαηαϵαδα. Since A3−A4>0, we have A1−A2>0. According to the above formulas, one hasβcαf((A2−A1)E*+Λ(θα+dα)dα(θα+ρα+dα),m1E*)=(ϵα+dα)E*. Define(7) Ψ(x)=βcαf((A2−A1)x+Λ(θα+dα)dα(θα+ρα+dα),m1x)−(ϵα+dα)x. Based on the hypothesis (H1), it is obvious that Ψ(0)=0 andΨ(Λ(θα+dα)A1−A2)=−Λ(θα+dα)(ϵα+dα)A1−A2<0. Since Ψ(x) is a continuous differentiable function with respect to x on the interval [0,Λ(θα+dα)A1−A2], it only needs to prove Ψ′(0)>0 so that Ψ(x)=0 has a positive solution E* on [0,Λ(θα+dα)A1−A2]. Therefore,Ψ′(x)=−βcα(A1−A2)dα(θα+ρα+dα)∂f(Λ(θα+dα)−(A1−A2)xdα(θα+ρα+dα),m1x)∂S+m1βcα∂f(Λ(θα+dα)−(A1−A2)xdα(θα+ρα+dα),m1x)∂I−(ϵα+dα). According to the hypothesis (H2), it can be further obtained thatΨ′(0)=m1βcα∂f(S0,0)∂I−(ϵα+dα)=(ϵα+dα)(R0−1), whereR0=βcαϵα(ϵα+dα)(δα+γα+κ1α+dα)∂f(S0,0)∂I. Hence, if the condition R0>1 is satisfied, Ψ(x)=0 exists a positive root. By calculation, we can getS*=Λ(θα+dα)−(A1−A2)E*dα(θα+ρα+dα),I*=ϵαδα+κ1α+γα+dαE*, Q*=δαηα+κ2α+dαI*,R*=(ρα+dα)S*+(ϵα+dα)E*−Λθα. Through calculation, it can be found that R*>0 can be ensured if the following inequality are satisfied.(8) Λραθα+A5E*>0, where A5=dα(ϵα+dα)(θα+ρα+dα)−(ρα+dα)(A1−A2). If A5≥0, inequality (8) clearly holds. If A5<0, we can deduce that(9) Λραθα+A5E*≥Λdα(ρα+θα+dα)[(ϵα+dα)(θα+dα)A1−A2−1]>0. Therefore, the mdoel (2) has at least one positive equilibrium. By the hypothesis (H4), one has(10) Ψ′(E*)=−βcα(A1−A2)dα(θα+ρα+dα)∂f(S*,I*)∂S+βcαϵαδα+γα+κ1α+dα∂f(S*,I*)∂I−(ϵα+dα)=−βcα(A1−A2)dα(θα+ρα+dα)∂f(S*,I*)∂S+βcαϵαδα+γα+κ1α+dα(∂f(S*,I*)∂I−f(S*,I*)I*)<0. According to inequality Ψ(E*)<0, the equilibrium P* is unique. Otherwise, there is another positive equilibrium point, defined by P*=(S*,E*,I*,Q*,R*). Therefore, one has Ψ′(E*)≥0, which is in contradiction with the previous derivation. □ Based on the center manifold theory [37], the existence of backward bifurcation is discussed.Theorem 3 Assume that the incidence ratef(S,I)is twice continuous differentiable with respect toS≥0,I≥0. Then, model(2)undergoes a backward bifurcation atR0=1, ifq12∂2f(S0,0)∂S2+2q1q2∂2f(S0,0)∂S∂I+q22∂2f(S0,0)∂I2>0, whereq1=γαθα+ηαδαθαηα+κ2α+dα−(θα+dα)(ϵα+dα)(δα+γα+κ1α+dα)ϵα, q2=dα(ρα+θα+dα). Proof The transmission probabilities β is selected as the bifurcation parameter. Solving R0=1 givesβ*=(ϵα+dα)(δα+γα+κ1α+dα)cαϵα(∂f(S0,0)∂I)−1. The Jacobian matrix of model (2) at P0 isJ(P0)=[−(ρα+dα)0−βcα∂f(S0,0)∂I0θα0−(ϵα+dα)βcα∂f(S0,0)∂I000ϵα−(δα+κ1α+γα+dα)0000δα−(ηα+κ2α+dα)0ρα0γαηα−(θα+dα)]. Through calculation, it can be found that the matrix J(P0)|β=β* has a unique zero eigenvalue, and the other eigenvalues have negative real parts. The left and right eigenvectors corresponding to the zero eigenvalue of matrix J(P0)|β=β* are represented as v=(v1,v2,v3,v4,v5) and w=(w1,w2,w3,w4,w5)T. The elements of the left eigenvector v are calculated as v1=v4=v5=0, v2=ϵαϵα+dα and v3=v3>0. Similarly, the right eigenvector w is given as w1=q1q2w3, w2=δα+κ1α+γα+dαϵαw3, w3=w3>0, w4=δαηα+κ2α+dα, w5=q3q2w3, whereq1=γαθα+ηαδαθαηα+κ2α+dα−(θα+dα)(ϵα+dα)(δα+γα+κ1α+dα)ϵα, q2=dα(ρα+θα+dα), q3=(ρα+dα)(γα+ηαδαηα+κ2α+dα)−ρα(ϵα+dα)(δα+γα+κ1α+dα)ϵα. Let x1=S, x2=E, x3=I, x4=Q and x5=R. And the k-th equation on the right-hand side of model (2) is denoted as Gk(k=1,2,⋯,5). According to the center manifold theory in [37], it can be calculated thata=∑k,i,j=15vkwiwj∂2Gk∂xi∂xj|P0,β=β*=v2∑i,j=15wiwj∂2G2∂xi∂xj|P0,β=β*=v2β*cαw32q22[q12∂2f(S0,0)∂S2+2q1q2∂2f(S0,0)∂S∂I+q22∂2f(S0,0)∂I2], andb=∑k,i=15vkwi∂2Gk∂xi∂β=v2w3cα∂(S0,0)∂I>0. According to the results in [37], the model (2) undergoes a backward bifurcation at R0=1, if the condition a>0 holds. Therefore, the proof of Theorem 3 is completed. □ 3.3 Stability analysis This subsection guarantees the local asymptotically stability of the equilibria for model (2) by establishing corresponding conditions.Theorem 4 Model(2)always has a unique disease-free equilibriumP0. Moreover, (i) The equilibriumP0is locally asymptotically stable, if the conditionR0<1holds. (ii) The equilibriumP0is unstable, if the conditionR0>1holds. Proof The characteristic equation corresponding to the Jacobian matrix J(P0) of model (2) at P0 is|λE−J(P0)|=(λ+θα+ρα+dα)(λ+dα)(λ+ηα+κ2α+dα)H(λ)=0, whereH(λ)=(λ+ϵα+dα)(λ+δα+κ1α+γα+dα)−ϵαβcα∂f(S0,0)∂I=λ2+(ϵα+δα+κ1α+γα+2dα)λ+(ϵα+dα)(δα+κ1α+γα+dα)−ϵαβcα∂f(S0,0)∂I. The characteristic equation |λE−J(P0)|=0 has three obvious characteristic roots, which are λ1=−θα−ρα−dα, λ2=−dα and −ηα−κ2α−dα, respectively. Based on the hypothesis (H3), the discriminant of H(λ) satisfiesΔ(H(λ))=[(ϵα+dα)+(δα+κ1α+γα+dα)]2−4(ϵα+dα)(δα+κ1α+γα+dα)+4ϵαβcα∂f(S0,0)∂I=(δα+κ1α+γα−ϵα)2+4ϵαβcα∂f(S0,0)∂I>0. Hence, H(λ)=0 has two real roots, defined as λ4 and λ5. Then, one hasλ4+λ5=−(ϵα+δα+κ1α+γα+2dα)<0, λ4λ5=(ϵα+dα)(δα+κ1α+γα+dα)−ϵαβcα∂f(S0,0)∂I=(ϵα+dα)(δα+κ1α+γα+dα)(1−R0). Therefore, we can deduce λ4+λ5<0 and λ4λ5>0, when R0<1. According to the conclusion of Lemma 1, P0 is locally asymptotically stable. Similarly, condition R0>1 can ensure that inequality λ4λ5<0 holds, which implies from Lemma 1 that the equilibrium P0 is unstable. This completes the proof of Theorem 4. □ Further, with respect to the positive equilibrium P*=(S*,E*,I*,Q*,R*) of model (2), the local asymptotic stability of P* will be considered. Therefore, the Jacobian matrix of model (2) at P* isJ(P*)=[−a10−βcαh20θαβcαh1−a2βcαh2000ϵα−a30000δα−a40ρα0γαηα−a5], wherea1=βcαh1+ρα+dα,a2=ϵα+dα,a3=δα+γα+κ1α+dα,a4=ηα+κ2α+dα,a5=θα+dα,h1=∂f(S*,I*)∂S,h2=∂f(S*,I*)∂I, The characteristic equation is|λE−J(P*)|=λ5+ξ1λ4+ξ2λ3+ξ3λ2+ξ4λ+ξ5, whereξ1=∑i=15ai,ξ2=a1(ξ1−a1)+a2(ξ1−a1−a2)+a3(a4+a5)+a4a5−ραθα−βcαϵαh2,ξ3=a2[a4a5+a1(a4+a5)]+a3[a4a5+a2(a1+a4+a5)+a1(a4+a5)]+a1a4a5+ϵαβ2c2αh1h2−ϵαβcαh2(a1+a4+a5)−θαρα(a2+a3+a4),ξ4=a3[a2(a4a5+a1(a4+a5))+a1a4a5]+a1a2a4a5+(a4+a5)ϵαβ2c2αh1h2−(a4a5+a1a5+a1a4)ϵαβcαh2−(a2a4+a3a4+a2a3)θαρα+ϵαθαβcα(ραh2−γαh1),ξ5=a1a2a3a4a5+a4a5ϵαβ2c2αh1h2−a1a4a5ϵαβcαh2−a2a3a4θαρα+a4ϵαθαβcα(ραh2−γαh1)−ϵαδαθαηαβcαh1. It is clear that ξ1>0. If ξi>0(i=2,3,4,5), the sufficient conditions can be derived as follows(11) Δ1=|ξ11ξ3ξ2|>0,Δ2=|ξ110ξ3ξ2ξ1ξ5ξ4ξ3|>0,Δ3=|ξ1100ξ3ξ2ξ11ξ5ξ4ξ3ξ200ξ5ξ4|>0, which can ensure that the equilibrium P* is locally asymptotically stable. Therefore, we can draw the following conclusions.Theorem 5 The endemic equilibriumP*of model(2)is locally asymptotically stable ifR0>1,ξi>0(i=2,3,4,5)and condition(11)holds. 4 Optimal control analysis Since the COVID-19 outbreak, many measures have been taken to limit the spread of the virus, including home quarantine, nucleic acid testing, wearing masks, COVID-19 vaccination and restrictions on population movement. These measures are mainly to reduce the number of contacts between susceptible individuals and infected individuals, so as to limit the spread of the virus. In this section, the control parameter u1 is used to describe the intensity of various control measures to reduce the number of contacts between susceptible individuals and infected individuals. Therefore, the corresponding fractional optimal control problem is considered as follows(12) minZ[u(t)]=∫0Tω1E(t)+ω2I(t)+ω3Q(t)+ω4u2(t)dt, subject to the state constraints(13) {0CDtαS=Λ−βcα(1−u(t))f(S,I)+θαR−(ρα+dα)S,0CDtαE=βcα(1−u(t))f(S,I)−(ϵα+dα)E,0CDtαI=ϵαE−(δα+γα+κ1α+dα)I,0CDtαQ=δαI−(ηα+κ2α+dα)Q,0CDtαR=ραS+γαI+ηαQ−(θα+dα)R, where Z[u(t)] represents the total cost caused by the relevant control strategy, ω1, ω2 and ω3 represent the weight coefficients corresponding to exposed group, infected group and isolated group, respectively, ω4 is the cost coefficient generated by the relevant control strategy, the corresponding feasible control domain isM={u(t)|0≤u(t)≤1,t∈[0,T]}. Before discussing the solution of FOCP (12)-(13), the corresponding Hamiltonian function is given byH=ω1E+ω2I+ω3Q+ω4u2+λ1(Λ−βcα(1−u)f(S,I)−(ρα+dα)S+θαR)+λ2(βcα(1−u)f(S,I)−(ϵα+dα)E)+λ3(ϵαE−(δα+γα+κ1α+dα)I)+λ4(δαI−(ηα+κ2α+dα)Q)+λ5(ραS+γαI+ηαQ−(θα+dα)R), where λ1, λ2, λ3, λ4 and λ5 are the adjoint variables. According to Pontryagin’s minimum principle and Ref. [25], we can draw the following conclusions.Theorem 6 Letu*be the optimal control variable of the FOCP(12)-(13), and the corresponding optimal state solution is(S*,E*,I*,Q*,R*), then there are adjoint state variablesλi(i=1,2,3,4,5), satisfying(14) {tRDTαλ1(t)=−[βcα(1−u)∂f(S,I)∂S+ρα+dα]λ1(t)+βcα(1−u)∂f(S,I)∂Sλ2(t)+ραλ5(t),tRDTαλ2(t)=ω1−(ϵα+dα)λ2(t)+ϵαλ3(t),tRDTαλ3(t)=ω2−βcα(1−u)∂f(S,I)∂Iλ1(t)+βcα(1−u)∂f(S,I)∂Iλ2(t)−(δα+γα+κ1α+dα)λ3(t)+δαλ4(t)+γαλ5(t),tRDTαλ4(t)=ω3−(ηα+κ2α+dα)λ4(t)+ηαλ5(t),tRDTαλ5(t)=θαλ1(t)−(θα+dα)λ5(t), with transversality conditions(15) tRDTα−1λi(T)=0,i=1,2,⋯,5. In addition, the optimal controlu*corresponding to the FOCP(12)-(13)in the feasible control domainMcan be obtained as followsu*=min{max{βcαf(S*,I*)(λ2−λ1)2ω4,0},1}. Theorem 7 From the Pontryagin’s minimum principle, it can be deduced that the adjoint system corresponding to the state system(13)satisfiestRDTαλ1(t)=∂H∂S,tRDTαλ2(t)=∂H∂E,tRDTαλ3(t)=∂H∂I,tRDTαλ4(t)=∂H∂Q,tRDTαλ5(t)=∂H∂R, with transversality conditionstRDTα−1λi(T)=0,i=1,2,⋯,5. The corresponding control equation is∂H∂u=0. Therefore, in the feasible control domainM, the optimal control can be obtained asu*=min{max{βcαf(S*,I*)(λ2−λ1)2ω4,0},1}. On the basis of Theorem 6 and Ref. [25], [26], [27], [28], [29], [30], [31], [32], we adopt FBSM (Forward Backward Sweep Method) to solve the FOCP, and the corresponding algorithm can be described as follows Step 1: Divide the time interval [0,T] into M sub intervals on average, and the corresponding grid node is {tk=kh,h=1,2,⋯,M}, where T=hM. Initialize u0(t)=1. Step2: The state system (13) is numerically solved by initialized control parameters and fractional-order predictor corrector algorithm. Step3: Based on the state vector obtained in Step 2, the R-L fractional-order adjoint system (14) with transversal condition (15) is numerically solved. Step 4: Based on the state vector and adjoint vector obtained in the above steps, the control u(tk) is updated by the following formula:u(tk)=min{max{βcαf(S(tk),I(tk))(λ2(tk)−λ1(tk))2ω4,0},1}. Step 5: The criterion of iteration termination is convergence. If the relative distance between the current variable value and the variable value in the previous iteration is less than the predetermined acceptable error, stop the iteration, otherwise return to Step 2. Based on the FBSM method, the FOCP of Caputo system can be solved numerically. In Step 2, the initial value problem of Caputo state system can be calculated according to the fractional-order predictor corrector method [33]. Inspired by the Ref. [25], [26], [27], [28], [29], [30], [31], [32], [33], the numerical scheme of R-L fractional-order adjoint system with transversality conditions in Step 3 is given. The following R-L fractional-order system are considered:(16) {tRDTαφ(t)=f(t,φ(t)),tRDTα−1φ(T)=φT, where f(·,φ(·))∈C2[0,T]. Taking R-L fractional-order integration on the left and right sides of the above system, we have(17) φ(t)=tRDTα−1φ(T)Γ(α)(T−t)α−1+1Γ(α)∫tT(ξ−t)α−1f(ξ,φ(ξ))dξ. The interval [0,T] is divided into uniform grid {tk=kh,h=1,2,⋯,M}, where t0=0, tM=T, h=TM. Hence, one can deduce(18) φ(tM−k−1)=φTΓ(α)(T−tM−k−1)α−1+1Γ(α)∫tM−k−1T(ξ−tM−k−1)α−1f(ξ,φ(ξ))dξ. Discretize the integral term in the above equation to obtain(19) ∫tM−k−1T(ξ−tM−k−1)α−1f(ξ,φ(ξ))dξ≈∫tM−k−1T(ξ−tM−k−1)α−1f^k(ξ,φ(ξ))dξ, where f^k(ξ) is the linear interpolating function of f(ξ) at discrete points {tj,j=0,1,2,⋯,M}. Further, we can get(20) ∫tM−k−1Tf^k(ξ,φ(ξ))(ξ−tM−k−1)1−αdξ=∑j=0k∫tM−j−1tM−jf^k(ξ,φ(ξ))(ξ−tM−k−1)α−1dξ=hαα(α+1)[kα+1−(k−α)(k+1)α]f(tM,φ(tM))+hαα(α+1)f(tM−k−1,φ(tM−k−1))+hαα(α+1)∑j=1k[(k−j)α+1+(k+2−j)α+1−2(k+1−j)α+1]f(tM−j,φ(tM−j)). Since the right side of formula (20) still contains the unknown term φ(tM−k−1), the estimated value of φ(tM−k−1) is given by referring to the predictor corrector method [33], which is defined as φm(tM−k−1). Similarly, according to the approximate method of estimation term in Ref. [33], it can be deduced that(21) φm(tM−k−1)=φTΓ(α)(T−tM−k−1)1−α+hαΓ(α+1)∑j=0kbjf(tM−j,φ(tM−j)), where bj=(k−j+1)α−(k−j)α. Further, the numerical format of R-L fractional-order system (16) is(22) φ(tM−k−1)=φTΓ(α)(T−tM−k−1)1−α+hαΓ(α+2)f(tM−k−1,φm(tM−k−1))+hαΓ(α+2)∑j=0kajf(tM−j,φ(tM−j)). whereaj={kα+1−(k−α)(k+1)αj=0,(k−j)α+1+(k+2−j)α+1−2(k+1−j)α+1j∈[1,k],1j=k+1, Therefore, according to FBSM and the above numerical format, the fractional optimal control problem (12)-(13) can be solved numerically. 5 Numerical simulations In the subsequent analysis, the infectivity of COVID-19 is described by saturation incidence rate. The commonly used form of saturation incidence rate is f(Sd,Id)=Sd(t)Id(t)1+σId(t), where σ is a positive number indicating the saturation factor, Sd and Id represent the density of susceptible group and infected group, respectively. The essence of saturated incidence rate is to use Id1+σId to describe the inhibitory effect caused by the increase of susceptible individuals or the crowding effect caused by the increase of infected individuals. Based on the above analysis, when the population size is measured by quantity, the corresponding saturated incidence rate can be described as(23) f(S,I)=SINp+σI, where Np is a constant representing the total population. During the COVID-19 outbreak, the size of the infected individuals will not exceed the total local population. Therefore, the above-mentioned inhibitory factor INp+σI has a similar inhibitory effect. In subsequent analyses, the trend of the COVID-19 pandemic is characterized by the above saturated incidence rate with inhibitory effect. The saturation incidence rate (23) obviously satisfies the hypothesis (H1)−(H3). Moreover, for S>0 and I>0, one has∂f(S,I)∂I−f(S,I)I=−σSI(Np+σI)2<0, which implies the saturation incidence rate (23) satisfies the hypothesis (H4). Therefore, the model (2) can be expressed as(24) {0CDtαS(t)=Λ−βcαS(t)I(t)Np+σI(t)+θαR(t)−(ρα+dα)S(t),0CDtαE(t)=βcαS(t)I(t)Np+σI(t)−(ϵα+dα)E(t),0CDtαI(t)=ϵαE(t)−(δα+γα+κ1α+dα)I(t),0CDtαQ(t)=δαI(t)−(ηα+κ2α+dα)Q(t),0CDtαR(t)=ραS(t)+γαI(t)+ηαQ(t)−(θα+dα)R(t), where the initial conditions are the same as in model (2). 5.1 Data resource The relevant data used in numerical simulation are from Johns Hopkins University (www.github.com/CSSEGISandData/COVID-19), Worldometer (www.worldometers.info) and the World Health Organization (www.who.int). There are three types of statistical data of the COVID-19 pandemic used in numerical simulation: the cumulative number of confirmed individuals, recovered individuals and dead individuals. Further, the current isolated cases can be measured by subtracting the sum of recovered cases and dead cases from the cumulative confirmed cases. 5.2 Fitting analysis This section verifies that the model (24) can effectively characterize the COVID-19 pandemic through the statistical data of currently isolated cases. Furthermore, the relevant theoretical results of this paper are verified based on statistical data. Since the outbreak of COVID-19 in late 2019, the Chinese government has adopted efficient quarantine measures. In mid-April 2020, the COVID-19 in China has been basically controlled. The data of current isolated cases of Hubei province, Hunan province and Jiangxi province in China are selected to identify the parameters of model (24) respectively. The data used in Figure 2 can be measured from the corresponding data provided by Johns Hopkins University. Further, through the effect of data fitting, it is demonstrated that the model established in this paper can effectively depict the spread of the COVID-19 outbreak. Before parameter identification, determine the relevant parameter values of model (24) according to the following analysis.• According to relevant statistics, the average life expectancy of China in 2020 is 77.5, and the corresponding natural mortality rate can be expressed by d=177.5×365. • During the outbreak of COVID-19, the population size remained balanced in the short term without significant fluctuations. Therefore, let Λ=dαNp. Fig. 2 The fitting effect diagram of model (24) on the isolated case. The solid line represents the fitting data and the circle represents the statistical data. Fig. 2 The parameters are identified according to the least square fitting, and the corresponding results are shown in Table 2. The fitting effect of model (24) on the isolated cases is shown in Figure 2. Meanwhile, the trends of susceptible, exposed, infected and temporarily removed individuals can be described, as shown in Figure 3 . According to the results of parameter identification, Figure 2 shows that model (24) can effectively describe the trend of isolated cases during the COVID-19 outbreak. The threshold R0 corresponding to Hubei Province, Hunan Province and Jiangxi Province can be obtained, as shown in Table 2. Further, Table 2 shows that the thresholds corresponding to these three provinces meet R0<1. Hence, the relevant conclusion of Theorem 4 is correct, which is verified by the curves in Figure 2 and Figure 3.Fig. 3 The trends of susceptible group, exposed group, infected group and temporarily removed group under the parameter identification results in Table 2. Fig. 3 Table 2 Parameter identification. Table 2Notation Hubei Hunan Jiangxi α 0.967 0.9971 0.9912 β 0.3765 0.6526 0.5266 c 11.2726 12.4872 9.1759 σ 4.8364×10−4 0.019 0.7864 ρ 0.1056 0.4027 0.1862 θ 0.0056 0.0316 0.0188 ϵ 0.3586 0.4365 0.4598 δ 0.5564 0.999998 0.999978 κ1 0.0398 0.0122 0.015 κ2 0.054 0.0128 0.0529 η 0.0893 0.1162 0.1189 γ 6.307×10−6 1.1586×10−5 3.8469×10−6 R0 0.3539 0.5863 0.4361 5.3 Optimal control analysis Through the previous analysis, the model (24) can characterize the COVID-19 outbreak. This section discusses the FOCP of model (24) based on measurement data, and provides theoretical guidance for current epidemic prevention and control. The FOCP is established corresponding to model (24) as follows:(25) minZ[u(t)]=∫0Tω1E(t)+ω2I(t)+ω3Q(t)+ω4u2(t)dt, subject to the state constraints(26) {0CDtαS=Λ−βcα(1−u)SINp+σI+θαR−(ρα+dα)S,0CDtαE=βcα(1−u)SINp+σI−(ϵα+dα)E,0CDtαI=ϵαE−(δα+γα+κ1α+dα)I,0CDtαQ=δαI−(ηα+κ2α+dα)Q,0CDtαR=ραS+γαI+ηαQ−(θα+dα)R. According to Theorem 6, the corresponding adjoint system is(27) {tRDTαλ1(t)=−[βcα(1−u)INp+σI+ρα+dα]λ1(t)+βcα(1−u)INp+σIλ2(t)+ραλ5(t),tRDTαλ2(t)=ω1−(ϵα+dα)λ2(t)+ϵαλ3(t),tRDTαλ3(t)=ω2−βcα(1−u)NpS(Np+σI)2λ1(t)+βcα(1−u)NpS(Np+σI)2λ2(t)−(δα+γα+κ1α+dα)λ3(t)+δαλ4(t)+γαλ5(t),tRDTαλ4(t)=ω3−(ηα+κ2α+dα)λ4(t)+ηαλ5(t),tRDTαλ5(t)=θαλ1(t)−(θα+dα)λ5(t), with transversality conditions(28) tRDTα−1λi(T)=0,i=1,2,⋯,5. The optimal control satisfies(29) u=min{max{βcαSI(λ2−λ1)2ω4(Np+σI),0},1}. Before solving the FOCP (25)-(29), the parameters of model (24) can be similarly identified based to the statistical data of current isolated cases. At present, due to the variation of COVID-19, the virus is still spreading repeatedly around the world. Since July 2022, the COVID-19 pandemic has gradually worsened in the Republic of Korea, and the number of virus carrier has rapidly increased. Based on the statistical data of the current isolated cases in the Republic of Korea, the parameters of model (24) are identified. the corresponding natural mortality rate is d=183.5×365. Different from the previous treatment method, the daily death rate and daily recovery rate are determined according to the relevant statistical data before parameter identification. It is assumed that the recovery rate η is the average of daily recovery rate, and the death rate κ2 is the average of the daily death rate. It can be calculated that η=0.0534 and κ2=3.7559×10−5 based on relevant data, as shown in Figure 4 . Therefore, only the remaining parameters of the model (24) need to be identified. Similarly, the results of parameter identification are obtained, as shown in Table 3. Based on the results in Table 3, the basic reproduction number R0=0.2744<1 can be calculated. According to the Theorem 4, it can be found that the disease-free equilibrium of the state system (24) is locally asymptotically stable. The corresponding fitting effect is shown in Figure 5 . Moreover, the simulation curves of other groups of model (24) are shown in Figure 6 .Fig. 4 The circle represents the data of daily death rate and daily recovery rate respectively, and the solid line represents the corresponding average value. Fig. 4 Fig. 5 Fitting effect of model (24) on current isolated cases in the Republic of Korea. Fig. 5 Fig. 6 The trends of susceptible, exposed, infected and temporarily removed groups under the parameter identification results in Table 3. Fig. 6 Table 3 Parameter identification. Table 3Notation Republic of Korea Notation Republic of Korea α 0.9182 ϵ 0.1736 β 0.2761 δ 0.516 c 9.0631 κ1 0.018 σ 4.8825×10−7 θ 0.0028 ρ 0.0439 γ 6.1462×10−6 Figure 5 shows that COVID-19 has spread rapidly in Republic of Korea since July 2022. By the end of September 2022, the transmission trend of COVID-19 has gradually slowed down in the Republic of Korea. Further, based on FBSM mentioned in Section 4, the FOCP (25)-(29) is solved numerically. Assume that the optimal control strategy is implemented for 90 days starting from 9 October, 2022. Select the corresponding weight parameters as: ω1=ω2=ω3=1 and ω4=10000. According to the results of parameter identification and FBSM, the FOCP (25)-(29) can be numerically solved. The corresponding control effect and optimal control u(t) are shown in Figure 7 . The data used in Figure 7 can be measured from the corresponding data provided by Worldometer.Fig. 7 (a)-(e) represent the control effects of optimal control for susceptible group, exposed group, infected group, isolated group and temporarily removed group respectively. (f) represents the corresponding optimal control curves. Fig. 7 From the results in Figure 7, it can be found that under the optimal control u1, the number of susceptible individuals exposed to the environment is significantly reduced, while also greatly reducing the number of virus carriers. This means that the corresponding optimal control strategy can effectively control the spread of COVID-19. 6 Conclusions An epidemic model is established to characterize the COVID-19 pandemic based on the Caputo fractional-order derivative. Some dynamic characteristics of the model are researched based on the threshold R0. According to the control strategies commonly used during the COVID-19 pandemic, the corresponding fractional optimal control problem is proposed. The numerical scheme of Riemann-Liouville fractional-order system with transversal conditions is given. 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==== Front Mater Today Proc Mater Today Proc Materials Today. Proceedings 2214-7853 Elsevier Ltd. S2214-7853(23)03601-5 10.1016/j.matpr.2023.06.205 Article Building construction: Impact of work from home on design and material choices Singh Sneh Walia Siddhant ⁎ Manipal University Jaipur, Jaipur, India ⁎ Corresponding author. 27 6 2023 27 6 2023 Copyright © 2023 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Sustainable Materials and Practices for Built Environment. 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Due to COVID-19 pandemic the term ‘Work from home (WFH), which was earlier only a theoretical concept for many, suddenly became necessary to continue various economic activities. The communication related infrastructures are often discussed in this context and how a sudden demand for it created a new avenue for certain businesses, but one very crucial aspect without which it is difficult to perform WFH is ‘suitable homes’ which provide necessary requirements to operate office work. This cross-sectional study focuses on investigating the issues faced by the students and working professionals who had to suddenly start working from home due to restricted movement because of lockdown measures. The study objective was achieved by analysing the unique situation through reviewing articles published in significant property portals, annual reports of major real estate developers & builders, conducting a focus group discussion to ascertain views of construction professionals on emerging requirements of WFH, a survey using structured questionnaire consisting of open, closed, and Likert scale questions from students and working professionals was carried out to understand dweller’s issues and requirements regarding WFH setup. The study findings indicate that residential dwellers working from home partially or full time are facing some common issues that require a focused approach towards design, material adaptability and a wider inclusion of Internet of things (IoT), and not just a single point solution of increasing space of existing residential unit, as indicated in annual reports of major realty players, thus the study also offers a framework which will be helpful for construction industry stakeholders in understanding the requirements of residential dwellers for performing WFH. Keywords Work from Home (WFH) IoT Covid-19 Indoor environmental quality Restricted movement Working professionals ==== Body pmc1 Introduction The novel coronavirus (COVID-19) pandemic has challenged social and economic activities in an unprecedented way forcing the government, private sector, and people, in general, to reconsider a wide variety of regular practices and look for new ways to complete the same tasks. The virus can spread from contact with an infected per-son through droplets generated when they cough, sneeze, speak or breathe. Known methods for reducing the risk of getting covid-19 are currently following local guidance (lockdown rules). Wearing well-fitted masks, staying at least 1 m away from others, proper ventilation, avoiding crowded places, avoiding touching surfaces, practicing good hand hygiene and vaccination [24]. The spread of coronavirus has impacted the whole world in various aspects due to the unavailability of mitigation measures, thus, to slow down its impact, the lockdown was adopted as a necessary step by the government worldwide. These lockdown measures initially halted various economic activities as only those economic activities were allowed to function, which were deemed essential. Lockdown forced many business organizations to look for alternative means to function, in general, they were employees working from home through the use of mobile and telephone communication, video conferencing via digital medium involving high-speed internet, various electronic gadgets like laptops, mobile, e-pads. The aim of this study is to investigate the issues faced by students and working professionals who had to suddenly start working from home due to restricted movement because of lockdown measures. Work from Home (WFH) though not a new concept. But before the pandemic, it was practiced in a very few select firms in India that too in a limited way [15], [22]. Some companies in Indian IT industry also offered option of flexi timing to its employee but its emphasis was to enhance work life balance of employees and work was performed from office [16], [23]. Some past studies have already been conducted to identify the advantages and disadvantages of WFH culture [21]. Some studies also examine the effect on productivity and wellbeing due to WFH as ‘new way of working’ [20]. According to a survey, net cost saving for 50% Work from home arrangements is approximately 1% only which isn’t significant since assuring every Work from Home arrangement to be effective is a challenge for companies [1]. Doing remote working is challenging considering the homes/flats are not constructed, keeping WFH as a requirement of dwellers. Lockdown led to various difficulties for residential dwellers who, due to movement restrictions by both central and state government lockdown guidelines, were now left with the only option of WFH. It would not be surprising to say that Covid 19 will become a reason for people to innovate home design due to WFH requirements, something similar to Spanish flu gave origin to vanity room at front door entry [2]. This study will examine the experiences of Indian students and working professionals who work from home. The study will use a mixed-methods approach, which includes a literature review, a focus group discussion, and a survey. The study aims to identify the challenges faced by students and working professionals when working from home, understand the impact of these challenges on their productivity and work-life balance, and develop recommendations for how homes can be made more suitable for working from home. 2 Methodology Covid 19 has a profound effect on human needs, including the dwelling units; world-wide, most people are working hard to survive in this uncertain economic disaster [2]. Working professionals and students who are forced to adjust to these new conditions have looked at their residential dwellings differently. Many new issues have emerged post this unique situation. Suppose these conditions persist (which is the case according to the majority of respondents in our survey). In that case, the construction industry will have to look for a more permanent solution for these new Work from Home requirements. To understand the requirements of WFH for Indian working professionals and students following a three-pronged approach was taken by authors: • Review of articles published by significant property portals to gain insights into their perspective on the issues related to work from home (WFH). Additionally, review of popular press articles and official company websites was conducted to understand the demands and requirements of WFH being addressed by builders and developers for new residential projects. • Focus group discussion with construction industry professionals to understand their take on WFH issues. • A cross-sectional survey was conducted on 114 Indian working professionals and students using an online questionnaire to investigate the challenges they face while working from home. The issues highlighted in the survey are ranked using a score scale for understanding where more focus is required from the construction industry while designing new residential dwellings (Fig. 1 ).Fig. 1 Methodological approach to study the demand of infrastructural changes in Residential buildings due to WFH requirements. 2.1 Market study To assess the issues about ‘Work from Home’ a review of the literature was done. The problem was explored by the authors through reviewing available literature, articles that appeared on property portals based on research conducted by analyzing the user data, and opinions of industry experts. The online property portals have become new marketplaces for buyers, sellers, developers, and property brokers since their emergence in Indian markets. The published articles in ‘research’ ‘knowledge center’ or ‘blog’ sections are based on expert opinions, user data of site visitors based on their search history, and market surveys related to the realty sector. To understand the issues related to WFH due to the unique situation of pandemic-related movement restriction measures a review of such articles was done by authors in Table 1 . From user data, it was clear that there is a growing need for more prominent space in residences which experts link to the need for more space for working from home [6]. Increased demand for homeownership is highlighted by the pandemic, focusing on the quality of the home and neighborhood. Before lockdown measures were enforced, it was observed that portal visitors searched for property near offices [6]. During the lockdown, they changed to the periphery of cities with a relative reduction in rents. Experts also observed that due to new working conditions, requirements for office-related furniture also increased [9]. The working professionals also faced issues with the lower number of electrical switches and plug points [8]. The rooms earlier used for other purposes were modified to new demands for office work, but they lacked necessary daylight, ventilation, acoustics, privacy, and common circulation areas, among other things. [4], [8], [10]. The full-equipped communication setup [10] secured Wi-Fi [5], [7], and the pre-lockdown network was not enough to perform WFH.Table 1 Adapted infrastructure & Design changes by Indian property portals to achieve WFH requirements. Customer requirements Magic bricks[7] No-broker [8] Housing [8] Sulekha[9] 99 Acres[4] Common floors[10] Quikr Homes[5] R1 Large Spaces x x R2 Increased number of Plug points/ Switches x R3 Better Daylight/ Ventilation x R4 Ergonomically designed Office Furniture x R5 Secure Wi-Fi/Network Connection x R6 Communication setup x R7 Work desk setup x x R8 Location change to city fringes x x R9 Emergency power backup x R10 Additional/ flexible spaces x x Review of popular press articles and official company websites: The homebuyers' preferences have changed due to new working conditions be-cause of the pandemic, which has led to the emergence of new residential segments. The lockdown measures have forced many to continue working from home. A review of annual reports from leading real estate companies has been conducted by authors, which highlights a need for technology-enabled device connectivity, the increasing demand for larger space, option of home offices, increase in need for individual spaces, additional multipurpose room or flexible spaces which can be utilized according to the need of home dwellers. A rise in demand for independent houses with open areas has also been observed (Table 2 ). WFH has also created many structural shifts by making thoughtful new flat types like 2LDK (Living, Dining & Kitchen) + personal workspace (launched by Krisumi Corporation),2.5/3.5 BHK homes where the additional space can be converted as per one's requirements. The independent floor concept has also picked up pace after the pandemic and leading realty players such as DLF are utilizing this opportunity by launching projects in this segment [17].Table 2 Changes in Consumer preferences due to WFH requirements. Real estate developers Changed Consumer preferences due to WFH requirements mentioned in Annual Reports RED 1 [11] Larger space Rise in coworking spaces Option of home offices RED 2[12] Requirements for individual space Additional multipurpose room RED 3[13] Demand for bigger houses RED 4[14] Demand for functional and flexible homes RED 5 [15] Demand for independent house with open areas RED 6[16] Need for larger and more expansive spaces Technology-enabled device connectivity for convenience Hygiene and sanitation facilities across the premises RED 7[17] Demand for 2.5 BHK and 3.5 BHK Demand for plotted developments Need for useful and workable homes that can fit into working areas 2.2 Focus group discussion During a focus group discussion, a group of 6–10 experts are invited to discuss an issue related to their field, with a moderator present to control the discussion. To gauge the views of construction professionals on the emerging requirements of WFH (work from home) and the necessary modifications or additions to residential dwellings to better accommodate this purpose, a focus group discussion was conducted with 8 construction professionals. The group consisted of five architects, two project managers, and one civil engineer. The following points were revealed during the discussion. • Ventilation and daylight requirement has increased Communication and network requirements have increased exponentially. • Noise cancellation of unwanted sounds in the background is affecting work quality. • Room cooling requirements have increased, which highlights the emerging need of HVAC/ green building concepts. • Plug point and electric switches requirements have increased as multiple devices have to run together. • Demand of sleek and good quality Partition wall. • Office Furniture requirements in homes have increased. • Requirement of good aesthetical background. • Requirement of new technology for smart home. The primary objective of the focus interview was to list out the WFH related problems and requirements of home dwellers identified by Construction professionals. 2.3 Survey In a recent work from home survey 2020 conducted by Gensler in USA only 12 percent of workers prefer to work from home full-time due to various challenges they face in working from home. Survey indicates that apart from socializing, community connectivity, and scheduled meetings, work from home setup requires a certain level of infrastructure requirements, essential amenities, and furniture facilities [3]. To understand issues faced by Indian professionals who have to adjust to this new normal of either working full time or partially from home, specific requirements in residential dwellings to perform WFH with ease, adjustments or alterations to be made in current dwelling designs an online survey was conducted by authors. The survey which was based on a structured questionnaire was conducted to understand the requirements of WFH of working professionals from across India. Total 112 responses were collected out of which 110 were found to be deemed fit. About 70% of the respondents were below 35 years of age, 57% male and 43% female. Respondents were also asked about the nature of their occupation, 46 percent of the respondents were full-time office workers, whereas 27% of respondents were in an occupation that required part office and part-time fieldwork. The survey data also shows that most respondents had at least one member doing WFH in the house. 25% of respondents mentioned that at least one member of the family was doing Work from home in the lockdown. Around 34% respondents in their dwellings had 2 members doing work from home. And around 31% of the dwellings had 3 or more than 3 members doing work from home during the lockdown, indicating the high demand for improved infrastructure requirements for WFH setup. The survey also identifies the dwelling types i.e., apartments or Villas to know more about their specific requirements. 62% of respondents have their WFH setup in Villa or independent houses and rest in apartment buildings. In response to what adjustments/alterations respondents have already made in their household for improving WFH conditions, it is found that 60% of the respondents up gradated their Network which include new broadband connection/ modem location & high-speed internet arrangements and 45% of respondents had to alter and made adjustment in furniture (Table 3 ).Table 3 Socioeconomic characteristics of Survey respondents. Gender Male 57% Age Female 43% < 26 47% 26–35 22% 36–50 20% 50 above 11% Occupation Full time office work 46% Full time field work 1% Part Office & part field work 24% Full time Student 25% Home Maker 4% Dwelling Unit Type Vila/Independent house 62% Apartment 38% No. of members doing WFH at one time 1/living alone 25% 2 34% 3 20% 4 14% 5 7% Adjustment / Alterations Alteration/adjustment made in furniture 45% Space reorganization 24% Addition/alteration of electrical points 30% Network upgradation 60% Alteration/ Adjustment made for noise cancelation 18% Changes required other than above 21% The Last part of the questionnaire was to analyze and rate the Consumer WFH requirement preferences. 58% of respondents find Noise cancelation issue is one area that requires maximum changes for WFH setup, whereas 36% of respondents identified no changes required for air conditioning issues during WFH (Fig. 2 ).Fig. 2 Noise cancelation and Communication are most rated issues which required maximum changes. All WFH requirements has been identified through market surveys of articles from property portals, and Annual reports from real estate developers and focus group interview i.e. communication-related issues, house/flat design & layout, house/flat space shortage, noise cancelation issues, furniture ergonomic & design issues, air conditioning issues & workstation Aesthetics. All Seven identified requirements are then rated in the survey using Likert scale of 1 to 7.(1) WeightedMeanValue19=x1w1+x2w2+x3w3+…..+xnwn/w1+w2+w3+….+wn Where: xn = number of respondents giving wn difficulty score to perform the mentioned activity (where n = 1…7). wn stands for weight assigned to the level of difficulty faced. The weight assigned to the highest difficulty level, w1, is 7. The weight assigned to the lowest difficulty level, w7, is 1. The weighted mean value has been calculated using eq.1, which is then used to give rank to the required preferences as the highest weighted mean receives maximum rank. Respondents have given ‘Noise cancelation’ as first rank, ‘Communication-related issues’ as second & Workstation Asthmatics as third. Air conditioning issues have received rank seventh. (Table 4 ).Table 4 The Rating of Consumer preferences due to WFH requirements. Features Rank 1 Rank 2 Rank 3 Rank 4 Rank 5 Rank 6 Rank 7 Respondents Weighted Total Weighted Mean Value Rank Communication related issues 25 16 33 14 9 4 9 110 536 19.14 2 House/flat design & layout 9 23 25 15 11 10 17 110 456 16.29 6 House/flat space shortage 17 17 25 18 5 7 21 110 468 16.71 5 Noise cancelation issues 22 31 22 11 8 6 10 110 540 19.29 1 Furniture ergonomic & design issues 14 24 29 11 10 8 14 110 491 17.54 4 Air conditioning issues 7 18 24 9 12 13 27 110 402 14.36 7 Workstation Aesthetics 21 21 23 16 9 10 10 110 509 18.18 3 The ranking received from the survey analysis gives a comprehensive insight about essential requirements which Construction Industry must address. Skewness and Kurtosis of responses received from 110 respondents was calculated to assess the normality of the data and the values of −0.47 and −0.96 were within the range of −2 to 2 and −7 to 7 and will be considered normal (George, 2011). Cronbach’s Alpha value (which measures internal consistency of data and is considered acceptable if greater than and equals 0.7) is 0.86 indicating good consistency. (Table 5 ).Table 5 Normality and Reliability test. Normality (N = 110) Value Skewness −0.4711701 Kurtosis −0.9573132 Reliability (N = 110) Cronbach's Coefficient alpha 0.8618164 2.4 Discussion 2.4.1 Materials selection and indoor environmental quality Residential dwelling units in general faces space constraints in metropolitan cities, while modern housing have advanced ventilation, but they deal with a limited fresh air circulation. Inadequately ventilated buildings trap fumes, scents, and other chemicals [25] which includes consumer products and cleaning agents to VOC (Volatile organic compound). These compounds evaporate (turn into a gas) at room temperature. Repeated VOC exposure reduces lung function and inflames airways. Formaldehyde in high concentrations is hazardous and irritating, causing headaches, dizziness, and mental impairment. Above 0.1 ppm, it can cause various health issues and allergic reactions [26]. Sometimes Urea-formaldehyde used in furniture and upholders as glue also emits formaldehyde at room temperature. For better Indoor Environmental Quality (IEQ) it is recommended to use High quality walk-off mats instead of traditional mats, which can be placed at the entrance of residential unit for collection of dirt due to footwear. This prevents dirt and mould regrowth. These mats maintenance is simpler and can also be easily vacuumed or cleaned by shaking out high-void volume mats. Another area which is often overlooked and can affect IEQ is maintenance of filtration facilities of residential building, for this, suitable schedule for substituting filtration vehicles and testing and sustaining exhaust systems in the building's must be part of facility management plan. Also building facility staff should know how to select, store, and handle hazardous waste; some cleaning supplies can degrade indoor air quality. Thermal comfort is another essential aspect that may impact homeowner performance in doing work from home. Many studies indicate that 1% to 16% productivity increases, saving significant amount of money per year [27]. Additional switches for controlling HVAC facilities may add to a project's preliminary costs, but energy savings from regulated temperatures offset these costs. To control misuse of personal switches, like fixing thermostats very high or keeping windows open, which leads to high energy costs, awareness can play a vital role and home dwellers must be trained in these aspects. Improved ventilation and real-time IEQ monitoring promote community health. These systems reduce COVID-19 vulnerability and promote public health. Different government and private body issued guidelines for mitigation of COVID-19 situation by following social distancing, but IEQ at home offices was rarely discussed. Roth et al. report that WFH may have negative health consequences owing to poor IEQ [28]. It was also observed by Yang et al. in their study that 90% and 50% of energy-efficient homes topped chronic exposure parameters for formaldehyde and TVOC, respectively [29]. The air pollutants enter homes and buildings through open doors and windows as well as through ventilation systems; the health risks posed by indoor air pollution are equivalent to those posed by pollution found outside. The AQI (air quality index) in outdoor areas of metropolitan cities (Table 6 ) is poor, so use of air purifier is advisable when working from home for longer durations. It is also recommended to close all the doors, windows, and ventilations in the building located in cities with poor AQI levels.Table 6 Air quality data of a week from (10–16 Nov. 2022) of 3 metro cities of India [30]. Cities AQI PM 2.5 PM10 CO NO2 SO2 Temp. Humidity Delhi Highest 490 364 441 2338 37 5 28 81 Lowest 113 41 115 545 12 2 16 25 Mumbai Highest 258 158 247 1472 41 10 33 73 Lowest 121 55 122 462 18 4 24 30 Bangalore Highest 183 85 58 490 6 3 28 97 Lowest 16 8 16 159 3 2 18 57 2.4.2 Importance of IoT in working from home Smart IoT applications can significantly improve the comfort and safety of Work from Home. Smart home systems can send alerts to registered devices or accounts about environmental data and alert home office workers. The thermostat applications can similarly monitor indoor room temperature and alerts the user to act or adjust the temperature by controlling/heating devices. The continuous use of electrical devices while doing WFH can negatively impact both energy bill cost of user and maintenance aspects of these devices (Table 7 ). Smart IoT applications can control turning on/off lights, appliances, doors, windows, etc. and help home office workers. Smart applications can alert users of abnormal or unexpected events and respond automatically if desired; this is important considering WFH maybe done by worker along with other personal work at home. The intrusion detection app can send images, audio, and video to the user. These applications can monitor home activity and alerts users to take security measures. Additionally, detect smoke/gas applications can sense the smart home environment for health and security by detecting light, ionizing air, and sampling it. This application can also alert nearby fire stations and users via email/SMS about potential health risks.Table 7 Design, IEQ, IoT consumer preferences. Design Material selection & IEQ IoT House/flat space shortage x x House/flat design & layout x x Workstation Aesthetics x Furniture ergonomic & design issues x X Air conditioning issues X x Noise cancelation issues x X x Communication related issues x x 3 Conclusion The requirements of working professionals for WFH setup are not just a single point ‘higher space’ issue as shown through this multipronged study. Through the survey conducted by authors it is clear that noise cancellation, communication, workstation aesthetics and furniture ergonomics are the top four concerns related to WFH which need urgent attention from construction industry. A review of annual reports of Real estate companies suggest that Realty players are mainly looking at WFH issues through the lens of increased space requirements which respondents in survey also highlighted, but other design related issues were ranked higher in priority by respondents over it and thus will require more focus from Indian construction industry who are involved in realty space. Focused group discussion of construction professionals has identified that noise cancelation, electrical plug point requirements, workstation aesthetics and furniture requirements should be addressed through design modifications and exploring new technologies as affordability of higher space is not suitable for all economic classes, this too suggest that looking at whole issue through space addition may not be the right answer. Points emerged based on multifaceted analysis have been addressed by authors and recommendations pertaining to design, material and IoT choices for home offices have been given (Table 8 ).Table 8 Adapted infrastructure & relation with Design, IEQ & IoT. Design Material selection & IEQ IoT Large Spaces x Increased number of Plug points/ Switches/ Net connections x Better Daylight/ Ventilation x X x Ergonomically designed Office Furniture x X Secure Wi-Fi/Network Connection x Communication setup X x Work desk setup x X Location change to city fringes Through this paper, authors have given a comprehensive framework for development of residential units equipped with work from home facilities for real state builders and contractors to understand the need of home dwellers and address the work from home issues. It's time to change the deep-seated behavior followed pre COVID 19 by construction industry. It is understandable that situation which prevailed during initial period of pandemic was unique and may never repeat but it's important to understand that hybrid or part office and part working from home will remain as part of normal working scenario for Indian workers and students, so it becomes important to define new norms and guidelines for constructing house/ dwelling units well suited for work from home. These guidelines will help the designers & builders to incorporate the changes & fulfil the requirements at the initial stages of planning which will avoid the post-construction changes and indirectly saves the overall cost and ensure comfortable WFH conditions. Limitation of this research is that it is based only on the perspectives of working professionals and students in India, and thus our findings may be restricted to this region. Future research could broaden the scope of investigation by exploring the needs and challenges of individuals who work from home in various parts of the world to provide more comprehensive understanding of this area of research. CRediT authorship contribution statement Sneh Singh: Investigation, Visualization, Writing – review & editing. Siddhant Walia: Data curation, Methodology, Validation, Writing – original draft. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. ==== Refs References 1 Work from Home and the impact on Corporate Real Estate, Knight Frank; https://content.knightfrank.com/research/2066/documents/en/work-from-home-indian-real-estate-residential-office-7431.pdf. 2 Marlin Chris, How COVID-19 will change the way we design our homes, World economic forum, 2020; https://www.weforum.org/agenda/2020/08/how-covid-19-will-change-what-we-call-home-ddfe95b686/ 3 Back to the Office, U.S. work from home survey 2020, Gensler, 2020; https://www.gensler.com/doc/gensler-us-work-from-home-survey-2020-briefing-1 4 Chari Vikram, Work from home changing homebuying preferences. 99acres.com, 2020; https://www.99acres.com/articles/work-from-home-changing-homebuying-preferences.html (retrieved 06 August 2021). 5 Sandhya Lakshmi C, Real Estate before and after COVID-19, Quikr Homes, 2020; https://www.quikr.com/homes/blog/real-estate before-and-after-covid-19+9651. 6 [Covid 19 Magicbricks Property Buyer’s Sentiment Survey 3rd Edition, Magicbricks, 2020; https://property.magicbricks.com/microsite/research-insights/src/pdf/COVID-19%20Property%20Buyer%20Sentiment%20Survey%203rd%20Edition_Nov%2010.pdf (accessed 16 September 2021). 7 Houses in city fringes in demand after Covid-19 outbreak: survey, The NoBroker Times; https://www.nobroker.in/blog/working-from-home-during-the-covid-19-pandemic/ (accessed 16 September 2021). 8 Amar Tendulkar, Interior and décor trends that are likely to gain preference, post the COVID-19 pandemic, Housing.com, 2021; https://housing.com/news/interior-and-decor-trends-that-are-likely-to-gain-preference-post-the-covid-19-pandemic/ (retrieved 06 August 2021). 9 Deepika Ravichandran, Home Decor Industry and the COVID-19 Battle, Sulekha, 2021; https://www.sulekha.com/interior-designers-decorators/home-decor-industry-and-the-covid-19-battle-5940-blog (retrieved 06 September 2021) 10 Basic Home Office Necessities, Commonfloor.com, 2021; https://www.commonfloor.com/guide/basic-home-office-necessities-57970 (accessed 06 August 2021). 11 Staying Resilient, Shobha Annual Report 2021; https://www.sobha.com/wp-content/uploads/2021/07/AR-2021.pdf (accessed 06 August 2021). 12 Annual report 2020-21, Oberoi realty, 2021; https://www.oberoirealty.com/pdf/2020/Oberoi_Realty_Annual_Report_2020-21.pdf 13 Annual Report 2020-21, Ashiana Housing Limited, 2021; https://www.ashianahousing.com/download/2020-21-Annual-Report.pdf. 14 Annual Report 2020-21, Prestige Estates projects Limited, 2021; https://www.prestigeconstructions.com/admin/uploads/investors/financial-performance/2020/annual/annualreport_fy_2021.pdf. 15 Power of discipline, Integrated Annual Report 2020-21, Kolte-Patil Developers Limited, 2021; https://www.koltepatil.com/assets/uploads/fianancial_statement/16297091011844968431.pdf (accessed 06 August 2021) 16 Annual Report 2020-21, Puravankara Limited, 2021; https://www.puravankara.com/backend/assets/uploads/investors_reports/375345c4ea660801dea9f961e9023334.pdf (accessed 10 September 2021). 17 Annual Report 2020-21, Macrotech Developers Limited (Lodha Group) 2021; https://s3.ap-south-1.amazonaws.com/lodhagroup.in-tfz/Annual+Report.pdf (accessed 10 September 2021). 20 Demerouti E. “New ways of working: Impact on working conditions, work–family balance, and well-being.” The impact of ICT on quality of working life 2014 Springer Dordrecht 123 141 21 Galanti T. Work From home during the COVID-19 outbreak: The impact on employees’ remote work productivity, engagement, and stress J. Occup. Environ. Med. 63 7 2021 e426 33883531 22 It took a pandemic for India Inc to accept the benefits of working from home, Quartz India; https://qz.com/india/1819365/india-inc-should-have-adopted-work-from-home-before-coronavirus/ (accessed 10 September 2021). 23 Deepak, R. Kanthiah Alias, and V. Balamurugan. “Flexible working hours is an emerging HR technology: a revolution in the world of work.” SNR College Road, Ganapathy Post, Coimbatore, Tamil Nadu 641006. 24 Coronavirus disease (COVID-19), World Health Organization; https://www.who.int/health-topics/coronavirus#tab=tab_1 (accessed 10 September 2021). 25 Steinemann A. Volatile emissions from common consumer products Air Qual. Atmos. Health 8 2015 273 281 10.1007/s11869-015-0327-6 26 US Consumer Product Safety Commission. (1997). An update on formaldehyde. US Consumer Product Safety Commission. 27 USGBC, L. (2009). Reference guide for green building design and construction. Washington, DC: US Green Building Council. 28 Roh T. Moreno-Rangel A. Baek J. Obeng A. Hasan N.T. Carrillo G. Indoor air quality and health outcomes in employees working from home during the covid-19 pandemic: a pilot study Atmosphere (Basel) 12 no. 12 2021 29 Yang S. Perret V. Hager Jörin C. Niculita-Hirzel H. Goyette Pernot J. Licina D. Volatile organic compounds in 169 energy-efficient dwellings in Switzerland Indoor Air 30 3 2020 481 491 32190933 30 AQI- Realtime pollution monitoring platform, https://www.aqi.in/ Air quality Index Purelogic Labs India Pvt. Further Reading 18 Sanjeev Sinha, Changing homebuyers’ preferences and emergence of new housing options amid the pandemic, Financial express, 2021; Changing homebuyers' preferences and emergence of new housing options amid the pandemic - The Financial Express (accessed 10 September 2021). 19 Stephanie Glen. “Weighted Mean: Formula: How to Find Weighted Mean” From StatisticsHowTo.com: Elementary Statistics for the rest of us! https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/weighted-mean/.
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==== Front Bull Acad Natl Med Bull Acad Natl Med Bulletin De L'Academie Nationale De Medecine 0001-4079 2271-4820 l'Académie nationale de médecine. Published by Elsevier Masson SAS. S0001-4079(23)00190-5 10.1016/j.banm.2023.02.014 Revue Générale Covid-19 : manifestations et complications neurologiques à la phase aiguë de la maladie☆ COVID-19: Neurological manifestations and complications during the acute phase of the diseasede Broucker Thomas Service de neurologie, hôpital Delafontaine, 93200 Saint-Denis, France 27 6 2023 27 6 2023 3 2 2023 28 2 2023 © 2023 l'Académie nationale de médecine. Published by Elsevier Masson SAS. All rights reserved. 2023 l'Académie nationale de médecine Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Les manifestations et complications neurologiques de la phase aiguë de la Covid-19 sont nombreuses. Elles concernent principalement le système nerveux central sous les formes fréquentes d’encéphalopathies, d’encéphalites et de pathologies neurovasculaires. Les manifestations neurologiques périphériques comportent principalement les polyneuropathies aiguës de type syndromes de Guillain-Barré et les neuromyopathies de réanimation. Ces manifestations ont été décrites pour la plupart lors de la première vague de la pandémie. Les aspects épidémiologiques, cliniques, paracliniques, physiopathologiques et thérapeutiques sont abordés dans cette revue générale de la littérature parue de 2020 à début 2023. The neurological manifestations and complications of the acute phase of COVID-19 are numerous. They mainly concern the central nervous system in the frequent forms of encephalopathy, encephalitis and neurovascular pathologies. Peripheral neurological manifestations mainly include acute polyneuropathies such as Guillain-Barré syndrome and intensive care neuromyopathies. Most of these manifestations were described during the first wave of the pandemic. The epidemiological, clinical, paraclinical, pathophysiological and therapeutic aspects are addressed in this general review of the literature published from 2020 to early 2023. Mots clés Infections à coronavirus Syndrome respiratoire aigu sévère Système nerveux central Manifestations neurologiques Keywords Coronavirus infections Severe acute respiratory syndrome Central nervous system Neurologic manifestations ==== Body pmcIntroduction La Covid-19 a trois ans au moment de l’écriture de cette revue générale. Très rapidement diffusée au monde entier, elle a été déclarée urgence de santé publique à portée internationale par l’Organisation mondiale de la santé (OMS) dès le 30 janvier 2020 [1]. Après trois ans la pandémie a touché près de 10 % de la population mondiale et a été la cause du décès de près de 7 millions de personnes [2]. L’Europe compte pour plus d’un tiers des cas et près d’un tiers des décès [3]. Les publications concernant les manifestations et les complications neurologiques de la Covid aiguë ont été précoces, dès les premiers mois de la pandémie, venant d’Asie, d’Europe et des États-Unis [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]. Des manifestations sensorielles spécifiques ont été décrites (anosmie principalement, agueusie). Des complications très fréquentes ont été rapportées dans le contexte de la réanimation respiratoire nécessitée par les cas graves [6]. Des manifestations comportementales ont fait l’objet de supputations physiopathologiques comme l’hypoxie heureuse ou « happy hypoxia » observée lors de la dégradation objective de l’hématose, trop bien vécue, sans anxiété, par les patients [15]. Des manifestations non spécifiques comme les céphalées ont augmenté sensiblement la fréquence des complications dites neurologiques rapportées dans les séries. La fréquence des encéphalopathies sévères survenant de manière retardée lors de l’aggravation de la Covid-19 ou se manifestant par un retard de réveil après sédation en réanimation a été vite repérée lors de la première vague de la pandémie en 2020, la plus sévère en termes de brutalité et d’intensité. D’emblée, en se basant notamment sur les enseignements des épidémies de SARS en 2004, et de MERS en 2012 et surtout sur les travaux de recherche physiopathologiques qui les ont suivies, une atteinte virale spécifique du système nerveux central par le SARS-CoV-2 a été suspectée qui, aujourd’hui encore fait l’objet d’affirmations reposant sur des hypothèses que les travaux disponibles depuis lors n’ont pas prouvées (voir pour revue [16], [17]). Les manifestations neurologiques rapportées plus de deux mois après le début de la Covid-19 aiguë sont constitutives de ce qu’il est convenu par l’OMS d’appeler le syndrome postCovid [18]. Elles seront abordées dans un autre article de ce même bulletin. Épidémiologie générale des manifestations neurologiques de la Covid-19 aiguë Il apparaît clairement que les manifestations neurologiques survenant lors de la Covid-19 ne touchent que très rarement les patients ambulatoires. L’anosmie, fréquemment rencontrée dans les coryza infectieux, dont les coronavirus sont responsables dans 10 à 15 % des cas, est un symptôme fréquent chez ces patients [19]. La cause neurologique de l’anosmie est probablement extrêmement rare par rapport à l’atteinte exclusive de l’épithélium sensoriel de soutien de l’appareil olfactif [20], [21]. De même l’atteinte du goût peut être rapportée à l’atteinte de l’olfaction, les capacités gustatives proprement dites n’étant pas touchées. Les atteintes du système nerveux périphérique dont le syndrome de Guillain-Barré et les atteintes de nerfs crâniens sont rares et sont repérées dans les séries hospitalières. Les céphalées sont fréquentes mais leur attribution à une atteinte neurologique est très largement surestimée, les méningites aiguës dans le cadre de la Covid-19 étant très rares et les céphalées faisant partie des syndromes viraux aigus communs [22]. La fatigue, souvent qualifiée de symptôme neurologique, est dans le même cas [23]. Les données descriptives hospitalières rassemblées lors de la première vague sont plus informatives. Elles indiquent une prévalence de manifestations/complications neurologiques de la Covid-19 comprise entre 8 et 12 % [7], [10], [13], [14]. Ces complications surviennent au cours de l’hospitalisation pour une Covid-19 plus ou moins sévère : 47 % des patients étaient admis en soins intensifs dans la série rétrospective sur 6 mois de Delorme et al. [13]. Les manifestations neurologiques peuvent être inaugurales et motiver en elles-mêmes l’hospitalisation dans des cas d’encéphalopathie ou d’infarctus cérébraux par exemple. Elles peuvent aussi survenir après une Covid-19 ambulatoire ou non, après un intervalle libre, dans les cas de syndromes de Guillain-Barré notamment. Les données hospitalières plus récentes sont rares, sans doute du fait de la meilleure prise en charge des formes graves de Covid-19 et de variants moins agressifs. Une étude récente de 169 patients hospitalisés à Suzhou (Chine) pour Covid-19 dû au variant Omicron dans 100 % des cas ne faisait état d’aucune complication neurologique ni d’aucun décès [24]. Épidémiologie descriptive des complications neurologiques Les données disponibles concernant les diverses complications et manifestations neurologiques de la Covid-19 aiguë sont très nombreuses mais disparates. Les petites séries et rapports de cas prédominent qui ont permis de décrire de manière exhaustive l’éventail des manifestations possibles. Peu de travaux mentionnent des groupes contrôles concomitants ou historiques concernant par exemple la pathologie vasculaire cérébrale ou le syndrome de Guillain-Barré. La série française multicentrique de Meppiel et al. a permis très tôt une description de la quasi-totalité des types d’atteintes neurologiques observables au cours de la Covid-19 aiguë lors de la première vague de la pandémie [14]. Ces données ont été publiées et confirmées durant la même période par d’autres séries hospitalières dont celles monocentriques et systématiques de Rifino et al., de Delorme et al., et de Romero-Sanchez et al. [7], [10], [13]. Ces atteintes sont centrales, périphériques, ou mixtes. Elles peuvent être en rapport avec le terrain, les complications de la réanimation, l’état septique, une défaillance d’organe sur terrain antérieurement débilité [13]. Elles peuvent aussi paraître spécifiques de la Covid-19 par leur fréquence, liée à l’ampleur de la pandémie et par leur physiopathologie au moins inflammatoire et/ou immunomédiée. L’étude récente de Grundmann et al. a montré que les complications neurologiques de la Covid-19 étaient moins nombreuses chez les patients hospitalisés traités par dexaméthasone, par remdesivir ou surtout par la combinaison des deux, que ce soit chez les patients sévères ou chez les patients non insulinorequérants. Dans cette étude, couvrant les années 2020 et 2021, les complications neurologiques concernaient 4,8 % et 4,5 % des patients sévères et non oxygénodépendants respectivement. Le traitement combiné permettait une réduction du risque de complications neurologiques de moitié [25]. Les complications neurologiques centrales Encéphalopathies Les encéphalopathies, définies comme un dysfonctionnement global non spécifique du système nerveux central sont les complications neurologiques les plus fréquentes, touchant entre 20 et 40 % des malades hospitalisés lors de la première vague de Covid-19. Elles sont d’autant plus fréquentes que les patients ont des antécédents de maladie neurologique, cognitive ou physique, handicapante (maladie d’Alzheimer, démence vasculaire, maladie de Parkinson, sclérose en plaques, myasthénie) [26], [27], [28], [29], et que la Covid-19 est sévère [7], [10], [14], [30]. La revue systématique et méta-synthèse de Manzano et al. dresse une description prototypique des encéphalopathies/ADEM (encéphalite aiguë disséminée)/AHLE (leucoencéphalite hémorragique aiguë) qui sont les formes cliniques fréquentes des encéphalopathies caractérisées (cf. plus bas) [31]. Rarement symptôme inaugural de la Covid-19, le délai de survenue des encéphalopathies est le plus souvent compris entre 8 et 30 jours. La symptomatologie est principalement faite d’un trouble de la vigilance dans 78 % des cas, qui peut être la cause de la nécessité des soins intensifs. Les signes de localisation moteurs plus que sensitifs sont observés dans 40–60 % des cas. Les crises épileptiques sont plus rares (11 %) de même que les atteintes du tronc cérébral (9 %). Un nombre non négligeable de cas était suspecté devant des anomalies persistantes lors de la levée de la sédation avec un éveil particulièrement retardé et lent. La spécificité des encéphalopathies observées lors de la Covid-19 sévère a été mise en question par le travail récent de Bernard-Valnet et al. devant la constatation de leur prévalence similaire avec celle des encéphalopathies survenant lors de SDRA (syndrome de détresse respiratoire de l’adulte) d’autres causes [32]. Cette constatation est d’ailleurs en phase avec la diminution majeure de la morbimortalité qui a accompagné la mise en place de thérapeutiques antivirales, anticoagulantes et anti-inflammatoires dans les cas de Covid-19 sévère et la survenue de variants plus contagieux, mais causes de Covid-19 moins sévères. Les traitements à visée anti-inflammatoire et immunologique des formes graves d’encéphalopathies diffuses hémorragiques ou non (corticoïdes, immunoglobulines polyvalentes, échanges plasmatiques) peuvent permettre une amélioration spectaculaire du tableau neurologique mais les séquelles neurologiques sont fréquentes [31]. Divers profils lésionnels ont été décrits en imagerie [33], [34]. L’IRM peut ne pas montrer d’anomalies particulières ou des anomalies témoignant d’une pathologie et sous-jacente (leucoaraïose, atrophie). Elle peut montrer des lésions multifocales évocatrices de vasculopathie : micro- ou macrohémorragies, lésions de la substance blanche pouvant être interprétées comme démyélinisantes [35], Il peut s’agir d’aspects évocateurs d’ADEM ou d’encéphalite hémorragique de Hurst (AHLE) [9], [36], [37], dont la démarcation avec le premier type peut apparaître arbitraire. D’autres formes clinicoradiologiques ont aussi été décrites dans ce spectre des encéphalopathies :• des cas d’encéphalopathie postérieure réversible [38], [39], [40] associant troubles de la vigilance ; • des symptômes de dysfonction corticale postérieure (visuelle), crises épileptiques partielles et aspect d’œdème vasogénique sous-cortical touchant aussi la substance grise en IRM ; • des cas d’encéphalite nécrosante aiguë [41], [42], [43] rapportés auparavant principalement au Japon dans des cas de grippe ou d’autres affections virales, ou encore d’atteinte réversible du splenium du corps calleux (cytotoxic lesion of the corpus callosum [CLOCC], antérieurement MERS pour mild encephalopathy with reversible corpus callosum lesion syndrome), notamment chez l’enfant dans le cadre du syndrome d’inflammation multisystémique [44], [45], [46], [47], [48], [49] dans lequel la ou les lésions en hyposignal ADC (œdème cytotoxique) médiane le plus souvent dans le splénium du corps calleux, s’amende en quelques jours. L’imagerie du tenseur de diffusion peut montrer des anomalies chez des patients Covid encéphalopathes dont l’imagerie standard ne montre pas d’anomalie [50]. À noter que la présentation des cas étiquetés ADEM (pour encéphalomyélite aiguë disséminée) n’avaient pas une présentation spécifique et ne correspondaient que rarement, en IRM et neuropathologiquement à une affection de type encéphalomyélite aiguë démyélinisante [51], [52]. L’électroencéphalogramme est décrit le plus souvent comme lent de manière aspécifique sans focalisation ni crises épileptiques. Mais des aspects plus inquiétants ont aussi été rapportés (voir revue dans [53]). Des aspects épileptiformes associés ou non à des crises épileptiques cliniques, des aspects de décharges périodiques latéralisées uni- (PLEDs) ou bilatérales (biPLEDs), des décharges pseudopériodiques frontales [53], [54], [55], [56]. À noter que l’EEG ne montrait pas d’anomalie sur plus de la moitié des enregistrements réalisés chez des malades de médecine ou de réanimation pour confusion, fluctuation de la vigilance, ou retard de réveil [57]. L’étude standard du liquide cérébrospinal est normale dans tous les cas et la recherche d’ARN de SARS-CoV-2 est négative dans tous les cas d’encéphalopathie où elle a été réalisée [13], [14], [58], [59]. Elle est exceptionnellement rapportée comme positive [60]. L’étude plus approfondie du LCS par Guasp et al. [61] a par contre mis en évidence un aspect cytokinique inflammatoire et des anomalies témoignant de lésions neuronales dans une population de 60 patients hospitalisés pour neuroCovid : élévation des cytokines IL-18, IL-6 et IL-8, dans le LCS et dans le sérum, élévation de la monocyte chemoattractant protein 1 (MCP-1) uniquement dans le LCS, élévation de l’IL-10, l’IL-1RA, IP-10, MIG et de la chaine légère du neurofilament (NfL) uniquement dans le sérum. Les profils cytokiniques étaient identiques chez les patients cliniquement diagnostiqués comme encéphalopathie et comme encéphalite, argument pour une communauté physiopathologique entre les deux entités, la principale différence étant une pléiocytose du LCS, un des critères de définition des encéphalites. À noter que seuls les taux élevés de protéine 14-3-3 et de NfL dans le LCS étaient corrélés au pronostic fonctionnel de récupération à 18 mois [59], [61]. Un travail similaire a été effectué par Bernard-Valnet et al. [59] chez 22 patients neuroCovid (sévères dans 13 cas) versus 55 contrôles neurologiques (maladies neuro-inflammatoires dont scléroses en plaques, ou non inflammatoires). Il existait un profil inflammatoire avec élévation de CCL-2, CXCL-8 et du vascular endothelium growth factor A (VEGF-A) dans le LCS chez les neuroCovid graves, interprété comme un témoin d’une altération des unités neurovasculaires cérébrales. L’aspect oligoclonal de la distribution des γ-globulines était identique à celle du sérum et associée à une élévation de l’albumine témoignant d’une altération de la barrière hématoencéphalique [59]. L’étude neurochimique de 47 patients Covid-19 de sévérité allant de légère à sévère par Kanberg et al. a montré une élévation de marqueurs plasmatiques d’atteinte neuronale (NfL) et astrocytaire (GFAp) chez les patients sévères [62]. L’étude de Pilotto et al. a montré dans une étude comparative un profil cytokinique et des marqueurs gliaux et neuronaux du liquide cérébrospinal témoignant d’une neuro-inflammation prédominante, identique à celle qui est observée dans les syndromes de relargage cytokinique et dans les ICANS (immune effector cell-associated neurotoxicity syndrome), effets secondaires des traitements par CAR T-cells. Les marqueurs neuronaux étaient élevés uniquement dans les formes sévères [63]. Enfin les LCS des ADEM/AHLE recensées par Manzano et al. étaient inflammatoires dans 30 % des cas témoignant dans ce contexte de Covid-19 des hésitations de classification des cas (encéphalopathies vs encéphalites) [31]. Plusieurs travaux neuropathologiques de patients décédés de Covid-19 ont été publiés [64], [65], [66], [67], [68], [69], [70], voir revue dans [71] et dans [51]. L’implication directe du virus SARS-CoV-2 dans la pathogénie des lésions cérébrales observées lors de formes graves de neuroCovid reste hypothétique à ce jour [51], [72]. Ces questions concernant l’association des manifestations neurologique avec la Covid-19 et leur imputabilité au SARS-CoV-2 ont été abordées tôt dans le cours de la pandémie [73]. De multiples travaux orientés à charge n’ont apporté jusqu’à présent que des arguments pour une pathogénie indirecte, inflammatoire, impliquant, d’une part, la physiopathologie décrite dans les encéphalopathies associées au sepsis, notamment l’« orage cytokinique » [74], d’autre part, une atteinte endothéliale et/ou péricytaire de l’unité neurovasculaire où la présence des récepteurs ACE-2 est attestée ainsi que celle de la protéine virale spike [75], [76]. Les anomalies observées peuvent être en rapport aussi avec les thérapeutiques agressives mises en œuvre comme l’oxygénothérapie extracorporelle (ECMO) [77]. Encéphalites et myélites Les encéphalites survenant dans le contexte de la Covid-19 sont responsables de 9,5 % des complications neurologiques enregistrées dans le registre multicentrique français lors de la première vague, dans 3,6 % dans le registre monocentrique de Bergame, et de 2 % dans le registre monocentrique parisien de la Salpêtrière [10], [13], [78]. Il est important de considérer la définition de cas utilisée dans ces travaux car les observations comportant la mise en évidence directe du SARS-CoV-2 par PCR dans le LCS sont rarissimes [79], [80]. La présence d’une réaction cellulaire dans le LCS peut être retenue comme élément de différenciation entre encéphalite et encéphalopathie, incluant de ce fait un tiers des encéphalomyélites aiguës disséminées/démyélinisantes [31], [78], [81]. L’infection par le SARS-CoV-2 doit être prouvée par ailleurs ou fortement suspectée [82]. En dehors de l’étude du LCS il n’y a pas d’élément clinique ou paraclinique permettant d’affirmer le diagnostic d’encéphalite dans le contexte de la Covid-19. Des diagnostics d’ADEM, ou d’encéphalite auto-immune ont été portés sur des tableaux cliniques et IRM peu évocateurs en eux-mêmes, sans prises de contraste, lésions associées à des lésions hémorragiques par exemple [9], [31]. À noter l’intérêt d’une thérapeutique proactive à visée anti-inflammatoire et immunologique décrite comme efficace voire très efficace dans ces cas de tableaux neurologiques sévères ou très sévères du fait de l’hypothèse physiopathologique para-infectieuse et de l’analogie qui peut être proposée avec les réactions encéphalitiques survenant lors des syndromes de relargages cytokiniques [83], [84]. Des cas de myélites ont été publiés, entrant probablement dans le cadre d’une pathologie para-infectieuse à rapprocher des cas suspectés d’encéphalomyélites aiguës démyélinisantes. La série de 43 cas de myélites transverses aiguës de Roman et al. recensées dans la littérature en 2020–2021 permet de retenir une latence de survenue située entre 15 h et 5 jours pour 32 % des cas et de 10 jours à 6 semaines pour 68 % des cas par rapport au début de la Covid-19, conduisant à des hypothèses physiopathologiques différenciées. L’atteinte médullaire est dorsale ou, plus souvent, cervicale et longitudinalement extensive en IRM dans 70 % des cas. Elle survenait dans le cadre d’une ADEM dans 8/43 cas. La PCR du SARS-CoV-2 était positive dans deux cas, dont une ADEM, dans le LCS, L’évolution sous traitement (corticoïdes, immunoglobulines polyvalentes iv) n’est pas rapportée [85]. Trois cas supplémentaires attribués à une vaccination contre la Covid-19 par le vaccin AZD-1222 sont recensés dans le même travail [85]. Quelques cas de méningites aiguës lymphocytaires associées à une Covid-19 ont été rapportés dans le registre multicentrique français (n  = 3). Ces cas n’étaient pas différents des méningites aiguës virales communes. Les recherches étiologiques (SARS-CoV-2, entérovirus, VZV) étaient négatives [78]. Pathologie neurovasculaire La pathologie neurovasculaire est la deuxième en fréquence parmi les complications neurologiques de la Covid-19, responsable de 16 à 38 % des manifestations neurologiques chez les patients hospitalisés [10], [13], [78]. Les accidents vasculaires ischémiques artériels sont les plus fréquents et peuvent être inauguraux, révélant la Covid-19 qui peut être asymptomatique par ailleurs. Des accidents hémorragiques ainsi que des thromboses veineuses cérébrales ont aussi été décrits [86], [87], [88], [89], [90], [91], [92], [93]. Une large étude multicentrique réalisée lors de la première vague concernant les patients Covid-19 hospitalisés ou se présentant aux urgences de 31 centres dans 4 pays a montré une prévalence des accidents vasculaires cérébraux de 1,13 % (172 cas sur 14 483 Covid-19). Parmi, 1,08 % avaient un infarctus cérébral, 0,19 % avaient une hémorragie cérébrale et 3/172 avaient une thrombose veineuse. La mortalité était importante : 38 % tous AVC, 58,3 % pour les hémorragies. Les facteurs pronostiques défavorables étaient le caractère cryptogénique de l’infarctus, l’âge plus élevé, et une lymphopénie à l’admission [90]. Une étude multicentrique multinationale de 275 unités neurovasculaires sur les 5 continents a montré une prévalence des AVC de 1,3 % dans la population Covid et de 2,9 % de Covid-19 dans la population neurovasculaire [94]. L’étude de Yaghi et al. a aussi montré la fréquence particulière des infarctus cryptogéniques (65,6 %) comparée à une population non-Covid concomitante [95]. Les patients Covid-19 étaient aussi plus jeunes, avaient un score NIHSS plus élevé et des D-Dimer et vitesse de sédimentation plus élevés. L’occlusion d’un gros vaisseau était un facteur pronostique péjoratif dans une autre étude new-yorkaise [96]. Une étude européenne multicentrique rassemblant 174 patients ayant un infarctus cérébral confirmait ces résultats [97]. L’étude multicentrique pronostique comparative de Calmettes (40 patients ayant un infarctus cérébral associé à une Covid-19) a montré une mortalité plus élevée, un score NIHSS plus sévère, un taux de complications thromboemboliques et des marqueurs inflammatoires plus élevés chez les patients Covid-19 par rapport aux contrôles [91], [92]. Autres atteintes neurologiques centrales Des cas de crises épileptiques isolées, d’altération transitoire de la vigilance, de lésions démyélinisantes uniques ou dévoilant une sclérose en plaques, de paraparésie subite, de myoclonus généralisé avec syndrome cérébelleux transitoire ont été recensés au cours de la Covid-19. Compte tenu de l’ampleur numérique de la pandémie, il est hasardeux de rapporter ces symptômes avec certitude à la Covid-19 sur le seul argument de la concomitance. Des manifestations psychiatriques ont aussi été décrites au cours de la phase aiguë. La plupart étaient expliquées par une encéphalopathie ou une encéphalite (confusion mentale, syndrome dysexécutif). Les cas de pathologie psychiatrique de novo (trouble anxieux, psychose, catatonie, manie aiguë) ou d’exacerbation d’une pathologie psychiatrique antérieure étaient rares [13], [98]. Dans les 6 mois postaigus les manifestations psychiatriques (troubles de l’humeur, troubles anxieux, troubles psychotiques) étaient par contre beaucoup plus fréquentes que les manifestations neurologiques [99]. Atteintes du système nerveux périphérique Syndrome de Guillain-Barré Le syndrome de Guillain-Barré est la principale complication neurologique périphérique de la Covid-19. Les premiers signalements ont été publiés dès l’été 2020 [100], [101]. Le registre français en recensait 15 cas compliquant des cas de Covid-19 hospitalisés entre le 1er mars et le 30 avril 2020, soit 6,8 % des 222 cas de complications neurologiques déclarées [14]. L’équipe italienne de Bergame en décrivait 17 sur 137 complications neurologiques soit 12,4 % [10]. L’intervalle entre la survenue des premiers symptômes de Guillain-Barré et les premiers symptômes de Covid-19 allait de −8 jours à +24 jours avec une médiane à +10 jours dans une revue des 18 cas publiés en 2020 [102]. L’électrophysiologie montrait une pathologie démyélinisante chez la plupart des patients. Les observations de paralysie faciale périphérique et d’ophtalmoplégie par atteinte des nerfs oculomoteurs peuvent être rapprochés de ce domaine nosologique compte tenu de leurs bilans paracliniques qui montraient uniquement une prise de contraste tronculaire des nerfs incriminés [103]. L’atteinte peut être accompagnée d’une ataxie avec diminution des réflexes tendineux, réalisant un syndrome de Miller-Fisher. Les anticorps anti-gangliosides anti-GD1b peuvent être positifs [104], [105]. La prise en charge thérapeutique est standard : échanges plasmatiques ou immunoglobulines polyvalentes iv, prise en charge et surveillance en soins intensifs adaptée à la gravité clinique, neurologique et respiratoire dans le contexte de la Covid-19. Une étude italienne a testé l’hypothèse d’une augmentation de prévalence pendant la première vague de la Covid-19 (mars–avril 2020) des syndromes de Guillain-Barré hospitalisés dans 12 hôpitaux en Lombardie et en Vénétie par rapport à la même période l’année précédente dans ces deux mêmes régions et hôpitaux. L’incidence de survenue du syndrome de Guillain-Barré a été multipliée par 2,6 en 2020 par rapport à 2019. Son incidence estimée dans la population Covid-19 était de 47,9/100 000, et de 236/100 000 chez les Covid-19 hospitalisés [106]. Un résultat inverse retrouvant une diminution d’incidence du syndrome de Guillain-Barré en 2020 a été rapporté au Royaume-Uni [107]. L’étude européenne de l’IGOS consortium concluait à un lien de causalité improbable entre Covid-19 et Guillain-Barré [108], contrairement au lien fort qui avait été démontré lors de l’épidémie de Zika virus en Polynésie française [109]. Neuropathies et myopathies de réanimation Les complications neurologiques périphériques de la réanimation les plus fréquentes sont les neuromyopathies dites « de réanimation » : jusqu’à 26 % dans la série de la Salpêtrière [13]. La présentation clinique associe une déficit moteur global sévère à une amyotrophie survenant chez un patient de réanimation chez lequel le sevrage de la ventilation peut même être compromis. Ces neuromyopathies ne sont pas spécifiques de la Covid-19 et compliquent des réanimations respiratoires longues et lourdes des SDRA, notamment en cas de traitement par corticothérapie, qui favorise l’atteinte musculaire, ou d’exposition aux curarisants non dépolarisants, qui favoriserait les neuropathies axonales motrices [110]. La prévalence de ces complications atteindrait 80 % des patients hospitalisés en réanimation pour SDRA, sources d’hospitalisations longues et de séquelles ultérieures [111]. Des atteintes de troncs nerveux périphériques ont aussi été décrites comme une paralysie des deux nerfs fibulaires [14] ou encore un syndrome de Tapia lié aux mesures réanimatoires dont l’intubation et la ventilation en procubitus [112]. Symptômes variés et associations d’atteintes centrale et périphérique Si les myalgies sont très fréquentes dans la Covid-19 symptomatique, touchant de 19 à 33 % des patients, des atteintes musculaires sévères ont été décrites, notamment des rhabdomyolyses aiguës ainsi que quelques cas de myosites éventuellement localisées (voir revue dans [113]). Des associations de manifestations neurologiques périphériques et centrales ont été rapportées dans le registre français. Il s’agissait de deux patients de réanimation présentant l’association d’une neuromyopathie de réanimation à une encéphalopathie pour l’un, à une encéphalite pour l’autre. Par ailleurs deux cas de syndrome de Guillain-Barré ont présenté l’un une encéphalopathie, l’autre un infarctus cérébral [14]. Conclusion L’irruption soudaine, massive, et mondiale de la Covid-19 a justifié une veille neurologique attentive pour déceler les diverses complications qui pouvaient lui être associées, les prendre en charge, et pour nombre d’entre elles, les prévenir par un traitement de plus en plus approprié à la physiopathologie de la maladie et à ce qui était de mieux en mieux compris des causes et mécanismes sous-tendant les atteintes neurologiques. Il reste des inconnues, objets de travaux à venir, dont la physiopathologie des encéphalopathies observées au cours de la Covid, rejoignant sans doute largement la physiopathologie des encéphalopathies du sepsis, et la pathogénicité du SARS-CoV-2, directe au niveau cellulaire, et indirecte sur le système immunitaire. Il faut en outre garder à l’esprit que le caractère massif de la pandémie rend l’imputabilité des manifestations neurologiques au SARS-CoV-2 lui-même, ou à la Covid-19 dans ses formes inflammatoires, incertaine. L’observation d’une concomitance ou d’une succession n’est pas preuve de causalité. Les manifestations et complications neurologiques de la phase aiguë de la Covid-19 sont une des causes de la prolongation de symptômes invalidants au-delà des 4 semaines qui définissent la Covid-19 aiguë. Elles constituent une des causes des syndromes postCovid, ou « Covid-longs » dont les études montrent que la prévalence diminue substantiellement dans l’année suivant la maladie aiguë et que les symptômes les plus fréquemment décrits évoquent plus un syndrome post infectieux de type fatigue chronique que des séquelles d’une atteinte durant la maladie aiguë. La pandémie n’est malheureusement pas terminée à ce jour mais semble évoluer vers une atténuation de la virulence des variants successifs. Néanmoins, malgré la vaccination de masse souvent associée à des réinfections par des formes de Covid-19 plus bénignes elles aussi protectrices, la mortalité reste supérieure à ce qu’on attendrait d’une « mauvaise grippe » [114]. Nul ne peut affirmer qu’un variant agressif ne survienne ultérieurement mais, à ce jour, les données disponibles concernant la Covid-19 et ses manifestations et complications touchant le système nerveux sont plutôt rassurantes. Déclaration de liens d’intérêts L’auteur déclare ne pas avoir de liens d’intérêts. ☆ Séance du 11/04/2023. ==== Refs Références 1 Déclaration du Directeur général de l’OMS relative à la réunion du Comité d’urgence du RSI sur le nouveau coronavirus (2019-nCoV) [Internet], [cité 28 janv 2023]. Disponible sur : https://www.who.int/fr/director-general/speeches/detail/who-director-general-s-statement-on-ihr-emergency-committee-on-novel-coronavirus-.(2019-ncov). 2 WHO Coronavirus (COVID-19) Dashboard [Internet]. [cité 28 janv 2023]. Disponible sur : https://covid19.who.int. 3 Data [Internet]. [cité 28 janv 2023]. Disponible sur : https://www.who.int/europe/data. 4 Wang Z. Yang B. Li Q. Wen L. Zhang R. Clinical features of 69 cases with coronavirus disease 2019 in Wuhan, China Clin Infect Dis 71 15 2020 769 777 32176772 5 Mao L. Jin H. Wang M. Hu Y. Chen S. He Q. 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Schröder A.S. Aepfelbacher M. Fitzek A. Heinemann A. Heinrich F. Dying with SARS-CoV-2 infection-an autopsy study of the first consecutive 80 cases in Hamburg, Germany Int J Legal Med 134 4 2020 1275 1284 32500199 67 Solomon I.H. Normandin E. Bhattacharyya S. Mukerji S.S. Keller K. Ali A.S. Neuropathological features of Covid-19 N Engl J Med 383 10 2020 989 992 32530583 68 Nauen D.W. Hooper J.E. Stewart C.M. Solomon I.H. Assessing brain capillaries in coronavirus disease 2019 JAMA Neurol 78 6 2021 760 762 33576767 69 Newcombe V.F.J. Spindler L.R.B. Das T. Winzeck S. Allinson K. Stamatakis E.A. Neuroanatomical substrates of generalized brain dysfunction in COVID-19 Intensive Care Med 47 1 2021 116 118 33034688 70 von Weyhern C.H. Kaufmann I. Neff F. Kremer M. Early evidence of pronounced brain involvement in fatal COVID-19 outcomes Lancet Lond Engl 395 10241 2020 e109 71 Cosentino G. Todisco M. Hota N. Della Porta G. Morbini P. Tassorelli C. Neuropathological findings from COVID-19 patients with neurological symptoms argue against a direct brain invasion of SARS-CoV-2: a critical systematic review Eur J Neurol 28 11 2021 3856 3865 34339563 72 Frank S. Catch me if you can: SARS-CoV-2 detection in brains of deceased patients with COVID-19 Lancet Neurol 19 11 2020 883 884 33031734 73 Ellul M. Varatharaj A. Nicholson T.R. Pollak T.A. Thomas N. Easton A. Defining causality in COVID-19 and neurological disorders J Neurol Neurosurg Psychiatry 91 8 2020 811 812 32503883 74 Mazeraud A. Righy C. Bouchereau E. Benghanem S. Bozza F.A. Sharshar T. Septic-associated encephalopathy: a comprehensive review Neurother J Am Soc Exp Neurother 17 2 2020 392 403 75 Nuovo G.J. Magro C. Shaffer T. Awad H. Suster D. Mikhail S. Endothelial cell damage is the central part of COVID-19 and a mouse model induced by injection of the S1 subunit of the spike protein Ann Diagn Pathol 51 2021 151682 33360731 76 Lee M.H. Perl D.P. Nair G. Li W. Maric D. Murray H. Microvascular injury in the brains of patients with COVID-19 N Engl J Med 384 5 2021 481 483 33378608 77 Khan I.R. Gu Y. George B.P. Malone L. Conway K.S. Francois F. Brain histopathology of adult decedents after extracorporeal membrane oxygenation Neurology 96 9 2021 e1278 e1289 33472914 78 Meppiel E. Peiffer-Smadja N. Maury A. Bekri I. Delorme C. Desestret V. Neurological manifestations associated with COVID-19: a multicentric registry Clin Microbiol Infect 27 3 2021 458 466 33189873 79 Moriguchi T. Harii N. Goto J. Harada D. Sugawara H. Takamino J. A first case of meningitis/encephalitis associated with SARS-Coronavirus-2 Int J Infect Dis 94 2020 55 58 32251791 80 Abenza-Abildúa M.J. Atienza S. Carvalho Monteiro G. Erro Aguirre M.E. Imaz Aguayo L. Freire Álvarez E. Encephalopathy and encephalitis during acute SARS-CoV-2 infection. Spanish Society of Neurology COVID-19 Registry Neurologia 36 2 2021 127 134 33549369 81 Abenza-Abildúa M.J. Novo-Aparicio S. Moreno-Zabaleta R. Algarra-Lucas M.C. Rojo Moreno-Arcones B. Salvador-Maya M.Á. Encephalopathy in severe SARS-CoV2 infection: inflammatory or infectious? Int J Infect Dis 98 2020 398 400 32712426 82 Ellul M.A. Benjamin L. Singh B. Lant S. Michael B.D. Easton A. Neurological associations of COVID-19 Lancet Neurol 19 9 2020 767 783 32622375 83 Cao A. Rohaut B. Le Guennec L. Saheb S. Marois C. Altmayer V. Severe COVID-19-related encephalitis can respond to immunotherapy Brain J Neurol 143 12 2020 e102 84 Pilotto A. Masciocchi S. Volonghi I. De Giuli V. Caprioli F. Mariotto S. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) encephalitis is a cytokine release syndrome: evidences from cerebrospinal fluid analyses Clin Infect Dis 73 9 2021 e3019 e3026 33395482 85 Román G.C. Gracia F. Torres A. Palacios A. Gracia K. Harris D. Acute transverse myelitis (ATM): clinical review of 43 patients with COVID-19-associated ATM and 3 post-vaccination ATM serious adverse events with the ChAdOx1 nCoV-19 vaccine (AZD1222) Front Immunol 12 2021 653786 33981305 86 Agarwal S. Scher E. Rossan-Raghunath N. Marolia D. Butnar M. Torres J. Acute stroke care in a New York City comprehensive stroke center during the COVID-19 pandemic J Stroke Cerebrovasc Dis 29 9 2020 105068 32807471 87 Cavalcanti D.D. Raz E. Shapiro M. Dehkharghani S. Yaghi S. Lillemoe K. Cerebral venous thrombosis associated with COVID-19 AJNR Am J Neuroradiol 41 8 2020 1370 1376 32554424 88 Chougar L. Mathon B. Weiss N. Degos V. Shor N. Atypical deep cerebral vein thrombosis with hemorrhagic venous infarction in a patient positive for COVID-19 AJNR Am J Neuroradiol 41 8 2020 1377 1379 32554423 89 Lapergue B. Lyoubi A. Meseguer E. Avram I. Denier C. Venditti L. Large vessel stroke in six patients following SARS-CoV-2 infection: a retrospective case study series of acute thrombotic complications on stable underlying atherosclerotic disease Eur J Neurol 27 11 2020 2308 2311 32761999 90 Siegler J.E. Cardona P. Arenillas J.F. Talavera B. Guillen A.N. Chavarría-Miranda A. Cerebrovascular events and outcomes in hospitalized patients with COVID-19: the SVIN COVID-19 Multinational Registry Int J Stroke 16 4 2021 437 447 32852257 91 Calmettes J. Peres R. Goncalves B. Varlan D. Turc G. Obadia M. Clinical outcome of acute ischemic strokes in patients with COVID-19 Cerebrovasc Dis Basel Switz 50 4 2021 412 419 92 Logroscino G. Beghi E. Stroke epidemiology and COVID-19 pandemic Curr Opin Neurol 34 1 2021 3 10 33278139 93 Cavallieri F. Sellner J. Zedde M. Moro E. Neurologic complications of coronavirus and other respiratory viral infections Handb Clin Neurol 189 2022 331 358 36031313 94 Nguyen T.N. Qureshi M.M. Klein P. Yamagami H. Mikulik R. Czlonkowska A. Global impact of the COVID-19 pandemic on stroke volumes and cerebrovascular events: a 1-year follow-up Neurology 100 4 2023 e408 e421 36257718 95 Yaghi S. Ishida K. Torres J. Mac Grory B. Raz E. Humbert K. SARS-CoV-2 and stroke in a New York Healthcare System Stroke 51 7 2020 2002 2011 32432996 96 Tiwari A. Berekashvili K. Vulkanov V. Agarwal S. Khaneja A. Turkel-Parella D. Etiologic subtypes of ischemic stroke in SARS-CoV-2 patients in a cohort of New York City Hospitals Front Neurol 11 2020 1004 33041972 97 Ntaios G. Michel P. Georgiopoulos G. Guo Y. Li W. Xiong J. Characteristics and outcomes in patients with COVID-19 and acute ischemic stroke: the Global COVID-19 Stroke Registry Stroke 51 9 2020 e254 e258 32787707 98 Varatharaj A. Thomas N. Ellul M.A. Davies N.W.S. Pollak T.A. Tenorio E.L. Neurological and neuropsychiatric complications of COVID-19 in 153 patients: a UK-wide surveillance study Lancet Psychiatry 7 10 2020 875 882 32593341 99 Taquet M. Geddes J.R. Husain M. Luciano S. Harrison P.J. 6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records Lancet Psychiatry 8 5 2021 416 427 33836148 100 Camdessanché J.P. Morel J. Pozzetto B. Paul S. Tholance Y. Botelho-Nevers E. COVID-19 and Guillain-Barré syndrome: response Rev Neurol (Paris) 176 7 2020 636 637 32419708 101 Alberti P. Beretta S. Piatti M. Karantzoulis A. Piatti M.L. Santoro P. Guillain-Barré syndrome related to COVID-19 infection Neurol Neuroimmunol Neuroinflammation 7 4 2020 1 102 De Sanctis P. Doneddu P.E. Viganò L. Selmi C. Nobile-Orazio E. Guillain-Barré syndrome associated with SARS-CoV-2 infection. A systematic review Eur J Neurol 27 11 2020 2361 2370 32757404 103 Dinkin M. Gao V. Kahan J. Bobker S. Simonetto M. Wechsler P. COVID-19 presenting with ophthalmoparesis from cranial nerve palsy Neurology 95 5 2020 221 223 32358218 104 Gutiérrez-Ortiz C. Méndez-Guerrero A. Rodrigo-Rey S. San Pedro-Murillo E. Bermejo-Guerrero L. Gordo-Mañas R. Miller-Fisher syndrome and polyneuritis cranialis in COVID-19 Neurology 95 5 2020 e601 e605 32303650 105 Dalakas M.C. Guillain-Barré syndrome: the first documented COVID-19-triggered autoimmune neurologic disease: more to come with myositis in the offing Neurol Neuroimmunol Neuroinflamm 7 5 2020 e781 32518172 106 Filosto M. Cotti Piccinelli S. Gazzina S. Foresti C. Frigeni B. Servalli M.C. Guillain-Barré syndrome and COVID-19: an observational multicentre study from two Italian hotspot regions J Neurol Neurosurg Psychiatry 92 7 2021 751 756 33158914 107 Keddie S. Pakpoor J. Mousele C. Pipis M. Machado P.M. Foster M. Epidemiological and cohort study finds no association between COVID-19 and Guillain-Barré syndrome Brain J Neurol 144 2 2021 682 693 108 Luijten L.W.G. Leonhard S.E. van der Eijk A.A. Doets A.Y. Appeltshauser L. Arends S. Guillain-Barré syndrome after SARS-CoV-2 infection in an international prospective cohort study Brain J Neurol 144 11 2021 3392 3404 109 Cao-Lormeau V.M. Blake A. Mons S. Lastère S. Roche C. Vanhomwegen J. Guillain-Barré Syndrome outbreak associated with Zika virus infection in French Polynesia: a case-control study Lancet Lond Engl 387 10027 2016 1531 1539 110 Plaut T. Weiss L. Electrodiagnostic evaluation of critical illness neuropathy StatPearls [Internet] 2022 StatPearls Publishing Treasure Island (FL) [cité 30 janv 2023]. Disponible sur : http://www.ncbi.nlm.nih.gov/books/NBK562270/ 111 Gonzalez A. Abrigo J. Achiardi O. Simon F. Cabello-Verrugio C. Intensive care unit-acquired weakness: a review from molecular mechanisms to its impact in COVID-2019 Eur J Transl Myol 32 3 2022 10511 36036350 112 Herath T. Lutchaman N. Naidu L. Wimalaratna S. Tapia's syndrome in a patient with COVID-19 Pract Neurol 23 2 2023 146 149 36198520 113 Hannah J.R. Ali S.S. Nagra D. Adas M.A. Buazon A.D. Galloway J.B. Skeletal muscles and Covid-19: a systematic review of rhabdomyolysis and myositis in SARS-CoV-2 infection Clin Exp Rheumatol 40 2 2022 329 338 35225218 114 El-Sadr W.M. Vasan A. El-Mohandes A. Facing the new Covid-19 reality N Engl J Med 388 5 2023 385 387 36724378
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==== Front J Am Acad Child Adolesc Psychiatry J Am Acad Child Adolesc Psychiatry Journal of the American Academy of Child and Adolescent Psychiatry 0890-8567 1527-5418 Elsevier S0890-8567(23)00203-4 10.1016/S0890-8567(23)00203-4 Article Table of Contents 27 6 2023 7 2023 27 6 2023 62 7 A4A7 2023 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmc
PMC010xxxxxx/PMC10294260.txt
==== Front J Med Vasc J Med Vasc Journal De Medecine Vasculaire 2542-4513 2542-4513 Published by Elsevier Masson SAS S2542-4513(20)30352-7 10.1016/j.jdmv.2020.10.011 R10 Atteintes dermatologiques au cours de l’infection à COVID-19 Klejtman T. 12⁎ 1 Service de médecine vasculaire, groupe hospitalier Paris Saint-Joseph, 75014 Paris, France 2 Université Paris Descartes, Paris, France ⁎ Auteur correspondant. 9 11 2020 11 2020 9 11 2020 45 S77S77 Copyright © 2020 Published by Elsevier Masson SAS. 2020 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Les manifestations cliniques les plus fréquentes de la maladie à coronavirus 2019 (COVID-19) [COrona VIrus Disease-19], sont la fièvre, une asthénie, des myalgies, des céphalées, une toux ou de la diarrhée. D’autres symptômes comme l’anosmie et l’agueusie ont ensuite été rapportés comme spécifiques à ce nouveau coronavirus, appelé SARS-CoV-2 (Severe Acute Respiratory Syndrome-CoronaVirus 2) [coronavirus 2 du syndrome respiratoire aigu sévère]. Des manifestations cutanées associées au SARS-CoV-2 ont rapidement été décrites, touchant environ 20 % des cas. Les lésions prédominaient sur le tronc, les mains et les pieds. Un exanthème maculo-papuleux généralisé était le plus fréquemment décrit, suivi par des lésions pseudo-varicelleuses ou une urticaire aiguë. Ces manifestations inflammatoires survenaient au début de la maladie et étaient parfois inaugurales. Des lésions cutanées vasculaires, comme les engelures, un livedo réticulé, un purpura ont également été rapportées. Contrairement aux lésions inflammatoires, les lésions vasculaires semblaient survenir plus tardivement dans l’évolution de la maladie. Il s’agissait d’un véritable challenge diagnostique, particulièrement pour les patients hospitalisés, afin de ne pas méconnaître d’autres diagnostics différentiels, comme une toxidermie ou des complications infectieuses (coagulation intra vasculaire disséminée par exemple). La physiopathologie de ces signes cliniques n’est pas complètement résolue. Il pourrait s’agir d’une réaction immunologique post-virale ou une localisation cutanée de la maladie. La différence de délai de survenue entre les lésions inflammatoires et vasculaires suggère une physiopathologie différente. De façon constante, les signes cutanés disparaissaient complètement, dans un délai de 10 jours en moyenne. Compte tenu de leur survenue fréquente chez les patients, sans autres signes généraux, les manifestations cutanées liées à la COVID-19 doivent être connues afin d’aider au diagnostic de cette maladie. Mots clés Dermatologie COVID-19 ==== Body pmcDéclaration de liens d’intérêts L’auteur déclare ne pas avoir de liens d’intérêt.
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==== Front mBio mBio mbio mBio 2150-7511 American Society for Microbiology 1752 N St., N.W., Washington, DC 37162223 00769-23 10.1128/mbio.00769-23 mbio.00769-23 Research Article editors-pickEditor's Pickfood-microbiologyFood MicrobiologyLaeA-Regulated Fungal Traits Mediate Bacterial Community Assembly https://orcid.org/0000-0002-0032-5126 Tannous Joanna a b Cosetta Casey M. c Drott Milton T. d e https://orcid.org/0000-0002-3207-1466 Rush Tomás A. b Abraham Paul E. b Giannone Richard J. b https://orcid.org/0000-0002-4386-9473 Keller Nancy P. a d npkeller@wisc.edu https://orcid.org/0000-0002-0194-9336 Wolfe Benjamin E. c benjamin.wolfe@tufts.edu a Department of Medical Microbiology and Immunology, University of Wisconsin—Madison, Madison, Wisconsin, USA b Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA c Department of Biology, Tufts University, Medford, Massachusetts, USA d Department of Plant Pathology, University of Wisconsin—Madison, Madison, Wisconsin, USA e USDA-ARS Cereal Disease Laboratory, St. Paul, Minnesota, USA Editor Taylor John W. University of California—Berkeley The authors declare no conflict of interest. 9 5 2023 May-Jun 2023 9 5 2023 14 3 e00769-2328 3 2023 3 4 2023 https://doi.org/10.1128/AuthorWarrantyLicense.v1 This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply. ABSTRACT Potent antimicrobial metabolites are produced by filamentous fungi in pure culture, but their ecological functions in nature are often unknown. Using an antibacterial Penicillium isolate and a cheese rind microbial community, we demonstrate that a fungal specialized metabolite can regulate the diversity of bacterial communities. Inactivation of the global regulator, LaeA, resulted in the loss of antibacterial activity in the Penicillium isolate. Cheese rind bacterial communities assembled with the laeA deletion strain had significantly higher bacterial abundances than the wild-type strain. RNA-sequencing and metabolite profiling demonstrated a striking reduction in the expression and production of the natural product pseurotin in the laeA deletion strain. Inactivation of a core gene in the pseurotin biosynthetic cluster restored bacterial community composition, confirming the role of pseurotins in mediating bacterial community assembly. Our discovery demonstrates how global regulators of fungal transcription can control the assembly of bacterial communities and highlights an ecological role for a widespread class of fungal specialized metabolites. KEYWORDS Penicillium fungal metabolites microbiome National Science Foundation (NSF) https://doi.org/10.13039/100000001 CAREER # 1942063 Wolfe Benjamin E. U.S. Department of Energy (DOE) https://doi.org/10.13039/100000015 SEED SFA Abraham Paul E. cover-dateMay/June 2023 ==== Body pmcINTRODUCTION Fungal specialized (or secondary) metabolites (SMs) gained attention in the 1900s following the discovery of the world’s first antibiotic, penicillin, produced by a Penicillium isolate (1). Since then, fungal SMs have come to play pivotal roles in medicine, agriculture, and biotechnology. Fungal SMs have been well-characterized in axenic cultures, including genetic (2–5), biochemical (6), and physiological aspects of their regulation and production (7–9). In cocultures, fungal SMs have been suggested to play roles as signaling molecules that mediate the communication of the fungus with its surroundings (10–13), virulence factors to support pathogenic lifestyles (14, 15), microbial inhibitors that shape the competition with other microorganisms for finite resources (16–18), or defenses against fungivores (19, 20). While these studies are critical first steps in understanding the potential functions of fungal SMs, the ecological roles of these compounds in multispecies communities are largely unknown. The activities of fungal SMs identified under laboratory conditions may not translate to natural communities if concentrations used in vitro are not reflective of those found in nature or if other community members are able to inactivate or alter these compounds. Purified and concentrated fungal SMs can inhibit the growth of some bacteria and fungi in large quantities in simplified lab environments (21, 22), but how naturally secreted fungal SMs operate in microbial communities is unknown. Fungal SMs could structure multispecies communities by mediating ecological interactions that favor certain species over others, resulting in a shift in community composition. We are unaware of studies that have demonstrated this scenario. One approach for identifying fungal SMs that mediate bacterial communities is by altering the activity of global regulators of fungal metabolites. In the fungal phylum Ascomycota, the production of many SMs is under the control of the global regulatory protein of the trimeric velvet complex, LaeA (23, 24). A growing body of research has emphasized the role of LaeA in regulating SM production in monocultures of Aspergillus, Fusarium, Penicillium, and other fungal genera (25–28). LaeA also regulates many biosynthetic gene clusters (BGCs), including the aflatoxin and cyclopiazonic acid gene clusters in Aspergillus flavus (25, 29), and the patulin gene cluster in Penicillium expansum (28). Besides its impact on the metabolome, LaeA was also reported to regulate other traits in filamentous fungi, such as conidiation (30), conidial morphogenesis (31), and sclerotia formation (32). LaeA-regulated fungal traits may play important ecological roles in multispecies microbial communities, but most studies of LaeA biology have been conducted with fungal monocultures. Several studies have explored how LaeA can mediate fungal strain competition and host-microbe interactions (8, 33), but the ecological roles of LaeA in the assembly of polymicrobial communities has not been characterized. Cheese rinds are microbial ecosystems where LaeA-regulated SMs could have significant ecological impacts. These ecosystems are composed of bacteria, yeasts, and filamentous fungi and form on the surface of many styles of cheese, including bloomy, washed, and natural rind cheeses (34). Penicillium spp. are frequently encountered in cheese rinds where they can be inoculated as industrial starter cultures (e.g., P. camemberti in Camembert or Brie) or can colonize cheese from natural populations of fungi (e.g., P. biforme, P. solitum, and P. nalgiovense in tomme style cheeses and clothbound cheddars [34, 35]). Species within this genus are prolific producers of SMs, including polyketides (e.g., patulin), nonribosomal peptides (e.g., roquefortine), and terpenes (e.g., expansolide) (36). While many Penicillium metabolites are valued as pharmaceuticals, such as the antibiotic penicillin (1) and the cholesterol-lowering drug lovastatin (37), others are considered mycotoxins, including the carcinogenic ochratoxin A (38), cyclopiazonic acid (39), and patulin (40). Penicillium species isolated from cheese rinds produce an extensive range of SMs, including mycotoxins (41, 42), and have been shown to impact the growth of neighboring bacteria (43–45), suggesting a potential of fungi to control bacterial community diversity through antibiotic production. In the present study, we determined the ecological significance of fungal SMs in the cheese rind model system by inactivating LaeA in Penicillium sp. strain MB. The exact species identity of this fungus is currently unknown due to limited availability of whole-genome sequences of isolates from this section of the Penicillium phylogeny; a previous whole-genome sequencing analysis of this strain placed it close to Penicillium polonicum (42). When this fungus was originally isolated from a natural-rind cheese, it was the dominant filamentous fungus growing on the cheese and was preventing typical growth of the normal fungal and bacterial communities found in natural-rind cheeses. Given its negative impacts on the cheese rind, we predicted that this strain produced metabolites that could alter microbial community assembly. When we deleted laeA in this strain, an in vitro cheese rind bacterial community increased in total abundance and shifted in composition to resemble bacterial communities grown without the fungus. Both transcriptomic analysis and metabolite profiling pointed to pseurotins as putative LaeA-regulated antibacterial compounds. Inactivation of pseurotin production in the WT strain through the disruption of the gene encoding the hybrid PKS-NRPS enzyme required for pseurotin synthesis eliminated much of the antibacterial activity and caused a shift in bacterial community composition that was similar to the ΔlaeA strain. This study demonstrates the ecological relevance of LaeA-regulated fungal SMs, their roles in shaping the assembly of multispecies bacterial communities, and their possible influence on the development of human food commodities. RESULTS The deletion of laeA impairs several physiological traits in Penicillium sp. strain MB. The deletion and complementation of laeA in Penicillium sp. strain MB resulted in four strains: MB_WT, MB_ΔlaeA, MB_HygR (to test for effects of inserting the hygromycin selectable marker into the genome), and MB_laeAc (complementation of the ΔlaeA strain) (see Fig. S1a to d in the supplemental material). The MB_ΔlaeA strain had altered pigmentation and reduced spore production relative to MB_WT, and complementation of the ΔlaeA strain (MB_laeAc) restored the WT phenotype (Fig. 1a and b). Differences in pigmentation were most striking on cheese curd agar (CCA), a medium that mimics cheese surface environments in cheese aging facilities (43, 46). No differences in pigmentation or sporulation were observed between the MB_WT, the MB_HygR, and the MB_laeAc strains. FIG 1 Deletion of laeA affects both morphological and physiological characteristics of Penicillium sp. MB strains (a) Colony aspect of Penicillium sp. MB strains grown on different media for 5 days at 25°C. GMM = glucose minimal medium, YES = yeast extract medium, CYA = Czapek Yeast Autolysate Agar, PDA = potato dextrose agar, CCA = cheese curd agar. Top and bottom views of the agar plates are shown in the top and bottom panels, respectively. (b) Spore counts for each strain over four days growth on GMM agar medium. The number of spores were assessed per standard area that was sampled by plugging the fungal colony using the shaft-attaching end of a p5000 pipette tip. (c) Percentage of germinated spores over 13 hours growth in GMM broth. Counting of germinated spores started 3 hours post-inoculation. There were no germinated spores between hours 3 to 6. In the bar graphs, the error bars represent one standard error of the mean and each dot represents a biological replicate (n = 3). One-way analysis of variance (ANOVA) was performed for each day for the sporulation data and each hour for the germination data. Dunnett’s multiple comparison test was used and compared to the MB_WT strain. (****) indicates P < 0.0001, (***) indicates P < 0.001, (**) indicates P < 0.01, (*) indicates P < 0.05, and no asterisk indicates not significant. For exact P-values for each treatment, see Data Set S1. 10.1128/mbio.00769-23.4 FIG S1 Genetic modifications conducted in Penicillium sp. strain MB. (a) Schematic representation of the genetic construct for laeA deletion in strain MB. The construct is constituted of a gene conferring resistance to hygromycin under TrpC promoter and terminator. (b) Southern blot analyzes of genomic DNA from the WT, the ΔlaeA, and HygR strains. The positions of the restriction enzyme cutting sites used for the Southern blot are shown on the construct schematic. Portions (10 μg) of total DNA from each strain were digested with the appropriate enzymes and subjected to Southern blot analysis using, respectively, the 5′ flank fragment (blue) and the 3′ fragment (grey) as probes. The 1-kb DNA ladder from New England Biolabs was used to determine the size of the expected bands. (c) Restriction map of plasmid pBC-phleo containing the ble gene for phleomycin resistance controlled by the Aspergillus nidulans gpdA promoter and the Saccharomyces cerevisiae CYC1 terminator. Restriction site used for cloning of the laeA gene is shown in red. (d) Southern confirmation of the single insertion of laeA using NotI as a restriction enzyme. The positive control corresponds to the digested PJT3 plasmid. (e) Map of plasmid pCS01 from which the hygromycin deletion cassette was amplified and used to knock-out the psoA gene in Penicillium sp. strain MB. (f) Scheme of the experimental strategy adopted to knockout the psoA gene in the Penicillium sp. strain MB using CRISPR-Cas9 system. The CRISPR components were delivered to the strain MB_WT protoplasts as a ribonucleoprotein (RNP) complex consisting of the Cas9 nuclease and single-guide RNA (sgRNA) targeting the PKS/NRPS conserved domain in the psoA gene sequence. The RNP complex will bind to the target sequence at the specific site and cut DNA double strands. The DNA double-strand breaks will be repaired by homology directed repair using the donor DNA template containing 1 kb of homology arms flanking the hph gene that confers resistance to hygromycin. (g) Resequencing the genomes of the ΔpsoA strains revealed the deleted regions by the CRISPR-Cas9-mediated knockout. Read mappings for the two different mutants are shown, and the deleted regions are represented by the large gap in both mappings. DNA resequencing reads are indicated by black lines. Download FIG S1, TIF file, 4.8 MB. https://doi.org/10.1128/AuthorWarrantyLicense.v1 This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply. The deletion of laeA resulted in an accelerated germination of spores, reaching 100% germination 13 h post-inoculation, while none of the control strains were able to achieve complete spore germination (Fig. 1c). Many fungi produce self-inhibitors that reduce germination rates, especially at high inoculum levels; we speculate that the deletion of laeA may results in the diminishment of these self-inhibitors (47). The deletion of laeA also resulted in reduced growth (as measured by colony diameter) regardless of the media (see Fig. S2). Collectively, these growth and development data demonstrate that LaeA regulates fungal traits that could have consequences for neighboring microbes. 10.1128/mbio.00769-23.5 FIG S2 Growth rates of Penicillium spp. on YES, PDA, CYA, and CCA. (a to c) Growth on YES medium (a), growth on PDA medium (b), and growth on CYA medium (c) are represented. In the box plots, the bar represents the standard errors of the means, and each dot represents a biological replication (n = 3). One-way analysis of variance (ANOVA) was performed for each bacterial community. Dunnett’s multiple-comparison test was used, and the results were compared to the WT strain. ****, P < 0.0001; ***, P < 0.001; **, P < 0.01; *, P < 0.05; no asterisk indicates not significant (ns). For the exact P values for each treatment, see Data Set 1. (d) Growth on CCA, where points and connecting lines with standard errors of the means bars were used from two independent experiments with five biological replications each. In experiment 2, replication 5 was removed due to no growth of the bacteria in the control. Two-way ANOVA was performed for each fungal strain. Dunnett’s multiple-comparison test was used and compared to the WT strain. ****, P < 0.0001; ***, P < 0.001; **, P < 0.01; *, P < 0.05; no asterisk indicates not significant (ns). For the exact P values for each treatment, see Data Set 1. Download FIG S2, TIF file, 0.9 MB. https://doi.org/10.1128/AuthorWarrantyLicense.v1 This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply. Penicillium traits regulated by LaeA mediate cheese rind bacterial community assembly and microbial interactions. To test how LaeA’s regulation of physiological or metabolic traits alters the development of cheese rind microbiomes, we grew all four Penicillium strains (MB_WT, MB_ΔlaeA, MB_HygR, and MB_laeAc) with a four-member bacterial community that represents the dominant taxa found in typical natural rind cheeses. We compared total bacterial community abundance (as total CFU) and bacterial community composition (relative abundance of each community member) at 3, 10, and 21 days post-inoculation across five different treatments: (i) bacteria alone, (ii) bacteria + Penicillium WT strain, (iii) bacteria + Penicillium ΔlaeA strain (MB_ΔlaeA), (iv) bacteria + Penicillium hygromycin resistance control strain (MB_HygR), and (v) bacteria + Penicillium laeA complement strain (MB_laeAc). After 3 days of incubation, community composition was similar across all treatments (Fig. 2a and b). However, the presence of MB_WT, MB_HygR, and MB_laeAc strains caused a significant restructuring of the bacterial community through their strong inhibitory effects at day 10 post-inoculation (Fig. 2c; see the Fig. 2 legend for PERMANOVA [permutational multivariate analysis of variance] statistics). The bacteria alone treatments were dominated by Actinobacteria (Brevibacterium [30%] and Brachybacterium [38%]), whereas the MB_WT, MB_HygR, and MB_laeAc treatments were Staphylococcus-dominated (>82% of the population) (Fig. 2c). In the presence of MB_WT, the absolute growth for all bacteria was significantly reduced, with Brevibacterium and Brachybacterium having nearly a 4-fold decrease in absolute growth, and Psychrobacter populations not reaching detectable levels (Fig. 2d). Comparable results were observed with the Penicillium MB_HygR and MB_laeAc strains. Strikingly, the abundance and structure of the bacterial community in the presence of MB_ΔlaeA was similar to the community grown in the absence of the fungus (a range of 57 to 68% Actinobacteria) (Fig. 2c). The shifts in community composition and dominance of Actinobacteria in the bacterium-alone and MB_ΔlaeA communities persisted through day 21 (Fig. 2e and f). FIG 2 Inactivation of LaeA increases bacterial diversity and abundance in cheese rind communities. The stacked barcharts show the shifts in the relative abundances of a typical rind bacterial community in the presence of four strains of Penicillium sp. strain MB after (a) 3, (c) 10, and (e) 21 days of incubation. Data are mean relative abundance from two independent experiments with five biological replicates each. The compositions of the Bacteria Alone and MB_ΔlaeA communities were not significantly different from one another, but were different from MB_WT, MB_HygR, MB_laeAc at 10 and 21 days (Day 3 PERMANOVA F = 0.9958, p = 0.425; Day 10 PERMANOVA F = 23.12, P < 0.0001; Day 21 PERMANOVA F = 11.14, P < 0.0001). The bar graphs show absolute abundances of individual bacterial community members from the same experiments as in (a), (c), and (e) in the presence of Penicillium sp. strain WT and mutants (MB_ΔlaeA, MB_HygR, and MB_laeAc) after (b) 3 (d), 10 and (f) 21 days of incubation. Each bar represents the mean with standard errors and each dot represents a biological replicate. One-way analysis of variance (ANOVA) was performed for each bacterium. Dunnett’s multiple comparison test was used and compared to the WT strain. (****) indicates P < 0.0001, (***) indicates P < 0.001, (**) indicates P < 0.01, (*) indicates P < 0.05, and no asterisk indicates not significant (ns). For exact P-values for each treatment, see Data Set S1. Penicillium species often co-occur with yeasts in cheese rinds (34, 43), and the presence of another fungus may dampen the inhibitory effects of Penicillium sp. strain MB on the bacterial communities. To test this, we repeated all community experiments with the addition of the common cheese rind yeast, Debaryomyces hansenii. We observed nearly identical patterns of inhibition of bacteria by MB_WT and loss of inhibition in MB_ΔlaeA communities in the presence of D. hansenii (see Fig. S3). This demonstrates that even with a more realistic two species fungal community, laeA-regulated traits of Penicillium sp. MB can control bacterial community diversity. 10.1128/mbio.00769-23.6 FIG S3 Community analysis in the presence of another common cheese rind fungal community member, the yeast Debaryomyces hansenii. Histograms show the shifts in a typical rind community (Staphylococcus, Brevivacterium, Brachybacterium, and Psychrobacter) in the presence Penicillium sp. strain MB_WT and mutants (ΔlaeA, HygR, and laeAc) and D. hansenii after 3 (a), 10 (c), and 21 (e) days of incubation. The data are mean relative abundances from two independent experiments. Histograms show the inhibition of community members grown in the presence Penicillium sp. strain MB_WT, mutant strains, and D. hansenii after 3 (b), 10 (d), and 21 (f) days of incubation. The data are from two independent experiments, with five biological replications each. In the box plots, the bar represents the standard errors of the means, and each dot represents a biological replication. One-way ANOVA was performed for each bacterial community. Dunnett’s multiple-comparison test was used and compared to the WT. ****, P < 0.0001; ***, P < 0.001; **, P < 0.01; *, P < 0.05; no asterisk indicates not significant (ns). For the exact P values for each treatment, see Data Set 1. Download FIG S3, TIF file, 1.8 MB. https://doi.org/10.1128/AuthorWarrantyLicense.v1 This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply. To determine the role of LaeA in mediating pairwise interactions between Penicillium sp. MB and each of the individual bacterial species, we cocultured each bacterium separately with the four Penicillium sp. MB strains on cheese curd agar. After 21 days, strains that had a functional LaeA (MB_WT, HygR, and MB_laeAc) inhibited the growth of all four bacteria (Fig. 3) with an inhibition hierarchy (log10% decrease alone versus MB_WT) of Psychrobacter (100% inhibition) > Brevibacterium (73% inhibition) > Staphylococcus (24% inhibition) > Brachybacterium (21% inhibition). These data demonstrate that Penicillium sp. MB strongly and directly inhibits the growth of individual cheese rind bacteria, and this inhibitory effect is mediated by LaeA. FIG 3 Pairwise interaction assays showing inhibition of bacterial strains grown individually in the presence of each of the four strains of Penicillium sp. strain MB. Data for (a) Staphylococcus, (b) Brevibacterium, (c) Brachybacterium, and (d) Psychrobacter are shown. Points indicate means and error bars indicate standard errors from two independent experiments with five biological replicates each. In experiment 2, replication 5 was removed due to no growth of the bacteria in the control. Two-way analysis of variance (ANOVA) was performed for each bacterial community. Dunnett’s multiple comparison test was used and compared to the WT strain. (****) indicates P < 0.0001, (***) indicates P < 0.001, (**) indicates P < 0.01, (*) indicates P < 0.05, and no asterisk indicates not significant (ns). For exact P-values for each treatment, see Data Set S1. Collectively, these community and pairwise data demonstrate that LaeA regulates some aspect of Penicillium sp. strain MB physiology or metabolism that results in differential inhibition of bacterial species growth in the cheese rind community. The LaeA-regulated factor(s) have the ability to completely transform the composition of the bacterial community. RNA sequencing reveals global alterations in the expression of specialized metabolite genes in the ΔlaeA strain. To identify SM biosynthetic gene clusters (BGCs) and other genes regulated by laeA in Penicillium sp. strain MB that might be driving bacterial-fungal interaction outcomes, we performed gene expression profiling using RNA sequencing (RNA-seq). RNA-seq libraries prepared from mycelium of MB_WT and MB_ΔlaeA harvested 48 h post-inoculation from CCA were examined for differential gene expression. This time point was selected because physiological differences between both strains were visually apparent (differences in pigmentation and spore production), and significant amounts of high-quality RNA could be obtained. Using a log2 ratio cutoff of 2 (corrected P value of <0.05), we identified 253 genes with decreased expression and 57 genes with increased expression in the MB_ΔlaeA strain (Fig. 4a; see also Data Set S2a). No transcripts for laeA were detected in the MB_ΔlaeA strain confirming the successful deletion of this gene. FIG 4 Deletion of laeA impairs production of metabolites with associated antimicrobial properties. (a) RNA-sequencing results showed a significant downregulation of biosynthetic genes in the pseurotin putative gene cluster when laeA was deleted. (b) Organization of the putative pseurotin biosynthetic gene cluster in Penicillium sp. strain MB and log2 ratio of expression in MB_ΔlaeA. Gene names in bold are those known to be involved in pseurotin production in Aspergillus species. (c) Visualization of zones of inhibition of crude extracts collected from all four Penicillium isolates on YES medium against Brachybacterium alimentarium evaluated by the disk diffusion method. See Fig. S4 for additional zone of inhibition data for other bacterial species. (d) Volcano plots representing the number of analytes significantly regulated in MB_ΔlaeA compared to the MB_WT strain on YES medium. The blue dots indicate pseurotin A and the other putative pseurotins identified in this analysis. (e) Comparison of the peak area corrected values for pseurotin A and the other putative pseurotins between strains. Each bar represents the means with +/− one standard error, and each dot represents a biological replicate (n = 3). One-way analysis of variance (ANOVA) was performed for each Penicillium strain. Dunnett’s multiple comparison test was used and compared to the MB_WT strain. (**) indicates P < 0.01 and no asterisk indicates not significant (ns). (f) Inactivation of psoA led to loss of antibacterial activity and restored bacterial community composition. The compositions of MB_ΔpsoA-1 and MB_ΔpsoA-2 bacterial communities were not significantly different from one another but were different from Bacteria Alone and MB_WT (PERMANOVA F = 49.97, P < 0.0001). (g) Histograms showing the inhibition of community members grown in the presence of the four strains of Penicillium sp. strain MB at 10 days post-inoculation. Data are mean relative abundance from three independent experiments with five biological replications each. In the box plots, the bar represents the standard errors of the means and each dot represents a biological replication. One-way analysis of variance (ANOVA) was performed for each bacterial community. Dunnett’s multiple comparison test was used and compared to the WT strain. (****) indicates P < 0.0001, (***) indicates P < 0.001, (**) indicates P < 0.01, (*) indicates P < 0.05, and no asterisk indicates not significant (ns). For exact P-values for each treatment, see Data Set S1. Using an enrichment analysis of differentially expressed genes’ GO terms, we identified depsipeptide, emericellamide, lactone, aspartic peptidase, indole alkaloid, fumagillin, epoxide, and alkaloid biosynthesis as pathways with greater than 30% of genes in a pathway being significantly downregulated (see Data Set S2b). All these biosynthetic pathways play major roles in SM production of fungi. The GO terms glucose, hexose, monosaccharide, and nucleobase transporters were enriched in the upregulated genes of the MB_ΔlaeA strain (see Data Set 2b). We used the list of downregulated genes and antiSMASH predictions to identify specific BGCs that might be related to the observed inhibition of bacterial growth. BGCs were identified by locating groups of adjacent downregulated genes with annotations for hallmarks of biosynthetic gene clusters, including polyketide synthases, nonribosomal peptide synthetases, terpene cyclases, and prenyltransferases (48). The most downregulated BGC in the MB_ΔlaeA strain contained genes homologous to the A. fumigatus fumagillin/pseurotin supercluster (49) (Fig. 4a). Pseurotins are a family of fungal alkaloids that have been reported as having antibacterial and insecticidal activities (50). A detailed analysis of the pseurotin BGC describing its genetic organization was reported in A. fumigatus (49), but pseurotin biosynthetic pathways and modes of antibacterial action are not fully known due to the presence of many intermediate compounds (51). The Penicillium sp. MB genome contains a 16-gene cluster with a complete set of predicted genes for pseurotin biosynthesis, including psoA, psoB, psoC, psoD, psoE, psoF, and psoG (Fig. 4b). Some genes essential for fumagillin biosynthesis, including the terpene cyclase gene (fmaA) are missing, suggesting the strain could synthesize pseurotin but not fumagillin (Fig. 4b). All these genes were highly downregulated (−4.7 to −7 log2-fold) and were among the most downregulated genes in the MB_ΔlaeA strain (Fig. 4a). Based on antiSMASH predictions (see Data Set S2c), the Penicillium sp. strain MB genome contains 42 regions that could encode putative specialized metabolites. Some of these other BGCs that were downregulated in the MB_ΔlaeA strain included a putative aspterric acid and quinolone BGC not known to have antibacterial activity, as well as other BGCs encoding putative metabolites (see Data Set S2a). None of these BGCs were downregulated to the same extent as the pseurotin BGC. Loss of laeA alters the production of all members of the 1-oxa-7-aza-spiro[4,4]non-2-ene-4,6-dione class of antibacterial natural products. Our findings highlighted a significant role for laeA in the assembly of microbial communities that may be partly due to a fitness cost associated with physiological traits of this mutant (Fig. 1). The downregulation of several BGCs in the MB_ΔlaeA strain suggested that differences in community structure between treatments could be due to antibacterial activities of laeA-regulated metabolites. To explore this hypothesis, we assessed metabolomic changes resulting from laeA deletion after 14 days of growth on four different media (CYA, PDA, YES, and CCA). Crude extracts collected from all four Penicillium strains were later screened for antibacterial activity using the disk diffusion method against the same bacterial species used in the community and pairwise interaction assays. Crude extracts collected from MB_WT growing on YES medium showed the highest antibacterial effects, with a clear circular zone of inhibition on all tested bacteria (Fig. 3c; see also Fig. S4a). Interestingly, crude extracts from MB_ΔlaeA cultures generated significantly smaller zones of inhibition than extracts from control strains regardless of the bacterial strain tested (see Fig. S4a). Crude extracts collected from cultures of the MB_ΔlaeA and the control strains did not show significant differences in their ability to inhibit bacterial growth, except against Psychrobacter (see Fig. S4b to d). Extracts collected from CCA cultures showed the lowest inhibitory effects on all tested bacteria, likely due to the high amount of fat in this medium resulting in low recovery of metabolites from the organic phase (see Fig. S4d). Similar problems with metabolite recovery have previously been attributed to the high fat content in milk (52). Therefore, we focused on exploring the metabolomic changes on YES medium to pinpoint the laeA-regulated metabolite(s) responsible for the shift in bacterial community composition. 10.1128/mbio.00769-23.7 FIG S4 Antibacterial activities of filtered crude extracts from cultures on different media. Crude extracts were collected from cultures of the different Penicillium strains on YES (a), PDA (b), CYA (c), and CCA (d) media. The diameter of the zone of inhibition was measured and analyzed. In the box plots, the bar represents the standard errors of the means, and each dot represents a biological replication (n = 3). One-way ANOVA was performed for each bacterial community. Dunnett’s multiple-comparison test was used and compared to the WT strain. ****, P < 0.0001; ***, P < 0.001; **, P < 0.01; *, P < 0.05; no asterisk indicates not significant (ns). For the exact P values for each treatment, see Data Set 1. Download FIG S4, TIF file, 0.9 MB. https://doi.org/10.1128/AuthorWarrantyLicense.v1 This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply. Analysis of metabolomic data on YES medium showed that the deletion of laeA led to a 2-fold decrease in the production of 62% of the 2,703 significantly differentially produced analytes and 2-fold increase in the production of 38% of all significantly differentially produced analytes in negative ionization mode (Fig. 4d). Pseurotins have a 1-oxa-7-aza-spiro[4,4]non-2-ene-4,6-dione core and multiple forms exist (A through F) with slight modifications to this core (53). Using a list of chemical formulas assigned to known metabolites produced by this Penicillium species and closely related species, we were able to identify peaks that correspond to putative pseurotins. Strikingly, the antibacterial metabolite pseurotin A showed a 140-fold decrease in the ΔlaeA mutant compared to the WT strain (P = 0.025) (Fig. 4e). The identification of pseurotin A (C22H25NO8) was confirmed by high-resolution mass spectrometry (HRMS) [M-H]– (m/z 430.1503, calculated for C22H24NO8 430.1507) and by HRMS/MS fragmentation. The mass spectrum showed the two fragmentation ions (m/z 270.0768 and 308.1137) consistent with the mass spectrum ion previously reported for pseurotin A (49). We could not perform experimental confirmation of other putative pseurotins due to the lack of commercially available standards or fragmentation databases. The published chemical formulas of pseurotins B, C, D, and E (54) were applied to MAVEN, which identified a significant reduction of production of these putative metabolites in the absence of laeA (Fig. 4e). The significant decrease in the synthesis of pseurotin A and the other pathway derivatives on YES medium matched the transcriptomic data showing a downregulation of the pseurotin gene cluster in the MB_ΔlaeA strain (Fig. 4a). Combined with previous reports of antibacterial properties of pseurotin (50), our concurring datasets suggested that pseurotins could explain the striking antibacterial activity of this Penicillium isolate. Inactivation of pseurotin production leads to a loss of antibacterial activity and restores bacterial community composition. To test whether pseurotins were the fungal metabolites regulating bacterial community structure, we disrupted the hybrid PKS-NRPS synthase gene psoA required for pseurotin production in Penicillium sp. strain MB using a CRISPR-Cas9 system (see Fig. S1e and f). The disruption of this gene in two independent mutants (MB_ΔpsoA-1 and MB_ΔpsoA-2) and the absence of Cas9 off-target cleavage events were confirmed using whole-genome resequencing (see Fig. S1g). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of MB_ΔpsoA-1 and MB_ΔpsoA-2 confirmed a lack of pseurotin A production (see Fig. S5). In contrast, MB_WT produced on average 1.36 mg of pseurotin A (see Data Set S1). 10.1128/mbio.00769-23.8 FIG S5 LC-MS data showing the absence of pseurotin production in the crude extracts of both MB_ΔpsoA strains compared to the MB_WT strain. (a) Chromatograms showing the absence of pseurotin A and putative pseurotin D peaks in the ΔpsoA mutants. (b) Fragmentation mass spectrum in positive ionization mode of pseurotin A in MB_WT strain compared to pseurotin A standard. (c) Fragmentation mass spectrum in positive ionization mode putative pseurotin D in MB_WT strain. The MS/MS spectrum shown for the putative pseurotin D is for reference purposes only and represents the differentially eluting isomer that was also affected by the genetic knockouts performed here. Download FIG S5, TIF file, 0.8 MB. https://doi.org/10.1128/AuthorWarrantyLicense.v1 This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply. When we repeated the bacterial community assembly assays described above with the ΔpsoA mutants, we saw two striking patterns (Fig. 4f and g). First, the two mutants did not reduce total bacterial growth as much as the MB_WT strain; while MB_WT had a 34.7% reduction in total CFU across all four bacterial species, the average total bacterial reduction across the two pseurotin mutants was only 13.9% (Fig. 4g). As we observed earlier (Fig. 2), Brevibacterium, Brachybacterium, and Psychrobacter were most inhibited by the MB_WT strain. Similar to coculture with MB_ΔlaeA, the ΔpsoA mutants had minor inhibitory impacts on these bacterial genera (Fig. 4g). Second, we observed that bacterial community composition was significantly different between the MB_WT and the MB_ΔpsoA-1 and MB_ΔpsoA-2 mutant communities. As with the ΔlaeA knockout above, the bacterial community composition shifted from dominance of Staphylococcus in the MB_WT community to a mix of all bacterial species in the MB_ΔpsoA-1 and MB_ΔpsoA-2 communities (Fig. 4f). These data combined with our transcriptomic and metabolomic data above demonstrate that the pseurotin BGC is regulated by LaeA in this Penicillium species and that the loss of pseurotin production eliminates much of the strong inhibitory effect of this fungus on bacterial community assembly. Components of the pseurotin biosynthetic gene cluster are widely distributed across the Ascomycota. Considering the strong ecological impacts of the pseurotin BGC, we used comparative genomics to identify other fungi whose genomes encode this cluster or closely related clusters with similar antimicrobial properties. Previous studies have demonstrated that the pseurotin/fumagillin supercluster was subject to rearrangements and gene loss in A. clavatus, Neosartorya fischeri, and Metarhizium anisopliae (49). Scanning across a broad sample of fungal genomes, we found that pseurotin genes are only present in the Ascomycota (Fig. 5; see also Fig. S6). Only a small subset of fungi are predicted to have all eight pseurotin genes, including species in the genera Aspergillus, Penicillium, Metarhizium, Tolypocladium, and Venustampulla (see Data Set S3). Of the 77 species of Aspergillus in this data set, only 11 have all 8 genes. Of the 24 species of Penicillium, only 2 (in addition to Penicillium sp. strain MB) have all 8 genes (Fig. 5a). Another closely related Penicillium strain that we isolated from a separate cheese production facility in the United States (Penicillium sp. strain 261) also has the full pseurotin BGC (Fig. 5b). FIG 5 Components of the pseurotin biosynthetic gene cluster are widespread across Ascomycota, but the full cluster is mainly found in Aspergillus and Penicillium species. (a) The subclade of Ascomycota identified as having higher prevalence of pseurotin cluster genes (Fig. S6) presented with a single leaf per species. Phylogenetic relationships were determined from the consensus of 290 maximum-likelihood trees constructed for benchmarking single copy orthologs (BUSCOs). The outer ring indicates the taxonomic order that species pertain to. Terminal branch lengths are not calculated. The number of pseurotin genes was determined using reciprocal best-hit BLAST analysis using the Aspergillus fumigatus pseurotin genes psoA, psoB, psoC, psoD, psoE, psoF, psoG, and fapR is indicated as the intensity of the tip color. (b) A phylogeny constructed in the same way as (a) representing a subset of species identified as having seven or eight pseurotin genes that were manually selected to represent taxonomic breadth. (c) An alignment of pseurotin gene clusters. Genes in the pseurotin cluster are labeled in blue while constituents of the intertwined fumagillin cluster are indicated in green. Regulatory genes of these clusters are labeled in orange. Coloration of genes is based on homology searches and visualizations performed by clinker. The weight of lines between homologous genes indicates the percent identity; genes sharing identity below 30% are not indicated. (d) A maximum likelihood phylogeny constructed from the concatenation of PsoA, PsoB, and PsoC sequences. Note that phylogenetic relationships between (d) and (b) are compatible, suggesting vertical transmission of the pseurotin gene cluster. The strain information of the species used in this analysis are provided in Data Set S3. 10.1128/mbio.00769-23.9 FIG S6 Frequency of genes associated with the pseurotin gene cluster (left) across fungal phyla (right). A total of 1.463 fungal genomes representing 808 species were obtained from the NCBI. Phylogenetic relationships between genomes were determined from the consensus of 290 maximum-likelihood trees constructed for single-copy orthologs (BUSCOs). Terminal branch lengths were not calculated. The number of pseurotin genes present in each genome was determined using reciprocal best-hit BLAST analysis using the Aspergillus fumigatus pseurotin genes psoA, psoB, psoC, psoD, psoE, psoF, psoG, and fapR. The orange arrow indicates a monophyletic clade within Ascomycota that was visually identified as having a higher prevalence of pseurotin genes. Download FIG S6, TIF file, 0.9 MB. https://doi.org/10.1128/AuthorWarrantyLicense.v1 This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply. Given the taxonomic breadth of fungi containing the pseurotin cluster, we sought to understand the evolutionary origins of this cluster within and outside the Ascomycota, including whether horizontal or vertical transmission explains patterns of pseurotin gene distribution. The overall history of this BGC appears consistent with vertical transmission and gene loss in some lineages; the topology of a phylogeny constructed from 290 benchmarking single copy orthologs (BUSCOs) was compatible with another phylogeny constructed using PsoA, PsoB, and PsoC sequences (Fig. 5b to d). We cannot entirely rule out the possibility of horizontal gene transfer, but such transfers would have to be ancient to allow for BGC and species histories to align. Patterns of vertical transmission are also apparent in the synteny of the BGC. For example, an inversion of several genes at one end of the cluster (fmaD-fapI) that is present in most Penicillium spp. but not in Aspergillus spp. is missing in the early-divergent Penicillium arizonense (Fig. 5c), suggesting that this mutation occurred sometime after the diversification of this genus. To explore the possibility of an ancient horizontal gene transfer event from outside the fungi, we BLASTed the PsoA protein against all NCBI databases, excluding fungi. The highest-scoring hit from this search (94% of the query cover, 32.38% identity) was an uncharacterized hypothetical protein that is annotated as PKS in the plant Carpinus fangiana (accession KAB8336832.1). However, the reciprocal BLAST of this protein against fungi found better hits (100% coverage and ~60% identity) against other uncharacterized PKSes and putative lovastatin nonaketide synthases. While our results demonstrate that the pseurotin BGC is widespread in a subclade of Ascomycota, further analyses exploring the evolution of this gene cluster are needed. DISCUSSION The chemistry, genetics, and physiology of fungal specialized metabolism are widely studied and recognized, but their ecological consequences in microbiomes are largely unknown. Previous genetic and -omics approaches identified key metabolites and pathways regulated by LaeA (28, 29, 55–57), but these data have not been integrated into a comprehensive model for studying microbial interactions that influence community assembly. By toggling LaeA between on and off, we were able to identify one class of metabolites produced by a strongly inhibitory fungus that allows it to dramatically remodel the bacterial communities of cheese. Our integration of physiological assays, transcriptomics, and metabolomics more broadly demonstrates how LaeA can be used to identify fungal metabolites that control the assembly of microbiomes. The intertwined pseurotin and fumagillin supercluster was initially identified in Aspergillus fumigatus and reported to be under the control of LaeA (49). Our study shows that this BGC exists in the cheese-isolated Penicillium sp. strain MB and is also positively regulated by LaeA (Fig. 4a and b). This metabolite was previously found to exhibit a range of bioactivities at moderate to high concentrations (up to 50 μg/mL) with potential therapeutic applications due to its immunosuppressive (58), antibacterial (50, 59), nematocidal (60), insecticidal (61), antiparasitic, and anticancer activities (62). Previous studies of antibacterial properties have focused on purified versions of the compound and have not used natural production from a host for assays. In our analysis, the production of pseurotin A by the MB_WT strain was roughly 2.7 times higher than the concentration used in the aforementioned studies (see Data Set S1) and may explain why this fungus has such strong inhibitory effects on bacterial communities. Characterization of how other fungi that produce pseurotins impact bacterial community assembly will further clarify how this class of metabolites plays roles in multispecies microbiomes. In addition, why pseurotins have antibacterial activity is not currently known, and additional work characterizing the mode of action is needed. In addition to its regulation and role in the cheese Penicillium isolate, we have found that components of the pseurotin BGC can be found intermittently in the Ascomycota, but not in any other fungal phyla. Pseurotin A has been reported in several different filamentous fungi, including species of Aspergillus, Penicillium, and more recently the distantly related taxon Metarhizium (51, 61, 63, 64). Our finding of all eight genes of this BGC in several other genera (Tolypocladium and Venustampulla) suggests that fungal pseurotins may be widely distributed across a range of fungal niches, from food production and indoor environments to forest soils. Additional studies of these fungi and neighboring bacteria are needed to fully characterize the ecological significance of pseurotins across ecosystems. Our data strongly suggest that pseurotins are key LaeA-regulated metabolites that can mediate bacterial community assembly, but we acknowledge that other fungal metabolites and traits could be playing roles in this system. Other BGCs, including some putative clusters with unknown functions, were downregulated in the transcriptome of the MB_ΔlaeA strain (see Data Set S2), suggesting that they are regulated by LaeA. These may also contribute to the antibacterial effects of Penicillium sp. strain MB. Future work using untargeted metabolomics and other approaches may identify these additional LaeA-regulated metabolites in this and other filamentous fungi that affect bacterial community assembly. Every time a consumer eats a piece of a naturally aged cheese, they ingest the many metabolites secreted into the cheese by rind microbes. Surprisingly little is known about the diversity and functions of these microbial metabolites (41, 65–68). Our work demonstrates that fungal SMs produced in cheese environments can serve as mediators of microbiome formation at concentrations relevant to naturally aged cheeses. The interactions described here occurred in a highly controlled laboratory environment and their relevance in full-scale cheese production remains to be determined. The Penicillium sp. strain MB used in this study was causing cheese production problems by inhibiting normal rind formation, suggesting that the disrupted community assembly observed in the lab could also happen in cheese caves. Many other filamentous fungi found in cheese rinds possess uncharacterized metabolites that might be responsible for similar negative outcomes. Systematic exploration of LaeA-regulated metabolites and their ecological roles will not only discover microbial mechanisms underlying traditional cheese-making but will also illuminate how fungal SMs mediate microbiome composition in all environmental niches where fungi reside. MATERIALS AND METHODS Microbial strain isolation and culture conditions. The Penicillium sp. strain MB used in this study was isolated from a natural rind cheese made in the United States. Its genome has been deposited in NCBI with accession number GCA_008931935.1. The exact species identification of this strain is unknown at this time because genomes of type strains of closely related species are not currently available. However, previous comparative genomic analysis suggests it is near P. polonicum in section Fasiculata of the genus Penicillium (69). The beta-tubulin marker gene (benA) of Penicillium sp. strain MB has a high similarity with two Penicillium species: 98.6% pairwise identity with a reference strain of P. cyclopium (strain CBS 14445) and 96.1% pairwise identity with a reference strain of P. polonicum (strain CBS 22228). Preliminary observations demonstrated that this Penicillium strain had potent antibacterial activity and was therefore an interesting strain to explore microbial interactions and secondary metabolite regulation through the inactivation of the global regulator of fungal secondary metabolism, LaeA. To prepare a fresh spore suspension prior to experiments, the Penicillium sp. strain MB was activated on glucose minimal media (GMM) (70) for 7 days at 25°C, and spores were harvested in 0.01% Tween 80 and counted using a hemocytometer. Bacterial strains were also maintained as glycerol stocks and streaked on brain heart infusion agar prior to experiments. The CYA, YES, PDA, and GMM agars were prepared as previously described (70), and the CCA medium was also prepared as previously described (43). Four bacterial strains (Staphylococcus equorum strain BC9, Brevibacterium aurantiacum strain JB5, Brachybacterium alimentarium strain JB7, and Psychrobacter sp. strain JB193) were used in bacterial community experiments. These species span three bacterial phyla (Firmicutes, Actinobacteria, and Proteobacteria) (43) that are most abundant in cheese rinds. They have been used as a model community in previous work from our lab (44, 45, 71) and have been demonstrated to have various responses to the presence of Penicillium (43, 45). This model community also has a well-defined community succession with Staphylococcus and Psychrobacter dominating early in succession (days 0 to 10) and the Actinobacteria Brevibacterium and Brachybacterium dominating later in succession (days 10 to 21). We did not use bacteria isolated from the same cheese where the Penicillium sp. strain MB was originally isolated because we were interested in identifying the basic ecological impacts of LaeA on bacterial communities, not the specific way in which this mold was interacting with bacteria on the cheese surface. By using previously well-characterized responding bacteria, we can compare our community assembly assays and interactions in this work to many previous experiments. We acknowledge that the bacteria that co-occurred with the Penicillium sp. strain MB may have had a different response compared to our model bacterial community. Construction of gene deletion cassettes. To knockout laeA in the Penicillium sp. strain MB, the isolate was first subjected to antimicrobial susceptibility testing toward hygromycin and phleomycin, two antibiotics commonly used by our group as selectable markers in fungal transformations. The isolate showed a confirmed sensitivity to both antibiotics. A three round PCR deletion strategy was used to replace the laeA open reading frame (ORF) in the Penicillium sp. strain MB with the hph gene, whose expression confers selection on hygromycin. The schematic representation of the laeA gene replacement with the hph gene is depicted in Fig. S1a. Each deletion cassette (5′flank-hph-3′flank) was constructed using three sequential PCRs. In the first PCR round, about 1 kb of genomic sequence that flanks either the 5′ or the 3′ end of the laeA ORF was amplified from strain MB using, respectively, the primer set PMB_KOlaeA_5′ or 3′F/R. The hph gene was amplified from plasmid pUCH2-8 using primers hph_F and hph_R. A second PCR was performed to assemble by homologous recombination the three individual fragments from the first-round PCR. The deletion cassettes were finally amplified using nested primer sets (PMB_KOlaeA_NestedF/R). To test whether pseurotin was involved in the antimicrobial activity and shifts in bacterial community composition observed with this Penicillium strain, we knocked out the psoA gene that encodes for the hybrid polyketide synthase-nonribosomal peptide synthetase (PKS/NRPS) of the putative pseurotin biosynthetic gene cluster to disrupt the production and accumulation of all pseurotin. The same strategy described earlier was adopted for the construction of the psoA deletion cassette. The primer sets PMB_KOpsoA_5′/R and 3′F/R were used to amplify the 1-kb homology arms flanking the psoA gene on the 5′ and 3′ ends, respectively. The hph gene under the control of the Tef1 promoter and terminator was amplified from the plasmid pCS01 using the primer set Hyg-tef1F/R. The plasmid map is given and annotated in Fig. S1e. A second PCR was performed to assemble by homologous recombination the three individual fragments from the first round PCR. The deletion cassette was finally amplified using the nested primer set (PMB_psoA_nestedF/R). The sequences of the primer sets used for the construction of the deletion cassettes are shown in Table S1. 10.1128/mbio.00769-23.10 TABLE S1 Primers used in this study for the construction of deletion cassettes and molecular cloning. Download Table S1, DOCX file, 0.02 MB. https://doi.org/10.1128/AuthorWarrantyLicense.v1 This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply. PEG-mediated protoplast transformation. To generate the deletion strains, a protoplast-mediated transformation protocol routinely used by our group was employed and optimized to achieve a successful protoplasting of Penicillium sp. strain MB. Briefly, 109 fresh spores from each strain are cultured in 500 mL of GMM broth supplemented with 1 g/L yeast extract for 12 h under 25°C and 280 rpm. Newly born hyphae were harvested by centrifugation at 8,000 rpm for 15 min and hydrolyzed in a mixture of 30 mg of lysing enzyme from Trichoderma harzianum (Sigma-Aldrich) and 20 mg of Yatalase (Fisher Scientific) in 10 mL of osmotic medium (1.2 M MgSO4 and 10 mM sodium phosphate buffer). The quality of protoplast was monitored under the microscope after 4 h of shaking at 28°C and 80 rpm. The protoplast mixture was later overlaid gently with 10 mL of chilled trapping buffer (0.6 M sorbitol and 0.1 M Tris-HCl [pH 7.0]) and centrifuged for 15 min under 4°C and 5,000 rpm. Protoplasts were collected from the interface, overlaid with an equal volume of chilled STC (1 M sorbitol, 10 mM Tris-HCl, and 10 mM CaCl2) and decanted by centrifugation at 6,000 rpm for 10 min. The protoplast pellet was resuspended in 500 μL of STC and used for transformation. For protoplast transfection, 100 μL of freshly isolated protoplasts and 5 μg of linear DNA containing the deletion cassette were mixed to a final volume of 200 μL of STC buffer. The contents were mixed by gently inverting the tubes. After 50 min incubation on ice, 1.2 mL of 60% (wt/vol) PEG solution (60 g of PEG 3350, 50 mM CaCl2, and 50 mM Tris-HCl [pH 7.5]) was added to the mixture, followed by incubation for an additional 20 min at room temperature. The mixture was supplemented with 5 mL of STC and mixed into 50 mL of SMM top agar (GMM supplemented with 1.2 M sorbitol) containing hygromycin at a final concentration of 150 μg/mL. The mixture was inverted several times, and each 5-mL portion was poured onto a selective SMM bottom agar plate. The transformation plates were incubated at 25°C for 5 to 7 days. (i) CRISPR/Cas9-mediated knockout of psoA gene. The large size of the psoA gene (~12 kb) made the standard transformation method described for laeA deletion ineffective. Therefore, we switched to a ribonucleoprotein-CRISPR-Cas9 (RNP-CRISPR-Cas9) system. The schematic representation of the CRISPR cas9 system used for engineering the psoA knockout strain is depicted in Fig. S1f. The fungal transformation steps followed were the same as described above, the only difference being the codelivery of the Cas9-gRNA RNP complex along with the linear donor DNA (psoA deletion cassette) to the protoplasts. The CRISPR RNA (crRNA) was designed on the PKS-NRPS conserved domain in the psoA sequence using the CRISPOR webtool (http://crispor.tefor.net/) that offers off-target and efficiency predictions. The selected crRNA (5′-GGAUCGAUCUUGAACAGCAG-3′) showed a predicted targeting efficiency of 100% and 0 predicted off targets. This crRNA was subjected to an additional confirmation using the RNAfold web server (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi) that allows to determine the gRNA secondary structure. The interpretation of the results and validation of the gRNA were done following the guidance by Hassan et al. (72). The crRNA and tracrRNA (IDT, catalog no. 1072534) were synthesized using the Alt-R CRISPR-Cas9 system from Integrated DNA Technologies (IDT; San Diego, CA). The crRNA and tracrRNA were mixed at a concentration of 200 nM each in IDT duplex buffer in a final volume of 20 μL. The gRNA complex was formed by incubation at 95°C for 5 min, followed by slowly cooling down for 20 min at room temperature, and then stored at −20°C. To form the RNP complex, 8.3 μL of HiFi Cas9 Nuclease (IDT, catalog no. 1081060), and 6 μL of the previously made gRNA was mixed in nuclease-free water to a 25-μL final volume. The mixture was incubated for 15 min at room temperature prior to use. (ii) Confirmation of gene deletion strains. After 5 to 7 days of incubation at 25°C, colonies grown on SMM plates supplemented with hygromycin (150 μg/mL) were subjected to a second round of selection on hygromycin. Single-spored transformants were later tested for proper homologous recombination at the ORF locus by PCR. To confirm deletion of laeA, 25 hygromycin-resistant transformants were isolated after a rapid selection procedure on SMM supplemented with hygromycin. The correct replacement of the laeA with the hph gene was first verified by PCR analysis of genomic DNA derived from the transformant strains using primers that amplify the laeA ORF. One ORF-specific confirmation primer set (PMB_laeA_F/R) was chosen for the strain. About 40% (10/25) of the monoconidial lines generated from primary transformants of strain MB were PCR-positive for the absence of the laeA ORF (data not shown). The positive deletion strains identified by PCR were further checked for a single insertion of the deletion cassette by Southern blot analysis. Probes corresponding to the 5′ and 3′ flanks of the laeA gene in each strain were labeled using [α-32P]dCTP (Perkin-Elmer, USA) according to the manufacturer’s instructions. A single-site integration of the deletion cassette was revealed in a single transformant of the strain MB (see Fig. S1b). A HygR control strain, which has the hygromycin cassette inserted into the genome but not at the target locus, was also included in the study as a control for absence of selectable marker gene effects. To confirm deletion of psoA in Penicillium strain MB_WT using Cas9/sgRNA RNP complex, four hygromycin-resistant transformants were isolated after a rapid selection procedure on SMM supplemented with hygromycin. Two out of four of the monoconidial lines generated were PCR positive for the absence of the psoA ORF using the primer set PMB_psoA_F/R (data not shown). Later, identification of exact gene deletion locations and assessment of off-target effects of the cas9 were analyzed by whole-genome resequencing of the two PCR- positive knockout strains. DNA was extracted from each ΔpsoA strain using a Qiagen DNeasy PowerSoil extraction kit. DNA was sent to the Microbial Genome Sequencing Center for library preparation and sequencing on an Illumina NextSeq 2000. To determine the specific location of CRISPR deletion, reads were mapped to the Penicillium sp. MB_WT genome using Bowtie. To assess whether any off-target mutations were caused by the CRISPR deletion of psoA, FreeBayes was used to identify variants in the mappings of both ΔpsoA strains. Overall, only two high-confidence single nucleotide polymorphisms (SNPs) were detected in both CRISPR knockout strains that could have resulted from off-target effects: one SNP (G→A with predicted amino acid substitution of D→N) in a gene annotated as “chromatin structure-remodeling complex subunit rsc1” and another SNP (DNA G→A with predicted amino acid substitution of P→L) in a predicted protein with an unknown function (see Fig. S1g). Construction and confirmation of complement strains. To confirm that the phenotype exhibited by the MB ΔlaeA strain is caused by the deletion of this specific gene, the MB ΔlaeA strain was complemented with a wild-type (WT) copy of the laeA gene using phleomycin as a selectable marker. Restriction sites for NotI were introduced at the predicted native promoter and terminator of the laeA gene using primers PMB_laeAcomp_F and PMB_laeAcomp_R. The laeA gene was cloned into the pBC-phleo plasmid at the multiple cloning site located within the lacZ gene (see Fig. S1c). The ligation of the digested insert into the recipient plasmid was performed using T4 DNA ligase (New England Biolabs) following the manufacturer’s instructions. The ligation reaction was later transformed into Escherichia coli DH5α competent cells according to the manufacturer’s directions (Thermo Fisher Scientific). Five white bacterial colonies were randomly selected from the blue-white screening lysogeny broth (LB) agar plate and screened for successful ligations by conducting a diagnostic restriction digest with the NotI restriction enzyme. The E. coli strain carrying the correct plasmid (labeled PJT3) was then grown in 50 mL of LB supplemented with chloramphenicol (35 μg/mL), and the plasmid DNA was isolated using a Quantum Prep-Plasmid Midiprep kit (Bio-Rad) according to the manufacturer’s instructions. Next, 10 μg of plasmid DNA was used for the transformation of the MB_ΔlaeA strain following the same protocol described above. Prior to transformation, the plasmid was linearized using the NotI restriction enzyme. Southern blotting was performed to confirm the single integration of laeA into the MB deletion strain using the laeA ORF sequence (amplified using the primer set laeA_ORF_F/R) for making the probe. Genomic DNAs from both laeA complement and deletion strains were digested with the same enzyme used for cloning. A positive control corresponding to the PJT3 plasmid was incorporated in the Southern blot analysis, and the MB_ΔlaeA strain was used as a negative control. 10 phleomycin-resistant transformants were isolated and subjected to Southern blot to confirm the single insertion of the laeA ORF. One strain out of 10 showed a single band of 2.8 Kb that matches the band obtained with the positive control (plasmid PJT3 generated after subcloning the laeA fragment into plasmid pBC-phleo). As expected, the MB_ΔlaeA mutant strain used as a negative control did not show any band (see Fig. S1d). Morphophysiological analysis. The impact of laeA deletion on the morphophysiological traits of the cheese Penicillium sp. strain MB was evaluated by monitoring the growth, sporulation, and germination of the MB_WT strain in comparison to the MB_ΔlaeA, MB_HygR, and MB_laeAc control strains. The phenotypic appearance and vegetative growth were evaluated on five different media: GMM, CYA, YES, and CCA. Spore production and germination were assessed on GMM agar and broth, respectively. For conidial counts, fresh spores from each strain were diluted to 105 spores/mL in GMM top agar and overlaid onto agar plates of the same medium. The plates were incubated at 25°C and agar plugs removed on the second, third, and fourth day post-inoculation were homogenized in 3 mL of 0.01% Tween 80 using the VWR 200 homogenizer. Total spore counts were made using a hemocytometer. Conidial germination rates were evaluated over a 24-h growth period using a Nikon Ti inverted microscope. A spore suspension of 105 spores/mL of GMM broth was prepared for each strain, and about 1 mL was distributed into three replicate wells of a 24-sterile well plate. Five pictures per well were taken an hour apart beginning 4 h postincubation. The number of germlings were counted for each strain and the percentage of germinated spores was plotted against time to estimate the germination rates. Community and pairwise interaction assays. To determine how the deletion of laeA impacted microbial community assembly, we reconstructed cheese rind bacterial communities on cheese curd agar (CCA) with each of the Penicillium strains and measured total bacterial community abundance (as total CFU) and bacterial community composition (relative abundance of each community member) at 3, 10, and 21 days of community assembly. Each member of a four-member bacterial community (Staphylococcus equorum BC9, Brevibacterium auranticum JB5, Brachybacterium alimentarium JB7, and Psychrobacter sp. strain JB193) was initially inoculated at 200 CFU per species in five treatments: (i) bacteria alone, (ii) bacteria + Penicillium WT strain, (iii) bacteria + Penicillium ΔlaeA strain (MB_ΔlaeA), (iv) bacteria + Penicillium hygromycin resistance control strain (MB_HygR), and (v) bacteria + Penicillium laeA complement strain (MB_laeAc). Penicillium strains were also inoculated at 200 CFU from experimental glycerol stocks (46). For each treatment, replicate communities were inoculated on the surface of 150 μL of cheese curd agar dispensed into each well of a 96-well plate. To determine bacterial community composition, communities were harvested from individual wells with a sterile toothpick, suspended in 500 μL of phosphate-buffered saline (PBS) in a 1.5-mL microcentrifuge tube, homogenized with a sterile micropestle, and serially diluted onto plate count agar with milk and salt (PCAMS) media (46). To selectively plate bacteria, 100 mg/L of cycloheximide was added to PCAMS. To quantify Penicillium abundance, 50 mg/L of chloramphenicol was added to PCAMS. Each of the four bacteria have very distinct colony morphologies, making it easy to determine the abundance of each community member. To determine whether the presence of another fungus could modify the inhibitory effects of Penicillium sp. strain MB, we repeated the community assays above with an isolate of the yeast Debaryomyces hansenii (see Fig. S3). This is a very widespread yeast in cheese rinds, has neutral or sometimes positive effects on the growth of cheese rind bacteria, and often co-occurs with Penicillium species (39). These experiments with the additional yeast were repeated as described above except that 200 CFU of Debaryomyces hansenii strain 135B were added to all treatments. Bacterial and fungal abundances were quantified as described above. Pairwise interactions between each individual bacterium and the four Penicillium strains (MB_WT, MB_ΔlaeA, MB_HygR, and MB_laeAc) were assessed using the same experimental setup as the community experiments. Each bacterium was inoculated on the surface of a well of a 96-well plate with PCAMS either alone or with 200 CFU of each of the four Penicillium strains. Bacterial abundance was determined at 3, 10, and 21 days by plating harvested cocultures on PCAMS supplement with 100 mg/L of cycloheximide. To determine the role of pseurotin in shaping cheese microbial communities, these community assays were conducted with the ΔpsoA-1 and ΔpsoA-2 strains. The experimental setup and data collection and analysis were identical to the experiments with the MB_WT, MB_ΔlaeA, MB_HygR, and MB_laeAc strains noted above. RNA sequencing analysis and antiSMASH BGC prediction. Transcriptome changes in Penicillium sp. strains MB_WT and MB_ΔlaeA were investigated using RNA-sequencing analysis of cultures growing on CCA medium. Inoculum of both strains were prepared from 1-week cultures on PCAMS medium. A 1-cm2 plug was taken from the leading edge of mycelium and homogenized in 500 μL of PBS. A 20 μL inoculum was spotted onto a CCA plate at three evenly spaced positions. After 48 h of growth in the dark at 24°C, the spots were about 1.5 cm in width. The MB_WT had produced blue colored spores whereas the MB_ΔlaeA spores were lighter in color. The entire fungal growth from each spot was cutoff from the CCA plates, placed in RNAlater (Qiagen), and stored at −80°C. Four biological replicates were sampled for each strain. RNA was extracted from one of the three spots from each replicate plate using the Qiagen RNeasy Plant minikit after grinding the sample in liquid nitrogen. Approximately 100 mg of ground fungal biomass was mixed in 750 μL of Buffer RLT supplemented with 10 μL of β-mercaptoethanol. The manufacturer’s recommended protocol was followed for RNA extraction, including an on-column DNase treatment. To isolate mRNA, the NEBNext Poly(A) mRNA magnetic isolation module (New England Biolabs) was used. This mRNA was used to generate RNA-seq libraries using the NEBNext Ultra II RNA Library Prep kit for Illumina according to the manufacturer’s recommended protocol. The RNA-seq libraries were sequenced using 125-bp paired-end Illumina sequencing on a HiSeq at the Harvard Bauer Core. Duplicate reads were removed, and the total number of reads was subsampled to 3.8 million forward reads that were used for read mapping and differential expression analysis. Reads were mapped to a draft genome of Penicillium sp. strain MB. Read mapping was performed with TopHat v2.1.0 (73). Differentially expressed genes were identified using DeSeq2 (74). Genes with a >5-fold change in expression and false discovery rate (FDR)-corrected P values of <0.05 were considered differentially expressed. To identify specific biological pathways that were enriched in the sets of downregulated or upregulated genes, we used a KOBAS 2.0 (75) to conduct a hypergeometric test on functional assignments from the Gene Ontology (GO) database (using the Aspergillus flavus genome as a reference for GO ID assignment) with Benjamini-Hochberg FDR correction. To identify putative BGCs in the Penicillium sp. strain MB genome beyond the pseurotin gene cluster, we used the fungal version of antiSMASH v 6.1.1 (76). Metabolite profiling by UHPLC-MS analysis. To determine the effect of laeA deletion on the biosynthetic metabolome of the Penicillium sp. strain MB, all four strains (MB_WT, MB_ΔlaeA, MB_HygR, and MB_laeAc) were cultivated by centrally inoculating 106 fresh spores on 60-mm petri dishes containing 10 mL of the agar media PDA, CYA, YES, and CCA. Three technical replicates per strain and condition were prepared. The cultures were incubated at 25°C for 2 weeks. After the incubation period, all cultures were freeze-dried (~3 g [dry weight]) and ground into 5 mL of sterile water. Soluble metabolites were later extracted by solvent extraction procedure using 5 mL of ethyl acetate. An organic metabolite fraction was generated by liquid-liquid partitioning and dried under vacuum. The crude extract was then dissolved in 400 μL acetonitrile-water (80:20 [vol/vol]) at a concentration of 100 μg/μL. Samples were later analyzed by UHPLC-MS as previously described (77). The total data set was first evaluated using the software MAVEN and the XCMS open-source package. Differential masses found via XCMS were filtered by having a maximum intensity greater than 4 × 104. Identified masses that had a maximum intensity lower than 4 × 104 were considered as background. A volcano plot was later constructed to determine statistically significant data points in crude extracts analyzed for both MB_WT and MB_ΔlaeA in negative ionization mode. For volcano plot construction, metabolites were filtered based on a P value of <0.05 and a fold change higher than 2 and lower than −2. To confirm the lack of pseurotin production by the two ΔpsoA mutants, crude extracts from 14-day cultures on YES agar medium of both MB_ΔpsoA and MB_WT were obtained and assessed by high-resolution parallel reaction monitoring (PRM) LC-MS/MS using a Vanquish uHPLC plumbed directly to a Q-Exactive Plus mass spectrometer (Thermo Scientific) outfitted with a 75-μm ID nanospray emitter packed with 15-cm Kinetex C18 resin (1.7-μm particle size; Phenomenex). Mobile phases included solvent A (95% H2O, 5% acetonitrile, 0.1% formic acid) and solvent B (30% H2O, 70% acetonitrile, 0.1% formic acid). Nanoliter flow rates were achieved by uHPLC split-flow and measured as 300 nL/min at the nanospray emitter. First, 5 μL of each sample was autoinjected prior to the split, leading to the separation and analysis of 10 nL of extract over a 30-min chromatographic method: 0 to 100% B over 17 min; 100 to 0% B over 3 min; hold at 0% B for 10 min to re-equilibrate the column. Pseurotin A was targeted for PRM analysis in positive-ion mode with a duty cycle that included a selected ion monitoring scan (427 to 437 m/z range; resolution, 35,000; 2 microscan spectrum averaging), followed by a PRM scan targeting the pseurotin A ion (m/z of 432.1653 [M+H]+; resolution 17,500; 2.0 m/z isolation window with a 0.5 m/z offset; normalized collision energy [NCE] in the HCD cell at 35). Sample extracts were measured across three technical replicates of each strain (WT_MB, MB_ΔpsoA1, and MB_ΔpsoA2), including 10 μM pseurotin A standard (Cayman Chemical). Standard addition experiments were also performed using the above PRM method on samples extracted from YES grown MB_WT strain to assess the pseurotin A concentration. All PRM data were analyzed using Skyline (78) and FreeStyle (Thermo Scientific) software. In vitro antimicrobial assay. To determine whether the findings observed with community and pairwise interaction assays are due to secreted metabolite(s), the antimicrobial activities of all crude exudates collected from cultures of MB_WT, MB_ΔlaeA, MB_HygR, and MB_laeAc strains on various media were evaluated using the paper disk agar diffusion method. The antimicrobial properties were assessed against the same bacterial strains used for community and pairwise interaction assays except the B. aurantiacum strain JB5 due to the inability of growing this strain for these in vitro experiments. Bacterial strains were first cultured in 5 mL of LB broth under 280 rpm at room temperature for 24 to 48 h. The optical density of the bacterial suspension was later adjusted to 1. One milliliter of the bacterial suspension was then added to 20 mL of LB top agar, and 5 mL was gently applied on agar dishes of the same medium. In sequence, sterile disks impregnated with 10 μL of extracts (at a concentration of 100 μg/μL) dissolved in acetonitrile-water (80:20 [vol/vol]) was placed over the bacterial culture plates. One disk containing the solvent previously used for resuspension was used as negative control. For each bacterium, one disk of ampicillin at a concentration of 100 μg/mL was applied as a positive control. All dishes were incubated at room temperature, for 24 to 48 h. At the end of the incubation period, each dish was examined, and inhibition halo diameters were measured. Comparative genomic analysis of the pseurotin gene cluster. To contextualize the ecological importance of pseurotin in Penicillium spp. relative to other fungi, we used a data set of all annotated publicly available genomes originally downloaded from NCBI on 20 April 2020. This data set comprised 1,464 genomes representing 808 species. We performed pairwise reciprocal-best hit analysis of all proteins in the Aspergillus fumigatus genome against all 1,464 genomes using methods described previously (79). The results of this analysis were used to identify psoA, psoB, psoC, psoD, psoE, psoF, psoG, and fapR orthologs across fungal phyla. We mapped the phylogenetic relationship of fungal genomes based on 290 benchmarking single copy orthologs (BUSCOs) as identified with BUSCO (80). We aligned sequences of each BUSCO using MAFFT and trimmed alignments using TrimAl (81) with the parameter -automated1. We constructed phylogenies for each gene using IQtree (82) after testing for the best fit model. We then created a single consensus tree using ASTRAL (83). We selected a subset of genomes to represent taxonomic breadth based on visual inspection of phylogenetic relationships between species where seven or eight pseurotin genes were found. In addition, we added a set of genomes that were not present on NCBI but were found to contain the pseurotin gene cluster from our analysis of this cluster in the Penicillium genus (see above). Phylogenetic relationships between these genomes were determined using the same methodology as described above. Pseurotin gene clusters were determined from antiSMASH (84) by selecting cluster calls that contained the psoA ortholog (as determined above). The resulting clusters were aligned and visualized using Clinker (85). When BGC border calls made by antiSMASH extended beyond the pseurotin BGC, we trimmed these calls to facilitate visualization of this gene cluster. Validation of gene calls in genome annotations was beyond the scope of this study. To explore the evolutionary history of the pseurotin gene cluster, we aligned PsoA, PsoB, and PsoC sequences of the selected species using MAFFT (86) using the same parameters described above. The resulting alignments were trimmed with TrimAl (81) and then concatenated to form a single sequence for each species. A phylogeny was generated from this alignment using IQtree (82). Phylogenies were visualized in R (87) using ggtree (88). Availability of biological materials. All unique materials, including the Penicillium sp. strain MB_WT isolated from cheese, the Penicillium sp. strain MB_laeA deletion mutant, and the bacterial strains used in the interaction and antimicrobial assays, are readily available from the authors upon request. Data availability. Sequence data that support the findings of this study have been deposited in the NCBI SRA database with accession numbers PRJNA861320 for the whole-genome resequencing data and PRJNA861316 for the RNA-seq data. The LC-MS raw data have also been deposited to MassIVE (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp), and data are available at ftp://massive.ucsd.edu/MSV000090563/ (data set ID MSV000090563, password: cheese). Source data used to create all figures are available in the supplemental material. 10.1128/mbio.00769-23.1 DATA SET S1 Statistical tests and exact P values for each experiment. Download Data Set S1, XLSX file, 0.6 MB. https://doi.org/10.1128/AuthorWarrantyLicense.v1 This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply. 10.1128/mbio.00769-23.2 DATA SET S2 RNA-seq and AntiSMASH data. (a) Differentially expressed genes between MB_WT and MB_ΔlaeA strains. (b). KOBAS pathway enrichment analysis of downregulated and upregulated pathways in WT versus laeA-deleted Penicillium sp. strain MB. (c) Overview of AntiSMASH results for the genome of Penicillium sp. strain MB. Download Data Set S2, XLSX file, 0.2 MB. https://doi.org/10.1128/AuthorWarrantyLicense.v1 This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply. 10.1128/mbio.00769-23.3 DATA SET S3 Reciprocal best-hit BLAST results demonstrating the presence of the nine different genes in the pseurotin BGC across many fungal genomes. 1, gene is present. Download Data Set S3, XLSX file, 0.1 MB. https://doi.org/10.1128/AuthorWarrantyLicense.v1 This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply. ACKNOWLEDGMENTS This study was supported by a grant from the National Science Foundation (CAREER 1942063) to B.E.W., a Secure Ecosystem Engineering and Design project funded by the Genomic Science Program of the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research as part of the Secure Biosystems Design Science Focus Area to P.E.A. Ruby Ye provided feedback on the manuscript and experimental design. Conceptualization was carried out by J.T., B.E.W., and N.P.K. Experiments were performed by J.T., C.M.C., M.T.D., and B.E.W. The article was written by J.T., C.M.C., and B.E.W. and revised with input from all authors. The figures and statistical analyses were made by J.T., C.M.C., T.A.R., and B.E.W., except for Fig. 4 (M.T.D.); see also Fig. S5 (R.J.G.), and Fig. S6 (M.T.D.). The RNA-seq experiments were conducted and analyzed by B.E.W. The LC-MS curation of data and analysis were conducted by R.J.G., P.E.A., T.A.R., and J.T. The study was supervised by N.P.K. and B.E.W. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). We declare there are no competing interests. ==== Refs REFERENCES 1 Fleming A. 1929. On the antibacterial action of cultures of a penicillium, with special reference to their use in the isolation of B. influenzae. Br J Exp Pathol 10 :226. 2 Peplow AW, Meek IB, Wiles MC, Phillips TD, Beremand MN. 2003. Tri16 is required for esterification of position C-8 during trichothecene mycotoxin production by Fusarium sporotrichioides. Appl Environ Microbiol 69 :5935–5940. doi:10.1128/AEM.69.10.5935-5940.2003.14532047 3 Bhatnagar D, Cary JW, Ehrlich K, Yu J, Cleveland TE. 2006. Understanding the genetics of regulation of aflatoxin production and Aspergillus flavus development. Mycopathologia 162 :155–166. doi:10.1007/s11046-006-0050-9.16944283 4 Georgianna DR, Payne GA. 2009. 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PMC010xxxxxx/PMC10294682.txt
==== Front mBio mBio mbio mBio 2150-7511 American Society for Microbiology 1752 N St., N.W., Washington, DC 37070986 00084-23 10.1128/mbio.00084-23 mbio.00084-23 Research Article editors-pickEditor's PickimmunologyImmunologyImprinted Anti-Hemagglutinin and Anti-Neuraminidase Antibody Responses after Childhood Infections of A(H1N1) and A(H1N1)pdm09 Influenza Viruses Daulagala Pavithra a Mann Brian R. b Leung Kathy a c d e Lau Eric H. Y. a c d Yung Louise a Lei Ruipeng f Nizami Sarea I. N. a Wu Joseph T. a c d Chiu Susan S. g https://orcid.org/0000-0003-2818-5089 Daniels Rodney S. h https://orcid.org/0000-0002-9078-6697 Wu Nicholas C. f Wentworth David b https://orcid.org/0000-0001-8217-5995 Peiris Malik a c i malik@hku.hk https://orcid.org/0000-0003-2493-3609 Yen Hui-Ling a hyen@hku.hk a School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China b WHO Collaborating Center for Surveillance, Epidemiology and Control of Influenza, Centers for Disease Control and Prevention, Atlanta, Georgia, USA c WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China d Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China e University of Hong Kong, Shenzhen Hospital, Shenzhen, China f Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA g Department of Paediatrics and Adolescent Medicine, Queen Mary Hospital and Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China h Francis Crick Institute, Crick Worldwide Influenza Centre, WHO Collaborating Centre for Reference and Research on Influenza, London, UK i Centre for Immunology and Infection (C2I), Hong Kong Science Park, Hong Kong SAR, China Editor Subbarao Kanta NIAID, NIH The authors declare no conflict of interest. 18 4 2023 May-Jun 2023 18 4 2023 14 3 e00084-2330 1 2023 29 3 2023 https://doi.org/10.1128/AuthorWarrantyLicense.v1 This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply. ABSTRACT Immune imprinting is a driver known to shape the anti-hemagglutinin (HA) antibody landscape of individuals born within the same birth cohort. With the HA and neuraminidase (NA) proteins evolving at different rates under immune selection pressures, anti-HA and anti-NA antibody responses since childhood influenza virus infections have not been evaluated in parallel at the individual level. This is partly due to the limited knowledge of changes in NA antigenicity, as seasonal influenza vaccines have focused on generating neutralizing anti-HA antibodies against HA antigenic variants. Here, we systematically characterized the NA antigenic variants of seasonal A(H1N1) viruses from 1977 to 1991 and completed the antigenic profile of N1 NAs from 1977 to 2015. We identified that NA proteins of A/USSR/90/77, A/Singapore/06/86, and A/Texas/36/91 were antigenically distinct and mapped N386K as a key determinant of the NA antigenic change from A/USSR/90/77 to A/Singapore/06/86. With comprehensive panels of HA and NA antigenic variants of A(H1N1) and A(H1N1)pdm09 viruses, we determined hemagglutinin inhibition (HI) and neuraminidase inhibition (NI) antibodies from 130 subjects born between 1950 and 2015. Age-dependent imprinting was observed for both anti-HA and anti-NA antibodies, with the peak HI and NI titers predominantly detected from subjects at 4 to 12 years old during the year of initial virus isolation, except the age-independent anti-HA antibody response against A(H1N1)pdm09 viruses. More participants possessed antibodies that reacted to multiple antigenically distinct NA proteins than those with antibodies that reacted to multiple antigenically distinct HA proteins. Our results support the need to include NA proteins in seasonal influenza vaccine preparations. KEYWORDS influenza imprinting neuraminidase hemagglutinin antigenic drift Research Grant Council, Hong Kong T11-712/19-N Peiris Malik Yen Hui-Ling Research Grant Council, Hong Kong T11-705/14N Peiris Malik Yen Hui-Ling cover-dateMay/June 2023 ==== Body pmcINTRODUCTION Influenza viruses continue to pose significant morbidity, mortality, and socioeconomic burden worldwide as antigenic variants that evade preexisting immunity continuously emerge, causing regular epidemics and infrequent pandemics. The most administered inactivated influenza vaccines induce neutralizing antibodies targeting the receptor-binding domain of the main surface glycoprotein, hemagglutinin (HA), and this strategy is supported by the establishment of the HA inhibition (HI) antibodies as a key correlate of protection (1). Other serological correlates of protection, such as HA-stalk reactive antibodies (2, 3) and anti-neuraminidase (NA) antibodies (4, 5), have been identified more recently through observational and human challenge studies. Specifically, anti-NA antibodies have been reported to protect against infection, reduce symptoms, and/or shorten the duration of viral shedding (2–7). Individual and population immunity are continuously shaped by influenza virus antigenic drifts and shifts. During primary exposure to the influenza virus, memory B cells are developed to react to a range of conserved and nonconserved viral epitopes. These memory B cells may bias future antibody responses upon exposure to an antigenic variant in a phenomenon referred to as “immune imprinting.” Immune response to a subsequent infection or vaccination is thus influenced by the antigenic similarity of later strains to an individual’s initial exposure. Experimental and observational studies have identified immune imprinting as an important driver of the anti-HA antibody immune landscape with both beneficial and disadvantageous impacts (8–11). The protective effect of immune imprinting has been observed in mice sequentially immunized with antigenic drift variants of A(H1N1) viruses, A/Puerto Rico/8/1934 (PR8) and its antigenic variant A/Puerto Rico/8/1934-S12a (S12a), which resulted in higher-affinity antibodies to the primed PR8 HA protein. However, these mice still developed antibodies that reacted to S12a, albeit at a lower HI titer (12). Despite developing a biased antibody response toward PR8, passive transfer of PR8-S12a-immune sera protected naive mice from S12a challenge. On the other hand, epidemiological and modeling studies suggest that immune imprinting provides limited protection against antigenically more distinct variants than the imprinted strain (13, 14) due to structural similarity and conserved protective epitopes unique to either group 1 HA or group 2 HA proteins. The immune imprinting patterns of anti-NA antibodies are relatively less studied due in part to the limited knowledge on the NA antigenic changes over time. Seasonal influenza vaccines have focused on generating neutralizing anti-HA antibodies against HA drift variants since the initial licensure in 1945. An observational study reported the age-dependent immune imprinting of anti-NA antibodies using historical N1 and N2 strains that were distantly related in antigenicity (15). It is unknown if age-dependent immune imprinting can be similarly observed using well-defined drifted NA proteins as the virus continues to evolve over time. As HA and NA drift variants evolve discordantly (16–18), the age-dependent anti-HA and anti-NA antibody responses have not been systematically compared in parallel. Here, we characterized the NA antigenicity of seasonal A(H1N1) viruses from 1977 to 1991 to complete the NA antigenic profile of A(H1N1) and A(H1N1)pdm09 viruses circulating in humans. By comparing imprinting patterns of anti-HA and anti-NA antibody responses, a broader cross-reactivity of anti-NA antibody responses than anti-HA antibody responses was observed. RESULTS Antigenic changes in the NA of A(H1N1) seasonal influenza virus, 1977 to 1991. To complete N1 NA antigenic mapping since its reemergence in 1977, we evaluated the NA antigenic changes among A(H1N1) vaccine strains recommended by World Health Organization (WHO) from 1977 to 1991, including A/USSR/90/77 (USSR/77), A/Brazil/11/78 (Brazil/78), A/Chile/1/83 (Chile/83), A/Singapore/6/86 (Singapore/86), and A/Texas/36/91 (Texas/91). The use of the recommended vaccine strains allowed us to focus on the dominant A(H1N1) strains that circulated from 1977 to 1991. However, with this approach we may have missed NA antigenic changes that were not preserved in the HA antigenic strains. The NA inhibition (NI) titers of polyclonal ferret antisera raised against the A(H1N1) viruses from 1977 to 1991 were measured against recombinant H6N1 viruses with NA genes derived from the A(H1N1) vaccine strains (19) using the enzyme-linked lectin assay (ELLA) (20). The use of H6N1 viruses in ELLA was to avoid the interference of H1-binding antibodies present in ferret postinfection sera. However, the use of H6N1 viruses may not completely prevent the interference of cross-reactive antibodies that bind to the stalk of H6 protein and inhibit NA activity through stearic hindrance (15). Since the NA coding sequence of Brazil/78 was identical to that of USSR/77, it was not included in the characterization. A bidirectional change ≥4-fold in NI antibody titers between two strains in two-way NIs was considered antigenically distinct. According to the two-way analysis (Fig. 1 and Table 1), the ferret antisera raised against USSR/77 showed an NI titer of 1:320 against the homologous antigen (Fig. 1A). The ferret antisera raised against Chile/83 showed a 2-fold reduction in NI titer against NA of USSR/77, while the NI reactivity of ferret antisera against USSR/77 showed a 4-fold reduction in NI titer against Chile/83 (Fig. 1B). As such, the antigenic change of NA between Chile/83 and USSR/77 was only evident in one direction. The ferret antisera raised against Singapore/86 showed a homologous NI titer against its own NA at a dilution of 1:320 (Fig. 1C). The antisera for Singapore/86 showed a ≥16-fold and ≥4-fold reduction in NI titer against NA of USSR/77 and Chile/83, respectively. Similarly, the ferret antisera raised against older strains (USSR/77 and Chile/83) also showed a ≥4-fold reduction in NI titer against NA of Singapore/86, suggesting an NA antigenic drift from Chile/83 to Singapore/86. The ferret antisera raised against Texas/91 poorly inhibited NAs of older strains with a ≥32-fold NI titer reduction, and the ferret antisera raised against older strains showed a ≥16-fold NI titer reduction against NA of Texas/91 (Fig. 1D), showing a pronounced change in NA antigenicity from Singapore/86 to Texas/91. FIG 1 Identification of NA antigenic variants from A(H1N1) viruses circulating between 1977 and 1991. NI titers were determined using ferret antisera and recombinant H6N1 viruses carrying NA protein derived from (A) USSR/77, (B) Chile/83, (C) Singapore/86, or (D) Texas/91. (E) Antigenic cartography map was generated using the endpoint NI titers, as shown in Table 1. NI titer values of 5 were assigned for all results below the ELLA limit-of-detection (NI titer <10). Recombinant H6N1 viruses (filled circle icons) and H1N1-raised ferret antisera (open square icons) are color-coded by N1 genetic similarity to USSR/77 (green), Chile/83 (CHL/83; orange), Singapore/86 (SGP/86; purple), and Texas/91 (TX/91; blue). Grid increments between two icons indicate a 2-fold difference in modeled NI titers with two units corresponding to a 4-fold reduction, three units to an 8-fold reduction, etc. TABLE 1 Extended NI analysis with ferret antisera raised against additional A(H1N1) viruses Postinfection ferret antisera Neuraminidase inhibition titer Viruses (H6N1) A/USSR/90/77 A/Chile/01/83 A/Singapore/06/86 A/Texas/36/91 USSR/77 320 80 10 10 HK/1977 320 80 20 10 Brazil/78 320 80 20 10 Lackland/78 320 80 20 10 Ostrava/80 80 160 40 10 Dakar/81 160 320 80 20 Finland/81 80 320 40 20 Firenze/83 80 320 40 10 Chile/83 160 320 a 80 20 Victoria/84 160 160 40 10 Singapore/86 20 80 320 20 Taiwan/86 10 80 160 <10 Sichuan/88 10 40 80 10 Texas/91 10 10 <10 320 a Homologous NI titers using matched vaccine strains and ferret antisera are shown in bold. The antigenicity of N1 protein derived from USSR/77, Chile/83, Singapore/86, and Texas/91 was further validated using available ferret sera raised against other A(H1N1) viruses that circulated in 1977 to 1988 (Table 1). Antisera raised against A(H1N1) that circulated between 1977 to 1984 showed a 4-fold change in NI titers against the NA derived from USSR/77 or Chile/83 viruses but reacted poorly (≥4-fold change) against the NA of Singapore/86 (Table 1). With the available ferret antisera, our result showed that A/Victoria/4/84 (Victoria/84) was the latest strain that showed similar antigenicity with USSR/77. Antisera raised against Singapore/86 and A/Taiwan/1/86 showed comparable NI titers against different NA proteins, suggesting they share similar antigenicity. Antisera raised against A/Sichuan/4/88 showed a 4-fold change in NI titer against the NA of Singapore/86 and a 32-fold change in NI titer against the NA of Texas/91, suggesting that its NA protein may be antigenically different from these viruses. An antigenic cartography map (Fig. 1E) produced from the endpoint NI titers of A(H1N1)-raised ferret antisera supports the antigenic relatedness of USSR/77 and Chile/83 NA proteins with a one-way antigenic change. In contrast, the NA of Singapore/86 is more antigenically distant to USSR/77 and Chile/83. From this antigenic map, it can be deduced that the NAs of USSR/77, Chile/83 and Singapore/86 evolved sequentially, while the NA of Texas/91 was both antigenically distinct and distant. The N386K amino acid change has led to a one-way NA antigenic change from USSR/77 to Singapore/86. Victoria/84 shared antigenicity with USSR/77 but was antigenically distinct from Singapore/86. We hypothesized that a key amino acid change may have occurred during the evolution from Victoria/84 to Singapore/86, leading to antigenic drift. By comparing the NA coding sequences of Victoria/84 and Singapore/86, a total of 15 amino acid changes (including 9 in the NA head domain) were identified (Table 2). The effects of S247N, K369R, N386K, and K434N (N1 numbering) amino acid substitutions were further investigated (Fig. 2A and B), as they were localized at antigenic sites previously identified for N1 or N2 proteins (18, 21–23). Among these four amino acid substitutions, the K434N change may add in a potential N-linked glycosylation site. FIG 2 Mapping amino acid changes responsible for the antigenic drift from USSR/77 to Singapore/86. (A) The NA enzyme active site and key amino acid changes, S247N, K369R, N386K, and K/R434N were mapped on the N1 tetramer of A/Vietnam/1203/2004 (PDB 2HU0) using PyMOL. (B) Magnified view of N1 monomer of A/Vietnam/1203/2004 (PDB 2HU0) with the active site residues highlighted in orange and critical amino acid residues highlighted in pink. (C) Reactivity of anti-USSR/77 ferret sera against NA protein derived from USSR/77, Singapore/86, or USSR/77 with different amino acid changes. (D) Reactivity of anti-Singapore/86 ferret sera against NA protein derived from USSR/77, Singapore/86, or USSR/77 with different amino acid changes. (E) Reactivity of anti-USSR/77 ferret sera against NA protein derived from USSR/77, Singapore/86, or Singapore/86 with K386N substitution. (F) Reactivity of anti-Singapore/86 ferret sera against NA protein derived from USSR/77, Singapore/86, or Singapore/86 with K386N substitution. TABLE 2 NA amino acid changes from USSR/77 to A/Singaporea Amino acid position 16 30 34 45 70 78 83 84 95 149 222 247 254 248 285 339 369 386 434 451 454 Virus strain  USSR/77 A I V H S K M T R V Q S R D T N K N K S V  HK/77 - - - - - - - - - I - - - - - - - - - - -  Brazil/78 - - - - - - - - - - - - - - - - - - - - -  Lackland/78 - - - - - - - - - - - - - - - - - - - - -  Ostrava/80 - - - - N - V - - - R - - N - T - - - - A  Dakar/81 - - - - N - V - - - R - - N - T - - - - A  Finland/81 - - - - N - V - - - R - - N - T - - - - -  Firenze/83 - - - - N - V - - - R - - N - T - - - - -  Chile/83 T - - - N - V - - - R - - N - T - - - - A  Victoria/84 T - - - N - V - - - R - - - - T - - R - A  Singapore/86 A V I Y N Q V A S - R N K N A T R K N G A a The dash sign (-) indicates identical amino acid to A/USSR/90/77. Using site-directed mutagenesis, S247N, K369R, N386K, or K434N amino acid changes were each introduced into the NA gene of the USSR/77 and recombinant H6N1 viruses were generated to determine NI titers. Anti-USSR/77 ferret sera reacted similarly to the wild-type (WT) and mutant USSR/77 NA proteins, with NI titers ranged from 1:160 to 1:320, suggesting that the S247N, K369R, N386K, or K434N amino acid changes did not significantly affect the binding of anti-USSR/77 ferret sera (Fig. 2C). The WT and mutant USSR/77 viruses were further tested using anti-Singapore/86 ferret sera, which did not react well with the WT or mutant USSR/77 NA proteins carrying the S247N, K369R, or K434N amino acid changes with NI titers ranged from 1:20 to 1:40 (Fig. 2D). However, the Singapore/86 antisera showed comparable NI titers (1:320 to 1:640) against the homologous Singapore/86 NA protein and the USSR/77 protein containing the N368K amino acid change. These results suggest that the change at residue 386 from N (USSR/77) to K (Singapore/86) may have affected the NA antigenicity. We further validated the role of residue 386 by introducing the K386N amino acid change into the NA protein of Singapore/86. Anti-USSR/77 ferret sera reacted well with the USSR/77 NA (NI titer at 1:320) and poorly with Singapore/86 NA (1:10), but the antisera showed increased reactivity against Singapore/86 NA containing the K386N change (1:80) (Fig. 2E). Interestingly, the anti-Singapore/86 ferret sera reacted similarly to Singapore/86 NA proteins with or without the K386N change (Fig. 2F). Taken together, these results showed that the amino acid change at residue 386 was associated with one-way NI titer change. An NA phylogenetic tree was constructed using representative A(H1N1) viruses circulated from 1977 to 2008, and amino acid changes at NA residue 386 were monitored over time (Fig. 3). A(H1N1) circulating from 1977 to 1984 contained asparagine at NA residue 386, corresponding to the related NA antigenicity of USSR/77 and Chile/83 viruses during this period. The N386K amino acid substitution emerged in 1986, leading to the antigenic drift observed from USSR/77 to Singapore/86. The N386K amino acid substitution was maintained among A(H1N1) viruses isolated from 1986 to 1988. Aspartic acid replaced lysine at NA residue 386 in 1988, prior to the emergence of the antigenically distinct Texas/91. The K386D substitution was observed in most of the A(H1N1) viruses circulating from 1989 to 2008. FIG 3 Phylogenetic tree of N1 sequences from seasonal A(H1N1) circulated from 1977 to 2008. A phylogenetic tree was generated using the full coding regions of the NA proteins from representative strains from 1977 to 2008. The strains are color-coded depending on the amino acid substitution on residue 386. The viruses with amino acid substitution of serine (S), asparagine (N), lysine (K), and aspartic acid (D) at residue 386 are shown in blue, green, orange, and pink, respectively. The age-dependent immune imprinting of anti-HA and anti-NA antibodies. With complete information on the HA and NA antigenic drift variants of A(H1N1) and A(H1N1)pdm09 viruses from 1977 to the present time, we were able to compare the age-dependent anti-HA and anti-NA antibody profiles in the population. Subjects (n = 130) aged 5 to 70 years (born between 1950 and 2015) were recruited for a cross-sectional study with sera collected from April 2020 to January 2021, when there was minimal community transmission of seasonal influenza viruses due to increased public health and social measures against SARS-CoV-2 in Hong Kong (24). This unique sample set allowed us to profile the long-lasting anti-HA and anti-NA antibody responses without the interference of a recent influenza virus infection. WHO-recommended A(H1N1) (n = 7) and A(H1N1)pdm09 (n = 2) vaccine strains with distinct HA antigenicity were used to determine HI titers for all 130 participants. We observed 96.2% (125/130) of the individuals developed ≥1:10 HI titer to a single or multiple HA antigens and 3.8% (5/130) did not show a detectable HI titer (<1:10) to any of the 9 HA antigens. By generating a heatmap using individually measured HI titers, we observed that 85.4% (111/130) of the participants developed ≥1:40 HI titers to at least one of the A(H1N1) or A(H1N1)pdm09 strains (Fig. 4A). Specifically, 17.7% (23/130) developed HI titer at ≥1:40 to a single antigen, and 67.7% (88/130) individuals developed cross-reactive HI antibodies to more than one antigen with HI titer ≥1:40. FIG 4 Age-dependent HI and NI antibody titers to antigenically distinct H1 HA and N1 NA of A(H1N1) and A(H1N1)pdm09 viruses from 1977 to 2015. (A) A heatmap of 130 individuals’ HI titers against antigenically distinct A(H1N1) and A(H1N1)pdm09 viruses. The HI titers of each individual were represented in a row, sorted according to the birth year, with the youngest subjects shown on the top and the oldest subjects at the bottom. HI titers were shown in the log2 scale, with the light-yellow shading indicating a higher HI titer and the black shading indicating a negative result (HI<1:10). (B) Generalized Additive Model (GAM) was fitted to the HI titers measured against A(H1N1) and A(H1N1)pdm09 viruses. The x axis represents the age of each individual at the year of virus isolation of each A(H1N1) and A(H1N1)pdm09 viruses. The y axis represents the HI titer. The fitted lines represent mean log2 HI titers against each A(H1N1) and A(H1N1)pdm09 viruses and are color coded by the virus strain. Individuals showing HI titer <1:10 are arbitrarily assigned the titer 5. (C) A heatmap generated using 130 individuals’ NI titers against antigenically distinct A(H1N1) and A(H1N1)pdm09 viruses. (D) GAM was fitted to the NI titers measured against A(H1N1) and A(H1N1)pdm09 viruses. The x axis represents the age of each individual at the year of virus isolation of each A(H1N1) and A(H1N1)pdm09 viruses. Individuals showing HI or NI titer <1:10 are arbitrarily assigned the titer 5. To explore age-dependent imprinting against the HA protein of A(H1N1) and A(H1N1)pdm09 strains, generalized additive models (GAM) were fitted to distinct HI titers against the age of the participants at the year of strain isolation (Fig. 4B). The peak HI titers for most of the A(H1N1) strains were consistently observed in individuals aged 4 to 10 years old (~7 years old) at the year of virus isolation. Interestingly, two HI peaks against USSR/77 were observed in individuals aged −21 years old (eg. born in 1998) and 4 years old (e.g., born in 1973) at the time when USSR/77 was isolated. No apparent peak was observed from the HI titers against the two A(H1N1)pdm09 viruses, California/09 and Michigan/15, suggesting the development of HI antibodies after primary A(H1N1)pdm09 infection was independent of birth year. These results confirm that early childhood exposure might lead to an age-dependent HI response against seasonal A(H1N1) viruses, while the HI response against A(H1N1)pdm09 viruses was less age dependent. Based on the knowledge of NA antigenic changes among A(H1N1) and A(H1N1)pdm09 strains, we further determine NI titers against these strains for all participants using ELLA. All 130 participants developed ≥1:10 NI titer to a single or multiple NA antigens, and most of the participants (95.4%) developed ≥1:40 NI titer to at least one of the antigenically distinct A(H1N1) and A(H1N1)pdm09 strains (Fig. 4C). Specifically, 6.2% (8/130) developed NI titer at ≥1:40 to a single NA antigen, and 90% (117/130) individuals developed cross-reactive antibodies to more than one NA antigen with NI titer ≥1:40. The percentage of individuals showing ≥1:40 cross-reactive NI titers was significantly higher than individuals showing ≥1:40 cross-reactive HI titers (P = 0.000011, Chi-square test), suggesting that anti-NA antibodies may confer greater cross-reactivity than anti-HA antibodies. By fitting NI titers against the age of the participants at the year of strain isolation, we observed that the peak NI titers for all the A(H1N1) strains were consistently observed from participants aged 6 to 12 years old (~9 years old) at the year of virus isolation, except for Texas/91 (Fig. 4D). The peak NI titer for Texas/91 was observed among individuals –7 years old (eg. born in 1998) when the strain was isolated, suggesting the NA antigenicity has remained unchanged for an extended period, as reported previously (17). Interestingly, we observed age-dependent NI responses against A(H1N1)pdm09 viruses, with peak NI titers detected from participants age 6 to 10 years old at the time of virus isolation. This pattern was distinct from the age-independent HI response against A(H1N1)pdm09, as shown in Fig. 4B. The results suggest that the development of NI antibodies to A(H1N1) and A(H1N1)pdm09 viruses were age dependent, with the primary exposure occurring within the first decade after birth leading to a long-lasting HI and NI imprint. Overall, the serological assays have identified 125 of 130 individuals who developed detectable antibody titer (≥1:10) for both HI and NI analysis, with 50.7% (66/130) individuals showing the highest HI and NI titers to a single antigen, while 8.5% (11/130) showed both NI and HI titers for multiple antigens. Among these 77 individuals, only 42.9% (33/77) developed concordantly high HI and NI titers against the same virus strain. The correlation between the number of individuals showing the highest NI and HI titers to a single antigen and multiple antigens was calculated using a Chi-square test and found no significant association (P = 0.225), irrespective of whether the individuals showed the highest titers to single or multiple antigens. These results support the discordant antigenic changes of influenza virus HA and NA proteins (16–18). DISCUSSION We characterized the antigenic changes of N1 NAs of A(H1N1) from 1977 to 1991, which completed the NA antigenic characterization of A(H1N1) and A(H1N1)pdm09 viruses circulating in humans since the 1950s, as the A(H1N1) virus reemerged in 1977 was genetically related to A(H1N1) virus that circulated in the 1950s (25). From 1977 to 2020, changes in H1 antigenicity have been documented with a total of 13 WHO-recommended A(H1N1) (n = 10) and A(H1N1)pdm09 (n = 3) vaccine strains. During this time, only 6 N1 antigenic changes occurred in A(H1N1) (n = 4) and A(H1N1)pdm09 (n = 2) viruses based on current and previous studies (17, 18, 23), supporting the dissonant nature of antigenic drift of NA from HA (16–18, 23). By profiling the anti-HA and anti-NA antibody responses against this antigenically different A(H1N1) and A(H1N1)pdm09 viruses of 130 subjects born between 1950 and 2015, we observed age-dependent imprinting of both anti-HA and anti-NA antibody responses with the peak HI and NI antibodies generally detected from subjects 4 to 12 years old at the year of isolation of each antigenically distinct A(H1N1) strains. Interestingly, the HI response against the A(H1N1)pdm09 viruses was age independent without apparent peak HI titer detected in any age group; however, the NI response against the A(H1N1)pdm09 was age dependent with the peak NI titers detected from those 6 to 10 years old at the year of virus isolation. We also observed that 90% of the participants possessed ≥1:40 NI titers against more than one antigenically different NA protein, while only 67.7% of the participants possessed ≥1:40 HI titers against more than one antigenically different HA protein, suggesting that the antibodies generated to target the NA were more cross-reactive than those generated to target the HA receptor binding domain. Anti-NA antibodies have been established as a correlate of protection against influenza virus infection (4–7). As HA antigenic changes occur more frequently, anti-NA antibodies are expected to provide prolonged protection against HA drift variants if these strains continue to share NA antigenicity. Furthermore, the more cross-reactive nature of the anti-NA antibodies may provide partial protection against NA drift variants. Our results support the inclusion of NA antigen as a component of annual influenza vaccine preparation. We identified that the NA of USSR/77, Singapore/86, and Texas/91 were antigenically distinct by two-way analysis, while there was a one-way antigenic change between the NA of USSR/77 and Chile/83. Kilbourne et al. (16) previously reported antigenic stasis of NA in A(H1N1) vaccine strains between 1980 and 1983, but a one-way antigenic change was observed from the study. Similarly, one-way antigenic change was observed while characterizing the N to K change at residue 386 from USSR/77 to Singapore/86, as changes in NI titers against USSR-N386K or Singapore-K386N were only observed with the use of heterologous ferret antisera. Previous studies by Sandbulte et al. and Gao et al. also identified that the antigenic drifts of N1 NAs were evident in one direction (17, 23). This raises the possibility that a one-way drift might not result in a complete loss of protection, as the homologous antisera were able to tolerate a single amino acid change associated with antigenic drift. This also coincides with the observation that most of our study subjects possess ≥1:40 NI titers against more than one antigenically different NA protein. The broader breadth of NI responses can be supported by the conserved epitopes (N273 and N309) mapped by Chen et al. (26) using human MAbs that cross-react with A(H1N1) and A(H1N1)pdm09 viruses from 1918 to 2017, although escape mutants have been selected with these cross-reactive MAbs in vitro (27). Wan et al. (28) recognized mouse Mab-binding epitopes (group B MAbs identifying residues 273, 338, and 339) that were conserved across NAs of A(H1N1) and A(H1N1)pdm09 viruses from 1918 to 2009. The amino acid substitution N386K was identified to be responsible for the one-way antigenic drift from USSR/77 to Singapore/86. While N386 was preserved among A(H1N1) strains circulated from 1977 to 1984, A(H1N1) strains circulated from 1986 to 1988 harbored K386 and formed a separate cluster in the N1 phylogenetic tree. The subsequent K386D change found in A(H1N1) strains circulated from 1988 to 2008 may similarly result in NA antigenic drift from Singapore/86 to Texas/91. Interestingly, the N386K change was also reported to cause a one-way NA antigenic change in the A(H1N1)pdm09 viruses. The N386K change was reported to result in the loss of a potential glycosylation site and the NA antigenic drift between California/09 and Michigan/15 (18, 23). Pair-wise epistatic amino acid substitutions leading to changes in local net charges at the NA antigenic site have been implicated in maintaining viral fitness during evolution (29), and further research is needed to identify the role of epistatic changes in the proximity of residue 386. Taken together, these results showed N386K evolved independently from A(H1N1) and A(H1N1)pdm009 viruses and suggested convergent evolution in N1 NA of human influenza viruses. Age-dependent imprinting of anti-HA and anti-NA antibody responses was observed from 130 subjects born between 1950 and 2015, with peak HI and NI antibodies generally detected from subjects at 4 to 12 years old when each of the antigenically distinct A(H1N1) strains was isolated. This finding is in accordance with previous studies showing age-dependent HI responses. Lessler et al. (30) estimated that virus neutralization titers measured against a panel of A(H3N2) viruses were the highest in subjects aged ~7 years at the time of strain isolation and declined smoothly thereafter across all strains. A similar study by Yang et al. (31) estimated the highest HI titers against a panel of A(H3N2) viruses at a median age of 4.3 years (IQR, 2.0 to 6.9 years) during strain isolation. Overall, the results suggest that primary exposure to seasonal influenza viruses occurred within the first decade of life, which agrees with the higher infection attack rates of seasonal influenza viruses in children than in adults (32, 33). Interestingly, the HI response against the A(H1N1)pdm09 viruses did not show an apparent age-dependent pattern. A higher infection attack rate of A(H1N1)pdm09 virus was similarly reported in children than in adults during the first year of the pandemic (34, 35). However, an increasing proportion of adults were infected by the A(H1N1)pdm09 virus in subsequent years (36), which may have weakened the age-dependent HI response against the A(H1N1)pdm09 virus. In comparison, the NI responses against the A(H1N1)pdm09 viruses were age dependent, with peak NI titers detected from those at 6 to 12 years old at the time of virus isolation. It is unclear if anti-NA antibodies raised against A(H1N1) viruses conferred cross-protection against A(H1N1)pdm09 virus in adults, while the age-dependent anti-NA antibodies against A(H1N1)pdm09 were developed among those who experience A(H1N1)pdm09 as the first influenza virus infection in life. Our study is limited by the vaccination history and recent infection history of the study subjects. However, sera were collected during 2020 to 2021 when minimal influenza virus activity in the community may have reduced the interference of recent influenza virus infection on HI and NI responses. HI and NI antibody responses continue to be shaped by repeated infection and vaccination with antigenically similar or distinct strains during our lifetime. Our study confirmed the age-dependent HI and NI responses and identified the more broadly cross-reactive nature of anti-NA antibodies than the anti-HA antibodies. Future longitudinal studies where individuals are followed up since birth could provide better insights into protection conferred by the anti-HA and anti-NA antibodies. Our study supports the inclusion of NA protein into annual influenza vaccine preparation and emphasizes the importance of routinely monitoring of NA antigenic drifts in parallel with HA evolution. MATERIALS AND METHODS Cell culture. Madin-Darby canine kidney (MDCK) cells and human embryonic kidney (HEK) 293T cells purchased from American type cell culture (ATCC) were grown in minimum essential media (MEM) and Opti-MEM, respectively (Gibco). The MEM used for cell culture was supplemented with 10% fetal bovine serum (Gibco), penicillin (100 U/mL) and streptomycin (100 μg/mL) (Gibco), vitamins (Sigma-Aldrich), and N-2-hydroxyethylpiperazine-N′-2-ethanesulfonic acid (HEPES, Gibco). The infection media (MEM, Gibco) used for viral infection was supplemented with 0.3% bovine serum albumin (Sigma-Aldrich), penicillin (100 U/mL) and streptomycin (100 μg/mL) (Gibco), vitamins (Sigma-Aldrich) and HEPES (Gibco). Viruses. Recombinant H6N1 viruses were generated for serological analysis using plasmid-based reverse genetics, as previously described (37). The NA genes of A(H1N1) and A(H1N1)pdm09 vaccine strains for the period 1977 to 2015 (A/USSR/90/77, A/Chile/01/83, A/Singapore/06/86 and A/Texas/36/91, A/Brisbane/59/07, A/California/04/09-pdm09, and A/Michigan/45/15-pdm09) were RT-PCR amplified and cloned into the pHW2000 vector. The HA gene of A/Teal/Hong Kong/W312/97 (H6N1) has been cloned into pHW2000, as described previously (38). The HA and NA plasmids were cotransfected with pHW181, pHW182, pHW183, pHW185, pHW187, and pHW188 derived from A/Puerto Rico/8/34 (PR8) in 293T cells using TransIT (Mirus) and Opti-MEM (Gibco) to generate recombinant H6N1 viruses. Transfection supernatants were harvested and passaged in MDCK cells at a multiplicity of infection (MOI) of 0.001 PFU/cell to prepare stock viruses. The HA and NA genes of all stock viruses were sequence verified using sanger sequencing prior to use. The egg-passaged A(H1N1) A(H1N1)pdm09 viruses, A/USSR/90/77, A/Singapore/06/86, A/Texas/36/91, A/Bayer/272/95, A/Beijing/07/95, A/New Caledonia/20/99, A/Brisbane/59/07, A/California/04/09-pdm09, and A/Michigan/45/15-pdm09, used in the hemagglutinin inhibition (HI) assay were kindly supplied by the center for disease control and prevention (CDC), Atlanta, Georgia and Francis Crick Institute, Midland, London. The viruses were propagated in 9- to 11-day-old specific pathogen-free (SPF) embryonated chicken eggs. The allantoic cavity of each egg was injected with 100 μL of virus diluted in phosphate-buffered saline (PBS), supplemented with penicillin-streptomycin (Gibco) and gentamicin (Gibco). The site of virus injection was sealed, and the eggs were incubated at 37°C for 48 h. The eggs were then transferred to 4°C to be kept overnight before harvesting the virus. Ferret antisera. Ferret antisera raised against wild-type A(H1N1) viruses (A/USSR/90/77, A/Hong Kong/117/77, A/Brazil/11/78, A/Lackland/3/78, A/Ostrava/12/80, A/Dakar/4363/81, A/Finland/10/81, A/Firenze/13/83, A/Chile/01/83, A/Victoria/4/84, A/Singapore/06/86, A/Taiwan/1/86, A/Sichuan/4/88, and A/Texas/36/91) were generously provided by Francis Crick Institute (Midland, London) and by CDC (Atlanta, GA). Study group and serum samples. Human serum for the cross-sectional study was collected from blood donors at 18 to 70 years of age (n = 110) from April to August 2020 (IRB number UW-132). The pediatric samples were collected for a study on SARS-CoV-2 infection in patients aged 5 to 17 years (n = 20) from May 2020 to February 2021 (UW 21–093) during the symptom onset of day 0 to 7 months as a part of a previous study. A total of 130 individuals aged 5 to 70 was included in the serology study. ELLA to measure the neuraminidase-inhibiting antibody titers. NI antibody titers were measured as previously described (20). First, the dilution of the virus that resulted in a 90 to 95% maximum signal was elected for use in serology. The sera were heat treated (56°C for 45 min), 2-fold serially diluted in phosphate-buffered saline–bovine serum albumin (PBS–BSA) and added to duplicate wells on a fetuin (Sigma-Aldrich) coated plate. An equal volume of the selected virus dilution was added to all serum-containing wells in addition to wells containing diluent without serum that served as a virus-only control. The plates were incubated for 16 to 18 h at 37°C, then washed with PBS–0.05% Tween 20 (PBS-T) before adding 100 μL/well peanut agglutinin conjugated to horseradish peroxidase (PNA-HRPO, Sigma-Aldrich). Plates were incubated at room temperature for 2 h and washed with PBS-T before adding O-phenylenediamine dihydrochloride (OPD, Sigma-Aldrich) to the plate. Then, 100 μL of the OPD substrate was added to each well on all plates and incubated in the dark, and the color reaction was stopped by adding 100 μL/well of 1 N sulfuric acid. The plates were read at 490 nm for 0.1 s using a microplate fluorimeter (FLUOstar OPTIMA F, BMG LABTECH). HI assay to measure the anti-HA antibody titers. Human sera were treated with receptor-destroying enzyme (RDE) II (Accurate, no. YCC340-122) overnight and were heat inactivated for 30 min at 56°C. The heat-inactivated sera were serially 2-fold diluted and incubated with A(H1N1) and A(H1N1)pdm09 viruses diluted to 8 HA/50 μL for 30 min at room temperature. Next, 0.5% turkey RBCs were added to the mixture and incubated for 30 min. The highest sera dilution that inhibited hemagglutination was recorded as the HI titer. Antigenic cartography. Antigenic cartography of endpoint ELLA two-way NI titers (Table 1) generated using recombinant influenza H6N1 viruses and polyclonal A(H1N1)-raised ferret antisera were optimized over 1,000 iterations into a two-dimensional (2D) antigenic landscape with the ACMACS-API software suite (version acmacs-c2-20161026-0717 and i19 build host) (39). NI titer values of 5 were assigned for all results below the ELLA limit-of-detection (NI titer <10). Modeled NI trends were rendered into 2D X/Y-mapping coordinates and annotated in tableau desktop (version 2022.3.0). All applied code sets are available from the authors upon request. Genetic analysis. The phylogenetic tree was constructed using the full coding region of the NA gene of 89 selected human A(H1N1) strains from 1977 to 2008 obtained from global initiative on sharing all influenza data (GISAID). NA nucleotide sequences were aligned, and the maximum-likelihood tree (bootstrap 500 replicates) was constructed using molecular evolutionary genetics analysis (MEGA, version 11.0.10). The constructed tree was visualized with the Geneious Prime 2023.0.1. Site-directed mutagenesis. Single amino acid changes, S247N, K369R, N386K, and R434N were introduced into the plasmid-encoding USSR/77-NA gene, and the amino acid change K386N was introduced into the plasmid-encoding Singapore/86-NA gene using the QuikChange II site-directed mutagenesis kit (Agilent Technologies). Statistical analysis. RStudio (version 1.3.1093) was used to generate generalized additive model (GAM) fitted to log2 HI and NI titer against the age of the individuals at the time of virus isolation using the mgcv package (version 1.8-33). ACKNOWLEDGMENTS This study was supported by RGC Theme-based Research Schemes, Hong Kong SAR, China (T11-705/14N and T11-712/19-N). We thank Ranawaka APM Perera from HKU for helpful discussions, Samuel S. Shepard from Centers for Disease Control and Prevention, and Reina Chau from General Dynamics Information Technologies, Inc. for their contributions in operationalizing ACMACS for portable and high-throughput usage. ==== Refs REFERENCES 1 Hobson D, Curry RL, Beare AS, Ward-Gardner A. 1972. The role of serum haemagglutination-inhibiting antibody in protection against challenge infection with influenza A2 and B viruses. 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==== Front ASAIO J ASAIO J MAT Asaio Journal 1058-2916 1538-943X Lippincott Williams & Wilkins Hagerstown, MD 37018815 00022 10.1097/MAT.0000000000001910 3 How to Do It Article Proof of Concept: Trans-atrial AngioVac Aspiration of Mitral Valve Thrombosis in a COVID-19 Patient Gerosa Gino * https://orcid.org/0000-0003-4472-6287 Ponzoni Matteo * Evangelista Giuseppe * Tessari Chiara * Tiberio Ivo † Molè Angelo * Zanella Fabio * Pittarello Demetrio † https://orcid.org/0000-0003-2360-2031 Tarzia Vincenzo * From the * Cardiac Surgery Unit, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padua, Padua, Italy † Anaesthesia and Intensive Care Unit, Department of Medicine, University of Padua, Padua, Italy Correspondence: Vincenzo Tarzia, Cardiac Surgery Unit, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health.University of Padua, Via Giustiniani 2, 35128 Padua, Italy. Email: v.tarzia@gmail.com; Twitter: @PonzoniMatteo. 04 4 2023 7 2023 04 4 2023 69 7 e342e345 8 2022 12 2022 Copyright © ASAIO 2023 2023 This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. In patients with a prohibitive surgical risk, the AngioVac cannula can be used to remove left-sided cardiac masses, as an off-label adaptation of the device. We herein describe a novel micro-invasive approach to gain access to the left atrium for the aspiration of a mitral valve mass in a patient affected by severe coronavirus disease 2019. Through a right anterior mini-thoracotomy, the right superior pulmonary vein was accessed and used to insert the aspiration cannula. A parallel venous-arterial extracorporeal membrane oxygenation (ECMO)-like circuit provided circulatory and respiratory support to ensure proper intra- and postoperative hemodynamic stabilization. AngioVac left-sided mitral valve COVID-19 STATUSONLINE-ONLY ==== Body pmcThe AngioVac system (AngioDynamics, Latham, NY) is a recent technology that has shown good efficacy and safety profiles for the treatment of venous and right-sided cardiac masses of different origins.1,2 Its versatility has enabled the off-label aspiration of left-sided masses in patients with a prohibitive surgical risk,3 in whom a conventional embolectomy may not be tolerated. It has been recognized that coronavirus disease 2019 (COVID-19) carries an abnormal activation of coagulation and fibrinolytic patterns, which can finally lead to an increased risk of thromboembolism in various sites.4,5 Despite adequate prophylaxis, venous thromboembolism can affect up to 47% of intensive care unit patients, with a significant impact on mortality.6 In this setting, a surgical embolectomy might not be offered to every patient, because of the high-risk localizations of the masses or the severe hemodynamic compromise of the patient. We herein describe the off-label use of the AngioVac system to treat a thrombus on the mitral valve in a patient affected by severe COVID-19. A novel approach through the right superior pulmonary vein via a right anterior mini-thoracotomy is illustrated. Technique A 68-year-old woman was admitted to the emergency department because of sudden onset of dysarthria, right-hand paralysis, and fever. Nasal swab testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was positive, whereas blood cultures were negative. A cerebral CT scan confirmed multiple minor ischemic strokes. Antiplatelet therapy with aspirin and antithrombotic prophylaxis with low-molecular weight heparin were started. Seriate transesophageal echocardiography revealed a floating mass of 20 × 15 mm on the atrial side of the mitral valve (Figure 1), not responding to prolonged anticoagulation therapy (total of 4 weeks of treatment). Biventricular function was preserved. The patient’s clinical conditions worsened rapidly, requiring invasive mechanical ventilation (P/F ratio 200–225) and inotropic infusions (dopamine and noradrenaline) caused by septic shock. Thoracic CT-scan confirmed diffuse ground-glass opacities, especially in the inferior lobes, consistent with severe COVID-19. Figure 1. Transesophageal echocardiography of the mass. Conventional surgery was considered prohibitive (EuroScore II 23.9%; society of thoracic surgeons (STS) score for morbidity or mortality 41.7%) because of the high risk of cerebral bleeding after full heparinization for cardiopulmonary bypass, hemodynamic instability, and respiratory compromise caused by COVID-19. We decided to aspirate the mass with the AngioVac system via a superior right pulmonary vein access through a right anterior mini-thoracotomy to prevent further mass embolisms, using a micro-invasive procedure while the patient was recovering from COVID-19. We opted for this access site to control potential bleedings during cannulation safely and to avoid complications related to trans-septal puncture. In addition, the right superior pulmonary vein allows closer access to the left atrium to ease the alignment of the cannula with the mitral valve and facilitate its manipulation during suction maneuvers. Moreover, a venous-arterial extracorporeal membrane oxygenation (ECMO)-like configuration of the extracorporeal circuit was planned, to ensure proper hemodynamic support during the procedure and provide further support in the postoperative period, if needed. The patient’s informed consent was obtained and the ethics committee approved the study (0063989; November 10, 2021). After surgical exposition, the right femoral vein was cannulated directly using a 21 Fr. (Maquet) venous cannula, while the femoral artery using a 23 Fr. (Maquet) arterial cannula (to adequately manage the increased blood return from the venous cannula + the AngioVac aspiration cannula) with the interposition of an 8 mm vascular graft. The ECMO support was started after partial heparinization with 5000 IU of unfractionated heparin (target activated clotting time: 180 seconds, as recommended for the venous-arterial ECMO circuit [Rotaflow, Maquet]), to reduce the risk of cerebral bleeding. A right mini-thoracotomy in the second intercostal space with dislocation of the third rib (to ensure a safer visualization of the right pulmonary veins, preserving the minimal invasiveness of the access) was performed. The right lung was excluded from ventilation using a double-lumen endotracheal tube. The pericardium was opened above the right anterior surface of the ascending aorta (to avoid phrenic nerve injury) and three retracting sutures were placed on the left side of the incised pericardium and four on the right side. This approach allowed adequate exposure of the right superior pulmonary vein, where two concentring purse-string 4-0 polypropylene pledgeted sutures were placed. A 26F GORE DrySeal (Gore & Associates, Newark, DE) was inserted and the 22F third-generation 20°-angled-tip AngioVac cannula was advanced through the sheath into the left atrium under transesophageal three-dimensional echocardiographic guidance (Figure 2). The AngioVac inflow line was connected to the ECMO venous line through a Y connector before the oxygenator. Two different centrifugal pumps were used to control the blood flows in the AngioVac and ECMO circuits separately (Bi-Pump configuration, Figure 3). The mass was completely aspirated without complications or significant valvular dysfunction (Video 1). The AngioVac cannula and the sheath were removed, while the venous-arterial femoral ECMO circuit was left in situ as planned to ensure postoperative cardio-respiratory support. The pathological analysis demonstrated the collected mass to be a thrombus. The patient’s clinical conditions improved gradually and she was successfully weaned from ECMO seven days later. Postoperative antithrombotic prophylaxis with low-molecular weight heparin was maintained during the whole postoperative hospital stay. Figure 2. Graphic representation of the surgical access to the left atrium using the right superior pulmonary vein. Figure 3. Representation of the Bi-Pump configuration. A, AngioVac cannula. B, Femoral venous line. C, Femoral arterial line. Video 1 1_ywn370it Kaltura Comment The AngioVac system has extended surgical indications to populations with a prohibitive risk for traditional surgery, being a versatile tool that can be adapted to the specific features of the disease and the patient’s clinical status.1–3 Our case describes a further application of the AngioVac technology to gain access to the left heart through the right superior pulmonary vein. A standard double purse-string suture allows for sheath stabilization and control of bleeding. Although working on left-sided structures on beating heart, accurate movements and synchronous visualization of the cannula through three-dimensional echocardiography are paramount to minimize the risk of mass embolization. Careful planning of the procedure permitted us to design a venous-arterial ECMO-like circuit that provided full intraoperative hemodynamic support. Including two centrifugal pumps (Bi-Pump configuration) ensured constant organ perfusion even during the suction maneuvers. Placing the oxygenator after the Y connection between the venous femoral line and the AngioVac inflow line guaranteed optimal blood oxygenation during and after the procedure, while pulmonary function recovered from COVID-19. Moreover, the oxygenator acts as an additional filter just before the reinfusion cannula, to further reduce the risk of mass debrides into the systemic line of the ECMO circuit. The current case provides some insights into how the current limitations of the AngioVac system could be alternatively managed to face life-threatening conditions in high-risk patients. First, the impossibility to use standard venous accesses (because of the inadequacy of peripheral vessels or the unusual location of the target mass) could be bypassed by performing direct cannulation of cardiac structures of interest (as in our case) or adopting alternative arterial peripheral accesses.4 Upgrading the extracorporeal circuit from a venous-venous bypass (as in the standard fashion) to a venous-arterial ECMO-like configuration allows a safe aspiration procedure even in case of hemodynamic compromise of the patient by providing concomitant circulatory support. At the same time, the ECMO circuit requires a lower range of anticoagulation. The possibility of mass fragmentation and subsequent embolization is an intrinsic risk of the AngioVac procedure itself7 and related concerns are magnified during off-label adoptions of the device for left-sided cardiac diseases. Ray et al. described the successful use of the Sentinel Cerebral Protection System for stroke prevention during the AngioVac aspiration of a mass in the right atrium in a patient with patent foramen ovale.8 In our case, we preferred to design an extracorporeal circuit with two independent centrifugal pumps which permitted the complete unloading of the heart. With this configuration, the heart is not ejecting while aspirating with the AngioVac cannula in the left atrium, thus reducing the risk of systemic embolisms during the procedure. The treatment of left-sided cardiac or intra-aortic masses represents the frontiers of the AngioVac system. Moreover, a venous-arterial ECMO-like configuration of the extracorporeal circuit might expand the current indications of the device, including hemodynamically unstable patients who may not tolerate a standard venous-venous bypass. Although technically feasible,1,4,5 larger studies are still needed to prove the efficacy and safety of the AngioVac system in these settings. The versatility of the AngioVac technology and the evolution of the AngioVac technique emblematize the surgical efforts to pursue a patient-tailored strategy in the most prohibitive clinical settings. Solid expertise and experience represent the bases for a safe medical innovation.9 Acknowledgments We thank Maddalena Braghetto for the graphical support. Disclosure: The authors have no conflicts of interest to report. ==== Refs References 1. Gerosa G Bagozzi L Tessari C : Proof of concept: Microinvasive AngioVac approach in renal cell carcinoma with atrial thrombosis. Ann Thorac Surg 112 : e193–e196, 2021.33676906 2. Tarzia V Tessari C Bagozzi L : Totally peripheral approach for ICD lead vegetation removal in a GUCH patient. J Cardiovasc Electrophysiol 32 : 1778–1781, 2021.33825266 3. Gerosa G Longinotti L Bagozzi L : Transapical aspiration of a mitral mass with the AngioVac system on a beating heart. Ann Thorac Surg 110 : e445–e447, 2020.32504600 4. Dao L Lund A Schibler CD Yoshioka CA Barsky M : A case of COVID-19-associated free-floating aortic thrombus successfully treated with thrombectomy. Am J Case Rep 22 : e933225, 2021.34822708 5. Gozgec E Ogul H Alay H : Left ventricular thrombus in a patient infected by COVID-19. Ann Thorac Surg 111 : e67, 2021.32735791 6. Middeldorp S Coppens M van Haaps TF : Incidence of venous thromboembolism in hospitalized patients with COVID-19. J Thromb Haemost 18 : 1995–2002, 2020.32369666 7. Moriarty JM Rueda V Liao M : Endovascular removal of thrombus and right heart masses using the AngioVac system: Results of 234 patients from the prospective, multicenter Registry of AngioVac Procedures in Detail (RAPID). J Vasc Interv Radiol 32 : 549–557.e3, 2021.33526346 8. Ray HM Al Rstum Z Saqib NU : Successful cerebral protection during removal of large right atrial thrombus with AngioVac in a patient with patent foramen ovale and recent embolic stroke. J Vasc Surg Cases Innov Tech 5 : 201–204, 2019.31289763 9. Tarzia V Gerosa G : The rules of medical innovation: Experience, creativity, and courage: reply. Ann Thorac Surg 112 : 2113–2114, 2021.
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==== Front Nurse Educ Nurse Educ NEDU Nurse Educator 0363-3624 1538-9855 Wolters Kluwer Health, Inc. 36877986 10.1097/NNE.0000000000001386 nedup4804p204 3 Articles Impressions of the Nursing Profession Among Nursing Students and New Graduates During the First Wave of COVID-19 A Qualitative Content Analysis Bongiorno Anne Watson PhD, APRN-BC, CNE bongioaw@plattsburgh.edu Armstrong Normadeane PhD, RN, ANP-BC narmstrong@molloy.edu Moore Geraldine A. EdD, RN-BC, AE-C gmoore@molloy.edu Mannino Jennifer Emilie PhD, RN, CNE jmannino@molloy.edu Watters Pamela PhD, MSS, MSCS pwatters@molloy.edu Cotter Elizabeth PhD, RN-BC ecotter@molloy.edu Kelley Randy DNP, RN, CCRN RKell004@Plattsburgh.edu Professor (Dr Bongiorno) and Assistant Professor (Dr Kelley), Department of Nursing, SUNY Plattsburgh, Plattsburgh, New York; Professor (Drs Armstrong, Moore, and Cotter) and Professor and Director of the PhD in Nursing Program (Dr Mannino), The Barbara H. Hagan School of Nursing and Health Sciences, Molloy University, Rockville Centre, New York; and Statistical Support Consultant (Dr Watters), Office of Graduate Academic Affairs, Molloy University, Rockville Centre, New York. Correspondence: Dr Armstrong, The Barbara H. Hagan School of Nursing and Health Sciences, Molloy University, 1000 Hempstead Ave, Rockville Centre, NY 11570 (narmstrong@molloy.edu). 7 2023 03 3 2023 03 3 2023 48 4 204208 16 2 2023 © 2023 Wolters Kluwer Health, Inc. All rights reserved. 2023 This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Background: The COVID-19 pandemic has been a defining event for the next generation of the nursing workforce. Complex pandemic practice environments have raised concerns for the preparation and support of novice nurses, even as a multitude of nurses leave the profession. Purpose: Researchers sought to examine nursing students' and new graduate nurses' impressions of the nursing profession in contrasting regions of New York State during the first wave of the COVID-19 pandemic. Methods: Inductive content analysis was performed on narrative text responses (n = 295) drawn from a larger multisite mixed-methods survey. Results: Five subconcepts were abstracted, leading to the main concept of shocked moral distress. Conclusion: Nursing students and new graduate nurses have experienced high levels of moral distress but remain committed to the profession. Building moral resilience, fostering ethical decision making, and implementing protective policies can reduce the incidence of moral distress. ethics moral distress nursing education pandemic ==== Body pmcThe COVID-19 pandemic has been a defining event for the next generation of the nursing workforce. Complex pandemic practice environments have raised concerns for the preparation and support of novice nurses. There is growing concern in the health care field about the moral distress experienced by nursing students and new graduates during the COVID-19 pandemic. Many students and new graduates have witnessed horrific suffering among family, friends, and coworkers. Nurses cared for patients with a highly virulent and contagious, novel disease in environments where resources were suboptimal.1 The nurse's duty to care posed great personal risk, with little experience of the realities of pandemic care practices such as changing isolation protocols, lack of proper personal protective equipment (PPE), and fear of contaminating themselves and/or loved ones.2 The duty to care is deeply ingrained in the nursing curriculum, but these added stressors were unprecedented in modern history. Moral distress results when nurses are unable to do what is considered morally right. This inability to adhere to the code of ethics and values inherent to the nursing profession leads to poor patient outcomes.3–9 Background The volume and level of patient acuity during the pandemic created extraordinary distress for nurses.10 Little was known at the start of this pandemic about how novice nurses handled these pressures. The literature remains sparse regarding the experiences of nursing students and new graduates in this pandemic. Two studies focused on strategies to develop moral resilience.11,12 These studies reported the lived experience of senior-level nursing students in Spain who experienced an abbreviated curriculum allowing them to graduate early and augment the nursing workforce. Participants reported concerns pertaining to safety, uncertainty, feelings of being overwhelmed, and fear but with a commitment to the profession.4,5 Goni-Fuste et al12 conducted a systematic review of the feelings and proficiencies of nursing students during pandemics and focused on their lack of preparedness, along with the need for improving safety guidelines. The study showed commonalities of nurses' insufficient knowledge on equipment supply and infection-control guidelines. Respondents were also fearful about the lack of PPE and the chance of contracting COVID-19, but the duty to care outweighed these concerns. Recommendations were for nursing programs to adopt robust curricula in pandemic preparedness to address the moral distress seen among students and new graduate nurses.12 Several related studies focused on the experiences of practicing nurses, specifically examining the ethical dimensions concerning pandemic care and experiences of critical care nurses.3,13 Sperling14 described how nurses responded to the perceived risk of contracting COVID-19 and their response to ethical dilemmas, where 74% of the sample believed that their duty to care meant that they must nurse these patients, despite facing grave personal risk. Hossain and Clatty15 examined how pivoting quickly from an ethical model of patient-centered care to a public health ethical approach created moral injury among nurses. For example, communication with patients' families ranged from difficult to near impossible in COVID wards. Nurses lacked disaster-response training to address rapidly changing protocols and unrelenting death rates in critically ill patients.16 These outcomes are expected in field-disaster nursing but almost unheard of in most acute care environments.17 One qualitative study of Chinese nurses analyzed the lived experience of coping with ethical dilemmas and personal risks while nursing patients with COVID-19; the research revealed moral distress and devotion to the duty to care as critical concerns.18 Critical care nurses related how the challenges of pandemic nursing created high levels of fear, anxiety, depression, and/or symptoms of posttraumatic stress disorder as well as spiritual and moral distress.13 Purpose The purpose of this study was to examine nursing students' and new graduate nurses' impressions of the nursing profession in contrasting regions of New York State during the first wave of the COVID-19 pandemic. Methods Data for this content analysis were drawn from a larger multisite mixed-methods study19 using a secure online survey platform sponsored by the university and stored in a password-protected file. Current bachelor of science in nursing (BSN) students and new graduates (within 1 year) of 3 programs in contrasting regions of New York State were emailed a survey link. The inductive content analysis extracted data from those participants who responded to the single narrative free-text response (n = 295). All reported being employed by and/or volunteering in the health care service industry during the first wave of the COVID-19 pandemic. In sum, 120 participants were in rural regions where COVID-19 cases were sparse at the time, whereas 175 participants in the US epicenter of the pandemic (the greater metropolitan area of New York City) experienced the pandemic more acutely (Table). This research study was approved by the institutional review boards of all participating colleges/universities. Table. Participants (n = 295)a Participants n Rural regions of New York State New graduate nurses 34 Upper-division students 46 Lower-division students 40 Total rural region 120 Greater metropolitan area of New York City New graduate nurses 64 Upper-division students 79 Lower-division students 32 Total greater metropolitan area of New York City 175 aAll students and graduates were from 4-year BSN programs. New graduate nurses are defined as having graduated their nursing program within 1 year; upper-division students are in the final 2 years of a 4-year program; and lower-division students are in the first 2 years of a 4-year program. Inductive content analysis, according to Kyngäs et al20 and Krippendorff,21 was performed on the narrative free-text responses of the single open-ended question: “How has the COVID-19 pandemic impacted your image of nursing?” The pathway of inductive content analysis began with an independent review of raw data by all researchers with the creation of open codes. Consensus was achieved when abstracting data into subconcepts, with further analysis into concepts, yielding a single main concept of shocked moral distress. Trustworthiness was maintained through independent coding, robust discussions, and consistency of the data analysis within the research team. Findings The open coding of data shared rich meanings. Five subconcepts were abstracted—image of nursing, commitment to the profession, fear, lack of organizational support, and muted moral distress—eliciting 1 main concept of shocked moral distress. Subconcepts The image of nursing was abstracted as a positive subconcept with characterizations of caring principles, heroism, and idealism: “My image of nursing has grown significantly because of the COVID-19 pandemic. This shows how resilient and strong nurses are.” Positive image was seen more frequently among respondents less exposed to the clinical realities of the pandemic: “It has made me idolize nurses more and want to fulfill my dream of being a nurse.” The subconcept of commitment to the profession was shared from multiple perspectives: “I saw how tough individual nurses are but how broken the health care system is. I want to be a nurse, but I don't want to accept the current health care system.” The subconcept of fear was strongly represented in data abstraction in the New York City sample: “I have seen the negative impacts that this field can have on all of the other aspects of a person's life ... the risk of exposing loved ones to illnesses such as COVID-19 has been on my mind a lot lately and the option of whether to pursue a career in bedside nursing or a ‘safer’ area has, too.” The subconcept of lack of organizational support was derived from sentiments conveying a sense of virtual abandonment by the health care system: “Nurses seemed disposable, mistreated and underfunded.” Multiple data points indicated how the lack of PPE contributed to safety concerns and rapidly deteriorating work conditions: “It shows how much the health care system needs to be reformed. When front-line workers lack resources and nurses are dying as a result, it's a problem”; “Not having enough PPE supplies for the first couple of weeks and trying to find supplies outside of the hospital on my own was really eye opening”; and “Nurses were placed in dangerous and difficult situations based on the fact limited PPE was provided.” Evidence of the subconcept muted moral distress was identified through statements of conflicting emotions, exhaustion, safety concerns, and fears. One nurse noted, “I have crippling anxiety and am completely terrified to begin working .... Nurses are not disposable ... they are nurses, not martyrs.” Others suggested, “It hurts to see others crying” and “Working on the frontline ... showed me how easy it is to get sick and so little is done for the nurse in return.” Main Concept: Shocked Moral Distress The main concept in this study, Shocked Moral Distress, is described as the explicit cognitive rejection of nursing conditions that were experienced by nurses, which became internalized and viewed as morally reprehensible. One participant said, “COVID-19 has made me realize that no one truly cares about nurses .... We are forced to reuse our PPE. We are short staffed, and we have to suffer the consequences because of it.” Another participant said: As a nursing student and before COVID-19 pandemic, we had more than enough protective gear to feel safe at doing our jobs. Soon after the pandemic, it became [sic] to my realization that I was being exposed and more vulnerable to the virus because of the lack of protective equipment. It felt as though my health was not as valuable as it should be. A participant explained, “It is a scary situation; I trust that I would be prepared to handle it because someone needs to be there for that patient.” Some participants' comments indicated some assimilation of the events with anticipatory growth, “I think nurses were caught in a difficult situation, attempting to protect the public while also protecting themselves during unprecedented times.” Discussion Researchers sought to examine nursing students' and new graduate nurses' impressions of the nursing profession in contrasting regions of New York State during the first wave of the COVID-19 pandemic. Pandemic nursing often involved providing care that felt incongruent with traditional core values and ethical education in nursing.3,14 This could be attributed to greater numbers of COVID-19 cases affecting densely populated areas. Abstracted subconcepts depicted mixed emotions of feeling undervalued, questioning one's own self-worth, and being fearful, yet remaining committed to the profession. Data demonstrated how participants were afraid of dying or bringing the virus home and exposing loved ones to the virus. Systemic and organizational issues impacted personal moral distress among the sample, and much of this was unexamined, indicative of muted moral distress.22 It is concerning that when individuals experience extreme emotions, unresolved conflict can build up to the point where suppressed or shocked moral distress occurs.23 Shocked moral distress often intensifies as nurses care for challenging and severely and morbidly ill patients.22,23 This cognitive rejection occurs when the nurse is charged to deal with the harsh realities of severe morbidity and frequent mortality among patients, resulting in a traumatic moral crisis. Shocked moral distress of frontline nurses was shared in this study, with participants relating severe mental anguish over the conditions of care and perceived disregard for the safety of nurses. Participants described feeling exposed and vulnerable, without protection, and often treating patients who were dying alone and some who were young. These sentiments permeated the entire sample, regardless of geographic and educational levels of participants, and their reports of “roller coaster” emotions included a sense of being critically wounded in an unpredictable situation. The main concept of shocked moral distress was abstracted from rich text descriptions and further analyzed according to Hanna's22 conceptualization of moral distress, revealing an underlying emotional struggle amidst challenging clinical and life experiences of pandemic nursing. The analysis indicated how shocked and muted moral distress, along with fear and lack of organizational support, led to conflicting attitudes toward the profession. Given the extreme pandemic care conditions in New York–area hospitals, it is important to remember that these conditions of care may have exceeded the limits of human ability. The content analysis demonstrated evidence of a worrying level of moral distress among this sample of novice nurses, indicative of a larger cohort who may be also experiencing a traumatizing entry into practice. Most concerning was the combined levels of shocked moral distress, muted moral distress, and fear. There is concern about the potential emergence of posttraumatic stress disorder and failure of nurses to thrive in the profession. d'Ettorre et al24 found significant evidence of mental health concerns among health care workers in a systematic review, citing a vital need for urgent interventions and protections industrywide. There are limits to how humans can deal with trauma, but there could also be opportunities for posttraumatic growth. Those in shocked moral distress can recognize it, seek help by talking with others, and grow from the experience. Muted moral distress includes a deeper inward reflective journey, which helps individuals learn from experience and achieve a sense of moral closure. However, if left unexamined, moral distress may be suppressed, which is most detrimental to the well-being of the individual and the one being cared for, as it results in emotional and physical symptoms of fatigue, headaches, and ultimately resulting in burnout.22,23 Hanna's conceptualization of moral distress may offer an important lens into the experiences of new graduate nurses and nursing students in pandemic nursing over time and lead to interventions for the amelioration of consequences. Limitations Limitations may include the extent to which the deep analysis of the data can occur using a single question. Because the sample is limited to a single state and is qualitative in nature, this may limit generalizability. Recommendations Building a Systems-Level Tool Kit to Combat Moral Distress The results indicate a need for tools that ameliorate moral distress in novice nurses. Educational interventions in schools of nursing and places of employment need to emphasize tactics to build moral resilience.25 Modalities should emphasize the values of ethical, safe, and quality patient-centered care, and nurse faculty and leaders need to be cognizant of the shocked and muted moral distress that occurs when conditions prevent students and new nurses from engaging in internalized ethics of care. Those who recognize moral distress can emerge stronger.7–9,17,26 Bell and Breslin26 recommended screening programs to identify concerns in the early stages. Church et al27 noted that institutions that offer nurse preceptor or residency programs often promote feelings of empowerment in a safe, supervised manner for new graduate nurses. Ethical Decision Making Nurses need strategies for ethical decision making. Encouraging nurses to participate in hospital- or systemwide ethics committees may prevent or diminish shocked moral distress.27 Nurses who participate in ethics committees have found it helpful to have dedicated time to examine the multiple perspectives of ethics, moral distress, and moral integrity in patient cases. Committee engagement for novice nurses facilitates therapeutic communication and relationship building, thus forming stronger bonds within the institution and the nursing profession.28 Pandemic Policy Policy implications include the need for nurses on the front lines to be a part of the team engaged in developing pandemic nursing guidelines. Pandemic policy should examine strategies such as wellness clinics, allocation of time for self-care, and system supports to create safety hubs for nurses moving forward. Using a strengths-based leadership approach with protective policies can assist nurses to engage in safe, quality practice. Ongoing holistic care programs are needed to promote mindfulness and social, emotional, and spiritual support. Race explained that new graduate nurses should be made aware of institutional resources and procedures for accessing those resources.28 Institutions that offer nurse preceptor or residency programs often promote feelings of empowerment in a safe, supervised manner for new graduate nurses.29 Conclusion Nursing students and new graduate nurses have experienced high levels of moral distress during the COVID-19 pandemic but remain committed to the profession. Future research needs to address the multiple dimensions of nursing practice with systems that enable nurses to feel safe from the perspective of social, emotional, physical, spiritual, and intellectual well-being. Building a tool kit to cope with moral distress, fostering ethical decision making, and implementing protective policies can reduce the incidence of moral distress and positively impact the future of the nursing profession. The authors declare no conflicts of interest. Early Access: March 3, 2023 Cite this article as: Bongiorno AW, Armstrong N, Moore GA, et al. Impressions of the nursing profession among nursing students and new graduates during the first wave of COVID-19: a qualitative content analysis. Nurse Educ. 2023;48(4):204-208. doi:10.1097/NNE.0000000000001386 ==== Refs References 1. Ramelet AS Befecadu FBP Eicher M Larkin P Horsch A . Postgraduate nursing students' experiences in providing frontline and backstage care during the Covid-19 pandemic: a qualitative study. J Prof Nurs. 2022;39 (2 ):165–170. doi:10.1016/j.profnurs.2022.01.01235272824 2. Kleemeier R . Survey: nurses fear going to work due to lack of protection from virus. Am Nurs. Published June 7, 2020. 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==== Front MCN Am J Matern Child Nurs MCN Am J Matern Child Nurs AJMCN Mcn. the American Journal of Maternal Child Nursing 0361-929X 1539-0683 Wolters Kluwer Health, Inc. 36943837 10.1097/NMC.0000000000000926 3 Feature Strategies to Address COVID-19 Vaccine and Pregnancy Myths Berkowitz Heather E. MSN, RN, CCRN Vann Julie C. Jacobson PhD, MS, RN Heather E. Berkowitz is a Clinical Nurse IV, UNC Health, Newborn Critical Care Center, The University of North Carolina at Chapel Hill, School of Nursing, Health Care Leadership & Administration, Chapel Hill, NC. Ms. Berkowitz can be reached via email at Heather.Berkowitz@unchealth.unc.edu Julie C. Jacobson Vann is the Clinical Associate Professor, The University of North Carolina at Chapel Hill, School of Nursing, Carrington Hall, Campus, Chapel Hill, NC. Dr. Jacobson Vann can be reached via email at jvann@email.unc.edu The authors declare no conflicts of interest. Jul-Aug 2023 27 6 2023 27 6 2023 48 4 215223 Wolters Kluwer Health, Inc. All rights reserved. 2023 This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses risks to pregnant women and their infants. The spread of misinformation about COVID-19 vaccination is a barrier to optimizing vaccination rates among women of childbearing age. We conducted an environmental scan to identify misinformation about COVID-19 vaccination, pregnancy, and fertility, and a review to identify evidence to refute misinformation and strategies to correct and prevent the spread of misinformation. Seven identified themes of misinformation are: the vaccine causes female infertility; can cause miscarriage; and can decrease male fertility; mRNA vaccines attack the placenta; pregnant and breastfeeding persons should not get the vaccine; the vaccine can change menstrual cycles; and vaccinated people can spread infertility symptoms to unvaccinated people. Strategies that can be implemented by social media platforms to help prevent misinformation spread and correct existing health misinformation include improving information regulation by modifying community standards, implementing surveillance algorithms, and applying warning labels to potentially misleading posts. Health services organizations and clinicians can implement health misinformation policies, directly recommend vaccinations, provide credible explanations and resources to debunk misinformation, educate patients and populations on spotting misinformation, and apply effective communication strategies. More research is needed to assess longer-term effects of vaccination among women of childbearing age to strengthen the defense against misinformation and to evaluate strategies that aim to prevent and correct misinformation spread about COVID-19 vaccinations. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses risks to pregnant women and their infants. The spread of misinformation about COVID-19 vaccination is a barrier to optimizing vaccination rates among women of childbearing age. This environmental scan identifies misinformation about COVID-19 vaccination, pregnancy, and fertility, reviews evidence to refute misinformation, and offers strategies nurses can use to correct and prevent the spread of misinformation. Key Words: Algorithms COVID-19 vaccines Infertility Misinformation Pregnancy SDCT ==== Body pmcFigure No caption available. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses major health risks to pregnant women and their infants (Centers for Disease Control and Prevention [CDC], 2022a; Delahoy et al., 2020; Woodworth et al., 2020). To reduce these risks, the CDC (2022b), American College of Obstetricians and Gynecologists (ACOG, 2021), Society for Maternal-Fetal Medicine (SMFM), and American Society for Reproductive Medicine (ASRM, 2021) recommend that women who are trying to conceive, pregnant, or breastfeeding receive the coronavirus disease of 2019 (COVID-19) vaccine. However, during the week of February 4, 2023, only 71.7% of those who are currently pregnant, age 18 to 49 years, had completed the primary COVID-19 vaccine series and 20.9% had received the updated bivalent booster dose before or during pregnancy (CDC, 2023). Commonly cited reasons people forgo the COVID-19 vaccination are the misconceptions that the vaccine may cause pregnancy complications, fertility problems, or give them COVID-19 infections (Ayhan et al., 2021; Sutton et al., 2021). Public trust in COVID-19 vaccines has been undermined by misinformation circulating on the internet and social media, and politicization of science and the COVID-19 vaccine, creating a culture of confusion and distrust (Bolsen & Palm, 2022; Cascini et al., 2022; Jennings et al., 2021; Lee et al., 2022). Background Misinformation about COVID-19 vaccinations is widespread. In one cross-sectional study of working adults in the United States, 57.6% reported being exposed to inaccurate information about COVID-19 vaccinations (Jennings et al., 2021). In a systematic review, identified themes of COVID-19 misinformation were medical misinformation, vaccine development, and conspiracies (Skafle et al., 2022). An analysis of all news spread on Twitter from 2006 to 2017, including approximately 126,000 unique stories shared by over 3 million distinct users, found that misinformation was 70% more likely to be shared and reach more unique users than the truth (Vosoughi et al., 2018). Vaccine misinformation did not begin with the COVID-19 infodemic (World Health Organization, 2020). A few historical examples of misinformation include childhood vaccines cause autism; the human papillomavirus (HPV) vaccine is not needed, safe, or recommended; and vaccines contain unsafe levels of toxins (Annenberg Public Policy Center, 2022; Davidson, 2017; Eggertson, 2010; Sonawane et al., 2021). In 1998, a case series of 12 children with regressive developmental disorders was published in the Lancet; parents of eight children attributed this problem to the measles, mumps, and rubella (MMR) vaccination (Eggertson, 2010). Despite retraction of this paper 12 years later, and other studies demonstrating that this association was false, this unsubstantiated fear has continued, in part because the MMR vaccine administration typically occurs near the time that signs of autism appear (Davidson, 2017). A cross-sectional study used data from the National Immunization Survey and Vaccine Adverse Event Report System to identify reasons that adolescents had not initiated the HPV vaccine series. The top reason reported by caregivers (n = 39,364) was concerns about vaccine safety (Sonawane et al., 2021). Other specified reasons were the HPV vaccine is not needed or not recommended, and adolescents were not sexually active (Sonawane et al., 2021). Some distrust in vaccines has been attributed to unfounded concerns that vaccines contain unsafe levels of toxins, such as antifreeze, formaldehyde, mercury, and aluminum (Annenberg Public Policy Center, 2022; Public Health.org, 2022). Historically, misinformation spreads through print news, television, public radio, and word of mouth (Posetti & Matthews, 2018). Although these routes continue to exist, social media, text messaging, and email chains have facilitated rapid and diffuse spread of misinformation, often without verification (Posetti & Matthews, 2018; Soll, 2016). Within social media platforms, algorithmic recommendations can create information silos often connecting like-minded followers (Agustin, 2021; Dunn et al., 2015). Process automation and artificial intelligence take these a step further, as robot technology (“bots”) emulates human behavior for content generation and engagement (Ferrara et al., 2016). These bots are frequently misused for social malintent by spreading misinformation and malware (Ferrara et al., 2016). Once misinformation is broadcast into the world, influencers spark peer-to-peer spread of information sharing on social media and word of mouth, much like the spread of an infectious disease (Hodas & Lerman, 2014). Circulation of misinformation has allowed the COVID-19 virus to thrive by encouraging noncompliance with public health recommendations, including quarantines, mask use, physical distancing, and vaccinations (Hornik et al., 2021; Lockyer et al., 2021). One survey of a nationally representative sample of US adults (n = 1074) found participants reporting higher beliefs in misinformation also reported lower compliance with mask-wearing and social distancing (Hornik et al., 2021). Among interview participants in another study conducted in the United Kingdom, widespread and diverse COVID-19 information was associated with reports of confusion, mistrust, and vaccine hesitancy (Lockyer et al., 2021). Confusion and mistrust have been linked to increased incidents of violence against frontline workers (Basis et al., 2021). In one survey, conducted in Italy to assess violence against health services workers during a COVID-19 campaign, 59% of responding nurses, doctors, and other health services workers reported some type of violent assault against them, primarily verbal abuse (Presti et al., 2022). We conducted an environmental scan and rapid review to explore misinformation about COVID-19 vaccination, pregnancy, and fertility, as well as evidence to refute the myths. Our objectives are to identify and summarize published misinformation that is being disseminated about the COVID-19 vaccination, pregnancy, and fertility, scientific evidence that refutes the identified myths, and strategies to correct and prevent the spread of misinformation. Methods One investigator (HEB) conducted Internet searches to identify examples of potential misinformation about COVID-19 vaccination and pregnancy and PubMed and CINAHL to identify scientific evidence to validate or dispute suspected misinformation, and strategies to prevent misinformation spread (Table 1 Supplemental Digital Content at http://links.lww.com/MCN/A84). We excluded resources not readily available in English. Prior to conducting searches, browsing histories, cache, and cookies were cleared from the web browser because personalization algorithms, prevalent on commercial search engines, social media, and personal social connections and browsing history, may prevent the discovery of some examples of pregnancy and infertility-related COVID-19 vaccination misinformation; this content is not aligned with our typical, evidence-based engagement preferences. Primary misinformation examples were identified through hyperlinks available in gray literature rather than social media platforms. Gray literature is defined as content produced outside of traditional publishing and distribution channels; it may also refer to user-generated web-content. Our search process was tracked using a matrix of search terms, by search platform and topic. We screened resources, by reviewing titles, sources of information, and abstracts, to assess relevance to our three topics: potential misinformation about COVID-19 vaccination and pregnancy; scientific evidence to validate or dispute misinformation; and strategies to prevent misinformation spread. When searching for misinformation, we assessed source titles of the first 25 web pages of results to determine baseline relevance for review. The screening process was tracked by documenting the number of identified and included resources by electronic search platform and search terms, for each of the three topics. We collected data for each information category until saturation, defined here as reviewing five resources without additional information. Information was abstracted from relevant resources and documented in three data tables: COVID-19 vaccination and infertility misinformation, evidence-based information about vaccination and infertility, and strategies to prevent COVID-19 vaccine misinformation (Table 2). We documented references, study contexts, methods, and outcomes for published studies, and summarized information by theme. TABLE 2. STRATEGIES TO ADDRESS COVID-19 VACCINE MISINFORMATION Social Media Platform Strategies Health Service Organizations Strategies Community Standards Users are required to agree to community standards that condemn the spread of misinformation Apply protocols to remove harmful health misinformation Apply consequences to users, ranging from lockouts to permanent account suspension Organizational Policy Social media is useful for communicating accurate health information during crisis Organizational policies should promote evidence-based information from trusted resources Surveillance Algorithms Artificial intelligence algorithms can surveil for antivaccine misinformation, including hashtags, images, and text Communicating Misinformation Direct recommendation to vaccinate Provide easy-to-understand and current information Fact sandwich communication Combine evidence with personal anecdotes Provide visual aids Motivational interviewing MisinfoRx Toolkit Warning Labels Posts identified as misinformation or disinformation are labeled as false, if not deleted Community Engagement Grass-roots efforts with trusted community members and vaccine ambassadors More effective in communities with low governmental trust Results Misinformation Themes and Evidence to Refute Misinformation We identified seven themes of misinformation about COVID-19 vaccination and pregnancy: the vaccine causes female infertility, the vaccine can cause miscarriage, the vaccine can decrease male fertility, mRNA vaccines attack the placenta, pregnant and breastfeeding persons should not get the vaccine, the vaccine can change menstrual cycles, and vaccinated people can spread infertility symptoms to unvaccinated people. COVID-19 Vaccination Causes Female Infertility One misinformation theme is the COVID-19 vaccine causes female infertility (Abbasi, 2022; Gregory, 2021; Hamel et al., 2021; Isaacs-Thomas, 2021; Kelen & Maragakis, 2021; North, 2021). A YouTube influencer, Zed Phoenix, falsely cited GlaxoSmithKline in June 2020, claiming that research indicated that 97% of women receiving the COVID-19 vaccine would become infertile (Gregory, 2021). This post went viral. The Kaiser Family Foundation conducted a survey of 1,519 adults in the United States in October 2021; one third of participants had heard that COVID-19 vaccines cause infertility, with 8% continuing to believe the false claim (Hamel et al., 2021). Scientific evidence disputes this misperception; COVID-19 vaccinations have not been shown to be associated with female infertility (Morris, 2021; Wesselink et al., 2022). In one prospective cohort study conducted in the United States and Canada (n = 2,126), fully vaccinated females in the United States and Canada were 1.07 times more likely to conceive than unvaccinated females (Wesselink et al., 2022). In a US study comparing implantation rates between persons who had received the COVID-19 vaccine and were SARS-CoV-2 seropositive, had seropositive-confirmed COVID-19 infections, and tested negative for COVID-19, using frozen embryo transfer, implantation rates and sustained implantation rates did not vary by study group (Morris, 2021). COVID-19 Vaccination Increases the Risk for Miscarriage There is a misconception is that COVID-19 vaccinations are associated with an increased risk of miscarriage (Abbasi, 2022; Gregory, 2021; Isaacs-Thomas, 2021). Several studies, including one in Norway and two in the United States, have found that risk for miscarriage is similar between persons who have and have not received COVID-19 vaccinations (Magnus et al., 2021; Shimabukuro et al., 2021; Zauche et al., 2021). COVID-19 Vaccination Negatively Affects Male Fertility Although there is a misconception that vaccination decreases male fertility by decreasing sperm count and possibly causes erectile dysfunction (Abbasi, 2022; Ramasamy, 2021), evidence does not support beliefs that COVID-19 vaccination has adverse effects on male fertility (Gonzalez et al., 2021). In a prospective cohort study, fully vaccinated males were as likely to conceive as unvaccinated males (Wesselink et al., 2022). In one US study, median sperm concentration in semen specimens were higher 75 days after the second mRNA COVID-19 vaccine dose compared with prevaccine levels (Gonzalez et al., 2021). COVID-19 mRNA Vaccines Attack the Placenta Social media posts claimed that mRNA vaccines, such as COVID-19 vaccines, target the placental protein Syncytin-1 because it is similar to a spike protein of the SARS-CoV-2 virus (Abbasi, 2022; Kelen & Maragakis, 2021; Reuters, 2021). These posts implied that mRNA vaccines attack the placenta, prevent healthy pregnancies, and potentially cause infertility. The gene sequence of the SARS-CoV-2 spike protein was compared with the human genome using the Basic Local Alignment Search Tool; no sequencing alignment was found between the spike protein and human genome (Nirenberg, 2020). In a prospective cohort study that examined and compared placentas of US women who had received the SARS-CoV-2 vaccine during pregnancy (n = 84) and those who had not received the vaccine (n = 114), the incidence of placental lesions was not higher in the vaccinated group (Shanes et al., 2021). Pregnant and Lactating Persons Should Not Receive COVID-19 Vaccine Some sources erroneously claim that pregnant and breastfeeding persons should refrain from COVID-19 vaccination (Hamel et al., 2021; Isaacs-Thomas, 2021). However, COVID-19 vaccination is recommended for pregnant, breastfeeding, and trying to conceive persons (ACOG, 2021; ASRM, 2021). Recommendations are supported by studies that reported no increase in preterm births or small for gestational age births among women who received a COVID-19 vaccine during pregnancy compared with unvaccinated women (Lipkind et al., 2022). In one study, babies born to mothers vaccinated during pregnancy had positive SARS-CoV-2 antibody titers (Trostle et al., 2021); and, in a case-control study, US mothers of babies hospitalized with a COVID-19 infection were less likely to have received the COVID-19 vaccine than mothers of infants hospitalized without COVID-19 (Halasa et al., 2022). Pregnant persons who are not vaccinated against COVID-19, and their babies, are at risk for adverse outcomes if mothers are infected while pregnant (Woodworth et al., 2020). Pregnant women are at increased risk for more severe COVID-19 illness that may require hospitalization and mechanical ventilation, presumably because of the increased physical demands of pregnancy (Fu et al., 2021; Woodworth et al., 2020). A COVID-19 infection during the second and third trimesters of pregnancy has also been linked to preterm birth, with more infants testing positive for COVID-19 when the mother tested positive within 1 week before birth (Woodworth et al., 2020). Risks of the COVID-19 virus for pregnant women and their babies far surpass unfounded myths of adverse effects of COVID-19 vaccination during pregnancy (Fu et al., 2021; Woodworth et al., 2020). COVID-19 Vaccination has Permanent Effects on Menstruation After receiving the COVID-19 vaccine, some women reported heavier than usual menstrual cycles, which led to the misbelief that the vaccination has possible permanent effects on menstruation (Abbasi, 2022; Brumfiel, 2021; Gregory, 2021; Isaacs-Thomas, 2021; Lu-Culligan & Epstein, 2021). Some reports of menstrual changes in the few cycles immediately after receiving the vaccine have been reported; however, research suggests that changes are not permanent (Edelman et al., 2022). In one US study (n = 3,959), vaccinated participants experienced an average increase in menstrual cycle length of 0.71 days in the cycle after vaccination; however, changes in cycle length resolved by the third cycle after vaccination (Edelman et al., 2022). Vaccinated Persons Can Spread Infertility to the Unvaccinated A misunderstanding of the mechanisms of herd immunity and COVID-19 vaccines has been linked to the belief that vaccinated persons can spread vaccine side effects, such as infertility, to unvaccinated persons (Brumfiel, 2021; Gregory, 2021). The current mRNA and viral vector COVID-19 vaccines do not contain live virus; therefore, the recipient is unable to spread the virus (CDC, 2022c). Any symptoms felt after vaccination are a part of the body's immune response to the vaccine and are also not able to be spread (CDC, 2022c). Correcting and Preventing the Spread of Misinformation We identified several strategies that help prevent misinformation spread and correct existing health misinformation. Social media platforms can improve information regulation by modifying community standards, implementing surveillance algorithms, and applying warning labels to potentially misleading posts (Broniatowski et al., 2021; Gabarron et al., 2021; Wang et al., 2021; Zhang et al., 2021). Health services organizations can implement health misinformation policies, apply effective community strategies, provide credible explanations and resources to debunk misinformation, and educate patients and populations on spotting misinformation (CDC, 2021a, 2021b; Gabarron et al., 2021; Murthy, 2021). Social Media Platform Improvements to Address Misinformation Community Standards. Social media platforms have taken some steps to develop and implement regulations and procedures to address the spread of false information (Gabarron et al., 2021). Facebook and Twitter require users to agree to community standards about misinformation (Meta, n.d.-a). Meta, the parent company of Facebook and Instagram, and Twitter have released statements condemning misinformation spread and promoting removal of “harmful health misinformation,” including regarding COVID-19 vaccination (Broniatowski et al., 2021; Facebook, 2022; Meta, n.d.-b; Twitter, 2021). Similarly, Twitter's misinformation policy defined COVID-19 misinformation and specifies consequences ranging from a 12-hour account lock to permanent account suspension (Twitter, 2021). However, Twitter indicated they stopped enforcing their COVID-19 misinformation policy as of November 23, 2022 (Twitter Safety, 2021). Surveillance Algorithms. Researchers have developed algorithms for surveillance of misinformation across social media platforms using artificial intelligence (Wang et al., 2021). Meta (2022a) describes their artificial intelligence model as a machine learning system that can perform human tasks, such as understanding text and photographs. Programmers can teach these models to identify posts that disregard community standards, such as misinformation (Meta, 2022a). Human reviewers then verify the model accuracy, and work to enhance accuracy over time. One team developed an artificial intelligence model to identify social media posts containing vaccine misinformation (Wang et al., 2021). This model used machine learning to analyze hashtags, images, and text to determine if a post contained antivaccine messaging. It was more than 90% accurate (Wang et al., 2021). Warning Labels. A warning screen may be placed over social media posts identified as having intentionally misleading content, if not deleted (Meta (2022b). For example, if an Instagram user attempted to view a video on false allegations of vaccine shedding, the user would see the following warning: “False Information: Reviewed by independent fact-checkers,” with an option for the user to click to learn why (@otogomes, 2021). Labeling social media posts with a misinformation warning has been shown to be an effective tool for highlighting posts as misinformation (Zhang et al., 2021). Health Services Approaches to Address Misinformation Organizational Policy. Social media can be useful for communicating accurate health information during a public health crisis (Chou et al., 2018; Mustafa et al., 2020; Yuksel & Cakmak, 2020). Health services organizations can leverage these tools by implementing policies that promote communication of evidence-based information from trusted sources on social media, for example, by recording videos with accurate information about COVID-19 vaccination and posting them on social media (CDC, 2021a; Chou et al., 2018). In one study that analyzed YouTube videos related to pregnancy and COVID-19 (n = 76), videos from physicians and news agencies had more views than videos recounting personal experiences (Yuksel & Cakmak, 2020). Communication Strategies. Health care providers and lay health advisers or vaccine ambassadors may apply communication approaches to address misinformation. As a starting point, clinicians need to make a clear recommendation to vaccinate (CDC, 2021b). Communication that comes from trusted members of a community may help to address misconceptions (CDC, 2021b). One communication approach to address misinformation involves using a combination of evidence and anecdotes (Arkes & Gaissmaier, 2012; de Wit et al., 2008; Perrier & Martin Ginis, 2018). To help audiences understand scientific evidence, visual displays such as fact boxes and pictographs may be used (Arkes & Gaissmaier, 2012). Motivational interviewing is a strong communication technique that has been shown to be associated with reduced vaccine hesitancy (Reno et al., 2018). This technique involves applying principles and skills, such as expressing empathy and acceptance, and use of open-ended questions, affirmations, reflective listening, and summaries (Miller & Rollnick, 1991). The MisinfoRx toolkit describes another communication technique, the Three “Cs” Approach, which stands for compassionate understanding, connection, and collaboration (Pasquetto et al., n.d.). Misinformation may also be corrected by providing current and easy-to-understand information from trustworthy sources (CDC, 2021a; Winters et al., 2021), for example, by presenting evidence in a fact sandwich (National Association of Community Health Centers, 2022; Winters et al., 2021). This method starts with presenting the truth with clear and memorable supporting facts. Second, alert the recipient about misinformation and explain how correct information could have been misinterpreted. Conclude by restating facts, using information that is more memorable than the misinformation (CDC, 2021a; National Association of Community Health Centers, 2022). Community Engagement. Misinformation often originates through social media, some news sources, YouTube videos, and speculation among friends and families (Hamel et al., 2021; Li et al., 2022; World Health Organization, 2020). A grass-roots effort can be used to correct misinformation using trusted members of the community, such as religious leaders, and messages tailored to communities' culture and beliefs (CDC, 2021a; Murthy, 2021). This approach may be more effective in communities with low trust in government sites or local health departments (CDC, 2021a; Murthy, 2021). Discussion We identified seven themes of COVID-19 vaccination misinformation. Published research refutes these myths (Lipkind et al., 2022; Magnus et al., 2021; Morris, 2021; Shanes et al., 2021; Trostle et al., 2021; Wesselink et al., 2022). We also identified strategies shown to address or prevent spread of misinformation and correct health misinformation. Some social media platforms have begun to implement strategies, such as community standards, search algorithms, warning labels, removing misinformation, and suspending user accounts when misinformation is posted (Meta, n.d.-a; Wang et al., 2021). Health services professionals can provide the public with access to credible information and use evidence-based communication strategies to promote vaccination (CDC, 2021a; Chou et al., 2018; Gabarron et al., 2021; Murthy, 2021; Pasquetto et al., n.d.). Our review has several limitations. It was challenging to identify misinformation because of the way commercial search engines and social media cater to end-users. Searches were limited by personalization algorithms that are prevalent on commercial search engines, social media, and personal social connections and browsing history. These algorithms may prevent the discovery of some examples of pregnancy- and infertility-related COVID-19 vaccination misinformation because this content is not aligned with our typical, evidence-based engagement preferences. Therefore, some key information may have been missed. Implications for Research Our findings point to the need for more research to assess longer-term effects of vaccination on fertility among pre- and peripubescent populations, and infants born to mothers vaccinated while pregnant. More robust research is needed to further assess each misinformation theme, focused on effects of COVID-19 vaccination on male and female fertility, conception, miscarriage, erectile dysfunction, menstrual changes, sex hormones, pregnancy, and breastfeeding. Research is needed to advance machine learning and artificial intelligence surveillance algorithms to capture all modes of audiovisual content creation while discouraging the spread of misinformation, yet maintaining freedom of speech (Wang et al., 2021; Zhang et al., 2021). Research is needed to assess effectiveness of misinformation retraction and correction, and communication strategies applied to COVID-19 vaccinations (Ecker et al., 2021; Swire-Thompson et al., 2022). Clinical Implications This environmental scan and rapid review has implications for clinical practice, specifically on effects of COVID-19 vaccination on pregnancy and fertility and strategies to address misinformation. The US Surgeon General states: “Limiting the spread of health misinformation is a moral and civic imperative that will require a whole-of-society effort” (Murthy, 2021, p. 2). Health services organizations should consider implementing misinformation policies and educating clinicians about misinformation correction and effective communication strategies. Nurses and other trusted health professionals are often the first line of defense in the battle against health misinformation (NORC, 2021; Saad, 2022). They may consider listening to patients' concerns and providing factual information in a way that is personal and easy to understand, applying communication strategies, such as motivational interviewing, providing credible resources to debunk misinformation, educating patients and populations on spotting misinformation, and assisting with research and evaluation to strengthen existing evidence about COVID-19 vaccination and strategies to address misinformation. Acknowledgment The authors thank Dr. Rebecca Kitzmiller, Clinical Associate Professor at the University of North Carolina at Chapel Hill, School of Nursing, for her assistance with planning this project. CLINICAL IMPLICATIONS There is strong evidence for nurses to support COVID-19 vaccination among pregnant women, persons of childbearing age, and others who may be concerned about possible effects of vaccination on pregnancy and fertility. Making a clear and direct recommendation for vaccinating is an important approach for addressing misperceptions. Nurses can work with their employers to develop internal policies that outline strategies for promoting evidence-based communication about COVID-19 vaccination. Nurses can consider identifying existing or developing fact sheets and other materials to support communication with patients and the public about COVID-19 vaccination. These materials should be easy to understand and may include a balance of anecdotes and evidence, presented with visual displays, such as fact boxes or pictograms. The public generally has a high degree of trust in nurses, which may be leveraged by nurses developing videos about pregnancy and COVID-19 vaccination and disseminating information on social media sites, clinic waiting rooms, and other sites. Evidence-based communication strategies, such as motivational interviewing and fact sandwiches, may be used by nurses when discussing vaccinations with patients and their families. Health services organizations can engage trusted community leaders and lay health advisors to communicate about vaccinations to enhanced credibility of accurate messages. ==== Refs References Abbasi J . (2022). Widespread misinformation about infertility continues to create COVID-19 vaccine hesitancy. 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==== Front Nurse Educ Nurse Educ NEDU Nurse Educator 0363-3624 1538-9855 Wolters Kluwer Health, Inc. 36877984 10.1097/NNE.0000000000001392 nedup4804pe116 3 Articles Choosing a Nursing Career During a Global Health Event A Repeated Cross-Sectional Study Avraham Rinat PhD, RN benamir@bgu.ac.il Wacht Oren PhD, EMT-P wacht@bgu.ac.il Yaffe Eli PhD, EMT-P Eliy@mda.org.il Grinstein-Cohen Orli PhD, RN grinstie@bgu.ac.il Departments of Nursing (Drs Avraham and Grinstein-Cohen) and Emergency Medicine (Drs Wacht and Yaffe), Recanati School for Community Health Professions, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel; and Magen David Adom, Israel (Drs Wacht and Yaffe). Correspondence: Dr Avraham, Department of Nursing, Recanati School for Community Health Professions, Faculty of Health Sciences, Ben-Gurion University of the Negev, POB 653, Beer Sheva, Israel (benamir@bgu.ac.il). 7 2023 03 3 2023 03 3 2023 48 4 E116E121 19 2 2023 © 2023 Wolters Kluwer Health, Inc. All rights reserved. 2023 This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Background: Although in the past, the decision to enter the nursing profession was mainly due to intrinsic motives, more recent generations present additional extrinsic career choice motives. The motivation of choosing a nursing career may be affected by global health events, such as COVID-19. Purpose: To examine the motivation for choosing a nursing career during COVID-19. Methods: A repeated cross-sectional study was conducted among 211 first-year nursing students at a university in Israel. A questionnaire was distributed during 2020 and 2021. Linear regression evaluated the motives that predict choosing a nursing career during COVID-19. Results: Intrinsic motives were the leading motives for choosing a nursing career in a univariate analysis. A multivariate linear model revealed that choosing a nursing career during the pandemic was associated with extrinsic motives (β= .265, P < .001). Intrinsic motives did not predict choosing a nursing career during COVID-19. Conclusion: Reassessment of motives among candidates may help the efforts of faculty and nursing to recruit and retain nurses in the profession. career choice COVID-19 global health events nursing profession self-determination theory SDCT STATUSONLINE-ONLY ==== Body pmcCareer choice may be one of the most important decisions made during one's life span, and it is influenced by many factors. Tendency toward career choice is developed mainly during childhood and adolescence and is influenced by gender role orientation, personality, educational experiences, and parental and peer interactions.1 Exploring why people enter the nursing profession reveals a number of reasons. Wu et al2 in their systematic review revealed 4 groups of factors influencing career choice among health care students: sociodemographic (gender and socioeconomic status); interpersonal factors (influence of family members and individuals in the profession); intrinsic factors (a desire to care for others and a personal interest in health care); and extrinsic factors (such as financial remuneration, job security, and professional prestige). In the past, the decision to enter the nursing profession was mainly due to the traditional intrinsic perception of nursing as a virtuous profession: altruistic, noble, caring, and compassionate.3,4 More recent generations present additional extrinsic career choice motives. For example, McLaughlin et al5 found that motivation in nursing students is no longer simply a desire to care but also the opportunity for self-development in the nursing profession. In a large cohort study performed in Sweden, “wanting to care for and help others” was the second reason for choosing nursing studies (among approximately 75% of participants). The main reason students had chosen nursing education was due to “the wide range of possible work tasks and areas” (80%).6 In this study, we refer to self-determination theory (SDT)—a theory of motivation that has recently been applied to health education.7,8 The SDT approach to human motivation highlights the importance of 3 universal psychological needs: autonomy, competence, and relatedness, needs that when satisfied may enable a person to experience a sense of well-being.9 Self-determination theory distinguishes between autonomous intrinsic motivators in life (eg, personal growth, community involvement) and some more extrinsic motivators (eg, wealth, prestige, self-image). People will seek goals that take into consideration the satisfaction of their basic needs.10 Career choice is influenced by both intrinsic and extrinsic motivators.11,12 Messineo et al studied the motivation for choosing nursing studies in the University of Palermo, within the theoretical framework of SDT. Using thematic analysis, the results show that nursing students' motivations can be organized according to the influence of extrinsic and intrinsic autonomous forms of motivation. The most frequent reasons were the desire to help others, and considering the nursing profession as a mission or a vocation, while the second most frequent motivation was related to job opportunities.7 Ben Natan and Becker13 specified the career choice motivations that influence individuals' choices such as helping others, interest, appreciation and self-fulfillment, challenge and excitement, creativity and responsibility, social assistance, and professional status. They stated that people motivated by a desire to help others, rather than by personal interest or challenges, are predisposed to choose a nursing career.13 Other studies support the notion that intrinsic rather than extrinsic motives are the leading factors for choosing a nursing career and revealed additional extrinsic motives of career choice, for example, employment security, flexible hours, high salary, promotion and responsibility, comfortable work conditions, short training, and the ability to work and study at the same time.14–18 Exposure to significant health events in the private and public environment may also contribute to decisions regarding career choice. Di Giulio et al19 found that compared with business students, students of health professions were significantly more exposed to severe personal illness, a relative's illness, death, or addiction, before beginning their professional studies. In March 2011, a large earthquake, tsunami, and nuclear explosion damaged northeastern Japan. A qualitative study that explored the reactions and interpretations of adolescents living in areas affected by the disaster revealed an association between childhood adversity and career choices. The researchers believe that exposure to trauma builds a source of resilience, as among these young people they found an optimistic future orientation and motivation to become a physician.20 There is also preliminary evidence that COVID-19 has influenced medical and nursing career choices. First, health care professions became more popular during the pandemic. For example, Zhang et al21 observed an increased preference for medical studies following the COVID-19 outbreak among high school students and their parents. Bai et al22 also reported an increase in the choice of nursing as a future career in China, from 50.9% before the COVID-19 pandemic to 62.7% after the onset of the pandemic. Second, COVID-19 has influenced career development within the health care profession. In a recent survey conducted among medical students at the University of Pennsylvania during the pandemic, the participants were asked whether they think that COVID-19 will affect their future choices in their medical career. Twenty percent of the students think that COVID-19 will affect their choice of specialty. In another study among a group of nursing students (n = 58), almost all students had decided to quit the nursing profession after graduating from university during the COVID-19 pandemic.23 However, some evidence exists that COVID-19 has had no influence on health care career choice, such as a study of 72 students in undergraduate and graduate-level medical laboratory science programs at a university in the United States that showed that for most students, the pandemic did not influence their decision to pursue this career.24 The global shortage of nurses is growing, and the number of required nursing graduates increases each year. In Israel, the number of nurses is already low, with 5 nurses per 1000 population, compared with the Organization for Economic Cooperation and Development (Israel) average of 9.4.25 Educators, policy makers, and health care professionals need to be aware of candidates' and graduates' career choice motivations and expectations to improve processes of nurses' recruitment and retention in the profession. The current COVID-19 pandemic may change the perceptions of both preservice and in-service public health professionals. The objective of this study was to evaluate the motivation for choosing nursing studies during COVID-19 as an example of a global health event among first-year nursing students, within the theoretical framework of SDT. Methods Setting, Study Design, and Sample A repeated cross-sectional survey study was conducted at the Department of Nursing, School of Health Professions, at Ben-Gurion University of the Negev University, Israel. Participants were first-year undergraduate nursing students who were recruited to the study at 2 points in time during the first month of the academic year, in October 2020 and in October 2021, using a convenience sampling method. A minimum sample size of 194 participants was required for this study based on α= .05, power = 0.20, and an expected correlation coefficient of at least 0.20.26 Research Variables and Measurements Participants completed a self-reported questionnaire to assess the outcome variable COVID-19 influence for choosing the profession, and the explanatory variables—intrinsic and extrinsic motives for choosing the profession. Participants also completed a demographic and socioeconomic characteristics questionnaire. The extent to which the participants chose nursing studies due to the COVID-19 pandemic was measured using 5 items created by the researchers (eg, “I chose to learn a healthcare profession following COVID-19” and “I chose to learn a therapeutic profession following COVID-19”). Participants rated their agreement with the items on a 5-point Likert scale from 1 = totally disagree to 5 = totally agree. A mean score was calculated to create the variable. For added consistency, this part was repeated in 5 questions, each question dealing with a slightly different part. Motives for choosing the profession were assessed using a list of 16 statements addressing the motives of health profession career choice adopted from a valid questionnaire by Romem and Anson.27 Participants rated each item on a 5-point Likert scale from 1 = totally disagree to 5 = totally agree. The original tool was organized under 5 themes (characteristics of the profession; socioeconomic status; childhood dream and personal inclination; previous familiarity with the profession; and family and social network support). In the current study, we organized the motives under 2 categories of intrinsic and extrinsic motives: 9 items were combined under 1 category (α= .50) and are referred to as intrinsic motives (eg, statements referring to the characteristics of the profession, childhood dream, and personal inclination). The other 7 items were combined under a different category (α= .62) and are referred to as extrinsic motives (eg, items relating to socioeconomic status, such as job security and prestige and family and social network support as motives for choosing the profession). Data Collection Data were collected during the COVID-19 pandemic, during the first month of 2 academic years: October 2020 and October 2021. An online anonymous questionnaire was distributed using electronic survey software. The Ethics Committee of the Faculty of Health Sciences at Ben-Gurion University of the Negev approved the study protocol (approval no. 45-2020). Participants' consent was obtained online, using a question of agreement to participate in the study at the beginning of the study questionnaire. Filling out the questionnaire was possible only if participants had checked “yes” for this question. Data Analysis Data were analyzed using IBM SPSS Statistics software (version 26, Armonk, New York). Descriptive analysis included means and standard deviations for interval variables and frequencies and proportions for categorical variables. Univariate analysis included independent t test, paired t test and Pearson correlation. Multivariate analysis included linear regression for the predictors of “COVID-19 motivation.” The statistical significance level was set at α value of less than .05. Results Out of 280 students who began their academic studies at the Department of Nursing in 2020 and 2021, 211 students completed the study questionnaire (compliance rate of 75.4%). No differences were found in sample characteristics between 2020 and 2021. Participants were mostly women born in Israel. Supplemental Digital Content Table, available at: http://links.lww.com/NE/B301, presents the sample characteristics and study variables distributions. We refer to the 2 points in the time of data collection as 1 comparable sample, after comparing the means of motives and COVID-19 motivation between participants in 2020 and participants in 2021. Three significant differences were found between the study years: “financial security” (t = 2.84, P = .005) and “enabling geographical mobility” (t = 2.20, P = .029), which were rated higher in 2020, and “previous familiarity with military service,” which was rated higher in 2021 (t =−2.25, P = .025). No other differences were noted. Although examining the means of each item separately, the 4 higher rated motives were (1) helping others, (2) diversity of work options, (3) work with people, and (4) natural tendencies, all of which are recognized as intrinsic motives (Table 1). Table 1. Descriptive Analysis of Nursing Career Choice Motives Motive Type Motive Mean SD Minimum Maximum Intrinsic motives Helping others 4.89 0.312 4 5 Diversity of work options 4.70 0.534 2 5 Work with people 4.64 0.527 3 5 Natural tendencies 4.39 0.655 2 5 Fits me the most 3.96 1.037 1 5 Previous acquaintance with a nurse 3.21 1.547 1 5 Childhood dream 2.98 1.163 1 5 Familiarity as a patient 2.66 1.423 1 5 Familiarity from military service 2.22 1.431 1 5 Extrinsic motives Potential for professional promotion 4.35 0.691 2 5 Family support 4.29 0.946 1 5 Friends support 4.18 0.972 1 5 Geographic flexibility 4.09 0.898 2 5 Professional prestige 3.97 0.931 1 5 Financial security 3.70 0.987 1 5 Financial constraints 1.74 0.918 1 5 Table 2 presents the α coefficients of the motivational constructs (ie, intrinsic, extrinsic, and COVID-19) and the mean, SD, and correlations between main study variables. COVID-19 motivation to choose nursing as a career was significantly lower than both intrinsic motivation and extrinsic motivation (t =−33.34, P < .001, confidence interval [CI]: −1.78 to −1.58; t =−32.10, P < .001, CI: <1.70 to −1.50, respectively). Intrinsic motivation rated higher than extrinsic motivation (t = 2.26, P = .02, CI: 0.01-0.14). Pearson correlations presented in Table 2 reveal weak positive association between COVID-19 motivation and both intrinsic motivation and extrinsic motivation (r = 0.209, P < .01; r = 0.310, P < .01, respectively). A positive correlation was also observed between intrinsic motivation and extrinsic motivation (r = 0.381, P < .01). Table 2. Constructs Reliability, Descriptive Statistics, and Correlation Coefficients of Study Variables Alpha Mean (SD) Intrinsic Extrinsic COVID-19 Intrinsic .50 3.8 (0.44) 1 Extrinsic .62 3.76 (0.50) 0.381a 1 Covid-19 .69 2.13 (0.72) 0.209a 0.310a 1 aP < .01. Multivariate linear regression for the variables that predict COVID-19 motivation of career choice is depicted in Table 3. The model was significant (F = 8.09, P < .001) and explained 14% of the variance in the motivation to choose the nursing profession following COVID-19. The model reveals that extrinsic motivation predicted COVID-19 motivation (β= 3.664, P < .001) but intrinsic motivation did not do so (β= 1.371, P = .172). Another significant predictor was religion, indicating that non-Jewish participants were less likely to choose the profession due to COVID-19 (β=−2.491, P = .014). Table 3. Linear Regression for Prediction of Choosing Nursing Career During Covid-19a B Standard Error β t P Constant 0.492 0.491 1.001 .318 Intrinsic motivation 0.158 0.115 .098 1.371 .172 Extrinsic motivation 0.376 0.103 .265 3.664 <.001 Religion (Jewish vs non-Jewish) −0.413 0.166 −.167 −2.491 .014 Gender (male vs female) 0.017 0.161 .007 0.107 .915 R 2 0.14 F (P) 8.09 (<.001) aDependent variable: choosing nursing career during COVID-19. Discussion COVID-19 has changed the world in many ways, and this includes the world of employment. This study aimed to explore changes in the motivation of students to choose the nursing profession during the pandemic period that represent a global health event. The results of this study indicate that during COVID-19, extrinsic motives but not intrinsic motives contributed to the decision to study nursing among the research participants. The leading extrinsic motives were either intrapersonal, for example, perception of nursing as a profession with the promotion opportunities and the geographic flexibility it enables, or interpersonal, for example, family and friends supporting the decision to study nursing. The trend in candidates' motivation to choose a nursing career from purely intrinsic motives toward a combination of intrinsic and extrinsic motives has been noted previously, prior to the COVID-19 pandemic,5–7 There is limited literature about the influence of global health events on nursing career choice.20 The COVID-19 pandemic is unique in that it lasted for an extended period and affected almost every location and population around the world. A few studies have shown an increase in demand for nursing studies during the COVID-19 pandemic.21,22 However, to the best of our knowledge, this is the first study that explores changes in motivation for nursing career choice during the pandemic. The fact that choosing nursing due to the pandemic is associated with extrinsic motives seems to support the trend seen in recent years of moving from intrinsic-based to mixed-based motives for choosing a nursing career.5–7 That is, people in the new world of work may enter the profession due to extrinsic factors as well.28,29 Another explanation may be the fact that COVID-19 has affected the stability of many other professions (eg, education, sales, and high-tech), causing people to look for a profession such as nursing. It will be important to follow those who were highly influenced by COVID-19 in their decision to become nurses in order to record their job experiences over time, for example, job satisfaction, burnout, worksite change frequency, and leaving rates. The higher average of the intrinsic motives in our study indicates that intrinsic motives are still dominant in the process of nursing career choice. This is encouraging, especially in light of the numerous adverse effects of the COVID-19 epidemic on almost all areas of life. The effect of COVID-19 on motivation for choosing the nursing profession was positive. The widespread recognition and appreciation that the medical profession received during the peak periods of the pandemic, as demonstrated by the applause for staff on the balconies,30 increased the prestige of the medical profession and interest in relevant fields of study. In Israel, only a slight intention to leave the profession was noted during the pandemic.31 Furthermore, there was an increase in demand for nursing studies according to the local press,32,33 and the Ministry of Health published an increase in the ratio of nurses per 1000 during this period.34 In fact, it can be cautiously claimed that the extrinsic motives that lead people to choose nursing career following COVID-19 were added to the intrinsic motives, which resulted in positive effect on the nursing career choice. Two extrinsic motives were higher in 2020 compared with 2021: the financial motive and the geographical motive. The authors believe that at the start of the pandemic, uncertainty and anxiety about the future, particularly in terms of a source of employment and income, were higher. It is worth examining the influence of these 2 motives during periods when there is no global health disaster. This study has several limitations. The cross-sectional design enabled correlational inference but not a causal effect. Because of the convenience sampling method, there is subrepresentation of important groups such as minorities and male gender populations that are currently growing within the nursing profession in Israel. In addition, data were collected in a single academic setting, may be subject to a specific acceptance policy of the setting, and to a specific nature of the learners, thus limiting generalizability. The study was conducted among individuals who already enrolled in the nursing program, thus excluding those who were not accepted to the program. The results may also be influenced by the country in which the study was conducted, due to local pandemic influence and country policies during COVID-19. Conclusions As previous global health events, COVID-19 has brought about numerous environmental and social changes, and it also seems to have affected people's motivation to choose a nursing career. High extrinsic/non–self-determined motivation encourages new students in their choice of a nursing career following COVID-19, more than intrinsic self-determined motivation. This finding emphasizes the need for managers and health-related stakeholders to adopt strategies that address individuals' expectations and motivations, in order to encourage them to join the health system. Such strategies could include increasing opportunities for professional development in nursing, advertising on social media to raise the prestige and the options of the nursing profession, which would be particularly suited to the current generation that is highly influenced by content on various media. In light of the steep increase in personnel shortage and leaving trends, gaining a true understanding of people's motivation to join the nursing profession is essential both for the recruitment of new students and for the retention of veteran professionals for the long term. Understanding current trends in motivation to join the nursing profession can contribute to the efforts to increase enrolment, enhance work satisfaction, and decrease the intention to leave the profession. Such efforts are needed now more than ever to help overcome the health professional shortage. It is important to continue this research to include additional subgroups among prelicensure nursing students (eg, participants in programs such as a fast track for academics, male students) and to explore strategies that may address these motives. The authors declare no conflicts of interest. Supplemental digital content is available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal's website (www.nurseeducatoronline.com). Early Access: March 3, 2023 Cite this article as: Avraham R, Wacht O, Yaffe E, Grinstein-Cohen O. 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==== Front Multimed Tools Appl Multimed Tools Appl Multimedia Tools and Applications 1380-7501 1573-7721 Springer US New York 35431608 12315 10.1007/s11042-022-12315-2 1215: Multimodal Interaction and IoT Applications A novel multi-modal depression detection approach based on mobile crowd sensing and task-based mechanisms http://orcid.org/0000-0002-8366-5491 Thati Ravi Prasad thati.raviprasad@gmail.com 1 Dhadwal Abhishek Singh 1 Kumar Praveen 1 P Sainaba 2 1 grid.433837.8 0000 0001 2301 2002 Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur, 440010 Maharashtra India 2 grid.448768.1 0000 0004 1772 7660 Department of Applied Psychology, Central University of Tamil Nadu, Tamilnadu, India 11 4 2022 134 31 3 2021 20 9 2021 17 1 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Depression has become a global concern, and COVID-19 also has caused a big surge in its incidence. Broadly, there are two primary methods of detecting depression: Task-based and Mobile Crowd Sensing (MCS) based methods. These two approaches, when integrated, can complement each other. This paper proposes a novel approach for depression detection that combines real-time MCS and task-based mechanisms. We aim to design an end-to-end machine learning pipeline, which involves multimodal data collection, feature extraction, feature selection, fusion, and classification to distinguish between depressed and non-depressed subjects. For this purpose, we created a real-world dataset of depressed and non-depressed subjects. We experimented with: various features from multi-modalities, feature selection techniques, fused features, and machine learning classifiers such as Logistic Regression, Support Vector Machines (SVM), etc. for classification. Our findings suggest that combining features from multiple modalities perform better than any single data modality, and the best classification accuracy is achieved when features from all three data modalities are fused. Feature selection method based on Pearson’s correlation coefficients improved the accuracy in comparison with other methods. Also, SVM yielded the best accuracy of 86%. Our proposed approach was also applied on benchmarking dataset, and results demonstrated that the multimodal approach is advantageous in performance with state-of-the-art depression recognition techniques. Keywords Depression detection Multi-modal Mobile crowd sensing Emotion elicitation Speech elicitation Machine learning ==== Body pmcIntroduction Depression has been a worldwide concern for a long time and continues to plague the global health agenda. According to the World Health Organization (WHO), more than 350 million individuals are estimated to suffer from depression. It is equivalent to 4.4% of the world’s population. Depression is forecasted to become the world’s leading health concern by the year 2030 [43]. COVID-19 has forced people throughout the world to stay indoors and minimize social interactions, Thus exacerbating the depression situation [32]. During the pandemic, the prevalence of depression in the general population is estimated to be 33%. COVID-19 not only impacts physical health concerns but also results in several mental illnesses. However, early diagnosis followed by appropriate treatment has proven to be successful in reducing its impact. Therefore, methods and tools for monitoring mental health are an immediate requirement [53]. Traditional methodologies rely on self-report or clinician consultation for spotting mental illness of an individual. Self-report has known limitations like inadvertence while filling up questionnaires and may be deemed unreliable. Clinician consultation depends on the physician’s expertise, patient’s budget, doctor’s availability, and various other parameters. Hence, auxiliary methodologies for detection of psychological ailments are essential and have drawn the attention of researchers to assist in early diagnosis of depression [26]. Several non-traditional strategies exist for depression detection. Ideally, all the indicators that a clinician utilizes could be modelled into machine learning(ML) algorithms to diagnose depression [38]. The majority of the approaches rely on various characteristics like visual manifestations, acoustic and linguistic communication, smartphone usage activities, social media content, physiological cues, etc. [14, 21, 24, 34, 45, 47, 48, 56]. Few approaches combine different modalities like visual with speech [38, 42]. Combining different modalities is not trivial as each modality behaves differently. For example, each modality source is in a different form (images in visual domain, and text in social media content). From the available literature in the field, it is evident that integrating the modalities provides promising results [4, 42, 46]. Collaborative efforts of researchers across various fields are involved in depression detection. Each subfield has different data sources with slightly different goals. Broadly, the majority of the data collection is based on Task/interview-based and mobile crowd sensing-based procedures. Many Task/interview-based depression detection methods consider only in-situ representations such as facial cues, pitch of the voice, etc. Several mobile crowd sensing based depression detection methods take into real-time (days/nights) markers from smartphones like location information, accelerometer, etc. These approaches are inadequate to address the problem completely as Task-based approaches lack historical context. In contrast, mobile crowd sensing-based approaches ignore non-verbal and verbal indicators that clinician primarily rely on. Thus, a methodology that addresses these limitations by combining these approaches is the need of the hour. With the advent of technology, smart phone has truly become an essential service for an individual in today’s world. Mobile Crowd Sensing(MCS) refers to a wide range of methods using mobile devices capable of sensing and computing, in which people share data and derive information to quantify and record behaviours of mutual interest. MCS is a key building block for evolving Internet of Things (IoT) applications [36]. The main advantage of MCS is that data can be collected without much user intervention. In the present study, MCS focuses on quantifying the history of the exhibition of symptoms (such as decreased levels of physical activity, lesser social interactions, reduced mobility, etc.) over a period of time. We rely on the smart phone usage records collected for a specific duration of two weeks to analyse individuals’ behavioural patterns. This work presents experiments by integrating smart phone usage patterns with task-based experimental modalities. Smart phone usage patterns were utilized to present historical information of symptom exhibition. Task-based experiments called emotion elicitation (triggering emotions to activate visual manifestations by showing pictures/video clips) and speech elicitation (triggering auditory responses by reading a predefined paragraph/open form of speech) were conducted to observe and study the momentary representations. The following are the main highlights/contributions of this work: To the best of our knowledge, this could be the first approach to integrate real-time smartphone usage patterns with the task-based modality for diagnosing depression. Creation of a novel tri-modality dataset with two weeks of smartphone usage data, visual and auditory cues of the participants. Designing an end-to-end machine learning pipeline, which involves multimodal data collection, feature extraction, feature selection, fusion, and classification to distinguish between depressed and non-depressed subjects. Extensive experimentation done using: various individual feature vectors from multi-modalities, features selection techniques, fused features, and machine learning classifiers such as Logistic Regression, Decision Tree(DT), Naive Bayes(NB), Random Forest(RF), Support Vector Machines (SVM), etc., for classification. Our findings demonstrate that the combination of statistical feature vectors from multimodal cues gave promising results compared to unimodal feature vectors, not only on our dataset but also on a benchmark open-source dataset. The rest of the paper is organized as follows. Section II contains the details of related work, covering the existing works on various depression detection techniques. Section III presents the proposed approach. Section IV presents our findings and results in the results section. Section V presents the conclusion. Finally, Section VI presents limitations of the proposed approach and future works in the same field. Related work According to American Psychiatric Association, which has released the Diagnostic and Statistical Manual of Mental disorders-V (DSM-V), depression is a common mental disorder that involves a continuous sense of sorrow and/or distinct lack of interest. In addition to these, four or more following symptoms are present: weight loss or gain, sleep difficulties, i.e., insomnia or hypersomnia, psychomotor retardation, fatigue or loss of energy, diminished ability to think or concentrate, feelings of worthlessness or excessive guilt, and suicidal thoughts. Depression results in clinically notable changes in cognition, emotion regulation, or behavior that reflect the individual’s psychological, biological, or developmental process, resulting in socially deviant behaviour. This condition persist for a minimum duration of two weeks [5]. Assessments are done through clinical consultations and questionnaire-based standard self-reports. Clinical consultations are conducted by psychiatrists, psychologists, experienced counsellors, etc. Table 1 gives details about a few standard self-reports. It usually takes 10 to 20 minutes to complete the questionnaire. Self-reports contain questions that are to be rated by an individual for the severity of their symptoms over a specific period of time. Each question records the response with 0 (not at all), 1(several days), 2(more than half the days), and 3(nearly every day). A score is formed by summing up all the responses. This score is used to diagnose depression and classifying the severity of the depression into different categories: mild, severe, etc. Table 1 Few standard self-reports Self-Report No. of Sample contents Categories of depression Questionnaire questions of the Questionnaires Patient Health 9 Sleep difficulties, excessive Mild, moderate, moderately Questionnaire(PHQ-9) [30] guilt, fatigue, suicidal ideation severe, and severe Beck Depression 21 Mood, self-hate, social minimal, mild, moderate, and Inventory(BDI-II) [8] withdrawal, fatigability severe depression Hamilton Rating Scale 17 Loss of interest, agitation, Normal, mild, moderate, and for Depression(HRS-D) [10] mood, loss of weight severe depression Quick Inventory of Depressive 16 oncentration, suicidal ideation, Normal, mild, moderate, and Symptomatology (QIDS) [51] sleep disturbance, self-criticism severe depression The following sub-sections briefly give an overview of the different works found in the field of depression diagnosis through facial, verbal, smart phone usage metadata, and multimodal cues. In every approach, the goals and various aspects such as the data collection process, data sources are different. Irrespective of these differences, each approach aims to explore innovative solutions that can assist in depression detection. Depression detection through smart phone usage indicators Some works on mobile crowd sensing have attempted to provide depression detection methods for the following reasons: First, most smartphones are equipped with multiple sensors that can continuously gather information about the users. This data can be monitored to understand behavioural patterns in real-time. Second, smartphones are unobtrusive, prevalent, and capable of data transmission to remote servers without requiring direct user interaction. Third, passive sensing applications that can run in the phone background to capture usage information and store it locally/server can be designed. Few are readily available, like SensusMobile [61], Funf journal application 1, etc. Fourth, smartphone-captured behavioral variations can be used as discriminative features for depression assessment. For e.g., people with depression are more likely to sleep lesser time than non-depressed people. This behavior pattern can be collected via brightness/light sensors present in the smartphone [41]. During a longitudinal study carried out by Masud et al. [37] in daily real-life scenarios, Inbuilt phone sensors such as the acceleration and Global Positioning System (GPS) sensor were used to classify physical activities and location movement patterns, respectively. Using a wrapper feature selection method, a subset of features were selected. Depression score was estimated using a linear regression model. SVM classifier was used to distinguish individual depression severity levels (absence, mild, extreme), with an accuracy of 87.2%. Fukazawa et al. [20] collected raw sensor data from mobile phone, such as brightness, acceleration, rotation/orientation, and application usage. The author used them to form higher-level feature vectors. The fusions of these feature vectors were able to predict the stress levels among the participants. The results demonstrate that the combined features extracted from smartphone log data can be used to predict stress levels. De Vos et al. [15] passively recorded geographical location data among healthy and depressed groups. From their results, it is evident that a strong correlation exists between geographical movements and depressed people. Depression detection through facial indicators Facial markers are extensively considered in depression diagnosis due to the following reasons: First, depressed individuals tend to have anomalous facial manifestations for e.g., fewer smiles, more frequent lip presses, prolonged activity on the corrugator muscle, sad/negative/neutral expression occurrence, fast/slow eye blinks, etc. Second, capturing visuals by web cameras has become effortless. Third, Several tools are now available to extract visual features, e.g., The Computer Expression Recognition Toolbox [35], OPENFACE [6], imotions2 etc. Wang et al. [58] have examined the facial cue changes between depressed and normal subjects in the same situation (while displaying positive, neutral, and negative pictures). To measure the facial cue changes on the face, they used person-specific active appearance model [11] to detect 68 point landmarks. Statistical features are extracted from distances between feature points of eyes, eyebrows, corners of the mouth to feed the SVM classifier. The classifier achieved 78% test accuracy. Girard et al. [22] have studied the relationship between facial manifestations and how the severity of depression symptoms changes over time. During a clinical interview of a longitudinal study, they measured Action Unit’s (AU) by Facial Action Coding System(FACS) [17, 18] between Low/High Symptom states. FACS has become the standard for muscle movements in the face. Each subtle muscle movement exhibited on the face is represented as AU. They found that AU 12 (Lip Corner Puller) is lower while AU 14 (Dimpler) is higher in a severe depressive state. Alghowinem et al. [2] have observed that the eyelids’ average distance (when opened) and duration of blinks vary between depressed and normal subjects. The findings conclude that depressed subjects tend to have a smaller average distance of the eyelids and duration of blink is higher than normal subjects. Alghowinem et al. [3] have also observed that head pose and movements significantly differ from depressed to normal people. They drew few conclusions: longer gaze time towards the right and down, slower head movements, and few head posture changes in depressed subjects. Depression detection through verbal indicators Acoustic features of speech play a vital role in diagnosis of depression for the following reasons: First, linguistic features (what subject speaks), paralinguistic features (how subject speaks), etc., are generally affected by the subject’s mental state. Second, the clinician uses verbal indicators. Several studies have found distinguishable prosodic features such as pitch, loudness, energy, formants, jitter, shimmer, etc., between depressed and non-depressed individuals. Third, the ease of recording and availability of tools to extract the features such as openSMILE [19], PRAAT,3 COVEREP [16], etc. Cummins et al. [12] have investigated good discriminative acoustic features that distinguish normal and depressed speakers. Features like Spectral centroid frequencies and amplitudes were computed using Mel-frequency Cepstral Coefficients (MFCC) then normalized. Multidimensional feature sets, i.e., combinations of those features have performed better when compared to single-dimensional features. They employed Gaussian mixture models to predict depressed and normal speakers. Further, Cummins et al. [13] analysed the effects of depression manifesting as a reduction in the spread of phonetic events in acoustic space. In their work, three acoustic variability measures: Average Weighted Variance (AWV), Acoustic Movement (AM), and Acoustic Volume, were used to model the trajectory of depressed speech in the acoustic space. They found that depressed groups often tend to have reduced vowel space when compared with healthy people. Scherer et al. [54] have investigated reduced vowel space’s association with the speech of individuals who exhibit depressive symptoms. They worked on a publicly available Distress Analysis Interview Corpus(DAIC) dataset [23]. They employed a voicing detection algorithm to detect voiced parts of the speech. COVERAP toolbox was utilized to track the first two formants (F1 and F2) in the voiced speech. Further, F1 and F2 were used to compute vowel space while uttering three kinds of vowel sounds i.e., /i/, /a/, and /u/. An unsupervised learning algorithm called K-means clustering(with k = 12 and c = 3) showed the association between vowel space and the depressed group. Depression detection through multi-modal indicators Recently some researchers have also tried to combine different modalities due to the following reasons: First, an individual modality’s contribution can be better understood when the convergence of modalities is carried out. Second, each modality has its own advantages. Hence a combination can yield better outcome. Third, compatible characteristics of the features exist. Williamson et al. [59] utilized feature sets derived from facial movements and acoustic verbal cues to detect psychomotor retardation. They employed Principal component analysis for dimensionality reduction and then applied the Gaussian mixture model to classify the combination of principal feature vectors. Alghowinem et al. [4] showed that the fusion of different modalities gives an improvement when compared to the individual modalities at hand. Their aim was to develop a classification-oriented approach, where features were selected from head pose, eye gaze, and verbal indicators of the depressed and healthy groups. Classification of these feature sets achieved the best results through the use of the SVM classifier. Williamson et al. [50] combined text, audio, and facial features to form hybrid fusion on a publicly available DAIC dataset [23]. Authors used deep learning for classification in thier study. Most of the studies discussed in current section employ ML based methods (Support Vector Machines, Gaussian Mixture Models, Random Forest, etc.,) but not deep learning methods. For this insufficient training data availability could be the reason. ML based methods can be trained on lesser data, i.e., when compared with ML, deep learning needs larger training data [44]. Another reason could be supervised ML is more powerful when a known relationship exists between the inputs and labels. i.e., numerous features can be extracted and then evaluated to improve model accuracy. Method and proposed approach Overview Figure 1 illustrates the overall architecture of the proposed approach. Our proposed approach has three stages; Stage 1: Data Collection, Stage 2: Feature Extraction, and Stage 3: Model Training and Testing. Fig. 1 The overall architecture of the proposed approach First of all, multi modal data collection was done by performing a participatory mobile crowd sensing experiment (where real-time smart phone usage data was collected over a period of 2 weeks) and a task-based experiment (where 15 minutes of visual and auditory responses were recorded). Post data collection, standard self reports were used to collect the ground truth. The acquired multimodal data was then used in the feature extraction stage as follows - After data pre-processing, low-level features were extracted from multimodal data modalities(smart phone, audio-visual modalities). High-level features were formed using low-level features. The statistical feature vectors were extracted from the high-level features.The statistical feature vectors and ground truth labels were used in the model-building stage. In the model training, the feature vectors from individual and combination of data modalities were used as inputs for feature selection techniques in order to train ML classifiers such as Logistic Regression(LR), Decision Tree(DT), Naive Bayes(NB), Random Forest(RF), Support Vector Machine(SVM)). These classifiers were trained for classifying depressed and non-depressed classes of participants. The model training is done as one time process. For testing, the trained models were then used on the new test data to predict the participant’s status. Data collection For mobile-sensor data collection, participants were volunteered through social networks, mailing lists, flyers, posters, and personal contacts. Among 143 responses received, 102 participants (56% female, mean age of 18-19 years) met the experiment’s eligibility criteria. Participants were eligible if they had smartphones, with access to the internet, could speak and read English, were over 18 years old, and lived in India. To improve data quality, several incentives were provided to the participants to participate with seriousness. The data was collected for over a period of two weeks. Two days before the start of the study, The research team assisted with the download, installation, and configuration of the SensusMobile application (refer to the About SensusMobile sub-section below). All the participants were instructed to: keep their phones with them charged throughout the day and enable the GPS and Bluetooth sensors for the duration of the study. Before taking informed written consent from the participants, the purpose of data collection and explanation of data was provided.The research team periodically checked the data at the server and contacted the participants in case of any discrepancies. At the end of the mobile-data collection phase, task-based data collection was performed in online mode to adhere Covid 19 guidelines and to accommodate participants across various regions. Participants were given appointments for Face to Face (30 minutes) Zoom sessions with members of the research team. The participants were instructed to turn their camera ON and be present in optimal lighting conditions with their headset before joining the session, preferably on a Desktop and if not on mobile. During the session, the researchers conducted tasks involving emotion [57] and speech elicitation [7] to record each subject’s facial and acoustic responses. Table 2 lists the experimental procedure with time duration involved to conduct emotion and speech elicitation. Table 2 Experimental procedure with time duration to conduct emotion and speech elicitation Experimental tasks Procedure Description(source) Duration Blank screen NA 1 minute Positive video The Circus(1928) / Charlie Chaplin, a known comedian, performs hilarious acts when he enters a lion cage. 3:32 minutes Emotion Elicitation Blank screen NA 1 minute Neutral video Abstract Shapes/colour bars 3 minutes Blank screen NA 1 minute Negative video The Champ(1979) / Little boy crying when his father is on the death bed. 3 minutes Break Blank screen NA 10 minutes Speech Elicitation Passage reading Short tale called “The North and the South Wind” 1 minute Free form speech Participant’s choice from a list appears on the monitor 2 minutes During online task-based experimental data collection, research assistants shared their screen/audio to perform emotion elicitation. In this task, various kinds (positive/neutral/sad) of multimedia clips selected from famous film clips in psychology [57] were shown to evoke the participant’s emotions. Prior to each clip, the experimenter stated that the screen would be blank for one minute (when participants were asked to clear their minds of all feelings, memories, and thoughts). After all the clips were presented, the participants were provided a break of approximately 10 minutes, then speech elicitation was performed. The participants were asked to provide their speech in two different conditions. In the first scenario, they were asked to read out tale (a phonetically balanced paragraph called “The North and the South Wind”) from the screen. Secondly, they were asked to provide an impromptu report on a topic of their choice from a list that appeared on the screen (e.g., memorable incident in life, their goals, etc.). The session was recorded for data pre-processing. Ground truth labelling The SensusMobile app was programmed to deliver instances of the PHQ-4(a subset of PHQ-9) survey (to be filled by participants) on a daily basis and a Patient Health Questionnaire(PHQ-9) Online Survey at the end of the mobile-data collection period (two weeks). PHQ-9/4 were selected in our study due to their high levels of consistency and statistical reliability/validity. Kroenke et al. [30] conducted a study to examine the validity of PHQ-9 for depression assessment. Their findings suggest that PHQ-9 is reliable/valid, and it is a helpful research tool for depression diagnosis. The participants also provided physical copies of completed PHQ-9 responses during the video-data collection phase. This questionnaire was collected in order to obtain a more “current” representation of the participants’ psychological state (compared to the mobile data collection phase). Based on the PHQ-9 Scores, each participant’s mental status was categorized into binary labels(Non-depressed= 0; those who show depressive symptoms = 1). To take care of outliers or inappropriate filling on the PHQ-9 report by the participants, a team of psychology research scholars from the Central University of Tamil Nadu were provided with scanned copies of the PHQ-9 questionnaires and the recordings of the participants(attained during task-based experimental data collection). The scholars reviewed these items and provided binary classifications using an amalgamation of both resources provided as well as another interview with the participant if needed. This additional step verification was performed in order to strengthen the validity of the ground truth labels leading to a more coherent dataset. Overall data set has been labelled with 54 non-depressed and 48 depressed subjects. About sensusmobile SensusMobile [61] is an open-source mobile crowd data collection application that runs in the background to access readings from device hardware sensors (i.e., accelerometer, GPS, gyroscope, etc.). The accessible sensor information can be stored locally on the device or transmitted to a remote server. Our project utilized Amazon Simple Storage Service Web Services for data collection to ensure that participant data gets stored periodically without user intervention. Prior to the actual study, several beta tests were performed with SensusMobile to calibrate settings to minimize battery consumption and reduce data redundancy. SensusMobile supports two methods of data sensing: 1) Listening (continuous data collection) and 2) polling (periodical triggering of probes to collect readings). Table 3 lists a subset of the data items collected from the participant’s smartphone for the study conducted. Table 3 A subset of the data items collected from the participant’s smartphone for the study conducted Data Collected Probe Used Listening/polling Intervals Acceleration Accelerometer Listening 1 reading / second Application Usage ApplicationUsageStats Polling 1 reading / 15 min Statistics Brightness LightDatum Listening 1 reading / second Bluetooth encounters BluetoothDeviceProximityDatum Polling scans and reads performed for 10 seconds each, between 30-second intervals Gyroscope values GyroscopeDatum Listening 1 reading / second GPS/ location LocationDatum Polling 1 reading / 15 min Screen unlocks ScreenDatum Polling 1 reading / 30 seconds Data pre-processing The goal of the data pre-processing stage was to facilitate the extraction of features from various sources, i.e., Smartphone usage data(.json) files along with visual and speech recordings. The research team extracted visual cues (.mp4) from participants via recorded visuals as marked in Fig. 2(a), and the marked red box was cropped for further use. Video portions where the participant is not visible or people other than the participant appeared in the recording were manually deleted. Openface [6] was used to extract low-level features from cropped versions of the recording. It is a state-of-the-art framework for extraction of low-level features like facial landmark location detection, eye gaze estimation, head pose estimation, and facial action unit recognition. In the proposed approach, higher-level statistical feature vectors were formed from these low-level features. Fig. 2 Samples during elicitation methods (a) Emotion Elicitation: participant’s facial clues are recorded while watching the neutral video (b) Speech Elicitation: participant’s speech is recorded while reading the phonetically balanced paragraph From the recorded meeting of the speech elicitation phase, as shown in Fig. 2(b) speech cues (.mp3) of the participants were extracted. The SOX tool4 was used for noise removal from the extracted speech content. Then, the Praat software tool5 was used for low-level acoustic feature extraction (pitch, intensity, formants, etc.). Feature extraction Those features extracted in the proposed approach were considered clinically significant [5] and supported by related work. It is believed that features computed from entire raw data obtained during the data collection of each modality give insightful information than on samples of information. For example, smart phone usage feature extraction with 14 days of smart phone usage data is more insightful than 3/7/10 days of data. In the following sub-sections, feature extraction is explained in detail. Smart phone usage feature extraction The following features were computed from collected smart phone usage data. Although clinically, those features extracted in the proposed work have no direct relation to depression, they can help quantify the individuals’ physical, cognitive, and environmental levels [55]. Features are as follows: Accelerometer probe features The accelerometer records dynamic or static forces the sensor is experiencing in x,y, and z directions. The acceleration probe reading consists of x, y, and z axes, which specify the axes’ acceleration. In this study, tri-axis readings were considered for feature extraction. From the raw three-axis accelerometer data, which was taken at 1800 samples per hour, the accelerometer magnitude was computed using (1). Further arithmetic mean of accelerometer magnitude was also computed from the accelerometer readings [27, 28]. 1 Magnitude=xi2+yi2+zi2 Where xi, yi, zi are the accelerometer readings at a given time instant i. Gyroscope probe features The Gyroscope sensor aids in determining the orientation of a device using the earth’s gravity. It tracks the rotations of the device in x,y and z directions. The gyroscope probe reading consists of x, y, and z axes, which specify the axe’s rotations. From the raw individual axis gyroscope data taken at 1800 samples per hour, the Variance of the individual axis was computed. Application usage probe features Smartphone applications were clustered into various categories from their google play store website entries. For Example- WhatsApp is part of the “communication” category The average amount of hours spent on each category per day was computed (using the TimeInForeground entry (provided by the sensor) as a variable). The subset of categories considered for the study were communication category, social category, entertainment category, health and fitness category, music and audio category, weather category, travel category, books and reference category, shopping category, events category, photography category, maps and navigation category, business category, etc. Location probe features Location probe, also known as GPS, periodically records the latitude and longitude entries of the user. Four samples per hour were collected. Using these samples, location variance [52] was calculated as the combined Variance of the latitude and longitude components as shown in (2). 2 Location Variance=log(σlat2+σlong2) Where σlat2,σlong2 are the Variance’s of latitude and longitude, respectively. Speed mean [52] was also extracted, i.e., mean of instantaneous speed’s obtained at each location sample. The instant speed was computed as the change in latitude and longitude values over two consecutive instants, as shown in (3) 3 Speed mean=(lati−lati−1ti−ti−1)2+(longi−longi−1ti−ti−1)2 Where lati, longi are the latitude and longitude at the time of sample i. Variance and mean were computed on instantaneous speed values along with total distance [52] -i.e. total geographical displacement using (4) 4 Total distance=∑i(lati−lati−1)2+(longi−longi−1)2 Where lati, longi are the latitude and longitude at the time of sample i. Bluetooth probe features Bluetooth probe was used to track nearby devices using Bluetooth via periodic information transmission. There were 12 samples recorded per hour, and the probe components were grouped day wise and the number of unique encounters was computed using addresses as an index. Features such as Average Unique Bluetooth encounters per day, Variance, and Standard Deviation of the number of unique devices encountered were used in the study. Light/brightness probe features The Light probe measures the illuminance of the device (user). The app recorded light levels 12 times per hour. Mean, Variance, and Standard Deviation of the readings were chosen as features. Screen unlock probe features The Screen Unlock probe is activated whenever the user unlocks his/her screen. This probe returns true (Boolean value) once the user unlocks the screen and the time stamp is recorded. The total count of all screen unlocks has been used as a feature [9, 40]. Table 4 summarizes all the smart phone usage features. Table 4 Smart phone usage features Parent feature Description Statistical features No. of extracted features Accelerometer probe To measure acceleration (the rate of change of velocity). We approximated accelerometer magnitudes using (1). Mean of the accelerometer magnitude was computed 1 Gyroscope probe To measure orientation of the phone. Axis-wise variance of entries were calculated 3 Application Usage probe App categories were extracted using their package references from play store Average amount of hours per day spent on each application category by the user 36 Location probe Raw readings were used to calculate location variance (2), speed mean (3) total distance (4). location variance (2) and its mean, Variance and mean of the instantaneous speed (3) and total distance (4) were calculated. 6 Bluetooth probe The entries are grouped day wise and the number of unique encounters were calculated using the Address entry. Day-wise mean, Variance and Standard Deviation of entries 3 Light (brightness) probe To measure the illumination of the device(user). Brightness probe readings are used here Mean, Variance and Standard Deviation of readings 3 Screen unlock probe The entries were divided on the basis of binary readings provided by the probe The percentage of entries where the screen_on entry is True with respect to the total number of entries was calculated 1 Visual feature extraction Two kinds of Openface’s low-level features were used for feature extraction. They are 1) 68 facial landmark location coordinates, their visualization is shown in Fig. 3(a). 2) Facial Action Coding System(FACS). FACS is a method for categorizing facial movements based on their appearance on the face. The facial Action Unit (AU) represents almost every subtle movement of muscles on the face. Figure 3(b) shows a few AU’s. A subset of AU (specifically: 1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 20, 23, 25, 26, 28, and 45) are recognized by Openface. Each AU has the following - AU occurrence (0 if the AU is absent and 1 if it is present) and AU intensities (degree of variability in the scale of 0 to 5, where 0,1 and 5 represent not present, minimum and maximum intensity, respectively). Fig. 3 Visual feature extraction (a) Visualization of 68 facial landmark location coordinates (b) Examples of few action units extracted from Cohn and Kanades database [25] In the present study, visual features are of two categories: 1) Geometrical features(using 68- landmark locations) and 2) Facial Action unit features(using FACS). Different geometrical features, i.e., displacement features, distance features, and region unit features were formed. Further statistical features were extracted from these two categories of features. Geometrical features Displacement Features Using one coordinate, the displacements of 6 specific landmark points (marked as dp1,dp2,dp3, dp4,dp5 and dp6 in Fig. 4) were computed using (5) 5 Displacement=(xi−xi+1)2+(yi−yi+1)2 where (xi, yi) denotes the landmark coordinates present in the frame i, (xi+ 1, yi+ 1) the same landmark coordinates in the frame i + 1 where i ranges from 0 to n − 1 (0 is the first and n − 1 is the last frame). Distance features Using two coordinates, Eight Euclidean distance values i.e mean squared distances (marked as d0,d1,d2,d3,d4,d5,d6, and d7 in Fig. 4) between two pairs of coordinates were computed using (6). 6 Distance=(xi−xj)2+(yi−yj)2 where (xi, yi) and (xj, yj) represents landmarks of two different coordinates in the same frame. These distances were calculated for all the frames. Region unit features Using more than two coordinates(as marked as A0, A1, and A2 in Fig. 4), the Area of the irregular polygon was used to compute the area of the mouth, left eye, and right eye using specific points in that region using (7) 7 Area=12∑(xiyi+1−xi+1yi) when i = n − 1, then i + 1 is expressed as 0. where (xi, yi),(xi+ 1, yi+ 1) to (xn− 1, yn− 1) represents the set of points in frame i. Facial action unit features Features from each AU occurrence and AU intensities were taken. Fig. 4 Geometrical Features representation using facial landmark locations From both: geometrical and action unit features, statistical features like mean, median, standard deviation, etc., listed in Table 5 were extracted. Table 5 Summary of the facial features Feature Feature Description Statistical features No. of category Name extracted features Displacement features Displacement (using (5)) of the six specific points as marked in Fig. 4 denoted by blue points as dp1 to dp6. Mean, median, minimum, maximum, kurtosis, mode, standard deviation, Root mean square, skewness for Each of 6 displacement points. (dp1 to dp6) 54 Geometrical features Distances features Distances(using (6)) between 8 pairs of points as marked in Fig. 4 denoted by black lines as d0 to d7 Mean, median, minimum, maximum, kurtosis, mode, standard deviation, Root mean square, skewness for Each of 8 distances. (d0 to d7) 72 Region Units Area of the mouth. Area of the left eye and Areaof the right eye(7) as marked in Fig. 4 is denoted by red irregular lines as A0, A1 and A3. Mean, median, minimum, maximum, kurtosis, mode, standard deviation, Root mean square, skewness for Areas of mouth, left eye and right eye. (A0 A1 and A2) 27 Facial Action Unit Features Action Unit features The facial action coding system is used to quantify the muscle movements on the face. AU occurrences present(1) or absent(0) for 18 AU. Mean, median, standard deviation, kurtosis, mode, Root mean square, skewness for each 18 AU present/ absent. 126 If present, AU intensities for 17 AU intensities. Mean, median, standard deviation, maximum, kurtosis, mode, Root mean square, skewness for each 17 AU intensities 136 Audio feature extraction Generally, depression diagnosis is subjective in nature which can be manipulated. So we assumed that acoustic features are more powerful than linguistic characteristics. Audio features were computed from the audio files which were recorded at the sampling frequency of 32000 Hz during the speech elicitation experiment. Table 6 lists the details of the features that were extracted. Table 6 Audio features Feature Description Statistical features No. of Name extracted features Pitch It is an approximation of the quasi-periodic rate of vibrations per speech cycle. mean, median, standard deviation, minimum, mode maximum, kurtosis, Root mean square, skewness 9 Intensity It is the measure of the perceived loudness. mean, median, standard deviation, minimum, mode maximum, kurtosis, Root mean square, skewness 9 Formants [F1,F2, F3,F4] They indicate resonating frequencies of the vocal tract. The formant with the lowest frequency band is F1, then the second F2, which occurs with 1000Hz intervals. mean, median, standard deviation, minimum, maximum, kurtosis, mode, Root mean square, skewness 36 Pulses A fundamental, audible, and steady beat in the voice. Count, Mean, standard deviation, variance 4 Amplitude It is the size of the oscillations of the vocal folds due to vibrations caused by speech biosignal. minimum, maximum, mean, Root mean square 4 Mean Absolute jitter It is the absolute difference between consecutive vocal periods, divided by the mean vocal period. Mean 1 Jitter (local, absolute) The absolute difference between consecutive periods, in seconds. Mean 1 Relative average perturbation jitter It measures the effects of long-term pitch changes like slow rise/fall in pitch. It is calculated as the average absolute difference between a period and its average and its 2 neighbours, divided by the mean period. Mean 1 5-point period perturbation Jitter It is calculated using the average absolute difference between a period and the average of it and its 4 closest neighbours, divided by the mean period. Mean 1 Mean absolute differences Jitter It is the absolute difference between consecutive differences between consecutive periods, divided by the mean period Mean 1 Shimmer It defines the short-term (cycle-to-cycle) tiny fluctuations in the amplitude of the waveform which reflects inherent resistance/noise in the voice biosignal. Mean 1 Mean Shimmer Average absolute difference between the amplitudes of consecutive periods, divided by the average amplitude. Mean 1 Mean Shimmer dB average absolute base-10 logarithm of the difference between the amplitudes of consecutive periods, multiplied by 20. Mean 1 3-point Amplitude Perturbation Quotient Shimmer It is calculated as the average absolute difference between the amplitude of a vocal period and the average of the amplitudes of its neighbours, divided by the average amplitude. Mean 1 5-point Amplitude Perturbation Quotient Shimmer It is the average absolute difference between the amplitude of a vocal period and the average of the amplitudes of it and its 4 closest neighbours, divided by the average amplitude. Mean 1 11-point Amplitude Perturbation Quotient Shimmer It is the average absolute difference between the amplitude of a vocal period and the average of the amplitudes of it and its 10 closest neighbours, divided by the average amplitude Mean 1 Mean absolute differences shimmer Average absolute difference between consecutive differences between the amplitudes of consecutive periods. Mean 1 Harmonicity of the voiced parts only It is used for measuring the repeating patterns in voiced speech signals. Mean 1 Mean autocorrelation It is used for measuring the repeating patterns in the speech signal. Mean 1 Mean harmonics-to-noise ratio It is a measure which gives the relationship between the periodic and additive noise components of the speech signal. Mean 1 Mean noise-to-harmonics ratio It is a measure which gives the relationship between the periodic and additive noise components of the speech signal. Mean 1 Fraction of locally unvoiced frames It is a fraction of pitch frames analysed as unvoiced pitch (75Hz) frames in a speech biosignal of a specified length. Mean 1 Number of voice breaks The number of distances between consecutive vocal pulses that are longer than 1.25 divided by the pitch floor. Hence, if the pitch floor is 75 Hz, all inter-pulse intervals which are longer than 16.6667 ms are called as voice breaks. Count 1 Degree of voice breaks This measure is the total duration of breaks between the voiced parts of the speech signal. Mean 1 Total energy Total energy of a vocal signal in air. Mean 1 Mean power The mean power of a speech signal in air. Mean 1 Feature selection Feature Selection is a mechanism to choose an optimum subset of features that improves classification efficiency with less complexity and computing costs. In the current study, numerous features (smart phone usage-53,visual-413, and audio-82) were extracted. Generally, high dimensionality of input features may lead to poor performance because feature space becomes huge and also it is observed that our dataset contains correlated features. Therefore, two different feature selection approaches were experimented to select the better features which improves the accuracy. Feature selection using correlation Correlation analysis is a statistical technique used for measuring the strength of the linear relationship between two or more attributes [1]. The Pearson correlation coefficient technique was used in the present study for three reasons:1) it is easy to implement,2) it suits our data (type), and 3) it is vastly used in the literature for depression detection [31, 33, 38, 39, 49, 60]. Given two attributes X and Y, which have ‘n’ values. The Pearson’s correlation coefficient (r) can be determined using (8). 8 r=Cov(X,Y)σXσY r is the correlation coefficient, Cov(X, Y ) is the covariance, σXand σY are the standard deviation of X and Y, respectively. Suppose X and Y are two set of values containing [x1, x2,⋯xn] and [y1, y2,⋯yn]. r value can be calculated using (9) 9 r=∑i=1n(Xi−X¯)(Yi−Y¯)∑i=1n(Xi−X¯)2∑i=1n(Yi−Y¯)2 where n is the size of the sample, Xi and Yi are the ith data value and X¯,Y¯ are the mean values of X, Y respectively. r value lies in between -1.0 and + 1.0. The r defines two parameters for any given two sets of values. They are:1) strength: it measures how much these sets are associated, higher the value greater the relationship) and 2) direction of the relationship: if one value increases in one set, then other value increases in another set or one value decreases in one set, then other value decreases in another set. In short, when both move in the same direction. it is called positive correlation. Converse is a negative correlation, i.e., in the opposite direction. 0 signifies there is no relationship. As the value reaches closer to + 1, the relationship becomes stronger.+ 1 indicates a perfect strong correlation. Similarly, those values close to -1 show a strong negative correlation. Figure 5 shows visualization of three kinds of correlations. Fig. 5 Visualization of three kinds of correlation :a) positive correlation b) negative correlation, and c) no correlation In the present work, the r values between the features and labels were examined. While examining, we observed a high correlation in the features themselves of our dataset. So, we decided to reduce the redundancy of the dataset by removing the highly correlated features. In all such cases, only one feature with a high correlation value with the label was selected, while other features were removed from the dataset. After deriving feature sets into the training and testing, a threshold correlation value(r) of 85% in the training data set was selected by experimentation(which resulted in improvement in terms of accuracy), and then resultant features were dropped in both the training and also testing dataset (to avoid overfitting). Feature transformation using dimensionality reduction A feature transformation technique called Principal Component Analysis (PCA) was used. PCA was applied to reduce/minimize the dimensions of the feature vector. The number of components in the resultant feature vector was based on the most promising principal components that have 95% variances to classify the labels. Normalization Each modality in our study belongs to various scales. Hence min-max normalization, i.e., scaling between 0 and 1 on the feature vectors on individual modality, was applied. The current study was performed on the normalized feature vectors. Figure 12(a) for visual representation. Results In this section, first, the efficacy of the extracted features using statistical analysis of Pearson’s correlation coefficients is described. Second, the classification results using ML classifiers (LR, DT, NB, RF, and SVM) on individual data modality and fused data modalities are presented. Lastly, the effectiveness of the proposed approach is demonstrated by comparing the results on a subset of feature vectors of a benchmarking dataset in depression detection called the Distress Analysis Interview Corpus (DAIC) dataset [23]. Statistical analysis This subsection describes the efficacy of the extracted features using statistical analysis to prove the capability of the features to predict the depressed or non-depressed subjects. Each feature value and corresponding binary class label was analysed using pair-wise comparison with Pearson’s correlation analysis. The r values(using (9)) were computed using this pair-wise comparison to find positive and negative correlated features. These r values were sorted to pick the top 10 correlated features in each category. Table 7 lists the top 10 features which were found to be positively correlated with the ground truth labels. It is worth noting that all the top 10 features are Action Unit (AU) features of the visual modality. Table has the AU feature name, its description, and r value (the strength of the correlation and direction). Table 8 lists the top 10 features which are negatively correlated. it contains feature name, its description, and r value. We have listed only 10 features for simplicity because the features extracted were numerous in the conducted study. Table 7 The top 10 positive correlated features with their description, r value (strength of correlation and direction) S.no Feature Name Description r value 1 AU 12 standard deviation (A_12_S) Lip corner puller intensity Standard deviation 0.64277 2 AU 12 root mean square (A_12_R) Lip corner puller intensity root mean square 0.62708 3 AU 12 maximum (A_12_M) Lip corner puller intensity maximum 0.562378 4 AU 12 mean (A_12_MN) Lip corner puller intensity mean 0.51846 5 AU 10 standard deviation (A_10_S) Upper lip raiser standard deviation 0.51244 6 AU 06 maximum (A_6_M) Cheek Raiser maximum 0.49279 7 AU 25 root mean square(A_25_R) Lips part root mean square 0.48731 8 AU 25 count (A_25_C) Lips part count 0.48315 9 AU 25 mean (A_25_M) Lips part mean 0.47884 10 AU 06 standard deviation (A_6_S) Cheek raiser standard deviation 0.473363 Table 8 The top 10 negative correlated features with their description, r value(strength of correlation and direction) S no Feature Name Description r value 1 AU 25 skewness (A_25_S) Lips part count skewness -0.44741 2 Fraction of locally unvoiced frames (F_L_U) It is a fraction of pitch frames analyzed as unvoiced pitch (pitch is 75Hz) frames in a voice. -0.3891 3 Degree of voice (D_V_B) This measure is total duration of breaks between the voiced parts of the speech signal -0.3784 4 AU 10 skewness (A_10_SK) Upper lip raiser skewness -0.36990 5 AU 09 skewness (A_9_S) Nose wrinkle skewness -0.34867 6 AU 25 kurtosis (A_25_K) Lips part kurtosis -0.34069 7 Pitch Skewness (P_SK) It is pitch’s skewness -0.3217 8 AU 12 skewness (A_12_SK) Lip corner puller skewness -0.3195 9 Shimmer APQ3 (SAQ) It is the average absolute difference between the amplitude of a vocal period and the average of the amplitudes of it and its 2 closest neighbours, divided by the average amplitude. -0.312 10 Mean absolute differences shimmer(MDS) Average absolute difference between consecutive differences between the amplitudes of consecutive periods. -0.3129 Figures 6 and 7 show the positive and negative correlations in the sample of participants, respectively. The feature vectors are normalized (0 to 1) for better understanding. To avoid clutter in the graphs, we have chosen only top five features rather then all the top 10 features listed in both categories. Fig. 6 Top 5 Positive correlated feature variations Fig. 7 Top 5 Negative correlated feature variations The graph in Fig. 6 shows the variations of positively correlated features between depressed and non-depressed subjects. For example, A_12_R (see Table 7 S.No 2) has lower values in depressed (1-5) and higher values in Non-depressed (6-10). The graph in Fig. 7 shows the variations of negatively correlated features between depressed and non-depressed subjects. For example, F_L_U(see Table 8 S.No 2) has higher values in depressed (1-5) and lower values in Non-depressed (6-10). Figures 8 and 9 show the participant’s wise variations in the positive and negative correlated features for a sample of participants, respectively. Fig. 8 Participant wise variations in Top 10 positive correlated features. Red and blue lines indicate the depressed and non-depressed participants, respectively Fig. 9 Participant wise variations in Top 10 negative correlated features. Red and blue lines indicate the depressed and non-depressed participants, respectively The graph in Fig. 8 shows insights into how the positively correlated features vary between depressed and non-depressed subjects. For example, Depressed subjects exhibit lower values in A_12_S (see Table 7 S.No 1) features and higher values for non-depressed subjects for the same feature. The graph in Fig. 9 shows insights into how the negatively correlated features vary between depressed and non-depressed subjects. Depressed subjects exhibit higher values in A_25_S (see Table 8 S.No 1) feature and lower values for non-depressed subjects for the same feature. To show single feature variations in all the participants, A_10_S (see Table 7. S.No 5) feature from the positive correlated feature set and A_25_S (see Table 8 S.No 1) feature from the negative correlated feature set were selected. Figures 10 and 11 show single feature variations in positive and negative correlated features, respectively. Fig. 10 Positive correlated single feature variation in all participants Fig. 11 Negative correlated single feature variation in all participants The graph in Fig. 10 shows how values are different in a single feature(positive correlated) between non-depressed and depressed subjects. For example the values of A_10_S (see Table 7. S.No 5) feature have higher values for the most non-depressed subjects and lower for the depressed subjects. The graph in Fig. 11 shows how values are different in a single feature(negative correlated) between non-depressed and depressed subjects. For example, the values of A_25_S (see Table 8 S.No 1) feature have lower values for the most non-depressed subjects and higher values for the depressed subjects. Classification results of individual modality and using feature fusion This subsection describes the classification results using a family of machine learning classifiers implemented on individual data modalities and by fused data modalities. ML classifiers like LR, DT, NB, RF and SVM were used with default hyper parameters. All the results presented are in terms of average accuracy because of the balanced dataset. The dataset was randomly categorised (without any overlap) into two components:80 percent training data and 20 percent testing data. Table 9 lists the classification results using individual modality (see Fig. 12b for visual representation) with:1) All the extracted feature vectors (see row# 1 to 5) then with feature selection mechanisms using the reduced feature vectors based on 2) Pearson’s statistical correlation analysis(see row# 6 to 10), and 3) PCA (see row# 11 to 15). Table 10 lists the classification results using feature fusion(see Fig. 12b for visual representation):1)concatenating all the features of the individual modalities.(see row# 1 to 5). 2) concatenating the reduced feature vectors of individual modalities, which are obtained with Pearson correlation analysis(see row# 6 to 10), and 3) Applying PCA over concatenated feature vectors of individual modality(see row# 11 to 15). Fig. 12 Summary of the investigated system configuration: a) feature preparation steps for smart phone usage, audio-visual modalities. b) using normalised feature vectors of different modalities:Individual and feature fusion techniques that were investigated Table 9 Average accuracy classification results for individual modalities S.no. Individual ML smart phone visual audio Average modalities Classifiers modality modality modality Accuracy # Acc # Acc # Acc 1 All features LR 53 61 415 70 82 60 64 2 DT 68 77 55 67 3 NB 62 78 68 69 4 RF 69 79 67 72 5 SVM 65 80 60 68 6 Pearson correlation reduced feature vector LR 45 60 166 79 57 67 69 7 DT 50 80 60 63 8 NB 58 66 61 62 9 RF 50 80 68 66 10 SVM 55 80 72 69 11 PCA LR 28-30 66 40-42 80 20-22 69 72 12 DT 68 69 52 63 13 NB 66 72 50 63 14 RF 66 79 50 65 15 SVM 69 80 69 73 Individual modality average 62 77 62 #- Number of features in a resultant feature vector, Acc-accuracy, and average accuracy corresponds to the row average to demonstrate each ML classifier used in different methods. Individual modality average corresponds to the column average of the individual modality Table 10 Average Accuracy classification results for fused modalities S.no. Fused ML smart-phone+ smartphone + video+ audio Method All modalities Classifiers audio modality video modality modality Average modalities # Acc # Acc # Acc Acc # Acc 1 Concatenate all features LR 135 82 468 78 497 81 80 550 83 2 DT 81 78 80 80 80 3 NB 82 72 75 76 80 4 RF 79 83 83 82 83 5 SVM 81 79 83 81 84 6 Concatenate Pearson correlation removed feature vectors LR 101 80 209 81 220 79 80 265 79 7 DT 81 79 82 81 80 8 NB 80 81 82 81 83 9 RF 85 80 80 82 85 10 SVM 83 83 84 83 86 11 95% of variance of PCA over concatenated feature vectors LR 40-42 75 30-32 79 40-42 79 78 50-55 78 12 DT 70 62 60 78 65 13 NB 79 72 79 77 74 14 RF 83 73 65 74 75 15 SVM 81 79 80 80 82 Fused modalities average 80 77 78 80 #- Number of features in a resultant feature vector,Acc-Accuracy, and method average corresponds to the row average to demonstrate each ML classifier used in different methods. Fused modalities average corresponds to the column average of the fused modality. Bold: fused modalities performed well when compared with the method average. Fused modalities average is the column average to demonstrate the average of each modality combination From Table 9, it is evident that visual results are more encouraging than smart phone usage and audio modalities in all the performed ways. The reason could be among the features extracted, visual modality features show a higher correlation than smart phone and audio modality features. (refer Section 4.1). From Table 10, using feature fusion, SVM with the concatenation based on Pearson’s correlation analysis showed the best performance, i.e., 86% accuracy(see row# 10).where as naive byes and random forest showed slightly lesser accuracy rates of 83% and 85%, respectively(see row #8-9). From Tables 9 and 10, the combination of modalities led to better performance in terms of accuracy. In most of cases, bi-modality performed better than uni-modality, tri-modality performed even better than bi-modality. From the results, we hypothesise that high correlation among the features contributes to redundancy in the dataset. This could confuse the classifier. Therefore removing the redundant features will enhance the performance of the system. Comparisons of results on benchmarking dataset Lastly, we demonstrate the effectiveness of the proposed approach by the comparison of the results on a subset of feature vectors on DAIC dataset(widely used benchmarking dataset in depression detection). However, it is to be noted that this dataset contains only visual and audio data modalities and lacks smartphone usage data. To the best of our knowledge, we could not find any benchmarking dataset with all the three data modalities that we have proposed. Hence we have chosen this dataset to compare the results with the two data modalities. The features (described in Section 3.4.2 visual feature extraction and Section 3.4.3 audio feature extraction) were extracted on the DAIC dataset, and the results were compared. Table 11 lists the average accuracies with the proposed feature vectors on ML Classifiers on DAIC dataset. Table 11 Results of proposed approach on DAIC Dataset S.no. ML Classifiers Fused All features Pearson correlation PCA modalities features reduced feature vector # Acc # Acc # Acc 1 Logistic Regression Audio 82 70 50 81 20-25 68 2 Video 230 81 72 83 20-25 80 3 Video + Audio 312 83 122 86 30-35 83 4 Decision Tree Audio 82 62 50 71 20-25 62 5 Video 230 80 72 80 20-25 82 6 Video + Audio 312 80 122 82 30-35 82 7 Naive Bayes Audio 82 55 50 70 20-25 70 8 Video 230 80 72 75 20-25 80 9 Video + Audio 312 80 122 82 30-35 80 10 Random Forest Audio 82 74 50 66 20-25 64 11 Video 230 85 72 80 20-25 81 12 Video + Audio 312 85 122 85 30-35 81 13 Support Vector Machines Audio 82 74 50 68 20-25 67 14 Video 230 85 72 83 20-25 82 15 Video + Audio 312 85 122 86 30-35 83 # - number of features in the feature vector. Acc-Accuracy, and BOLD: Best accuracies obtained Note- DAIC dataset does not contain all the low-level openface feature sets. Hence we extracted statistical feature vector on the available low-level feature vector of DAIC dataset From Fig. 13, it is evident that LR and SVM using audio and video achieved 86% accuracy. Hence we believe that our approach can work on any kind of depressive diagnosis detection with similar cues. Fig. 13 Comparision of accuracies over ML classifiers with feature selection methods: All Features, Pearson’s correlation analysis, and PCA using both audio and video An alternative measure of accuracy,the Receiver Operating Characteristic(ROC) [29] is shown in Fig. 14. In ROC curve,a graph between true positive rate(sensitivity) and false positive rate(specificity) of ML classifiers were plotted. Curves were plotted using Pearson’s correlation reduced feature vector, which gave the best accuracy. The Area under the ROC Curve called as AUC(Area Under Curve) is also provided is Fig. 14. it can be seen that, the highest Score of AUC (79%) for SVM classifier that indicates better performance over other classifiers in classifying depressed and non-depressed subjects. Fig. 14 ROC curve of ML classifiers Conclusion This study investigated multi modal features extracted from MCS and task/interview based mechanism to identify depressed and non-depressed participants. For this purpose, the user data was collected in a unique way by acquiring their smartphones usage data, emotion and speech elicitation mechanisms. In our research, we designed and experimented with an end-to-end machine learning approach, which involves multimodal data collection, feature extraction, feature selection, feature fusion, and classification to determine and distinguish depressed and non-depressed subjects. We experimented with: various features from multimodalities individually, and by fusing them, features selection techniques based on PCA and Pearson’s correlation analysis, and different machine learning classifiers such as Logistic Regression, Decision Tree, Naive Bayes, Random Forest, Support Vector Machines for classification. Our findings suggest that combining features from multiple modality performs better than any single data modality and the best classification accuracy is achieved when features from all the three data modality are fused. Also feature selection method based on Pearson’s correlation coefficients improved the accuracy in comparison to using all the features and other selection technique like PCA. Amongst different machine learning classifiers that we experimented with, SVM yielded the best accuracy of 86%. Our proposed approach was also applied on a benchmarking dataset, and results demonstrated multimodal approach to be advantageous in performance with state-of-the-art depression recognition techniques. Limitations and future work It is a common problem in similar kind of studies, that the limited number of participants because of the selection criteria could affect the result analysis. A large-scale study using a clinically validated depression diagnosis is preferable. Demographic labels(gender, age, marital status, etc.), which are the main factors for depression diagnosis, can be further explored. For the study, we have manually collected verbal and non-verbal cues using zoom meetings. Developing an automatic assessment technique using a mobile application that could monitor the mobile phone usage patterns and check the verbal and nonverbal indicators(when user provide permission) would be more beneficial. In the present work, we have used a popular technique of Pearson’s correlation for statistical analysis. However, different statistical analysis techniques and their comparative study could reveal the more scientific significance of the work. Our proposed approach needs at least 14 days of smart phone usage patterns for classification, which could be a little costly. Any machine learning algorithm has to maintain a trade-off between the time duration required and output prediction. Future studies will build a classification approach with 3/7/10 days of the most influential factors that contribute to depression diagnosis. Our future work will explore semantic cues in verbal, head pose and eye gaze features in visual, along with skin conductance and heartbeat in physiological modalities along with a focus on more advanced smart phone usage variables. Acknowledgements The authors wish to thank all the participants who helped to do this research. Declarations Ethics approval The experimental procedure used for the study is approved by the Institutional Committee of Visveswaraya National Institute of Technology, Nagpur, India. For this research study, volunteer’s smart phone usage logs, visual and verbal data were used only after obtaining informed written consent forms. Conflict of Interests There is no conflict of interest. 1 https://www.funf.org/journal.html 2 https://imotions.com/ 3 https://www.fon.hum.uva.nl/praat/ 4 http://sox.sourceforge.net/ 5 https://www.fon.hum.uva.nl/praat/ Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Agarwal S (2013) Data mining: data mining concepts and techniques, 203–207 (IEEE) 2. Alghowinem S, Goecke R, Wagner M, Parker G, Breakspear M (2013) Eye movement analysis for depression detection, 4220–4224 (IEEE) 3. 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==== Front Indian J Med Microbiol Indian J Med Microbiol Indian Journal of Medical Microbiology 0255-0857 1998-3646 Indian Association of Medical Microbiologists. Published by Elsevier B.V. S0255-0857(22)00048-2 10.1016/j.ijmmb.2022.03.003 Original Research Article Study of immunogenicity, safety and efficacy of covishield vaccine among health care workers in a tertiary cardiac care centre Mahadevaiah Ashwini ∗ Doddamadaiah Chaithra K S Sadananda Cholenahalli Nanjappa Manjunath SJICR, Mysuru, India ∗ Corresponding author. 11 4 2022 April-June 2022 11 4 2022 40 2 200203 7 10 2021 7 3 2022 9 3 2022 © 2022 Indian Association of Medical Microbiologists. Published by Elsevier B.V. All rights reserved. 2022 Indian Association of Medical Microbiologists Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Purpose The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) might be curtailed by vaccination. We assessed the safety, and immunogenicity of Covishield vaccine among Health care workers (HCWs) in a tertiary cardiac care centre. Methods It's a prospective analytical study, conducted at Sri Jayadeva Institute of cardiovascular science and research centre, Mysore, between January 2021 to May 2021. Pre and Post vaccination SARS CoV2 IgG antibodies were assessed among 122 HCWs. Interval between two doses in this study were 4 and 6 weeks. Adverse events following immunisation b(AEFI) and efficacy were assessed and followed up for two month post vaccination. Results Post vaccination seropositivity was 69.67% in overall study participants. Seropositivity and P/N ratio median value in uninfected and infected group were 60.43% (n ​= ​55),3.47 (IQR: 2.56–5.22) and 96.77% (n ​= ​30),9.49 (IQR: 7.57–12.30) respectively (P ​< ​0.001). Seropositivity and P/N ratio after 4 and 6 weeks were 48.3% (n ​= ​60), 2.95 (IQR: 1.91–4.24), and 83.8% (n ​= ​31), 4.88, (IQR: 3.39–6.43) respectively (P ​< ​0.001). AEFI after first and second dose was 72.9% and 27.8% (p ​< ​0.05) respectively. The most common symptoms after both doses of vaccination were local pain (73% & 88.2%), followed by fever (38.2% & 26.5%). The average duration of symptoms in both doses was 1.75 days. Of 122 participants only 10 (8.19%) had breakthrough infection after two doses of vaccination with mild severity. Conclusion Covishield vaccine has showed seropositivity of 69.67%.It has acceptable level of safety profile. Seropositivity and P/N ratio has increased with increase in interval between two doses. Though it has not prevented breakthrough infection it has certainly reduced the severity of infection. Keywords Covishield Health care workers (HCWs) Adverse events following immunisation (AEFI) Seropositivity ==== Body pmc1 Introduction Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused a human pandemic of coronavirus disease 2019 (COVID 19). COVID-19 was first reported in November 2019 in Wuhan, China, and subsequently spread worldwide. WHO declared covid19 ‘A Pandemic’ on March 11, 2020. As of July 10, 2021, more than 186 million cases have been confirmed, with more than 4.01 million confirmed deaths [1]. Healthcare workers (HCWs) are at high risk of acquiring infection with COVID-19 resulting in mortality, morbidity, mental stress, disruption of patient care, risk of transmission to patients and family members. Therefore, protection of HCWs from COVID-19 and early detection, isolation and treatment has become a priority worldwide. Currently there is no specific therapy and vaccine for COVID-19 as it is new to humankind and the nature of protective immune response is poorly understood. It is even unclear that which vaccine strategies will be the most successful. Therefore, it is imperative to develop various vaccine platforms and strategies in parallel. To meet the urgent need for a vaccine, a new pandemic vaccine development paradigm has been proposed that compresses the development timeline from 10–15 years to 1–2 years [2]. Among more than 160 vaccine candidates worldwide, a handful of them entered phase I, II, and III clinical trials [3]. M/s Serum Institute of India, Pune has presented a Recombinant Chimpanzee Adenovirus vector vaccine (Covishield) encoding the SARS-CoV-2 Spike (S) glycoprotein with technology transfer from AstraZeneca/Oxford University. After detailed deliberations, Subject Expert Committee has recommended for the grant of permission for restricted use in emergency situation [4]. Our study aims to assess the immunogenicity, safety and efficacy of Covishield vaccine among HCWs in a tertiary cardiac care centre. Aims and objective 1) Assessment of antibody response in HCWs following 2 doses of Covishield vaccination 2) Comparison of post vaccination antibody response between covid infected and uninfected HCWs. 3) Comparison of prevaccination and postvaccination antibody response in infected group. 4) Comparison of postvaccination antibody response between 4 and 6 weeks interval between two doses of vaccination in uninfected group. 5) Assessment of overall safety profile following vaccination. 6) Assessment of break through Covid infection in the vaccinated HCWs. 2 Materials and methods 2.1 Study design and participants A prospective analytical study was conducted at Sri Jayadeva Institute of Cardiovascular Science and Research, Mysuru between January 2021 to May 2021. A total of 122 HCWs of all staff cadre, > 18 years of age were included in the study. Both COVID infected and uninfected staffs were included. Prevaccination screening for SARS CoV2 IgG antibodies was performed. All staff with prior history of covid infection and those who were positive for pre vaccination antibodies were labelled as infected group (n ​= ​31). Staff with no prior antibodies and no history of covid infection were labelled as uninfected group (n ​= ​91). A subgroup of participants delayed the 2nd dose of vaccine by 2 weeks for various reasons; therefore incidentally we had 2 groups who took second dose of vaccine at 4 and 6 weeks interval. Written consent from all the participants and ethical committee approval were obtained. 2.2 Vaccine administration 2 doses of 0.5 ​ml of COVISHIELD vaccine was administered intramuscularly into deltoid region at the interval of 4 weeks and in subset of the participants at 6 weeks interval. 2.3 Methods 2 ​ml of blood sample was collected just prior to vaccination and after 28 days of second dose of vaccine. IgG antibodies for SARS CoV2 were assessed using COVID KAWACH IgG MICROLISA Kit (Microwell ELISA test for the qualitative detection of COVID 19 IgG antibodies in serum). The antigen used is SARS CoV 2 virus whole cell antigen coated on to the microtiterplate. Though the kit is recommended for sero surveillance, in this study, Positive to Negative ratio (P/N ratio) which is Sample OD (Optical Density) divided by Mean Negative control, was also determined and analysed comparatively between different groups. Test procedure and interpretation of the result was done according to kit manufacturer's instructions. Sample OD more than cut off and P/N ratio more than 1.5 was considered as positive. Number of participants positive for IgG antibodies before and after vaccination, P/N ratio comparison between COVID infected and uninfected group was assessed and statistically analysed. At the time of vaccination participants were given questionnaires to be filled in if they encountered any Adverse Events Following Immunization (AEFI), which included symptoms, duration and severity observed for a period of one month post vaccination. Participants were instructed to report any symptoms suggestive of COVID infection, if tested positive they were isolated and treated according to severity. Breakthrough covid infection in vaccinated HCWs was observed for a period of two months which coincided with peak of second wave of covid pandemic in India. 2.3.1 Limitation of the study Control group with placebo could not be included to study the efficacy of the vaccine as no health care worker could be denied of vaccine in the prevalent pandemic situation. 2.4 Statistical analysis Descriptive statistics is used to present all outcomes, Mann Whitney U test is used for P/N ratio comparison. AEFI were analysed using Chi square test and Mann Whitney U test used for the comparison of duration of symptoms. A P value less than 0.05 shows statistical significance. Data entered in Microsoft excel and analysed using SPSS version 20.00. 3 Results Drugs Controller General of India (DGCI) permitted COVISHIELD vaccine for restricted use in emergency situation on January 3rd, 2021. The healthcare workers of Sri Jayadeva Institute of Cardiovascular Science and Research, Mysuru received their first dose of vaccine on January 23, 2021 followed by second dose after 4 week interval, and in a subset of study group at 6 weeks interval. Of 122 sample size 13 participants had past history of covid infection at least 2 months prior to vaccination. Prevaccination screening for IgG antibodies for SARS CoV2 was done in all participants. 26 (21.31%) were tested positive for antibodies. Out of 26 only 8 had previous history of COVID infection, rest 18 had no prior history of covid infection. 5 participants in the infected group were negative for antibodies. After prevaccination screening the total no of participants in the infected group were 31 and 91 were uninfected. We have assessed the baseline characteristics, seropositivity rate to vaccine and P/N ratio in the two groups. In this study population the range of age varied from 19 to 69 years with an average age of 35years. 53% of study group were male [Table 1 ].Table 1 Baseline characteristics of the study population. Table 1Variables Frequency Percentage Age, (range, Mean ​± ​SD) 19–69 years, 35.33 ​± ​6.93 Gender Male 65 53.30% Female 57 46.70% Designation Doctor 20 16.40% Staff Nurse 57 46.70% Technician 27 22.10% DEO 3 2.50% Group D 13 10.70% Security 2 1.60% H/O Covid Infection Yes 13 10.70% Infected group Yes 31 25.40% Uninfected group Yes 91 74.59% Postvaccination seropositivity for SARS CoV2 IgG antibody was 69.67% in overall study participants. Seropositivity and P/N ratio median value in uninfected and infected group were 60.43% (n ​= ​55), 3.47 (IQR: 2.56–5.22) and 96.77% (n ​= ​30), 9.49 (IQR: 7.57–12.30) respectively (P value ​< ​0.001). The Pre and post vaccination P/N ratio in the infected group was 3.31 (IQR: 2.68–4.54) and 9.49 (IQR7.57–12.30) respectively (P value ​< ​0.001). Seropositivity and P/N ratio after 4 and 6 weeks were 48.3% (n ​= ​60), 2.95 (IQR: 1.91–4.24), and 83.8% (n ​= ​31),4.88, (IQR: 3.39–6.43) respectively (P value ​< ​0.001). [Table 2 and Table 3 ].Table 2 Seropositivity among study population. Table 2 Frequency Percentage IgG Positive Prevaccination Yes 26 21.31% No 96 78.68% IgG Positive Post vaccination Yes 85 69.67% No 37 30.32% IgG Positive Post vaccination in uninfected group(n ​= ​91) Yes 55 60.43% No 36 39.56% IgG Positive Post vaccination in infected group(n ​= ​31) Yes 30 96.77% No 01 3.22% Table 3 Comparison of PN Ratio among the study population. Table 3Group Median Inter Quartile Range Test Statistic P Value Post vaccination group 6.522 <0.001 Infected 9.49 7.57–12.30 Uninfected 3.47 2.56–5.22 Infected group 5.793 <0.001 Pre vaccination 3.31 2.68–4.54 Post vaccination 9.49 7.57–12.30 Post vaccination in uninfected group 3.761 0.001 4 ​Weeks 2.95 1.91–4.24 6 ​Weeks 4.88 3.39–6.43 AEFI were recorded in the study population. After first dose, 72.9% participants reported AEFI which reduced to 27.8% following second dose (P value ​< ​0.05). The most common symptoms after I and II dose of vaccination were local pain (73% & 88.2%), followedby fever (38.2% & 26.5%), myalgia (31.5% & 11.8%), headache (30.3% & 44.1%) respectively. The average duration of symptoms post vaccination in both doses is 1.75 days. Only one participant reported symptoms lasting for more than 7 days. Majority had mild illness in both doses (81.81%) [Table 4 , Fig. 1 ].Table 4 Descriptive assessment of safety profile of the study population. Table 4Side Effects After first dose After second dose P Value Yes, n (%) No, n(%) Yes, n (%) No, n(%) Local Pain 65 (73%) 24 (27%) 30 (88.2%) 4 (11.8%) 0.072a Fever 34 (38.2%) 55 (61.8%) 9 (26.5%) 25 (73.5%) 0.222a Giddiness 10 (11.2%) 79 (88.8%) 1 (2.9%) 33 (97.1%) 0.149a Headache 27 (30.3%) 62 (69.7%) 15 (44.1%) 19 (55.9%) 0.145a Local swelling 6 (6.7%) 83 (93.3%) 1 (2.9%) 33 (97.1%) 0.416a Body ache 28 (31.5%) 61 (68.5%) 4 (11.8%) 30 (88.2%) 0.025∗a Fatigue 10 (11.2%) 79 (88.8%) 2 (5.9%) 32 (94.1%) 0.371a Low Backache 9 (10.1%) 80 (89.9%) 2 (5.9%) 32 (94.1%) 0.462a Duration, (range, Mean±SD) days 1–15 days, 1.75 ​± ​1.89 1–10 days, 1.85 ​± ​2.03 0.795b Duration <3 ​Days 76 (85.4%) 5 (5.6%) 27 (79.4%) 7 (20.6%) 0.021∗a 4–7 ​Days 4 (4.5%) 79 (88.8%) 3 (8.8%) 31 (91.2%) 0.407a >7 ​Days 1 (1.1%) 82 (92.1%) 1 (2.9%) 33 (97.1%) 0.511a P ​< ​0.05 shows significance. a Chi square test, b Mann Whitney U test, Fig. 1 Frequency of most common AEFI after I and II dose of COVISHIELD vaccine. Fig. 1 Incidence of covid infection over a period of 2 months after vaccination was found to be very less in our study population. Out of the 122 participants only 10 (8.19%) had developed infection after vaccination with mild severity and none among the infected group had Reinfection after vaccination. 4 Discussion In this prospective analytical study we assessed immunogenicity, safety and efficacy of covishield vaccine. Two doses of covishield vaccine were administered to our HCWs from January to March 2021, as a part of vaccination program by government of India for priority group. Of 122 total study population, 31 were grouped under infected group based on both history of covid infection and presence of prevaccination covid antibodies and rest were included in uninfected group (n ​= ​91). Out of 13 participants who had history of covid infection 8 (61.5%) had IgG antibodies with P/N ratio median value 3.31 (IQR: 2.68–4.54). Different studies have given different rate of seropositivity in covid infected patients. Pyoeng Guyn Choe et al. [5] in their study conducted in South Korea has reported 71% seropositivity in asymptomatic and 100% seropositivity in severe Covid infection after 8 weeks of postinfection. In a similar study by Etienne Brochot et al. [6] out of 151 samples of mild to severe Covid infected cases, seropositivity was 100% for anti RBD, 2 were negative for anti S2. In the same study out of 25 samples of asymptomatic covid positive cases, only 56% were seropositive after 2 weeks of post infection. Therefore the rate of seropositivity after covid infection depends on severity of the disease and duration from the time of infection. Out of uninfected participants 18 (16.5%) had Prevaccination IgG antibodies, attributing to asymptomatic or mild infection unnoticed. In a seroprevalence study conducted by Tanu Singal et al. [8] in HCWs conducted in a private hospital in Mumbai, seropositivity was 4.3% in asymptomatic and 70% in symptomatic untested participants. All staff was administered 2 doses of covishield vaccine. Second dose of the vaccine was administered at the interval of 4 weeks (n ​= ​90), 32 participants happened to miss the second dose at 4 weeks but received the dose at 6 weeks. Post vaccination screening for IgG antibodies were done after one month of last dose. Out of 122 participants 85 (69.67%) were found to have IgG antibodies. In the infected group (n ​= ​31), postvaccination IgG antibodies were detected in 30 (96.77%) participants. One infected participant who was negative for antibodies in prevaccination was found to be negative in post vaccination also. In a similar study conducted by Gabriele Anichini, M.S. et al. [9], one participant out of 100 HCWs was negative for IgG antibodies following natural infection and vaccination. In the uninfected group (n ​= ​91), 55 (60.43%), were found to have IgG antibodies postvaccination. The median value of P/N ratio in the infected and uninfected groups were 9.49 (IQR: 7.57–12.30) and 3.47 (IQR: 2.56–5.22) respectively (P value ​< ​0.001). This attributes to booster effect of vaccine in infected group. A similar study by Gabriele Anichini, M.S. et al. [9] has reported significant difference in antibodies titer between infected and uninfected HCWs and reported that after the administration of a single dose of vaccine, the humoral response against SARS-CoV-2 in persons with a history of SARS-CoV-2 infection is greater than the response in previously uninfected participants who have received a second dose. Accordingly Vaccination process stimulates B cell only whereas natural infection stimulates both T cell and B cell, asconcluded by Catherine J. Reynolds et al. [10]. Comparative study between two different intervals between two doses of the vaccine was done in uninfected group only to avoid influence of past natural infection on seropositivity. There was found to be statistically significant difference in the Seropositivity and P/N ratio between two intervals, 48.3%, 2.95 (IQR: 1.91–4.24) and 83.8%, 4.88 (IQR: 3.39–6.43) at 4 and 6 weeks respectively (p ​< ​0.01). Clinical trial group also suggests extended period between two doses [11]. Any vaccine is associated with AEFI, which is a major road block to successful universal vaccine coverage. Our study observed AEFI for a period of one month after each dose. Localized illness like pain, swelling, redness, itching, abscess, weakness and generalized illness such as fever, headache, myalgia, giddiness, low backache, radiating pain, generalized rashes, anaphylaxis, cough, sore throat, and neurological pain were considered as AEFI. Incidence of AEFI after I dose was 72.9% compared to 27.8% after second dose of vaccine (P ​< ​0.05) (Fig. 1). The most common symptom was local pain after both dose of vaccine, followed by fever in the first dose and headache in the second dose. The trend of symptoms was similar after both doses. Majority of the participants, 85.4% after first dose and 79.4% after second dose had symptoms lasting for less than 3 days, only one participant following both dose complained of symptoms lasting for more than 7 days. The average duration of symptoms post vaccination in both doses is 1.75 days. Majority of the participants had mild illness following both doses. None of them had severe illness or illness requiring admission. In a similar study by Pedro M Folegatti et al. [12], pain was the most common symptom and self-limiting with no major adverse events. Severity being highest on first day after vaccination. Similar study conducted by Merryn Voysey et al. [13] has also demonstrated acceptable safety profile of ChAdOx1 nCoV-19 vaccine. Post vaccination covid infection was reported by 10 participants with mild severity. None from the infected group had breakthrough infection. Natural immunity followed by booster effect of vaccine has conferred higher protection than vaccine only. With regard to advent of variants of COVID 19 and also non availability of data on waning of COVID antibodies and its clinical implication it is imperative to consider booster dose based on this observation. In a study conducted by Ezgi et al. [14] in a cohort of 417 who had either, Pfizer – Biotech or Moderna vaccine, two women had vaccine breakthrough infection. This observation indicate potential risk of infection even after successful vaccination which emphasizes on importance of covid appropriate behaviour. 5 Conclusion Covishield Vaccination among health care workers has shown good immunogenicity with a seropositivity rate of 69.67% in overall study participants, and 60.43% in those who had no prior natural infection. Pain is the most common AEFI, lasting less than 3 days with no major adverse events in our study. Seropositivity and P/N ratio has increased with increase in interval between two doses by 2 weeks. Covid Vaccine has shown a booster effect on those who had past natural infection. Booster doses may be considered in future. Though it has not prevented breakthrough infection it has certainly reduced the severity of infection. Credit author statement Dr C N Manjunath, Dr Sadananda K S - Conceptualization, Methodology, Dr Ashwini M - Data curation, writing- Original draft preparation, Visualization, Investigation , Supervision, Writing- Reviewing and Editing, Dr Chaithra D - Data curation, writing- Original draft preparation, Writing- Reviewing and Editing. Funding Sri Jayadeva Institute of Cardiovascular Science and Research. Declaration of competing interest None. ==== Refs References 1 World health Organization (Who) Coronavirus disease (COVID-19) dashboard Available from: https://covid19.who.int 2020 2 Nicole Lurie Saville Melanie Hatchett Richard Halton Jane Developing Covid-19 vaccines at pandemic speed N Engl J Med 382 2020 1969 1973 10.1056/NEJMp2005630 https://www.nejm.org/doi/full/10.1056/NEJMp2005630 32227757 3 Kaur S.P. Gupta V. COVID-19 Vaccine: a comprehensive status report Virus Res 288 2020 198114 10.1016/j.virusres.2020.198114 32800805 4 Press statement issued by the drugs controller general of India (DCGI) on restricted emergency approval of COVID-19 virus vaccine, New Delhi, 2021, HFW//DGCI Media Statement on COVID VACCINE/2021/2. 5 Choe P.G. Kim K.H. Kang C.K. Suh H.J. Kang E. Lee S.Y. Kim N.J. Yi J. Park W.B. Oh M.D. Antibody responses one year after mild SARS-CoV-2 infection J Kor Med Sci 36 21 2021 e157 10.3346/jkms.2021.36.e157 6 Brochot Etienne Demey Baptiste Antoine Touzé Belouzard Sandrine Jean Dubuisson Schmit Jean-Luc Duverlie Gilles Francois Catherine Castelain Sandrine FrancoisHelle Anti-Spike, anti-Nucleocapsid and neutralizing antibodies in SARS-CoV-2 hospitalized patients and asymptomatic carriers Front Microbiol 2020 10.3389/fmicb.2020.584251 8 Singhal T. Shah S. Naik R. Kazi A. Thakkar P. Prevalence of COVID-19 antibodies in healthcare workers at the peak of the pandemic in Mumbai, India: a preliminary study Indian J Med Microbiol 38 3 & 4 2020 461 463 10.4103/ijmm.IJMM_20_308 33154264 9 Anichini G. Terrosi C. Gandolfo C. SARS-CoV-2 antibody response in persons with past natural infection N Engl J Med 385 1 2021 90 92 10.1056/NEJMc2103825 33852796 10 Reynolds C.J. Pade C. Gibbons J.M. Prior SARS-CoV-2 infection rescues B and T cell responses to variants after first vaccine dose (published online ahead of print) Science 2021 eabh1282 10.1126/science.abh1282 11 Pimenta D. Yates C. Pagel C. Gurdasani D. Delaying the second dose of covid-19 vaccines BMJ 372 2021 n710 10.1136/bmj.n710 33737404 12 Folegatti Pedro M. Ewer Katie J. Aley Parvinder K. 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==== Front ADV TRADIT MED (ADTM) Advances in Traditional Medicine 2662-4052 2662-4060 Springer Singapore Singapore 640 10.1007/s13596-022-00640-8 Research Article In silico ADMET, molecular docking and molecular simulation-based study of glabridin’s natural and semisynthetic derivatives as potential tyrosinase inhibitors http://orcid.org/0000-0001-5149-9401 Kumari Arti arti.mbio@patnawomenscollege.in 1 kumar Rakesh rakeshbis@cub.ac.in 2 Sulabh Gira gira23yadav@gmail.com 3 Singh Pratishtha rai.pratishtha86@gmail.com 4 Kumar Jainendra jainendrak@gmail.com 5 Singh Vijay Kumar vksingh@cub.in 2 Ojha Krishna Kumar kris@cub.ac.in 2 1 grid.412457.1 0000 0001 1276 6626 Department of Biotechnology, Patna Women’s College, Patna, Bihar India 2 grid.448755.f 0000 0004 1764 7337 Department of Bioinformatics, Central University of South Bihar, Gaya, Bihar India 3 grid.411639.8 0000 0001 0571 5193 Department of Pharmacology, Manipal TATA Medical College, Manipal Academy of Higher Education, Jamshedpur, Manipal India 4 grid.10706.30 0000 0004 0498 924X School of Life Sciences, Jawaharlal Nehru University, New Delhi, India 5 grid.499253.0 Patliputra University, Patna, Bihar India 11 4 2022 119 8 1 2022 24 3 2022 © The Author(s), under exclusive licence to Institute of Korean Medicine, Kyung Hee University 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Hyper-pigmentation conditions may develop due to erroneous melanogenesis cascade which leads to excess melanin production. Recently, inhibition of tyrosinase is the main focus of investigation as it majorly contributes to melanin production. This inhibition property can be exploited in medicine, agriculture, and in cosmetics. Present study aims to find a natural and safe alternative molecule as tyrosinase inhibitor. In this study, human tyrosinase enzyme was modelled due to unavailability of its crystal structure to look into the degree of efficacy of glabridin and its 15 derivatives as tyrosinase inhibitor. Docking was performed by Autodock Vina at the catalytic core enzyme. Glabridin effects on melanoma cell lines was also elucidated by analysing cytotoxicity and effect on melanin production. Computational ADME analysis was done by SwissADME. Molecular dynamic simulation was also performed to further evaluate the interaction profile of these molecules and kojic acid (positive inhibitor) with respect to apo protein. Notably, four derivatives 5′-formylglabridin, glabridin dimer, 5′-prenyl glabridin and R-glabridin exhibited better binding affinity than glabridin. Glabridin effectively inhibited melanin production in a dose dependent manner. Among these, 5′-formylglabridin displayed highest binding affinity with docking score − 9.2 kcal/mol. Molecular properties and bioactivity analysis by Molinspiration web server and by SwissADME also presented these molecules as potential drug candidates. The study explores the understanding for the development of suitable tyrosinase inhibitor/s for the prevention of hyperpigmentation. However, a detailed in vivo study is required for glabridin derivatives to suggest these molecules as anti-melanogenic compound. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s13596-022-00640-8. Keywords Hyperpigmentation Melanogenesis Tyrosinase Glabridin 5′-formylglabridin Kojic acid Molecular simulation Docking Melanoma Skin whitening ==== Body pmcIntroduction Glabridin {systematic name: 4-[(3R)-8,8-dimethyl- 3,4-dihydro-2H-pyrano[2,3-f] chromen-3-yl] benzene-1,3-diol} is a polyphenolic flavonoid compound from liquorice (Glycyrrhiza glabra, Fabaceae). It was first characterized in 1976 (Saitoh et al. 1976). Glabridin has been reported as main iso-flavanoid of licorice and its content in dried roots varies from 0.08 to 0.35% (w/w) (Simmler et al. 2013; Hayashi et al. 2003). Glabridin is known as species specific marker compound of Gycyrrhiza glabra Linn and is absent in certain species of the Glycyrrhiza genus (Lim, 2016). It also has phytoestrogen properties and displays broad range of biological activities. Several studies have reported pharmacological activities of glabridin mainly in terms of antibacterial (Kumari et al. 2020, 2019a; Fukai et al. 2002), antifungal (Gupta et al. 2008), anti-oxidant (Fukai et al. 2000), anti-inflammatory, antiviral, anti-allergenic, neuroprotective (Cui et al. 2008), anti-osteoporotic (Yu et al. 2008), anti-tumorigenic properties (Choi, 2005; Hsu et al. 2011). Damle (2014) and Simmler (2013) reviewed pharmacological properties of Glycyrhhiza glabra and also talked about skin whitening properties of G. glabra extracts. The role of glabridin in skin whitening needs more investigation. There has been a research gap after the study performed by Yokata et al. 1998 which state that glabridin contains two hydroxyl groups which are important for the inhibition of tyrosinase and melanin synthesis. The hydroxyl group at the 4′position is more closely related to the inhibition (Yokota et al. 1998). Melanin absorbs harmful ultraviolet radiations (UVR) and protects the skin (Pillaiyar et al. 2018). Conglomeration of melanin pigments leads to hyper-pigmentary disorders (Cestari et al. 2014). Melasma, post inflammatory hyper pigmentation, Café- an- lait, age spots, solar lentigo, erythema dyschromicum perstans, prurigo pigmentosa, Linea niagra, freckels, lentigines are common dermatological problems (Baurin et al. 2002) and don’t have the advance treatment strategies presently. Tyrosinase (EC.1.14.18.1, Syn. Polyphenol oxidase) is the key enzyme of the Melanogenesis pathway and catalyses the conversion of tyrosine into dihydroxyphenylalanine (DOPA), and then, DOPA into dopaquinone (Shi et al. 2002; Kobayashi et al. 1995). Both steps are rate limiting reactions of the melanin biosynthesis pathway (Kumari et al. 2019b; Radhakrishnan et al. 2013; Raper, 1928; Kobayashi et al. 1995; Borges et al. 2001). As over-expression of brain tyrosinase catalyses production of dopaquinones in the process of age-dependent neuromelanin production, which in excess leads to neuronal damage, implicating the tyrosinase potential in neurogenerative diseases like Parkinson’s and Huntington’s diseases (Pillaiyar et al. 2017; Cavalieri et al. 2002; Vontzalidou et al. 2012; Hasegawa et al. 2010; Tessari et al. 2008; Greggio et al. 2005; Nithitanakool et al. 2009). Tyrosinase has also been associated with the browning of vegetables and fruits during post-harvest and handling processes, leading to quick degradation (Yi et al. 2010; Friedman, 1996; Mayer, 1986). The role of tyrosinase has been further explored in the moulting process of insects and wound healing (Liu et al. 2007; Pillaiyar et al. 2017). Structurally, this multifunctional enzyme’s catalytic core has two copper (III) atoms, surrounded by three histidine residues which act as catalytic centre of the enzyme (Matoba et al. 2006; Masum et al. 2019). The most prominent approach for the development of melanogenesis inhibitors is the down regulation of tyrosinase as strong correlation has been observed between tyrosinase and melanin pigmentation (Masum et al. 2019; Pillaiyar et al. 2017). Targeting tyrosinase is the key to comprehend the knowledge of melanin inhibition mechanism which can help to improve hyper pigmentary conditions (Lee et al. 2020). Several tyrosinase inhibitors are reported as potential therapeutic and cosmetic agents for various hyperpigmentation conditions and skin related issues respectively. Tyrosinase inhibitors can stumble the high tyrosinase activity and play notable role as remedies for skin disorders (Pillaiyar et al. 2017). Apart from their role in medicine, tyrosinase inhibitors have attracted much attention in the cosmetic industry (Pillaiyar et al. 2017). Nature has always been a great reservoir of the novel compounds which can be exploited to obtain various drugs (Mushtaq et al. 2018) and these phytochemicals and their derivatives are naturally produced by plants as secondary metabolites which helps in defence against pathogens (Isah 2019, Kumari et al. 2021). Several studies have reported better results from derivatives in the treatment of diseases than the parent compound (Haudecoeur et al. 2017). However, in medicine, tyrosinase inhibitors are important clinical antimelanoma drugs but only a few compounds are effective and safe. Thus, the hunt for safe cosmetic agents demands identification of new molecules/substance with more powerful actions, and without any side effect (Ali and Naaz 2015). Thus, to enhance the knowledge of anti-melanogenic natural compounds, we have conducted the virtual docking studies of glabridin and its various structural derivatives with the modelled human tyrosinase enzyme (hTYR) to evaluate their anti-tyrosinase efficacy and thus they could potentially prevent abnormal pigmentation. It also reports the effect of glabridin on the melanoma cells for its anti-melanogenic potentiality. The aim of the current study is to find a suitable candidate for the development of new skin whitening agent for hyper pigmentary disorders or cosmetic purpose which may have lesser adverse effects. Materials and methods The effort was to find the anti-tyrosinase molecule using derivatives of glabridin. This study includes the natural compounds present in Glycyrhhiza glabra plant which are proposed to have anti-tyrosinase activity in traditional usages. All derivatives included in the study are (R)- glabridin [4484219], 5′prenyl glabridin [23641093], 2′,4′-O- dimethyl glabridin [480856], glabridin dimer [5481978], R- Hispaglabridin A [4484221], R-Hispaglabridin B [5318057], 5′-formylglabridin [46230506], 4′-O-Methyl preglabridin [44257508], 4′-O-prenyl glabridin [480861], 4′-O-methyl glabridin [5319664], 3′-hydroxy-4′methoxy glabridin [10338211], 2′-O-methylglabridin [74830193], 2′-O,5′-C-diprenylglabridn [480868], Hispaglabridin A [442774] and Hispaglabridin B [15228661]. These are structural derivatives of glabridin and can be obtained from the extract of Glycyrhhiza glabra plant by HPLC purification except Glabridin dimethyl ether (2′,4′-O- dimethyl glabridin) and 2′-O-methylglabridin. These two compounds are semisynthetic and Belinky et al. (1998) synthesized them by the following method. Glabridin (100 mg, 0.31 mmol) in a round bottom flask was dissolved in acetone (2 ml) and methyl iodide (30 ml, 0.33 mmol) and K2CO3 (64 mg, 0.46 mmol) were added. The reaction mixture was heated to 50 °C with stirring, and after 6 h, the conversion reaction was determined by HPLC analysis. Glabridin (35%), 2′-O-methylglabridin (30%), 4′-O-methylglabridin (20%) and 2′,4′-O-dimethylglabridin (15%) were obtained. These compounds were separated and purified on a silica gel column, using CH2Cl2 and then CH2Cl2/3% CH3OH as eluents. 2′,4′-O-dimethylglabridin can be synthesized in a higher yield by using excess of methyl iodide and potassium carbonate in the initial reactions. Kojic acid was used as positive control for the docking studies and for in vitro studies glabridin (62% pure) was purchased from Kan Phytochemicals Pvt. Ltd.; Sonipat, Haryana. The experimental workflow employed is displayed in Fig. 1.Fig. 1 Experimental workflow employed during the present study Homology modelling of hTYR and secondary structure prediction Due to non-availability of an experimentally established high—resolution X-ray crystal structure of hTYR in the Protein Data Bank, a three-dimensional structural model of the same was built using MODELLER (Webb et al. 2014) based on homology modelling. Tyrosinase protein sequence (Homo sapiens) (Accession no AAB60319.1) was retrieved from the NCBI protein database (https://www.ncbi.nlm.nih.gov) (Johnson et al. 2008) and used as query sequence to perform BLASTp. A structural model of the human tyrosinase was constructed using MODELLER using published crystal structure of 5, 6‐dihydroxyindole‐2‐carboxylic acid oxidase of human RCSB Protein Data Bank (Berman et al. 2007) (PDB ID: 5M8Q). The modelling template showed maximum 44.44% identity and query coverage 81%. Modelling was done for the catalytic domain by removing 25 amino acids from the start along with a short sequence of 59 residues from 471 to 529 of the peptides. Two copper molecules were added to the catalytic centre obtained from Bacterial tyrosinase (Matoba et al. 2006). The overall quality of the modelled structure was validated through the Ramachadran plot using Rampage server. The secondary structure of the human tyrosinase enzyme sequence was also determined by self-optimized prediction method (SOPMA) (Combet et al. 2000). The secondary structure of protein is important to understand the three-dimensional structure of the protein and its function. The default parameters of window width 17, similarity threshold 8 and number of states 4 were used for this study. Structure preparation of ligands and target modelled hTYR Glabridin and its derivatives (supplementary data-Table 1S) were retrieved from PubChem database to investigate their possible impact as inhibitor of human tyrosinase. 3D structures of glabridin derivatives were downloaded in sdf format. The sdf files were converted into pdbqt file format using open babel tool on Linux version 16.04. Active site prediction and molecular docking of ligands with modelled hTYR Active site prediction was performed by CastP web server (Tian et al. 2018). Docking studies were carried out on Autodock Vina (Trott and Olson 2010) which carries out automated preparation steps for both receptor and ligands that include the addition of partial charges and required hydrogen atoms. Docking of ligand molecules was performed on predefined grid obtained from the template structure in complex with kojic acid (Lai et al. 2017). The grid size was 50 × 50 × 50 and dimension for X, Y and Z coordinates − 15.715, 5.183 and − 20.640, respectively. These parameters were also used to dock glabridin derivatives using Autodock Vina executed on Linux platform. The docking was validated by best possible conformation of each ligand and the best confirmation of each compound was selected based on the lowest docking score and their binding interactions measured in terms of Gibbs free energy (∆G). The docked structures (protein–ligand complexes) stabilize through several binding interactions such as polar, hydrophobic, hydrophilic, pi-pi stacking, salt bridge etc. The molecular interactions were analysed and evaluated based on their polar and hydrophobic interactions using structure visualization tool Pymol version 2.3 (DeLano 2002), Chimera10.1 (Pettersen 2004) and LigPlot (Wallace et al. 1995). Anti-melanogenic activity of glabridin on melanoma cell line The anti-melanogenic activity of glabridin was evaluated on B16F10 cell line. The cytotoxicity of glabridin on murine melanoma cell lines (B16 melanoma cells) and effect of glabridin concentrations on the melanin production by melanoma cells was observed. Effect of glabridin on Melanin production by B16 melanoma cells Melanin contents in cultured B16 melanoma cells were measured according to the method of Oikawa et al. (1973) with slight modifications. B16 melanoma cells were seeded (initial density of cells 0.4 × 104 cells/cm2) in T75 culture flasks, and glabridin was added to the culture medium with concentrations from 1.0 to 5 µg/ml. After 3 days culture, the cells were collected by brief trypsinization and subsequent centrifugation, and then treated with 5% trichloroacetic acid, ethyl alcohol: diethyl ether (3:1) and diethyl ether successively in this order. The cells were dissolved with 2 M NAOH containing 10% DMSO. Doxorubicin was used as positive control. The melanin contents were measured with Agilent Cary 50 UV Visible spectrophotometer at 400 nm. Results were analysed statistically using statistical software Prism 5 via one-way analysis of variance at P < 0.05 with control. Cell viability assay (MTT assay) B16F10 cells were grown to confluence in T75 cm2 flask supplemented with Dulbecco’s Modified Eagle Medium and 10% fetal calf serum in CO2 incubator with 5% CO2. Cells were seeded at a density of 1 × 104 cells per well in DMEM medium. Twenty-four hours post seeding, cells were treated at concentrations ranging from 5 to 200 µg/ml of glabridin for different time intervals (24 and 48 h). After the exposure times, MTT was added to a final concentration of 0.5 mg/ml medium and the plates were incubated for 4 h at 37 °C. The purple formazan crystals formed were dissolved in DMSO and read at 570 nm in a micro-quant plate reader. The assay was carried out in triplicates. The results were expressed as % inhibition. Drug likeness and In-silico ADME prediction Early detection of ADME properties reduces the failure in clinical phases and also minimises the load of synthesis of compounds for testing its potentiality for drug. Hence, it has become a vital tool in drug candidate identification. On this note, in silico prediction of the ADME properties (absorption, distribution, metabolism and excretion) was performed using SwissADME web tool (Daina et al. 2017) to determine the activity of these molecules within human body. These pharmacokinetic parameters were evaluated for the glabridin and its 15 derivatives to investigate their drug candidate chance. SMILES of each compound was obtained from PubChem database and was used for the analysis. The drug likeness of the molecules was predicted by adopting Lipinski’s Rule of 5. The rule of 5 predicts molecules with more than 5H-bond donors, 10-H bond acceptors, molecular weight more than 500 Da and the logP greater than 5 likely to had poor absorption and permeation of molecular entities (Ibrahim et al. 2020). Molecular dynamics simulation study MD simulation has been performed using GROMACS 5.1.2. (Berendsen et al. 1995) to analyse the structural stability of tyrosinase upon ligand binding, under GROMOS96 43a1 force field (Pol-Fachin et al. 2009). MD simulations for apo-protein (TYR) along with all of the receptor-ligand complexes, TYR-Kojic acid, TYR-5′- formylgrabridin, TYR-5′ prenylglabridin, TYR- Glabrdin dimer, TYR- 3′-hydroxy-4′-methoxyglabridin and TYR- (R)-glabridin, were performed in triclinic periodic boundary conditions. The topologies for ligand molecules were prepared using PRODRG (Schüttelkopf and Van Aalten 2004). The systems were prepared with solvation using SPC water model at 1 nm marginal radius, followed by neutralization by adding the significant number of Na+ ions. The final systems, consisted of Protein, Ligand (except for TYR), Na+ ions and solvent, was subjected to energy minimization step for each system was performed using steepest decent integrator for 5000 iterations using the 0.01 energy step. After that, NVT (Berendsen et al. 1995) and NPT (Berendsen et al. 1984) ensembles were employed for temperature and pressure coupling and equilibration with leap-frag integrator for 50,000 steps (100 ps). Finally, 50,000 ps production simulations of each system were performed 2 fs time interval (Millan et al. 2017). The RMSD of backbone, RMSF of C-alpha atoms, SASA, Rg, and hydrogen bond were retrieved from MD simulation trajectories, and analysis plots were prepared using OriginPro. Result and discussions Homology modelling Homology modelling of hTYR was successfully done. Stereo chemical properties of 3D protein structure of hTYR, obtained through the structure model algorithm (Fig. 2A) and validated with several validation tools associated with save server, were analysed through the Ramachandran plot. Ramachandran plot analysis indicated that more than 90% amino acid residues accommodate in the allowed region (Fig. 2B). These properties improved after energy minimization through YASARA server and 94% residues lay in the allowed region of Ramachandran plot. As the tyrosinase activity is dependent on the presence of two copper ions, they were added to the catalytic centre from the bacterial tyrosinase (Lai 2017). Secondary structure prediction results (Sup Table 2S) and homology model were analogous to each other. Majority of studies have implicated mushroom tyrosinase and bacterial tyrosinase (da Silva et al. 2017; Lima et al., 2014; Radhakrisnan et al. 2013). However, differences in the binding pockets of mushroom and human tyrosinase have been reported (Garcia -Borron, 2002). Only few researchers have reported modelling of human tyrosinase enzyme (Lee et al., 2020; Nokinsee et al. 2015) but in their modelling studies, the template similarity was very low. Nokinsee et al. (2015) used protein template, tyrosinase of B. megaterium (PDB Id- 3NQ1) which had highest identity with human tyrosinase of only 33.5%, whereas in present study template used for homology modelling is Human tyrosinase related protein (PDB ID 5M8Q) which showed 44.6% identity with hTYR.Fig. 2 A Three-dimensional structure of the modelled human tyrosinase (hTYR) (Two red dots in the centre represents Copper metal ions essential for the catalytic activity), B The Ramachandran plot for the modelled protein: the residues occurring red coloured region are in allowed region, residues in yellow region are in generously allowed region. (97% residues are in the allowed region where as 1.6% residues are in generously allowed region Interaction study between protein and ligand complexes Active site prediction by Cast p resulted in several active sites. Among major active sites, site 1 active site was found to be the largest pocket. Kojic acid binding coordinates with template protein were similar to the coordinates of site1 active site of modelled enzyme. The docking of the glabridin and its derivatives was successfully achieved with the Autodock vina tools and the results were analysed in term of the binding energy. The docking results of Glabridin and kojic acid are presented in Fig. 3 (3A—glabridin and hTYR docking; 3B—Kojic acid and hTYR docking). Docking scores in terms of binding affinity for glabridin and its derivatives are presented in Table 1. Docking structures of the molecules which showed lesser docking energy score than glabridin are presented in Fig. 4A, B, C and D. Cast P analysis of docked structures of these molecules also confirmed that all ligand molecules were docked in the active pocket (site 1) of the modelled tyrosinase and amino acid composition of this site is presented in Table 3S (suppl). Few representative glabridin derivative molecules bound in the active pocket 1 has been presented in Fig. 5. Glabrdin and its all 15 derivatives were docked in the active pocket of the enzyme which indicate that they may interfere with tyrosinase activity. The hydrophobic interaction of these ligand molecules along with kojic acid was calculated using LigPlot and generated interactions are shown in Fig. 6 (where the ray like structures represent the hydrophobic relationship between the atoms as well as the molecules) except for glabridin dimer due its complex structure. The hydrophobic interaction was calculated within the 4 A° region around ligand molecules.Fig. 3 Docking structure of ligands (Glabridin and Kojic acid). A: Docking picture of glabridin with modelled  hTYR, B: Docking picture of Kojic acid with modelled hTYR Table 1 List of glabridin & glabridin’s derivatives and Kojic acid with their docking score (Gibb’s free energy) Name of Ligand Dock Score (Gibb’s free energy) 3′- hydroxy 4′ methoxy-glabridin  − 8.0 4′-O- methylglabridin  − 7.6 4′-O-prenyl glabridin  − 8.0 4′-O-methyl preglabridin  − 7.6 5′- formylglabridin  − 9.3 5′- prenylglabridin  − 8.8 2′-O, 5′-C-diprenyl glabridin  − 8.6 Glabridin dimethylether  − 7.6 Glabridin dimer  − 9.2 Hispaglabridin A  − 7.9 2′-O- methylglabridin  − 8.1 (R) -glabridin  − 8.9 (R)- hispaglabridin A  − 8.0 (R)- hispaglabridin B  − 8.6 Hispaglabridin B  − 7.9 Glabridin  − 8.8 Kojic Acid  − 6.8 Fig. 4 Docking structure of glabridin derivatives- A: 5′formylglabridin ligand docked with modelled hTYR (Dock score:  − 9.3), B: Glabridin dimer ligand docked with modelled hTYR (Dock Score:  − 9.2), C: 5′ prenylglabrdin ligand docked with modelled hTYR (Dock Score:  − 8.8), D: (R)—Glabridin ligand docked with modelled hTYR (Dock score:  − 8.9) Fig. 5 Cast p generated files analysed in Chimera 10.1 which shows that all ligands were docked in the site 1 active pocket whereas the orientation of ligands are different Fig. 6 Hydrophilic and hydrophobic interaction between ligand (glabridin, 5′- formylglabridin, Kojic acid, R- glabridin, 5′prenylglabridin and 3′- hydroxy -4′ methoxyglabridin) with modelled hTYR Among all derivatives, 5′-formylglabridin comes out to be the best molecule based on its docking score (− 9.3). Other important molecules which can be analysed in vivo for their anti-tyrosinase activity along with 5′-formylglabridin are glabridin dimer, 5′-prenyl glabridin and (R)—glabridin which showed attractive binding modes with docking scores − 9.2, − 8.8, − 8.9 and − 8.6 respectively. Glabridin binds to tyrosinase by forming totally 3 hydrogen bonds with residue SER355, ASN 339, Cu 501, where C’2 of glabridin binds to SER355 and Cu501, C’4 of glabridin binds to ASN339. This is in agreement with study of Yokata et al. 1998. It can be inferred that both group of glabridin are important for the inhibition activity of the glabridin. Cu forms the active centre of the enzyme. Hence, glabridin interfere with the tyrosinase activity. This study revealed the anti-tyrosinase property of glabridin and its derivatives. Our study shows that, the amino acids involved in the H-bonding with kojic acid are not involved in the H-bonding with glabridin whereas those residues are involved in other type of interactions and this may suggest the non-competitive inhibition mechanism. This result is in accordance with the reports of Chen et al. 2016. We comparatively analysed the stability of glabridin, kojic acid, 5′-formyl glabridin, R- glabrdin, 5′ -prenylglabridin and 3′Hydroxy- 4′ methoxy binding with human tyrosinase and report the interacting residues in Table 2. Interacting residues of other molecules are tabulated in Suppl. Table 4S. 5′- formylglabridin forms four hydrogen bonds with residues ARG 171, ASP 161, ILE 173, GLU 178, R- glabridin forms 3 H-bond with SER 355, ASN 339 and Cu 501 whereas 5′ prenylglabridin forms only one hydrogen bond with ARG171. The binding mode of selected ligands in the active site of human tyrosinase model involved some very positive hydrophobic interactions. Important residues which are either involved in H-bonding or interact with other kind of interaction which are common with kojic acid are HIS 338, HIS 177, HIS 342, SER 350, VAL 352 and ASN 339 and possess very important role in interaction with these ligands. The residues which are limited to glabridin and its derivatives interaction are PHE 322, SER 330, SER 355, ILE 343, HIS 155, ARG 171 which could have important roles in the inhibition.Table 2 Comparative analysis of molecular interaction of kojic acid and ligand molecules (Glabridin and its few derivatives) with tyrosinase enzyme In vitro testing the effect of glabridin on to tyrosinase Treatment of B16 melanoma cells with glabridin at concentration 1, 1.5 and 2 µg/ml was found to decrease the melanin concentration effectively. Each group was statistically correlated with control as well as with other groups. Statistical analysis showed that the reduction in melanin content was highly significant at each treatment with respect to untreated cells. Analysis of significance level among different treatment showed that 2.5 µg/ml was significant than 1.5 µg/ml and similarly 2 µg/ml was significant than 1 µg/ml dosages of glabridin (Fig. 7A).Fig. 7 Testing the effect of glabridin on melanin production in B16F10 melanoma cell lines; A: Effect of glabridin’s different concentrations on melanin production is dose dependent. Significant reduction in melanin production was observed with increase in dosages, B: Effect of glabridin on cell viability. B16F10 cells were treated with varying concentrations of glabridin (5–200 µg/ml) for 24 h and 48 h. Bright field images were captured, and representative images for control and treatments are provided to indicate the morphological changes at two concentrations (5 and 40 µg) of glabridin. Cellular viability was measured via MTT assay and cell viability was calculated in percentage and IC 50 was also determined Cytotoxicity assay Morphological changes were observed in melanoma cells exposed to glabridin and showed toxicity in concentration dependent manner (Fig. 7B). Glabridin was shown to inhibit the melanin production in dose dependent manner and 1 µg/ml of glabridin significantly inhibited the melanin production in comparison to the untreated cells as control. The cytotoxicity analysis of glabridin was analysed from 5 to 200 µg/ml. Dose dependent cytotoxicity showed by glabridin was calculated as percentage of inhibition (Supplementary data 1S and 2S). The effect of compounds on the capability of cells to replicate is used as an index of toxicity; IC50 calculated for 24 h was 22.78 µg/ml, and for 48 h, it was 13.82 µg/ml which suggest that it could be efficiently used for the purpose (Supplementary data 3S). Drug-likeness and in silico ADME prediction analysis A molecule should fulfil certain criteria to become a potent drug molecule. Drug development procedure involves the assessment of ADME properties of a molecule which stands for absorption, distribution, metabolism and excretion. A potent molecule should reach the target in optimum concentration and stay there in bioactive form long enough for the expected biologic event to occur (Daina et al. 2017). Pharmacokinetics profile of the small molecule is important in drug discovery process. Swiss ADME results were categorised in 6 areas namely physiochemical properties, lipophilicity, water solubility, pharmacokinetics, drug likeness and medicinal chemistry. Bioavailability radar provides the first glance at the drug-likeness of a molecule. ADME results detailed in Table 3 reveals that, all derivatives of glabridin acquire acceptable drug like properties in addition to the other drug likeness parameters. All derivatives satisfied all the rule, (MW ≤ 500 Da, LogP < 5, nHBD ≤ 5, NHBA ≤ 10 and TPSA < 140 Å2) except glabrdin dimer which has molecular weight more than 500 Da and 2′-O,5′-C-diprenylglabridn whose log P value was higher than 4.15. All the compounds water solubility is moderate to poor. None of the compound are violating more than one rules of Rule 5 which is acceptable in case of natural compounds especially the molecular weight. All molecules have TPSA < 140, and most importantly the logKp which denotes the skin permeability is within the standard range of − 8.0 to − 1.0. A parameter analysed by Molinspiration web server is enzyme inhibition availability. All molecules have enzyme inhibition capability which ranged between 0.37 and 0.63. All the molecules included in this study showed the ADME parameters (Molecular weight, Log P, no of hydrogen bond donor, no of hydrogen bond acceptor, no of rotatable bond, TPSA, no Lipinski rule violation, Log Kp, Enzyme inhibition capability, Synthetic accessibility) results within favourable range. Thus, all have good oral bioavailability or permeability. Similar analysis was performed by Ibrahim et al. 2020 for some derivative molecules as elevators of p53 protein levels. Molecular properties and bioactivity analysis by Molinspiration web server (data tabulated as supplementary material 5S) projected all molecules as enzyme inhibitor. All molecules in this study are projected as potential drug molecule. Even though majority of our molecules could be considered further for analysis as drug candidate based on ADME properties and calculation of molecular properties and bioactivity score prediction by Molinspiration web server, we present only few molecules [Glabridin, 5′-formylglabridin, 5′- prenylglabridin, Glabridin Dimer, R- Glabridin and 3′-hydroxy-4′- methoxyglabridin] having lower binding affinity score than glabridin for the molecular dynamic simulation studies in comparison to positive control kojic acid along with Apo protein.Table 3 Lipinski properties of glabridin and its derivatives with SwissADME Name MW LogP nHBD nHBA nRotB TPSA Lip Vio Log Kp EI SA 2-D structure Bio-rad Glabridin 324.37 3.89 2 4 1 58.92 0  − 5.52 0.50 4.04 (R)- glabridin 324.37 3.89 2 4 1 58.92 0  − 5.52 0.50 4.04 5′prenyl glabridin 392.49 5.82 2 4 3 58.92 0  − 4.56 0.54 4.58 Glabridin dimethyl ether (2′,4′-O- dimethyl glabridin) 352.42 4.54 0 4 3 36.92 0  − 5.23 0.37 4.26 Glabridin dimer 648.74 6.95 4 8 0 117.84 1  − 5.23 0.58 6.44 R- Hispa glabridin A 392.49 5.82 2 4 3 58.92 0  − 4.56 0.63 4.58 R-Hispa glabridin B 390.47 5.17 1 4 1 47.92 0  − 5.01 0.42 4.45 5′-formyl glabridin 352.38 3.90 2 5 2 75.99 0  − 5.68 0.39 4.06 4′-O-Methyl pre glabridin 340.41 4.86 2 4 4 58.92 0  − 4.93 0.42 3.86 4′-O-prenyl glabridin 392.49 5.71 1 4 4 47.92 0  − 4.64 0.46 4.53 4′-O-methyl glabridin 338.40 4.21 2 5 2 68.15 0  − 5.72 0.43 4.19 3′-hydroxy-4′methoxy glabridin 354.40 3.86 2 5 2 68.15 0  − 5.72 0.43 4.19 2′-O-methylglabridin 338.40 4.21 1 4 2 97.72 0  − 5.38 0.45 4.15 2′-O,5′-C-diprenylglabridn 460.60 7.64 1 4 6 47.92 1  − 3.69 0.47 5.10 Hispaglabridin A 392.49 5.82 2 4 3 58.92 0  − 4.56 0.63 4.58 Hispaglabridin B 390.47 5.17 1 4 1 47.92 0  − 5.01 0.42 4.45 MW Molecular weight, LogP Log of octanol/water partition coefficient, nHBA Number of hydrogen bond acceptor (s), nHBD Number of hydrogen bond donor (s), nRotB Number of rotatable bonds, Log Kp Log of skin permeability, TPSA Total Polar surface area. EI Enzyme inhibition, SA Synthetic accessibility, 2D- Structure- 2-dimensional structure, Bio-RAD Bioavailability Radar (6 vertices represents six parameters- Lipophilicity, Size, Polarity, solubility and flexibility). Range of standard values for drug like molecule: Lipophilicity- 0.7 < LogP3 <  + 5.0; size—150 g/mol < MV < 500 g/mol, polar-20A°2 < TPSA < 130 A°2; Insolubility- 0.25 < FractionCsp3 < 1; flexibility- 0 < no of rotatable bond < 9 Comparison of Tyr (apo-protein) with Tyr in complex with Kojic acid, glabridin and its derivatives Comparative analysis of simulated trajectories of native protein, control ligand kojic acid and 5′-formylglabridin, glabridin, 5′ prenylglabridin, (R)—Glabridin and 3′ hydroxy—4′ methoxyglabridin were analysed in terms of root mean square deviation (RMSD), root mean square fluctuation (RMSF) and Radius of gyration (Rg) and Solvent Accessible Surface Area (SASA) plots with respect to time. The MD simulations studies were used to compare the binding of Glabridin and its derivatives with tyrosinase. The RMSD of backbone showed slight increase at initial stages, which was stabilized later during the course of simulation (Fig. 8A). Comparative RMSD analysis suggested that the binding of ligands molecules stabilises the TYR structure compared to the RMSD observed in apo-protein. The average RMSD of backbone was highest for TYR itself in comparison to TYR in complex with different ligands (Table 4). It depicts more stability of the complex as lower RMSD values indicate formation of more stable complexes (Sen Gupta et al. 2020). RMSF was plotted to compare and analyse the flexibility behaviour of the complex residues. The RMSF of C-alpha atoms of protein were higher in between residues 175–275 of TYR, which is binding region for Kojic acid and its derivative, rest of the structure remained stable (Fig. 8B). Protein structure compactness and stability can correctly be defined in terms of the radius of gyration (Rg) (Bhardwaj et al. 2020; Kalhor et al. 2020). The average Rg value for apo-protein is depicted near 2.13 nm by a small fall from the initial to the end of MD simulation. The Kojic acid-TYR complex showed the small fall till 10,000 ps (Fig. 8C) after that it maintained the structural stability till the end of simulation. The average Rg for Kojic acid-TYR complex is near 2.1 nm, although the average Rg value for TYR-grabridin complex and complex of glabridin derivatives with TYR is > 2.1 nm. The decline in total SASA score confirms less availability of residues to the solvent and further compaction upon binding of the ligands, which is quite evident in the case of TYR- Kojic acid and TYR- 5′ formylgrabridin complex and other complexes (Fig. 8D). The decrease in total SASA is validated by decrease in the number of intramolecular hydrogen bonds (Fig. 9), since hydrogen bonds are important for stable and precise binding (Sigala et al. 2009; Qiu et al. 2018). All complex of glabridin and its derivatives with TYR appears more reliable and stable in terms of RMSD, RMSF and Rg in comparison to Kojic acid complexed with TYR. Comparative analysis of SASA results of tyrosinase and its complex with kojic acid and other complexes suggested that glabridin and its derivatives are the stable ligand which shows its potential binding to TYR. Similar analysis was performed by Umesh et al. 2021 for identifying new anti-CoV drug chemical from Indian spices targeting SARS-CoV-2 main protease.Fig. 8 Molecular dynamic simulation studies of modelled Human Tyrosinase enzyme (hTYR), control ligand kojic acid complexed with TYR and 5′- formylglabridin ligand complexed with hTYR. A: Graphic presentation of root mean square deviation (RMSD) of apo-proteins and complex structures. B: Graphic presentation of root mean square fluctuation (RMSF) of apo-proteins and complex structures. C: Graphic presentation of radius of gyration (Rg) of apo-proteins and complex structures. D: Graphic presentation of Solvent Accessible Surface Area (SASA) of apo-proteins and complex structures Table 4 The average RMSD of protein backbones during the molecular dynamic simulations System Average RMSD of protein Backbone TYR (apo protein) 3.5 Å TYR- Kojic acid 3.5 Å TYR-5′-formylglabridin 3.2 Å TYR-5′-prenylglabridin 3.4 Å TYR-glabridin dimer 3.2 Å Fig. 9 Graphic representation of Hydrogen bonding pattern of complexes of TYR enzyme with kojic acid, glabridin and its derivatives during molecular dynamic simulation Conclusion The current approaches employed in identification of tyrosinase inhibitors recognises glabridin and its natural and semi- synthetic derivatives for their bioactive potential as tyrosinase inhibitor. Glabridin derivatives have not been explored for their role in tyrosinase inhibition. The primary objective of the present study was to search for the potential and effective molecules that could inhibit tyrosinase or suppress the production of melanin so that they can be used as effective and safe skin care and cosmetic agents. Through molecular docking in virtual model of human tyrosinase (hTYR) we demonstrated the inhibitory effect of glabridin and glabridin derivatives. The glabridin derivatives molecules exhibited active binding with human tyrosinase. Manual ranking of these molecules based on the binding scores projected 5-formylglabridin as having the highest binding affinity with the docking score -9.3. HIS 338, HIS 177, HIS 342, SER 350, VAL 352, ASN 339, PHE 322, SER 330, SER 355, ILE 343, HIS 155 and ARG 171 were found to be important residues for binding of ligands in the active site of human tyrosinase model. Glabridin effectively controlled the melanin production in B16F10 melanoma cells. Even though, all the glabridin derivatives successfully docked with the modelled tyrosinase enzyme but, four molecules namely 5′-formylglabridin, glabridin dimer, (R)-Glabridin and 5′prenylglabridin were identified as potent inhibitor of hTYR based on their higher binding affinity than glabridin. ADME properties analysis projected all studied molecules as drug candidate because they followed all criteria required for absorption, distribution, metabolism and excretion and didn’t violate the rule 5. Further, molecular simulation studies also confirmed the effective binding of glabridin, 5′-formylglabridin, R- glabridin, 5′- prenylglabridin, glabridin dimer and 3′-hydroxy-4′methoxyglabridin with hTYR authenticating the binding of it for inhibition mechanism and appears better candidate than the commonly used kojic acid for hyper pigmentary conditions. However, the complete prospects of these compounds need to be explored further in wet lab studies before being used as an ingredient in anti- hyper pigmentary formulations. Supplementary Information Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 233 KB) Funding Funding not available. Data Availability The data that supports the findings of this study are available in the supplementary material of this article. Declarations Ethical statement This article does not contain any studies involving animals performed by any of the authors. This article does not contain any studies involving human participants performed by any of the authors. Conflict of interest Arti Kumari has no conflict of interest. Rakesh kumar has no conflict of interest. Gira Sulabh has no conflict of interest. Pratishtha Singh has no conflict of interest. Jainendra Kumar has no conflict of interest. Vijay Kumar Singh has no conflict of interest. Krishna Kumar Ojha has no conflict of interest. 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==== Front Environ Dev Sustain Environ Dev Sustain Environment, Development and Sustainability 1387-585X 1573-2975 Springer Netherlands Dordrecht 35431619 2298 10.1007/s10668-022-02298-3 Article Why are some countries cleaner than others? New evidence from macroeconomic governance http://orcid.org/0000-0002-1107-2523 Akan Taner taner.akan@istanbul.edu.tr 1 Gündüz Halil İbrahim halil.gunduz@istanbul.edu.tr 2 Vanlı Tara vanli.tara@gmail.com 1 Zeren Ahmet Baran ahmetbaranzeren@gmail.com 1 Işık Ali Haydar alihaydar.isik@comu.edu.tr 13 Mashadihasanli Tamerlan tamerlan.mashadihasanli@ogr.iu.edu.tr 1 1 grid.9601.e 0000 0001 2166 6619 Faculty of Economics, Department of Economics, Istanbul University, Beyazıt, 34452 Istanbul, Turkey 2 grid.9601.e 0000 0001 2166 6619 Present Address: Faculty of Economics, Department of Econometrics, Istanbul University, Beyazıt, 34452, Istanbul, Turkey 3 grid.412364.6 0000 0001 0680 7807 Faculty of Political Science, Department of Economics, Çanakkale Onsekiz Mart University, Çanakkale, Turkey 11 4 2022 2023 25 7 61676223 30 8 2021 14 3 2022 © The Author(s), under exclusive licence to Springer Nature B.V. 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. This study aims to investigate why some countries are cleaner than the others with reference to macroeconomic governance (MEG) in order to explain how major macroeconomic aggregates should be governed to mitigate environmental pollution at the level of economic systems. Using per capita carbon dioxide emissions (CPC) as the proxy for air pollution, and macro-non-financial governance (MNFG) and macro-financial governance (MFG) as the proxies for MEG, the study introduces the systemic and fragmented governance of green complementarities (GCMs) and dirty complementarities (DCMs) as analytic concepts to compare the MEG models for managing pollution in 13 high-income countries (HICs), 10 upper-middle-income countries (UMICs), and nine lower-middle-income countries (LMICs) for the period 1994–2014. The paper concludes that (i) HICs reduced their CPC levels thanks to adopting green systemic governance by creating GCMs between both MNFG and MFG variables in the long run; (ii) UMICs experienced a remarkable increase in their CPC levels due to adopting dirty systemic governance by creating DCMs between the MNFG variables, but prevented pollution from being higher through creating GCMs between the MFG variables; and (iii) LMICs experienced the highest comparative increase in CPC due to adopting a fragmented governance in managing both MNFG–pollution and MFG–pollution nexus. Keywords Pollution Macroeconomic governance Complementarities Growth issue-copyright-statement© Springer Nature B.V. 2023 ==== Body pmcIntroduction Environmental pollution is the world’s leading environmental cause of morbidity and mortality. Of the major types of environmental pollution, air pollution1 is the most significant environmental health risk, causing the death of more than seven million people annually (WHO, 2021; Burney & Ramanathan, 2014; Dockery et al., 1993; Graff Zivin & Neidell, 2013; Kampa & Castanas, 2008; Saxena & Srivastava, 2020).2 91% of the world’s population and 98% of residents of cities with populations greater than 100,000 in low- and middle-income countries live in places where ‘nine out of ten people worldwide breathe air containing levels of pollutants that exceed World Health Organization limits’ (UNEP, 2021a, b). Furthermore, a direct relationship has recently been established between the rapid increase in COVID-19 contagion and atmospheric pollution acting as a carrier and booster of the pandemic (Pozzer et al., 2020). In addition to its deadly health effects, air pollution also reduces labor productivity, increases health expenditures, and reduces crop yields. Its indirect effects originate in the reallocation of production factors across the economy, changes in international trade, and savings, which are induced by relative price changes (Adhvaryu et al., 2014; Ebenstein et al., 2016; Graff Zivin & Neidell, 2012; Hansen & Selte, 2000). It is projected that exposure to PM2.5 concentration would increase by 50% by 2030 if no new policies are implemented (UN, 2021c). The total annual costs of air pollution are projected to increase from 0.3% in 2015 to 1.0% by 2060 (Lanzi, 2016), with a 1 μg/m3 increase in PM2.5 concentration estimated to reduce real GDP by 0.8% (Dechezleprêtre et al., 2019). It is well established that air pollution is caused by human emissions of substances into the atmosphere as a negative externality of environmentally hazardous models of production (e.g., industrialization, exploration, and mining), consumption, trade, finance, urbanization, and population growth (Cole, 2004; Omri, 2013; Ukaogo et al., 2020; Xu & Lin, 2016). Thus, the mitigation of air pollution is an integral component of the United Nations Sustainable Development Goals in many areas ranging from atmosphere, health and population, employment and decent work to food security and sustainable agriculture, sustainable cities, and human settlements (UN, 2021b; Rafaj et al., 2018). As a corollary, the Ministerial Declaration of the United Nations Environment Assembly emphasizes ‘the need for rapid, large-scale, and coordinated action against pollution’ (UNEP, 2018: 3). Against this backdrop, it is suggested that a full-fledged restructuration is indispensable for the air pollution–economy nexus by integrating environmental and economic policies through a long-term governance of ecology, economy, and public health. (Liu et al., 2018). This paper hypothesizes that the basic way of achieving such a transformation is to create a complementary dynamic between major macroeconomic variables in reducing air pollution. In order to substantiate this argument, the paper first divides macroeconomic governance into macro-non-financial governance, MNFG, and macro-financial governance, MFG, based on the functional clustering of major macroeconomic variables. (For a complete list of the abbreviations used in the paper, see Note 3.3) Second, the paper introduces two modes of economic governance in managing macroeconomy–pollution nexus, systemic and fragmented governance. Third, the paper introduces green and dirty complementarities to explain the effects of macroeconomic variables on pollution under systemic and fragmented modes of macroeconomic governance. Fourth, the paper focuses on long-run (cointegrated) relationships between macroeconomic governance and takes carbon dioxide emissions, CO2, as the proxy for air pollution given that CO2 is a stock pollutant whose effect lasts more than one century. Fifth, the paper sets up two models that measure the impact of major financial and non-financial macroeconomic aggregates on pollution by (i) using Pedroni and Kao panel cointegration tests and panel autoregressive distributed lag (ARDL) technique executed by pooled mean group estimator (Pesaran & Smith, 1995; Pesaran et al., 1999), and (ii) by taking 13 high-income, 10 upper-middle-income, and 9 lower-middle-income countries for the period 1994–2014 based on the availability of statistically consistent set of data for all twelve variables included in the analysis. The paper continues as follows. Section 2 explains the pollution–macroeconomy nexus from a complementarity-theoretic perspective. Section 3 presents the data and methodology. Section 4 presents the empirical findings. Section 5 makes a structured discussion on the paper’s contributions, the policy implications of its findings at national and international level, and the relevance of its methodology and findings for future work on macroeconomy–pollution nexus. Section 6 provides the conclusion. Explaining the macroeconomic governance–pollution nexus Three main gaps can be found in the extant literature regarding the pollution–macroeconomy nexus that this paper aims to address. The first is the exclusively aggregative approach of the EKC hypothesis. The second relates to the ambiguity of the findings. The third is the fragmented selection of analytical variables. First, as economic growth is in effect a matter of rise or fall in income, most studies investigate either pollution–growth or pollution–income nexus using the U-shaped or N-shaped environmental Kuznets curve (EKC) hypothesis (Kuznets, 1955; Bhattarai et al., 2009; Álvarez-Herránz et al., 2017). In their seminal study, Grossman and Krueger (1991) provided an empirical test of EKC hypotheses. Thereafter, several studies, such as Dinda and Coondoo (2006), Managi and Jena (2008), Stern (2004), and Shafik (1994), carried out their investigation on linear and nonlinear relationships between economic activity and emissions. However, the EKC hypothesis exclusively tests the aggregate growth–pollution nexus, but does not explain how each component of growth affects pollution in time or if their effects diverge from each other, which may restrain the specification of variable-specific policy options. Second, an extensive review of the extant literature on the effects of macroeconomic variables on pollution demonstrates that the findings are too ambiguous (see Appendix 1, table 12). For example, the EKC hypothesis holds for both HICs and UMICs (Dong et al., 2020a, 2020b) or only for HICs (Lau et al., 2018; see Appendix 1, Table 13for more detailed and diverse results for the EKC hypothesis). Trade openness variably increases pollution (Shahbaz et al., 2013), reduces pollution in LMICs and UMICs but not in HICs (Halicioglu, 2009; Lau et al., 2018), has no significant impact on pollution (Javid & Sharif, 2016), has a positive effect only in the long run (Al-Mulali et al., 2018; Lv & Xu, 2019), or has a negative impact in the short run (Lv & Xu, 2019). Foreign direct investment has no effect on pollution (Wang et al., 2020; Nasir et al., 2019), a negative effect on pollution in HICs but a positive effect in developing countries (Khalil & Inam, 2006; Lau et al., 2018), a U-shaped effect in HICs (Christoforidis & Katrakilidis, 2021), a negative effect in both developed and developing countries (Essandoh et al., 2020), a negative effect only in developing countries (Pradhan, 2021), or a negative effect only in LMICs (Nguyen et al., 2020). Financial development reduces CO2 emissions in HICs (Khaskheli et al., 2021) but increases emissions in LMICs and UMICs (Ehigiamusoe & Lean, 2019; Nasir et al., 2021; Thampanya et al., 2021), reduces CO2 emissions in HICs, UMICs, and LMICs (Godil et al., 2020; Neog & Yadava, 2020), has no effect on pollution for HICs but has a negative effect on pollution in UMICs and LMICs (Di Vita, 2008), or has no effect on pollution for all cases (Katircioğlu, 2012; Öztürk & Acaravcı, 2013). Third, the variables included in the analysis for macroeconomy–pollution nexus are in most cases selected fragmentedly, which might cause the findings to be inconsistent or ambiguous, reducing the efficiency of policy proposals (see ‘Variables’ in Tables 12 and 13 in Appendix 1). In fact, there may be a general perception of the MEG–pollution nexus. Economic development, growth, energy consumption, and trade openness may increase pollution faster in LMICs and UMICs than in HICs in both the short and long run (Dong et al., 2020a; Ehigiamusoe, 2019; Wu et al., 2018). Because the developing world uses energy-intensive production technologies based on fossil fuels—predominantly nonrenewable energy sources (Dong et al., 2019, 2020b; Hu et al., 2018; Ehigiamuoe & Lean, 2019)—developed countries generally have illiberal trade models based on the export of dirty commodities (Fakher, 2018; Lv & Xu, 2019). Furthermore, developing countries typically do not have green or strict regulations on FDI; neither do they have nation-wide standardized regulations on environmental quality (Danlami et al., 2019; Song, Li, et al., 2021; Song, Zhang, et al., 2021; Song, Zhang, et al., 2021; L. Zhang et al., 2021) nor effective, democratic, and nation-wide standardized mechanisms of environmental governance (Zhang et al., 2021). Likewise, public awareness of environmental degradation tends to be low (Mujtaba et al., 2020; Zhang et al., 2021). CPC levels in our case countries of three income groups support, prima facie, this general perception. As Fig. 1 illustrates, in the period 1994–2014, CPC declined by 0.8 percent in HICs and increased on average by 2.1 percent and 2.9 percent in UMICs and LMICs, respectively. However, as noted above, the findings on the macroeconomy–pollution nexus for each country group are so ambiguous that they are hardly able to produce a clear-cut group-specific relationship pattern for HICs, UMICs, and LMICs; this is because the perception of the nexus is based on an estimation using not a systematic but rather a fragmented set of macroeconomic variables.Fig. 1 Per capita carbon emissions in HICs, UMICs, and LMICs, 1994–2014. Source: World Bank (2020) Given these three points, we first focus on the root cause of the generation of income and growth, macroeconomic governance, rather than to the income or growth itself. This is significant because pollution–income or pollution–growth nexus is dependent on the pollution–macroeconomy nexus. It is the mode of macroeconomic governance, as detailed below, that determines the rate of growth or the level of income in a country. Accordingly, we do not make an EKC-based analysis, because we suggest that greening economic systems in a full-fledged manner requires the greening of overall macroeconomic structure with its financial and non-financial dynamics (Heijdra & Heijnen, 2013). Such an approach entails disaggregating the macroeconomy–pollution nexus by bringing all these individual variables into the analysis simultaneously rather than investigating the aggregate growth–pollution nexus itself. On this basis, second, we cluster the major macroeconomic variables into two groups and then set up our econometric models to test the effects of the macroeconomic variables in each group on pollution in terms of both their individual and complementary effects. This is necessary both for structuring the effects of major macroeconomic variables on air pollution to remove the potential shortcomings of the ambiguous findings and for coming up with structured policy suggestions not only for the relationships between each macroeconomic variable and pollution but also for the overall macroeconomy–pollution nexus. In so doing, we introduce two modes of economic governance in managing the macroeconomy–pollution nexus based on green and dirty complementarities. Economic governance can be defined, in broad strokes, as a patterned or unpatterned system of interaction between public or private actors for adjusting or monitoring one or more market structures under the guidance of predetermined rules and procedures. In specific, macroeconomic governance is the collective ordering of micro–macro policy choices in deploying financial and non-financial institutions, underlying public–private and national–international economic relations.Macroeconomic governance can basically be financial and non-financial based on the functional clustering of major macroeconomic variables. An economic system relies mainly on two functions. The first is the real economic activity consisting of production, consumption, trade, and foreign direct investment. The second is the financial flows that fund these activities at public and private sectors. Macro-non-financial governance is a policy mix that consists of the propensity of (a) governments to adjust the size of their purchases; (b) non-financial sector to adjust the size of fixed capital investment; (c) non-financial businesses to adjust the size of imports or exports; and (d) foreign direct investors to adjust the size of their investments across countries. In a similar vein, macro-financial governance represents a policy mix that consists of (a) the adjustment of the size of government debt for financing public purchases; (b) the adjustment of gross savings (to buy financial assets such as debt securities, corporate equities, and mutual fund shares, or the depositing of money as time and savings deposits); (c) the adaptation of an accommodative or non-accommodative monetary policy mainly by changing the quantity of (broad) money; (d) the transferring of portfolio investment funds across countries, and (e) the development of financial markets by financial investors and intermediaries. It is hence the collective ordering of governments’, businesses’, central banks’, foreign direct investors’, portfolio investors’, and financial investors’ choices that determine economic performance, the rate of growth, or the level of income in a country or between countries. We suggest that it is the same ordering that determines the impact of macroeconomic governance on ecological pollution including air pollution. This impact can be explained based on the complementary dynamics of macroeconomic aggregates. Complementarities can be defined as the mutual reinforcement among a certain group of variables in part or all of a social structure that improves or worsens clustering relative to alternative configurations (Hall & Soskice, 2001). There would emerge four types of relationships between the components of macro-financial and macro-non-financial governance in affecting pollution: (i) a ‘green’ complementary relationship in reducing pollution, (ii) a ‘dirty’ complementary relationship in increasing pollution, (iii) the conflation of the first two options, which is more realistic, and (iv) the lack of any relationship. We sketch out the first two cases. The delineation of the last two options is a matter of empirical analysis. For example, based on the literature summarized in Table 1 on the potential pollution-reducing and pollution-increasing impacts of the major macroeconomic variables, governments can stimulate the development of greener products by funding R&D in energy efficiency as well as directly subsidizing firms to produce them. The greater energy efficiency and the lower cost of green production may enable firms to invest more funds in cleaner capital goods and technologies, thus becoming more internationally competitive in green products. Accruing higher revenue from green goods may stimulate governments to maintain their policy perspective. At the same time, greater savings may both reduce aggregate demand and augment financial development by increased investment in financial assets such as shares. Further financial market development can enable firms to have faster and easier access to cheaper and long-term funds, consolidating their ability to sustain their green production strategy for the predictable future. Monetary policy can complement this virtuous cycle by first providing necessary liquidity to financial markets and then achieving equilibrium in the quantity of money, respectively. Thus, green complementarities—GCMs—would emerge (i) between government consumption expenditures, private investment expenditures, and exports in the MNFG–pollution nexus, and (ii) between savings, financial market development, and monetary policy in the MFG–pollution nexus.Table 1 Examples for pollution-reducing and pollution-increasing impacts of major macroeconomic aggregates Variable Pollution-reducing impact achieved by Pollution-increasing impact caused by Gross fixed capital investment Investment in less energy-intensive sectors, lower use of natural resources, and energy efficiency thanks to using cleaner technologies and production techniques (Sarkodie & Strezov, 2018) Energy-intensive production using higher quantity of factors of production (scale effect) or energy inefficient/environmentally unfriendly technologies (Bilan et al., 2019; Rahman & Ahmad, 2019) Government consumption expenditures Stimulus programs in or consuming clean and renewable energy, environmentally friendly public goods such as efficient public transportation and high-speed trains, and green buildings (Halkos & Paizanos, 2013; Ghalipour and Farzanegan, 2018) Higher quantity of government expenditures underlying higher emissions by stimulating higher fixed capital investment, creating a multiplier impact on investment and consumption expenditures (Dai et. al., 2012; Halkos & Paizanos, 2013; Galinato and Islam, 2014; Lopez and Palacios, 2014; Islam and Lopez, 2015) Exports and Imports Encouraging competition in producing clean products (Shahbaz et. al., 2013); an export-led strategy based mainly on raw materials and other agricultural products (Javid & Sharif, 2016; Zhang & Gangopadhyay, 2012); achieving energy-efficient production by importing clean capital goods and technologies (Javid & Sharif, 2016) Exporting natural resource-intensive goods or using dirty technologies in producing exports; importing pollution-intensive products or dirty technologies (Khalil & Inam, 2006; Nasir, 2019; Solarin, 2017; Halıcıoğlu, 2009) Foreign Direct Investment Encouraging R&D in reducing emissions, using less energy-intensive production techniques and green technologies, particularly in high-income countries (Demena & Afesorgbor, 2020; Marques & Caetano, 2020) Using outdated technologies, investing in most polluting industries or consuming arable lands (Nasir et al., 2019; Zhang, 2011; Xie et. al., 2020; Kivyiro & Arminen, 2014) Savings Reducing the rate of domestic aggregate demand or trade in goods and services (Hamilton & Clemens, 1999); Stimulating financialization and deindustrialization (Palley, 2013) Growth-oriented intermediation of savings by finance sector, in particular in developing countries (Aghion et al., 2016), when growth rests upon energy-intensive techniques and consumption patterns Broad money Contractionary monetary policy stimulating lower quantity of domestic demand due to increasing interest rates, reserve ratios, and causing credit contraction; stimulating the purchase of green products by green quantitative easing programs (Dafermos et. al. 2018) Expansionary economic policy stimulating critical public expenditures on or private investment in environmental protection, energy-efficient technologies, and environmental R&D Expansionary monetary policy stimulating higher investment, energy consumption, and consumption expenditures due to reducing interest rates, reserve ratios, and causing credit expansion (Islam and Lopez, 2015; Al-mulali & Sab, 2012; Gök, 2020) Contractionary economic policy curtailing critical public expenditures on or private investment in environmental protection, energy-efficient technologies, and environmental R&D; structural adjustment or austerity measures causing deforestation or excessive extraction and the use of underpriced natural resources for comparative cost advantage (Holden et al., 1998) Public debt Using public funds in applying tax cuts for or financially stimulating green technologies and products, supporting R&D in green technologies (Carratù et. al., 2019; Cetin and Bakirtaş, 2020) Funding expansionary government policies, in particular above a certain threshold, stimulating higher carbon emissions (Clootens, 2017); applying tax cuts for or financially stimulating dirty technologies or products; either not supplying or substantially reducing the funds for R&D in green technologies, discouraging the use of energy-efficient technologies Portfolio investment Supplying higher quantity of funds (Klein & Olivei, 1999) in sustainable economies or to non-financial businesses that invest in less energy-intensive sectors and use green technologies; large-scale withdrawal both squeezing and destabilizing the supply of funds, underlying lower quantity of investment and consumption expenditures as well as the quantity of imports not only during but also after the crises due to the adoption of austerity or structural adjustment measures (Grabel, 1996) Stimulating higher growth in particular in the short run in environmentally unsustainable economies (Klein & Olivei, 1999; Shahbaz et al., 2020); destabilizing macroeconomic and financial systems due to high risks of liquidity and unregulated financial openness, causing instability in the supply of financial funds, in particular foreign currency and tempting short-termism in continuing to use dirty technologies and energy-intensive investment for sustaining comparative cost advantages (Reinhard and Reinhart, 2009) Financial development A sophisticated financial system urging reputational finance and thus encouraging firms to be environmentally responsible (Dasgupta et al., 2001); stimulating environmental R&D and technological innovation, increasing the efficiency of energy consumption (Omri et. al., 2015; Tamazian and Bhaskara Rao, 2010; Tamazian et al., 2009; Shahbaz et. al., 2013); declining propensity to invest or consume due to financialization (Palley, 2013) Stimulating higher quantity of fixed capital investment enabling firms to buy higher quantity of factors of production in dirty sectors, fostering industrialization, enabling consumers to consume more through cheaper and less costly credits, and stimulating higher FDI in particular in the long run (Sadorsky, 2010; Shahbaz & Lean, 2012; İbrahim & Vo, 2021; Khan & Ozturk, 2021; Hunjra et. al., 2020) Energy consumption (Zhang et al., 2019; Wang et. al., 2020; Muhammad, 2019; Ummalla & Goyari, 2020) Moreover, the macroeconomy–pollution nexus may also operate in the exact opposite way. Governments can provide insufficient funds to stimulate green products both by underfunding R&D in energy efficiency and by eliminating subsidies to firms to produce them. With insufficient government stimulus, firms may continue using more energy-intensive and energy-inefficient technologies, increasing their competitive power in dirty goods. Accruing higher revenue from dirty goods may stimulate governments to maintain their policy perspective. In terms of the MFG–pollution nexus, the reduced savings may result from higher investment and consumption expenditures, leaving a lower level of investment in shares. The consequent slow or inadequate level of financial market development may stimulate firms to maintain their dirty production strategy, concentrating on short-run gains by using cheap natural resource-intensive energy instead of transitioning to long-run productivity and cost advantages as a result of energy efficiency. A contractionary monetary policy can worsen this vicious cycle by not supplying the necessary quantity of liquidity to financial markets and by worsening economic predictability as a result of failing to meet the demand for money. Consequently, there would emerge dirty complementarities (DCMs) (i) between government purchases, investment expenditures, and exports in the MNFG–pollution nexus, and (ii) between savings, financial market development, and monetary policy in the MFG–pollution nexus. Evidently, GCMs and DCMs or their conflation in a third case lead to an especially complex set of policy choices and outcomes (Warford et al., 1997: 48). The key point of today under the rising threat of COVID-19 and imminent other pandemics is how to govern these choices and outcomes. We suggest two broad modes of governance, systemic governance, and fragmentation, which can be used to explain pollution–macroeconomy nexus in terms of the joint environmental impact of the cited choices and outcomes. Explaining their ‘joint’ impact in a ‘multivariate’ setting is significant as it is their joint, complementary or simultaneous rather than separate impact per se that determines the overall level of pollution (Apsimon et al., 2009; Warford et al., 1997: 65–80; Girma, 1992). Systemic mode of governing pollution–macroeconomy nexus can be defined as a process of decision-making, regulation, or monitoring that aims to manage present and evolutionary complexities of this nexus. There are two points to be noted. First, it refers to governing the mix of GCMs and DCMs, so as either (i) to achieve a transformation into sustainable growth or (ii) to sustain current profits, employment, rents, or other interests, creating a regime of unsustainable growth. A mode of governance that achieves first and second targets based on GICs and DICs, respectively, can be considered as green and dirty systemic governance, respectively. Both targets arise out of the convergence of major and separate economic and environmental policy choices. The lack of systemic governance ends up with fragmentation or disjointed policy choices, causing up either the deterioration of environmental pollution or the failure in achieving green growth. In specific, fragmentation makes sense with either (a) the lack of any relationships between the MEG’s variables and pollution or (ii) the conflicting impacts of the MEG’s variables on pollution. Each option illustrates the lack of a convergence between the MEG’s variables in reducing or mitigating pollution. In parallel with the above-noted third case, there might be a mix of GCMs and DCMs between environmental and macroeconomic policy choices under both systemic and fragmented modes of governance. This mixed order can, inter alia, turn out in one of two ways: (i) MNFG and MFG can separately increase or decrease pollution, or (ii) only one of them would have an impact on pollution, whereas the other would not. In theoretical terms, we suggest that the specific drivers of the differences between the three country groups can and should be explained by making a system-level analysis of the macroeconomic determinants of pollution in these countries. For this purpose, we bring major macroeconomic aggregates into the analysis, as illustrated in Fig. 2. In empirical terms, we run a multivariate causality analysis taking MNFG and MFG variables together as independent variables in two separate models. In so doing, it will be possible, (i) first, to explain the drivers of pollution in terms of how MNFG and MFG variables converge or diverge from each other in reducing or increasing pollution through GCMs, DCMs, or disjointed strategies between themselves, and (ii) then devise structured and systemic policy suggestions to mitigate CPC for each group.Fig. 2 Macro-financial and macro-non-financial governance Data and methodology The paper has two major points in selecting an econometric technique for the estimation of the pollution–macroeconomy nexus. The first is to estimate the long-run and short-run causal relationships between pollution and macroeconomic governance. The second point is to make a holistic analysis of the pollution–macroeconomy, as noted above. The long run4 matters when it comes to establishing the existence or the lack of a stable and dynamic relationship between the two or more variables, which is an indication of a systemic or fragmented mode of governance for managing the nexus, respectively. Systemic governance can be inferred when there is a mutual reinforcement between the majority of the major variables of MNFG and MFG in reducing or increasing pollution in the long run. The majority matters because we can expect a noticeable and sustained increase or decrease in pollution when the majority of the MNFG’s and MFG’s variables complement each other or converge to the extent of reducing or increasing pollution, respectively. Fragmentation can be inferred when (i) there is a mutual reinforcement between the few of the same variables in increasing or decreasing pollution, or (ii) there is a lack of relationship between them in the long run.Fig. 3 The effects of MNFG’s and MFG’s variables on CPC in HICs Fig. 4 The effects of MNFG’s and MFG’s variables on CPC in UMICs The econometric technique to cover the two points noted above is panel data cointegration that estimates, first, the long-run and short-run relationships, and second, in a multivariate setting. The first point matters as it illustrates the existence or the lack of comovement or trending relationship between the variables. A trending relationship is an indication of an ordered mode of governance because it illustrates in macroeconomic terms that variables complement each other or have a mutually reinforcing relationship in achieving a certain purpose. This achievement may be because of an action that may or may not be concerted. The focal point of our analysis is the complementary or non-complementary relationship itself. The second point in this regard matters as it illustrates the existence or the lack of a system-level trending relationship in a complementary manner. The ‘complementary relationship’ here refers specifically to the existence of convergence between the majority or few variables of a model in reducing or increasing pollution (see Figs. 3, 4, and 5).Fig. 5 The effects of MNFG’s and MFG’s variables on CPC in LMICs The paper used per capita carbon dioxide emissions (CPC) as a proxy for environmental pollution. The data sources for both CPC and for the MNFG and MFG’s variables are given in Table 2. The case countries are 13 HICs, 10 UMICs, and 9 LMICs (Table 3). The basic criteria in selecting the sample countries were first to be able to gather all the data in a statistically consistent manner and second to cover the longest time available, considering that the higher the degrees of freedom, the more robust the cointegration analysis would be.Table 2 List of variables and their descriptions Variable Definition Source cpcit Per capita carbon dioxide emissions World Development Indicators (WDI) MNFG’s Variables (Model 1) gexdit Government consumption expenditures* WDI fcfit Investment expenditures* WDI expit Exports* WDI impit Imports* WDI fdiit Inward foreign direct investment* WDI engyit Per capita energy use WDI MFG’s Variables (Model 2) bmnit Broad money* WDI svgit Gross savings* WDI prftit Inward portfolio investment* WDI pbdtit Public debt* IMF Global Debt Database fmdit Financial market development IMF Financial Development Index Database Annual data between 1994 and 2014 (T=21) for three different income groups; *As percent of GDP; Source: World Bank (2020); IMF (2020a, b) Table 3 Country groups High income (HICs) Upper middle income (UMICs) Low income (LMICs) 1 Australia Argentina Bangladesh 2 Chile Botswana Cameroon 3 Czech Republic Brazil India 4 Denmark China Kenya 5 Israel Indonesia Morocco 6 Korea, Rep Malaysia Nigeria 7 Norway Mexico Pakistan 8 Singapore South Africa Philippines 9 Sweden Thailand Sri Lanka 10 Switzerland Turkey 11 UK 12 USA 13 Uruguay A vast majority of the studies use CO2 emissions to investigate the nexus between economic growth and environmental degradation (Bibi & Jamil, 2021; Cialani, 2007; Destek et al., 2020; Dinda & Coondoo, 2006; Dogan & Inglesi-Lotz, 2020; Franklin & Ruth, 2012; Friedl & Getzner, 2003; Lazăr et al., 2019; Ongan et al., 2021; Zhang & Gao, 2016). The reason why we select CPC for pollution is that our primary focus is on the long-term relationships between the macroeconomy and pollution. Pollutants are twofold: flow and stock pollutants. Flow pollutants only have an immediate effect on the environment. The atmospheric lifetime of sulfur dioxide (SO2), nitrogen oxide (NOx), and carbon monoxide (CO) is 1–4 days, 2–5 days, and 1–3 months, respectively. But stock pollutants accumulate in the air, and their effects last a century or longer (Lieb, 2004; Liu & Liptak, 2000; Moore, 2009; Ukaogo et al., 2020). Furthermore, a high correlation between SO2, NOx, and CO2 demonstrates that CO2 emissions matter for the short run as well (Angelopoulos et al., 2010; Fischer & Heutel, 2013; Smulders, 2004). In other words, the key analytical concern between stock and flow pollutants is that the former matter both for the long and short run, whereas the latter are relevant only for the short run. The fact that CO2 data are consistently available for a large number of countries is another key reason for selecting it as a proxy for pollution. We developed two models, which can be written in the following formal algebraic forms. The first model estimates the long-run and short-run relationships between MNFG and pollution, whereas the second between MFG and pollution. The variables represented by the acronyms are defined in Table 2.1 lncpcit=flngexdit,lnfcfit,lnexpit,lnimpit,lnfdiit,lnengyit,e1,it, 2 lncpcit=flnbmnit,lnsvgit,lnpbdtit,lnprftit,lnfmdit,lnengyit,e2,it, where i=1,2,…N; t=1,2,…T and e1,it, and e2,it are error terms. The panel cointegration tests comprise four steps: (i) first, to estimate cross section dependence between the panels, (ii) second, to run panel unit root tests, (iii) third, to estimate the cointegration relationship, and (iv) fourth, to estimate long-run and short-run models if the cointegrated relationship is established (see Appendix 2for technical details on panel unit root, cross section, and cointegration tests). In panel cointegration tests, the existence of cross section dependence or independence is significant in selecting either the first- or the second-generation panel unit root tests. (In the existence of cross section dependence, the second-generation unit roots tests should be used as they allow cross section dependence.) We ran the cross section dependence test using the Breusch and Pagan (1980) Lagrangian multiplier (LM) test, the Pesaran et al. (2008) bias-adjusted LM test, and the Pesaran (2004) CD test. According to the results in Tables 4 and 5, we fail to reject the null of cross section independence for both Model 1 and Model 2 in all country groups, because either all or the majority of the tests are not statistically significant at 1 percent, 5 percent, or 10 percent level.Table 4 The results of cross-sectional dependence tests for Model 1 HICs UMICs LMICs Test Statistic p value Statistic p value Statistic p value LM 66.72 0.8149 50.29 0.2720 33.17 0.6039 LM adj  − 3.672 0.0002  − 0.5759 0.5621  − 1.791 0.0732 LM CD 1.277 0.2014 1.27 0.2041 0.1365 0.8914 Given the results of cross section dependence, we use the following first-generation panel unit root tests that precondition cross section independence both for Model 1 and for Model 2: (i) LLC, proposed by Breitung (2001), Levin et al. (2002); (ii) IPS, proposed by Im et al. (2003); and (iii) ADF-PP, proposed by Maddala and Wu (1999) and Choi (2001) (Table 6).Table 5 The results of cross-sectional dependence tests for Model 2 HICs UMICs LMICs Test Statistic p value Statistic p value Statistic p value LM 97.05 0.0710 45.31 0.4590 32.31 0.6449 LM adj 0.8137 0.4158  − 1.638 0.1014  − 2.255 0.0241 LM CD 1.488 0.1368 1.27 0.2041  − 0.5977 0.5501 The results from the panel unit root tests using individual intercept and trend models are reported in Tables 6, 7, and 8 for HICs, UMICs, and LMICs, respectively. We conclude that all the variables in Model 1 and Model 2 are non-stationary at level and become stationary at first differences as the results of either all or the great majority of the panel unit root tests are not statistically significant at level and are statistically significant at first differences, respectively.Table 6 The Results of Panel Unit Root Tests for HI Countries Variables LLC Breitung IPS ADF PP Level First Diff Level First Dif Level First Dif Level First Dif Level First Dif lncpcit  − 0.22  − 9.42* 3.97  − 1.60* 0.95  − 8.92* 29.53 135.12* 28.32 218.49* lngexdit  − 2.07  − 10.46*  − 2.61  − 5.77*  − 2.55  − 9.58* 41.63 131.28* 32.66 247.53* lnfcfit  − 4.55  − 9.06*  − 1.74  − 3.81*  − 2.92  − 8.72* 53.34 119.56* 27.38 140.37* lnexpit  − 1.17  − 12.69*  − 4.32  − 5.85* 0.23  − 10.84* 28.00 147.75* 23.06 176.14* lnimpit  − 3.06**  − 11.23* 0.67  − 5.40*  − 2.86  − 11.95* 49.20 165.81* 50.39 267.13* lnfdiit  − 9.09  − 11.72*  − 3.12  − 5.17*  − 6.34  − 10.80* 86.88 138.83* 149.67 264.76* lnengyit  − 0.90  − 10.60* 4.98  − 5.22*  − 0.42  − 10.36* 35.75 152.02* 37.55*** 205.87* lnbmnit 0.47  − 10.67* 0.87  − 7.46*  − 0.20  − 9.23* 26.33 126.55* 36.22*** 171.21* lnsvgit  − .70***   − 9.66*  − 2.24  − 3.10*  − 2.75  − 10.20* 44.73 143.32* 26.47 272.60* lnprftit  − 1.04  − 14.68*  − 4.39  − 6.90*  − 6.25  − 14.86* 18.31 196.07* 18.83 256.44* lnpbdtit  − 1.01  − 4.03* 0.51  − 2.90* 0.66  − 5.30* 24.02 72.94* 9.30 64.29* lnfmdit  − 4.35  − 9.15* 0.02  − 5.17*  − 3.41  − 8.82* 58.89 117.32* 94.12 156.06* *, **, and *** denote statistical significance at the 1 percent, 5 percent, and 10 percent levels, respectively Table 7 The Results of Panel Unit Root Tests for UMI Countries Variables LLC Breitung IPS ADF PP Level First Diff Level First Dif Level First Dif Level First Dif Level First Dif lncpcit 0.38  − 6.16* 0.49  − 3.95*  − 1.09  − 5.75* 24.90 67.04* 16.20 105.87* lngexdit 1.67  − 3.04*  − 2.85  − 2.86*  − 2.80  − 5.19* 6.92 61.60* 6.61 105.35* lnfcfit 0.59  − 4.63*  − 2.21  − 4.72*  − 3.24  − 5.06* 10.90 58.59* 10.71 91.71* lnexpit 1.16  − 2.02* 1.26  − 5.47*  − 4.79  − 5.97* 5.68 71.33* 5.20 135.16* lnimpit 0.59  − 5.87*  − 1.87  − 8.08*  − 1.70  − 7.29* 31.28*** 87.61* 41.14 144.87* lnfdiit  − 0.87  − 11.54*  − 3.64  − 2.52*  − 5.03  − 10.22* 17.31 101.29* 23.98 163.95* Inengyit  − 2.37***  − 5.96* 0.55  − 5.32*  − 2.34  − 6.82* 37.45 83.39* 20.71 116.15* lnbmnit  − 0.67  − 4.65*  − 1.05  − 5.31*  − 2.66  − 7.51* 39.16 92.39* 28.59*** 141.21* lnsvgit  − 1.00  − 7.13*  − 3.30  − 4.28*  − 2.36  − 7.58* 33.55 85.37* 28.01 113.96* lnprftit 0.31  − 11.16*  − 3.10  − 4.83*  − 6.22  − 10.27* 10.41 117.08* 9.14 187.83* lnpbdtit 1.14  − 14.67*  − 0.56  − 2.83*  − 1.43  − 9.55* 35.01 69.39* 46.77 89.18* lnfmdit  − 3.07  − 8.22*  − 2.58  − 7.78*  − 1.44  − 8.32* 28.36 100.93* 26.28 146.59* *, **, and *** denote statistical significance at the 1 percent, 5 percent, and 10 percent levels, respectively Table 8 The Results of Panel Unit Root Tests for LI Countries Variables LLC Breitung IPS ADF PP Level First Diff Level First Dif Level First Dif Level First Dif Level First Dif lncpcit 0.12  − 6.94*  − 0.83  − 2.93*  − 0.67  − 7.36* 21.68 78.48* 17.83 86.90* lngexdit  − 1.16  − 9.40*  − 2.45  − 6.74*  − 0.87  − 7.87* 21.26 82.78* 14.44 87.31* lnfcfit  − 3.69  − 4.76*  − 0.63  − 3.85*  − 0.75  − 7.01* 24.41 83.27* 24.84 87.23* lnexpit  − 1.56  − 5.31*  − 0.36  − 4.39*  − 1.20  − 6.24* 31.63 72.87* 41.85 117.62* lnimpit  − 1.83  − 6.97*  − 1.80  − 7.36*  − 0.47  − 7.56* 20.75 80.93* 23.83 134.04* lnfdiit  − 2.22  − 11.84* 1.08  − 1.75*  − 3.36  − 5.03* 61.81 65.54* 66.50 161.52* lnengyit  − 0.21  − 7.71* 2.82  − 2.59* 0.63  − 6.39* 18.53 73.14* 10.10 74.81* lnbmnit 2.45  − 3.17*  − 2.44  − 3.98*  − 2.05  − 5.69* 36.17 64.99* 27.40 93.71* lnsvgit  − 2.19  − 6.37* 0.40  − 4.92*  − 1.33  − 6.68* 28.96 71.82* 17.14 114.50* lnprftit  − 2.08  − 5.03* 1.81 1.94*  − 2.26  − 6.94* 25.24 100.23* 25.64 127.83* lnpbdtit 1.86  − 5.63* 0.63  − 5.22* 1.51  − 4.21* 12.69 46.34* 5.22 44.13* lnfmdit  − 1.94  − 7.21*  − 0.50  − 7.15*  − 1.99  − 6.03* 31.22 66.00* 17.96 93.25* *, **, and *** denote statistical significance at the 1 percent, 5 percent, and 10 percent levels, respectively For cointegration analysis, we use two widely used tests, Pedroni (1999, 2004) and Kao (1999). Table 9 documents the results of the panel cointegration tests for Model 1 and Model 2. For both HICs and UMICs, we can reject the null hypothesis of no cointegration for both models as either all or the majority of Pedroni and Kao test statistics are significant at 1 or 5 percent level. For LMICs, we fail to reject the null hypothesis for Model 1 but can reject for Model 2 as the majority of Pedroni and Kao test statistics are not and are significant at 1 or 5 percent levels, respectively. Thus, we conclude that there are cointegration (long-run) relationships between CPC and the MNFG variables (Model 1) only for HICs and UMICs, and between CPC and the MFG variables (Model 2) for all country groups.Table 9 Pedroni and Kao cointegration results Model 1 Model 2 Pedroni Kao Pedroni Kao HICs Statistic p value Statistic p value Statistic p value Stat p value Modified Phillips–Perron t 37.553 0.0001 20.659 0.0194 42.001 0.0000  − 16.024 0.0545 Phillips–Perron t  − 35.985 0.0002 19.797 0.0239  − 18.128 0.0349  − 18.236 0.0341 Augmented Dickey–Fuller t  − 31.769 0.0007 15.277 0.0633  − 16.464 0.0498  − 33.198 0.0005 UMICs Modified Phillips–Perron t 36.855 0.0001  − 15.744 0.0577 27.277 0.0032  − 34.157 0.0003 Phillips–Perron t  − 35.209 0.0002  − 18.230 0.0342  − 36.103 0.0002  − 29.812 0.0014 Augmented Dickey–Fuller t  − 34.879 0.0002  − 17.178 0.0429  − 34.022 0.0003  − 23.146 0.0103 LMICs Modified Phillips–Perron t 31.473 0.0008  − 0.8423 0.1998 30.757 0.0010  − 37.080 0.0001 Phillips–Perron t  − 14.349 0.0757  − 13.430 0.0896  − 34.326 0.0003  − 31.609 0.0008 Augmented Dickey–Fuller t  − 14.835 0.0690  − 0.1873 0.4257  − 19.816 0.0238  − 20.107 0.0222 The fourth step in estimating panel cointegration models after establishing the cointegrated relationship is to estimate long-run and short-run relationships between variables, for which we use panel autoregressive distributed lag approach. For running panel ARDL models, we used a dynamic panel estimator, PMG.5 The long-term models have been constructed as follows:3 Incpcit=γ1i+∑j=1pγ2iIncpci,t-j+∑i=0qγ3iInfcfi,t-j+∑i=0qγ4iIngexdi,t-j+∑i=0qγ5iInexpi,t-j+∑i=0qγ6iInimpi,t-j+∑i=0qγ7iInfdii,t-j+∑i=0qγ8iInengyi,t-j+ε1,it 4 Incpcit=δ1i+∑j=1pδ2iIncpci,t-j+∑i=0qδ3iInsvgi,t-j+∑i=0qδ4iInbmni,t-j+∑i=0qδ5iInpbdti,t-j+∑i=0qδ6iInprftii,t-j+∑i=0qδ7iInfmdi,t-j+∑i=0qδ8iInengyi,t-j+ε2,it where Incpc is the logged dependent variable for both the models; the remaining (logged independent) variables such as Infcf and Insvg are the MNG’s and the MFG’s variables, which are defined in Table 2; i = 1,….N are cross section units; t = 1,….T are time periods; p and q are optimal lag orders; γ1i and δ1i are the group-specific intercepts; γ2i⋯γ8i and δ2i ... δ8i are long-term coefficients; and ε1it and ε2it are the error terms. After estimating the long-run model, we construct the short-term (error correction) model as follows:5 ΔIncpcit=α1i+∑j=1pα2iΔIncpci,t-j+∑j=0qα3iΔInfcfi,t-j+∑j=0qα4iΔIngexdi,t-j+∑j=0qα5iΔInexpi,t-j+∑j=0qα6iΔInimpi,t-j+∑j=0qα7iΔfdii,t-j+∑j=0qα8iΔInengyi,t-j+δ3iECTi,t-1+ϵ1,it 6 ΔIncpcit=β1i+∑j=1pβ2iΔIncpci,t-j+∑j=0qβ3iΔInsvgi,t-j+∑j=0qβ4iΔInbmni,t-j+∑j=0qβ5iΔInpbdti,t-j+∑j=0qβ6iΔprfti,t-j+∑j=0qβ7iΔInfini,t-j+∑j=0qβ8iΔInrexci,t-j+δ3iECTi,t-1+ϵ2,it where Δ is the first-difference operator and ECTi represents the error correction term obtained from the long-run model. That the ECT is negative and statistically significant indicates that the short-run deviations adjust to the long-run equilibrium, and δ indicates the speed of this adjustment. Tables 10 and 11 indicate the results of long-run and short-run estimations using PMG estimator, respectively. We do not estimate Model 1 for LMICs as there is no cointegrated relationship between MNFG’s variables and CPC. As the ECT for the both models estimated for HICs and UMICs as well as for Model 2 estimated for LMICs is statistically significant and negative, we conclude that short-run deviations from the long-run equilibrium converge to long-run equilibrium in the models and therefore we can interpret the estimated coefficients for the long-run model.Table 10 The results of panel ARDL models with PMG estimator (long run) HICs UMICs LMICs Variables Coeff p value Coeff p value Coeff p value Model 1 constant  − 0.227 0.007*  − 0.262 0.021**  − 0.249 0.035** Δlngexdit  − 0.627 0.000* 0.221 0.003*  − 0.211 0.028** Δlnfcfit  − 0.719 0.000* 0.120 0.002* 0.277 0.067*** Δlnexpit  − 0.325 0.003* 0.090 0.043**  − 0.004 0.954 Δlnimpit 0.427 0.001*  − 0.109 0.198 0.242 0.001* Δlnfdiit 0.060 0.000*  − 0.011 0.544 0.038 0.004* Δlnengyit 1.229 0.000* 0.529 0.000* 0.081 0.477 Model 2 constant  − 0.142 0.010*  − 0.381 0.021**  − 0.384 0.005* Δlnbmnit  − 0.253 .000*  − 0.235 .000*  − 0.002 .981 Δlnsvgit  − 0.194 .014**  − 0.116 .028**  − 0.031 .547 Δlnprftit  − 0.136 .000*  − 0.123 .000*  − 0.022 .115 Δnpbdtit  − 0.182 .000*  − 0.123 .000*  − 0.211 .000* Δlnfmdit  − 0.011 .791  − 0.001 .991 0.027 .395 Δnengyit 0.557 .002* 1.311 .000* 0.835 .000* *, **, and *** denote statistical significance at the 1, 5, and10% levels, respectively. ECT represents the coefficient of the error correction term The appropriate lags have been selected as 1 via the BIC Table 11 The results of panel ARDL models with PMG estimator (short run) Variables HICs UMICs LMICs Coeff p value Coeff p value Coeff p value Model 1 Constant  − 1.085 0.008*  − 0.998 0.025*  − 0.327 0.027** Δlngexdit 0.585 0.002*  − 0.117 0.095***  − 0.082 0.334 Δlnfcfit 0.181 0.136 0.088 0.281  − 0.048 0.563 Δlnexpit  − 0.374 0.545  − 0.107 0.201  − 0.020 0.661 Δlnimpit 0.248 0.543 0.095 0.223  − 0.004 0.918 Δlnfdiit  − 0.007 0.373 0.018 0.033**  − 0.001 0.840 Δlnengyit 0.971 0.000* 0.573 0.081*** .599 0.062*** Model 2 Constant  − 0.058 0.052***  − 2.144 0.020**  − 0.766 0.009* Δlnbmnit 0.001 0.987 .071 0.163 0.202 0.121 Δlnsvgit  − 0.073 0.260 0.051 0.256 0.073 0.132 Δlnprftit  − 0.003 0.843  − 0.013 0.723  − 0.024 0.270 Δnpbdtit  − 0.081 0.324 0.045 0.137 .035 0.586 Δlnfmdit 0.221 0.322 0.054 0.002* 0.082 0.058*** Δnengyit 1.071 0.000* 0.576 0.022** 1.331 0.128 *, **, and *** denote statistical significance at 1, 5, and 10% levels, respectively. The appropriate lags have been selected as 1 via the BIC Empirical findings As Fig. 3 illustrates, an increase in the key variables of MNFG (fixed capital formation, government expenditures, and exports) and in all the MFG variables, except financial market development, caused a decline in CPC in the HICs in the long run. This result illustrates that HICs achieved a reduction in their CPC levels by adopting a systemic mode of macroeconomic governance in operating GCMs. We infer this ‘green systemic governance’ for the long run considering that the great majority of the variables of MEG converge in creating GCMs. That imports and inward foreign direct investment cause an increase in CPC does not change our result as both the majority and the key variables of macroeconomic variables converge in creating GCMs, which underlies the reduction in CPC levels in these countries in the long run. In the short run, only government expenditures out of MNFG variables have a positive effect on CPC. The remaining MNFG variables and all the MFG variables have no short-run relationship with CPC. Thus, there is an institutional fragmentation in governing the macroeconomy–pollution nexus for HICs in the short run, which may have hindered a faster and/or higher reduction in pollution levels in these countries. As Fig. 4 illustrates, an increase in the key variables of MNFG (fixed capital formation, government expenditures, and exports) causes an increase in CPC in the UMICs. Thus, we can conclude that there is a systemic mode of MNFG in increasing CPC levels in the UMICs—‘dirty systemic governance.’ This does not mean that the countries make an intentional effort to increase their CPC levels, but rather that the convergence of public–private investment policy and public consumption policy leads to this outcome. However, the key domestic variables of MFG (broad money, public debt, and savings) converge in creating a green complementary effect. There are two relevant points in terms of the unified impact of the MNFG and MFG in the UMICs on CPC. First, the production, consumption, and trade variable of the MNFG model that have a direct impact on CPC may have underlay the increase in CPC in the UMICs. In contrast, that the MFG was executed in the form of green systemic governance can be suggested as an underlying factor that helps explain why the increase in pollution in UMICs was lower than that in LMICs. When it comes to the short run, only the foreign direct inflows out of the MNFG variables have a statistically significant positive impact on CPC, and only financial market development of the MFG variables has a statistically significant negative impact on CPC. We can thus safely conclude that there was a fragmented governance model in the examined UMICs in the short run, which may have factored into the remarkable increase in CPC in these countries. We can conclude that the macroeconomy–pollution nexus was managed by a fragmented governance model in LMICs, which may have underlay the highest increase in pollution compared to the HICs and the UMICs in the 1994–2014 period. This conclusion lies in two facts. First, as Fig. 5 illustrates, there is no long-run cointegrated relationship between MNFG’s variables and CPC, and there is no statistically significant relationship between MFG’s major variables and CPC, too, except a negative effect running from portfolio investment to CPC. What may have consolidated this result is the lack of a statistically significant relationship between CPC and MFG’s variables in the LMICs in the short run. Our study confirms a number of findings in the extant literature about the relationships between individual macroeconomic variables and pollution. For example, our findings for the HICs confirm that real economic activity in HICs reduces pollution (Chen & Taylor, 2020; Hamit-Haggar, 2012; Salari et al., 2021). Specifically, our results confirm the negative effects running from FDI to CO2 (Bulus & Koc, 2021) and from financial market development to CO2 (Shoaib et al., 2020), and the positive effect running from imports to CO2 in HICs (Ali & Kirikkaleli, 2021). Our empirical findings for UMICs confirm, in general, that real economic activity increases CO2 emissions in these countries (Ahmed & Shimada, 2019; Bhat, 2018; Ummalla & Goyari, 2020), in particular regarding the positive effect running from government expenditures to CO2 (Carlsson & Lundström, 2001; Fan et al., 2020), from international trade to CO2 (Wu et al., 2021a, 2021b), and from investment expenditures to CO2 (Nugraha & Osman, 2018; Shahbaz et al., 2020). Our findings for LMICs also confirm, in general, that real economic activity increases CO2 emissions in these countries (Antonakakis et al., 2017; Ben Jebli et al., 2020; Ehigiamusoe & Lean, 2019; Halkos & Gkampoura, 2021; Zaman & Moemen, 2017), specifically confirming the positive effect running from imports to CO2 (Alola & Joshua, 2020) and from FDI to CO2 (Lau et al., 2018; Shi et al., 2020). Discussion Pollution in general and air pollution in specific are the most significant environmental causes of worldwide morbidity and mortality. Underlying are the emissions of substances into the atmosphere by human activities such as production, consumption, trade and finance, which are governed predominantly by macroeconomic policies. Given these two basic facts, the paper aimed to provide new evidence for how to structure macroeconomic policies in a way to reduce environmental pollution, which might be a part of a rapid, large-scale, and coordinated action against pollution, required by the United Nations Environment Assembly (UNEP, 2018: 3). This section, in this context, aims to make a structured discussion on (i) the paper’s contributions to the understanding and governance of macroeconomy–pollution nexus, (ii) the policy implications of the paper’s findings at national and international level, and (iii) the implications of the paper’s findings for future work on macroeconomy–pollution nexus. The first contribution of this paper is to introduce the systemic and fragmented governance of GCMs and DCMs as analytical tools to explain the effect of each variable of macroeconomic governance on air pollution in conjunction with the others, thereby providing systemic insight into how the macroeconomic governance–pollution nexus works as a whole. This matters because a ceteris paribus approach is neither realistic nor explanatory and may be misleading when used for cultivating policy suggestions. In specific, this contribution matters for cultivating feasible and conclusive policy inferences by explaining and understanding (i) what to do to make an economic system environmentally friendly, (ii) how to decide what to do first, and (iii) how to restructure variable-specific policy options (See policy implications). The second contribution of the paper is to illustrate the need for disaggregating the EKC hypothesis in order to demonstrate how each component of growth rather than only aggregate growth itself affects pollution in time so that variable-specific policy suggestions may be imagined. Given the results for each country group, it can be suggested that the higher the level of income, the more environment friendly the macroeconomic governance. Evidently, this result confirms the EKC hypothesis in terms of the comparative performance of high-income, upper-middle, and lower-income countries but not in terms of the changing levels of income for the same country groups over time. In other words, we did not test the ordinary EKC hypothesis. Instead, we first grouped countries based on their levels of income and then investigated the effects of macroeconomic variables on pollution in the selected countries. However, it turns out that a dynamic approach to the macroeconomy–pollution nexus using the EKC hypothesis would be a valuable exercise to understand the changing effects of macroeconomic governance on pollution for the same group of countries with changing levels of income. Evidently, such a dynamic approach would yield the formulation of stage-specific policy inferences for governing the relationship between each variable of macroeconomic governance and pollution. The third contribution of the paper is to introduce how to make a full-fledged analysis of pollution–macroeconomy nexus by a systematic selection of macroeconomic variables. In the extant literature, most of the macroeconomic variables have been examined in terms of their effects on pollution. But either only few variables have been selected or these variables have been modeled regardless of the systemic conduct of macroeconomic regime. A key result of this way of action is that there has yet to be formulated common characteristics for macroeconomy–pollution nexus for HICs, UMICs, and LMICs. The paper contributed to the joint explanation of the effect of macroeconomic variables on pollution, thereby mitigating the ambiguity of the findings on various income groups. The paper has both national and international policy implications for governing macroeconomy–pollution nexus. At national level, it becomes evident that HICs should attach specific and primary importance to their foreign (real) economic relations, because both imports and FDI in these countries increase CO2 emissions, whereas the domestic (real) economic activity altogether reduces it. Specifically, HICs should prioritize greening financial market development as it is a significant variable in providing both public and private (real) economic actors with necessary funds for the said reorganization. The UMICs should reorganize the relationship between their entire domestic (real) economic activity and pollution rather than the relationships of only one of these variables (e.g., government expenditures, investment expenditures, and exports) with pollution. Apparently, any variable-specific policy option in reorganizing these relationships free from each other cannot be expected to create a green economic model in UMICs. Greening financial market development should also be achieved simultaneously with this reorganization as it will be impossible to achieve in the absence of the necessary financial funds. When it comes to LMICs, they need a system-wide reorganization both for domestic and for foreign real economic activities. In so doing, they may prioritize fixed capital formation, exports, and imports over government expenditures. From the above group-specific policy proposals, there emerges a meta-regime of macroeconomic governance at the international or global level in orchestrating the requirements of these proposals, too. A systemic mode of green macroeconomic governance may be used as a best practice or benchmark for international organizations to make a green world possible, as the fragmented, variable-specific or country-specific policy approach may not be sufficient to drive a worldwide transformation of the macroeconomy–pollution nexus. In this regard, international organizations, including the IMF, the World Bank, and the OECD, which either impose their structural adjustment programs or make policy recommendations to their member countries, should take concerted action in cooperation with organizations such as the United Nations, the European Environment Agency, and the Earth System Governance Project in greening macroeconomic regimes. The necessity of systemic or green approaches in dealing with the macroeconomy–pollution nexus becomes even more urgent during the pandemic and in the post-COVID-19 era. First, as noted earlier, air pollution exacerbates the deadly effects of COVID-19 (Pozzer et al., 2020) and the WHO makes recurrent warnings about the pandemic ahead (WHO, 2020). Second, there has already been a process of deglobalization and economic recession with rising protectionism, disruption of global supply chains, a sharp fall in international commercial and financial flows, an increasing shortage of raw materials and final products, sharp slumps in stock markets, a heightened volatility of financial asset prices, and finally, a rapid drop in (domestic and foreign) aggregate demand (WEF, 2020). Thus, short-run policy actions focusing exclusively on stimulating quantitative growth at all costs may make economic systems less sustainable in the long run by either disrupting the complementary effects of the majority of the MEG variables in HICs in reducing pollution or by deepening the pollution-increasing effects of the MNFG variables in UMICs or in LMICs. A long-run, focused fine-tuning of macroeconomic and environmental priorities may be possible only by handling these trade-offs from a systemic approach elaborated on in this paper. Six principal points have emerged from our study that can be investigated by further work. The first is to explain the weight and comparative significance of MNFG and MFG in the overall increase or decrease in CPC levels (not only by looking at coefficient values but also by comparing the joint impact of their respective variables). The second is to study how long-run and short-run modes of MEG can be organized so as to complement each other in creating GCMs. This is relevant in particular for (i) how the pollution-reducing effects of long-run and green systemic governance in the HICs can be complemented by the same mode of governance in the short run, and (ii) how the long-run and green systemic governance model for managing the MFG–pollution nexus in the UMICs can be extended to the MNFG–pollution nexus, and how it can be adapted in the short run both for the MNFG–pollution and for the MFG–pollution nexus. The third issue is to deepen the analysis by explaining the specific GCMs between the MEG variables in each country group or in individual countries; the models used in the paper investigated system-wide complementarities and did not illustrate the specific complementarities that create mutual reinforcement between the MEG variables in reducing or increasing pollution. Another area for further research is to determine if green systemic governance can work better when adapted by concerted action, possibly through the comparison of coordinated and liberal market economies. The sixth and final point is to study the existence and adaptability of systemic governance in an international context during a stage when there is an urgent need for a complementary action by national, regional, international, and global actors in tackling severe environmental challenges. Conclusion The paper concludes, first, that the clustering of major macroeconomic variables into macro-financial and macro-nonfinancial governance yields to the systematic explanation of the relationships between macroeconomic variables and pollution. Second, such a systematic approach yields to the formulation of system-wide policy proposals in a way that demonstrates, first, what to do to make an entire macroeconomic system environmentally friendly, then, how to decide on what to do first to achieve this objective, and finally how to restructure variable-specific policy options. Third, the green and dirty complementarities as analytic tools that mediate the clustering of the converging pollution-increasing or pollution-decreasing effects of macroeconomic variables on pollution underlie the systematization and structuring of both the theoretical analysis and empirical policy inferences. Fourth, it is the systemic and fragmented modes of governance that underlie the explanation of the macroeconomy–pollution nexus from a complementary-theoretic approach by presenting a framework through which the existence or absence of complementarities between macroeconomic variables and pollution can be contextualized into an ordered analytic perspective. Fifth, (i) the systemic and fragmented governance models of GCMs and DCMs are, inter alia, the key drivers of countries’ CPC levels, and (ii) environmental pollution can be tackled effectively when adopting green systemic governance in managing the variables of MNFG and MFG both in the long run and in the short run for all country groups. Sixth, it turns out that the EKC hypothesis needs to be systematically disaggregated for the understanding of how each component of macroeconomic governance has a dynamic effect on pollution. This disaggregation is necessary both (i) for explaining if the EKC hypothesis holds for the relationship between each component of growth and pollution in the same direction and magnitude as that between aggregate growth and pollution itself, (ii) thereby coming up with variable-specific policy suggestions to illustrate when and how to green each macroeconomic variable. Appendix 1 See Tables Table 12 Literature review on the effects of macroeconomic variables on CO2 No Author Country and Period Variables Method Results 1 Salari et al. (2021) States across USA (1997–2016) CO2 emissions, Energy Consumption, GDP Static and dynamic models A long-run relationship exists between various forms of energy consumption and CO2 emissions at the state level The relationship between CO2 emissions and GDP is inverted-U shaped, providing sufficient evidence to support the environmental Kuznets curve (EKC) hypothesis across states 2 Adedoyin and Zakari (2020) UK 1985–2017 UK’s CO2 emissions in tons per capita (CO2), real GDP (RGDP), energy consumption (EU) economic policy uncertainty (EPU) ARDL, Granger causality The model indicates that EPU is the most beneficial in the short run, since it decelerates the growth of CO2 emissions, but its continued usage in the UK has a dubious effect, as CO2 emissions continue to increase 3 Chen and Taylor (2020) Singapore 1900–2017 A heavy metal (chromium, Cr) is utilized as a proxy for environmental quality in this case. GDP, energy use Granger Causality Findings verified the EKC hypothesis about Cr emissions in Singapore. Additionally, the findings show that Singapore’s post-industrial growth may have contributed to the region’s pollution havens 4 Essandoh et al. (2020) Developed and developing countries 1991–2014 CO2 emissions, international trade, and FDI inflows Granger causality The decreasing trend in foreign direct investment tends to impede the detrimental effects of CO2 emission 5 Bulus and Koc (2021) Korea 1970–2018 CO2, FDI, GDP, energy use, renewable energy, government expenditures, exports, imports ARDL N-shaped link between GDP per capita and CO2 emissions per capita. Furthermore, the PHH is somewhat applicable in Korea, and the negative impact of FDI on environmental quality is generally restricted. Additionally, government spending increases the quality of the environment 6 Akbar et al. (2021) 33 OECD nations from 2006 to 2016 Healthcare spending, carbon dioxide (CO2) emissions, and the human development index (HDI) Panel vector autoregression -Healthcare expenditures, CO2 emissions, and HDI, exhibit a causal relationship -Healthcare expenditures and CO2 emissions exhibit bidirectional causality, implying that CO2 emissions significantly increase healthcare expenditures in OECD countries 7 Valodka et al. (2020) EU Countries 2000–2016 CO2 emissions and imports The multi-regional input–output (MRIO) approach The findings indicate that the EU did not reduce CO2 emissions but rather outsourced them 8 Ali and Kirikkaleli (2021) Italy Asymmetric influence of trade, renewable energy, and economic growth on consumption-based CO2 emissions The Gregory–Hansen test for cointegration, Markov switching regression, Nonlinear autoregressive distributed lag (NARDL), and a frequency domain causality test -Import has a positive asymmetric effect on consumption-based CO2 emissions, implying that increasing import is associated with a decline in consumption-based environmental quality -Export, renewable consumption, and economic growth all help Italy reduce consumption-based CO2 emissions 9 Thampanya et al. (2021) 61 countries classified as high- and middle-income economies 1990–2018 The influence of positive and negative shocks in financial development on CO2 emissions Linear and nonlinear ARDL (NARDL) Financial development factors in reducing CO2 emissions in the long term for high-income economies, it increases CO2 emissions and thereby degrades environmental quality in middle-income economies 10 Sephton and Mann (2016) UK 1857–2007 GDP per cap, CO2, SO2 Nonlinear cointegration, threshold cointegration Inverted U-shaped relationship 11 Shahbaz et al. (2016) 25 Developed Economies 1970–2014 Carbon emissions, non-renewable energy GDP CIPS test, Westerlund cointegration, Granger causality Globalization increases carbon emissions for most of the developed countries 12 Giovanis (2013) UK 1991–2009 Household income, weather data, demographic and household characteristics Dynamic panel data No evidence of EKC hypothesis 13 Friedl and Getzner (2003) Austria 1960–1999 GDP, CO2, trade, structural change Time series cointegration Cubic (i.e., N-shaped) relationship between GDP and CO2 14 Franklin and Ruth (2012) US 1800–2000 CO2, GDP per cap, Gini coefficient, ratio of exports to imports, inflation adjusted energy prices Time series, level, cubic; OLS, Prais–Winsten AR (1) regression model Inverted U-shape 15 Fosten et al. (2012) UK 1830–2008 CO2, SO2 and GDP per cap Cointegration, nonlinear error correction CO2 and SO2 emissions having an inverse-U relation with real GDP per capita 16 Hamit-Haggar (2012) Canada 1990–2007 Industrial energy, CO2, GDP Pedroni cointegration test, FMOLS, VECM Granger causality Inverted U-shaped relationship 17 Shoaib et al., 2020 G8 and D8 nations 1999–2013 Financial development and CO2 emissions PMG panel ARDL approach In the long run, financial development has a substantial and beneficial effect on carbon emissions at the 1% statistical level in both panels. Financial development and energy consumption have a greater influence on the D-8 and G(8) nations, respectively. Energy consumption and trade openness have a beneficial effect, but GDP has a substantial effect in reducing carbon emissions by 1% statistically 18 Ahmed and Shimada (2019) 30 Emerging and developing countries 1994–2014 GDP constant USD prices, gross fixed capital formation, labor force, CO2, renewable and non-renewable energy consumption Panel co-integration test, FMOLS and DOLS GDP and non-renewable energy consumption cause the increase in CO2 emissions 19 Banday and Aneja (2019)  5 BRICS Countries 1990–2017 GDP constant USD prices, renewable energy consumption, non-renewable energy consumption, CO2 Bootstrap Dumitrescu and Hurlin panel causality There is unidirectional causality from GDP to CO2 for India, China, Brazil, South Africa and no causality for Russia 20 Bhat (2018) 5 BRICS countries 1992–2016 GDP at market prices, gross fixed capital formation, labor force, population GDP per head of population, renewable energy consumption, non-renewable energy consumption, CO2 Panel cointegration Population, per capita income, and non-renewable energy consumption increase CO2 emissions 21 Muhammad (2019) 68 countries—developed, emerging and Middle East and North Africa countries 2001–2017 GDP, energy consumption per capita, CO2, labor force, gross national expenditure, financial development, population, urban population, trade openness, bank financial development, merchandise trade SUR, GMM CO2 emissions increase in all countries because of energy consumption. CO2 emissions increase, while the energy consumption decreases in developed and MENA countries but energy consumption increases and CO2 emissions decrease in emerging countries due to the increase in economic growth 22 Ummalla and Goyari (2020) 5 BRICS countries 1992–2014 GDP, labor force, CO2, clean energy consumption, energy consumption, population Panel cointegration, panel Granger causality Energy consumption and GDP increase CO2 while clean energy consumption significantly reduces it 23 Vo et al. (2019) 5 ASEAN members 1971–2014 CO2, energy consumption, renewable energy consumption, GDP per capita, population Granger causality and VECM There is no long-run relationship among CO2 emissions, energy consumption, renewable energy, population growth, and GDP in the Philippines and Thailand, but there is a relationship in Indonesia, Myanmar, and Malaysia 24 Pradhan et. al. (2021) 5 BRICS nations 1992–2014 CO2, energy use, GDP per cap, FDI Panel cointegration, FMOLS and DOLS Foreign direct investment reduces CO2 emission 25 Fan et al. (2020) China 2007–2015 CO2, population, government expenditure, energy consumption, GDP Decomposition analysis Disparities in government expenditure play an important role in regional emission inequality 26 He et al. (2020) 5 BRICS countries 1970–2018 CO2, trade, FDI Bootstrap ARDL CO2 emissions have a causal relationship with trade 27 Wu et. al. (2021b) China 2000–2017 CO2, trade, GDP Decomposition method International trade increases CO2 emissions 28 Zhao and Yang (2020a, 2020b) 29 Chinese provinces 2001–2015 CO2, financial development, GDP, energy consumption, urban population Panel data analysis The regional financial development has significantly lagged inhibitory effects on CO2 emissions. Moreover, in the long run, there is a two-way causality between the variables 29 Nugraha and Osman (2018) Indonesia 1971–2014 CO2, energy consumption, household final expenditures, agriculture sector, industry sector ARDL and Granger causality An increase in household final consumption expenditure has a negative effect on CO2 emission in the short term in Indonesia 30 Al-mulali and Sab (2012) 19 selected countries 1980–2008 CO2, broad money, domestic credit provided by banking sector, domestic credit provided to private sector, GDP, energy consumption Panel data analysis Broad money increases the CO2 emission level in these countries 31 Mitić et al. (2020) 9 Balkan countries 1996–2017 CO2, gross fixed capital formation, industry, services Panel cointegration tests and panel causality tests Gross fixed capital formation has statistically significant on CO2 emissions 32 Shahbaz et. al. (2013) South Africa 1965–2008 CO2, GDP, financial development, trade, coal consumption ARDL bounds testing and ECM Trade openness improves the quality of environment 33 Halkos and Paizanos (2013) 77 selected countries 1980–2000 CO2, GDP, government expenditure Panel data analysis Government expenditures have a negative direct effect on CO2 emissions 34 Gholipour and Farzanegan (2018) 14 MENA countries 1996–2015 CO2, government expenditure, trade openness, resource rents, weather conditions ECM Government expenditures directly reduce CO2 emissions in MENA countries 35 Xie et. al. (2020) 11 emerging countries 2005–2014 CO2, FDI, GDP, population, energy consumption, trade openness Panel smooth transition regression (PSTR) An increase in FDI has a significant influence on CO2 emissions and population Energy consumption and trade openness are the key factors in increasing CO2 emissions 36 Nasir et al. (2019) ASEAN-5 economies 1982–2014 CO2, FDI, GDP, financial development, bank credit to bank deposit Panel data analysis An increase in FDI will cause an increase in CO2 emissions in emerging ASEAN countries 37 Carlsson and Lundström (2001) 75 selected countries 1975–1995 CO2, GDP, political freedom, size of government, freedom to trade with foreigners, structure and use of markets, price stability and legal security Panel data analysis An increase in the government expenditures may indirectly lead CO2 emissions to increase 38 Halicioglu (2009) Turkey 1960–2005 CO2, energy use, GDP, foreign trade The ARDL bounds testing and Granger causality An increase in trade inflows causes CO2 emissions to increase 39 Chandran and Tang (2013) ASEAN-5 economies CO2, energy consumption, GDP, FDI Granger causality Foreign direct investment has an insignificant impact on CO2 emissions 40 Shahbaz et. al. (2020) China 2007–2015 CO2, investment, population, GDP, technological innovations, exports, FDI Bootstrapping autoregressive distributed lag modeling (BARDL) There is a positive relationship between investment and carbon emissions 41 Zhao et. al. (2016) China 1993–2013 CO2, fossil fuels consumption, total energy consumption, gross output value, fixed asset investment, gross fixed asset investment Extended logarithmic mean Divisia index (LMDI) An increase in investment leads CO2 emissions to increase 42 Ben and Ben (2021) 12 countries (MENA region) (1970–2015) Co2 emissions, real GDP per capita, real GDP per capita square, energy use, trade openness, FDI inflows, financial development Panel threshold regression model There is a strong regime dependence relationship between income and air pollutants Carbon emission patterns differ among countries with identical energy intensities 43 Halkos et al. (2021) 119 countries 32 lower middle income 7 low income (2000–2018) Total electricity production, population, electricity production from oil, gas and coal sources, electricity production from renewable sources, excluding hydroelectric, electricity production from hydroelectric sources, CO2 emissions, GDP per capita, population density Fixed effect and GMM and Granger causality EKC hypothesis is confirmed for high- and upper-middle-income countries For low-income levels GDP per capita has negative effect on CO2; however, from a certain threshold, higher GDP per capita increases CO2 emissions While electricity production from fossil fuels causes environmental degradation, electricity production from renewable sources has an inverse relationship with CO2. For low- and lower-middle-income countries, population diversity is a small driver of CO2 emissions 44 Dong et al. (2020) 130 countries (1997–2015) -CO2 emissions from fuel combustion, GDP Decomposition (identity) analysis UMI countries are the main contributors to recent CO2 emission growth For the last two decades, while income increase had positively affected global CO2 growth, declining energy intensity had a mitigating effect 45 Abban et al. (2020) 44 Countries, 16 low- and lower middle-income countries (1995–2015) CO2 emission, GDP per capita, GDP squared, EI (kilograms of oil equivalent), FDI inflows Westerlund–Edgerton cointegration, AMG estimation EKC is only confirmed in HICs. Bidirectional causal effect between CO2 and FDI Except LMICs, there is a bidirectional relation among EI and CO2 emissions For LMICs, there is a one-way causal effect from CO2 to EI, and bidirectional relation among GDP and CO2 emissions Unidirectional causal effect from GDP to CO2 emissions in HICs and LICs 46 Khaskheli et al. (2021) 19 Low-income countries (1990–2016) Environmental degradation is estimated by CO2 emissions, Private credit by banks as a percentage of GDP, GDP per capita, International trade percentage of GDP, population Panel smooth transition regression model (PSTR) The environmental measures of low-income countries are nonexistent. However, the implementation of measures mitigates environmental quality by decreasing CO2 emissions FD has a positive relation with CO2 in low regimes; however, on higher regimes effect turns in to negative GDP, international trade, and population has a positive effect on CO2; however, in higher regimes, it has a diminishing effect 47 Alola and Joshua (2020) 217 countries with low, lower middle, upper middle and high income (1970–2014) Renewable energy, fossil fuel, globalization, CO2 emission Panel pooled mean group and Granger causality Fossil fuel energy usage is the leading cause for increased carbon emissions in each of the included income groups Except lower-middle-income group, renewable energy negatively and globalization positively affect CO2 emissions In the short run, renewable energy usage and globalization improve environmental quality; however, their impacts turns into negative in the long run 48 Wu et al. (2021a) 56 countries; 14 lower middle income, low income (1991–2018) CO2 emissions, GDP, urbanization, energy, consumption per capita, industry, value-added, export Principal component analysis and CCEMG Urbanization and economic structure increases CO2 emissions on LMIC and LIC, GDP has bidirectional relation with CO2 on all country groups 49 Pham et al. (2020) 44 Lower middle-income countries (2003–2014) CO2 emissions, merchandise exports on GDP, merchandise imports on GDP, net FDI inflow on GDP, real GDP, renewable energy consumption Pooled mean group regression Merchandise import, FDI and renewable energy consumption mitigate environmental quality. -Merchandise export worsens it 50 Shi et al. (2020) 147 countries; 37 lower-middle income 16 low-income (1995–2015) CO2 emissions, total population, net inflow of international tourists, total primary energy consumption per capita, expenditure of inbound tourists per capita, GDP per capita IPAT equation and cointegration and Granger causality The main contributor of global CO2 emissions is primary energy consumption For low-income countries, expenditure of inbound tourists per capita has a positive impact on CO2 emissions As countries’ income decreases, the impact of tourism on CO2 emissions increases 51 Danlami et al. (2019) LMI and Middle East and North African (MENA) countries (1980–2011) CO2 emissions, GDP growth, gross capital formation, FDI -Energy production Two separate ARDL models for LMI and MENA countries and FMOLS for the two regions over the same period GDP and FDI have a positive relationship with CO2 emissions Contribution of CO2 emissions by MENA countries is immense 52 Acheampong (2019) 46 Sub-Saharan African countries (2000–2015) CO2 emissions, real GDP per capita growth, energy consumption, trade openness, population, urbanization, financial development (6 variables, domestic credit to private sector as a share of GDP, domestic credit to private sector by banks as a share of GDP, domestic credit to private sector by financial sector as a share of GDP, broad money as a share of GDP, liquid liabilities, international capital flow) GMM While financial development has a detrimental effect on the environment, FDI has a moderating role on CO2 emissions The direct and indirect effect of financial development has mixed results among income groups and regions 53 Dong et al., (2019) 110 countries; LI 10 LMI 28 (1980–2015) Emission coefficient, population income level, energy intensity, energy consumption structure, CO2 emission Extended logarithmic mean and Divisia index While CO2 emissions continue to increase, the effects of driving forces of CO2 emissions are similar in all periods Main driving forces of CO2 emissions, income and population, respectively Main mitigating factors are energy intensity and energy consumption structure, respectively Countries that positively contribute to environmental quality by reducing CO2 reductions most effectively were mainly UMI countries For HICs, energy intensity was the primary mitigating factor For low-income countries, the main mitigating factor was energy consumption UMI countries will mitigate environment by 2030 at a higher level than the other income country groups 54 Ehigiamusoe and Lean (2019) 122 countries; 13 low income, 32 lower middle income (1990–2014) Energy consumption, economic growth, financial development, CO2 emissions DOLS and FMOLS Energy consumption has a positive effect on CO2 emissions regardless of income groups While economic growth and financial development reduce CO2 emissions in HI countries, its environmental effects are detrimental for middle-income and low-income countries 55 Lau et al. (2018) 100 countries 13 low-income, 28 lower middle-income, 25 upper middle income, 34 high income (2002–2014) CO2 emissions, GDP per capita, FDI, trade, rule of law, control of corruption GMM EKC hypothesis is only valid in high-income countries Except low-income countries, rule of law has a positive impact on the environment For high-income countries, FDI, control of corruption has positive impact on CO2 emissions. However, trade openness has adverse effects on the environment For developing countries, while trade openness contributes to CO2 reduction, FDI has adverse environmental effects 56 Zaman and Moemen (2017) 90 countries (25 low 42 lower middle and upper middle income 23 high income) (1975–2015) Carbon dioxide emissions, GDP per capita, GDP per capita square, FDI, inflows, trade openness, population, energy use, agriculture, value-added, industry, value-added, services, value-added, health expenditures per capita, government expenditures on education Panel GMM and panel fixed effect regression EKC hypothesis is confirmed Sectoral value added has positive impact on CO2 emissions, Industry value-added, service value-added and energy consumption increase CO2 emissions Because of the shift of polluting industries from developed countries to developing countries, low-income countries are the most polluting countries 57 Antonakakis et al., (2017) 106 countries; 12 low, 24 lower middle income (1971–2011) Real GDP per capita, CO2 emissions, final consumption of total energy consumption (5 subcomponents (1) Electricity (2) Oil (3) Renewable (4) Gas (5) Coal energy) Panel VAR Enduring growth aggravates the greenhouse gas emissions. EKC hypothesis cannot be confirmed There is a bidirectional causality between economic growth and energy consumption There is not any statistically significant evidence for the fact that renewable energy consumption is conducive to economic growth 12, Table 13 Literature review on the EKC hypothesis No Author Country and period Variables Method Results 1 Bibi and Jamil (2021) Latin America, East Asia and the Pacific, Europe and Central Asia, South Asia, the Middle East and North Africa, and Sub-Saharan Africa (2000–2018) Per capita CO2 emissions, per capita GDP, trade openness, FDI, education financial development indicator, institutional quality Random effect and fixed effect models The EKC hypothesis is supported in all the regions except in the Sub-Saharan Africa region. Consequently, different regions have dissimilar EKC relationships 2 Shikwambana et al. (2021) South Africa (1994–2019) GDP, CO2, black carbon (BC), SO2, CO The sequential Mann–Kendall (SQMK) EKC hypothesis showed an N-shape for SO2 and CO. Emissions levels are generally correlated with economic growth 3 Amar (2021) UK (1751–2016) Per capita GDP, CO2 emissions Dynamic correlation, Squared cross-wavelet coherency The EKC hypothesis holds in the UK 4 Ongan et al. (2021) USA (1990–2019) CO2 emissions, Per capita disposable income, per capita renewable, fossil energy consumptions ARDL The undecomposed model does not detect evidence of the EKC hypothesis for the USA. However, the decomposed model (where the per capita income series is decomposed into its increases and decreases as two new time series and only one series, which contains income increases, is used) strongly does so 5 Minlah and Zhang (2021) Ghana (1960–2014) Per capita CO2 emissions, per capita GDP VAR Bootstrap rolling window Granger causality Environmental Kuznets curve for carbon dioxide emissions for Ghana is upward sloping. Thus, EKC does not hold 6 Adeel-Farooq et al. (2020) Association of Southeast Asian Nations (1985–2012) Per capita GDP, Methane emissions, Energy consumption, Trade openness Mean group (MG) Pooled MG (PMG) Economic growth causes CH4 emissions to decrease 7 Jiang et al. (2020) 286 cities in China and 228 cities and counties in South Korea (2006–2016) Per capita GRP, per capita SO2 emissions, SO2 emissions intensity, SO2 emissions density, employment, share of manufacturing, industry in GRP, energy consumption, population density Simultaneous equation model (SEM) There is an inverted U-shaped pattern in metropolitan areas and a U-shaped pattern of non-metropolitan areas 8 Dogan and Inglesi-Lotz (2020) Austria, Bulgaria, Finland, France, the Netherlands, Sweden, and Turkey (1980–2014) GDP, CO2 emissions, Industry value added, Energy intensity, Urbanization, Population Fully modified OLS (FMOLS) EKC hypothesis does not hold where higher levels of industrialization promote reductions in the emission levels. The channel might be through access to modern, cleaner, more efficient technologies that promote environmentally friendly behaviors of the overall economy 9 Pata and Aydin (2020) Brazil, China, Canada, India, Norway and the USA (1965–2016) GDP, Ecological footprint, Hydropower energy consumption Fourier bootstrap ARDL The EKC does not hold in the top six hydropower energy consuming countries -There is no causal nexus between hydropower energy consumption and ecological footprint Hydropower energy is not used effectively enough to reduce ecological footprint 10 Lazăr et al. (2019) Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia (1996–2015) Per capita CO2 emissions, Per capita GDP, Per capita energy consumption, Index of economic freedom Mean Group (MG) estimator Mean Group Fully Modified Least Squared (MG-FMOLS) estimator Augmented Mean Group (AMG) Aggregate results reveal an increasing nonlinear link between GDP and CO2 for the group of CEE countries. However, at a disaggregated or country level, the relationship between GDP and CO2 is diverse among CEE countries, namely: N-shaped, inverted-N, U-shaped, inverted-U, monotonic, or no statistical link 11 Sephton and Mann (2018) USA (1857–2007) GDP per capita, CO2, SO2 Nonlinear cointegration, threshold cointegration Inverted U-shaped relationship 12 Yang et al. (2015) 29 Chinese provinces (1995–2010) CO2, industrial dust, Ind. gas, Ind., smoke, Ind. SO2, Ind. waste water, GDP, % of exports, imports, domestic trade, ratio of entry of FDI/GDP, population density Fixed and random effects models Positive linear relationship EKC does not hold 13 Bölük and Mert (2015) Turkey (1961–2010) CO2, GDP per cap electricity production from renewables ARDL Inverted U-shape 14 Zhang and Zhao (2014) 28 Chinese provinces (1995–2010) GDP per cap, energy intensity, income CO2, inequality, urbanization, share of industry sector in GDP Fixed effect model N-shape 15 Shahbaz et al. (2013) Indonesia (1975–2011) CO2, GDP per cap, energy consumption per cap, real domestic credit to private sector per cap, trade openness ARDL VECM Granger Causality Economic growth and energy consumption increase CO2 emissions, while financial development and trade openness compact it Bidirectional causality between CO2 and GDP Financial development Granger causes CO2 emissions 16 Giovanis (2013) UK (1991–2009) Household income, weather data, demographic, household characteristics Fixed effects model, Arellano–Bond GMM, binary logit model with fixed effects No evidence of EKC hypothesis 17 Franklin and Ruth (2012) USA (1990–2000) GDP, CO2, service and manufacturing, employment, Gini coefficient, real fuel prices, Genuine Progress Indicator, trade OLS Inverted U-shaped relationship 18 Hamit-Haggar (2012) Canada (1990–2007) Industrial energy, CO2, GDP FMOLS, VECM Granger causality Inverted U-shaped relationship 19 Jayanthakumaran et al. (2012) India–China (1971–2005) GDP per cap, CO2 energy consumption, ratio of exports plus imports to GDP, manufacturing value added ARDL Growth and structural changes in manufacturing, and increased energy consumption influence CO2 emissions in China Income and energy consumption increase emissions in India The role of structural change in India is ambiguous 20 Fosten et al. (2012) UK (1830–2008) CO2, SO2, GDP per cap OLS Error correction model CO2 and SO2 emissions have an inverse-U relation with GDP per capita 21 Franklin and Ruth (2012) The USA (1800–2000) Co2, GDP per cap, Gini coefficient, ratio of exports to imports, inflation adjusted energy prices OLS Prais–Winsten AR(1) Inverted U-shape 22 Soytas and Sari (2009) Turkey (1960–2000) Energy consumption; carbon emissions; labor, gross fixed capital investment; GDP VAR Toda–Yamamoto No long-run causal link between income and emissions 23 Dutt (2009) 124 countries (1960–2002) CO2, GDP per capita, governance, political institutions, socioeconomic conditions, population density, education Robust OLS, fixed effect model Linear between 1960 and 1980; Inverted U-shape between 1984 and 2002 24 Managi and Jena (2008) 16 states in India (1991–2003) GSP, SO2, No2, and suspended particular matter Productivity measurement technique EKC exists between environmental productivity and income The effect of income on environmental productivity is negative 25 Halicioglu (2008) Turkey (1960–2005) CO2, energy, GDP. Foreign Trade ARDL Granger causality Strong connection between GDP and CO2 26 Soytas et al. (2007) USA (1960–1995) Energy, GDP VAR Granger causality EKC does not hold in the case of USA In the long run, the main cause of CO2 emissions in the USA is energy consumption 27 Dinda and Coondoo (2006) 88 countries (1960–1990) CO2 per cap, GDP per cap ECM/fixed effects model Bidirectional relationship Results confirm pollution haven hypothesis 28 Friedl and Getzner (2003) Austria (1960–1999) GDP, CO2, trade, structural change Cointegration structural model N-shaped relationship between GDP and CO2 13. Appendix 2 Panel unit root, cross section dependence, and panel cointegration tests Panel unit root tests Levin et al. (2002) and Breitung (2001) tests are estimated using the following models:1 Δyit=κi+αyit-1+∑j=1kdijΔyit-j+εit and2 Δyit=κi+αyit-1+βit+∑j=1kdijΔyit-j+εit Tests use panel versions of the augmented Dickey–Fuller (ADF) unit root test (with and without a trend). α is restricted to be identical across cross-sectional units, but the lag order for the first difference terms to vary across cross-sectional units is allowed in these tests, which in this study are countries. Pooled ordinary least squares (POLS) estimates Eqs. 1 and 2 for tα critical values are tabulated by Levin et al. (2002) via Monte Carlo simulations for various combinations of N and T commonly employed in applied work. H0:α=0 and H1:α<0 are the null and the alternative hypothesis, respectively. Under the H0, there is a unit root, while under the H1 alternative there is no unit root. While the Levin et al. (2002) test requires bias correction factors to correct for cross-sectionally heterogeneous variances to ensure efficient POLS estimation, the Breitung (2001) test achieves the same result by appropriate variable transformations. α, in Eqs. 10 and 11, is restricted to be identical across countries under both the null and alternative hypotheses is one of the drawbacks of the Levin et al. (2002) and Breitung (2001) tests. Im et al. (2003), here after IPS, is proposed a t-bar test using the following model:3 Δyi,t=αi+ϑit+θt+ρiyi,t-1+∑j=1pφi,jyi,t-j+vi,t. It has the advantage over the Levin et al. (2002) and Breitung (2001) tests it does not assume that all countries converge toward the equilibrium value at the same speed under the alternative hypothesis and thus is less restrictive. The IPS test is the adjusted version of ADF individual unit root test statistics. As T and N goes to infinity, the IPS statistics is asymptotically normally distributed. The null and alternative hypothesis of IPS test:H0:ρi=0foralliH1:ρi<0i=1,2,…,Nρi=0i=N1+1,N1+2,…,N, While H0 assumes that each series in the panel has a unit root for all cross-sectional units, H1 means stationary of panel series. The test statistic of IPS unit root test is modeled as follows:4 t¯T=1N∑i=1Nti,tPi where ti,t is the ADF t-statistics for the unit root tests of each country and Pi is the lag order in the ADF regression. The test statistic is calculated as follows:5 AT=NTt¯T-EtTvartT The values for EtitPi,0 are obtained from the results of the Monte Carlo simulation carried out by IPS. Simulations indicate that the t¯T statistics is more powerful even for small sample sizes in the presence of no cross-sectional dependency. Cross section dependence tests A key limitation of the Breusch and Pagan (1980) LM test is to exhibit substantial size distortions when N > T. The CD test proposed by Pesaran (2004) converged to a normal distribution with a mean of 0 and a variance of 1 under the null hypothesis of cross-sectional independence when both N and T → ∞. Further, Pesaran et al. (2008) proposed a bias-adjusted LM test that successfully controlled the size and maintained reasonable power in panels with exogenous regressors and normal errors, even when the cross section mean of the factor loadings was close to zero where the CD test had little power. Simulation evidence provided by Pesaran et al. (2008) indicates that the test had a good size and power for T > 20. All three tests were relevant for our analysis as N < T and T > 20 in both our models. Panel cointegration tests Kao (1999) develops two types of panel cointegration tests, the Dickey–Fuller (DF) and augmented Dickey–Fuller (ADF) types. The tests are based on the estimated residuals derived from the following long-run regression model:6 yit=αi+xit′β+ϵit, where yit and xit are assumed to be I1. The structure of estimated residuals is as follows:7 ϵ^it-1=ρϵ^it+vit, where ρ is assumed to be common for all units. Under the null hypothesis, all tests show the absence of cointegration, whereas the alternative hypothesis indicates the existence of cointegration. All tests are asymptotically distributed as N0,1. Pedroni (1999) assumes that all panels have individual slope coefficients in Eq. (6). Allowing unit-specific ρi instead of ρ, the proposed panel cointegration tests are obtained by testing for a unit root in the estimated residuals. The test statistics areModifiedPP-t≡TN-1/2∑i=1N∑t=1Te^it-12-1∑t=1Te^it-1Δe^it-α^i PP-t≡N-1/2∑i=1Nσ^i2∑t=1T∫^it-12-1/2∈^it-1Δ∈^it-α^i ADF-t≡N-1/2∑i=1N∑t=1Ts~∗2∈^it-12∗-1/2∑t=1N∈^it-1∗Δ∈^it∗ where λ^i=12σ^i2-s^i2 and S~N,T∗2=1N∑i=1NS^∗2. The null and alternative hypotheses areH0:ρi=1 H1:ρi<1, for all i. Under the null hypothesis, there is no cointegration for all spesific units, whereas the alternative hypothesis indicates cointegration. Acknowledgements The paper is not under consideration by any other journal. Funding The authors did not use any fund for preparing or publishing this paper. Declarations Conflict of interest The authors have no conflict of interest with any people or organization. Consent for the publication All authors have consent for the publication of this paper in Environment, Development and Sustainability journal. 1 Air pollution denotes the presence of one or more contaminants in the atmosphere ranging from gas, dust, and fumes to mist, smoke, or vapor. 2 Air pollution causes a number of potentially deadly diseases and illness such as lung disease, asthma, cardiovascular and heart disease, cancer and pneumonia, premature deaths, and many hazardous environmental problems such as acid rain, smog, and haze. 3 Country groups Macroeconomic governance Complementarities HICs Upper-middle-income countries MEG Macroeconomic governance GCMs Green complementarities UMICs High-income countries MNFG Macro-non-financial governance DCMs Dirty complementarities LMICs Lower-middle-income countries MFG Macro-financial governance 4 In our analysis, long run denotes a time period when the macroeconomic variables and CO2 emissions comove in a patterned manner. The ‘patterned’ here denotes, for example, a relationship dominated by green or dirty complementarities (see Figs. 3, 4, and 5). Short run denotes the subperiods when this patterned relationship diverges from the long-run equilibrium of the patterned relationship. That the ECT is negative and statistically significant denotes that these short-run divergences from the long-run equilibrium converge to the long-run equilibrium. 5 The main advantage of panel ARDL approach (Pesaran et al., 1999) is to be able to estimate cointegrated relationships irrespective of the order of integration, which might be I(0), I(1) or a mix of I(0) and I(1). Panel ARDL cannot, however, be employed when the dependent variable is not I(1) or any of the variables is I(2). As all variables in the both models we established are I(1), we are able to run oanel ARDL for our models. The PMG estimator highlights both pooling by the homogeneity restrictions on the long-run coefficients and averaging across groups to obtain means of the estimated error correction coefficients and the other short-term parameters of the model. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Abban OJ Analysis on the nexus amid CO2 emissions, energy intensity, economic growth, and foreign direct investment in Belt and Road economies: Does the level of income matter? Environmental Science and Pollution Research 2020 27 10 11387 11402 10.1007/s11356-020-07685-9 31965501 Acheampong AO Modelling for insight: Does financial development improve environmental quality? 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==== Front Environ Resour Econ (Dordr) Environ Resour Econ (Dordr) Environmental & Resource Economics 0924-6460 1573-1502 Springer Netherlands Dordrecht 35431457 678 10.1007/s10640-022-00678-x Article How Do Carbon Taxes Affect Emissions? Plant-Level Evidence from Manufacturing Ahmadi Younes yahmadi@ucalgary.ca 1 Yamazaki Akio a-yamazaki@grips.ac.jp 12 Kabore Philippe pkabo048@uottawa.ca 3 1 grid.22072.35 0000 0004 1936 7697 Department of Economics, University of Calgary, Calgary, AB Canada 2 grid.444282.c 0000 0001 2105 7362 National Graduate Institute for Policy Studies (GRIPS), Tokyo, Japan 3 grid.28046.38 0000 0001 2182 2255 Department of Economics, University of Ottawa, Ottawa, ON Canada 11 4 2022 2022 82 2 285325 8 3 2022 © The Author(s), under exclusive licence to Springer Nature B.V. 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. This paper investigates how carbon taxes affect emissions by examining British Columbia’s revenue-neutral carbon tax in the manufacturing sector. We theoretically demonstrate that carbon taxes can achieve emission reductions while increasing production. Recycling carbon tax revenues to lower corporate income tax rates encourages investments, allowing plants to emit less per unit of output. Using detailed confidential plant-level data, we evaluate this theoretical prediction by exploiting the treatment intensity through plants’ emission intensity. We find that the carbon tax lowers emissions by 4 percent. Furthermore, we find that the policy had a positive output effect and negative emission intensity effect, suggesting that the carbon tax encouraged plants to produce more with less energy. We provide initial evidence showing how a revenue-neutral carbon tax may achieve emission reductions while stimulating the economy. Keywords Carbon tax Energy Revenue-recycling Manufacturing emission JEL Classification H23 Q5 L6 Smart Prosperity Research Networkhttp://dx.doi.org/10.13039/501100013259 Policy Research Center, National Graduate Institute for Policy Studies issue-copyright-statement© Springer Nature B.V. 2022 ==== Body pmcIntroduction At the 21st Conference of Parties1 in Paris (December 2015), countries, by consensus, adopted the first universal climate agreement to tackle global warming. Several countries have already implemented carbon pricing policies to reduce their greenhouse gas (GHG) emissions.2 After the Paris agreement, there is a general expectation in the international community that these policies would be expanded. Many countries are now proposing to achieve the net-zero emissions by 2050 through more ambitious climate action plans, including carbon pricing. Theoretical models show that a uniform carbon tax is an effective tool to achieve emission reduction targets at the lowest economic costs.3 However, the political feasibility of the policy is still heavily debated among policymakers and the public because of its potential adverse effects on the economy. Some even argue that the current carbon prices around the world are set too low to reach emission reduction targets.4 For many countries to achieve the net-zero emission goal, it is essential to understand the effectiveness of carbon-pricing policies in reducing emissions. Thus this paper takes advantage of a unique plant-level dataset to investigate the effect of the carbon tax, implemented by British Columbia (BC) in 2008, on GHG emissions from manufacturing plants. The carbon tax in BC was unexpectedly announced in February 2008 and has been in effect since July 2008. The tax rate initially began at $10 per tonne of CO2 equivalent (CO2eq) and increased by $5 annually, reaching $30 in 2012. The tax applies to all fossil fuels purchased within BC and covers 77% of provincial emissions (Harrison 2012). There are three reasons why this policy is ideal for estimating the causal effect of a carbon tax on GHG emissions. First, the tax is comprehensive, applying to all fossil fuels purchased by all plants within BC. Second, its tax rate is high compared to other existing carbon policies, so companies are more likely to change their behavior in response to the policy.5 Third, the fact that the tax was introduced shortly after its unexpected announcement eliminates any anticipatory effects (i.e., actions prior to the implementation of the policy) as plants presumably did not have enough time to adjust their behavior. Our empirical strategy is motivated by a simple model of monopolistic competition with heterogeneous plants exposed to a carbon tax, which is borrowed from Yamazaki (2022). We start by decomposing the plant-level emission responses into scale effect and technique effect.6 By allowing plants to invest in energy-saving technologies, we theoretically show that carbon taxes can reduce emissions while producing more. This is possible through recycling tax revenues from the carbon tax to lower the corporate income tax (CIT) rates. The carbon tax alone would reduce emissions at the cost of output (i.e., negative scale effect), while the CIT reduction would ameliorate the distortion in the capital market and encourage plants to be more efficient in both energy consumption and production (i.e., positive technique effect). We further show that the magnitude of emission responses increases monotonically with plants’ emission intensity. Therefore, it is reasonable to assume that high emission-intensive plants are more affected by the carbon tax than low emission-intensive plants. Using the theoretical insights, we design a difference-in-differences (DID) estimator allowing for differential treatment intensity. We use plant-level emission intensity as a measure of exposure to the carbon tax. As the magnitude of plants’ exposure to the carbon tax monotonically increases with their emission intensity. We contend that plants with high emission intensity are more likely to respond to the policy by adjusting their operation or production technologies than the low emission-intensive plants. Our augmented DID estimator compares changes in emission differences between high emission-intensive and low emission-intensive plants in BC with changes in the same emission differences in the rest of Canada before and after the unilateral implementation of the carbon tax.7 Our estimator identifies the relative emission responses between the high and low emission-intensive plants. Furthermore, we exploit the panel structure of the data by including various fixed effects to control for possible unobserved confounding factors, such as commodity price shocks, provincial geographic characteristics, and industry factor intensities. We estimate the emission effect of the policy using the confidential plant-level manufacturing dataset, the Annual Survey of Manufacturing (ASM). This dataset consists of detailed information on plant-level manufacturing activities, such as fuel expenditures, total sales, and employment. What is unique about this dataset is that having access to plant-level fuel expenditures allows us to construct the most comprehensive plant-level GHG emission dataset for Canada.8 Manufacturing sector accounts for a relatively small portion of BC’s total emissions; however, there are three reasons why focusing on this sector provides valuable insights about the effectiveness of carbon taxes. First, while limited to manufacturing plants, the ASM dataset allows us to calculate plant-level emissions and emission intensity while other publicly available datasets cannot. Second, manufacturing plants in BC are relatively emission-intensive, making this sector more likely to respond to the policy than other sectors. Third, a large variation in the emission intensity of manufacturing plants allows us to capture an extra source of variation across plants and design a more credible estimation strategy. We find that the BC carbon tax lowered GHG emissions. The point estimate shows a statistically significant reduction in emissions by 4 percent.9 Furthermore, we show that the policy increased outputs, suggesting that the carbon tax provided enough incentives for plants to take actions to produce more with less (fossil-fuel) energy. Our findings are quite appealing, especially to policymakers, because implementing a carbon tax could both reduce emissions and strengthen the economy. There are potentially two factors that may contribute to the increased outputs. First, the amount of money the BC government returned to the economy was about 15% more than what the carbon tax collected in all years between 2008 and 2016 (e.g., the BC carbon tax raised $1.2 billion in 2012-13 and returned $1.4 billion). This is mainly because the BC government announced the reduction of personal and CIT rates based on the projected carbon revenue, and the actual revenue was less than the projected revenue. This means that the BC economy received a net reduction in taxes. Second, the revenue recycling feature of the policy may have played an important role in generating the positive output effect. The revenues collected from the carbon tax were used to lower the rates of corporate and personal income taxes. Theoretically, a reduction of the CIT rate increases investments and capital formation, resulting in lower emission intensity and higher output. As emission-intensive plants in BC are more capital intensive, these plants receive larger benefits from the CIT cut relative to the low-emission-intensive plants. Therefore, the output of high emission-intensive plants could increase, and their emission intensity could decrease relative to the low emission-intensive plants. This argument is consistent with the results found in our paper. Yamazaki (2017, 2022) has a similar argument regarding the importance of the revenue recycling feature of the BC carbon tax, and our results are consistent with their findings.10 In addition to the plant-level emission responses, we adapt an approach developed by Najjar and Cherniwchan (2021) to decompose the aggregate emission response into the scale, technique, and selection effects.11 This allows us to discuss the aggregate implications by using the point estimates for the scale, technique, and selection effects in this paper. We find that the aggregate manufacturing emission decline in response to the policy. This decomposition exercise illustrates that a reduction in aggregate emissions would be mainly a result of the scale and technique effects. The size of the selection effect is limited, suggesting that the emission responses to the policy are dominated by the intensive margin adjustments of the surviving plants. A large number of studies examine the effect of carbon taxes on GHG emissions using simulation methods, such as Manne et al. (1990), Goto (1995), Floros and Vlachou (2005), and Wissema and Dellink (2007). Although they find that a uniform carbon tax would lead to a significant reduction in GHG emissions, it is difficult to solely rely on these findings for designing future policies. What we need is more of evidence-based policy suggestions. The empirical findings, thus far, from ex-post analyses are limited and concentrated on the aggregate emission responses to carbon taxes.12 For instance, Bohlin (1998) and Andersson (2019) both investigate the effect of the Swedish carbon tax, implemented in 1991. Bohlin finds that the transportation sector was not affected, and emissions from industrial sectors increased due to exemptions that decreased the effectiveness of the policy. It, however, finds that GHG emissions declined in the heating sector as a result of substitution from coal to biofuel. On the other hand, Andersson uses a synthetic control method and finds that transportation emissions declined by 11 percent. Lin and Li (2011) use a DID method to estimate the emission effect of carbon taxes in Scandinavian countries and the Netherlands. They find that there was no significant effect in Denmark, Sweden, and the Netherlands, and that the carbon tax in Norway led to a substantial increase in GHG emissions from the oil and gas sector due to tax exemptions. This paper provides new evidence to this literature by examining the micro-level responses to a carbon tax. This paper is closely related to Metcalf (2019) and Pretis (2020) as they both investigated the aggregate emission response to the BC carbon tax. Metcalf finds that the BC carbon tax reduced aggregate emissions between 5 and 8 percent, although the estimates are sensitive to the specifications. Pretis shows that the results of Metcalf (2019) are not robust and finds that the BC carbon tax did not have a (statistically) significant effect on aggregate emissions. It further investigates the emission effects for six sectors and finds that emissions from the transportation sector declined.13 The paper concludes that the carbon tax rate was too low for the policy to have any impacts. Pretis (2020)’s inability to detect a (statistically) significant emission reduction for the industrial sector may be that the industrial sector consists of a mix of many subsectors with different emission intensity, possibly suffering from the aggregation bias. We address this issue by utilizing the micro-level data to directly observe the plant-level emission intensity in the manufacturing sector and employ the augmented DID estimation. Lastly, this paper provides theoretical predictions of emission responses from carbon taxes. We do so by adapting the model of Yamazaki (2022). Yamazaki theoretically shows that carbon taxes can increase manufacturing productivity by recycling tax revenues to reduce CIT rates. It allows plants to invest in energy-saving technologies and explicitly models the plant-level responses from both the carbon tax and the revenue recycling through the CIT reduction. The paper finds that the BC carbon tax negatively affects productivity while the CIT reduction increases it, offsetting the negative carbon tax effect. We extend the model of Yamazaki (2022) to show how the carbon tax affects plant-level emissions through output and emission intensity responses. Although not tested, we further emphasize the importance of the revenue recycling feature of the policy on plant-level emission responses. The remainder of the paper is organized as follows. Section 2 provides an overview of the BC carbon tax and its features. Section 3 presents the theoretical framework. The description of the data and empirical methodology are presented in Sect. 4. Section 5 presents the estimation results and robustness checks. Section 6 discusses the aggregate implications of our plant-level estimates. Section 7 concludes. The results of additional empirical analyses, and additional tables and figures regarding the data are reported in Appendix. Overview of the BC Carbon Tax The BC’s Liberal government announced the new climate policy agenda in its throne speech in February 2008. The target of the policy was to reduce BC’s GHG emissions by 33 percent (i.e., 10 percent below the 1990 level) by 2020. Additionally, all electricity generators were required to have zero emissions by 2016. Two months after the throne speech, the BC government announced its intention to join five U.S. states in developing a regional cap and trade system called the Western Climate Initiative. This announcement was completely unexpected because the Liberal government had been previously criticized by environmentalists for supporting off-shore oil and gas explorations, a large decline in its environmental budget, and proposals for two new coal-fired electricity power plants (Harrison 2012). Those in the business community with close ties to the Liberal government were taken by surprise. Jock Finlayson, the Executive Vice President of the BC Business Council, said:The throne speech was a huge surprise, not just to my organization but to everybody in the corporate community. There really was not any advance notice, either through public statements or even through back channels. I actually dropped my coffee cup, full of coffee, when I was watching the live broadcast. (Harrison 2012). The carbon tax rate initially began at $10 per tonne of CO2eq and increased by $5 annually, reaching $30 in 2012.14 The $10 carbon tax represented an increase of 2.4 cents per liter for gasoline and a $20.8 increase per ton for coal. These numbers rose to 7.2 cents per liter for gasoline (equivalent to 4.4% of the final price) and $62.4 per tonne of coal (equivalent to 55% of the final price) at the tax rate of $30 per tonne of CO2eq. The tax covers all fossil fuels purchased within BC, covering 77% of total provincial emissions (Murray and Rivers 2015).15 The policy is comprehensive and includes all plants in BC.16 The tax is designed to be revenue-neutral. The revenue is returned to consumers and businesses through a direct transfer to low-income individuals (a one time $100 Climate Action Dividend per adult in the initial year), a decline in income taxes (around 2% reduction in 2008 and 5% reduction in 2009 for those who have an annual income of less than $70,000), a decline in general corporate income taxes (from 12 to 10 percent), and a reduction in small corporate income taxes (from 4.5 to 2.5 percent in the first three years after the implementation of the policy). According to the budget and fiscal plan for 2013, the carbon tax raised about $1.2 billion in revenues for 2012–2013 and returned about $1.4 billion to consumers. Theoretical Framework In this section, we briefly explain how a revenue-neutral carbon tax affects manufacturing emissions and motivate our empirical strategy discussed in Sect. 4. We adapt a simple model of Yamazaki (2022), who theoretically shows that carbon taxes can positively affect manufacturing productivity through recycling the tax revenues to lower the CIT rates. It allows plants to invest in energy-saving technologies and explicitly models the plant-level responses from both the carbon tax and the revenue recycling through the CIT reduction. We extend the model of Yamazaki (2022) to show how the policy affects plant-level emission through output and emission intensity responses. To begin, let Z≡ex denote manufacturing plant’s emission, where e and x are its emission intensity and output, respectively. Taking logs and totally differentiating this emission equation yields:3.1 Z˙=e˙+x˙ where Z˙=dZ/Z, and so on (i.e., “˙" denotes a percentage change). This shows that emission responses to any shocks, including a carbon tax, can be decomposed into two channels. The first term is referred to as the technique effect, while the second term is referred to as the scale effect.17 We explicitly show further how these two effects are affected by a revenue-neutral carbon tax. Consider a partial equilibrium model with an iso-elastic demand for manufacturing goods:3.2 x=p-σB where B is a constant representing aggregate quantity and price indexes, p is the price for the manufacturing goods, and σ>1 is elasticity of substitution between differentiated goods. Following Copeland and Taylor (1994), there is a joint production technology for manufacturing plants:3.3 x=A(1-θ)F(K,L) 3.4 Z=φ(θ,IA)F(K,L) where capital (K) and labor (L) are used to produce the potential output, F(K, L). We can think of x to be the net output because some are allocated to abatement. φ(θ,IA) is an abatement function, satisfying φ(0,IA)=1,φ(1,IA)=0, and ∂φ/∂θ<0. θ∈[0,1] is a fraction of inputs allocated to abatement. This means that the level of emission decreases with abatement, but at the cost of output. Now following Forslid et al. (2018), the abatement function is expressed as follows:3.5 φ(θ,IA)=(1-θ)1/αΩ(IA) with 0<α<1, and Ω(IA) is the abatement augmenting technology, which is a function of abatement investment, IA. It satisfies dΩ(IA)/dIA>0 and is the reciprocal of the amount of emission produced per output. This is a technological parameter for the abatement activity. Equation (3.5) reflects that plants can reduce their emissions by increasing θ or increasing the abatement investment. Using Eqs.(3.3), (3.4) and (3.5), output can be expressed as,3.6 x=A(Ω(IA)Z)αF(K,L)1-α With this formulation, one can think of Z as an input and re-interpret it as energy.18 First, we show an expression for e by solving plant’s cost minimization problems. Cost Minimization From Eq. (3.6), we can see that a plant chooses how much capital and labor for the production of the potential output, F, while choosing the cost-minimized combination of the potential output and energy. By solving the former cost minimization problem with F(k,l)=kβl1-β, the minimum cost of producing a unit of F can be expressed as:3.7 cF(r~,w~)=κβr~βw~1-β where κβ≡β-β(1-β)β-1, r~≡(1-λktc)r, and w~≡(1-tc)w. r and w are the prices of capital and labor, respectively. tc is the CIT rate. The cost of labor is fully deductible for tax purposes while only a portion λk≥0 of the capital cost is deductible.19. λk is a highly stylized representation of many CIT systems, intending to reflect the distortionary features of the CIT with regard to capital. The typical case would be λk<1 because the real cost of capital is not fully deductible. This is because only the nominal cost of debt finance is fully deductible while that of equity finance is not, or because tax depreciation is different from economic depreciation. This incomplete deductibility of capital costs is a source of distortions from the CIT. It increases the before-tax rate of return on the marginal investment required to generate the after-tax hurdle rate of return.20 This tax wedge between the before- and after-tax rate of return on the marginal investment is known as the marginal effective tax rate (METR) on capital,21 which we discuss further below. When λk=1, the full opportunity cost of capital is deducted, and the CIT is a tax on economic profit (i.e., the CIT is not distortionary).22 Next, by solving the latter cost minimization problem, the minimum cost of producing a unit of x can be expressed as:3.8 cx(τ~,cF)=καA-1Ω(IA)-αcF1-ατ~α where κα≡α-α(1-α)α-1. τ~≡(1-tc)τ. τ is the carbon tax inclusive energy price. The cost of energy is fully deductible for tax purpose.23 From Shephard’s lemma, the conditional input demand for energy is expressed as:3.9 z=1AΩ(IA)α(α1-αcFτ~)1-α By using the definition of energy intensity, Eq. (3.9) is the expression for energy intensity as Eq. (3.8) is a unit cost function, i.e., z=e. What’s left to show is the expression for the abatement technology, which plays an important role in shaping the technique effect through investment. The optimal abatement investment is derived from the plant’s profit maximization. Profit Maximization The plant sets the pricing rule given the abatement investment and then chooses how much to invest in abatement given the pricing rule. Maximizing profits by a monopolistic competitive manufacturing plant yields a pricing rule:3.10 p=σσ-1cx1-tc Using Eqs. (3.2) and (3.10), plant’s profit can be expressed as:3.11 π=B(σ-1)σ-1σ-σ(1-tc)σcx1-σ-(1-tc)IA Similar to labor cost, the abatement investment cost is fully deductible.24 Following Forslid et al. (2018), we assume that Ω(IA)=IAρ with ρ>0. Plugging Eq. (3.8) into (3.11), and then maximizing plant’s profit with respect to abatement investment IA yields:3.12 IA=Aσ-1γ((1-γ)Γ)1γτ-α(σ-1)γ(1-λktc1-tc)-β(1-α)(σ-1)γ where Γ≡Bσ-σ(σ-1)σ(κβrβw1-β)(1-α)(1-σ) and γ≡1-αρ(σ-1)>0.25 Notice again that when the costs of capital investments are fully deductible, i.e., λk=1, Eq. (3.12) becomes independent of the CIT. This is simply because there is no distortion in the capital investment market when the CIT is levied on pure profit. In this simple model, one can redefine (1-λktc)/(1-tc) to be (1+tMETR), where tMETR is the METR on capital. When λk<1, tMETR is an increasing function of tc. Equation (3.12) shows that the abatement investment is a decreasing function of the carbon tax. While this may not be intuitive, Forslid et al. (2018) point out that the abatement investment intensity is an increasing function of the carbon tax.26 This positive effect is an encouraging policy response towards the emission reduction. On the other hand, the abatement investment is a decreasing function of the CIT,27 and thus a decreasing function of the METR. This implies that the reduction of the CIT rate has a positive effect on abatement investment through the reduction of the METR.28 Lowering the before-tax rate of return required on the marginal investment allows more capital projects that were not feasible before, such as energy-saving technologies. Thus, the overall effect of the policy on the abatement investment is ambiguous as the carbon tax and CIT reduction work against each other. Putting all together yields:3.13 e=1A(α1-α)1-αIA-αρ((κβrβw1-β)(1+tMETR)βτ)1-α⏟pr≡After-tax relative price between F and z This shows that both the carbon tax and CIT affect emission intensity through two channels, the after-tax relative price between the potential output (F) and energy (z), and the abatement investment. First, the carbon tax directly affects the emission intensity negatively by decreasing the relative price between the potential output and emission. As expected, it increases the cost of emissions, inducing plants to reduce the level of emissions per unit of output. On the other hand, as explained above, although the effect of the carbon tax on the abatement investment is ambiguous, it could be positive through the market competition. Second, as pr is an increasing function of the CIT, a fall in the CIT makes pr smaller, making the potential output cheaper through the reduction of the METR. This induces a substitution away from z to F to produce a unit of x. As a result, more resources are allocated for the abatement to maintain the same level of the net output with more F and less z. Thus, this reduces emission intensity. At the same time, the reduction of the CIT rate increases abatement investment through the reduction of the METR, resulting in a fall in emission intensity. From these channels, the implementation of the carbon tax with the reduction of the CIT could reduce emissions through the technique effect. Next, we demonstrate how the policy affects the scale effect. Plugging Eq. (3.10) into (3.2) yields:3.14 x=ψIAαρσ(1+tMETR)-σβ(1-α)τ-ασB where ψ=(σ(1-σ)-1καA-1(κβrβw1-β)(1-α))-σ. This shows that a carbon tax negatively affects the scale effect by increasing the cost of production while it allows plants to produce more through the increase of the abatement investment. Thus, depending on the size of these two effects, the effect of the carbon tax on the output could go either way. On the other hand, the reduction of the CIT positively affects the scale effect from both channels, directly through the METR and abatement investment.29Fig. 1 These figures plot the plant responses to a carbon tax. Z~ is defined as Ω(IA)Z, which is the abatement-technology augmented emission. This allows us to model plants choosing to invest in energy-saving technologies so that they can lower the policy burden, i.e., allowing for plants to produce more with less energy. Panel (a) depicts the most obvious response to the policy, i.e., scaling down the production to avoid the tax burden, resulting in a fall in energy consumption and emission. Panel (b) depicts how plants can produce more with less energy by investments, i.e., Z is reduced, but Z~ is increased due to the increase in IA To summarize, Eqs. (3.13) and (3.14) show that implementing a carbon tax alone would reduce emission at the cost of output. Yet, the negative scale effect can be mitigated by the abatement investment. On the other hand, when the carbon tax revenues are used to lower the rate of the CIT, there is a possibility that plants can lower their emissions while producing more. This is possible because the reduction of the CIT ameliorates the distortion in the capital market and encourages the abatement investment. We demonstrate a simple case in Fig. 1. To simplify the notations, we define Z~ as Ω(IA)Z, which is the abatement-technology augmented emission, presented in Eq. (3.6). Panel (a) depicts the most obvious initial response to the policy, i.e., scaling down the production to avoid the tax burden, resulting in a fall in energy consumption and thus emission. On the other hand, panel (b) depicts how plants can produce more with less energy by investments in response to the CIT reduction, i.e., Z is reduced, but Z~ is increased due to the increase in IA. Although Fig. 1 is an oversimplified version of what our theory predicts, it provides a clear motivation for an empirical investigation on how all these effects pan out. Finally, we take a step further to connect our theory to empirical design. Plugging Eq. (3.12) into (3.13) and (3.14) yields:3.15 e=ϕeΓ-αργ(1+tMETR)β(1-α)γτα-γγ 3.16 x=ϕxΓαρσγ(1+tMETR)-σβ(1-α)γτ-ασγ where ϕe=Aαρ-1γ(α1-ακβrβw1-β)(1-α)(1-γ)-αργ and ϕx=Aσγ(σ1-σκα(κβrβw1-β)(1-α))-σ(1-γ)αρσγ. Then by plugging Eqs. (3.15) and (3.16) into the emission equation, we have:3.17 Z=ϕeϕxΓαρ(1-σ)γ(1+tMETR)β(1-α)(1-σ)γταγ(1-σ)-1 Now totally differentiating Eq. (3.17) with respect to the carbon tax (τ) yields:3.18 dZdτ=(α(1-σ)γ-1)exτ⏟(-)+(β(1-α)(1-σ)γ)ex1+tMETR⏟(-)d(1+tMETR)dτ⏟(-)=[(1-σ)[α(1-ρ)+εβ(1-α)]-1γ]exτ where ε≡d(1+tMETR)dττ(1+tMETR)∈(0,-1) is the elasticity of the METR with respect to the carbon tax. As the carbon tax rate increases, the reduction of the CIT rate increases due to the revenue-neutrality of the policy. Equation (3.18) shows that the effect of the carbon tax on plant’s emissions is a monotonic function of plant’s emission intensity.30 This means that the emission responses would be larger for emission-intensive plants. The sign of the function is ambiguous because the emission effects from the carbon tax and revenue recycling through the reduction of the CIT rate work against each other. This theoretical prediction motivates our empirical strategy, i.e., exploiting the plant-level variation in emission intensity to identify the emission effect. Our simple theory presented in this section, not only connects the theory to the empirics, but also helps us explain our findings better. Empirical Analysis This section discusses the econometric design to estimate the emission effect of the BC carbon tax. The simple model illustrates that the size of the policy exposure depends on plants’ emission intensity. We take advantage of the confidential dataset to directly observe the plant-level emission intensity to measure the policy exposure, which is discussed in detail below. Methodology As motivated by Eq. (3.18), we employ a difference-in-differences estimation with differential treatment intensity and estimate the following equation:4.1 lnElipt=β(Kp×Dt×EIl)+αl+λl′t+ϕi′t+δpt+ϵlipt where lnElipt is the log of GHG emissions from plant l of industry i (at 6-digit NAICS level) in province p at year t. Kp is a dummy variable that takes the value of one for BC and zero for all other provinces. Dt is a dummy for the post-policy period, which is equal to one after 2008 and is equal to zero otherwise. EIl is the average pre-policy emission intensity for plant l. We fix EIl at the pre-policy level because the emission intensity after 2008 would be an outcome variable and would change due to the carbon tax. αl is the plant fixed effects that capture plant specific time-invariant characteristics, as well as industry and province time-invariant characteristics that affect GHG emissions. λl′t is the high emission-intensive plant by time fixed effects. We denote l′ as a group of plants whose EIl is greater than a threshold. We use the 70th percentile of emission intensity in the whole sample as the threshold.31 These fixed effects capture any high emission-intensive plant-specific time shocks. ϕi′t is sector (at 2-digit NAICS level) by year fixed effects that capture any sector-specific time shocks. δpt are province by year fixed effects that capture any province-specific and nationwide time shocks. ϵlipt is the idiosyncratic error term. The interaction term allows us to isolate the emission effect of the BC carbon tax by exploiting three sources of variation.Table 1 The Tax Burden of the BC Carbon Tax for Various Industries Subsector (NAICS) Emission intensity (t/$1K) Tax paid as % of output Tax paid ($1K) Panel A. Canada 5 most emission intensive Non-metallic mineral product (327) 0.529 1.06 64,065 Chemical (325) 0.205 0.41 87,897 Paper (322) 0.200 0.40 73,380 Primary metal (331) 0.182 0.36 93,134 Petroleum and coal product (324) 0.092 0.18 38,966 5 least emission intensive Miscellaneous (339) 0.020 0.04 1,020 Leather and allied product (316) 0.019 0.04 61 Clothing (315) 0.019 0.04 349 Transportation equipment (336) 0.011 0.02 13,990 Computer and electronic product (334) 0.007 0.01 828 Average 0.087 0.17 23,095 Panel B. British Columbia 5 most emission intensive Non-metallic mineral product (327) 0.827 1.65 10,801 Paper (322) 0.300 0.60 20,115 Primary metal (331) 0.236 0.47 7,901 Textile mills (313) 0.153 0.31 13 Chemical (325) 0.149 0.30 1,446 5 least emission intensive Electrical equipment (335) 0.027 0.05 41 Miscellaneous (339) 0.023 0.05 128 Textile product mills (314) 0.023 0.05 48 Clothing (315) 0.012 0.02 19 Computer and electronic product (334) 0.008 0.02 86 Average 0.140 0.28 2,697 This shows the top and bottom five subsectors (3 digit NAICS) in terms of their emission intensities and the average among all subsectors in the dataset. We multiply the average tax rate during the 2008-2012 period (i.e., $20/tCO2e) by subsectors’ pre-policy average emission intensity to calculate the average cost imposed on subsectors, reported in column 2. The last column reports the average tax paid for the corresponding subsectors (i.e., multiplying the average tax rate by subsectors’ pre-policy average emission) The first two sources of variation are intuitive. As the policy was implemented in 2008, it created provincial and temporal variations. We can simply compare plants in BC with plants in other provinces before and after the implementation of the policy. The third source of variation originates from the difference in emission intensity across plants. The simple model illustrates that the emission response depends on plants’ emission intensity. Intuitively, we claim that high emission-intensive plants have a much larger incentive to reduce their emissions in response to the policy because they would bear a higher cost per output, shown in Table 1. On the other hand, the tax burden for low emission-intensive plants is almost negligible, providing little incentive for them to respond to the policy.32 This allows us to treat low emission-intensive plants as less-affected or control group.33 Yamazaki (2017) also exploits the variation in the industry emission intensity at the national-level. As our data allows us to directly observe the plant-level emission intensity, we can compare plants based on the intensity of their policy exposure more accurately. These three sources of variations allow us to employ an augmented DID estimation method. It compares the emission differences between high emission-intensive plants and low emission-intensive plants in BC relative to the same emission differences in the rest of Canada before and after the implementation of the policy.34 There are several threats to the identification that are worth mentioning here. First, the carbon tax in BC may alter the output level in other provinces through the inter-provincial trades of intermediate goods. Through a cost pass-through, it would make it more expensive for plants in other provinces to produce with the imported intermediate goods from BC. The magnitude of this change depends on the bilateral trade cost. The control group being (indirectly) affected by the policy violates the stable unit treatment value assumption (SUTVA). To test the severity of this concern, we performed a robustness test by using only provinces that have very low trade flows with BC. The baseline estimation results presented in the later section are robust to this sample difference.35 Second, one of the unfortunate challenges in identifying the effect of the BC carbon tax is that the timing of the implementation coincides with the Great Recession. Although the negative impacts of the Great Recession may be different across provinces due to the substantial differences in the composition of their economies, it is unlikely that it also had the differential impacts across plants based on their emission intensity, high vs. low emission intensive plants. Being able to exploit the variations at the granular level allows us to mitigate this concern, especially with the fixed effects. Third, even though the policy announcement was unexpected, it is possible for plants to respond to the policy prior to the actual implementation. Although this may not be as much a concern as the first two threats above, it is worth exploring. In addition, it is also important that we do not capture the differences in pre-existing trends. We examine the anticipatory responses and pre-existing trends using the flexible estimation method, presented in Sect. 5.2. Fourth, there was a significant change in the price of natural gas in BC in 2009 and 2014. As the sample period for this study is from 2004 to 2012, the price change in 2014 is not a concern, but the price change in 2009 may be. In the augmented DID design, we control for sector specific shocks at the 2-digit NAICS code. Therefore, if the impact of the change in natural gas price is not different between the high emission-intensive and low emission-intensive plants, our estimation method can isolate the impact of the policy from the effect of change in the natural gas price. β is the coefficient of interest. It estimates the average effect of the BC carbon tax on GHG emissions from treated plants during the 2008–2012 period. The identifying assumption requires that there are no high emission-intensive plant by province specific shocks to GHG emissions that are contemporaneous to the adoption of the BC carbon tax. In other words, there should not be any other factors aside from the carbon tax that changes the GHG emissions of (more) treated plants differently than those of untreated (or less treated) plants. This assumption fails if, for instance, there is an economic shock that affects high emission-intensive and low emission-intensive plants differently across provinces. We exclude Alberta and Québec as control provinces because they implemented similar policies in 2007. Data Fig. 2 Steps for calculating emission intensity To identify the causal effect of the BC carbon tax on GHG emissions, we construct plant-level emission data. To do so, we use a confidential plant-level data set, the Annual Survey of Manufacturing (ASM), which includes (but not limited to) plant-level fuel purchases, shipment destinations, sales, final products, plant location, and plant total production costs. The ASM dataset allows us to calculate plant-level emissions and emission intensity, which cannot be done with other publicly available datasets. To construct our measure of GHG emissions, we collect fuel prices for various cities in all provinces over time, and then divide fuel purchases by fuel prices to determine the fuel quantities for each plant.36 Finally, using the embodied GHG emission of each fuel type,37 we calculate GHG emissions at the plant-level and divide by the plant’s output value to find the emission intensity. This is the most comprehensive plant-level dataset for GHG emissions in Canada. These steps are shown in a simple flowchart in Fig. 2. Quick (2014) shows that calculating emissions by fuel consumption is a more accurate way to determine GHG emissions when compared to using observed emissions from emissions monitoring systems. Linn et al. (2015) show that these two alternative measures of emissions are very consistent with each other, and the results are not statistically different. In sum, previous research suggests that the lack of emissions data in the ASM dataset is not of concern with regards to our analysis. Our method of calculating GHG emissions should be more accurate than using self-reported emissions or at least consistent with it.Table 2 Summary statistics Panel A. Key variables BC ROC High Low All High Low All Emission (tons) 4,362 179 1,772 5,460 337 1,818 Energy expenditure ($1K) 1,203 112 526 1,713 252 675 Output ($1K) 21,051 7,752 12,816 27,502 19,287 21,661 Salary workers 10 9 9 17 14 15 Production workers 43 26 33 53 42 45 Age 8.57 8.57 8.57 8.56 8.54 8.54 Total expenses ($1K) 23,769 8,327 14,207 33,805 20,777 24,543 Panel B. Emission (log) BC ROC Low High Diff Low High Diff Pre-policy (2004-2007) 3.87 5.85 1.98*** 4.18 6.10 1.92*** (1.39) (1.97) [0.046] (1.41) (1.86) [0.018] Post-policy (2008-2012) 3.93 5.77 1.84*** 4.25 6.12 1.88*** (1.50) (2.03) [0.043] (1.55) (1.92) [0.017] Difference-in-differences -0.139*** -0.044** [0.059] [0.022] Triple differences -0.095 [0.061] This shows summary statistics for key variables in the dataset. Panel A breaks the data into two ways: (1) BC and ROC, and (2) high emission-intensive and (High) and low emission-intensive plants (Low). It reports the mean of key variables for each category. Panel B shows the means of emissions in log for the same categories as Panel A. In addition, we break the data into the pre- and post-policy periods and report the differences in the means for each period, shown in column 3 for BC and ROC. We perform t-test on such differences. Standard deviations are reported in the parenthesis, while standard errors are reported in brackets. In the last two rows of Panel B, we manually calculate the emission effect of the policy by the difference-in-differences and triple differences ***Significant at the 1 percent level, **Significant at the 5 percent level, *Significant at the 10 percent level Table 2 presents summary statistics of key variables in the data.38 To motivate our empirical strategy further, in Panel A, we report the means of key variables for four categories: (1) high emission-intensive plants in BC (BC-High), (2) low emission-intensive plants in BC (BC-Low), (3) high emission-intensive plants in ROC (ROC-High), and (4) low emission-intensive plants in ROC (ROC-Low). These show that the means of these key variables for High and Low are reasonably similar between BC and ROC, which is important for the identification. Panel B shows the means of emission in log for the same categories as Panel A. Furthermore, we break the data into the pre- and post-policy periods and report the differences in the means for each period, shown in column 3 for BC and ROC. We perform t-test on such differences. In the last two rows of Panel B, we manually calculate the emission effect of the policy by the difference-in-differences and triple differences. These naïve calculations of the emission effect provide suggestive evidence that the policy might have contributed to the emission reduction, and that it is worth moving forward with a more rigorous econometric technique to isolate its emission effect. In addition, we present the trends of emission for BC and ROC in Fig. 3. Panel (a) shows the average emission trends for BC and ROC, while Panel (b) shows the trends of differences in average emissions between high and low emission intensive plants for BC and ROC. These figures also corroborate the suggestive evidence from Table 2, i.e., emissions decline in BC relative to ROC. Moreover, these also show that the pre-policy trends are reasonably parallel between BC and ROC, justifying the use of the augmented DID method. As our data is survey data, there is an issue of missing data, in particular 32 percent of manufacturing plants in the data do not report their energy expenditure by fuel types.39 These plants are excluded from the analysis. There are three reasons why some plants do not report their energy expenditures: (1) plants were not active in the relevant years; (2) plants did not fill the fuel expenditure section of the survey; (3) those plants are administrative plants and not manufacturing plants, and so they do not use any fuels. There is no correlation between the size of plants and missing data for energy expenditure. Therefore, if plants that did not report their energy expenditure were not active for a reason other than the carbon tax, or are not systematically different from other plants, there will be no selection problem that undermines the identification strategy. Moreover, the sample is restricted to include plants that appear in the dataset at least once before and once after the implementation of the BC carbon tax.Fig. 3 This figure plots emission trends for BC and ROC. Panel (a) presents the trends of average emission for BC and ROC, while Panel (b) presents the trends of differences in average emissions between high and low emission intensive plants for BC and ROC Another concern is that the ASM dataset does not include electricity generation plants. The electricity generation in BC is primarily from hydro, which emits negligible emissions, and thus would not be of concern in our analysis. Furthermore, plants are taxed only for their direct purchases of fossil fuels; therefore, we focus only on direct GHG emissions from manufacturing plants and abstract from indirect emissions from electricity consumption. Results The results are presented in the following subsections. Section 5.1 presents the estimates of Eq. (4.1), while Sect. 5.2 shows the results from robustness checks. Section 5.3 explores the heterogeneous responses to the policy. Section 5.4 estimates the scale and technique effects of the policy to discuss the emission reduction mechanisms through the decomposition exercise. Main Results The results of four specifications based on Eq. (4.1) are reported in Table 3. As we are using the constructed emission data based on fuel expenditures from a survey-based dataset, there is a concern of measurement error in both the outcome (i.e., plant-level GHG emissions) and treatment variable (i.e., emission intensity of plants).40 Following Chowdhury and Nickell (1985), we address the measurement error by taking the average of both emission and emission intensity variables across plants within the same city, industry (6-digit NAICS code), province, and year, i.e., the outcome variable is now lnEcipt where c denotes city. With this measurement error correction (MEC), we estimate the following equation:5.1 lnEcipt=β(Kp×Dt×EIcip)+αci+λci^t+ϕi′t+δpt+ϵcipt where EIcip is the average pre-policy emission intensity for industry i in city c of province p. Instead of the plant fixed effects, we include αci, which is the city by industry (6-digit NAICS) fixed effects. λci^t is the high emission-intensive city-industry by time fixed effects. We denote ci^ as a collection of city-industry pair whose EIcip is greater than a threshold (70th percentile of emission intensity). Chowdhury and Nickell (1985) show that dividing the sample into different groups and taking the average within each group would reduce the measurement error to a large extent. This is especially true when the variable with measurement error is serially correlated. In our case, the emission and emission intensity variables have a high level of serial correlation over time and across plants within the same 6-digit NAICS code. Each industry-city pair contains about 3 plants. We expect that taking the average of both emissions and emission intensity in these two dimensions reduces the measurement error. This approach would reduce the attenuation bias and improve the precision of the standard error estimations. As shown in Eq. (5.1), the downside of this approach is that we cannot control for confounding factors at the plant-level. However, given the size of each city-industry pair, the city by industry fixed effects are still as powerful as the plant fixed effects in controlling for the confounding factors.Table 3 Baseline Estimates for Emissions Plant-level City by industry-level (MEC) (1) (2) (3) (4) (5) (6) (7) (8) Kp×Dt×EIl −0.20 −0.15 −0.44*** −0.39*** (0.16) (0.16) (0.08) (0.07) Kp×Dt×EIcip −0.26** −0.23** −0.37*** −0.34*** (0.10) (0.10) (0.05) (0.05) Plant Y Y Y Y City × industry Y Y Y Y Sector × time Y Y Y Y Controls Y Y Y Y MEC Y Y Y Y N 117,445 117,445 77,937 77,937 41,548 41,548 27,462 27,462 R2 0.90 0.91 0.93 0.93 0.93 0.93 0.95 0.95 Dependent variable for columns (1) through (4) is log of plant-level emission, while that for columns (5) through (8) is log of city by industry-level emission. EIl is the average pre-policy emission intensity for plant l, while EIcip is the average pre-policy emission intensity for city c, industries i, and province p. Dt is a dummy for the post-policy period, which is equal to one after 2008 and is equal to zero otherwise. Kp is a dummy variable that takes the value of one for BC and zero for all other provinces. Industry refers to the 6-digit NAICS industry while sector refers to the 2-digit NAICS industry. Columns (3), (4) and (7), (8) includes additional controls, i.e., plant’s age, input-output ratio, the number of plants owned by the parental firm, and export volume. MEC stands for measurement error correction. All specifications include high emission intensive plant by time FEs, and province by time FEs. To account for serial correlations and within sector correlations, standard errors are clustered by province by sector (at 2-digit NAICS), reported in parentheses ***Significant at the 1 percent level, **Significant at the 5 percent level, *Significant at the 10 percent level We present the results in Table 3.41 First, four columns report coefficients from estimating Eq. (4.1), whereas the last four columns report coefficients from estimating Eq. (5.1). Plant-level estimates include the plant fixed effects, while the MEC estimates include the city by industry (6-digit NAICS) fixed effects. In all columns, we control for high emission-intensive plant by time fixed effects, and province by time fixed effects. Sector (at 2-digit NAICS) by time fixed effects are included in columns (2), (4), (6), and (8). We also add additional control variables as the robustness checks. We include plant’s age, input-output ratio, the number of plants owned by the parental firm, and export volume. Standard errors are clustered at the level of province by sector (2-digit NAICS).42 The sample spans 2004 to 2012 and includes only plants that appear in the data set at least once before and once after implementation of the carbon tax. All specifications show negative signs with similar magnitudes, implying that the carbon tax had a negative impact on the manufacturing emission in BC. While the point estimates without the MEC (columns 1 and 2) are not statistically different from zero, adding the additional controls makes the coefficients statistically significant and slightly larger than those without the controls.43 As expected, the MEC improves the precision of the estimations so that the coefficients from columns (5) through (8) are all negative and statistically significant. Although adding the sector by time fixed effects reduces the size of the coefficients slightly, the point estimates are robust to the inclusion of such fixed effects. Using the point estimate from our preferred specification (column 6), the carbon tax reduced the plant-level manufacturing emission, on average, by 4 percent.44 Robustness Checks As we attempt to estimate the causal effect of the policy on emissions, it is important that we explore the robustness of our main estimates presented in the previous subsection. We conduct a series of robustness checks below and find little evidence that undermines our main results. Anticipatory Effect Fig. 4 This figure plots the point estimates from the event-study method estimation, treating 2006 as the base year. The solid red line indicates the year of the policy implementation. The y-axis is the percentage change in emission, while the x-axis is year Despite the quick implementation of the policy, plants might have anticipated the policy and changed their behavior prior to the implementation of the policy. The policy was announced unexpectedly, but plants might still get informed prior to the announcement. To test for the presence of an anticipatory response, we use an event-study method to investigate the evolution of the emission effects during the sample period, treating 2006 as the base year.45 We estimate the following equation:5.2 lnElipt=∑t∈T′βt(γt×Kp×EIl)+αl+λl′t+ϕi′t+δpt+ϵlipt where T′={2004,2005,2007,2008,...,2012}. γt is the year dummy. If there is no anticipatory effect, the emission effect for 2007 should be zero. In addition, this event study analysis allows us to test whether the main estimates are not driven by the difference in the pre-policy emission trends between treated and control plants. Similar to the anticipatory effect, the emission effects should be zero for all years during the pre-policy period (2004-2007) if there is no difference in the pre-policy trends, i.e., β2004=β2005=β2007=0. The results from estimating Eq. (5.2) are shown in Fig. 4. The point estimates for the pre-policy period are all close to zero (i.e., precisely estimated zero), which confirms that there is no anticipatory response to the policy or difference in the pre-policy emission trends between treatment and control groups. It is clear from the figure that the emission effects are declining after the implementation of the policy.Fig. 5 This figure plots a kernel density distribution of 1000 placebo estimates of the emission effects of the carbon tax. The x-axis is the placebo emission estimates Permutation Test To explore the robustness of our results further, we perform a permutation test based on placebo carbon taxes (Bertrand et al. 2004). Based on Eq. (4.1), the treatment variable is the interaction of three variables, i.e., province × year × emission intensity, allowing us to randomly select a set of a different year, province, and policy exposure intensity to construct a “placebo carbon tax." We estimate the emission effect of the placebo carbon tax and then repeat this process 1,000 times to generate a distribution of the placebo effects. As these placebo carbon taxes are randomly constructed, the emission effect, on average, should be zero. Figure 5 plots a kernel density distribution of the emission effect of the placebo carbon taxes. The mean of the placebo estimates is centered around zero, and moreover, the point estimates from the main results in Table 3 fall in the extreme left tail of the distribution. This suggests that the emission effects identified in the main estimates are not biased by the spillover effects, which validates the SUTVA in this context. Dynamic Panel Estimates Table 4 Addressing the autocorrelation HAC Lagged GMM (1) (2) (3) (4) (5) (6) Kp×Dt×EIcip −0.26*** −0.23*** −0.192*** −0.165*** −0.189*** −0.162** (0.07) (0.07) (0.057) (0.06) (0.06) (0.06) lnEcipt-1 0.4*** 0.399*** 0.421*** 0.423*** (0.01) (0.014) (0.034) (0.033) Sector × time Y Y Y N 41,548 41,548 36,397 36,397 31,601 31,601 R2 – – 0.95 0.95 – – AR(1) test – – – – 0 0 AR(2) test – – – – 0.23 0.25 Dependent variable is log of city by industry-level emission. EIcip is the average pre-policy emission intensity for city c, industries i, and province p. Dt is a dummy for the post-policy period, which is equal to one after 2008 and is equal to zero otherwise. Kp is a dummy variable that takes the value of one for BC and zero for all other provinces. Industry refers to the 6-digit NAICS industry while sector refers to the 2-digit NAICS industry. All specifications employ the measurement error correction and include city by sector FEs, high emission intensive plant by time FEs, and province by time FEs. The coefficients in columns (1) and (2) are the same as columns (5) and (6) of Table 3 but the values in parentheses are the heteroskedasticity and autocorrelation consistent (HAC) standard errors. Columns (3) and (4) add the lagged dependent variable (lnEcipt-1). Columns (5) and (6) estimate the Arellano and Bond dynamic panel model. We also report the p-value from the AR(1) and AR(2) tests ***Significant at the 1 percent level, **Significant at the 5 percent level, *Significant at the 10 percent level Another potential issue in our main estimation is that we do not allow for a possibility of persistence in emissions. This means that we need to consider the autocorrelation in the emission equation. We check the robustness of our main results by taking the autocorrelation issue into account in three ways. First, we re-estimate Eq. (4.1) with the heteroskedasticity and autocorrelation consistent (HAC) standard errors. Second, we simply add the lagged dependent variable in Eq. (4.1). Third, we employ Arellano and Bond (1991)’s GMM estimator to address the potential biases in the dynamic panel data model. Table 4 shows that the main estimates presented in Table 3 are robust to allowing for the persistency of emissions. The estimates are statistically significant and negative, while the magnitudes are also similar. We also test the existence of the autocorrelation, presented in the last two rows of columns (5) and (6). These confirm that including the one-year lagged emission is sufficient in the dynamic panel model as the existence of the 2nd order autocorrelation is rejected at the AR(2) test. Heterogeneous Effects The analyses to this point have focused on the average effects of the carbon tax on plant emission. To take advantage of the rich dataset, we explore the heterogeneous responses to the policy based on different plant characteristics. We do this by grouping plants into three dimensions. First, we allow the emission effect to differ across large, medium, and small plants based on the size of their production. Second, we explore whether the firm structure matters for the emission responses, i.e., singly-owned plants and multi-plant firm’s plants. Third, we allow for the differential effects based on their sectoral trade intensity. The results are reported in Table 5.Table 5 Effects of the BC carbon tax by different plant characteristics Plant size Firm structure Trade intensity Large Medium Small Single-plant Multi-plant High Medium Low Kp×Dt×EIl −0.18*** −1.54* −0.59** −1.14*** −0.14*** −0.47*** −0.15 −0.36 (0.039) (0.905) (0.233) (0.404) (0.029) (0.038) (0.226) (0.489) N 23,053 23,447 23,603 66,838 11,099 34,022 25,057 14,858 # of Plants 3,957 3,968 3,971 11,299 1,944 5,836 5,074 2,569 R2 0.94 0.94 0.94 Plant size is determined by output. A plant is large if its output is above the 70th percentile, medium if its output is between the 35th and 65th percentiles, and small if its output is below the 25th percentile. Under Firm structure, we compare plants that are singly owned with plants whose parental firm owns multiple plants. For Trade intensity, we first group subsectors (3-digit NAICS) into three groups based on their trade intensity and create a dummy for each group. Then we interact these subsectoral dummies with our main treatment variables. High trade-intensive subsectors are 327, 323, 337, 321, 332, 324, and 312. Medium trade-intensive subsectors are 311, 339, 326, 322, 325, 331, and 336. Low trade-intensive subsectors are 333, 314, 313, 316, 315, 334, and 335. All specifications include plant FEs, high emission intensive plant by time FEs, sector (2-digit NAICS) by time FEs, province by time FEs, and plant-level control variables. To account for serial correlations and within sector correlations, standard errors are clustered by province by sector (at 2-digit NAICS), reported in parentheses ***Significant at the 1 percent level, **Significant at the 5 percent level, *Significant at the 10 percent level There are several interesting results worth discussing. First, the medium and small plants respond more than the large plants. In particular, the size of the emission reduction is the largest for the medium plants. This may imply that the policy burden falls more on the medium plants than large or small plants. Second, the singly-owned plants respond to the policy more than the multi-plant firms’ plants. These heterogeneous responses are consistent with results found in Yamazaki (2022) that the medium singly-owned plants are the ones responding to the policy the most. Lastly, plants in the high trade-intensive subsectors respond more to the policy than plants in the medium or low trade-intensive subsectors. This is also consistent with the claim that the emission-intensive and trade-exposed industries are the ones that are most susceptible to the policy, losing their competitiveness in the global market. For this reason, although their emission reduction may come from the decline in production size, it is also possible that they respond to the policy by improving their energy efficiency to lower their policy burden. Decomposition: Scale Versus Technique Effects As discussed in Sect. 3, emissions can decrease by technological improvement (technique effect), a reduction in output (scale effect), or both. This implies that the 4 percent reduction in emission found in the previous subsection could be solely due to the scale effect, which would mean that the emission reduction would necessarily come at the cost of manufacturing output. To explore the possible mechanisms behind the emission reduction, we directly investigate the scale and technique effects of this policy. We re-estimate Eq. (4.1) with the log of output and the log of emission intensity being the dependent variables. The results are shown in Table 6. Contrary to the prior concern regarding the scale effect described above, Table 6 shows an interesting and appealing result, i.e., the estimated scale effects are statistically significant and positive. This suggests an increase of the manufacturing output in response to the policy. The point estimate shows that the plant-level output increases, on average, by 1.8 percent.46 If the scale effect is positive, emissions can decline only through the improvement of technology. The estimated technique effects, shown in Table 6, confirm that the policy led to a decline in emission intensity. The point estimate from column (4) suggests that emission intensity decreased, on average, by 6 percent.47Table 6 Estimates for Scale and Technique Effects Output Emission intensity (1) (2) (3) (4) Kp×Dt×EIl 0.059* 0.097*** −0.208 −0.325*** (0.032) (0.018) (0.141) (0.096) MEC Y Y N 117,445 41,548 117,445 41,548 R2 0.95 0.96 0.78 0.82 Dependent variable is log of plant-level output and log of plant-level emission intensity. EIl is the average emission intensity for plant l from the pre-policy period. Dt is a dummy for the post-policy period, which is equal to one after 2008 and is equal to zero otherwise. Kp is a dummy variable that takes the value of one for BC and zero for all other provinces. It includes high emission intensive plant by time FEs, province by time FEs, sector (2-digit NAICS) by time FEs, and city by industry (6-digit NAICS) FEs. The measurement error correction (MEC) is applied to the specification in columns (2) and (4). To account for serial correlations and within sector correlations, standard errors are clustered by province by sector (2-digit NAICS), reported in parentheses ***Significant at the 1 percent level, **Significant at the 5 percent level, *Significant at the 10 percent level Based on our simple model in Sect. 3, there are two possible channels through which this particular policy could generate this positive technique effect (i.e., the reduction in emission intensity). The first is that the carbon tax could directly provide an incentive for plants to invest in energy-saving technologies.48 This is because plants may wish to lower the long-run financial costs of paying the carbon tax. The second channel is through the reduction of the CIT rates. As a CIT is essentially a tax on capital, reducing its rate would improve distortion in plants’ decision on capital. This may incentivize plants to invest. What is different from the first channel is that this channel could also explain the positive output effect found in this subsection because plants may also invest in productivity-enhancing technologies. As lowering the user costs of capital provides incentives for all types of capital, not just energy-saving related capital, these investments may allow plants to produce more with the same amount of inputs or even fewer inputs. This is why it may be possible for plants to reduce emissions while producing more.49 One concern here is the measure of output. The ASM does not provide the quantity of output produced, instead it records the total sales (i.e., the product of the price and quantity). The increase in output found in this section can also be due to the increase in price. Although there is no direct way to test or isolate the price effect, we argue that this may not be much of a concern in this particular context because a majority of plants in the sample are heavily traded internationally.50 This implies that their output prices are determined at the world market, not set by individual plants. This is especially true for Canadian manufacturing plants as Canada is considered as a small open economy. Yamazaki (2022) confirms this view in the context of productivity. Putting together the results, manufacturing plants seem to respond to a revenue-neutral carbon tax by investing in both energy-saving and productivity-enhancing technologies, allowing them to lower emissions while producing more. Aggregate Implications In our model, Eq. (3.1) shows that the plant-level emission responses can be decomposed into the scale and technique effects. Using the same decomposition technique, we discuss how these plant-level responses translate into the response in aggregate manufacturing emissions. In this section, we show how we can use the estimates (scale and technique effects) identified in Sect. 5.4) to quantify the effect of the policy on the aggregate emission.51 Following Cherniwchan et al. (2017), we suppose that the manufacturing, m, is composed of a continuum of plants, and we can express aggregate manufacturing emission as:6.1 Zm=∫0nmzm(n)dn=∫0nmxm(n)em(n)dn where zm(n), xm(n), and em(n) are plant n’s emission, output, and emission intensity, respectively. ni denotes the marginal plant that is endogenously determined by the industry’s profitability. Taking logs and differentiating yield:6.2 Z˙m=∫0nmx˙m(n)ηm(n)dn+∫0nme˙m(n)ηm(n)dn+nmηm(nm)n˙m where ηm(n)dn is plant n’s share of manufacturing emission. The first term of Eq. (6.2) is the scale effect, while the second term is the technique effect for the aggregate emission. Unlike the plant-level decomposition, there is an additional term, i.e., the third term is the selection effect. This effect captures the change in manufacturing emission from plant entries and exits in response to the policy. We adapt an approach developed by Najjar and Cherniwchan (2021) to derive an empirical analogue to Eq. (6.2). Let t index time, such that manufacturing emission at time t is defined as Zmt=∫0nmtxmt(n)emt(n)dn, where xmt(n), emt(n), and nmt are analogous to their counterparts in Eq. (6.2). Then, the change in manufacturing emission between t-1 and t can be expressed as52ΔZmt=∫0nmtxmt(n)emt(n)dn-∫0nmtxmt-1(n)emt-1(n)dn+∫nmt∈nEnterxmt(n)emt(n)dn-∫nmt∈nExitxmt-1(n)emt-1(n)dn By following the similar algebra in Najjar and Cherniwchan (2021), we can show that the percentage change in manufacturing emission, Z˙mt=Zmt-Zmt-1Zmt-1, is:6.3 Z˙mt=∫0nmtηmt-1(n)s˙mt(n)dn+∫0nmtηmt-1(n)e˙mt(n)dn+∫nmt∈nEntryzmt(n)Zmt-1dn-∫nmt∈nExitηmt-1(n)dn+∫0nmtηmt-1(n)s˙mt(n)e˙mt(n)dn The first four terms of Eq. (6.3) are the scale, technique, and selection (entry and exit) effects that we discussed above, while the final term is an interaction effect between the scale and technique effects. Najjar and Cherniwchan argue that this can be interpreted as the approximation error in Eq. (6.2) caused by focusing on small, instead of potentially large, changes. As we already have the estimates for the scale and technique effects from Sect. 5.4, we now need to estimate the selection effect. We explain how we estimate the selection effect and present the result here. We estimate the following equation:6.4 Njpt=β(Kp×Dt×EIjp)+αjp+λjp^t+ϕj′t+δpt+ϵjpt where Njpt is either the number of entering or exiting manufacturing plants in industry j (4-digit NAICS) in province p at year t. The interaction term, Kp×Dt×EIjp, is the treatment variable, which is defined as the same as Eq. (4.1) except we use EIjp. EIjp is the average pre-policy emission intensity for industry j in province p. Instead of the plant fixed effects, we include αjp, which is the industry (4-digit NAICS) by province fixed effects. λjp^t is the high emission-intensive industry-province by time fixed effects. We denote jp^ as a collection of industry-province pairs whose EIjp is greater than a threshold (70th percentile of emission intensity). ϕj′t is the subsector (3-digit NAICS) by time fixed effects while δpt is the province by time fixed effects. Finally, ϵjpt is the idiosyncratic error term at industry by province by time. The results are reported in Table 7. These suggest that the extensive margin responses to the policy are rather limited. We find no statistically significant exit effect while the policy increase the number of entrants by 3 plants (column 4). This amounts to 33% of the average entries in BC’s manufacturing industries (at 4-digit NAICS).53 Investigating the selection effect of the policy is important, especially for the policymakers, because the public worries that putting a price on carbon emissions would push some businesses into bankruptcy, exiting the market. Despite this concern, the results presented here suggest otherwise. The increase in the number of entrants in response to the policy may be an appealing finding. Even with the carbon tax in place, manufacturing firms are establishing new plants in BC. Although not tested, this may be because the corporate income taxes are reduced in BC. Some firms may find it profitable to locate their plants in BC despite the carbon tax as the financial benefits coming from the CIT reduction may outweigh the costs from the carbon tax.Table 7 Estimates for Selection Effects Exit Entry (1) (2) (3) (4) Kp×Dt×EIjp 0.56 0.20 2.74 3.06** (0.73) (0.66) (1.88) (1.32) Sub-sector × time Y Y N 5,939 5,939 5,920 5,920 R2 0.77 0.80 0.78 0.81 Dependent variable is the number of exiting or entering plants. EIjp is the average emission intensity for industry-province pair from the pre-policy period. Dt is a dummy for the post-policy period, which is equal to one after 2008 and is equal to zero otherwise. Kp is a dummy variable that takes the value of one for BC and zero for all other provinces. All specifications include industry by province FEs, high emission intensive industry by time FEs, province by time FEs. Columns (2) and (4) include subsector (3-digit NAICS) by time FEs. To account for serial correlations, standard errors are clustered by province by industry (4-digit NAICS), reported in parentheses ***Significant at the 1 percent level, **Significant at the 5 percent level, *Significant at the 10 percent level Now with all the point estimates for the scale, technique, and selection effects, we can calculate the change in aggregate emissions using Eq. (6.3). We report each component of Eq. (6.3) in Table 8 and add the total effect in column 5. The results show that the aggregate emission is estimated to decline by 4%, which is mainly a result of the scale and technique effects. This decomposition exercise illustrates that the reduction of the aggregate manufacturing emissions comes from the intensive margin adjustments of the surviving plants to the policy.Table 8 Decomposition of Aggregate Emission Effects Scale Effect Technique Effect Selection Effect Interaction Effect Total Exit Entry 1.7% −5.67% −0.0026% 0.03% −0.096% −4.04% This table shows the scale, technique, selection, and interaction effects of the aggregate emission change. Column 5 reports the total effect Discussion and Conclusion This paper takes advantage of a unique confidential plant-level dataset and uses the revenue-neutral carbon tax in BC as an ideal setting to estimate the effect of a carbon tax on GHG emissions from manufacturing plants. We directly observe the plant-level policy exposure through emission intensity, allowing us to employ an augmented DID estimation. This method allows us to isolate the causal effect of the carbon tax on manufacturing emissions. We find that the BC carbon tax led to a decline in plant-level manufacturing emissions by 4 percent. Furthermore, we explore the mechanisms behind this emission reduction. We find that output increased by 1.8 percent while emission intensity decreased by 6 percent in response to the policy. This suggests that, on average, manufacturing plants respond to the revenue-neutral carbon tax by producing more with less energy. We argue that this may be possible because reducing corporate income taxes encouraged plants to invest in both energy-saving and productivity-enhancing technologies, allowing plants to be more efficient in their production. We also find considerable heterogeneity in emission responses to the policy across plants with different characteristics, such as plant size, ownership types, and trade intensity. For example, singly-owned plants are affected more negatively than multi-plant firms’ plants. Although not formally tested, we hypothesize that the appealing findings of this paper may come from the revenue-neutrality of this policy, especially the reduction of the corporate income tax. Our simple theory shows that emission reduction from the carbon tax alone is likely to come at the cost of output. Thus, recycling the carbon tax revenues to reduce the CIT might have played a major role in how emissions are reduced in the manufacturing sector, possibly through investments. Finally, we use the plant-level estimates found in this paper to quantify the effect of the policy on aggregate emissions. We find that a reduction of aggregate emissions comes from the intensive margin adjustments (i.e., the scale and technique effects) of the surviving plants to the policy. Plant entries and exits in response to BC carbon tax are limited. What would be important to investigate in future research is the long-run effect of the policy. This paper has already demonstrated the importance of the technique effect for emission reductions in response to the policy. Many argue that it takes time for investments to substantially impact emission reductions, productivity enhancement, or even both. Thus, investigating the long-run effect of this policy would provide a fruitful contribution to both the literature and public policy. Furthermore, we could better understand the magnitude of each component of a revenue-neutral carbon tax by identifying the emission effect from the carbon tax and CIT reduction separately. Appendix A: Model In this appendix, we show that the size of the technique and scale effects also depends on a plant’s emission intensity and that the signs are also ambiguous. Totally differentiating Eqs. (3.15) and (3.16) with respect to the carbon tax yield:A.1 dedτ=(αγ-1)eτ⏟⪌0+(β(1-α)γ)e1+tMETR⏟(+)d(1+tMETR)dτ⏟(-)=[α[1-ρ(1-σ)]+εβ(1-α)-1γ]exτ A.2 dxdτ=(-ασγ)zτe-1⏟(-)+(-β(1-α)σγ)z1+tMETRe-1⏟(-)d(1+tMETR)dτ⏟(-)=[-σ[α+εβ(1-α)]γ]zτe-1 Similar to Eqs.(3.18), (A.1) and (A.2) are both a function of a plant’s emission intensity. As the signs of these equations are ambiguous due to the same reason as Eq. (3.18), these provide a motivation for the estimation equation for the technique and scale effects in Sect.5.4. Appendix B: Additional Results In this appendix, we present additional results discussed in the main text. B.1 Difference-in-Differences Estimations As an alternative to Eqs. (4.1) and (5.1), one can estimate a more conventional DID estimator, i.e., compare plants in BC with plants in the rest of Canada before and after the policy:B.1 lnElipt=β(Kp×Dt)+αl+ϕi′t+ϵlipt where all is defined as in Eq. (4.1). While this approach is intuitive and simple, it is difficult to isolate the causal effect of the policy on emission responses, especially when other concurring events happened along with the implementation of the policy, such as the Great Recession. If these confounding factors affect plants in different provinces in the same way, the fixed effects would take care of biases from these factors. Unfortunately, we imagine that the Great Recession had differential impacts across provinces because provinces have different industrial compositions, have access to different international markets, and because some provinces are natural resource-based economies (i.e., Alberta, Saskatchewan, and Manitoba encounter less impact from the recession). To mitigate this issue, we directly exploit the variation in the plant-level policy exposure using the emission intensity in Eq. (4.1). This allows us to include the fixed effects at a more granular level, possibly capturing differential effects of the Great Recession across provinces and industries.Table 9 Difference-in-differences Estimates for Emissions (1) (2) (3) (4) (5) (6) Kp×Dt −0.06 −0.053 −0.11* −0.08 (0.05) (0.06) (0.058) (0.07) EIl×Dt −0.2 −0.28 (0.23) (0.13) Plant Y Y Y City × industry Y Y Y Measurement error correction Y Y Y N 117,445 42,459 13,946 5,194 35,227 13,373 R2 0.90 0.93 0.92 0.94 0.90 0.93 Sample Full Only BC Only high EI Dependent variable is log of plant-level emission. EIl is the average emission intensity for plant l from the pre-policy period. Dt is a dummy for the post-policy period, which is equal to one after 2008 and is equal to zero otherwise. Kp is a dummy variable that takes the value of one for BC and zero for all other provinces. Industry refers to the 6-digit NAICS industry while sector refers to the 2-digit NAICS industry. All specifications include sector by time FEs. To account for serial correlations and within sector correlations, standard errors are clustered by province by sector (at 2-digit NAICS), reported in parentheses. Columns (1) and (2) use the full sample, while columns (3) and (4) use only plants in BC, comparing high emission-intensive plants with low emission-intensive plants before and after the policy within BC. Columns (5) and (6) use only high emission-intensive plants, comparing high emission-intensive BC plants with high emission-intensive non-BC plants before and after the policy. Measurement error correction is applied to columns (2), (4), and (6). For these specifications, the dependent variable is log of city by industry-level emission and EIl is replaced with EIcip. ***Significant at the 1 percent level, **Significant at the 5 percent level, *Significant at the 10 percent level Table 9 reports the results from estimating Eq. (B.1), columns (1) and (2). In addition to the DID estimator explained above, we also estimate two other DID estimators. First, we compare the high emission-intensive and low emission-intensive plants in BC only. This would address the bias from the differential shocks across provinces but would suffer from the differential shocks between high emission-intensive and low emission-intensive plants. The results of this DID are reported in columns (3) and (4) of Table 9. Second, we only use the high emission-intensive plants, allowing us to compare the high emission-intensive plants in BC with those in the rest of Canada before and after the policy, reported in columns (5) and (6) of Table 9. Conversely, this would address the bias from the differential shocks between the high emission-intensive and low emission-intensive plants, but would suffer from the differential province shocks. Although most point estimates are not statistically different from zero, they all suggest that the carbon tax reduces manufacturing emissions. This is consistent with the findings discussed below. As pointed out by Yamazaki (2017), the lack of statistical significance in Table 9 could be due to the lack of variation in the treatment variables to precisely estimate the emission effect, which is why Eq. (4.1) is important for the identification. B.2: Testing the Robustness with Different Threshold Levels for High Emission Intensive Plants Table 10 Different Threshold Levels for High Emission Intensive Plants (1) (2) (3) (4) (5) (6) (7) (8) Kp×Dt×EIcip −0.26** −0.23** −0.32*** −0.28** −0.29*** −0.25** −0.23** −0.21** (0.10) (0.10) (0.11) (0.12) (0.11) (0.11) (0.09) (0.1) Sector × time Y Y Y Y High emission intensive plant × time 70th percentile Y Y 50th percentile Y Y 60th percentile Y Y 80th percentile Y Y N 41,548 41,548 41,548 41,548 41,548 41,548 41,548 41,548 R2 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 Dependent variable is log of city by industry-level emission. EIcip is the average pre-policy emission intensity for city c, industries i, and province p. Dt is a dummy for the post-policy period, which is equal to one after 2008 and is equal to zero otherwise. Kp is a dummy variable that takes the value of one for BC and zero for all other provinces. Industry refers to the 6-digit NAICS industry while sector refers to the 2-digit NAICS industry. All specifications include city by sector FEs and province by time FEs, and the measurement error correction is applied to all. Columns (1) and (2) are taken from columns (3) and (4) of Table 3. In columns (3) ∼ (8), we changed the threshold level for high emission intensive plant by time FEs. To account for serial correlations and within sector correlations, standard errors are clustered by province by sector (at 2-digit NAICS), reported in parentheses. ***Significant at the 1 percent level, **Significant at the 5 percent level, *Significant at the 10 percent level In estimating Eq. (4.1), we arbitrarily choose the 70th percentile of plant-level emission intensity as a threshold for the high emission-intensive plant by year FEs. We explore the robustness of our main estimates by using the different threshold levels, i.e., 50th, 60th, and 80th percentiles. The results are presented in Table 10. Columns (1) and (2) are taken from columns 5 and 6 of Table 3. The rest of the columns explore the different threshold levels. The first impression from these results is that the main estimates are robust to the different threshold levels, i.e., the coefficients are statistically significant and negative. B.3: Indirect Test of SUTVA Table 11 Different control groups: Provinces with limited trade with BC Emission Output Emission intensity (1) (2) (3) (4) (5) (6) Kp×Dt×EIcip −0.27*** −0.247*** 0.084*** 0.086*** −0.35*** −0.33*** (0.081) (0.083) (0.028) (0.027) (0.084) (0.085) Sector × time Y Y Y N 11,794 11,794 11,794 11,794 11,794 11,794 R2 0.94 0.94 0.96 0.96 0.82 0.82 Dependent variable is log of city by industry-level emission. EIcip is the average pre-policy emission intensity for city c, industries i, and province p. Dt is a dummy for the post-policy period, which is equal to one after 2008 and is equal to zero otherwise. Kp is a dummy variable that takes the value of one for BC and zero for all other provinces. It includes high emission intensive plant by time FEs, province by time FEs, sector (2-digit NAICS) by time FEs, and city by industry (6-digit NAICS) FEs. The measurement error correction is applied to all the specifications. To account for serial correlations and within sector correlations, standard errors are clustered by province by sector (2-digit NAICS), reported in parentheses. The selected provinces are Newfoundland and Labrador, Prince Edward Island, Nova Scotia, New Brunswick, Manitoba, and Saskatchewan. ***Significant at the 1 percent level, **Significant at the 5 percent level, *Significant at the 10 percent level One of the important assumptions for the identification is the SUTVA. As discussed in the main texts, this could be violated by the general equilibrium (GE) effects through the inter-provincial trades of intermediate goods. From a cost pass-through, it would make it more expensive for plants in other provinces to produce with the imported intermediate goods from BC. The magnitude of this change depends on the bilateral trade cost. The control group being (indirectly) affected by the policy violates the SUTVA. To test the severity of this concern, we re-estimate Eq. (4.1) using only provinces that have very low trade flows with BC. The selected provinces are Newfoundland and Labrador, Prince Edward Island, Nova Scotia, New Brunswick, Manitoba, and Saskatchewan. Table 11 shows that the estimates are similar to those in the main text, i.e., they are statistically significant and negative. The magnitudes are also similar. These results provide a piece of evidence that the violation of the SUTVA through the GE effects is not warranted. B.4 Indirect Test of Price Effect for Scale Effect Analysis In Sect. 5.4, we explore the scale effect of the emission responses to a carbon tax, i.e., emission falls from the output decline. We implicitly assume that the prices are stable. We indirectly explore this by interacting our main treatment variable with subsector trade intensity. The results are presented in Table 12. We show that the coefficient shown in Table 3 is fairly close to the coefficient for highly trade-intensive subsectors in Table 12, implying that our assumption on price effect may be justified.Table 12 Interaction with industry trade intensity lnQ (1) (2) Plant-level MEC Kp×Dt×EIl × High trade-intensivei 0.03 0.123*** (0.04) (0.03) × Medium trade-intensivei 0.15 0.019 (0.11) (0.108) × Low trade-intensivei −0.74 −0.46 (0.747) (0.77) N 117,445 41,548 R2 0.95 0.96 Dependent variable is log of plant-level output. EIl is the average emission intensity for plant l from the pre-policy period. Dt is a dummy for the post-policy period, which is equal to one after 2008 and is equal to zero otherwise. Kp is a dummy variable that takes the value of one for BC and zero for all other provinces. Both specifications include high emission intensive plant by time FEs, province by time FEs, and sector (2-digit NAICS) by time FEs. Plant-level estimates includes the plant FEs while the MEC estimates includes city by industry (6-digit NAICS) FEs. To account for serial correlations and within sector correlations, standard errors are clustered by province by sector (2-digit NAICS), reported in parentheses. High trade-intensive subsectors are 327, 323, 337, 321, 332, 324, and 312. Medium trade-intensive subsectors are 311, 339, 326, 322, 325, 331, and 336. Low trade-intensive subsectors are 333, 314, 313, 316, 315, 334, and 335. Please refers to Table 14 for the corresponding NAICS codes. ***Significant at the 1 percent level, **Significant at the 5 percent level, *Significant at the 10 percent level B.5: Robustness Check with Matching (Re-Weighting) Method Despite the advantage of the augmented DID method, one can even take one step further to ensure the credibility of the main estimates by combining regression and the matching method. To best utilize our rich confidential dataset, we do this by re-estimating Eq. (4.1) with weights based on the estimated propensity-scores. This would ensure the similarity between treated and control plants as well as overcome the issue of the curse of dimensionality in other matching methods. We estimated the propensity-scores with many plant characteristics, such as output, labor, energy, export volume, industry type, total expenditure, total revenues, and R&D expenditure. The results are presented in Table 13. Columns (1) and (2) are equivalent to columns (2) and (4) in Table 3, while columns (3) and (4) are equivalent to columns (6) and (8) in Table 3. There are two important remarks worth discussing from this. First, the estimates are considerably similar to those in Table 3. Second, the point estimate in column (1) is now statistically significant when the point estimate in column (2) in Table 3 is not. This implies that even at the plant-level analyses, there is a negative emission effect in response to the carbon tax. This provides, not only robust findings, but also convincing support for the main estimates.Table 13 Baseline estimates with the propensity-scores weighted method Plant-level City by industry-level (1) (2) (3) (4) Kp×Dt×EIl −0.25** −0.38*** (0.10) (0.07) Kp×Dt×EIcip −0.18* −0.27*** (0.09) (0.05) Plant Y Y City × industry Y Y Controls Y Y MEC Y Y N 90,402 77,153 40,990 26,940 R2 0.93 0.94 0.94 0.95 Dependent variable for columns (1) and (2) is log of plant-level emission while that for columns (3) and (4) is log of city by industry-level emission. EIl is the average pre-policy emission intensity for plant l, while EIcip is the average pre-policy emission intensity for city c, industries i, and province p. Dt is a dummy for the post-policy period, which is equal to one after 2008 and is equal to zero otherwise. Kp is a dummy variable that takes the value of one for BC and zero for all other provinces. Industry refers to the 6-digit NAICS industry while sector refers to the 2-digit NAICS industry. Columns (2) and (4) include additional controls, i.e., plant’s age, input-output ratio, the number of plants owned by the parental firm, and export volume. MEC stands for measurement error correction. All specifications include high emission intensive plant by time FEs, province by time FEs, and sector by time FEs. To account for serial correlations and within sector correlations, standard errors are clustered by province by sector (at 2-digit NAICS), reported in parentheses. ***Significant at the 1 percent level, **Significant at the 5 percent level, *Significant at the 10 percent level B.6: Decomposition In Sect. 6, we derived the empirical analogue of the manufacturing emission given by Eq. (6.2). We heavily follow Najjar and Cherniwchan (2021) and show more algebraic steps to get to Eq. (6.3) and explain how we can use the estimates presented in this paper to discuss the aggregate emission response to the policy. We start from the equation for the change in manufacturing emission between t-1 and t:ΔZmt=∫0nmtxmt(n)emt(n)dn-∫0nmtxmt-1(n)emt-1(n)dn+∫nmt∈nEnterxmt(n)emt(n)dn-∫nmt∈nExitxmt-1(n)emt-1(n)dn This can be rewritten as:ΔZmt=∫0nmt(xmt(n)-xmt-1(n))emt(n)dn+∫0nmtxmt-1(n)(emt-emt-1(n))dn+∫0nmt(xmt(n)-xmt-1(n))emt-1(n)dn-∫0nmt(xmt(n)-xmt-1(n))emt-1(n)dn+∫nmt∈nEnterxmt(n)emt(n)dn-∫nmt∈nExitxmt-1(n)emt-1(n)dn Then, with further algebra, this reduces to:ΔZmt=∫0nmtΔxmt(n)emt-1dn+∫0nmtxmt-1(n)Δemt(n)dn+∫nmt∈nEnterxmt(n)emt(n)dn-∫nmt∈nExitxmt-1(n)emt-1(n)dn+∫0nmtΔxmt(n)Δemt(n)dn Finally, dividing this by Zmt-1 yields our empirical decomposition, given by Eq. (6.3) in the main text. Next, we show how we can use our estimates presented in the paper in Eq. (6.3). Again, following Najjar and Cherniwchan (2021), x˙mt(n) and e˙mt(n) can be calculated as:x˙mt(n)=βx^,if plantn is treated0,otherwisee˙mt(n)=βe^,if plantn is treated0,otherwise where βx^ and βe^ are causal estimates of the average change in plant output and emission intensity due to the policy, respectively. They are the estimates presented in Sect. 5.4. Letting the share of the manufacturing emission at t-1 from treated plants be given by ηmt-1Treated=∫n∈Treatedηmt-1(n)dn, the scale (SC) and technique (TE) effects can be expressed as:SC^=βx^ηmt-1Treated,TE^=βe^ηmt-1Treated To construct an expression for the selection effect, ∫nmt∈nEntryzmt(n)Zmt-1dn-∫nmt∈nExitηmt-1(n)dn, we need estimates of the policy’s effects of the plant entries and exits and information on the average emission share of the entering and exiting plants. In Sect. 6, we present causal estimates of the average plant entries and exits in response to the policy, denoting it as β^Entry and β^Exit. Then, the selection (SE) effect can be expressed as:SE^=β^Entryη¯mtEntry-β^Exitη¯mt-1Exit where η¯mtEntry and η¯mt-1Exit are the average entering and exiting plants’ share of manufacturing emission, respectively. Lastly, the interaction effect in Eq. (6.3) can be expressed by substituting the estimates for x˙mt(n) and e˙mt(n) as:IE^=βx^βe^ηmt-1Treated Putting all these together, we have:Z˙mt=βx^ηmt-1Treated⏟SC+βe^ηmt-1Treated⏟TE+β^Entryη¯mtEntry-β^Exitη¯mt-1Exit⏟SE+βx^βe^ηmt-1Treated⏟IE Appendix C: Data In this appendix, we present additional information about our data. Table 14 Manufacturing Industries at 3-digit NAICS code 311 Food manufacturing 312 Beverage and tobacco product manufacturing 313 Textile mills 314 Textile product mills 315 Clothing manufacturing 316 Leather and allied product manufacturing 321 Wood product manufacturing 322 Paper manufacturing 323 Printing and related support activities 324 Petroleum and coal product manufacturing 325 Chemical manufacturing 326 Plastics and rubber products manufacturing 327 Non-metallic mineral product manufacturing 331 Primary metal manufacturing 332 Fabricated metal product manufacturing 333 Machinery manufacturing 334 Computer and electronic product manufacturing 335 Electrical equipment, appliance and component manufacturing 336 Transportation equipment manufacturing 337 Furniture and related product manufacturing 339 Miscellaneous manufacturing 311 Food manufacturing 312 Beverage and tobacco product manufacturing 313 Textile mills 314 Textile product mills 315 Clothing manufacturing 316 Leather and allied product manufacturing 321 Wood product manufacturing 322 Paper manufacturing 323 Printing and related support activities 324 Petroleum and coal product manufacturing 325 Chemical manufacturing 326 Plastics and rubber products manufacturing 327 Non-metallic mineral product manufacturing 331 Primary metal manufacturing 332 Fabricated metal product manufacturing 333 Machinery manufacturing 334 Computer and electronic product manufacturing 335 Electrical equipment, appliance and component manufacturing 336 Transportation equipment manufacturing 337 Furniture and related product manufacturing 339 Miscellaneous manufacturing Table 15 Size of output and emission intensity in BC Emission intensity (percentile) Output ($1K) Below 10th percentile 4,877 Between 10th and 25th percentile 3,577 Between 25th and 50th percentile 3,141 Between 50th and 75th percentile 4,156 Between 75th and 90th percentile 5,666 Above 90th percentile 6,388 This shows the size of output for 6 categories based on the emission intensity percentile in BC Table 16 Summary statistics Canada BC Dropped In-sample Dropped In-sample Output ($ millions) 11.89 15.71 7.93 8.39 (119) (153) (20) (35) Labor expenses ($ millions) 2.71 9.26 4.46 4.46 (4.8) (4.5) (0.6) (0.6) # of salary worker 3.9 11.6 3.3 7.1 (16.19) (42.04) (9.81) (20.04) # of production worker 11.27 34.32 9.98 23.38 (48.42) (114) (25.48) (58.97) Plant age 8.04 7.17 7.95 6.98 (3.04) (3.35) (3.10) (3.40) Total expense ($ millions) 13.92 17.98 8.35 9.25 (174) (163) (20.4) (46.4) This shows summary statistics for key variables. In the main text, we mentioned that we excluded some plants as they do not report their energy expenditure. We denote Dropped as those plants dropped from the data while In-sample indicates those plants that are kept in the data for the estimation. We show a summary statistics for Canada and BC Fig. 6 This figure plots a distribution of average plant-level emission across subsectors (3-digit NAICS). The solid red line represents the average plant-level emission among all subsectors Fig. 7 This figure plots output trends for BC and ROC. Panel (a) presents the trends of average output for BC and ROC, while Panel (b) presents the trends of differences in average outputs between high and low emission intensive plants for BC and ROC 1 Conference of Parties is the formal annual meeting of the United Nations Framework Convention on Climate Change (UNFCCC) Parties. In these meetings, the member countries assess countries’ progress in reducing greenhouse gas emissions and negotiate climate change agreements. 2 There are 64 carbon pricing policies implemented worldwide, and the price ranges from less than $1 (Poland) to $137 (Sweden). See World Bank (2021) for mode details. In fact, Canada is now proposing to increase the federal carbon price to $170 by 2030. See https://www.cbc.ca/news/politics/carbon-tax-hike-new-climate-plan-1.5837709 3 A uniform carbon tax is a per-unit charge on fossil fuels based on their carbon embodiment, applied to all consumers at the same rate. The effect of a carbon tax on GHG emissions is less pronounced when the carbon tax is revenue neutral (i.e., all the tax revenues from the policy are returned to consumers to maintain the government revenues constant). Theoretical models show that the effect depends on how the tax revenue is recycled. 4 Pretis (2020) shows no identifiable aggregate emission response from British Columbia’s carbon tax. 5 Québec was the first province to introduce a carbon tax, but the tax rate is only around $3 per tonne of CO2eq and does not include all emitters. Some Scandinavian countries have carbon taxes as high as $150. However, the effective tax rates are smaller due to many tax exemptions, and in some cases, the energy excise taxes were removed and replaced by carbon taxes. 6 Antweiler et al. (2001) refer to the emission response by increasing the production size as scale effect while referring to the emission response by changing the production technology that improves emissions per unit of output as technique effect. 7 Some, such as Andersson (2019), argue that the carbon tax may have a general equilibrium effect and lead to carbon leakages into other provinces, violating the stable unit treatment value assumption. To minimize this concern, we also estimate the emission effect using only provinces that have very low trade flows with BC because we expect very limited carbon leakages into these provinces. The selected provinces are Newfoundland and Labrador, Prince Edward Island, Nova Scotia, New Brunswick, Manitoba, and Saskatchewan. The baseline estimation results are robust to this sample difference. The results are presented in Table 11 in Appendix B. 8 Alternatively, one can use the facility-level emission data available at Environment Canada, known as Greenhouse Gas Reporting Program (GHGRP). This data includes only large industrial emitters that emit more than 100 kilotonnes per year. The reporting threshold was reduced to 50 kilotonnes in 2009 and further to 10 kilotonnes in 2018. We believe that our data is better suited as it covers all manufacturing plants and provides more variation, while the facility-level emission data only covers the large facilities. 9 We also find considerable heterogeneity in emission responses to the policy across plants with different characteristics, such as plant size, ownership types, and trade intensity. For example, singly-owned plants are affected more negatively than multi-plant firms’ plants. 10 Yamazaki (2017) also argues that there is a positive demand effect from lowering the personal income tax, which could also help to explain the positive output effect found in this paper. 11 Najjar and Cherniwchan (2021) refer selection effect to be the change in aggregate emission through plant entries and exits. 12 There are considerable numbers of ex-post analyses investigating cap-and-trade policies, such as the European Union Emissions Trading System (see Martin et al. (2016) for a review) and US Regional Greenhouse Gas Initiative (e.g., Fell and Maniloff (2018)). 13 It also shows that the industrial emissions, including manufacturing, declined but its point estimate was not statistically different from zero. 14 The rate was kept at $30 until 2018, when increased to $35 on April 1. It continues to increase by $5 annually and will reach $50 in 2022 (Ministry of Finance 2017). An annual increase of $5 was postponed in 2020 due to the COVID-19. 15 The uncovered emissions are associated with emissions produced by landfill facilities, non-combustion emissions from the agriculture sector, most fugitive emissions, and industrial emissions that do not come from burning fossil fuels. 16 There are no manufacturing industries that are exempted from the carbon tax. The agriculture sector was exempted from the tax after 2012, which is not included in our analysis because the focus of this paper is on manufacturing plants. 17 This type of decomposition exercise has been used extensively in the literature, e.g., Antweiler et al. (2001), Cherniwchan et al. (2017), and Najjar and Cherniwchan (2021). 18 We assume that there is a one-to-one mapping between energy and emission. Yamazaki (2022) argues that a concept of abatement in Copeland and Taylor (1994) is still relevant here, although the regulation they consider is either emission tax or emission standard. Once we interpret Z as energy and θ as a fraction of inputs allocated to energy-saving activities, such as R&D expenditure allocated to energy-saving technology, the formulation of Copeland and Taylor (1994) is still valid. Tombe and Winter (2015) also argue that “one might loosely interpret abatement as any costly activity that lowers the use of emissions-relevant energy, such as substitution between different fuel types.” For this reason, investment in energy-saving technology, fuel switching, and factor substitution can all be interpreted as abatement in the definition of Copeland and Taylor (1994). 19 The incomplete deductibility of capital costs is a common way to represent more complex CIT systems. See Haufler and Schjelderup (2000), McKenzie and Ferede (2017), and Fuest et al. (2018) as examples. 20 A hurdle rate is the minimum rate of return acceptable by investors. 21 The concept of the METR has been widely used since the work of King and Fullerton (1984). 22 In principle, λk>1 is also possible when the tax system subsidizes capital due to accelerated depreciation, investment allowances, and investment tax credits. However, a back-of-the-envelope calculation using the model parameters from the literature shows that λk is around 0.77 for British Columbia and is likely to be always less than 1. Thus, we assume λk<1 for this paper. λk can be calculated with information on the METR. 23 In Canada, fuel costs are fully deductible as business expenses. See https://www.canada.ca/en/revenue-agency/services/tax/businesses/topics/sole-proprietorships-partnerships/business-expenses.html. 24 Although Yamazaki (2022) also allows the abatement investment cost to be not fully deductible like capital cost, we abstract away from that for the illustrative purposes. 25 In order to satisfy the second order condition of the profit maximization problem, γ has to be positive. See Appendix of Yamazaki (2022) for the verification. 26 This can be easily verified by using Eqs.(3.12) and (3.16). 27 See Yamazaki (2022) for the verification. 28 In addition, lowering the METR increases investments in general. Lowering the user costs of capital encourages plants to invest more. This may also make plants more productive through a more traditional manner, i.e., an increase in A. 29 As before, the reduction in the CIT also leads to higher investment in other capitals, leading to higher output. 30 We also totally differentiate Eqs. (3.15) and (3.16) with respect to the carbon tax, discussed in Appendix A. By doing so, we show that the size of the technique and scale effects also depend on plant’s emission intensity. These confirm that the effects of this revenue-neutral carbon tax on plants’ emissions through the technique and scale effects are ambiguous due to these countervailing forces. 31 We also explored the different threshold levels, such as 50th, 60th, and 80th percentile. Results are robust to using these different thresholds, shown in Table 10 in Appendix B.2. 32 Low emission-intensive plants may still have an incentive to reduce their emissions if they pay a considerable amount of tax (i.e., their energy expenditure is large if their output level is high enough). Especially if fuel switching requires only a fixed cost (e.g., a fixed cost to buy new machinery that works with electricity rather than coal and natural gas), then plants’ incentives to invest depends only on the absolute value rather than the per unit cost of the carbon tax. Table 1, however, shows that low emission-intensive plants pay much less carbon tax in absolute terms relative to high emission-intensive plants. We also show that high emission-intensive plants produce, on average, higher levels of output in Table 15 of Appendix C. 33 For an average plant below the 70th percentile in emission intensity, the carbon tax imposes a charge less than 0.05 percent of the plant’s total costs. 34 To ensure the credibility of our estimates, we also estimate Eq. (4.1) with weights based on the estimated propensity scores. This would ensure the similarity between treated and control plants. The propensity scores are estimated using many plant-characteristics, such as output, labor, energy, industry, etc. The results are presented in Table 13 in Appendix B. 35 The results of this robustness check are presented in Table 11 in Appendix B. 36 Fuel prices for gasoline, diesel, propane, light fuel oil, and heavy fuel oil are retrieved from Natural Resource Canada (2016), prices for natural gas are retrieved from Statistics Canada (2015), and coal prices are retrieved from Natural Resource Canada (2012). The fact that, for each fuel, we use the average price in major cities in each province is a potential source of concern. This average price can be different from the exact price that each plant faces because plants may have different contracts and strategies for buying their fuels. This difference creates a certain degree of error in measuring plant-level GHG emissions. However, if the measurement error does not vary systematically with the treatment (i.e., the error is not larger or smaller for plants that are more exposed to the policy and only after the carbon tax is introduced), it will only increase the noise in the data, inflating the standard errors, but it would not undermine our ability to identify the effect of interest. 37 The embodied GHG emissions by fuel type are available on the Environment Canada website. 38 To visualize the data better, we also include a few figures (Figs. 6 and 7) that may be useful in Appendix C. 39 We provide summary statistics for those plants that are excluded from the analysis in Table 16 in Appendix C. 40 Measurement error in the dependent variable is less of concern because it only reduces precision in estimating the standard error, but the coefficient would be unbiased. The measurement error in emission intensity is more of concern because it causes attenuation bias, as it biases the estimates downward. For more details regarding measurement errors in panel data, see Griliches and Hausman (1986). We use plants’ emission intensity prior to 2008, meaning that the measurement error would not be correlated with the treatment variable. 41 In Appendix B.1, we discuss the importance of Eqs. (4.1) and (5.1). We present the estimates from a more conventional DID estimator. 42 We also cluster the standard errors at the province level as well as province by 3-digit NAICS subsector level, and results are similar. Mackinnon and Webb (2020) show that under-clustering (i.e., clustering at the 3-digit NAICS subsector by province) suffers from a severe over-rejection, implying that ignoring the within-province correlation is worse than having too few cluster groups (i.e., clustering at the province level). Thus, we cluster at the 2-digit NAICS sector by province. 43 Despite the statistically significant results from columns (3), (4), (7), and (8), these control variables, especially input-output ratio, the number of plants owned by the parental firm, and export volume, may be bad controls as they could also be the outcome variables. Including these may bias the results. In addition, adding these controls drops about 30 percent of the data used in the estimates without the controls. For these reasons, we prefer to treat these estimates as the robustness checks. 44 This 4 percent reduction is calculated by 100×(e(β^ΔEI¯l)-1) where ΔEI¯l is the difference of the average emission intensity between high emission-intensive and low emission-intensive plants in BC. We also calculated the upper and lower bounds for the emission effect, which are 0.5 and 7.6 percent reduction, respectively. 45 This method is also referred to as a flexible estimation. 46 This 1.8 percent increase is calculated using the same method as the emission effect, and its upper and lower bounds are 2.5 and 1.2 percent, respectively. 47 This 6 percent decline is calculated using the same method as the emission effect, and its upper and lower bounds are 2.5 and 9 percent reduction, respectively. 48 This can be mathematically seen in Eq. (A.1) in Appendix A, specifically in the first term. Even if the carbon tax revenues were not recycled (i.e., the second term becomes zero without the revenue recycling), the effect of the carbon tax alone on emission intensity is ambiguous. 49 Although we theoretically show that these are the possible explanations, we never formally test these channels in this paper. For this reason, we do not claim that these are the only explanations. To test these possible explanations, we need to separately estimate the emission effect of the carbon tax and CIT reduction. 50 We indirectly explore this by interacting our main treatment variable with subsector trade intensity. The results are presented in Table 12 in Appendix B. We show that the coefficient shown in Table 3 is fairly close to the coefficient for highly trade-intensive subsectors in Table 12, implying that our assumption on price effect may be justified. 51 We present the mathematical derivations of the decomposition equation in Appendix B.6. 52 While Najjar and Cherniwchan (2021) assume that plant only exit, we allow plants to both enter and exit. 53 Although not statistically significant, 0.2 amounts to 4% of the average exits in BC’s industries. Previous versions of this paper were circulated under the title “How Effective are Carbon Taxes in Reducing Emissions? Evidence from the Revenue Neutral Carbon Tax in British Columbia, Canada.” We would like to thank Scott Taylor, Pamela Campa, Jared Carbone, and Ken McKenzie for their invaluable comments. Ahmadi acknowledges generous financial support from Smart Prosperity Research Network (SPRN) of the University of Ottawa. Yamazaki acknowledges generous financial support from the Policy Research Center (PRC) at the National Graduate Institute for Policy Studies. This paper also benefited from comments by Stephan Litschig, Katrin Millock (editor), Nouri Najjar, Yutaro Sakai, three anonymous referees, and discussants/participants at various conferences and seminars. We are grateful to many members of the Canadian Centre for Data Development and Economics Research (CDER) at Statistics Canada for their advice on data issues. The views expressed herein are those of the author and do not necessarily reflect the views of SPRN, PRC, or CDER. All remaining errors are our own. 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==== Front Sportorthopa¨die-Sporttraumatologie 0949-328X 1876-4339 S0949-328X(22)00127-2 10.1016/j.orthtr.2022.03.012 Review Influence of physical activity on well-being at times of the COVID-19 pandemic: a review Einfluss körperlicher Aktivität auf das Wohlbefinden zu Zeiten der COVID-19-Pandemie: eine ÜbersichtsarbeitSymanzik Cara ab⁎ Hagel Clara c Hotfiel Thilo de Engelhardt Martin d John Swen Malte ab Grim Casper df a Institute for Interdisciplinary Dermatological Prevention and Rehabilitation (iDerm) at Osnabrück University, Osnabrück, Germany b Department of Dermatology, Environmental Medicine and Health Theory, Institute for Health Research and Education (IGB), Faculty of Human Sciences, Osnabrück University, Osnabrück, Germany c Department of New Public Health, Institute for Health Research and Education (IGB), Faculty of Human Sciences, Osnabrück University, Osnabrück, Germany d Center for Musculoskeletal Surgery Osnabrück (OZMC), Klinikum Osnabrück, Osnabrück, Germany e Department of Orthopedic Surgery, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany f Institute for Health Research and Education (IGB), Faculty of Human Sciences, Osnabrück University, Osnabrück, Germany ⁎ Corresponding author: Dr. rer. nat. Cara Symanzik, B.Sc., M.Ed., Institute for Interdisciplinary Dermatological Prevention and Rehabilitation (iDerm) and Department of Dermatology, Environmental Medicine and Health Theory at Osnabrück University, Am Finkenhügel 7a, D-49076 Osnabrück, Germany. Tel.: +49 541 969 7448, Fax: +49 541 969 2445. 11 4 2022 11 4 2022 . 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background Physical activity (PA) has been shown to be advantageous to one's health. Coronavirus disease 2019 (COVID-19) lockdown measures have reportedly led to substantial decrease of PA and to drastic reduction of well-being (WB). In light of this, the purpose of this review was to assess the impact of PA on WB during the COVID-19 pandemic. Material and Methods In May 2021, Literature search was conducted in Pubmed/Medline. The eligible publication period was one year from the search date. Results Synthesis of results from eight publications reporting on data from around 100 countries showed that PA positively influences diverse dimensions of the multivariate construct of WB, all of them relating to mental health. Young adults and women showed lowest PA concomitant with lowest self-reported WB. Conclusions Reduced PA levels resulted in lower WB levels, which might have a negative impact on mental health. Forthcoming, initiatives will be needed to facilitate PA – ideally whilst promoting joy of moving – in consideration of pandemic circumstances. By this means it will be possible to effectively promote WB and to prevent arising mental health issues. The current findings are fundamental to develop suitable approaches to improve PA in pandemic situations. Zusammenfassung Hintergrund Körperliche Aktivität (PA) hat sich als gesundheitsfördernd erwiesen. Die Eindämmungsmaßnahmen der Coronavirus-Krankheit-2019 (COVID-19) führten zu einem Rückgang der PA und zu einer Verringerung des Wohlbefindens (WB). Das Ziel dieser Arbeit war, Auswirkungen von PA auf WB während der COVID-19-Pandemie zu evaluieren. Material und Methoden Eine Literatursuche in Pubmed/Medline wurde im Mai 2021 durchgeführt. Der eingeschlossene Publikationsszeitraum betrug ein Jahr ab Recherchedatum. Ergebnisse Die Synthese der Ergebnisse aus acht Publikationen, die über Daten aus rund 100 Ländern berichteten, zeigte, dass PA verschiedene Dimensionen des multivariaten Konstrukts von WB positiv beeinflusst; alle mit Bezug zur psychischen Gesundheit. Junge Erwachsene und Frauen zeigten die niedrigste PA, die mit dem niedrigsten selbstberichteten WB einherging. Schlussfolgerungen Reduzierte PA führte zu niedrigerem WB, was sich generell negativ auf die psychische Gesundheit auswirken könnte. In der Zukunft werden Initiativen benötigt, um PA – idealerweise mit gleichzeitiger Förderung der Bewegungsfreude – unter Berücksichtigung der pandemie-bedingten Umstände zu erleichtern. Nur auf diese Weise wird es möglich sein, WB effektiv zu fördern und aufkom Menden psychischen Problemen vorzubeugen. Die aktuellen Erkenntnisse sind grundlegend, um geeignete Ansätze zur Verbesserung der PA in Pandemiesituationen zu entwickeln. Keywords COVID-19 Exercise Pandemic Physical activity Well-being Schlüsselwörter COVID-19 Körperliche Aktivität Pandemie Sport Wohlbefinden ==== Body pmcIntroduction It is scientific consensus that physical activity (PA) is generally beneficial to one's health. Those who engage in regular PA can improve their overall well-being (WB) as well as their physical, mental, and social health. As a practical matter, PA may be considered medicine since it protects the mind and body against physical and mental illnesses .[13] According to the World Health Organization (WHO), PA is understood as any physical movement that is generated by the skeletal muscles and requires energy and describes all activity including movement in leisure, for transportation to and from locations, or as part of a person's occupation .[26] PA is influenced by various determinants which comprise individual (e.g., sociodemographic, biomedical, and skill-related descriptors), interpersonal (e.g., social surroundings and support), and environmental (e.g., living and working environment) factors .[3], [7], [8], [22] To prevent transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19), far-reaching restrictions in daily life had to be undertaken involving so-called lockdown measures. Necessary social distancing comprised not only restraints of social contacts in the private life but also remote working scenarios as well as closings of local shops and facilities, such as sports venues. A substantial decrease of PA across diverse population groups throughout the COVID-19 pandemic has already been reported .[13], [17], [24] Concomitantly, drastic reduction of WB has been observed at a global scale under aforementioned pandemic conditions .[23] Against this backdrop, this review aims at evaluating the influence of PA on WB at times of the COVID-19 pandemic. Important parameters which should be addressed in the future for facilitating PA in consideration of pandemic circumstances as well as particularly affected populations shall be identified. Perspectivity, this review seeks to contribute to the development of appropriate approaches to generally improve PA in pandemic situations. Methods Literature searches were performed in May 2021. A time period of 1 year retrospective to the search date was taken as eligible publication period. We systematically searched for covid-19 AND (exercise* [TI] OR physical activity* [TI]) AND (wellbeing [MeSH] OR wellbeing [TI] OR well-being [TI]) in Pubmed/Medline. Eligibility criteria following the PICO (population, intervention, control, and outcomes) scheme are listed in Table 1 . Only studies reporting on participants in early or mid-adulthood (18 to 59 years) written in English or German were considered. In general, all studies not meeting the inclusion criteria were excluded. Further, studies not reporting on relevant aspects about the influence of PA on WB (e.g., assessment of another factor influencing the evaluated parameter of PA and WB) as well as studies not reporting on the impact of the COVID-19 pandemic at societal level (e.g., only patients with acute SARS-CoV-2 infection) were excluded.Table 1 Eligibility criteria following the PICO (population, intervention, control, and outcomes) scheme. Table 1Criterion Inclusion Populationa COVID-19 Intervention exercise* OR physical activity* Control n/a Outcomes wellbeing OR well-being a In this review, population has to be understood rather as ‘problem’; n/a, not applicable. Results Study selection A PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 flow diagram [19] of the literature search is presented in Figure 1 . Initial searches yielded 25 separate study records, which were then evaluated. There were no new references found after manually searching references (forward and backward snowballing). After excluding publications that were irrelevant because they did not provide data on the influence of PA on WB at times of the COVID-19 pandemic, we obtained a final number of 8 papers for this review.Figure 1 PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 flow diagram. Characteristics of included studies All included papers (n = 8) report on cross-sectional studies using online surveys/questionnaires to collect data. 1 study exclusively evaluated data from France, 1 study exclusively looked at data from New Zealand, and 1 study exclusively explored data from Canada. 2 studies examined data exclusively from the United Kingdom (UK). 1 study looked at data collected in 99 countries, among them Austria, Brazil, China, Finland, Germany, Greece, Iceland, Iran, Chile, Malaysia, Philippines, Russia, Spain, Switzerland, Taiwan, Turkey, the UK, and the United States of America (USA). 1 study examined data collected in Australia, Ireland, New Zealand, and the UK. In 7 of 8 (87.5%) studies reporting on gender, the proportion of women amongst the study population was 79.3% (n = 1), 59.5% (n = 1), 79.3% (n = 1), 68.8% (n = 1), 73.0% (n = 1), 75.2% (n = 1), and 70.7% (n = 1). Within 8 of 8 (100%) studies giving detailed information about the age of the participants, the age ranged from 18 to 59 years (n = 1) and averaged 42 ± 15 years (n = 2), 34.1 ± 14.4 years (n = 1), 34.1 ± 14.2 years (n = 1), 43 ± 13 years (n = 1), 40.2 ± 13.5 years (n = 1), and 44.5 ± 14.8 years (n = 1). Synthesis of results Influence of physical activity on well-being Different levels (high, moderate, and low) of PA and their correlation with varying dimensions of WB are summarized in Table 2 . People with low PA show higher Depression Anxiety and Stress Scales (DASS)-9 scores and lower World Health Organization (WHO)-Five Well-Being Index scores compared to people with moderate or high PA .[6] Further, people with low PA present with worse scores for WB – with regard to depression, anxiety, and stress – compared to people with moderate or high PA .[25] People with high PA overall showed better scores in terms of mental health .[16], [18] Especially people with high PA – who practiced PA daily – evinced the most positive status of WB with a proven correlation between training frequency and WB .[1], [16], [18] Moderate to high PA positively correlated with self-reported psychological detachedness, relaxation, self-mastery, control over leisure time, satisfaction of needs, and subjective vitality .[9] A positive relation between PA and WB has been shown regardless of severely limited mobility associated with the pandemic conditions .[15] Data on differences between inactive and active people show that generally people who overall practiced less PA – regardless of being in the group of inactive or active people – reported on less value, joy, self-confidence, support, and possibilities of being active [16]. Compared to active people, inactive people had an overall worse state of mental health [16]. Moreover, better scores on mental health were reported for people practicing PE within a group of people and not alone .[16] Table 2 Correlation between physical activity (PA) and well-being (WB). If not mentioned otherwise, data are displayed as mean ± standard deviation. Table 3Study WB Increased PA / high PA Consistent PA / moderate PA Reduced PA / low PA Lesser and Nienhuis 2020 [16] MHC score 49.34 ± 12.30 (IA); 48.92 ± 12.50 (A) 50.54 ± 11.51 (IA); 50.56 ± 10.53 (A) 44.42 ± 13.07 (IA); 48.13 ± 12.35 (A) Lesser and Nienhuis 2020 [16] Social 15.83 ± 5.59 (IA); 14.96 ± 5.88 (A) 15.75 ± 5.38 (IA); 15.63 ± 5.06 (A) 13.50 ± 5.89 (IA); 14.93 ± 5.36 (A) Lesser and Nienhuis 2020 [16] Emotional 11.66 ± 2.61 (IA); 11.88 ± 2.60 (A) 12.23 ± 2.47 (IA); 11.97 ± 2.43 (A) 10.62 ± 3.05 (IA); 11.47 ± 2.82 (A) Lesser and Nienhuis 2020 [16] Psychological 21.88 ± 5.58 (IA); 22.05 ± 5.70 (A) 22.51 ± 5.59 (IA); 22.97 ± 4.70 (A) 20.22 ± 6.02 (IA); 21.39 ± 5.97 (A) Lesser and Nienhuis 2020 [16] GAD-7 9.87 ± 4.26 (IA); 9.65 ± 4.74 (A) 8.83 ± 4.71 (IA); 9.62 ± 4.76 (A) 11.24 ± 4.66 (IA); 10.98 ± 4.46 (A) Brand et al. 2020 [1]a POMS During exercise: 4-2 → 0.121 During exercise: 3-2 → 0.020 During exercise: 1-2 → -0.241 0-2 → -0.0248 Faulkner et al. 2021 [6] WHO-5 score 55.53 ± 19.54 58.48 ± 20.45 40.52 ± 19.97 Faulkner et al. 2021 [6] DASS-9 2.22 ± 1.94 2.09 ± 1.89 3.65 ± 2.39 Faulkner et al. 2021 [6] Anxiety 0.84 ± 1.44 0.65 ± 1.30 1.24 ± 1.85 Faulkner et al. 2021 [6] Stress 2.26 ± 1.92 2.13 ± 1.85 3.03 ± 2.21 Nienhuis and Lesser 2020 [18] MHC score 49.63 ± 12.18 50.70 ± 10.99 45.36 ± 13.37 Nienhuis and Lesser 2020 [18] Social 15.75 ± 5.60 15.81 ± 5.13 13.90 ± 5.89 Nienhuis and Lesser 2020 [18] Emotional 11.77 ± 2.52 12.07 ± 2.31 10.75 ± 3.09 Nienhuis and Lesser 2020 [18] Psychological 22.14 ± 5.58 22.79 ± 5.32 20.55 ± 6.16 Nienhuis and Lesser 2020 [18] GAD-7 9.96 ± 4.41 10.00 ± 4.60 11.20 ± 4.78 Wood et al. 2021 [25] SWEMWBS 21.9 ± 3.4 21.7 ± 3.0 19.9 ± 4.5 Wood et al. 2021 [25] Depression 8.7 ± 7.1 9.5 ± 6.9 14.8 ± 11.1 Wood et al. 2021 [25] Anxiety 4.2 ± 5.3 4.8 ± 5.5 8.8 ± 9.0 Wood et al. 2021 [25] Stress 11.7 ± 6.8 13.0 ± 8.1 16.4 ± 10.2 a Data are displayed as estimates; A, active people; DASS, Depression Anxiety and Stress Scales; GAD-7, Generalized Anxiety Disorder Scale-7; IA, inactive people; MHC, Mental Health Continuum; PA, physical activity; POMS, Profile of Mood Scale; SWEMWBS, short version of the Warwick–Edinburgh Mental Wellbeing Scale; WB, well-being; WHO-5, World Health Organization (WHO)-Five Well-Being Index. Age-related and sex-related differences In general, young adults practiced less PA than all other age groups [6] and were shown to have worse scores on WB compared to older adults .[9] Overall, women practiced less PA compared to men and simultaneously showed more generalized anxiety than men – Generalized Anxiety Disorder Scale (GAD)-7 scores averaged to 10.40 ± 4.63 for women and to 8.74 ± 4.63 for men .[6] Collectively, women also reported on less WB compared to men .[9] Women who conducted PA had higher Mental Health Continuum (MHC) scores (average of 49.60 ± 11.66) by contrast with women who did not practice PA (average of 47.82 ± 12.89) .[18] Low PA in women has reportedly mainly resulted from self-perceived barriers as well as little facilitations for conducting PA .[18] However, when PA was practiced more positive changes related to movement behavior were reported for women than for men .[6] Physical activity – motivation, importance, and places Women who were able to maintain their levels of PA during the COVID-19 pandemic were shown to have greater autonomous motivation for practicing PA .[18] Low self-effectiveness regarding PA was associated with depressive symptoms and no joy of moving .[18] WB was highest in people practicing PA out of joy of moving .[15] Regarding self-assessed importance of PA, 54.3% of 171 people reported to think that PA has become more important during lockdown periods than before .[25] People who reportedly had low PA showed worse scores for all measures of WB (overall well-being, depression, anxiety, and stress) than people practicing moderate or high PA .[25] Increased expenditure of time for PA was associated with enhancement of psychological well-being, reduction of depression and anxiety, and improved life satisfaction .[25] The share of people (n = 171) practicing PA at home rose by 34.3% for activities indoors as well as by 25.4% for activities outdoors .[25] Discussion In this review we were able to show that there is an influence of PA on WB at times of the COVID-19 pandemic. Self-reported WB is highest in people practicing PA to moderate or high extent whereby PA wields influence on diverse dimensions of WB, which all are associated with mental health; the lowest self-reported WB is found in people with low PA .[1], [2], [6], [9], [15], [16], [18], [25] These findings are similar to earlier research that found a significant decrease in WB during the COVID-19 pandemic, for a variety of causes .[11] Interestingly, it was shown that a further decrease in PA leads to lower self-reported WB regardless of someone generally being rather active or inactive in terms of practicing PA .[16] Furthermore, it was shown that negative changes in terms of movement behavior – characterized by reduced PA – are connected to a worse state of WB .[6] As the studies included in this review were conducted in various countries from many regions of the world – one study even examining data from 99 countries [1] – it can be assumed that the regulations influencing PA in various territories all exert similar effects on WB. Further, the findings of this review seem to be relevant on a global scale. Whereas the general negative impact of the pandemic on mental health has been addressed in previously conducted studies ,[10] our review is – to the best of our knowledge – the first to reflect upon the influence of PA on WB during the COVID-19 pandemic. Actions associated with containment of the COVID-19 pandemic – such as lockdown measures, social distancing, and remote working – generally entail far-reaching implications on daily life. Aforementioned sanctions and especially remote working led, inter alia, to long periods of sitting associated with a worse state of WB [6], [9] – and further genesis of cardiovascular diseases and metabolic diseases such as diabetes [4], [12] – which is especially relevant for a high share of white-collar workers working from so-called home offices for reasons of social distancing. Adverse effects of sitting can easily be ameliorated by PA ;[5] preventative strategies in this field – which are tailored to the needs of workers under pandemic circumstances – could be conceptualized and explored in future studies. It was also shown that young adults practiced PA to a lesser extent compared to all other age groups, resulting in reduced self-reported WB .[6], [9] Women were reported to practice less PA compared to men and consequently to have lower self-reported WB than men .[6], [9] One explanation of age-related and sex-related differences mentioned in literature is that in general affective disposition and processes of mood regulation differentiate depending on factors such as age and sex .[9] Important obstacles for women with regard to practicing PA were found to be self-perceived barriers as well as little facilitations for conducting PA, in which context a lapse of childcare due to social distancing should be named .[14], [18] Future strategies for promoting PA in women might focus on those identified hindrances. A pleasing trend was seen regarding substitution of sports venues which were closed within lockdown measures .[20], [25] It was shown that many people practiced PA at home (indoors and/or outdoors) which can be positively emphasized .[25] At this juncture, it should be mentioned that self-reported WB was higher in people practicing PE within a group .[16] In connection with increasing numbers of people practicing PA outdoors at home, PA in groups conducted outdoors could prospectively contribute to better WB and still conform to necessary COVID-19 measures. It seems reasonable to scrutinize current physical activity recommendations against the backdrop of changes in daily life. It could be assumed that the WHO recommendation of at least 150 to 300 minutes of moderate-intensity aerobic physical activity or at least 75 to 150 minutes of vigorous-intensity aerobic physical activity for adults aged 18 to 64 years [26] might not be sufficient to account for ceased daily movement due to restrictions associated with the pandemic (i.e., remote working etc.). Moreover, the necessary amount of PA for WB might be higher under the specific pandemic situation than under normal circumstances, e.g., in terms of an expedient coping strategy for experienced stress. A limitation of this review is that all analyzed articles solely referred to self-reported survey data. It should be mentioned that self-reported data is inherently of subjective nature and might be influenced by under-estimations or over-estimations of individuals. However, the utilization of validated questionnaires in the included studies can be emphasized positively. Decreasing levels of PA have justifiably been proclaimed another pandemic within the COVID-19 pandemic .[24] The results of this review overall indicate that in the future strategies are needed for facilitating a consistent or even increased level of PA whilst decreasing levels of PA should urgently be circumvented in order to make use of the beneficial effects of PA on WB. Strategies should focus on young adults and women as they might profit particularly of improved PA routines. Acute cogitable approaches might be creating easy opportunities for practicing PA as well as supporting coping strategies for dealing with individually perceived barriers for not practicing PA. In this, increasing daily activity could be focused as a key role as even daily activities such as stairclimbing have been shown to lead to an enhancement of WB .[21] Against the background that adherence to COVID-19-associated measures might stay necessary for a while, longstanding strategies are needed to promote PA in the general population in order to contribute to better WB. In future research, the elderly as well as children and adolescents might be a focus group worth paying particular attention to. Conclusion The findings of this study indicate that PA has a beneficial effect on various dimensions of WB at times of the COVID-19 pandemic. Decreasing levels of PA led to decreasing levels of WB and might consequently exert an adverse influence on mental health. In the future, strategies are needed to facilitate practicing PA – at best whilst promoting joy of moving – in consideration of the pandemic circumstances. Only this way will it be possible to effectively promote WB and to prevent possibly arising mental health problems. Conflict of interest statement The authors declare that no conflicts of interest exist. ==== Refs References 1 Brand R. Timme S. Nosrat S. When Pandemic Hits: Exercise Frequency and Subjective Well-Being During COVID-19 Pandemic Front Psychol 11 2020 570567-570567 2 Coyle C. Ghazi H. Georgiou I. The mental health and well-being benefits of exercise during the COVID-19 pandemic: a cross-sectional study of medical students and newly qualified doctors in the UK Ir J Med Sci 190 2021 925 926 33150537 3 Dishman R.K. Sallis J.F. Orenstein D.R. The determinants of physical activity and exercise Public Health Rep 100 1985 158 171 3920714 4 Dunstan D.W. Owen N. Less Sitting for Preventing Type 2 Diabetes Diabetes Care, online ahead of print 2021 10.2337/dci2321-0028 5 Ekelund U. Tarp J. Fagerland M.W. Johannessen J.S. Hansen B.H. Jefferis B.J. Whincup P.H. Diaz K.M. Hooker S. 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Drastic Reductions in Mental Well-Being Observed Globally During the COVID-19 Pandemic: Results From the ASAP Survey Front Med 8 2021 578959 24 Wilke J. Mohr L. Tenforde A.S. Edouard P. Fossati C. González-Gross M. Sánchez Ramírez C. Laiño F. Tan B. Pillay J.D. Pigozzi F. Jimenez-Pavon D. Novak B. Jaunig J. Zhang M. van Poppel M. Heidt C. Willwacher S. Yuki G. Lieberman D.E. Vogt L. Verhagen E. Hespanhol L. Hollander K. A Pandemic within the Pandemic?. Physical Activity Levels Substantially Decreased in Countries Affected by COVID-19 Int J Environ Res Public Health 18 2021 2235 33668262 25 Wood C.J. Barton J. Smyth N. A cross-sectional study of physical activity behaviour and associations with wellbeing during the UK coronavirus lockdown J Health Psychol, online ahead of print 2021 10.1177/1359105321999710 26 World Health Organization, Physical activity, 2020, URL: https://www.who.int/news-room/fact-sheets/detail/physical-activity.(last accessed 8 October 2021).
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==== Front Comput Sci Eng Comput Sci Eng 1103111 MCSE CSENFA Computing in Science & Engineering 1521-9615 1558-366X IEEE 35414796 10.1109/MCSE.2020.3037033 Theme Article: Computational Science in the Fight against Covid-19, Part II Early COVID-19 Pandemic Modeling: Three Compartmental Model Case Studies From Texas, USA https://orcid.org/0000-0002-6513-8305 Pierce Kelly A. Pierce Kelly A. kpierce@tacc.utexas.edu https://orcid.org/0000-0002-7070-3321 Ho Ethan Ho Ethan eho@tacc.utexas.edu Wang Xutong Wang Xutong wangxutong@utexas.edu https://orcid.org/0000-0002-9321-6876 Pasco Remy Pasco Remy pasco@utexas.edu Du Zhanwei Du Zhanwei duzhanwei0@gmail.com Zynda Greg Zynda Greg gzynda@tacc.utexas.edu Song Jawon Song Jawon jawon@tacc.utexas.edu Wells Gordon Wells Gordon gwells@csr.utexas.edu Fox Spencer J. Fox Spencer J. spncrfx@gmail.com Ancel Meyers Lauren Ancel Meyers Lauren laurenmeyers@austin.utexas.edu The University of Texas at Austin Austin TX 78712 USA 1 2021 10 11 2020 23 1 2534 18 9 2020 24 10 2020 31 10 2020 26 2 2021 2020 IEEE This article is free to access and download, along with rights for full text and data mining, re-use and analysis. mcse-pierce-3037033 mcse-pierce-3037033.pdf mcse-pierce-3037033.pdf The novel coronavirus (SARS-CoV-2) emerged in late 2019 and spread globally in early 2020. Initial reports suggested the associated disease, COVID-19, produced rapid epidemic growth and caused high mortality. As the virus sparked local epidemics in new communities, health systems and policy makers were forced to make decisions with limited information about the spread of the disease. We developed a compartmental model to project COVID-19 healthcare demands that combined information regarding SARS-CoV-2 transmission dynamics from international reports with local COVID-19 hospital census data to support response efforts in three metropolitan statistical areas in Texas, USA: Austin-Round Rock, Houston-The Woodlands-Sugar Land, and Beaumont-Port Arthur. Our model projects that strict stay-home orders and other social distancing measures could suppress the spread of the pandemic. Our capacity to provide rapid decision-support in response to emerging threats depends on access to data, validated modeling approaches, careful uncertainty quantification, and adequate computational resources. CDC Contract CDC contract 75D-301-19-C-05930 National Institutes of Health 10.13039/100000002 3R01AI151176-01S1 Tito's Handmade Vodka This work was supported by CDC Contract CDC contract 75D-301-19-C-05930, NIH Grant 3R01AI151176-01S1, and Tito's Handmade Vodka. ==== Body pmcT he novel coronavirus SARS-CoV-2 emerged from Wuhan, China, in late 2019 and sparked the Coronavirus Disease 2019 (COVID-19) pandemic as it spread worldwide in the early months of 2020. Early estimates of rapid growth with three-day doubling times and high mortality rates painted a grim picture. Preliminary projections suggested that, without interventions, the healthcare infrastructure would be overwhelmed and COVID-19 mortality might exceed 2 million deaths in the United States alone. Public health messaging initially focused on “flattening the curve” to reduce healthcare burdens and buy time to ramp up surveillance and develop effective response strategies. Policymakers turned to mathematical models of disease transmission to help with interpreting data, projecting healthcare demands, and assessing mitigation measures. Over the last two decades, modeling has become a core tool for pandemic planning and decision support during emerging threats including the 2009 H1N1 flu pandemic, the 2015–2016 Ebola epidemic, and the 2015–2016 Zika virus pandemic.1 Such models are designed to synthesize information on disease progression and transmission dynamics with local epidemiological data to provide situational awareness, project future spread, hospitalizations, and mortality, and evaluate possible interventions. Here, we describe key challenges in modeling early COVID-19 spread and strategies for constructing data-driven models in the face of uncertainty to provide situational awareness and pandemic projections for three Metropolitan areas in Texas. COVID-19 data and uncertainty As the COVID-19 pandemic emerged in U.S. cities, there was great uncertainty regarding the transmission and severity of the virus. Two categories of data are required for robust modeling: 1) disease transmission and severity data, including the impact of behaviors that reduce transmission, and 2) surveillance data. Disease Transmission and Severity Data Pathogen natural history data describe mode of transmission, transmission rates, length of incubation or latent periods, timing and duration of symptom onset, and fatality rates. These data are typically aggregated from published studies, which are scarce for new pathogens. Given the speed in which COVID-19 was spreading, many groups chose to publish initial reports on medRxiv instead of waiting to disseminate following peer review. Despite the quick response by the scientific community to share results and data, uncertainty in these key aspects of SARS-CoV-2 natural history remained. For example, early reports suggested individuals without symptoms were infectious. But it still remains unclear what fraction of infections is transmitted by these asymptomatic individuals. There is still debate on whether these cases would be more accurately described as subclinical, and how many asymptomatic cases are in fact presymptomatic and eventually progress to symptom onset.2,3 These uncertainties are very important for modeling efforts. Many models are designed to capture uncertainties in these estimates when enough research is available to derive estimates of parameter distributions. This is not always possible, and in many cases knowledge from similar pathogens is used to fill in the gaps. Many modeling groups, including our own, borrowed from a rich history of influenza modeling to make reasonable assumptions about aspects of SARS-CoV-2 natural history that were not yet fully described in the literature. Surveillance Data Disease surveillance systems typically track suspected and laboratory confirmed infection case counts and deaths. As SARS-CoV-2 spread, local, state, and federal agencies realized that hospitals may approach their patient capacities, and rapidly developed additional surveillance systems to track hospital census.4 Laboratory confirmation for SARS-CoV-2 infection required the development of novel diagnostics. Early delays in the manufacturing and distribution of testing kits hampered efforts to track the early spread of the virus. As testing ramped up, the demand caused backlogs in testing laboratories and exacerbated reporting delays. While many of these hurdles have been addressed, testing data remain an unreliable indicator of local transmission given the considerable spatiotemporal variability in testing availability and priorities and the high proportion of asymptomatic and mildly symptomatic cases.5–7 Mortality data paint a more complete but time-lagged picture of severe COVID-19 prevalence. The average time to death from the date of hospitalization is 14 days.5–7 Hospital census data—total number of patients hospitalized for COVID-19 related complications on a daily or weekly basis—provide an earlier indication of pandemic spread, given the estimated 5.9 day lag between symptom onset and hospitalization.5–7 We expect the majority of severe COVID-19 cases will seek hospitalization, and have thus prioritized hospitalization data when estimating transmission rates for our models. Early COVID-19 Models There are many COVID-19 models that project cases, hospitalizations, and deaths, and they differ in their model structure, assumptions, and calibration. Early attempts to project the course of the pandemic relied on estimates from other respiratory viruses and from initial reports from China. For example, the widely cited projections of pandemic waves in the US and UK from Imperial College London used a previously developed individual-based influenza model parameterized with data from the Wuhan, China COVID-19 epidemic.3 As US case and death count data became available, models including the high-profile dashboard produced by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington calibrated projections based on local conditions.8 Simultaneously, the pandemic research community developed a diverse portfolio of COVID-19 models that leveraged local, regional, and national surveillance data as well as cell phone mobility data.9 Many of these models now contribute to the national COVID-19 mortality forecasting ensemble, maintained by the Centers for Disease Control and Prevention.10 Three Case Studies in Texas, USA Our research team responded to requests from policy makers in three metropolitan statistical areas (MSAs) in Texas for data-informed projections of COVID-19 hospitalizations that were more locally relevant than the national and state-level models available in the early epidemic. To meet these requests, the UT COVID-19 Modeling Consortium quickly adapted existing models for influenza virus epidemics to project healthcare needs for Austin-Round Rock MSA,5 Houston-The Woodlands-Sugar Land MSA,6 and Beaumont-Port Arthur MSA.7 We summarize the findings of those three reports, with a focus on how differences in available data in these MSAs drove changes to modeling infrastructure and approach. METHODS We used hospital census data (total count of in-hospital patients with confirmed COVID-19 diagnosis per day) to derive model-based estimates of key parameters governing the viral transmission rate in our models. Our model code and details on our workflow are available in GitHub at https://github.com/UT-Covid/compartmental_model_case_studies. Selection of Texas MSA We selected Austin-Round Rock MSA, Houston-The Woodlands-Sugar Land MSA, and Beaumont-Port Arthur MSA based on the availability of reliable COVID-19 hospital census data. Hereafter, we refer to these regions as Austin, Houston, and Beaumont. Both Austin and Houston encompass large metropolitan areas with populations of 2.2 million and 7 million people, respectively. Beaumont has a smaller population of approximately 410,000 people. Austin has a lower estimated proportion of high-risk individuals than the other two MSAs.5–7 Data Sources We acquired hospital census data from stakeholders to inform our parameter estimation and projections:• Dell Medical School at the University of Texas at Austin collected comprehensive daily COVID-19 hospital census data from hospitals in Austin starting March 13, 2020. • The Southeast Texas Regional Advisory Council (SETRAC) provided COVID-19 hospital census data for Houston and Beaumont. Daily data were updated on a weekly cadence, beginning April 2, 2020. SARS-CoV-2 Transmission Model We use a compartmental model based on SARS-CoV-2 transmission natural history to model transmission of the virus within each MSA, as described in detail in Wang et al.5–7 (see Figure 1). The model tracks the changing number of individuals in distinct disease compartments. Newly infected individuals move from susceptible (S) to exposed (i.e., infected but not yet symptomatic or infectious) (E), followed by an infectious period that could be symptomatic (IY) or asymptomatic (IA). Asymptomatic individuals recover (R) without ever developing symptoms. Symptomatic individuals could either become hospitalized (IH) or recovered (R). Hospitalized individuals then recover (R) or die (D). The population is divided into five age classes (0–4 yr, 5–17 yr, 18–49 yr, 50–65 yr, and +65 yr) and transmission is governed by age specific contact rates for home, school, work, and other locations. The model assumes that there is no travel in or out of each MSA, given the COVID-19 stay-home orders at the time of analysis. The full list of parameters is provided in the Table A1 in Appendix and in our reports.5–7 FIGURE 1. Compartmental model of COVID-19 transmission in a U.S. city. Each age and risk subgroup is modeled with a separate set of compartments. Upon infection, susceptible individuals (S) progress to the exposed (E) compartment and then to either symptomatic infectious (IY) or asymptomatic infectious (IA) compartments. All asymptomatic cases eventually progress to a recovered class where they remain protected from future infection (R); symptomatic cases are either hospitalized (IH) or recover. Mortality (D) varies by age group and risk group and is assumed to be preceded by hospitalization. Used, with permission, from Wang et al.5–7 Deterministic model: We used a deterministic implementation of our model for parameter estimation of the transmission rate, transmission rate reduction, and start date. We also used the deterministic model to initialize simulations for the Houston and Beaumont reports as described in the following sections. Stochastic model: We used a stochastic implementation of our model to capture critical sources of uncertainty regarding the transmission dynamics and severity of the virus. Specifically, we draw random deviates from distributions rather than assuming fixed values for the following parameters: duration of the latent period, duration of the infectious period, relative infectiousness of asymptomatic cases, length of hospital stay for survivors, and length of hospital stay for nonsurvivors, as described in our reports.5–7 We assume triangular distributions because they make minimal assumptions regarding the shape of the distributions while capturing a minimum, maximum, and mean value for the parameter. In addition, the number of individuals transitioning from one compartment to the next at each time step is determined by a Poisson random variable to capture variability in the disease progression process. Model Calibration and Uncertainty Estimation We assumed that the initial transmission rate of SARS-CoV-2 prior to the implementation of social distancing measures (β0) was fixed and used hospital census data prior to any mandated interventions, if available, to estimate β0. We further assume that shelter-in-place orders reduced the transmission rate by a fixed amount, and used hospital census data following implementation of mandated interventions to estimate this reduction (κ). These parameters were estimated using nonlinear least squares.5–7 Briefly, for both β0 and κ, we searched the interval [0, 1] for values that minimized mean squared error in the observed hospital census data and the number of people in the hospitalized compartment of our model. Preintervention Data: We were able to estimate the transmission rate prior to intervention (β0) for Austin using hospital census data from March 13 to March 24, 2020. However, data from this early period were not available for Houston or Beaumont. Thus, we assumed that the baseline transmission rate would be the same for these MSAs as for Austin. Postintervention Data: Stay-home orders were enacted on March 24, 2020 in Austin, March 27, 2020 in Houston and March 28, 2020 in Beaumont. For the Austin area, we fit our model to COVID-19 hospitalization data covering this entire range to estimate the reduction in transmission (κ). In Houston and Beaumont, we fit our model to COVID-19 hospitalization data that were available beginning April 2. Uncertainty Quantification: To indirectly estimate confidence intervals for κ, we ran 100 stochastic simulations for each possible value of κ between 0%–100%, at 5% increments.5–7 We then calculated the binomial probability that the 95% prediction interval for each simulation would contain the observed data. We report the 95% confidence interval for κ as the minimum and maximum values for which this binomial probability is greater than 0.05. Estimating the Date of Pandemic Emergence We estimated the date on which SARS-CoV-2 began spreading in the Austin area based on the date of the first reported COVID-19 hospital admission (March 13, 2020) and simple assumptions about the early transmission rate of the virus, prior to stay-home measures. Specifically, we assumed a three-day doubling time5 and that approximately 4% of cases require hospitalization.5–7 For the other two regions, we did not know the date of the first COVID-19 hospitalizations. Therefore, we took an alternative approach in which we evaluated a range of possible start dates. For each date, we estimated κ using the nonlinear least squares method mentioned above and selected the start date and κ combination that minimized the root mean squared error between the simulated and observed hospitalization counts. Hospitalization Projections We performed stochastic simulations using the estimated pandemic emergence date β0 and κ values to project the daily hospital census in each MSA. All simulations started with a single infected individual on the estimated start date of the local epidemic for the MSA under consideration. Transmission rate reduction began the day interventions took effect in each MSA: March 24, 2020 for Austin, March 27, 2020 for Houston, and March 28, 2020 for Beaumont. Filtering Plausible Simulations Some stochastic simulations failed to result in epidemics, that is, the initial clusters of cases died out before producing a large-scale epidemic. Given that epidemics actually occurred, we filtered out such runs. For Austin, we excluded all simulations that had no cases on the date the local stay home order was enacted (March 24, 2020). For the Houston and Beaumont MSAs, we started each simulation using a deterministic implementation of our model, in which all parameters were fixed to their median values, and then switched to a stochastic model when ten daily incident cases were achieved. Computing Resources Parameter estimation (start date, transmission rate, transmission rate reduction) and stochastic simulations were conducted on the Frontera Supercomputer at the Texas Advanced Computing Center (TACC). RESULTS We estimated healthcare demands for Austin, Houston, and Beaumont in the early stages of the COVID19 pandemic. We shared reports of these results with local officials and posted them online.5–7 Here, we review our projections and the challenges we faced while racing to provide time-sensitive situational awareness. Data Availability and Speed of Analysis We began our assessment of Austin on April 16th and delivered a completed report four days later (April 20), using COVID-19 hospitalization data from March 13th through April 16th. The Houston and Beaumont analyses required additional methods development since data were only available after April 2, likely several weeks after the pandemic emerged in these two areas. To address the missing early data, we assumed that the early transmission rate in Houston and Beaumont was identical to that estimated for Austin. These two reports took 8–9 days to complete. Estimation of Epidemic Emergence Date and Impact of Stay-Home Orders For Austin, we estimated initial transmission rate (β0) and impact of stay-home orders (κ) using nonlinear least squares fitting. For Houston and Beaumont, we estimated the date of epidemic emergence and κ using a combination of nonlinear least squares fitting and a search across possible start dates. We estimated that COVID-19 began spreading in Austin, Houston, and Beaumont on February 15, February 10, and February 27, 2020, respectively. Furthermore, the local stay-home orders reduced COVID-19 transmission rates by 94% (95% CI: 55–100%) in Austin, by 99% (95% CI: 80–100%) in Houston, and by 85% (95% CI: 70–100%) in Beaumont (see Figure 2). We used stochastic simulations to indirectly derive confidence intervals for κ, and applied two methods to restrict simulations to only those in which the epidemic eventually grew exponentially. In Austin, we filtered out all simulated outbreaks that died out after a few cases; in the other areas, we initialized our simulations using a deterministic model that did not allow for stochastic fade out. The first method produced larger confidence intervals than the second method (see Figure 2). FIGURE 2. Estimated reduction in transmission rate during April 2020 stay-home orders (κ) for the Austin, Houston, and Beaumont areas. Bars indicate 95% confidence intervals for our estimates of κ. COVID-19 Hospitalization Projections In our initial reports, we projected COVID-19 hospitalizations, ICU patients, and ventilator demand through mid-August, 2020.5–7 The projections are initialized on the estimated emergence date of the pandemic. In Figure 3, we provide excerpts from those projections through May 15. Our 95% prediction intervals captured reported hospitalizations for Austin and Houston, where hospitalizations had seemingly begun to plateau before we generated our projections. Beaumont hospitalizations did not plateau until after our projections were made, and dipped below the lower 95% prediction bound shortly after the release of our report. FIGURE 3. Observed and projected COVID-19 hospitalization in the (A) Austin, (B) Houston, and (C) Beaumont areas from March 13–May 15, 2020. Filled points indicate reported hospital census data included in parameter estimation for our reports; empty circles indicate hospital census data reported after parameter estimation was complete; red lines and shading indicate median predictions; and 95% prediction intervals; gray shading indicates the period prior to local stay-home orders; solid vertical lines mark the dates when reports were released. Issue With Uncertainty Quantification Our stochastic simulations of COVID-19 transmission in Austin produced two types of outcomes. Roughly 90% resulted in epidemic trajectories that mirrored the reported hospitalizations; the remaining 10% produced outbreaks that faded out before the epidemic began growing exponentially. This resulted in wide prediction intervals that do not fully convey the bimodal nature of the variation [see Figure 3(A)]. The narrower prediction intervals for Houston and Beaumont relative to Austin stem from a change in our methodology [Figures 3(B) and (C)]. We employed a deterministic version of the model until there were at least ten daily incident cases and then switched to a stochastic version to ensure that all simulations progress to the point of wide community spread. Computational Resource Utilization We utilized TACC's Frontera Supercomputer for all computations. Each model run—solving the system of equations defining the compartmental model for a number of time steps under a single set of parameters—was assigned to one of the 56 cores on a single Frontera Intel Xeon Platinum 8230 node. All runs were independent (trivially parallel) and were batched as single node jobs. We conducted 6330 model runs (excluding development and debugging runs). A single deterministic fitting run was used to estimate κ for Austin, and 29 deterministic fitting runs were used to select the best κ and pandemic emergence date combinations for Houston (17 possible dates considered) and Beaumont (12 possible dates considered). A total of 6300 stochastic simulations were performed with estimated parameters, of which 4200 were hybrid deterministic and stochastic runs (2100 each for Houston and Beaumont) and 2100 were purely stochastic runs (Austin). Table 1 outlines the wall clock time per run and the total time needed to complete the results summarized in this article. Table 1 Model run types and wall clock times. Run type N runs Single core wall clock time (s)a Single node wall clock time (s)b Deterministic fitting 30 189 189 Stochastic 2100 6.31 240 Hybrid 4300 12.6 968 Total 6330 208 1397 aAverage single core wall clock time on Frontera Intel Xeon Platinum 8280 from 10 benchmark runs. bAverage wall clock time multiplied by the number of 56 core Intel Xeon Platinum 8280 nodes required for the run volume specified. DISCUSSION Our rapid development of models to provide COVID-19 situational awareness in Texas highlighted the importance of 1) data availability, 2) modeling infrastructure, 3) reliable uncertainty quantification, and 4) computational resources. Data Availability The authors of this study include national experts in modeling the transmission dynamics of viruses who were tapped by federal, state, and local policymakers and public health agencies to provide urgent analyses as the COVID-19 pandemic emerged across the U.S. Throughout the pandemic, they have served on the Austin-wide COVID-19 leadership team, providing model-based projections and policy guidance for city leaders, all area healthcare systems and public health officials throughout the pandemic. Through these efforts, the research team has had unprecedented access to comprehensive daily COVID-19 hospitalization data starting from the first reported case on March 13, 2020. Data for Houston and Beaumont were provided by the Southeast Texas Regional Advisory Council (SETRAC), which helps to coordinate emergency healthcare responses across the Texas Gulf Coast, an area that includes Houston and Beaumont and is prone to hurricanes and flooding. SETRAC quickly pivoted its data sharing infrastructure to collect and disseminate COVID-19 hospitalization data. However, comprehensive COVID-19 hospital reporting was not required until April 2020. Thus, earlier data for estimating baseline transmission rates in March 2020 were not available for these two regions. Throughout the pandemic, our unique partnership with healthcare systems in Austin has provided critical data for forecasting the pandemic and providing actionable decision support.9,11,12 Although our models are scalable to other cities and states, lack of access to granular, standardized, and reliably reported COVID-19 hospitalization data has been a major impediment to such efforts. Modeling Infrastructure We define modeling infrastructure broadly as a combination of expert knowledge, established methods and models (including software), and a data pipeline for model inputs. In March 2020, researchers at the University of Texas at Austin partnered with Texas Advanced Computing Center to establish the UT COVID-19 Modeling Consortium which brought these complementary needs under one roof. The consortium includes experts across diverse fields, including infectious disease modeling, optimization, statistics, social sciences, and software development. Moreover, many of its members have a long track record of collaborating on translational research bringing epidemic science and data to the frontline of public health. The rapid pivot from influenza modeling to COVID-19 modeling was made possible by key similarities between the two viruses. First, influenza and SARS-CoV-2 are spread by similar types of contacts and have similar risk factors.3 This similarity allowed us to make use of the age-specific contact matrices and risk structures that we had previously developed for influenza modeling. Second, both viruses have latent periods and can be described by similar compartmental model frameworks. Given these similarities, we were able to adapt the influenza model for COVID-19 by altering the values of key parameters describing the disease progression and transmission dynamics of SARS-CoV-2. New data have since shown more differences between these two viruses, and we have been able to incorporate these updates quickly through software modifications. Uncertainty Quantification Reliable uncertainty quantification is crucial to accurately communicating plausible outcomes to the public and to policy makers. We incorporate uncertainty directly with our stochastic simulations, where some parameters are drawn from distributions and where transitions between disease states have a random component. The result of these stochastic simulations is an array of possible epidemic trajectories that capture plausible outcomes for a given parameter set. We found that our methodology for initializing epidemic simulations produced too many unrealistic simulations and inflated our uncertainty in projecting hospitalizations, even after the most unrealistic simulations were removed. We updated our simulation initialization methodology to produce more realistic simulations, and we believe these results to better capture the realistic uncertainty in our model projections. In our full reports, we also explore multiple possible transmission rate reduction scenarios both inside and outside of our estimated confidence bounds to highlight the dependence of future trajectories on individual behavior and intervention policies.5–7 Computational Resources We made extensive use of TACC's Frontera supercomputer for both model development and analysis. Once our workflow was optimized, we were able to produce the approximately 2000 simulations needed for each report in under one hour by taking advantage of the highly parallel processing capabilities of Frontera CPU nodes. This resource coupled with the real-time multifaceted support of TACC staff allowed us to complete the analysis and reports within four to nine days of data receipt. Computational resources were never a limiting factor. In the months since the release of these three reports, pandemic policies, human behavior, and, consequently, the pandemic itself have evolved. We have updated our models continually to reflect our changing understanding of the situation, to make use of new data sources, and to support decision making at multiple scales, including the evaluation of stay-home orders, physical distancing recommendations, masking requirements, and testing priorities. Stemming from these early reports, we now maintain three dashboards that provide real-time COVID-19 situational awareness and short-term mortality and healthcare projections across Texas and the U.S.9 Our collaborative consortium and access to the world-class computational resources at TACC has enabled and accelerated our ability to provide actionable models for the public and decision-makers. CONCLUSION Access to expertise, data, modeling infrastructure, and computational resources has enabled rapid and high impact modeling in support of front-line COVID-19 response efforts. Academic groups such as the UT COVID-19 Modeling Consortium can rapidly expand access to expertise, modeling infrastructure, and computational resources by leveraging collaborator networks and engaging with stakeholders. However, data access relies on external public health and clinical systems. These systems have been heavily stressed by the pandemic in ways that have impeded data collection and dissemination. While hospitalization data have proven to provide a robust signal for COVID-19 prediction models, these data are not consistently reported. This restricts the application of our most reliable prediction models to a selected set of cities that report hospitalization data; localities without access to hospitalization data may have reduced situational awareness. Now in the tenth month since the emergence of SARS-CoV-2, reporting of more traditional surveillance data including confirmed case and mortality data access are still often inconsistent and missing key context for interpretation (e.g., testing policies and test sensitivity and specificity). In addition to advocating for continued investment in the basic and applied science resources that enable the work of UT COVID-19 Modeling Consortium, we also advocate for investment in public health systems that facilitate disease surveillance and case reporting. These systems include decision making frameworks to guide resource allocation and data collection policy during public health crises, infrastructure for data dissemination (e.g., data repositories), and trained personnel who can collect, aggregate, and disseminate not only surveillance data but the metadata that allow stakeholders to contextualize those data. Expanding these critical resources while solidifying connections between researchers, technologists, clinicians, emergency responders, and public health and governmental authorities will be essential to preventing and containing future pandemic threats. ACKNOWLEDGMENTS The authors acknowledge three anonymous reviewers for their helpful reviews of this manuscript. The authors also acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC and visualization resources that have contributed to the research results reported within this article. URL: http://www.tacc.utexas.edu. This work was supported by CDC Contract CDC contract 75D-301-19-C-05930, NIH Grant 3R01AI151176-01S1, and Tito's Handmade Vodka. Kelly A. Pierce is a research associate with the Scalable Computational Intelligence Group, Texas Advanced Computing Center, University of Texas at Austin, Austin TX. She received the Ph.D. degree in ecology, evolution, and behavior in 2014 and has a background in data science and mathematical epidemiology. Contact her at kpierce@tacc.utexas.edu. Ethan Ho is a computational biologist in the Life Sciences Computing Group, Texas Advanced Computing Center, University of Texas at Austin. He received the bachelor's degree in mathematics, biochemistry, and molecular biology. Contact him at eho@tacc.utexas.edu. Xutong (Suzanna) Wang is a graduate student with the Department of Integrative Biology, University of Texas at Austin, Austin TX and received the Master of Science degree in statistics in 2019. Contact her at wangxutong@utexas.edu. Remy Pasco is a graduate student in Operations Research with the University of Texas at Austin, Austin TX. He received the Master of Engineering, Financial Engineering degree in 2012. Contact him at pasco@utexas.edu. Zhanwei Du is a postdoctoral fellow with the Department of Integrative Biology, University of Texas at Austin, Austin TX. He received the Ph.D. degree in computer science in 2015. Contact him at duzhanwei0@gmail.com. Greg Zynda is a bioinformatics scientist in the Life Sciences Computing Group, Texas Advanced Computing Center, University of Texas at Austin, Austin TX. He received the Ph.D. degree in informatics in 2020 and has a background in computational biology. Contact him at gzynda@tacc.utexas.edu. Jawon Song is a research associate in the Life Sciences Computing Group, Texas Advanced Computing Center, University of Texas at Austin, Austin TX. She received the Ph.D. degree in structural bioinformatics in 2010. Contact her at jawon@tacc.utexas.edu. Gordon Wells is a program manager with the Center for Space Research, University of Texas at Austin, Austin TX. He received the D.Phil. degree in atmospheric physics and paleoclimatology in 1992. Contact him at gwells@csr.utexas.edu. Spencer J. Fox is the Associate Director of the UT COVID-19 Modeling Consortium in the Department of Integrative Biology, University of Texas at Austin, Austin TX. He received the Ph.D. degree in ecology, evolution, and behavior in 2018. Contact him at fox@austin.utexas.edu. Lauren Ancel Meyers is the Executive Director of the UT COVID-19 Modeling Consortium and Cooley Centennial Professor in the Department of Integrative Biology, University of Texas at Austin, Austin TX. She received the Ph.D. degree in biological sciences in 2000. Contact her at laurenmeyers@austin.utexas.edu. APPENDIX Table A1 Model parameters. Symbol Quantity Value β 0 transmission rate 0.035 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\frac{1}{{{\gamma ^Y}}}$\end{document} 1γY symptomatic recovery rate Triangular(21.2, 22.6, 24.4) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\frac{1}{{{\gamma ^A}}}$\end{document} 1γA asymptomatic recovery rate Triangular(21.2, 22.6, 24.4) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\frac{1}{{{\gamma ^H}}}$\end{document} 1γH hospitalized recovery rate 1/14 τ symptomatic proportion 82.1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\frac{1}{\sigma }$\end{document} 1σ exposure rate Triangular(5.6, 7, 8.2) P proportion of pre-symptomatic transmission 12.6 ⍵E relative infectiousness, exposed \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\frac{{({\frac{{YHR}}{\eta } + \frac{{1 - YHR}}{{{\gamma ^Y}}}}){\omega ^Y}\sigma P}}{{1 - P}}$\end{document} (YHRη+1-YHRγY)ωYσP1-P ⍵A relative infectiousness, asymptomatic 0.47 IFRL low risk infection fatality ratio [0.00092, 0.0022, 0.034, 0.25, 0.64] IFRH high risk infection fatality ratio [0.0092, 0.022, 0.34, 2.5, 6.4] YFRL low risk symptomatic fatality ratio [0.0011, 0.0027, 0.041, 0.31, 0.78] YFRH high risk symptomatic fatality ratio [0.011, 0.027, 0.41, 3.1, 7.8] YHRL low risk hospitalization ratio [0.028, 0.022, 1.3, 2.9, 3.4] YHRH high risk hospitalization ratio [0.28, 0.22, 13, 29, 34] HFR hospitalization fatality ratio [4.0, 12, 3.1, 11, 23] h high-risk proportion, age specific [8.2825, 14.1121, 16.5298, 32.9912, 47.0568] η symptom onset to hospitalization rate 0.1695 π symptomatic hospitalization rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\frac{{({{\gamma ^Y}*YHR})}}{{\eta + ({{\gamma ^Y} - \eta })YHR}}$\end{document} (γY*YHR)η+(γY-η)YHR μ rate from hospitalization to death 1/14 ν hospitalization fatality rate [0.039, 0.12, 0.030, 0.10, 0.23] ICU proportion hospitalized in ICU [0.15, 0.20, 0.15, 0.20, 0.15] Vent proportion in ICU needing ventilation 0.67 dICU duration of ICU stay (days) 10 dv duration of ventilation (days) 10 Values given as five-element vectors are age-stratified with values corresponding to 0-4, 5-17, 18-49, 50-64, 65+ year age groups, respectively. Adapted from references,5–7 with permission. ==== Refs REFERENCES 1. C. Rivers, et al. , “Using ‘outbreak science’ to strengthen the use of models during epidemics,” Nature Commun., vol. 10 , no. 1 , pp. 3102–3102, 2019, doi: 10.1038/s41467-019-11067-2.31308372 2. X.-L. Jiang, et al. , “Transmission potential of asymptomatic and paucisymptomatic severe acute respiratory syndrome coronavirus 2 infections: A 3-family cluster study in China,” J. Infect. Dis., vol. 221 , no. 12 , pp. 1948–1952, Jun. 2020, doi: 10.1093/infdis/jiaa206.32319519 3. N. M. Ferguson, et al. , “Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand,” pp. 3–20, 2020. [Online]. Available: imperial.ac.uk, doi: 10.25561/77482. 4. “COVID-19 guidance for hospital reporting and FAQs for hospitals, hospital laboratory, and acute care facility data reporting. Updated October 6, 2020,” [Online]. Available: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf 5. X. Wang, R. Pasco, K. Pierce, Z. Du, S. Fox, and L. A. Meyers, “COVID-19 healthcare demand projections: Austin, Texas,” UT COVID-19 Model. Consortium, 2020, doi: 10.15781/503k-c813. 6. K. Pierce, et al. , “COVID-19 healthcare demand projections: Houston-The Woodlands-Sugarland MSA, Texas,” UT COVID-19 Model. Consortium, 2020, doi: 10.15781/z822-gr62. 7. K. Pierce, et al. , “COVID-19 healthcare demand projections: Beaumont-Port Arthur MSA, Texas,” UT COVID-19 Model. Consortium, 2020, doi: 10.15781/j8dw-ev41. 8. IHME COVID-19 health service utilization forecasting team and C. J. Murray, “Forecasting the impact of the first wave of the COVID-19 pandemic on hospital demand and deaths for the USA and European Economic Area countries,” medRxiv 2020.04.21.20074732, Apr. 2020, doi: 10.1101/2020.04.21.20074732. 9. “UT Austin COVID-19 modeling consortium,” The UT COVID-19 Modeling Consortium, Austin, TX, 2020. Accessed: Oct. 23, 2020. [Online]. Available: https://covid-19.tacc.utexas.edu/ 10. “COVID-19 forecasts: Deaths,” Centers for Disease Control and Prevention, Atlanta, GA, 2020. Accessed: Sep. 09, 2020. [Online]. Available: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html 11. D. Duque, D. P. Morton, B. Singh, Z. Du, R. Pasco, and L. A. Meyers, “Timing social distancing to avert unmanageable COVID-19 hospital surges,” Proc. Nat. Acad. Sci. USA, vol. 117 , no. 33 , pp. 19873–19878, Aug. 2020, doi: 10.1073/pnas.2009033117.32727898 12. X. 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PMC009xxxxxx/PMC9000345.txt
==== Front Applied Surface Science Advances 2666-5239 2666-5239 The Author(s). Published by Elsevier B.V. S2666-5239(22)00036-8 10.1016/j.apsadv.2022.100246 100246 Article Journey of organ on a chip technology and its role in future healthcare scenario Singh Deepanmol a⁎ Mathur Ashish bd Arora Smriti c Roy Souradeep d Mahindroo Neeraj a a Department of Pharmaceutical Sciences, School of Health Sciences, University of Petroleum and Energy Studies, Dehradun, Uttarakhand 248007, India b Department of Physics, University of Petroleum and Energy Studies, Dehradun, Uttarakhand 248007, India c Department of Allied Health Sciences, School of Health Sciences, University of Petroleum and Energy Studies, Dehradun, Uttarakhand 248007, India d Centre for Interdisciplinary Research and Innovation (CIDRI), University of Petroleum and Energy Studies, Dehradun, Uttarakhand 248007, India ⁎ Corresponding author. 11 4 2022 6 2022 11 4 2022 9 100246100246 22 3 2022 31 3 2022 © 2022 The Author(s). Published by Elsevier B.V. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Organ on a chip refers to microengineered biomimetic system which reflects structural and functional characteristics of human tissue. It involves biomaterial technology, cell biology and engineering combined together in a miniaturized platform. Several models using different organs such as lungs on a chip, liver on a chip, kidney on a chip, heart on a chip, intestine on a chip and skin on a chip have been successfully developed. Food and Drug administration (FDA) has also shown confidence in this technology and has partnered with industries/institutes which are working with this technology. In this review, the concepts and applications of Organ on a chip model in different scientific domains including disease model development, drug screening, toxicology, pathogenesis study, efficacy testing and virology is discussed. It is envisaged that amalgamation of various organs on chip modules into a unified body on chip device is of utmost importance for diagnosis and treatment, especially considering the complications due to the ongoing COVID-19 pandemic. It is expected that the market demand for developing organ on chip devices to skyrocket in the near future. Keywords Organ on chip technology Microfluidic cell Heart on chip Kidney on chip Liver on chip Body on chip ==== Body pmcIntroduction Organ on a chip (OOAC) is a novel in-vitro micro-scale biomimetic platform that helps in reproducing physiological environment of human organs. This technology involves cell biology, engineering and material sciences to simulate in-vivo tissues [1]. In-vivo experiments on animals and humans are common to study physiological functions of the body but several alternative methods, including 2D and 3D in-vitro models and computational approaches, have also been explored in last two decades. Since 2011, when US president announced the start of project on “human on chip” by the US research agencies, researchers across the globe became curious to understand and work on the possibilities of this concept. It has advantage of miniaturization, integration, low consumption and accurate control of parameters such as concentration gradient, fluid shear stress, organ–organ interaction, tissue–tissue interface [2]. Organ on a chip model has shown immense usefulness in drug discovery process. It can be used for hit-to-lead optimization, toxicological studies, physiological studies, pharmacokinetic studies and phenotyping screening [3]. The induced pluripotent stem cell (iPSC) technology which was introduced in 2006 became a common and effective source for supplying human cells for different organs such as brain, heart, spinal cord, kidney etc. The origin of 2D cell culture is traced to Ross Harrison, who studied neural tissue growth as early as in 1907. Mid 1950s was era of 2D cell culture advances when establishment of “L” cell line and human HeLa cell line of a cancer patient were done, both continue to drive cell culture research even today [4]. The 2D cell culture has been the mainstay for scientists to understand human physiology, culture stem cells, study diseases, cell-cell interaction, tissue imaging, drug discovery, toxicology and drug metabolism since 1900s [5]. However, it has its own disadvantages such as it is unable to mimic the actual in vivo scenario where in cell-to-cell contact is further fortified by extra-cellular matrix. The cells are not monolayers but intricately arranged as tissues wherein cell-cell interaction also leads to mechanical force being exerted by one cell on another. Thus, cells in 2D do not depict the true physiology of the organ. Cells in 2D cultures experience no gravity. Further, 2D culture does not give layers of cells to study drug efficacy and penetration into cells. Thus, it failed to provide accurate micro-environment for drug discovery [6]. Adding to this, it is known that many drug discovery studies have faced a set back at phase III level of development either due to toxicity or lack of efficacy and thus affected the economics of drug discovery process adversely [7, 8]. The 2D cultured cells have altered cell morphology, show cell flattening leading to cytoskeletal and changes in shape of nucleus [9,10]. The cells in 2D culture get exposed to equal amounts of media, oxygen being in suspension while to model cancers, a mass of cells has to be generated where oxygen tension and nutrient concentrations are much less in the inner layers of the mass than outer layers [11]. The 2D cultured cells are always proliferating and do not depict the state of tissues where all cells are at different stages of growth and cell cycle. Scaling up of 2D cell cultures has been difficult. Last but not the least, an important drawback is that the cells in 2D are in stress [12]. Late 20th century saw a surge in development of 3D cell culture technologies. This recent advent of 3D culturing design and technologies allowed various improvements in cell research and led to a more accurate depiction of cellular processes. However, a new technology brings newer challenges. The advantages of 2D cultures are that the scientific community is trained on 2D cell culture, imaging protocols are well developed, it is easier to image cells in x-y plane than to do a 3D imaging, comparative literature on 2D cells is available in plenty and it is inexpensive to do a 2D culture.3D technology offers many advantages such as it mimics biological environment more, allows accurate research on cell–cell interaction and cell-ECM (extra cellular matrix) interactions. Various studies in drug discovery and cancer conducted on 3D cell culture achieved better accuracy [13, 14, 12]. 3D cell culture also helped in subverting research on animal models and thereby making the research more humane. The gene expression studies correlate better with 3D models of cells [15, 16, 17]. Fig. 1 shows graphical representation of 2D cell culture, 3D cell culture and organ on a chip model. There are numerous ways to culture cells in 3D which can be classified into scaffold based or scaffold free. The scaffold based models include polymeric hard material-based support, hydrogel-based support, hydrophilic glass fibre, and organoids each with unique applications, advantages and disadvantages. The non-scaffold based methods include hanging drop microplates, magnetic levitation, and spheroid microplates with ultra-low attachment coating; each has its unique advantage and disadvantages [18].Fig. 1 Graphical representation of 2D cell culture, 3D cell culture and Organ on a chip model. Fig 1 However, 3D culture have limitations such as the matrices/scaffolds used may have unwanted viral, human derived hormonal components making them less adaptable to clinical set up, detachment of 3D cultures is difficult [4, 19]. They suffer from batch to batch variability, isolation of nucleic acids/ proteins from 3D cell culture is much more difficult [19]. Further, cytometry analysis requiring single cells are optimized for 2D cell culture suspensions. The most recent development post 3D cell culture is its amalgamation with microfluidics to build cell based sensors on a chip (where 2D cultured static cells were used) now advanced to organ on a chip [20]. Table 1 summarizes the limitations and advantages of 2D and 3D cell culture.Table 1 Comparison of features of 2D cell culture with that of 3D. Table 1Features/Characteristics 2D cell culture 3D cell culture Cell Morphology Flattened/Elongated Only 2D expansion is allowed Native cell phenotype is preserved Cells grow in 3 dimensions Exposure to cell medium Cells receive equal exposure to media Cells are maintained in same phase of growth gases diffuse into and out cells more evenly Cells in center of 3 mass are less exposed to media Due to differential exposure of media, cells are in different phases of growth Diffusion of gases is uneven in inner layers versus outer layer of cells Cell differentiation Cells in 2D do not differentiate well Cells in 3D culture differentiate well Cell junctions No real mimic of cell-cell junctions are formed in 2D cell cultures Cell-cell junctions are observed and mimic real cell junctions Drug sensitivity Cells are often unrealistically susceptible to drugs Cells are often resistant to drugs compared to 2D and results are comparable to in vivo models Cell proliferation Very rapid, unnatural Realistic, mimics natural conditions Imaging and analysis Existing procedures are standardized for 2D cells Imaging analysis is standard and easier for 2D cells Procedures are not standardized for 3D cells Imaging and analysis is difficult for 3D cells Gene expression Gene expression does not mimic that in-vivo models Gene expression more accurately depicts that in- vivo models Cost Low High/expensive The first-generation microfluidic biological devices ideally called as ‘cells on a chip’. Microfluidics involves study and manipulation of fluids in microliter scale (μL) confined within channels with dimensions of few tens to hundreds of micrometres. The earliest microfluidics devices allowed improved understanding of micro analytics, molecular analysis (such as microarrays) and also had applications in defence [21]. The amalgamation of microfluidics and 3D cell cultures adds another dimension to cell biology research leading to better mimicking of in-vivo cell environment. It allow studies of biological organs with minute volumes of fluid. They add multiple dimensions to cell research for they can be easily miniaturized, are easy to use, highly sensitive and robust, and adaptable towards a high throughput design. 3D Bioprinting has allowed fast paced development of organ on a chip microfluidic devices [22, 23]. Some applications of organ on a chip models are described in Table 2 .Table 2 Summary of applications of Organ on a Chip technology. Table 2Organ on a Chip Applications/ Model References Lung on a Chip Model pulmonary edema, in-vivo environment for human airways, model for viral infection [24, 25], Brain on a Chip Blood Brain Barrier functioning, Neural Network [26, 27, 28, 29] Heart on a Chip Electrical stimulation, cardiac electrophysiology and different heart diseases [30, 31, 32] Liver on a Chip Liver specific Protein Synthesis, [33, 34] Kidney on a chip Drug induced nephrotoxicity, Glomerular filtration [35, 36] Skin on a Chip Dermal diffusion testing, toxicology studies, efficacy testing, wound healing, inflammation, repair, ageing and shear stress studies [37, 38] Gut on a Chip Drug pharmacokinetics, host-gut microbiota cross talk, and nutrition metabolism [39] Types of organ on a chip models In recent years, researchers developed different models for different organs on a chip, for example; kidney on a chip [40], lung on a chip [41], heart on a chip [42], skin on a chip [43], pancreas on a chip [44], brain on a chip and blood brain barrier on a chip [45]. Microfluidic technologies that enabled developing organ on a chip models overcame some of the current limitations such as:a Organ's exposure to fluids in motion which is often directional. b Nutrients flowing in and the waste flowing out. c A gradient flow can be established leading to dosing studies becoming more amenable. d Multiple cell types can be layered thus mimicking organs. e Studies are done on human derived cells, therefore physiologically much more relevant. f Organ engineering can be done (with precise deletion/mutation) and studied in isolation unlike if it were an animal where other mutations would interfere. The greatest revolution in research that came about by organ on a chip microfluidic devices is in areas of high throughput drug screening [46, 47], single cell analysis [48], cell–cell interaction, cell-ECM studies [49] cell co-culture [50], neuronal models [51, 52] and fluid gradient involving studies such as bacterial chemotaxis [53], drug screening, precision medicine (shown in Fig. 2 ), cancer cell migration and axon growth [54, 55, 56, 57].Fig. 2 Graphic representing organ on a chip development for disease modeling and therapy development for precision modeling. Fig 2 Fig. 3 Schematic of lung on chip portraying two sections namely - air channel and blood channel which mimics the human lung [24]. Fig 3 Lung on chip Pulmonary diseases are reported to be the fifth most common cause of death globally. Several new interventions are tried to facilitate the treatment of pulmonary diseases. Huh et al. from Wyss Institute of Harvard University were the pioneers in developing lung on a chip model [58]. In 2015, Huh and co-workers prepared an architecture and dynamic microenvironment surrogate to alveolar–capillary unit of the living human lung (as shown in theFig. 3). The system was then used to conduct nano-toxicological study in which production of intracellular reactive oxygen species (ROS) in response to alveolar exposure to nanoparticles was examined. It was also used to model pulmonary edema in human lungs [24]. Cigarette smoking is the major cause for clinical exacerbations with patients with asthma and chronic obstructive pulmonary disease (COPD) Benam et al. developed a chip which breathes smoke in and out to study impact of smoking on lungs. They confirmed that experimental results obtained by using the lung-on-a-chip model were close to those from animal experiments. The team studied the up regulation and down regulation of genes due to smoking and also discovered novel biomarkers using this model [59]. Shrestha et al. fabricated simple open well lung on a chip model that could simulate the in-vivo environment of airway. They tested the cell viability, mucus secretion, cell permeability and P-gp expression on cell surface and found promising results for toxicology tests, permeability assays, pulmonary drug delivery studies. The effect of cigarette smoke on interlukins (IL-6 and IL8) followed by treatment with budesonide was studied using Calu-3 cells on lung-on a chip model(Jesus Shrestha, 2019). Human airway on a chip was used for evaluating repurposing of the drugs for COVID-19. The model was developed for viral infection, cytokine production and circulating immune cells using human bronchial epithelium and pulmonary endothelium. Drugs such as nafamostat, olsetamivir, amodiaquine and hydroxychloroquine werestudied against pseudotypedSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and influenza A [25]. Brain on chip Engineering full structure and function of brain is difficult, yet some specific parts/functions such as structure of spinal cord, unidirectional neural network, myelination process and BBB (Blood Brain Barrier) have been successfully recapitulated. Brain on a Chip designs are broadly classified into 4 groups and are summarized in Table 3: microfluidic models, compartmentalized models, hydrogel-based models and spheroid models. Each type of design has advantages in a particular application. Mold lithography, contact printing, hydrogel casting and 3D printing are the most common manufacturing processes for brain on chips [60].Table 3 Different Brain on a chip models and their applications. Table 3Models Applications References Microfluidic models Study of tight junctions, and BBB functions [61] Compartmentalized models Study of diseases development and behavior associated with neural networks [27, 28, 29] Hydrogel based models These models biomimic in-vivo microenvironment. They allow cells to interact, migrate, and propagate in 3D. They also exhibit transport properties that mimic in vivo conditions [62] Spheroid models Disease modeling and drug discovery [63] Three-dimensional neurospheroids based microchip with constant flow of fluid was developed for in-vitro study of Alzheimer's disease. The toxic effects of amyloid β were studied by Park et al. for understanding destruction of neural networks. The destruction of neural networks was found to be significantly more as compared to when studied under static conditions [64]. Another model for neural differentiation and maturation was developed for analysis of complex cell and tissue behaviour. Through this brain on a chip model the migration of human neural progenitors in response to CXCL12, a key chemokine present in brain was studied [65]. Interconnected brain on a chip systems have also been made to study interaction of various cells of neurovascular unit as well as to study the interactions between brain and other organs [66, 67]. Blood brain barrier (BBB) maintains the homeostasis of brain by regulating traffic of molecules between blood and CNS. Any disturbance in this barrier results in several neurological disorders. Patient specific stem cells have been combined with microfluidic platform to form personalized human BBB-chip that simulates BBB and can predict the inter-individual variability. These chips were used to study the alterations in BBB function in diseased conditions. Huntington's disease (HD) was studied with respect to the alteration in the barrier functions using induced brain microvascular endothelial cells. The study was based on variability in the permeability of fluorescently labeled dextrans (different molecular weights) across iPSC based BBB chips derived from healthy as well as HD patient. No significant variation in the permeability for the healthy humans but significant increase in some dextrans in HD patient was observed [26]. Sances et al. were able to develop spinal cord on a chip model using induced pluripotent stem cell (iPSC)-derived ventral spinal neurons which were co-cultured with brain endothelial cells. The uniqueness of this model is that it recapitulated the vascular-neural interaction and gene specific activation that enhanced the neuronal function and in-vivo like signatures. This model is proposed to provide platform to study, functional, pathological and disease mechanisms for therapeutic discoveries [68]. Heart on chip Cardiovascular diseases (CVD) are leading cause of death in several countries. The major challenges of cardiovascular drug development are: (a) CVD animal models are poor predictors of human responses (b) adverse effects are organism dependent and (c) Lengthy and costly process. In a study analyzing the drug pipeline of AstraZeneca between 2005 and 2010, it was found that 82% of projects were closed in preclinical phase because of safety concerns. Cardiovascular related failures were around 17%, highest amongst all other organs analyzed [30]. Heart on a chip is used to study different functions of heart such as electrical stimulation, cardiac electrophysiology and different heart diseases. This model is also anticipated to be used in understanding particulate matter toxicity in human body [59]. Heart on a chip is useful in simulation of hypoxia, tachycardia model of arrythmogenesis and mechanical stimulation experienced by cardiac cells. It has also been used in combination with stem cells for therapeutic testing or precision medicine [31]. Kim and colleagues developed a multichannel microfluidic device containing fibrin matrix (to enable cell growth) which was used to study vasculogenesis and angiogenesis. They were able to mimic the perfusable vascular network and the barrier function. The vascular networks showed higher production of nitric oxide with dynamic environment as compared with the static conditions [32]. Microfluidic chip based on human induced pluripotent stem cells (iPSCs) – derived 3D cardiac microtissues was used to study contractile function with the help of MUSCLEMOTION software. Heart microchip device was also used to study the effect of isoproterenol, a β-adrenoceptor agonist, on the beating rate of 3D cardiac microtissues. The entire particle displacement distance was correlated with the beating rate with respect to the increased dose of isoproterenol [69]. Ischemia on a chip model was developed integrating intra and extracellular compartments with bioelectronic device. The model was able to mimic  temporary coronary occlusion and reversibly activated hypoxia-related transduction pathways in HL-1 cardiac model cells [70]. Beating heart on a chip was developed with highly functional micro-engineered cardiac tissues which can be used to predict hypertrophic changes in cardiac cells. The device has ability to generate cardiac microtissues with increased mechanical and electrical coupling amongst neighboring cells. The model showed positive chronotropic effect with isoprenaline, hence, shows its possible use in drug discovery and toxicity studies [26]. Liver on chip Most of the drugs fail in preclinical trials due to their hepatotoxic effects [34]. Liver on a chip model has a potential to be an economic and rapid alternative to animal studies [71]. Liver on a chip has offered advantage to study organogenesis, disease mechanisms, liver disease models, drug development and personalized medicines [72]. A microchip containing hepatocytes co-cultured with Kuppfer cells was developed to study the role of liver cells in hepatitis B virus (HBV). The model was compared with already existing 2D and 3D hepatic spheroids and results showed that due to constant recirculating nutrients and oxygenated media, the chip-based model can be used for long term study (at least 40days post seeding). The model can be used for host/pathogen interactions, biomarkers development, treatment responses and other therapeutic interventions [73]. A study done by Tostoes et al. showed that human liver-specific protein synthesis, CYP450 activity, and phase II and III drug-metabolizing enzyme gene expression activity was maintained in a perfusion bioreactor system for two to four weeks [33].The liver on a chip models have also been used to study hepatotoxicity of non-steroidal anti-inflammatory drugs (NSAIDS). Primary rat hepatocytes were used for toxicity testing of diclofenac and acetaminophen in a perfusion incubator liver chip [74].Layers of Caco2 (for gut) and HepG2 (for liver) cells were used to study first pass metabolism of apigenin using microfluidic chip. CYP450 activity and absorptive property of Caco2 and HepG2 cells was also studied [75]. Using the same cells, Lee et al. predicted the first pass metabolism of paracetamol [76]. A spheroid based microfluidic chip with rat primary hepatocytes and hepatic stellate cells (HSCs) was developed as a model for alcohol liver injury. The role of HSCs was studied in the recovery of liver with alcohol liver disease [77]. Presently, there is no specific treatment of Non-alcoholicsteatohepatitis (NASH) which is mainly due to the absence of models that can recapitulate liver cellular microenvironment and the complexities of NASH. Freag et al. developed NASH on a chip to study disease pathogenesis and development of anti-NASH drugs [78]. Kidney on a chip Kidney cells are at much less shear rate as compared to endothelial cells or lung cells. The initial design of Kidney on a chip had two compartments, the first compartment represented urinary lumen and has a fluid flow and the other chamber mimics interstitial space. The device utilized rat tubular cells. [79] In 2013, Jang et al. reported development of a kidney-on-a-chip microfluidic model. The model could successfully exhibit number of primary cilia, expression of sodium-potassium ATPase and aquaporin 1, albumin uptake, and glucose reabsorption [80]. Drug induced nephrotoxicity studies were successfully done using microchips [35]. Membrane permeability and drug induced toxicity was studied for cisplatin, gentamycin and cyclosporine A [81]. In one of the study, glomerular function was studied with mature human podocytes derived from human induced pluripotent stem (hiPS) cells that could mimic the adriamycin induced albuminaria [36]. Kidney on a chip has also been utilized in multiple organ on a chip models (described below). Podocyte is a glomerular visceral epithelial cell which acts as a size and charge selective barrier to plasma proteins and injury to podocytes can cause proteinurea. Podocytes on a chip has been tried by researchers but the system is challenging as sophisticated culturing is required. [82]. Skin on a chip The source of skin cell lines are induced pluripotent stem cells (iPSC) or commercially available reconstructed skin tissues (EpidermFT, Epiderm, EpiSkin, StratiCELL) [83]. Skin on a chip models have been used for dermal diffusion testing, toxicology studies, efficacy testing, wound healing, inflammation, repair, ageing and shear stress studies [37]. Kim et al. studied the anti-ageing effects of curcumin and coenzyme Q10 using pumpless skin on a chip model [84, 85]. Wufuer et al. developed skin on a chip model and studied structural and functional features of the skin. Skin inflammation and edema was simulated through the model using TNF-α and the cytokine levels were studied. The anti-inflammatory response of steroidal drug, dexamethasone, was studied using this model as shown in Fig. 4 [38].Fig. 4 (A) Schematic representation of the human skin edema model, (B) Inflammation induced by TNF-α damages tight junctions, resulting in vascular leakage and efficacy testing with dexamethasone [38]. Fig 4 Sriram et al. [86] studied the barrier function and epidermal morphogenesis through fibrin based dermal matrix using organ on a chip model [86]. The diffusion studies through dermal layer including the role of efflux transporters, penetration and drug-drug interactions have been studied by many researchers [87, 88]. Gut on a chip Artificial gut has been engineered on a chip with controlled microenvironment containing different types of human cells such as intestinal epithelial, endothelial and immune cells. The intestinal villi microstructures have also been reproduced to simulate in-vivo microenvironment of intestine [89]. Different types of cells used for this model are Caco2, human umbilical vein endothelial cells, intestinal organoids, human intestinal microvascular endothelial cells, human lymphatic microvascular endothelial cells and peripheral blood mononuclear cells. Gut on a chip has been used to study drug pharmacokinetics, host-gut microbiota cross talk, and nutrition metabolism [39]. Microfluidic microhole trapping array has been used to study the cellular permeability of propranolol, naproxen, furosemide, antipyrine, verapamil, atenolol, piroxicam, hydrochlorothiazide, cimetidine, and carbamazepine [90]. Gut on a chip microfluidic device-based model was developed for studying the Coxsackie B1 virus infection and interaction between host and infective virus [91]. Grassat and group studied the impact of intestinal mechanical forces on Shigella infection [92]. Multiple organs on a chip Intestine–kidney chip was successfully developed to study the absorption and nephrotoxicity of digoxin in combination with cholestyramine and verapamil [93]. An intestine-liver microchip was developed with three sections comprises of intestinal cells, liver cells and breast cancer cells. Caco2 cells (for intestine) and HepG2 cells (for liver) were used to study absorption and hepatic metabolism of cyclophosphamide, epirubicin, 17-β estradiol, and soy isoflavone. Drugs/substances after passing through the HepG2 cells were further analyzed for the anticancer activity using MCF-7 cells (mimics human breast carcinoma). The bioassay was performed with ease with lower consumption of cells as shown in Fig. 5 [94].Fig. 5 Microchip for simultaneous study of absorption, hepatic metabolism and anticancer activity [94]. Fig 5 Fig. 6 Important factors for high throughput organ on a chip model development. Fig 6 Three organs small intestine, liver and lung were simulated on a microfluidic chip. The organ-to-organ network was prepared through microporous membrane and microchannels. Caco-2, HepG2, and A549 cell were used to represent the small intestine, liver, and lung respectively. Due to perfusion environment and high oxygen permeability of polydimethylsiloxanes (PDMS), the device was able to co-culture three types of cells for at least more than 3 days. This model was used to study the pharmacokinetics of three anticancer drugs (epirubicine, irintecan and cyclophosphamide) and the results suggested that the device can replicate the bioactivity of anticancer drugs on target cells [94]. Micro-engineered chip devices Most common methods, type of microarchitectures and materials used for fabrication of microchips are mentioned in Table 4 [95, 96, 97].Table 4 Methods and materials used for construction of microengineered chips. Table 4Micro-fabrication Methods Materials Type of Microarchitectures Photolithography, Soft lithography, 3D printing, computer numerical code micromilling Polydimethylsiloxanes (PDMS), Hydrogels, Gelatin methacryloyl, Polyamindes, Polymethylmethacrylate, Polyvinyl chloride Single layer microfluidic device, 3D compartmentalization, Microfluidic vascular networks Most of the organs have a multimodular structure and are comprised of cells that play a characteristic function in the body such as gas exchange in the lung alveoli, metabolism in the liver and absorption in the villi of the gut [98]. Most popular source of biological tissues for different organ on a chip models are embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and adult stem cells (ASCs) [1]. Different types of cells used for simulating the organs on a chip are summarized in Table 5 .Table 5 Common sources of cells used in organ on a chip technology. Table 5Organs Cells References Lungs human induced pluripotent stem cells [99] Liver HepG2 cells [75] Kidney mature human podocytesderived from human induced pluripotent stem [36] Brain human induced pluripotent stem cells derived neural cells [100] Heart human induced pluripotent stem cells (iPSCs) – derived 3D cardiac cells [69] Gut Caco 2 cells [39] Skin Induced pluripotent stem cells (iPSC) or commercially available reconstructed skin tissues (EpidermFT, Epiderm, EpiSkin, StratiCELL) [83] Organ on a chip system: regulatory authorities and market size Although organ on a chip technology is still in its infancy stage, regulatory bodies and top pharma companies are increasingly showing interest in this technology. Till now organ on a chip technology has not been specifically classified by any major regulatory body such as the United States Food and Drug Administration (U.S. FDA), the European Medicines Agency, and/or the Medicines and Healthcare Products Regulatory Agency in the United Kingdom. However, a survey showed that most developers of organ on a chip technology follow one of the following three guidelines: ISO 9001:2015, FDA 21 CFR Part 58, and FDA FD&C Act Section 507 [101]. National Center for Advancing Translational Science (NCATS) USA, the USFDA, and the US Defense Advanced Research Projects Agency (DARPA) collaborated to develop organ on a chip for screening drug safety and effectiveness before approval for first in human studies [102]. In 2017, the US FDA announced an agreement with a company named Emulate Inc, (Boston, MA) for evaluation of company's organs-on-chips technology at the agency's Center for Food Safety and Applied Nutrition (CFSAN). The aim of this project was to understand the usefulness of this technology for predicting the harmful effects of certain potential chemicals on the human body [102].FDA awards contract to Harvard University's Wyss Institute for Biologically Inspired Engineering for developing countermeasures to treat acute radiation syndrome (ARS). ARS illness affects combination of organs on exposure to high dose of radiation. The institute will develop organs-on-chips models that simulate of radiation damage in the lung, gut, and bone marrow and then use these models to test medical countermeasures for its treatment [103]. In December 2018, a collaborative study by international space station through collaborative research program national center for advancing translational sciences (NCATS) at National Institute of Health (NIH) and center for advancement in sciences in space in partnership with NASA was planned using tissue chips. The aim was to understand the role of microgravity on human health and disease. These chips are expected to behave life astronauts’ body experiencing same kind of rapid change [104]. In May 2019, the projects took their first mission and the chips that were studied mimicked lungs, bone marrow, bone and cartilage, kidney and blood brain barrier. In the next year, study of chips mimicking intestine and heart tissues was conducted. In December 2020, a project focusing on the prevention of osteoarthritis after join injuries, muscle wasting and reversal of heart tissue damage was started. In May 2021, the second trip of project started in May 2019, focus was on understanding the kidney stone formation and effect of microgravity on the functioning of kidney was started. In 2020, the global Organ-on-a-Chip market size is approximately USD 41 million and is expected to reach USD 303.6 million by 2026. In next 7 years, liver segment at a growth rate at a CAGR of 35.4% and kidney segment at a CAGR of 36.1%. Major factors for the growth of organ on a chip technology is increase in collaborations between pharmaceutical companies and organ-on-chip manufacturers for early drug detection and emphasis on the alternatives to animal testing models. Asia-Pacific (China, India and Japan) is expected to witness the highest growth rate and North America to hold largest market share during the forecast period [105]. Some of the major companies developing organ on a chip technology as shown in Table 6 [106]Table 6 Major companies developing organ on a chips models. Table 6Company Country Area Emulate U.S. Lungs-on-chip, gut-on-chip and even blood-brain-barrier-on-chip Mimetas Netherlands Kidney, gut, tumors and many others Elvesys France Microfluidic systems AxoSim U.S. Aims to develop the special microfluidic chips to fight cancer TaraBiosystems U.S. Focuses on developing heart on a chip Nortis Bio U.S. Kidney on a chip BioIVT U.S. Pancreatic islets and the lung airway epithelium are established models AlveoliX Switzerland Human lung on a chip TissUse Germany Multi organ on a chip BiomimX Italy Specialized in the generation of predictive models of human organs and pathologies to test new drugs Technology readiness level, EU Horizon 2020 technology, is a scale that indicates the maturity of a technology and classifies it to different levels. Peer review publications reports application of this technology in operational environment (Technology Readiness Level - TRL 7) and potential of the technology to be used in preclinical trials [101]. Conclusion and future roadmap Although, organ on a chip technology has achieved ground breaking success so far yet there is still a long way to go. Apart from the challenges related to surface adsorption effects and poor mixing of fluids in the microfluidic devices, there are several other modification required (Organs on chip review). Presently, most of the organ on a chip models cover single or combination of few organs. As every individual organ on a chip model is a piece of an entire puzzle, it becomes important to interconnect these models to develop one single chip that mimics all the major organs. Moving towards body on a chip technology, it is important to consider inter-organ scaling, common media and its flow rate and the interdependent functionality of different organs. Although, major organs are simulated using this technology but still there are many other organs such as adipose tissue, retina and placenta to name a few, for which very less studies are done. In recent studies, a cross talk between microbiome and host metabolism has been highly appreciated. Hence, it becomes important to integrate the microbiome research with organ on a chip technology [106]. Regulatory agencies should also lay guidelines for the validation of organ on a chip technology for variety of potential applications including disease model development [101]. There is a need to transform organ on a chip technology into high throughput organ on a chip technology to expedite the screening process. Parallelization of models, standardized and scalable platform, validation, automation and online data analysis are the important factors shown in Fig. 6 which needs to be incorporated for a high throughput organ on a chip models [107]. Market research shows that North America might hold the largest share of 49% of organ on a chip technology as they might adopt the change from 2D and 3D cell cultures. Pharmaceutical companies, biotechnology companies, academic and research institutions are the major segments to utilize this technology. Increase in research funding across the globe in pharmaceutical/biotechnology sector and growing number of clinical trials based on cell-based therapy will boost the organ on a chip market. [108]. To summarize, organ on a chip technology provides wealth of opportunities including drug toxicity and efficacy studies, in-vitro analysis of biochemicals, pathogenesis study of diseases and metabolic activities of human cells. Declaration of interest The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. ==== Refs References 1 Wu Q. Liu J. Wang X. Organ-on-a-chip: recent breakthroughs and future prospects BioMed. Eng. OnLine 19 2020 2 Sun W. Chen Y.Q. Luo G.A. Zhang M. Zhang H.Y. Wang Y.R. Hu P. Organs-on-chips and its applications Chin. J. Anal. Chem. 44 2016 533 541 3 Esch E.W. Bahinski A. Huh D. Organs-on-chips at the frontiers of drug discovery Nat. Rev. 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Luo D. Shuler M.L. March J.C. Microscale 3-D hydrogel scaffold for biomimetic gastrointestinal (GI) tract model Lab Chip 11 2011 389 392 21157619 90 Yeon J.H. Park J.K. Drug permeability assay using microhole-trapped cells in a microfluidic device Anal. Chem. 81 2009 1944 1951 19203200 91 Villenave R. Wales S.Q. Hamkins-Indik T. Papafragkou E. Weaver J.C. Ferrante T.C. Bahinski A. Elkins C.A. Kulka M. Ingber D.E. Human Gut-on-a-Chip supports polarized infection of Coxsackie B1 virus in vitro PLoS One 12 2017 e0169412 92 Grassart A. Malardé V. Gobaa S. Sartori-Rupp A. Kerns J. Karalis K. Marteyn B. Sansonetti P. Sauvonnet N. Bioengineered human Organ-on-Chip reveals intestinal microenvironment and mechanical forces impacting shigella infection Cell Host Microbe 26 2019 435 444 e4 31492657 93 Li Z. Su W. Zhu Y. Tao T. Li D. Peng X. Qin J. Drug absorption related nephrotoxicity assessment on an intestine-kidney chip Biomicrofluidics 11 2017 034114 94 Imura Y. Sato K. Yoshimura E. Micro total bioassay system for ingested substances: assessment of intestinal absorption, hepatic metabolism, and bioactivity Anal. Chem. 82 2010 9983 9988 21090751 95 Beverung S. Wu J. Steward R. Jr. Lab-on-a-Chip for cardiovascular physiology and pathology Micromachines (Basel) 11 2020 898 96 Sosa-Hernández J.E. Villalba-Rodríguez A.M. Romero-Castillo K.D. Aguilar-Aguila-Isaías M.A. García-Reyes I.E. Hernández-Antonio A. Ahmed I. Sharma A. Parra-Saldívar R. Iqbal H.M.N. Organs-on-a-Chip module: a review from the development and applications perspective Micromachines (Basel) 9 2018 536 97 Yum K. Hong S.G. Healy K.E. Lee L.P. Physiologically relevant organs on chips Biotechnol. J. 9 2014 16 27 24357624 98 Huh D. Kim H.J. Fraser J.P. Shea D.E. Khan M. Bahinski A. Hamilton G.A. Ingber D.E. Microfabrication of human organs-on-chips Nat Protoc 8 2013 2135 2157 24113786 99 Nawroth J.C. Barrile R. Conegliano D. Van Riet S. Hiemstra P.S. Villenave R. Stem cell-based Lung-on-Chips: the best of both worlds? Adv. Drug Deliv. Rev. 140 2019 12 32 30009883 100 Jendelova P. Sykova E. Erceg S. Neural stem cells derived from human-induced pluripotent stem cells and their use in models of CNS injury Results Probl. Cell Differ. 66 2018 89 102 30209655 101 Allwardt V. Ainscough A.J. Viswanathan P. Sherrod S.D. Mclean J.A. Haddrick M. Pensabene V. Translational roadmap for the Organs-on-a-Chip industry toward broad adoption Bioengineering 7 2020 112 102 Fitzpatrick S. Sprando R. Advancing regulatory science through innovation: in vitro microphysiological systems Cell Mol. Gastroenterol. Hepatol. 7 2019 239 240 30585159 103 United States Food and Drug Administration (2019) Organs-On-Chips for Radiation Countermeasures. https://www.fda.gov/emergency-preparedness-and-response/mcm-regulatory-science/organs-chips-radiation-countermeasures (accessed on 9/8/2021),. 104 National Aeronautics and Space Administration (2018) Small Tissue Chips in Space a Big Leap Forward for Research. https://www.nasa.gov/tissue-chips accessed on 9/8/2021. 105 Valuates Reports. 2021. Organ-On-Chip (OOC) Market Size is USD 303.6 Million by 2026 at CAGR 39.9%. (Accessed on 9/8/2021). Available: https://www.prnewswire.com/in/news-releases/organ-on-chip-ooc-market-size-is-usd-303-6-million-by-2026-at-cagr-39-9-valuates-reports-856995001.html (Accessed on 9/8/2021). 106 Zhang B. Radisic M. Organ-on-a-chip devices advance to market Lab Chip 17 2017 2395 2420 28617487 107 Probst C. Schneider S. Loskill P. High-throughput organ-on-a-chip systems: current status and remaining challenges Curr. Opin. Biomed. Eng. 6 2018 33 41 108 Market Research Report Inkwood Research (2019) Global Organ On a Chip Market Forecast 2019-2027. https://inkwoodresearch.com/reports/global-organ-on-a-chip-market/ accessed on 9/8/2021.
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==== Front J Perianesth Nurs J Perianesth Nurs Journal of Perianesthesia Nursing 1089-9472 1532-8473 Published by Elsevier Inc. on behalf of American Society of PeriAnesthesia Nurses. S1089-9472(22)00031-4 10.1016/j.jopan.2022.01.013 Editorial Opinion Travel Nursing: Price Gouging or Supply and Demand? Odom-Forren Jan PhD, RN, CPAN, FASPAN, FAAN ⁎ College of Nursing, University of Kentucky, Lexington, KY ⁎ Address correspondence to Jan Odom-Forren, College of Nursing, University of Kentucky, 751 Rose Street, Lexington, KY 40536 11 4 2022 4 2022 11 4 2022 37 2 153154 © 2022 Published by Elsevier Inc. on behalf of American Society of PeriAnesthesia Nurses. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcI have been in the literature the past few weeks reading about moral distress, moral injury, compassion fatigue, and burnout of nurses. The focus lately has been on the crisis in nursing during the COVID-19 pandemic. However, I and others believe the crisis started much before the pandemic. The pandemic has just stretched the limits and shown what was bubbling underneath the surface. Dall'Ora et al1 conducted a review focused on burnout in nursing. In summary, they found that adverse job characteristics, such as high workload, low staffing levels, long shifts, and low control over the work environment were associated with nursing burnout. They also stated that the association between turnover and burnout needs more research, but it is not hard to see the tie-in. And it is especially interesting to know that the studies used in the review were conducted before the stress of the Covid-19 pandemic. I also have been reading these past few weeks about travel nursing. It seems that many nurses are choosing to forgo their regular jobs and move to travel nursing. Travel nursing requires that a nurse sign up with a travel agency and work short-term, usually 13-week contracts. If the hospital or other healthcare agency still needs a nurse and the nurse has enjoyed working at the facility, the contract can be extended. The benefits of travel nursing include picking a geographic area that a nurse would like to see, higher pay—much higher pay than the typical staff nurse makes, and most importantly, control of your own workplace environment. If you are working in a facility with a toxic environment, you can wave bye as you leave after 13 weeks. What is the downside to travel nursing? Some nurses like more stability or perhaps, because of family obligations, cannot travel. Some nurses prefer to work where there is more familiarity with policies and procedures. In some facilities who have travel nurses working side by side with their staff, there are tensions over the pay differential. The demand for travel nurses has increased exponentially;2 it increased by 35% in 2020 and is expected to have increased by 40% more in 2021.3 There is an ongoing discussion now about “price-gouging.” Price gouging refers to retailers and others taking “advantage of spikes in demand by charging exorbitant prices for necessities, often after a natural disaster or other state of emergency.”4 , (para 1) Legislators have asked the White House Covid-19 team to look into whether travel nurse agencies are price gouging during the pandemic, and some states are trying to cap travel agency salaries.5 Is it price gouging or supply and demand? Nurses in great numbers are retiring, leaving nursing, and changing positions. Hospitals believe the travel agencies are price-gouging. Others believe that the price of a travel nurse relates to supply and demand.3 One point of agreement is that something must be done to facilitate addressing fundamental problems with healthcare staffing.3 Significantly, outcomes of poor staffing related to burnout results in poor patient safety, poor quality of care, and adverse events.1 Is the answer to cap the salaries for travel nurses thus reducing the supply? Or is the answer to work hard at the root problem which is pointing to workplace environment? I have been in meetings where some nurse leaders believe that nurses are moving to travel nursing only for the money. Is money the only motivator? It does not appear to be so. In a review of 91 papers on nursing burnout, pay was not found to be an associated factor if you assume that burnout may be a factor in the move to travel nursing.1 The problem is that we do not have the data to know why nurses become travel nurses. We do not even know how many perianesthesia nurses have left work in a PACU or Day Surgery Unit to travel. When I searched CINAHL and PubMed, there are various articles about becoming a travel nurse, very little peer-reviewed papers published before the pandemic, and no research related to travel nursing during the pandemic. So anecdotally, I asked a nurse to tell me three major priorities as to why she had left her position in an intensive care unit to travel. Her answers were: 1) Flexibility of schedule—13-week assignments with a potential for breaks in between; 2) Poor pay and poor treatment as a full-time employee; 3) Opportunity to see new places. This nurse went further to say that if hospitals are not going to prioritize retaining their most experienced staff, then people like her are going to look elsewhere to make nursing more tolerable. She went even further to state that travel nursing is used as an excuse to deflect from poor leadership or undervalued nursing. And if you think she is the only one with those thoughts, check out #nursetwitter. I believe that many nurses leave to travel to control the working environment, to control their schedules. What is the root of the problem? Lack of resources to provide a safe environment (eg, personal protective gear, N-95 masks), no consistent communication from leaders, no significant effort to retain experienced staff, and high nurse/patient ratios. Those are just a few of important issues. No one is asking to leave the unit understaffed while they attend resiliency training or attend a pizza party.6 As Udod et al7 state: “…nurses need more than psychological support to allay their concerns: They need food, rest, and a sense of safety.” Focusing on individual nurse resiliency puts the responsibility on the nurse and not on how the organization can support nurse efforts.7 What are some concrete strategies that can be done? One suggested strategy is to appropriate money so that the National Health Care Workforce Commission, established in 2010, can meet. The finances to begin the commission's work became politicized and was never funded, so the commission never met. The first Chair-designee was Dr. Peter Beurhaus who is the expert in all things related to the nursing workforce.8 Another strategy is to improve working conditions with nurse to patient ratios. California's law regarding nurse/patient ratio has reduced nurse injuries by one third9 and increased patient safety and nurse retention.10 Transformational leadership is effective in terms of maintaining consistent communication and effective role modeling.6 An emphasis on retention of experienced nurses is also needed. The days of showing nurses who burnout to the door knowing that other newer nurses who require less salary are right around the corner is over. We have an acute nursing shortage driven in part by the issues that were boiling under the surface and by the stress of the pandemic. Nurses need to be paid in a manner that shows their value. I have wondered often if hospitals had increased salaries and had clear, consistent communication if many of the nurses who left to travel would still be there. We need policymakers to assist in allocating funding for the provision of essential personal protective equipment for frontline nurses and ensuring a safe workplace for nurses by legislating appropriate health and safety measures.11 We also need the help of policymakers to appropriate funding for increasing enrollment at nursing schools and funding for the increased space and staff that will require. However, at the same time, we cannot keep tossing our new graduate nurses out into toxic workplace environments which is why improvement in the workplace is a priority. The bottom line is that we cannot continue with the healthcare system as it currently exists. National nursing organizations that are not 501(c)3 need to become involved at the policy level. Those of us who do belong to non-profit organizations like ASPAN can still become involved at the individual level. If you are in an area that uses travel nurses to staff your unit, how would you staff if salaries were capped and no one came….supply and demand. If you are in leadership how many staff members can you keep by supporting a safe environment and increased pay. I am interested to hear your opinions (jan.forren@uky.edu). The ideas or opinions expressed in this editorial are those solely of the author and do not necessarily reflect the opinions of ASPAN, the Journal, or the Publisher. Conflict of interest: None to report. ==== Refs References 1 Dall’Ora C Ball J Reinius M Griffiths P. Burnout in nursing: a theoretical review Hum Resour Health 18 2020 41 32503559 2 Yang YT Mason DJ. COVID-19’s impact on nursing shortages, the rise of travel nurses, and price gouging Health Affairs Forefront 2022 Available at: https://www.healthaffairs.org/do/10.1377/forefront.20220125.695159/ Accessed January 31, 2022 3 Bernstein L. As Covid Persists, Nurses are Leaving Staff Jobs — and Tripling Their Salaries as Travelers 2021 Washington Post Available at: https://www.washingtonpost.com/health/covid-travel-nurses/2021/12/05/550b15fc-4c71-11ec-a1b9-9f12bd39487a_story.html Accessed January 31, 2022 4 Morton H. Price gouging state statutes. 2021. Available at:https://www.ncsl.org/research/financial-services-and-commerce/price-gouging-state-statutes.aspx. Accessed January 29, 2021. 5 Rodriguez S. White house urged to look into price gouging by nurse staffing agencies. 2022. Available at: https://revcycleintelligence.com/news/white-house-urged-to-look-into-price-gouging-by-nurse-staffing-agencies. Accessed February 16, 2022. 6 Brittain AC. Is resiliency training the answer to burnout? J Prof Nurs 38 2022 A3 A4 35042597 7 Udod S MacPhee M Baxter P. Rethinking resilience: nurses and nurse leaders emerging from the post-COVID-19 environment JONA 51 2021 537 540 8 McDonough JE. Old wine in a new bottle—time for a national health care workforce commission. Available at:https://www.milbank.org/quarterly/opinions/old-wine-in-a-new-bottle-time-for-a-national-health-care-workforce-commission/. Accessed January 31, 2022. 9 Leigh P. Higher nurse-to-patient ratio law improves nurse injury rates by one-third. 2015. Available at: https://blogs.cdc.gov/niosh-science-blog/2015/05/06/patient-ratio/. Accessed January 31, 2022. 10 Anders RL. Patient safety time for federally mandated registered nurse to patient ratios Nurs Forum 56 2021 1038 1043 34227123 11 De Raeve P Adams E Xyrichis A The impact of the COVID-19 pandemic on nurses in Europe: a critical discussion of policy failures and opportunities for future preparedness Int J Nurs Stud Adv 3 2021 100032
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==== Front Lancet Respir Med Lancet Respir Med The Lancet. Respiratory Medicine 2213-2600 2213-2619 Elsevier S2213-2600(22)00091-1 10.1016/S2213-2600(22)00091-1 Comment Myopericarditis after COVID-19 vaccination: unexpected but not unprecedented Ryan Margaret ab Montgomery Jay ac a Immunization Healthcare Division, Defense Health Agency, Falls Church, VA, USA b University of California San Diego, San Diego, CA 92134, USA c Walter Reed National Military Medical Center, Bethesda, MD, USA 11 4 2022 7 2022 11 4 2022 10 7 624625 Published by Elsevier Ltd. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcIn the midst of the devastating COVID-19 pandemic, rapid development of highly effective vaccines was enthusiastically welcomed. Unfortunately, myopericarditis after COVID-19 vaccination was an unanticipated adverse event. In The Lancet Respiratory Medicine, Ryan Ruiyang Ling and colleagues commendably review the risk of this adverse event in the context of risk after other vaccines.1 Their study provides an important perspective on the historical global experience with cardiac adverse events after vaccination. Ling and colleagues applied rigorous statistical analyses to the available literature and confirmed the conclusions of other reviewers. Specifically, the overall incidence of myopericarditis after COVID-19 vaccination (18·2 cases [95% CI 10·9–30·3] per million doses) is not higher than expected outside of the context of vaccination, and not significantly higher than the incidence of myopericarditis reported after the standard immunisations included in the study, such as influenza vaccines (1·3 [0·0–884·1], p=0·43 vs COVID-19 vaccines). There is, however, an important demographic and vaccine-related component to this adverse event that is obscured in reporting the overall incidence. The risk of myopericarditis in young males after their second dose of mRNA COVID-19 vaccine is remarkably higher than expected. This pattern has been seen before. As Ling and colleagues found when they reviewed the extant literature, myopericarditis risk is well established after receipt of live-replicating smallpox vaccine. Notably, in a study by Oster and colleagues2 of myocarditis after mRNA COVID-19 vaccination, the rate of myocarditis reported in the highest-risk group of recipients (105·86 cases [95% CI 91·65–122·27] per million doses in males aged 16–17 years receiving a second dose) approached the historical rate of myopericarditis after smallpox vaccination (132·1 cases [81·3–214·6] per million doses) according to Ling and colleagues' study.1 US military professionals, who are very familiar with adverse events following smallpox vaccination, were among the first to observe myocarditis cases after mRNA COVID-19 vaccines,3 most likely because the US military includes a large number of young men who received two doses of COVID-19 vaccine very early in the 2021 pandemic vaccine rollout. Although there are common demographic and clinical features between the myopericarditis cases that followed smallpox vaccine and those that followed mRNA COVID-19 vaccines, better understanding of the pathophysiology of these adverse events following vaccination is an important area for future research. Because smallpox vaccination has very limited global application in the modern era, the experience of mRNA COVID-19 vaccination must now propel the field forward. Analyses of the pathology and immunological mechanisms behind these demographic-dependent adverse events following vaccination are likely to advance our understanding of cardiology and immunology.4, 5, 6 These advances could spur the development of safer vaccines or precision vaccination practices.7 Ling and colleagues' analysis1 also raises important questions about whether cardiac adverse events following vaccination have historically been well evaluated outside of the realm of smallpox vaccine. In a literature review spanning 75 years, it is remarkable that the study team identified only five publications addressing myocarditis following immunisations other than smallpox or COVID-19 vaccination. The 7 million vaccine doses described in these publications represent a small fraction of the billions of vaccinations administered globally every year.8 This challenge might impact the interpretation of the results. Among the populations who received billions of vaccine doses after which myopericarditis was not observed or very rarely observed, published literature might not exist; reassuring data from background populations would not be captured in analyses of the literature, such as those conducted by Ling and colleagues. The safety signal observed after COVID-19 vaccination is, therefore, even more important to fully investigate.9 Reports of unexpected adverse events—albeit rare and limited to a specific subset of vaccine recipients—have the potential to damage vaccine confidence at a crucial point in the pandemic response. Like Ling and colleagues, all professionals who have described myopericarditis following COVID-19 vaccination have emphasised that the benefits of vaccination far outweigh the risks during the current pandemic. Nonetheless, scientific knowledge and public health strategies must continue to evolve. Alternative vaccine platforms, vaccine doses, or vaccine schedules could reduce the risk of rare adverse events and must be explored in the context of changing infection risk.10 Vaccine confidence is one our most valuable resources, and it is dependent upon trust in public health. Trust is a fragile commodity that is strengthened by reporting challenges transparently and addressing these challenges with scientific rigour and appropriate concern. We declare no competing interests. ==== Refs References 1 Ling RR Ramanathan K Tan FL Myopericarditis following COVID-19 vaccination and non-COVID-19 vaccination: a systematic review and meta-analysis Lancet Respir Med 2022 published online April 11. 10.1016/S2213-2600(22)00059-5 2 Oster ME Shay DK Su JR Myocarditis cases reported after mRNA-based COVID-19 vaccination in the US from December 2020 to August 2021 JAMA 327 2022 331 340 35076665 3 Montgomery J Ryan M Engler R Myocarditis following immunization with mRNA COVID-19 vaccines in members of the US military JAMA Cardiol 6 2021 1202 1206 34185045 4 Kiblboeck D Klingel K Genger M Myocarditis following mRNA COVID-19 vaccination: call for endomyocardial biopsy ESC Heart Fail 2022 published online Feb 23. 10.1002/ehf2.13791 5 Mormile R Myocarditis and pericarditis following mRNA COVID-19 vaccination in younger patients: is there a shared thread? Expert Rev Cardiovasc Ther 20 2022 87 90 35180029 6 Hajjo R Sabbah DA Bardaweel SK Tropsha A Shedding the light on post-vaccine myocarditis and pericarditis in COVID-19 and non-COVID-19 vaccine recipients Vaccines (Basel) 9 2021 1186 7 Kennedy RB Ovsyannikova IG Palese P Poland GA Current challenges in vaccinology Front Immunol 11 2020 1181 8 Strategic Advisory Group of Experts on Immunization The Global Vaccine Action Plan 2011–2020. Review and lessons learned (WHO/IVB/19.07) 2019 World Health Organization Geneva 9 Arnaud M Bégaud B Thurin N Moore N Pariente A Salvo F Methods for safety signal detection in healthcare databases: a literature review Expert Opin Drug Saf 16 2017 721 732 28490262 10 Shiri T Evans M Talarico CA The population-wide risk-benefit profile of extending the primary COVID-19 vaccine course compared with an mRNA booster dose program Vaccines (Basel) 10 2022 140 35214599
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==== Front Lancet Rheumatol Lancet Rheumatol The Lancet. Rheumatology 2665-9913 Elsevier Ltd. S2665-9913(22)00099-6 10.1016/S2665-9913(22)00099-6 Comment Pre-exposure anti-SARS-CoV-2 monoclonal antibodies in severely immunocompromised patients with immune-mediated inflammatory diseases Goulenok Tiphaine a Delaval Laure a Delory Nicole a François Chrystelle a Papo Thomas a Descamps Diane bc Ferré Valentine Marie bc Sacré Karim ad a Département de Médecine Interne, Hôpital Bichat, Assistance Publique Hôpitaux de Paris, Université de Paris, Paris, France b Département de Virologie, Hôpital Bichat, Assistance Publique Hôpitaux de Paris, Université de Paris, Paris, France c INSERM UMR 1137 IAME, équipe DESCID Paris, Paris, France d INSERM UMR 1149, Centre de Recherche sur l'Inflammation, Laboratoire d'Excellence Inflamex, Paris, France 11 4 2022 7 2022 11 4 2022 4 7 e458e461 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcPatients with immune-mediated inflammatory diseases are at a higher risk of severe COVID-19, in part, due to immune suppression induced by treatment.1, 2 France began vaccinations against COVID-19 in December, 2020, and patients with immune-mediated inflammatory diseases receiving immunosuppressive drugs were prioritised for vaccination.3 It has since been demonstrated that patients with immune-mediated inflammatory diseases—especially those receiving anti-CD20 agents—mount a suboptimal humoral response to COVID-19 vaccination.4, 5 These patients are thus candidates for additional strategies to protect them from COVID-19. Our objective was to determine whether pre-exposure prophylaxis with tixagevimab and cilgavimab—a monoclonal antibody combination with neutralising activity against alpha (B.1.1.7), beta (B.1.351), gamma (P.1), delta (B.1.617.2), and omicron (B.1.1.529) SARS-CoV-2 variants—might be of benefit to patients with immune-mediated inflammatory diseases who did not generate a humoral response to mRNA vaccination. Tixagevimab plus cilgavimab was granted Emergency Use Authorisation by the Agence Nationale de Sécurité du Medicament in France in December, 2021, for pre-exposure prophylaxis against COVID-19 in individuals with severely compromised immune systems due to immunosuppressive medications who might not mount an adequate humoral response to COVID-19 vaccination.6 Immunosuppressive medications included anti-CD20 agents, Bruton's tyrosine kinase (BTK) inhibitor, azathioprine, cyclophosphamide, and mycophenolate mofetil. An inadequate humoral response to vaccine was defined by a serum SARS-CoV-2 anti-spike IgG (anti-S) titre of less than 264 binding antibody units (BAU)/mL 2–4 weeks after receiving a fourth vaccine dose.6 We screened all outpatients with immune-mediated inflammatory disease (n=219) who were immunised via the vaccine task force set up between March and May, 2021, in our national centre for rare immune-mediated inflammatory diseases (Internal Medicine Department, Bichat Hospital, Paris, France).7 Of these patients, 165 were taking immunosuppressive drugs. 70 patients were treated with azathioprine, mycophenolate mofetil, anti-CD20 agents, or a combination thereof (no patients were receiving a BTK inhibitor or cyclophosphamide). All patients were fully vaccinated against SARS-CoV-2 (initial three-dose series with an mRNA-based COVID-19 vaccine and a fourth dose ≥6 months later), and were considered for pre-exposure prophylaxis. All 70 patients were contacted and invited for screening for SARS-CoV-2 anti-S titres in serum shortly after the fourth vaccine dose, and those patients with inadequate antibody titres were scheduled to receive an infusion with tixagevimab and cilgavimab. 44 (63%) of 70 patients were screened, and 17 (39%) had inadequate anti-S titres (0–256 BAU/mL [median 0·6]) and were eligible for pre-exposure prophylaxis. 12 (71%) of these eligible patients had received anti-CD20 agents. The absence of adequate humoral responses after vaccination was associated with age older than 60 years, obesity, and the use of anti-CD20 drugs (table ). Due to logistical constraints (appendix pp 1–2), only ten of the 17 eligible patients received anti-SARS-CoV-2 monoclonal antibodies at a median of 40·5 days (IQR 22–55) after the fourth vaccine dose and a median of 7·5 days (4–25) after antibody screening (appendix pp 3–6). All patients who received prophylactic monoclonal antibodies had a negative SARS-CoV-2 RT-PCR result on the day of infusion.Table patient characteristics All on immunosuppressants (n=165) Severely immunocompromised*(n=70) Screened for humoral response post vaccine (n=44) Anti-S IgG† >264 BAU/mL (n=27) Anti-S IgG <264 BAU/mL (n=17) p value Age, years 48 (37–62) 47 (37–61) 49 (37–63) 43 (35–57) 60 (43–69) 0·07 Gender Female 104 (63%) 45 (64%) 25 (57%) 14 (52%) 11 (65%) ns Male 61 (37%) 25 (36%) 19 (43%) 13 (48%) 6 (35%) ns Autoimmune diseases 74 (45%) 41 (59%) 25 (57%) 16 (59%) 9 (53%) ns Systemic lupus erythematosus 42 24 15 11 4 .. Immune cytopenia 5 4 2 1 1 .. Sjögren's syndrome 2 0 0 0 0 .. Systemic sclerosis 4 1 1 0 1 .. Mixed connective tissue disease 5 3 1 1 0 .. Immune encephalitis 3 2 2 0 2 .. Myositis 13 7 4 3 1 .. Systemic vasculitis 49 (30%) 23 (33%) 15 (34%) 9 (33%) 6 (35%) ns Small-vessels vasculitis 24 16 11 6 5 .. Large-vessels vasculitis 10 1 1 1 0 .. Behcet's disease 15 6 3 2 1 .. Other 42 (25%) 6 (9%) 4 (9%) 2 (7%) 2 (12%) ns Sarcoidosis 14 0 0 0 0 .. Autoinflammatory diseases 11 1 1 0 1 .. IgG4-related diseases 8 3 1 1 0 .. Relapsing polychondritis 3 0 0 0 0 .. Unclassified 6 2 2 1 1 .. BMI >30 kg/m2 30 (18%) 10 (14%) 5 (11%) 1 (4%) 4 (24%) 0·06 Diabetes 22 (13%) 8 (11%) 5 (11%) 2 (7%) 3 (18%) ns COPD, pulmonary fibrosis 12 (7%) 6 (9%) 3 (7%) 2 (7%) 1 (6%) ns Treatment Mycophenolate mofetil 43 (26%) 29 (41%) 18 (41%) 11 (41%) 7 (41%) ns Azathioprine 26 (16%) 18 (26%) 10 (23%) 7 (26%) 3 (18%) ns Anti-CD20 39 (24%) 29 (41%) 21 (48%) 9 (33%) 12 (71%) 0·03 Anti-CD20 plus mycophenolate mofetil 6 6 4 1 3 .. Anti-CD20 plus azathioprine 6 5 3 1 2 .. Data are median (IQR), n (%), or n. Comparison was performed between patients with immune-mediated inflammatory diseases with (anti-S >264 BAU/mL) and without (anti-S <264 BAU/mL) adequate humoral responses after complete vaccination. The Mann-Whitney test was used to compare continuous variables. The Fisher's exact test was used to compare dichotomous variables. BAU=binding antibody units. BMI=body-mass index. COPD=chronic obstructive pulmonary disease. Anti-S=SARS-CoV-2-spike antibodies. ns=not significant (p>0·1). * Due to immunosuppressive medications included anti-CD20 agents, Bruton's tyrosine kinase inhibitor, azathioprine, cyclophosphamide, and mycophenolate mofetil. † Anti-S titres were measured 2–4 weeks after the vaccine booster administration, using the automated Abbott SARS-CoV-2 IgG kit (chemiluminescent microparticle immunoassay; Abbott, IL, USA) according to the manufacturer's instructions. PCR-confirmed COVID-19 occurred in eight (47%) of the 17 patients who did not mount an adequate humoral response to vaccination, with infection occurring a median of 49 days (IQR 39–96) after the fourth vaccine dose (appendix p 7). All but one SARS-CoV-2 infection was due to the omicron variant. Among patients with COVID-19, five required hospitalisation, four of whom received supplemental oxygen; all eight patients received therapeutic anti-SARS-Cov-2 monoclonal antibodies, three received dexamethasone, and one received anakinra. One patient died (appendix p 7). Of the eight patients with PCR-confirmed COVID-19, seven did not receive prophylactic monoclonal antibodies. The patient who received tixagevimab and cilgavimab and developed COVID-19 had only mild disease and did not require admission to hospital. No serious adverse effects after administration of prophylactic SARS-CoV-2 monoclonal antibodies were reported. Of note, COVID-19 also occurred in one (4%) of the 27 patients who had adequate humoral responses after vaccination, and in one patient who was not screened for humoral response (appendix p 9). Our study showed that more than a third of severely immunocompromised patients with immune-mediated inflammatory diseases did not mount an adequate antibody response to SARS-CoV-2 vaccination, and pre-exposure administration of tixagevimab and cilgavimab was associated with a lower risk of COVID-19. All patients with immune-mediated inflammatory diseases who are receiving immunosuppressive drugs should be screened for humoral response to vaccination based on anti-S titres. The absence of anti-SARS-Cov-2 antibodies after full vaccination identifies patients who are at high risk and are eligible for anti-COVID-19 monoclonal antibody prophylaxis. Successful administration of anti-SARS-CoV-2 monoclonal antibodies requires accurate identification of high-risk patients, rapid assessment of humoral responses to vaccination based on anti-S titres, and an easily accessible site for anti-SARS-CoV-2 monoclonal antibody administration. The rate of COVID-19 was surprisingly high in our patients with immune-mediated inflammatory diseases who did not receive anti-SARS-CoV-2 monoclonal antibodies. A likely explanation for this is that patients in our cohort were highly exposed to SARS-CoV-2 during the omicron spread that occurred in France during the fifth wave of COVID-19 in January, 2022 (appendix p 10).8 The onset of a highly contagious variant that was spreading rapidly across the country and was dominant at the time of this study, along with the absence of adequate humoral response to vaccination, might explain the high infection rate. We did not observe differences in age, gender, immunosuppressive therapy, comorbidity, vaccine type, or timing of vaccinations between patients who received or did not receive anti-SARS-CoV-2 monoclonal antibodies (appendix pp 3–6). The median time from infusion to last follow-up (50 days, IQR 43–54) in the treatment group was longer than the time from humoral screening to development of COVID-19 (16·5 days, 5–22) in the control (untreated) group, suggesting that patients in both groups have had approximately the same exposure to SARS-CoV-2 (appendix pp 5–6). Conversely, the decrease in the incidence of COVID-19 observed over time (appendix p 10) might also have artificially inflated the benefit of prophylactic anti-SARS-CoV-2 monoclonal antibodies. It is also possible that patients who did not receive prophylactic antibodies were infected with SARS-CoV-2 before they were able to be treated. Although we have no evidence that our patients were in close contact with individuals infected with SARS-CoV-2, we cannot rule out the possibility that prophylactic treatment might have been given to patients after virus exposure. Three of the eight patients who developed COVID-19 were living in nursing homes and thus were at a higher risk of exposure to SARS-CoV-2. The threshold used to determine an adequate humoral response (264 BAU/mL) was consistent with a previously published study,9 and we observed a strong correlation between anti-S IgG titres and pseudoneutralisation activity (appendix p 11). Our study had several limitations. The anti-S titres in response to vaccine were not available for all patients, but the characteristics of patients deemed eligible for pre-exposure monoclonal antibody therapy were not different from those who were not screened for vaccine response (appendix p 8). The sample size was small, and the study was done at a single centre. The findings of this study should thus be interpreted with caution, and future studies with larger sample sizes and more homogeneous follow-up times for treatment and control groups are necessary to confirm these results. Despite these limitations, our study is, to our knowledge, the first to report on the real-world clinical use of prophylactic anti-SARS-CoV-2 monoclonal antibodies in patients with immune-mediated inflammatory diseases, with initial evidence for clinical benefit. In conclusion, our findings support the use of timely pre-exposure administration of prophylactic anti-SARS-CoV-2 monoclonal antibodies to prevent COVID-19 in patients with immune-mediated inflammatory diseases who are severely immunocompromised and do not generate an adequate humoral response to vaccination. Because it is unclear which SARS-CoV-2 variants will become dominant over the next few months, the benefits of pre-exposure prophylaxis will need to be updated over time. TG and KS designed the study, directed the project, and wrote the Comment. TG and VMF did the analysis. LD, ND, CF, TP, and DD were involved in project development and edited the Comment. All authors reviewed and approved of the final Comment. DD received consulting fees from VIIV Heath Care, Gilead Sciences, Merck Sharp & Dohme, and Janssen Cilag; and support for attending meetings or travel, or both, from VIIV Heath Care and Gilead Sciences. TP received support for attending meetings or travel, or both, from Swedish Orphan Biovitrum. VMF received support for attending meetings or travel, or both, from Gilead Sciences. All other authors declare no competing interests. Patients and the public were not involved in the design, conduct, reporting, or dissemination plans of this research. Patient consent was not required for this study. The authors acknowledge Jean-Francois Alexandra, Marie-Paule Chauveheid, Julie Chezel, Antoine Dossier, Maureen Marie-Joseph, Julien Rohmer, Diane Rouzaud, and Celine Mendes from the Bichat Hospital Internal Medicine Department for their invaluable help. The study was supported by Agence Nationale pour la Recherche (grant number ANR-21-COVR-0034 COVALUS) and by the Agence Nationale de la Recherche sur le SIDA et les Maladies Infectieuses Emergentes, AC43 Medical Virology and Emergen Program. Supplementary Material Supplementary appendix ==== Refs References 1 FAI2R /SFR/SNFMI/SOFREMIP/CRI/IMIDIATE consortium and contributors Severity of COVID-19 and survival in patients with rheumatic and inflammatory diseases: data from the French RMD COVID-19 cohort of 694 patients Ann Rheum Dis 80 2020 527 538 33268442 2 Mageau A Papo T Ruckly S Survival after COVID-19-associated organ failure among inpatients with systemic lupus erythematosus in France: a nationwide study Ann Rheum Dis 81 2022 569 574 34893471 3 Filière de Santé des Maladies Auto-immunes et Auto-inflammatoires Rares FAI2R Recommendations for patients with autoimmune or autoinflammatory diseases during COVID-19 epidemic period https://www.fai2r.org/actualites/covid-19/ (in French). 4 Boekel L Steenhuis M Hooijberg F Antibody development after COVID-19 vaccination in patients with autoimmune diseases in the Netherlands: a substudy of data from two prospective cohort studies Lancet Rheumatol 3 2021 e778 e788 34396154 5 Jyssum I Kared H Tran TT Humoral and cellular immune responses to two and three doses of SARS-CoV-2 vaccines in rituximab-treated patients with rheumatoid arthritis: a prospective, cohort study Lancet Rheumatol 4 2022 e177 e187 34977602 6 Agence Nationale du Médicament et des Produits de Santé Tableau-ATU-RTU https://ansm.sante.fr/tableau-atu-rtu/tixagevimab-150-mg-cilgavimab-150-mg-solution-injectable-evusheld (in French). 7 Goulenok T Francois C Mendes C Improving COVID-19 vaccine coverage in patients with autoimmune and inflammatory diseases J Rheumatol 49 2022 118 119 34654731 8 Santé Publique France COVID-19: epidemiological update. Weekly report. Week 3. 27 January 2022. Point épidémiologique hebdomadaire https://www.santepubliquefrance.fr/maladies-et-traumatismes/maladies-et-infections-respiratoires/infection-a-coronavirus/documents/bulletin-national/covid-19-point-epidemiologique-du-27-janvier-2022 2022 9 Feng S Phillips DJ White T Correlates of protection against symptomatic and asymptomatic SARS-CoV-2 infection Nat Med 27 2021 2032 2040 34588689
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==== Front Lancet Lancet Lancet (London, England) 0140-6736 1474-547X World Health Organization. Published by Elsevier Ltd S0140-6736(22)00580-3 10.1016/S0140-6736(22)00580-3 Correspondence What comes next in the COVID-19 pandemic? Fisher Dale a Suri Sameera b Carson Gail c on behalf of the GOARN collaborators a Yong Loo Lin School of Medicine, National University of Singapore, 119228, Singapore b GOARN Operational Support Team, WHO, Geneva, Switzerland c International Severe Acute Respiratory and Emerging Infection Consortium, University of Oxford, Oxford, UK 11 4 2022 11 4 2022 © 2022 World Health Organization. Published by Elsevier Ltd. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcThe COVID-19 pandemic is not over, but with collaboration and solidarity, we can transition to a manageable endemic disease state sooner and better mitigate the most severe health and socioeconomic impacts. In this third year of pandemic response, society needs to focus on improved implementation of effective interventions to end the acute phase. Governments and health authorities have the necessary knowledge and tools in hand, in the form of vaccines, diagnostics, and therapeutics, but equitable availability of these tools remains a challenge globally. Today's decisions and efforts will continue to affect the pandemic's overall health, social, and economic toll. According to Our World in Data, 700 000 deaths were recorded as COVID-19 related between January and March, 2022, and only 14·5% of people in low-income countries have received at least one dose of a COVID-19 vaccine. SARS-CoV-2 variants continue to emerge as trust between governments and their constituents is tested, rendering sustained implementation of broad community-based interventions challenging. In many communities, crucial non-COVID-19 health services are yet to be fully restored to pre-pandemic levels. The emergency phase of the COVID-19 pandemic will eventually end, but when will be determined by collective actions. Likewise, what is learned and how society grows from this experience can still be influenced. The next pandemic need not catch the world so unprepared. The extraordinary nature of this pandemic calls for extraordinary analyses at global, national, and organisational levels. Society must reflect on what has been learnt about ourselves, our communities, our governance, and our preparedness and response systems. SARS-CoV-2 has caused too much harm in terms of death, morbidity, careers, relationships, finances, plans, and dreams for us to fall short of rigorous and independent after-action appraisal of the pandemic response. Communities have a right to understand why and how the pandemic response unfolded the way it did and to be assured improvements will be made. National and global leaders must use the knowledge gained from this pandemic and its reviews to ensure more robust multidisciplinary governance and equitable health and public health systems going forward. A fresh approach to global health security is needed as well as the development of better measures of preparedness, with a greater emphasis on collaboration and equity. We call for improved funding of partners to enhance both preparedness efforts and alert and rapid response capabilities at both national and international levels. Sustained financing for institutions is necessary to train future leaders and build a global response workforce that embraces multidisciplinary scientific and public health networks as a core component. Immediate operational response needs at the country and local levels must be supported with sufficient resources. Since its inception in 2000, the Global Outbreak Alert and Response Network (GOARN) has grown to encompass 270 partners and has responded to almost every major national and international outbreak through deployment of more than 3500 experts to over 100 countries. Drawing from this experience, we offer recommendations in the appendix outlining important next steps at this stage of the COVID-19 pandemic that would enable communities to better mitigate the health and societal impacts of the next pandemic. For Our World in Data COVID-19 deaths see https://ourworldindata.org/grapher/cumulative-deaths-and-cases-covid-19 For more on GOARN see https://extranet.who.int/goarn/ For Our World in Data COVID-19 vaccinations see https://ourworldindata.org/covid-vaccinations?country=OWID_WRL Supplementary Material Supplementary appendix We declare no competing interests.
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==== Front Lancet Respir Med Lancet Respir Med The Lancet. Respiratory Medicine 2213-2600 2213-2619 Elsevier Ltd. S2213-2600(22)00059-5 10.1016/S2213-2600(22)00059-5 Articles Myopericarditis following COVID-19 vaccination and non-COVID-19 vaccination: a systematic review and meta-analysis Ling Ryan Ruiyang a† Ramanathan Kollengode MD ac†* Tan Felicia Liying a Tai Bee Choo PhD ab Somani Jyoti FACP ad Fisher Dale Prof FRACP ad MacLaren Graeme MSc ac a Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore b Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore c Cardiothoracic Intensive Care Unit, National University Heart Centre, National University Hospital, Singapore d Division of Infectious Diseases, Department of Medicine, National University Hospital, Singapore * Correspondence to: Dr Kollengode Ramanathan, Cardiothoracic Intensive Care Unit, National University Heart Centre, National University Hospital, Singapore 119228 † Contributed equally 11 4 2022 7 2022 11 4 2022 10 7 679688 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background Myopericarditis is a rare complication of vaccination. However, there have been increasing reports of myopericarditis following COVID-19 vaccination, especially among adolescents and young adults. We aimed to characterise the incidence of myopericarditis following COVID-19 vaccination, and compare this with non-COVID-19 vaccination. Methods We did a systematic review and meta-analysis, searching four international databases from Jan 1, 1947, to Dec 31, 2021, for studies in English reporting on the incidence of myopericarditis following vaccination (the primary outcome). We included studies reporting on people in the general population who had myopericarditis in temporal relation to receiving vaccines, and excluded studies on a specific subpopulation of patients, non-human studies, and studies in which the number of doses was not reported. Random-effects meta-analyses (DerSimonian and Laird) were conducted, and the intra-study risk of bias (Joanna Briggs Institute checklist) and certainty of evidence (Grading of Recommendations, Assessment, Development and Evaluations approach) were assessed. We analysed the difference in incidence of myopericarditis among subpopulations, stratifying by the type of vaccine (COVID-19 vs non-COVID-19) and age group (adult vs paediatric). Among COVID-19 vaccinations, we examined the effect of the type of vaccine (mRNA or non-mRNA), sex, age, and dose on the incidence of myopericarditis. This study was registered with PROSPERO (CRD42021275477). Findings The overall incidence of myopericarditis from 22 studies (405 272 721 vaccine doses) was 33·3 cases (95% CI 15·3–72·6) per million vaccine doses, and did not differ significantly between people who received COVID-19 vaccines (18·2 [10·9–30·3], 11 studies [395 361 933 doses], high certainty) and those who received non-COVID-19 vaccines (56·0 [10·7–293·7], 11 studies [9 910 788 doses], moderate certainty, p=0·20). Compared with COVID-19 vaccination, the incidence of myopericarditis was significantly higher following smallpox vaccinations (132·1 [81·3–214·6], p<0·0001) but was not significantly different after influenza vaccinations (1·3 [0·0–884·1], p=0·43) or in studies reporting on various other non-smallpox vaccinations (57·0 [1·1–3036·6], p=0·58). Among people who received COVID-19 vaccines, the incidence of myopericarditis was significantly higher in males (vs females), in people younger than 30 years (vs 30 years or older), after receiving an mRNA vaccine (vs non-mRNA vaccine), and after a second dose of vaccine (vs a first or third dose). Interpretation The overall risk of myopericarditis after receiving a COVID-19 vaccine is low. However, younger males have an increased incidence of myopericarditis, particularly after receiving mRNA vaccines. Nevertheless, the risks of such rare adverse events should be balanced against the risks of COVID-19 infection (including myopericarditis). Funding None. ==== Body pmcIntroduction Globally, more than 10 billion doses of COVID-19 vaccines have been administered as of March, 2022.1 The side-effects of vaccination are usually mild and self-limiting; however, myopericarditis is increasingly being reported after COVID-19 vaccination.2 It has been postulated that the mRNA in the vaccine might activate aberrant innate and acquired immune responses that potentially trigger myocardial inflammation as part of a systemic reaction. Although a number of mechanisms have been suggested, the actual mechanism for the pathogenesis of post-vaccine myopericarditis has not been established.3, 4, 5, 6, 7, 8, 9 Myopericarditis is a rare complication of vaccination against viruses, and has previously been linked only to smallpox vaccination.10 A study in Israel, however, suggested that mRNA COVID-19 vaccines significantly increase the risk of myocarditis, particularly in males and in people aged 16–39 years.11 In addition, numerous case reports and series have been published on myopericarditis in people vaccinated against COVID-19.12, 13 Whether these findings reflect a true increase in incidence or merely improved reporting and recall bias remains inconclusive.14 We conducted a systematic review and meta-analysis comparing the incidence of myopericarditis following vaccination against COVID-19 with that following vaccination against other diseases to explore the risk of myopericarditis in subpopulations receiving COVID-19 vaccinations and to quantify the incidence of myocarditis, pericarditis, and mortality after receiving a vaccine. Research in context Evidence before this study The risk of myopericarditis following COVID-19 vaccination has been subject to considerable scrutiny both by the scientific community and the general population given the increased reporting of such events, especially in young adults. In some studies, the risk of myopericarditis was up to three times higher than that of controls. Notably, myopericarditis has been associated with mRNA COVID-19 vaccines, and multiple immunological mechanisms have been proposed for this. We searched four international databases between Jan 1, 1947, and Dec 31, 2021, for studies reporting on myopericarditis among people receiving vaccines using the keywords “vaccines”, “myocarditis”, and “pericarditis”, without any language restrictions. We identified 4919 studies from the search strategy, of which 22 observational studies were relevant to our study. Although we found previous systematic reviews that pooled the incident cases of myopericarditis following vaccination, we did not identify any meta-analyses evaluating the proportion of people who develop myopericarditis following vaccination. Added value of this study Our meta-analysis was conducted to determine if the increased reporting of myopericarditis was a true increase in incidence or a result of improved reporting systems and recall bias. Among 260 million people who received more than 405 million vaccine doses as reported in studies and databases, we found that the incidence of myopericarditis was not elevated after COVID-19 vaccination (18 cases per million vaccine doses) when compared with after non-COVID-19 vaccination (56 cases per million vaccine doses) or relative to the background pre-pandemic incidence rate of myopericarditis. In people who received a COVID-19 vaccine, a significantly higher incidence of myopericarditis was found in males (vs females), those younger than 30 years (vs those aged 30 years or older), those receiving a second dose of vaccine (vs a first or third dose), or those receiving an mRNA vaccine (vs a non-mRNA vaccine). Using robust-variance estimation methods to account for intra-study correlation, decreasing age (excluding people younger than 12 years) was associated with an increased incidence of myopericarditis. Implications of all the available evidence In the general population, the risk of myopericarditis after receipt of COVID-19 vaccination is low. The incidence of myopericarditis from COVID-19 vaccination also appears to be lower than that from COVID-19 infection. However, the incidence of myopericarditis for young men after mRNA COVID-19 vaccination appears higher than expected. These findings might be of interest to policy makers determining national vaccination protocols, particularly as many countries will be encouraging a booster dose of vaccination during 2022. Finally, our findings inform the general public of the rarity of myopericarditis, placing the risks into perspective and allowing for a more informed decision regarding COVID-19 vaccination. Methods Search strategy and selection criteria This study was registered with PROSPERO (CRD42021275477) and conducted in accordance with the PRISMA statement (appendix p 3).15 The study protocol is available online. We searched four databases (MEDLINE via Pubmed, Embase, Cochrane, and Scopus) for relevant studies, published in English, using the keywords “vaccines”, “myocarditis”, and “pericarditis”, from Jan 1, 1947, to Dec 31, 2021 (appendix p 6). Grey literature was searched by reviewing the reference lists of included studies and review articles. Observational studies reporting on people in the general population who had myopericarditis in temporal relation to receiving vaccines were included in our review. We excluded randomised controlled trials, case reports, studies that reported on a specific subpopulation of patients, non-human studies, and studies in which the number of doses was not reported. Data collection and risk of bias assessment Data were collected using a prespecified data extraction form (appendix p 7). Where data were not explicit, we calculated the incidence using the reported number of patients with myopericarditis, the number of vaccine doses and types administered, and the incidence rate, as appropriate. Intra-study risk of bias was rated using the Joanna Briggs Institute (JBI) checklist for prevalence studies.16 Overall certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. The screening of studies, data collection, and risk of bias assessment were done independently in duplicate by RRL, FLT, and KR; disagreements were resolved by consensus. Data synthesis The primary outcome was the incidence of myopericarditis after any vaccination; secondary outcomes included the incidence of myocarditis, pericarditis, and mortality after any vaccination. Given the heterogeneity in reporting of individual cases of myopericarditis and pericarditis, we define in our review myopericarditis as an umbrella term describing myocarditis, pericarditis, or cases with features of both myocarditis and pericarditis, as reported in the databases or defined within the individual studies. Among studies that reported on myocarditis and pericarditis individually, we pooled the incidence rates of both conditions accordingly. Statistical analyses were done using R version 4.0.1. We conducted random-effects meta-analyses (DerSimonian and Laird) and computed 95% CIs with the Clopper-Pearson method.17, 18 Although we initially intended to use the Freeman-Tukey double arcsine transformation, several concerns about its use in meta-analyses of rare events were raised, and hence we opted instead to use the logit transformation for our analyses.19, 20 We also did a sensitivity analysis excluding any database and preprint data and studies with high risks of bias (JBI score <7) to assess the impact of intra-study risk of bias on the reporting of myopericarditis. Publication bias was assessed by visual inspection of funnel plots as well as by Egger's test. We analysed the difference in incidence of myopericarditis among prespecified subpopulations including the type of vaccine (COVID-19 and non-COVID-19 vaccines, and mRNA COVID-19 vaccine and non-mRNA COVID-19 vaccine) and age (paediatric [<18 years] and adult [≥18 years]) using the random-effects Q test. We further investigated the differences between individual non-COVID-19 vaccines (smallpox, influenza, and mixed [defined by the individual studies and including varicella; yellow fever; oral polio vaccine; measles, mumps, and rubella; meningococcal; and diphtheria, pertussis, and tetanus]) with COVID-19 vaccines. As there have been concerns about myopericarditis being more common in young men receiving their second dose of COVID-19 vaccination,21 we compared its incidence by sex (male and female), age group (<30 or ≥30 years), and dose (first, second, and third) specifically for COVID-19 vaccines. As inter-study heterogeneity between observational studies of large sample sizes tends to be overestimated by I 2 statistics, we assessed the heterogeneity as part of the GRADE approach, accounting for both quantitative heterogeneity (using I 2 statistics, exploring for sources of heterogeneity using subgroup analysis and meta-regression) and qualitative heterogeneity (distribution of the point estimates and degree of overlap of the 95% CIs of studies in the forest plots).22, 23 p<0·05 was considered to indicate significance in our analysis. The pooled incidence of myopericarditis and mortality are presented as cases per million vaccine doses. Post-hoc analysis Given the amount of attention myopericarditis in COVID-19 vaccination among younger people (particularly males) has received, we did an inverse-variance weighted meta-regression between the age and the incidence of myopericarditis among four studies that provided age-stratified data for vaccinees.21, 24, 25, 26 To account for intra-subject correlation, we estimated SEs using robust-variance estimates, incorporating a random-effects term for each study, and a moderator term for age, which was modelled as a continuous variable.27 We clustered the pooled estimates around each unique study identifier to derive the robust-variance estimates for SE. In addition, we evaluated differences in the incidences of myocarditis and pericarditis between COVID-19 and non-COVID-19 vaccines. Finally, to estimate the baseline incidence of myopericarditis from COVID-19 infection, we did a rapid review of the literature and pooled the incidence of myopericarditis among patients with COVID-19 infection (appendix p 8). We included studies with at least ten adult patients with COVID-19 reporting on myopericarditis, and excluded any case reports, reviews, post-mortem studies or studies that did not report the number of patients with COVID-19. In the event of overlapping studies, we included the largest study and excluded other studies. Role of the funding source There was no funding source for this study. Results Of 4919 studies, 156 full-text publications were reviewed. 22 observational studies totalling 405 272 721 vaccine doses were included in the meta-analysis (figure 1 ; appendix pp 10–17).13, 21, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 11 studies reported on 395 361 933 doses of COVID-19 vaccines,13, 21, 24, 25, 26, 29, 30, 35, 36, 41, 43 six studies reported on 2 900 274 doses of smallpox vaccines,28, 31, 33, 39, 40, 44 two studies on 1 521 782 doses of influenza vaccines,34, 42 and three studies on a variety of non-COVID-19 vaccines (such as varicella; yellow fever; oral polio vaccine; measles, mumps, and rubella; meningococcal; diphtheria, pertussis, and tetanus; BCG; hepatitis; and typhoid; 5 488 732 doses).32, 37, 38 Across the nine studies that specified the type of COVID-19 vaccine, 290 730 653 doses of mRNA vaccines and 51 969 677 doses of non-mRNA vaccines were administered. Definitions of pericarditis, myocarditis, and myopericarditis in individual studies or databases are summarised in the appendix (pp 10-17). The intra-study risk of bias and GRADE assessment are also provided in the appendix (pp 18–20); apart from one study,25 all studies were of good quality (JBI score >7).Figure 1 Flow diagram of study identification and inclusion The overall incidence of myopericarditis was 33·3 cases (95% CI 15·3–72·6) per million vaccine doses (high certainty, Egger's test p=0·12; figure 2 ; appendix p 21). Sensitivity analyses excluding studies with high risks of bias and databases found that the pooled incidence of myopericarditis for COVID-19 and non-COVID-19 vaccines did not change significantly (appendix p 22).Figure 2 Incidence of myopericarditis following vaccination in studies investigating COVID-19 and non-COVID-19 vaccines The pooled incidence of myopericarditis following vaccination was 18·2 cases per million doses of COVID-19 vaccine and 56·0 cases per million doses of non-COVID-19 vaccine (p=0·20). The overall incidence of myopericarditis in the general population did not differ significantly after receipt of COVID-19 vaccines (18·2 cases [10·9–30·3] per million doses, high certainty) compared with non-COVID-19 vaccines (56·0 [10·7–293·7], moderate certainty, p=0·20; figure 2). Comparing COVID-19 vaccines with each type of non-COVID-19 vaccine found a significant difference between subpopulations (global p<0·0001). The incidence of myopericarditis was 132·1 (81·3–214·6) per million doses of smallpox vaccine (p<0·0001 vs COVID-19 vaccines), 1·3 (0·0–884·1) per million doses of influenza vaccine (p=0·43), and 57·0 (1·1–3036·6) per million doses for studies reporting on a variety of vaccines (p=0·58; appendix p 23). Between the adult subgroup (26·0 cases [11·8–57·4] per million doses; 298 508 729 doses, 14 studies) and paediatric subgroup (18·4 [4·7–72·9]; 12 145 663 doses, six studies), the incidence of myopericarditis did not differ significantly (p=0·67; appendix p 24). Among COVID-19 vaccines, the incidence of myopericarditis was significantly higher (p=0·0010) among those who received mRNA vaccines (22·6 cases [12·2–42·0] per million doses; 290 730 653 doses, nine studies; figure 3 ) than among those who received non-mRNA vaccines (7·9 [7·2–8·7]; 51 969 677 doses, three studies). Furthermore, incidence of myopericarditis was significantly higher in people younger than 30 years than in people aged 30 years or older, in those receiving a second dose of vaccination than in those receiving a first or third dose, and in males than in females (table ; appendix pp 25–27). Among people younger than 30 years, the incidence of myopericarditis was approximately ten times higher in males than in females; for people older than 30 years, the incidence was around three times as high in males than in females (table; appendix pp 28–29). Further details on the demographic and clinical characteristics of patients with myopericarditis following vaccination are summarised in the appendix (pp 30–35). Time from vaccination to symptom onset was reported heterogeneously and hence these data were not pooled; nonetheless, most studies reported a window of 1–2 weeks before symptom presentation.Figure 3 Incidence of myopericarditis following vaccination in studies investigating mRNA and non-mRNA COVID-19 vaccines The pooled incidence of myopericarditis following COVID-19 vaccination was 22·6 cases per million doses of mRNA vaccine and 7·9 cases per million doses of non-mRNA vaccine (p=0·0010). Table Subgroup analyses among people who received COVID-19 vaccines Studies, n Vaccine doses, n Myopericarditis cases per million vaccine doses (95% CI) p value Type of vaccine .. .. .. 0·0010 mRNA 913, 21, 24, 25, 26, 29, 35, 36, 41 290 730 653 22·6 (12·2–42·0) .. non-mRNA 335, 36, 41 51 969 677 7·9 (7·2–8·7) .. Age* .. .. .. <0·0001 ≥30 years 321, 24, 26 143 154 756 2·9 (1·8–4·7) .. <30 years 521, 24, 25, 26, 29 30 564 464 40·9 (18·4–90·9) .. Dosing* .. .. .. <0·0001 First dose 813, 21, 25, 26, 29, 30, 35, 36 54 971 473 7·2 (3·8–14·0) .. Second dose 813, 21, 25, 26, 29, 30, 35, 36 46 754 686 31·3 (14·1–69·8) .. Third dose 135 2 643 203 3·0 (1·5–6·1) .. Sex* .. .. .. 0·0019 Female 521, 24, 26, 35, 29 123 336 615 5·1 (2·3–11·5) .. Male 521, 24, 26, 35, 29 110 454 182 23·0 (8·9–59·4) .. Sex by age group* Age <30 years .. .. .. <0·0001 Male 521, 24, 25, 26, 29 14 532 527 59·7 (29·8–119·4) .. Female 421, 24, 26, 29 16 161 957 5·3 (3·6–8·0) .. Age ≥30 years .. .. .. 0·034 Male 321, 24, 26 66 729 801 4·0 (2·4–6·8) .. Female 321, 24, 26 76 424 955 1·7 (0·9–3·1) .. Forest plots of the studies included in these subgroup analyses are provided in the appendix (pp 25–29). * Data extracted from the Therapeutic Goods Administration (Australian Government Department of Health)26 on Dec 31, 2021, were not amenable for these analyses; therefore, we opted to use data from our previous most recent update (Oct 15, 2021), in which data of sufficient granularity were provided; all other analyses were conducted on the basis of data extracted on Dec 31, 2021. Meta-regression among five studies based on the age-stratified incidence of myopericarditis after COVID-19 vaccination using robust variance estimates found that age was negatively associated with myopericarditis (regression coefficient −0·069 [95% CI −0·094 to −0·045], p=0·0030, figure 4 ).21, 24, 25, 26, 29 Figure 4 Effect of age on incidence of myopericarditis following COVID-19 vaccination Strata-level meta-regression between age and logit-transformed robust-variance estimated incidence of myopericarditis following COVID-19 vaccination. Bubble sizes correspond to the weights of each study, which are computed as an inverse of the SE of the strata-level pooled estimate. Horizontal error bars correspond to the range of ages that each strata represents. Excluding people younger than 12 years, for whom few data were reported in the studies included, the incidence of myopericarditis increases as the mean age of each subgroup decreases. A post-hoc analysis was done to investigate the incidence of myopericarditis in patients with COVID-19 (appendix pp 8–9, 36–37). Of 6181 studies, we assessed 393 full-text records and included 21 studies with 2 453 491 patients hospitalised with COVID-19 and had clinical or radiological suspicion for myopericarditis,45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65 among whom there were 48 904 cases of myopericarditis (1·1% [95% CI 0·5–2·2]; appendix p 37). Across all vaccines, the incidence of myocarditis was 16·0 cases (95% CI 8·2–31·2) per million doses (180 995 007 doses, seven studies, moderate certainty; appendix p 38). The incidence of myocarditis was significantly lower (p<0·0001) among those receiving COVID-19 vaccines (8·9 [6·7–11·8]; 179 664 350 doses, five studies) than those receiving non-COVID-19 vaccines (79·4 [63·6–99·0]; 1 330 657 doses, two studies). Pericarditis had an incidence across all vaccines of 16·7 cases (5·8–48·0) per million doses (169 138 458 doses, seven studies, moderate certainty; appendix p 39), and did not differ significantly (p=0·64) between COVID-19 vaccines (10·1 [5·8–17·4], 166 286 019 doses, three studies) and non-COVID-19 vaccines (20·0 [1·2–328·5]; 2 852 439 doses, four studies; appendix p 39). The pooled all-cause mortality following vaccination was 7·8 deaths (95% CI 1·8–34·7) per million doses (240 709 487 doses, ten studies, high certainty), and overall mortality was similar (p=0·93) between COVID-19 vaccines (8·4 [2·0–35·9]; 238 540 345 doses, five studies) and non-COVID-19 vaccines (7·2 [0·2–217·5]; 2 169 142 doses, five studies; appendix p 40). Discussion Our systematic review and meta-analysis shows that the incidence of myopericarditis in people who received COVID-19 vaccines was not significantly different from that in people who received non-COVID-19 vaccines in general, and was lower than that in people who received smallpox vaccines. Thus, the overall risk of myopericarditis appears to be no different for this very new group of vaccines against COVID-19 than for traditional vaccines against other pathogens. We also found that young men have a higher incidence of myopericarditis than others receiving mRNA COVID-19 vaccinations. Among the general population, the background pre-pandemic incidence of myopericarditis varies greatly depending on age and sex,66, 67, 68 and it is possible that it has been underestimated because of the existence of subclinical myopericarditis.69 Overall, the background incidence of myopericarditis is estimated to be between 9·5 and 21·6 per million people per month,67, 68 whereas the expected incidence of myopericarditis in vaccine recipients was 2·4 to 550 per million vaccinees.70 In our meta-analysis, the incidence of myopericarditis following vaccination was 18·2 cases (95% CI 10·9–30·3; 8·9 cases of myocarditis and 10·1 cases of pericarditis) per million COVID-19 vaccine doses and 56·0 (10·7–293·7) per million doses of non-COVID-19 vaccines. The background incidence of myocarditis is 8·3–16·7 per million people per month69 and of pericarditis is 4·78–21·67 per million people per month.71, 72 Notably, the specific incidence of myopericarditis after smallpox vaccination was significantly higher than after COVID-19 vaccines, and the incidence following influenza and other vaccines was similar to that following COVID-19 vaccines. The studies reporting on smallpox vaccination were primarily done in US military personnel, most of whom would be young men, and could account for the increased incidence of myopericarditis in smallpox vaccinees. The increased incidence of myopericarditis after non-COVID-19 vaccination might suggest that myopericarditis is a side-effect of the inflammatory processes induced by vaccination and is not uniquely a result of exposure to SARS-CoV-2 spike proteins through COVID-19 vaccination or infection. The risks of such infrequent adverse events are outweighed by the benefits of vaccination, which include a lower risk of infection, hospitalisation, severe disease, and death from COVID-19.73, 74 In people aged 30 years or older, the incidence of post-vaccination myopericarditis was 2·9 cases (95% CI 1·8–4·7) per million vaccine doses. Being aware of a possible association between COVID-19 vaccination and myopericarditis, clinicians might have had an inherently lower threshold for investigating a patient with non-specific chest pain after COVID-19 vaccination, eventually leading to a diagnosis of myopericarditis. Additionally, given current robust vaccine surveillance systems and the fact that COVID-19 vaccines have received a much higher degree of scrutiny than previous vaccines, the possibility of relative under-reporting of adverse events following non-COVID-19 vaccinations cannot be excluded, despite mass vaccination of more than 6 billion people in the past year. Our analysis found that myopericarditis was more common among those who were male and under the age of 30 years. The findings of our analysis appear to be concordant with the literature: male sex and younger age groups are more susceptible to myopericarditis after COVID-19 vaccination.11, 24 Previous studies have shown that myocarditis after the second dose of an mRNA COVID-19 vaccine occurs clinically in approximately one in 10 000 young males,75 which is approximately 50–100 times higher than expected (based on claims made in 2017–19 from the IBM MarketScan Commercial Research Database).76 In the general population before the COVID-19 pandemic, the incidence of myocarditis was generally higher in males, and highest in young adults.77, 78 Thus far, guidelines for COVID-19 vaccine-induced myopericarditis have mainly focused on early diagnosis and treatment,79, 80, 81, 82 while some have recommended avoiding strenuous exercise for 2 weeks following vaccination.83 Several national guidelines also highlight the indications and contraindications for vaccine subtypes in this context.80, 83 Although the prognosis of this self-limiting condition is generally good, long-term outcomes for affected patients after 3 months and 6 months are currently awaited.84 In people who received a COVID-19 vaccine, our results showed that myopericarditis was nearly four times as common in those receiving an mRNA vaccine than a non-mRNA vaccine and in those receiving their second dose of vaccine compared with a first or third dose. Similarly, a large study from Israel showed that mRNA COVID-19 vaccination was associated with a higher risk of myocarditis than the background population rate (risk ratio 3·24 [95% CI 1·55–12·44]). Over 90% of people with myocarditis after mRNA COVID-19 vaccination were male, with a median age of 25 years (IQR 20–34). The authors also highlighted an increased risk of myocarditis following COVID-19 infection (risk ratio 18·28 [95% CI 3·95–25·12]).2 A study of cardiac MRI in young athletes recovered from COVID-19 showed a prevalence of myocarditis of 2·1%,85 whereas our post-hoc analysis of myopericarditis in patients hospitalised with COVID-19 with radiological or clinical suspicion of myopericarditis found a prevalence of 1·1% (95% CI 0·5–2·2). It is well recognised that such rare adverse reactions are unlikely to be identified in phase 3 trials because sample sizes are not large enough to capture these events. Following the initial publication of results from phase 3 trials of mRNA vaccines, post-marketing evaluation, including those by the US Vaccine Adverse Event Reporting System, provides opportunities to implement vaccine programmes with more precision. As of December, 2021, the omicron variant is spreading rapidly around the world and is set to be the dominant variant globally in early 2022. Consequently, vaccination and booster vaccines will be of considerable importance,86, 87 particularly for mRNA vaccines, which can be manufactured rapidly.88 Just as different population groups have been found to be more susceptible to thrombosis with thrombocytopenia syndrome (TTS) after COVID-19 vaccination,89 different population groups (in our analysis, those of male sex and younger age) are more susceptible to myopericarditis. Just as there are appropriate strategies to address TTS, reasonable policies—such as preferentially offering a non-mRNA vaccine to males, particularly those younger than 18 years—could be considered to manage the risk of myopericarditis, while considering the overall benefits and harms of the vaccines. These policies will become more crucial as more countries begin offering booster doses of COVID-19 vaccines to more people under the age of 30 years. However, the risk and benefit calculations on such policy-making decisions must take into account the local epidemiology (ie, the incidence rate of COVID-19 infection at the time and location that the decision is being made), whether there are other non-mRNA COVID-19 vaccines available, and the risk of morbidity from COVID-19 infection for that particular group, while recognising that such factors and decisions will be dynamic during a pandemic. It is also important to interpret the risks and benefits in the context of the background incidence of myopericarditis across subpopulations—ie, the risk of myopericarditis will depend on the prevailing prevalence of COVID-19 locally and at the time of vaccination. There are three main strengths of our study. First, with a sample size of more than 400 million vaccine doses, to our knowledge, this study is the largest to quantify the incidence of myopericarditis post-vaccination. Second, we compared the incidence of myopericarditis between COVID-19 and non-COVID-19 vaccines, which gives an indication of whether COVID-19 vaccines increase the rate of myopericarditis compared with other routine non-COVID-19 vaccinations. Third, the analyses between subpopulations within those receiving COVID-19 vaccines help to clarify potential at-risk populations and could contribute to driving better vaccination policy-making decisions. Nonetheless, we recognise several limitations of our analysis. Most of the studies included in our review did not report on outcomes of patients younger than 12 years receiving vaccination against COVID-19, as vaccination of this younger age group is relatively recent. As such, the findings of our review are not generalisable to children in that age group. Additionally, the comparisons made between COVID-19 and non-COVID-19 vaccines were made indirectly across studies from different time periods. There are far more sensitive tools (eg, MRI, widespread echocardiography, or biopsy) being used currently that did not play as large a role in diagnosing myopericarditis previously in people receiving non-COVID-19 vaccines. This disparity introduces heterogeneity to the reporting and treatment of myopericarditis, which results in potential confounders within our analysis. There are other important vaccines (including, but not limited to, those against hepatitis, Haemophilus influenzae, pneumococcus, and diphtheria, pertussis, and tetanus) that were under-represented in our analysis, suggesting that cases of myopericarditis after these commonly used vaccines occurred very rarely. Furthermore, the 95% CIs for the pooled estimate of non-COVID-19 vaccines were relatively wide, most likely due to two main factors: heterogeneity and variability in the type of vaccine (for which we conducted a subgroup analysis of non-COVID-19 vaccine subtypes to explore as a potential source of heterogeneity), and imprecision resulting from a smaller sample size than that for COVID-19 vaccines. Because COVID-19 vaccines were developed in response to a new global pandemic, they have been administered at an unprecedented rate, with millions of doses given within a short period, unlike any of the comparator non-COVID-19 vaccines. As such, the relative incidence of myopericarditis following COVID-19 vaccination should be interpreted in this context, although it is probably more accurate than the incidence of non-COVID-19 vaccines. Our analysis is also based on study-level data, which limited our analysis of subpopulations. Although we were able to partially account for this by conducting a strata-level meta-regression analysis by age, more granular data are required to better guide the clinical decision-making process. Our analysis also uses data from registries and databases, which are inherently limited by the lack of longitudinal data, and some of the coded cases of myopericarditis might turn out to not have myopericarditis following further investigation of the symptoms. Some studies only reported the number of doses of vaccines that were administered. As a result, we had to analyse the incidence of myopericarditis by doses and not patients. Most of the studies included in our analysis did not report on myocarditis or pericarditis specifically, but grouped both complications under the umbrella term myopericarditis. Nonetheless, these remain the best data available on myopericarditis following vaccination. Additionally, myopericarditis occurring in temporal relation with COVID-19 vaccination cannot always confirm a diagnosis of vaccine-induced myopericarditis, as it is difficult to distinguish it from myopericarditis due to other causes. Finally, our review was unable to account for the disease burden or severity of myopericarditis, which, while usually mild and self-limiting, can take a more fulminant course eventually requiring mechanical circulatory support. There are also other side-effects that were not addressed in this study that might influence a person's decision to receive a vaccination. In conclusion, this meta-analysis of more than 400 million doses of vaccines suggests that the overall incidence of myopericarditis following COVID-19 vaccination is similar to that in the published literature on its incidence after influenza vaccination, and is lower than the incidence after live smallpox vaccination. The incidence of myopericarditis in younger males after mRNA COVID-19 vaccination is higher than expected by comparison with other age groups. The scale of mass global vaccination and enhanced surveillance might account for the increased reporting of this adverse event in the context of COVID-19 vaccination. Nonetheless, certain subpopulations—those of male sex or younger age and those receiving an mRNA vaccine, particularly the second dose—appear to be at increased risk of myopericarditis following COVID-19 vaccination. These findings are important additions to the conversation when weighing the risks and benefits of COVID-19 vaccination during this pandemic. Although the results of our analysis place the risks of COVID-19 vaccination into perspective, the decision to vaccinate should be informed by appropriately weighing the benefits and harms of COVID-19 vaccination, the local risk of exposure to COVID-19 infection at the time, and the risk of myopericarditis from COVID-19 infection itself. This online publication has been corrected. The corrected version first appeared at thelancet.com/respiratory on May 10, 2022 Data sharing This manuscript makes use of publicly available data from the included studies and their supplementary information files; therefore, no original data are available for sharing. Declaration of interests KR has received honoraria for webinars unrelated to the topic from Baxter. All other authors declare no competing interests. Supplementary Material Supplementary appendix Acknowledgments We thank Suei Nee Wong (Medical Library, National University of Singapore) for her assistance with the search strategy and Megan Ruien Ling (Yong Loo Lin School of Medicine, National University of Singapore) for her assistance with the screening of studies and data collection. Contributors KR and RRL designed the study and drafted the manuscript. RRL, KR, and FLT contributed to the search strategy, screening of articles, and data collection. RRL and FLT contributed to the risk of bias assessment and made the tables and figures. RRL, BCT, and KR contributed to data analysis and interpretation. KR, RRL, GM, JS, DF, and BCT contributed to critical revision of manuscript for intellectually important content. All authors provided critical conceptual input, interpreted the data analysis, and read and approved the final draft of the manuscript. RRL, FLT, and KR accessed and verified the data. 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==== Front Pharm Res Pharm Res Pharmaceutical Research 0724-8741 1573-904X Springer US New York 35411507 3257 10.1007/s11095-022-03257-3 Research Paper Role of Disease Progression Models in Drug Development Barrett Jeffrey S. jbarrett@c-path.org 1 Nicholas Tim timothy.nicholas@pfizer.com 2 Azer Karim kazer@axcellatx.com 3 Corrigan Brian W. brian.corrigan@pfizer.com 2 1 grid.417621.7 Rare Disease Cures Accelerator Data Analytics Platform, Critical Path Institute, Tuscon, AZ 85718 USA 2 grid.410513.2 0000 0000 8800 7493 Global Product Development, Pfizer Inc, 445 Eastern Point Rd, Groton, CT 06340 USA 3 Axcella Therapeutics, 840 Memorial Drive, Cambridge, MA 02139 USA 11 4 2022 113 1 3 2022 5 4 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The use of Disease progression models (DPMs) in Drug Development has been widely adopted across therapeutic areas as a method for integrating previously obtained disease knowledge to elucidate the impact of novel therapeutics or vaccines on disease course, thus quantifying the potential clinical benefit at different stages of drug development programs. This paper provides a brief overview of DPMs and the evolution in data types, analytic methods, and applications that have occurred in their use by Quantitive Clinical Pharmacologists. It also provides examples of how these models have informed decisions and clinical trial design across several therapeutic areas and at various stages of development. It briefly describes potential new applications of DPMs utilizing emerging data sources, and utilizing new analytic techniques, and discuss new challenges faced such as requiring description of multiple endpoints, rapid model development, application of machine learning-based analytics, and use of high dimensional and real-world data. Considerations for the continued evolution future of DPMs to serve as community-maintained expert systems are also provided. KEY WORDS decision-making disease progression model drug development MIDD ==== Body pmcIntroduction A fundamental tenet of model-informed drug development (MIDD) is incorporation of available information to inform the development of a new medicine [1]. For many diseases, understanding the course of disease as a function of time, disease severity, and impact of treatment can aid in answering questions related to impact of a new medicine, and how it can be of greatest value to the patient. A model-informed approach offers an ideal solution to incorporate all the types of information available to the researcher across disparate sources. It serves as the framework to integrate knowledge, and to build and grow an expert understanding of both disease and treatment impacts that can inform the development and use of medicines. Disease progression modeling (DPM) integrates mathematical functions and underlying scientific pathophysiologic principles to quantitatively describe the time course of disease progression. The key concepts and developments leading to their increased use have been well-described previously [2, 3]. Historically, these models have been empiric, but more recent examples include semi-mechanistic, systems biology, and systems pharmacology approaches [4]. Over time, the complexity of data types used, analytic approaches, and potential applications have continued to grow. However, irrespective of the underlying modeling techniques, these models normally contain three elements essential for use in drug development (see Fig. 1). The three components of DPMs allow for use across a variety of applications at various stages in drug development.Fig. 1 Components of a Disease Progression Model for Use in Drug Development. DPM Applications During development, MIDD strategies encompass a variety of model types as tools to inform different decision points and problems, ideally in an integrated and complimentary manner. Each model type offers a different approach and may be constructed on different data. They can inform different problems or may be used together to offer fidelity to a particular decision (assuming they make the same recommendation). Often, model types evolve in parallel and inform each other, and may be used interchangeably. DPMs have become an important tool in the Quantitive Clinical pharmacologist’s toolkit, and the application of DPMs to inform decision making has grown over time with the realization of their potential. They have become one of the most common applications for models in drug development. The utility of connecting disease progression models with clinical trial simulations was identified early, dating back to the pivotal work of Nick Holford and others in Parkinson’s and Alzheimer’s Disease [5–7]. These early examples illustrated the approach and benefit to understanding disease progression linked to clinical outcomes and illustrated the potential of data sharing and meta-analyses. These analyses provided insights int the nature of the drug effects within the clinical trials used as a data source. Since then, a diverse array of similar examples across multiple therapeutic areas have been completed and made available in the public domain [8–10]. An important learning from these initial clinical trial-informed efforts was that data pooled solely from one or a few completed clinical trials rarely contained sufficient information to fully characterize disease progression (e.g., insufficient duration of observation). As investigators explored other applications of DPMs, it became necessary to utilize a broader array of existing and emerging data sources, especially if the intent was to fully characterize and link progression attributes and biomarkers to long-term disease outcomes or if accounting for real-world experience. Over time, there have been significant advances in linking observed outcomes back to the underlying mechanisms of drug action, and to related biomarkers. Mechanistic-based models inform decisions related to pathway and target selection, candidate selection, biomarker strategy, patient selection and optimal study design for early signals of efficacy [2]. Mechanistic DPMs may identify patient populations most likely to respond to therapy. They can identify patient-responder phenotypes that inform enrollment criteria and that aid in assessment of commercial value by determining the prevalence of the proposed target indication. They can answer the longitudinal design-related clinical study questions (duration of study, optimal timing of assessments). DPMs can assess impact of drug combinations, especially when combined with a Quantitive Systems Pharmacology (QSP) model. In more recent examples, QSP models form the basis for disease progression models themselves [11]. A common application of DPMs in drug development is use of QSP-linked DPMs to design and interpret clinical Proof of Concept, guide portfolio decisions including franchise ranking and DPM-linked clinical trial simulation models to evaluate design scenarios and judge the probability of technical success (PTS) across various design options [1, 12]. The trial simulation application is particularly important given the stage and cost of development at this juncture (typically phase 2 or 3 but can also reflect post-marketing efforts). Beyond clinical trial simulation, there are also applications in health economic and clinical outcomes research supported by both academic and governmental agencies that could inform both funding allocation and policy decisions respectively [13, 14] and amongst the provider and payer communities to support formulary and reimbursement policy and decisions [15]. At later stages of development this type of model framework can be used to evaluate clinical study designs including endpoints, substrata, and sample size as well as clinical operations including patient and site selection. Data Sources and Requirements for DPMs The extent to which modeling efforts are successful often depends upon the availability and appropriateness of the data used to construct them, development of analysis plans apriori, and alignment of MIDD deliverables with timing of decision points with the team responsible. Irrespective of the planned application, and given the increasing diversity of DPM application [2], early investment and planning in data requirements is warranted to maximize a DPM’s value. Early development and planning also allows for model enhancement over time, and to add data as it is generated and to answer emerging questions in a timely manner. A fundamental concept in MIDD is that the systematic collection and quantification of results from all available sources is required to best inform decision making at every stage [1]. As such, data requirements for a DPM model may vary based on the intended context of use (COU). As model requirements change with stage of development, different data may be required to inform new COU as the utility of the model is challenged by later-stage development questions. An early-stage model may be informed by natural history data, patient registry data and preclinical data that describe mechanistic underpinnings of the relevant disease biology (like QSP model requirements). In later stages, the model may be augmented by earlier patient studies within the program, advancements in understanding of the disease, or from patient data from previous programs in the same population. While providing flexibility in the types of information that can be incorporated, these disparate data types (large, survey-based, and often unstructured data with small, structured data or simply parameter estimates with distributional data or assumptions) can also create a challenge for assumptions around suitability for data integration and model definition. In general, the data used to inform DPMs has evolved with the growing complexity of data sources available (see Fig. 2). Early DPM examples primarily were constructed using individual level data taken from within clinical trials and natural history studies used to inform the model. These individual level DPMs were based on clinical endpoints, incorporated linear progression of disease and potential symptomatic or disease modifying treatment effects [5, 6], but the data itself was often not available outside of the organizations that generated it. When published, this information was typically aggregated to a set of summary statistics. As individual level data was not readily available to the larger group of quantitative scientists, aggregate literature data was used to characterize disease progression in longitudinal model based meta-analyses (MBMA) [7, 8]. These MBMA were able to incorporate more information across industrial and academic sources which were previously unavailable. Further evolution of DPM branched into combined individual and aggregate data derived longitudinal MBMA [7, 40].Fig. 2 Flow of Information from Data Source to DPM Development Utilization*. *Information Propagates from Data Sources (light green nodes) to Utilization (brown, tan, lavender, and pink nodes) through Data Types (red nodes) and Model Types (light red, purple, and light purple nodes). The Evolution of DPMs is Depicted by Linkage Color. The light blue linkages are the earliest, which expanded to include natural history data sources (dark blue) and Aggregate Level Data Types (green). The Current State Includes Non-clinical Data Sources and Mechanistic DPM (peach). Real World Evidence (orange linkages) is Depicted as Potential Future State. As biomarker data (e.g., imaging, HbA1c concentrations, etc.) became more prevalent in clinical trials, their use in DPMs also increased leading to more complex, semi-mechanistic models. Non-clinical sources were utilized, leading to sets of physiological parameters which furthered the development of mechanistic DPM type models. In our current state, real world evidence is also being incorporated into different data types. Information flows from source, or observation, to developmental utility (Fig. 2). Differing data sources (Clinical Trials, Natural History studies, non-clinical experiments, and real-world evidence) provide the basis for data types (individual clinical endpoint, aggregated clinical endpoints, clinical biomarkers, [16–18] physiological parameters). DPM types (individual, aggregate, and mechanistic) characterizing these data types, or combinations of such, are utilized for a range of development decisions. The DPM transforms the information into quantitative knowledge which is actionable. DPMs may incorporate information from different sources, and data types, and are constructed using a range of methodologies. Though different, an overlap in utility is apparent in which different types of DPM can address the same development question (eg – the time course of a novel clinical trial). The progression of a model can be thought of as part of a model development lifecycle (MDLC) in which model structures are evolved in an iterative fashion, each building on the previous work. Implicit to the concept of a MDLC is the addition of more information, thereby increasing precision of parameter estimates, or improved characterization of clinical endpoints or trajectory of disease [18]. A future state may be the merging of empirical and mechanistic approaches into holistic DPMs describing both pathophysiology of the disease and the distribution of clinical endpoints supporting development utility ranging from the selection of mechanism to providing a Bayesian prior for more efficient study design. The past decade has also seen significant advances in the regulatory science real-world data (RWD) and Real-World Evidence (RWE) framework. These advancements along with regulatory guidance [19] have driven the increased use of from electronic health records and claims databases to provide the basis for evidence in support of drug effectiveness (RWE). The use of RWD has now also become an important data source for DPMs. RWD sources include electronic health records (EHRs), claims and billing activities, product and disease registries, patient-generated data including in home-use settings, and data gathered from other sources that can inform on health status, such as mobile devices. The addition of various RWD sources may also be relevant to incorporate the clinical signs and symptoms of clinical care into a DPM. Such data allows the evaluation of the existing standard of care and the performance of existing treatments to be considered and can be useful if the DPM is coupled with a clinical trial simulation model [20]. Analytic Methodologies Initial approaches Cook and Bies [2] describe three broad classes of DPMs: empirical, semi-mechanistic, and systems biology DPMs, with their application and subsequent appearance in the literature occurring in that order. With advancements in the types of DPM models used, and increased complexity of data types, there has also been an evolution in analytic methodologies that have developed. Initial empiric models describing subjective scoring utilized linear and non-linear mixed effects models. Subsequently more complex models such as asymptotic progress, physiological turnover, and growth and decay models have been utilized and have been well described [21]. The next section focuses on more recent advances in analytic techniques. Latent Variable Disease Progression Models In many cases, clinical endpoints are composites of prespecified observations (or assessments) which are combined as a single measure of disease state. Due to the way in which they are defined, these endpoints may be bounded at one or both ends which may cause complications when modeling near the boundaries. Additionally, each of the assessments, or subscales, can contribute different amount of information to the underlying understanding of disease state depending on severity. Empirical model approaches have been developed which characterize the progression of disease as a latent variable. In this methodology the disease is indirectly characterized based on information from a series of clinical endpoints. Applications range in complexity from a logit transformation in which the probability of an endpoint is characterized, to models utilizing item response which characterize the probability for each of the endpoint components (subscales). Latent variable disease progression modeling, charactering primary clinical endpoints, has been employed in myriad of indications. Selected examples can be seen in rheumatoid arthritis [22–25], psoriasis [26, 27], ulcerative colitis [24], and Alzheimer’s disease [28, 29] Applications of item response disease progression modeling have been applied to Alzheimer’s disease [30, 31], Parkinson’s disease [31, 32], and multiple sclerosis [33]. In these examples the bounded nature of the clinical endpoint has been appropriately described enabling the interpretation of underlying disease progression, and in some examples a treatment effect, to be established. This type of modeling has also shown utility in being able to simulate different response rates through clinical trial simulation. The item response methodology, characterizing the information for each of the subcomponents of a composite scale, enables clinical trial simulation into the subscales. A further benefit of the item response model is that it can be used to integrate different variants of a clinical scale [32] and potentially future applications integrating multiple clinical scales which describe different levels of disease severity (ex—CDR-SOB, ADAS-COG, NPI, SIB). Natural language Processing and Machine Learning There has been significant progress in the availability of knowledge and data across biological and human scales, facilitated by advances in omics technologies, digital data platforms integrating clinical trial and patient registry data, and hospital or primary care data such as EHR, claims. For the Life Sciences industry, the question has become where these data can be systematically leveraged for advancing knowledge on human disease, and for developing innovations in medical treatments or vaccines for disease eradication or management. This exponential increase in data generation and availability is met with commensurate improvements in computer technology and advancement in computational models geared for handling big data science. Take for example the problem of researching the literature for novel or historical insights on disease or drug mechanisms. With the increasing size of the PubMed corpus (several million full text articles), it is increasingly difficult to research specific topics comprehensively without additional search tools and capabilities. Natural language processing (NLP) has been an evolving discipline, with deep roots in computer science and discrete mathematical modeling among others, that is progressed to be an important tool for assimilating large amounts of knowledge, example from PubMed, and building knowledge graphs representing relationships across fields or variables of interest to the researcher [34]. These graph models can be quantitatively mined for specific information and surfacing the relevant data or knowledge the research is looking for in a systematic and data driven way. Given the extent and evolution of data and knowledge that DPMs rely on for both mechanistic and patient level information, NLP models can be an invaluable tool for integrating and assimilating the relevant knowledge and data from respective knowledge sources. Advances in measurement technologies have also facilitated the interrogation of broad metabolic or proteomic scale characteristics of disease and drug action. These have over time played important roles in advancing our understanding of disease [35, 36] One notable and timely example is host-virus interactions and how DNA or RNA based viruses can evade the immune system, or hijack cellular machinery to reproduce and spread. Although these data have the potential to elucidate disease mechanisms or drug action, they also have brought forward computational and technical challenges due to the size of the data and complexity of potential interactions. Advancement in machine learning (ML) based models coupled with computing technology have allowed us to tackle increasingly larger data sets and derive biological or mechanistic while maintaining statistical rigor and soundness. As we apply ML models to big data derived from mechanistic data sets (e.g., omics data), we can advance specific biological mechanisms or biomarker strategies that can subsequently be represented in DPMs [37]. With larger scale data, and applications where causality or inference is less important, for example, medical outcome of an imaging-based diagnostic, Artificial Intelligence based models, grounded in deep learning models such as large neural networks, have been the tool of choice. These AI based approaches have successfully alleviated the burden of manual readouts where machine readouts based on AI technology is validated and are also increasingly utilized as a platform [38] in early drug discovery to advance potential novel targets forward based on volumes of discovery biology and chemical libraries. Regulatory Considerations for DPM use Often, DPMs are developed de novo for use within a development program and used to describe the data contained within an individual submission. In some cases, the model is subsequently published. This approach is inefficient and limits the potential for models to evolve. Regulatory Path to model qualification and COU A longstanding interest of the global regulatory community as potentially enabling significant progress in drug development has been the application of scientific advances as new tools to aid the development process. Such tools have been shown to speed up the availability of new products that may be safer and more effective. The Center for Drug Evaluation and Research (CDER) of the US FDA has undertaken multiple initiatives to support the development of new drug development tools (DDTs). Among these efforts has been the creation of a formal qualification process, described in a formal guidance [9] that CDER can use when working with submitters of DDTs to guide development as submitters refine the tools and rigorously evaluate them for use in the regulatory process. The DDT qualification process is intended to expedite development of publicly available DDTs that can be widely employed. Drug developers can use a DDT that has been qualified within a specific context of use (COU) [FDA Guidance 2020] for the qualified purpose during drug development if: [1] The study is conducted properly, [2] the DDT is used for the qualified purpose and [3] at the time of qualification, there is no new information that conflicts with the basis for qualification. Once a DDT has been qualified, CDER reviewers feel more confident in the application of the DDT within the qualified COU and do not have to re-confirm DDT utility. Qualification is an expectation that within the stated COU, the DDT can be relied on to have a specific interpretation and application in drug development and regulatory review. The COU describes the way the DDT is to be used and the purpose of the use. A complete COU statement describes the circumstances under which the DDT is qualified and the boundaries within which the available data adequately support use of the DDT. Once a DDT has been qualified for a specific COU in drug development, it can be used to produce analytically valid measurements that can be relied on to have a specific use and interpretable meaning. The DDT can then be used by drug developers for the qualified context in IND, NDA, and BLA submissions without the relevant CDER review group reconsidering and reconfirming suitability. The process for DDT qualification provides a framework for interactions between CDER and DDT submitters to guide the collection of data to support a DDT’s prospectively specified COU. The qualification process consists of three stages: [1] an initiation stage, [2] a consultation and advice stage, and [3] a review stage for the qualification determination. The appropriate review offices will participate in the entire qualification process for the DDT. The goal of the process is to reach a determination about the adequacy of the submitted data to support DDT qualification within a COU. An important future goal of the COU process would be the establishment of more formalized planning regarding governance and provenance of disease progression models and the underlying code. Provenance is an important aspect given that the COU should carry some version of an “expiration date” as the demands and utility of models in this category evolves with data, knowledge of disease biology and the pool of agents and procedures used to treat target diseases. Caretakers of the various models should represent the mutual desires of regulators and the scientific community. Illustrative Example Applications Evolution of DPMS in Neurodegenerative Disorders (Alzheimer’s) The evolution of DPMs in medicines development for Alzheimer’s disease (AD) over the last three decades illustrates how DPMs have evolved with increased treatment options, understanding of disease, improved analytic techniques, emergence of new data types, and increased trial design complexity (see Table I). It also highlights how investigators build on previous knowledge to continue to evolve models and to develop them into expert systems for each disease.Table I Some Illustrative Disease Progression Models across Therapeutic Areas. Model Reference Therapeutic Area and Class Data Informing Model Model Structure and Type Application Holford et al. 1992 [5] Alzheimer’s disease Alzheimer’s patient data (placebo, tacrine 40 mg, tacrine 80 mg) for 10 weeks Nonlinear mixed effects Description of symptomatic effects of tacrine in Alzheimer’s patients from a single trial Barendregt et al. 2003 [13] Asthma/epidemiology Victorian Burden of Disease Study/Australian Bureau of Statistics Multi-state life table A generic model for the assessment of disease epidemiology Kowalski et al. 2008 [49] Surgery Pain Data from a parallel-group, placebo-controlled study evaluating single oral doses of placebo, 3, 10, and 60 mg SC-75416 capsules, and 50 mg rofecoxib with 50 patients per group Nonlinear logistic-normal Dose selection and clinical development Hutmacher et al. 2008 [22] Rheumatoid Arthritis (RA) Dose ranging in 264 RA patients Introduction of indirect latent response variable Dose Selection Ito et al. 2010 [7] Alzheimer’s disease 576 mean ADAS-cog changes from baseline data points of 52 trials in the literature, representing data from approximately 19,972 patients and more than 84,000 individual observations Nonlinear mixed effects Trial duration determination for disease modifying agents Ito et al. 2011 [39] Alzheimer’s disease 817 mild to moderate patients in a Natural history study Nonlinear mixed effects Incorporation and use of biomarkers as predictive markers of disease progression Hu et al. 2011 [26] Psoriasis Two Phase III studies in severe Psoriasis Informative dropout/Longitudinal Mixed Effects logistic regression Support of dose regimen and support for alternative regimens Hu et al. 2011 [50] Psoriasis Two Phase III studies in severe Psoriasis Latent variable with standard logit transofrmation to bound tails Improvement to previous model by bounding outcomes Rogers et al. 2012 [40] Alzheimer’s disease Literature and Patient-level data from Open registry Beta-regression First model approved in the FDA Fit for Purpose Pathway. Shared model Ueckert et al. 2014 [30] Alzheimer’s disease 2744 patients, ADAS-Cog Item response Theory Early example of application of IRT to DPMs Conrado et al. 2014 [28] Alzheimer’s disease 4495 patients ADAS-COG11 from Coalition Against Major Disease Database (15 RCTs) Beta regression Model refinement and verification rates of progression of earlier publications Hu et al. 2014 [51] Methodology NA Latent variable indirect response New Method Description Novakovic et al. 2017 [33] Multiple sclerosis (RRMS) 1319 multiple sclerosis patients Item response theory Estimation of drug disease modifying effect Karelina et al. 2017 [52] Alzheimer’s TG576 mouse data, longitudinal soluble and insoluble CSF and plasma A-beta healthy and patient data, Pib-PET data (healthy subjects) Mechanisitic (QSP) Estimation of required trial duration for observation of disease modifying effects in AD Kaddi et al. 2018 [4] Acid Sphingomyelinase Deficiency Literature (enzyme kinetics) preclinical studies, natural History Study, Phase I patient studies, Mechanistic (QSP) with 4 sub-models (pk, molecular, cellular, organ) Assessment of systemic pharmacological effects in adult and pediatric patients, variability within and across these patient populations, and extrapolation of treatment response from adults to pediatrics Gottipati et al. 2019 [32] Parkinson’s disease 554 patients UPDRS and MDS-UPDRS Item response Louis et al. 2019 [48] None Riemannian Geometry Learning for Disease Progression Modelling Riemannian manifold learning Method development Kim et al., 2020 [53] Alzheimer’s disease 645 patients (129 SCI, 270 AMCI, and 246 ADD) followed up more than three times to obtain CDR-SB scores at the Samsung Medical Center from Jan. 2003 to Dec. 2015. Data shared by author upon request Mixed-effect predictive model expressed in SAS Understanding of the effect of education on cognitive trajectories – no described regulatory use Fourage et al., 2021 [54] Centronuclear myopathy 59 patients, 15 with DNM2 mutation and 44 patients with mutation in the MTM1 gene. Patients had been evaluated every 3 months under 2 years of age, every 6 months between 2 and 6 years of age, and, for patients older than 6, at 6 months and 12 months after enrolment and then once a year Bayesian predictive model—Proc MCMC in SAS 9.4 Model adequately predicted the natural evolution of patients over the duration of the study and will facilitate future trial designs that can cope with disease rarity Rao et al. 2021 [11] COVID-19 Literature (in vitro mechanistic data), observational and RCT trial summary data across multiple studies Integrated multi-endpoint QSP model of within-host SARS-CoV-2 viral dynamics and the immune response Utilized for duration Selection for Paxlovid™ Wang et al. 2022 [55] COPD real-world longitudinal Electronic Medical Record (EMR) database of over 300,000 patients Unsupervised learning/Markov Model no AD DPMs were some of the first reported in the literature, and were used to describe the symptomatic effects of cholinesterase inhibitors in AD patients by Holford and Peace [5] and Ito et. al. [7] utilized summary level literature data from 52 studies representing nearly 20,000 patients to describe impact of disease severity and age on yearly progression of the most commonly used clinical outcome measure, and to further describe treatment effect. The model was used to describe both symptomatic and disease modifying effects, and to determine expected differences in highlighted their use to describe effects observed in clinical trials. With advancements in understanding of disease biology, and incorporation of specific biomarkers and genetic tests in studies, DPMs were able to further characterize factors impacting progression of AD. Ito et al. [39] published further work based on a natural history study that incorporated imaging data, biomarkers, and genetic information. Subsequent work was undertaken by Rogers et. al. in collaboration with Ito et al., the Critical Path Institute and FDA to utilize both patient level and summary level data that were available [40]. A beta-regression approach was used that allowed for both data types to inform the model. This model also formed the basis for a fit-for-purpose pathway for drug development and was the first tool deemed suitable under that regulatory program. The model and supporting materials were made available as open source for community use. With increasing understanding that late-stage patients may have progressed too far to respond to disease modifying agents, a shift to testing disease modifying agents at the earlier stages of AD with resultant slower rates of progression, there was a need to understand whether existing elements of the ADASD-cog were more sensitive to detecting treatment effect and/or if new endpoints would be needed in patients with mild cognitive impairment. Ueckert et al. [30] applied Item Response Theory (IRT) to determine which items within the ADAS-cog provided the most information by stage of disease. In parallel to advances in DPMs, both systems biology and systems pharmacology models also advanced and provided even further insights into relationship between the emerging imaging, genetic and protein biomarkers, and trial outcomes. Karelina et al. [52] looked at how mechanistic translational models can allow for prediction of long-term clinical trials at various stages of disease. Systems biology approaches capture the disease in the broader context of CNS neurodegeneration and help provide insights into potential targets and pathways for exploration [41]. Inflammation and Immunology MIDD has been applied in the inflammation and immunology areas to characterize disease progression and to provide dosing rationale for a myriad of indications such as ulcerative colitis, psoriasis, and rheumatoid arthritis [22]. In these indications the progression of disease has been implemented through placebo (or standard of care) and active treatment indirect response functions in which the clinical endpoints have been described using bounded outcome methodologies [22]. While many of these examples are applied to the observations from a single study or combined studies for a single novel therapeutic, the expectation of a mature disease progression model is to synthesize information across clinical studies and new molecular entities (NMEs). Hu et al. [51] applied information from multiple studies, and NMEs, utilizing an empirical model describing the expectation of disease and standard of care to provide a phase 2 dose regimen decision for a novel therapeutic in psoriasis. The Immunology and Inflammation therapeutic area has a wealth of information and has provided the opportunity for a holistic model describing the behavior of disease and standard of care in a clinical trial. Such models provide a basis for trial design quantifying the duration of treatment needed to observe an effect. As an informative prior they enable a reduction in the number of patients needed to demonstrate the effect of a novel treatment. In both cases the application of DPMs would accelerate development decision making while maintaining scientific rigor. Rare Disease: Bronchopulmonary Dysplasia Comprising approximately 8000 diverse disease states linked only by their “rare” prevalence designation (disease or condition that affects less than 200,000 people in the United States by the US FDA) [42] is a broad array of conditions that often begins at birth or soon thereafter sometimes with very short life expectancy and rarely in a manageable condition in adulthood [42]. It is only through the Orphan Drug Act of 1983 that this therapeutic area has been properly incentivized for financial motivation to spur private sector R&D to make inroads to the myriad of diseases in this class. A recently supported effort of the FDA, Critical Path Institute, and International Neonatal Consortium (INC) has promoted the execution of pilot projects that generate RWE to support regulatory decision making in neonatal drug development. One such pilot is focused on developing a validated definition of Bronchopulmonary dysplasia (BPD). In addition to the definition, the BPD pilot will also assess the extent to which a large, multisource aggregation of RWD will allow identification of validated risk factors for, and surrogate endpoints representing, BPD, and the inclusion of these in clinical trial simulations that help identify risk factors and surrogate that are fit-for-purpose for hypothetical studies aiming to prevent or treat BPD and its related long-term complications. The backbone of the proposed trial simulations will be a qualified, fit-for-purpose disease progression model (and likely other models). While BPD is described as a disease, in fact its better classified as a syndrome – a condition of a premature neonate requiring “oxygen supplementation at a particular level and for a particular duration in the postnatal period” [57]. The probability of a BPD diagnosis depends mainly on gestational age and birth weight. Babies born after only 22–24 weeks of development have an 80% chance of being diagnosed with BPD [58]. At this age, lungs are just starting to develop alveoli and the premature exposure to air breathing disrupts this process. While knowledge of the many factors involved in alveologenesis is steadily accumulating, specific endotypes remain to be defined with the quantitative detail needed for both QSP and disease progression models. A major caveat is that this knowledge is obtained primarily in a range of model systems and using a variety of manipulations to induce BPD-like lungs. Longitudinal data reflecting disease progression are very limited, both in humans and model animals. In humans, longitudinal data in neonates are limited for obvious reasons. Besides records of oxygen supplementation, there is some longitudinal data on the efficiency of gas exchange, showing that premature neonates with BPD are less efficient compared to neonates without BPD and that both improve over time [59]. Ultrasound imaging also appears promising as a source of data easily obtained from neonates [60, 61]. At present, a landscaping exercise is in progress to assemble credible mechanistic and RWD sources that would be the foundation of both QSP and disease progression models for BPD. Figure 3 illustrates the nature of the repeated measure data of current clinical and convenience sampling and the gaps particularly at the onset of disease progression where sampling is limited or nonexistent. It is hoped that this effort even if it does not support the full COU desired will be a starting point for future models and encourage more informative BPD trials with sampling that compliments current data and knowledge gaps. It is also the hope and intent that the landscaping and integration of mechanistic, RWD and disease progression data, and incorporation into a coupled disease-progression and QSP platform can facilitate the discovery and translation of novel targets and identify optimal timepoints for therapeutic intervention.Fig. 3 Schematic of BPD Disease Progression with Variables of Clinical Interest Linked to Stage of Progression. Neuroscience Evolving Approaches (Systems Biology/Data Science) Mechanism-driven DPMs (mDPMs) describe the time evolution of disease characteristics with fit for purpose mechanistic description and can often be tied to QSP models to provide a more comprehensive representation of underlying biology for the respective mechanisms. mDMPs provide decision value across the discovery and translational medicine continuum, such informing the design and interpretation of POC/POM clinical studies, and informing the biomarker strategy, as tied to a disease or to a therapeutic MOA. This begs the question – how does one inform mechanisms that can be incorporated into mDPMs Various data sources are often relied upon when interrogating disease mechanisms or drug MOA, such as non-clinical in-vitro or in-vivo models. Although these efforts and tools generate important data, but not sufficient by themselves. Systems biology has deep academic roots and has over time extended its reach from basic science application, elucidating system wide etiology of disease and drug action, into more recently having increasingly direct influence on key drug discovery and development milestones. e.g., identifying MOA of a compound, or facilitating translation into the clinic. The advantage and promise of systems biology as a discipline is the data driven, scientifically objective approach to discovery and elucidation of disease mechanisms and drug action. One data source we have discussed earlier here is the importance of disease registries for the assembly and engineering of DPMs. These same registries can be additionally utilized as an important source of identification of underlying biology implicated along the time evolution of disease progression. Big data approaches such as metabolomics and proteomics (derived from patient samples from these registries) have been invaluable tools in discovery of novel mechanisms implicated in disease. When coupled with advanced data science approaches e.g., machine learning, they represent an innovative and data driven pipeline for discovery of disease mechanisms, and incorporation into mDPMs or mDPM-QSP platforms. Multi-endpoint QSP Models: COVID-19 A recent example for a novel oral treatment for COVID-19 illustrates the flexibility of a DPM to integrate information from disparate sources and to build on existing models by incorporating rapidly emerging data to quickly answer important questions regarding drug development [11]. The model provided understanding across several different biomarker endpoints, and clinical outcomes, and was used to inform study design (specifically treatment duration). In this example, a QSP model of the pathogenesis and treatment of SARS-CoV-2 infection streamlined and accelerated the development and Emergency Use Authorization of a novel medicine to treat COVID-19. Utilizing an updated version of a previously published preliminary model of the immune response to SARS-CoV-2 infection (significantly updated with emerging data from a curated dataset spanning viral load and immune responses in plasma and lung) allowed for in silico exploration of the uncertainties of clinical trial design to rapidly inform development decisions for upcoming clinical trial duration. The authors identified a population of parameter sets to generate heterogeneity in pathophysiology and treatment and tested this model against published reports from interventional SARS-CoV-2 targeting Ab and anti-viral trials. Upon generation and selection of a virtual population, they matched both the placebo and treated responses in viral load in these trials. They extended the model to predict the rate of hospitalization or death within a population. To validate this approach, they showed the model matched a published subgroup analysis of patients treated with neutralizing Abs. By simulating intervention at different timepoints post infection, the model predicted efficacy is not sensitive to interventions within five days of symptom onset, but efficacy is dramatically reduced if more than five days pass post-symptom onset prior to treatment, as borne out in the clinical trial [43]. Challenges and Opportunities Challenges exist for routine, standardized approaches for the development and use of well-characterized and robust DPMs. These include but are not limited to the following:more consistent evaluation and regulatory feedback regarding the construction and utility of DPMs more diverse and collaborative drug development culture which embraces the contributions of a truly multidisciplinary community that is required to develop such models as opposed to other model types in the MIDD toolbox○ a more collaborative environment for the sharing of data, code, and models. ○ a more collaborative and neutral governance and provenance environment that is both efficient and comprehensive. Despite these challenges, progress has been made in all these areas. Recent publications and FDA public meetings [2, 9] provides initial thoughts on best practices for DPMs. As the field is still evolving and the COU for the various DPM types can be very varied, a heavily prescriptive approach is unwarranted though these initial thoughts form the basis of what will surely evolve as a meaningful guide. Collaboration happens of course but the more consistent engagement of academic thought leaders particularly those in the disease biology arena of targeted therapeutic areas should be more commonly expected. Notions of model ownership need also to be examined and resolved so more can contribute and benefit from such collaborations. An important requirement for the future success of DPM is the collaborative spirit and effort that must guide the next generation of models and ultimately enhance their utility beyond drug development purposes. As alluded to previously, this will require a relaxed view of model ownership and a broader adoption of open science principles. As some have pointed out [44], despite the increasing availability of Open Science (OS) infrastructure and the rise in policies to change behavior, OS practices are not yet the norm. The benefits are clear it would seem—less error-prone and more visible models, not only to peers from the same and other scientific disciplines but also greater penetration to the public, who can appreciate the economic benefits of knowledge dissemination. Moreover, engaging in OS practices facilitates the sharing and reuse of data, materials, and code in the scientific community [45, 46], contributes to enriched scholarly output and literacy, and increases trust in the process [47] The obstacles to meaningful OS adoption are typically grounded in financial concerns over intellectual property (IP) and heavily constrained by past legal practice. An important evolution for this collaboration and more consistent OS engagement will require legal agreements and data use agreements more focused on shared IP where financial incentives are agreed upon without constraining the creative process and the OS approach. Conclusions and Path Forward Despite past challenges, the use of DPMs to inform drug development is becoming routine. DPMs flexibility in allowing integration of information from various sources in a quantitive manner make them indispensable for use in informing trial design and improving confidence in decision making during all stages of drug development. They are now routinely accepted and used in support of drug submissions worldwide. The key questions surrounding DPMs are no longer whether they have validity, do they add value and where they can be applied, but rather how their use can be expanded to incorporate emerging complex data types, to answer more and more complex development questions stemming from new modalities and emerging health risks, and how to do so quickly and efficiently so they accelerate development of new medicines. There is a growing need for disease models to inform multiple safety, biomarker, and efficacy endpoints simultaneously, a requirement that may not be suited for classical empiric DPM approaches. While ML approaches are used to recognize patterns in large data, and complex statistical methodologies [48] have been proposed, they lack the underlying ability to integrate basic pharmacologic principles and drug-specific information that Quantitive clinical pharmacology “expert-systems” like QSP models afford, and that can allow for hypothesis generation (i.e. for identification of new targets or pathways). In addition, QSP approaches, based in fundamental principles of pharmacology, allow for models to build. grow and evolve as new information emerges. They can be shared and maintained by a community of users [45]. Finally, going forward, DPMs may be combined with other emerging tools and technologies to decrease patient burden. While randomized controlled trials have been considered the standard for demonstration of efficacy, there has been a significant drive for increased patient inclusivity and use of patient centric designs to minimize patient burden and to provide maximal benefit to patients seeking clinical trials as a care option. Hybrid study designs that include features of RCTs with use of RWD can combine the advantages of both [62]. A potential synergy is DPMS utilizing RWD as an informative Bayesian prior to augment control arms of a study. An appropriate drug-disease-trial model could significantly minimize the number of patients needed in the control arm, improving likelihood that a patient receives active therapy. This could be of particular benefit for populations that are not part of initial approvals and that are typically included in post-approval commitments, such as pediatrics by minimizing the number of patients needed in the control arms and maximizing the likelihood of being randomized to active treatment. Declarations Conflicts of interests/Competing interests The authors received salary support from the Critical Path Institute (JB), Pfizer Pharmaceuticals (BC and TN) and Axcella Therapeutics (KA). Other than salary support, the authors did not receive support from any organization for the submitted work. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs REFERENCES 1. Lalonde RL Kowalski KG Hutmacher MM Ewy W Nichols DJ Milligan PA Model-based drug development Clin Pharmacol Ther 2007 82 1 21 32 10.1038/sj.clpt.6100235 17522597 2. 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==== Front Kidney Int Kidney Int Kidney International 0085-2538 1523-1755 International Society of Nephrology. Published by Elsevier Inc. S0085-2538(22)00272-1 10.1016/j.kint.2022.04.003 Letter to the Editor Early treatment with sotrovimab monoclonal antibody in kidney transplant recipients with Omicron infection Chavarot Nathalie 12∗ Melenotte Clea 23 Amrouche Lucile 12 Rouzaud Claire 23 Sberro-Soussan Rebecca 12 Pavie Juliette 4 Martinez Frank 12 Pouvaret Anne 23 Leruez-Ville Marianne 25 Cantin Delphine 6 Fourgeaud Jacques 25 Delage Claire 1 Vimpere Damien 7 Peraldi Marie Noëlle 12 Legendre Christophe 12 Lanternier Fanny 23 Zuber Julien 12 Scemla Anne 12 Anglicheau Dany 12 1 Department of Nephrology and Kidney Transplantation, Necker-Enfants Malades Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France 2 Université de Paris, Paris, France 3 Department of infectious Diseases and Tropical Medicine, Necker-Enfants Malades Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France 4 Infectious Disease Department, Hotel Dieu Hospital, Assistance Publique Hôpitaux de Paris (APHP), Paris Centre Hôtel-Dieu, Paris, France 5 Virology Laboratory, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France 6 COVID-19 Screening Center, Emergency Department, Hôtel Dieu Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France 7 Adult Intensive Care Unit, Department of Anaesthesiology, Necker-Enfants Malades Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France ∗ Correspondence: Nathalie Chavarot, Service de Néphrologie et Transplantation Rénale Adulte, Hôpital Necker-Enfants Malades, 149, Rue de Sèvres, 75015 Paris, France. 12 4 2022 12 4 2022 © 2022 International Society of Nephrology. Published by Elsevier Inc. All rights reserved. 2022 International Society of Nephrology Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcTo the editor: Early data about coronavirus disease 2019 (COVID-19) related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant (B.1.1.529) suggest that it may be less severe than prior variants of concern in the general population.1, 2, 3 However, our preliminary data (NC, personal communication, January 28, 2022) about Omicron infection in kidney transplant recipients (KTRs) suggest that the disease is associated with severe forms in this vulnerable population with low postvaccinal immune responses. Sotrovimab monoclonal antibody has been demonstrated to reduce disease progression in high-risk patients with mild-to-moderate COVID-19 before the Omicron era.4 Recent studies assessed that, in contrast with other monoclonal antibodies, it remained active against the Omicron spike.5 We aimed to compare the clinical outcomes of the first 25 KTRs treated with sotrovimab for mild-to-moderate Omicron COVID-19 with KTRs who did not receive sotrovimab. Sotrovimab was available in our institution (Necker Hospital, Paris, France) from January 25, 2022. KTRs with a high risk for progression of COVID-19 (because of older age [≥55 years] or because they had at least 1 of the following risk factors: diabetes, obesity [body mass index >30, estimated glomerular filtration rate <30 ml/min], coronary artery disease, or chronic lung disease) who presented with mild-to-moderate Omicron COVID-19 after this date were treated with sotrovimab (a single 500-mg, 1-hour infusion). The control group consisted of the first 100 consecutive KTRs who experienced Omicron infection before January 25. We excluded patients who received pre-exposure prevention with tixagévimab/cilgavimab. A total of 25 patients (21 men [84%], median age of 54 years, interquartile range: 46–62 years) who developed an Omicron infection between January 14 and February 13, 2022, received sotrovimab (Table 1 ). Sixteen of 23 (69.6%) patients with available data had a COVID-19 serostatus predictive of a poor protection against Omicron (seronegative or weakly seropositive [<264 binding antibody units/ml] and/or treated with casirivimab/imdevimab). Antibody titers of seropositive patients are available in Supplementary Table S1). No infusion-related reaction was observed. Median time between symptom onset and sotrovimab infusion was 5 (interquartile range: 3–9) days. (Eight patients [32%] were treated after day 5 [up to day 13] of symptom onset.) Although sotrovimab-treated patients presented more risk factors associated with severe COVID-19 (significantly more men and more underlying comorbidities; Table 1), Omicron infection was less severe (less mortality and less severe disease [mortality and/or intensive care unit admission]) compared with controls (Figure 1 ). In the sotrovimab group, 4 (16.0%) patients were hospitalized, of whom, 1 patient required intensive care unit admission and no patients died. The patient admitted in intensive care unit received sotrovimab at day 11 after symptom onset. In contrast, 35 patients (35%) were hospitalized for Omicron disease in the control group. Among them, 17% required intensive care unit admission (9% needed mechanical ventilation) and 11% died.Table 1 Baseline and COVID-19 characteristics of KTRs infected with Omicron variant who received or not sotrovimab Variables Sotrovimab-treated KTRs (N = 25) Nonsotrovimab-treated KTRs (N = 100) P Age, median (IQR) 54 (46–62) 53 (37.8–52) 0.599 Sex (males), n (%) 21 (84.0) 54 (54.0) 0.006 BMI, kg/m2, median (IQR) 24 (22–25.6) 25.5 (22.6–30) 0.162 BMI >30 kg/m2, n (%) 2 (8.0) 23 (24.5) 0.101 Hypertension, n (%) 20 (80.0) 81 (82.7) 0.773 Coronary artery disease, n (%) 6 (24.0) 13 (13.3) 0.217 Diabetes mellitus, n (%) 8 (32.0) 34 (34.7) 1.000 Chronic lung disease, n (%) 5 (20.0) 4 (4.1) 0.017 eGFR <30 ml/min per 1.73 m2,a n (%) 8 (32.0) 7 (7.1) 0.003 KT >1, n (%) 5 (20.0) 21 (21) 1.000 Induction immunosuppressive therapy, n (%)  Antithymocyte globulin 7 (31.8) 46 (46) 0.246  Basiliximab 14 (63.6) 44 (44) 0.105  Rituximab at induction 4 (16) 8 (8) 0.256 Maintenance immunosuppressive therapy  Calcineurin inhibitors, n (%) 20 (80.0) 75 (75.0) 0.794  Azathioprine, n (%) 2 (8.0) 6 (6.0) 0.660  Mycophenolic acid, n (%) 18 (72.0) 84 (84.0) 0.246  Dose, mg/d, median (IQR) 1000 (1000–1500) 1000 (1000–1500) 0.407  mTOR-i (everolimus), n (%) 1 (4.0) 3 (3.0) 1.000  Steroids, n (%) 24 (96.0) 96 (96.0) 1.000  Dose, mg/d, median (IQR) 8 (6–10) 7.5 (5–10) 0.179  Belatacept, n (%) 3 (12.0) 21 (21.0) 0.402 Anti-SARS-2 mRNA vaccination (Pfizer–BioNTech), n (%) 23 (92.0) 88 (92.6) 1.000  1 injection 1 (4.0) 1 (1.1) 0.377  2 injections 1 (4.0) 5 (5.3) 1.000  3 injections 16 (64) 58 (61.1) 0.822  4 injections 3 (12.0) 24 (25.3) 0.188 Positive serology at Omicron infection, n (%) 7/23 (30.4) 21/45 (46.7) 0.298 Anti-S titer, BAU/ml 192 (30–744) 260 (60–1010) 0.349  Previous history of COVID-19, n (%) 2 (8.0) 13 (13) 0.734 Characteristics of Omicron infection  Time between KT and Omicron infection, yr, median (IQR) 7 (5–14) 6 (2.8–11) 0.140  Clinical symptoms at presentation, n (%) N = 23 N = 86  Cough 16 (69.6) 46 (51.1) 0.236  Asthenia 10 (43.5) 45 (50.0) 0.489  Fever 14 (60.9) 37 (41.1) 0.160  Rhinitis 6 (26.1) 36 (40.0) 0.229  Myalgia 5 (21.7) 31 (34.4) 0.223  Sore throat 8 (34.8) 30 (33.3) 1.000  Diarrhea 5 (21.7) 23 (25.6) 0.791  Headache 9 (39.1) 22 (24.4) 0.206  Dyspnea 0 (0) 15 (16.7) 0.037  Asymptomatic 2 (8.7) 7 (7.8) 1.000  Time between symptom onset and sotrovimab injection, d, median (IQR) 5 (3–9) – –  Follow-up after infection, d, median (IQR) 30 (27–34) 20 (11–27) 0.011 BAU, binding antibody units; BMI, body mass index; COVID-19, coronavirus disease 2019; eGFR, estimated glomerular filtration rate; IQR, interquartile range; KT, kidney transplantation; KTRs, kidney transplant recipients; mTOR, mammalian target of rapamycin; S, spike. Bold values of P < 0.05 were considered statistically significant. a Determined with the Modification of Diet in Renal Disease equation. Figure 1 Kaplan-Meier curves representing (a) mortality and (b) severe Omicron coronavirus disease 2019 (COVID-19) in kidney transplant recipients infected with Omicron variant and treated or not with sotrovimab. Omicron infection appears to be severe in KTRs. Our study reports the first cohort of KTRs treated with sotrovimab for Omicron infection. Although these patients presented high risk for progression to severe disease, the severity of COVID-19 was lower than the historical control group, concordant with findings in the general population. Interestingly, the rate of patients with SARS-CoV-2–positive immune response was similar (and low) in both groups. Despite its retrospective character and the relatively short follow-up, our findings show that the sotrovimab-neutralizing anti-SARS-CoV-2 antibody can prevent severe COVID-19 in KTRs infected with the Omicron variant and can be safely proposed in outpatient KTRs. Data Statement The data that support the findings of this study are available from the corresponding author at Nathalie.chavarot@aphp.fr. Disclosure All the authors declared no competing interests. Supplementary Material Supplementary File (Word) Supplementary File (Word) Table S1. Antispike titers in postvaccinal seropositive kidney transplant recipients treated or not with sotrovimab. ==== Refs References 1 Maslo C. Friedland R. Toubkin M. Characteristics and outcomes of hospitalized patients in South Africa during the COVID-19 Omicron wave compared with previous waves JAMA 327 2022 583 584 34967859 2 Ulloa A.C. Buchan S.A. Daneman N. Brown K.A. Estimates of SARS-CoV-2 Omicron variant severity in Ontario, Canada JAMA 327 2022 1286 1288 35175280 3 Iuliano A.D. Brunkard J.M. Boehmer T.K. Trends in disease severity and health care utilization during the early Omicron variant period compared with previous SARS-CoV-2 high transmission periods—United States, December 2020-January 2022 MMWR Morb Mortal Wkly Rep 71 2022 146 152 35085225 4 Gupta A. Gonzalez-Rojas Y. Juarez E. Early treatment for Covid-19 with SARS-CoV-2 neutralizing antibody sotrovimab N Engl J Med 385 2021 1941 1950 34706189 5 Hoffmann M. Krüger N. Schulz S. The Omicron variant is highly resistant against antibody-mediated neutralization: implications for control of the COVID-19 pandemic Cell 185 2022 447 456.e11 35026151
PMC009xxxxxx/PMC9001010.txt
==== Front J Infect J Infect The Journal of Infection 0163-4453 1532-2742 The British Infection Association. Published by Elsevier Ltd. S0163-4453(22)00201-8 10.1016/j.jinf.2022.04.019 Letter to the Editor Inhibition of endocytic recycling of ACE2 by SARS-CoV-2 S protein partially explains multiple COVID-19 related diseases caused by ACE2 reduction Ren Yongwen abc Lv Lu bc Li Peng bc Zhang Leiliang abc⁎ a Department of Clinical Laboratory Medicine, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China b Department of Pathogen Biology, School of Basic Medical Sciences, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China c Medical Science and Technology Innovation Center, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China ⁎ Corresponding author at: Department of Clinical Laboratory Medicine, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China. 12 4 2022 7 2022 12 4 2022 85 1 e21e23 6 4 2022 © 2022 The British Infection Association. Published by Elsevier Ltd. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcDear editor In this Journal, Li and colleagues described the comparative biology of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) receptor ACE2 and provided some explanation of the host range.1 Reduction of ACE2 protein level leads to a variety of diseases, such as lung injury, hypertension and abnormal coagulation.2 It has been reported that the protein level of ACE2 decreases during SARS-CoV-2 infection.3 To explain this phenomenon, we hypothesize that SARS-CoV-2 disrupts the homeostasis of ACE2 by interfering its trafficking to plasma membrane. Sorting nexin 27 (SNX27) mediates endocytic recycling of cargo proteins containing C-terminal PSD95/Dlg1/ZO-1 (PDZ)-binding sequences from endosomes to the plasma membrane, preventing their lysosomal degradation.4 Human ACE2 (hACE2) contains a SNX27-PDZ domain binding motif DVQTSF in its C terminus (Fig. 1 A), indicating that ACE2 may interact with SNX27. Indeed, GST pulldown experiments demonstrated that hACE2 but not hACE2T803A/F805A associated with both PDZ domain of SNX27 and full length SNX27 (Fig. 1B). Immunofluorescence staining showed that the surface signal of ACE2T803A/F805A was weaker than that of wild type ACE2, suggesting that binding to SNX27 was crucial for the plasma membrane targeting of ACE2 (Fig. 1C). Moreover, silencing SNX27 by siRNA reduced the surface level of ACE2, confirming that ACE2 was delivered to the cell surface through SNX27 (Fig. 1D). Taken together, those results demonstrated that endocytic recycling of ACE2 was mediated by SNX27.Fig. 1 Endocytic recycling of ACE2 is mediated by SNX27. (A) Human ACE2 contains a SNX27 PDZ binding motif in the C-terminus. (B) Both SNX27 and its PDZ domain associate with ACE2 but not T803A/F805A mutant of ACE2 in GST pulldown experiments. Lysates from 293T cells transfected with the constructs expressing HA-tagged wild type hACE2 and T803A/F805A mutant of hACE2 were pulled down by GST–SNX27, GST-SNX27 PDZ or GST. Input represents 2% of total cell lysates. (C) Surface level of T803A/F805A mutant of ACE2 is weaker than that of wild type ACE2. HeLa cells transfected with the constructs expressing HA-tagged wild type hACE2 and T803A/F805A mutant of hACE2 were stained with HA antibody. ACE2-HA and T803A/F805A mutant of ACE2-HA are in red. Nucleus stained with DAPI is in Blue. Scale bar: 10 μM. Relative ACE2 intensity was normalized by quantifying at least 20 cells through Image J. ****, p value < 0.0001. (D) Silencing SNX27 by siRNA reduces surface level of ACE2. HeLa cells treated with siRNA against SNX27 for 3 days and then transfected with the construct expressing HA-tagged wild type hACE2 were stained with HA antibody. ACE2-HA is in red. Nucleus stained with DAPI is in Blue. Scale bar: 10 μM. Relative ACE2 intensity was normalized by quantifying at least 20 cells through Image J. ****, p value < 0.0001. HeLa cells were treated with siRNA against SNX27 for 3 days and then blotted with antibodies against SNX27 or β-actin. Fig 1 SARS-CoV-2 spike (S) protein also contains a potential SNX27 binding motif MTSC in the cytoplasmic tail (Fig. 2 A), indicating that S may associate with SNX27. GST pulldown experiments confirmed that S interacted with both PDZ domain of SNX27 and full length SNX27 (Fig. 2B). Further GST pulldown experiments showed that S but not T1238A mutant of S associated with both PDZ domain of SNX27 and full length SNX27, suggesting T1238 in S was critical for its SNX27 association (Fig. 2C). Since both S and ACE2 interacted with PDZ domain of SNX27, we hypothesized that SARS-CoV-2 S competed with ACE2 for associating with SNX27. To test this hypothesis, we performed GST pulldown experiments and found that overexpression of GFP-S but not GFP reduced the binding ability of ACE2 to PDZ domain of SNX27 or full length SNX27 (Fig. 2D and E). However, when overexpressing SARS-CoV-2 S-T1238A in which the SNX27 binding affinity was abolished, the association of ACE2 with SNX27 PDZ or SNX27 was recovered, suggesting that S blocked SNX27-ACE2 interaction through its association with SNX27 (Fig. 2F and G). Compared with GFP, GFP-S but not GSP-S-T1238A mutant reduced the surface level of ACE2, which suggested that S suppressed the endocytic recycling of ACE2 through SNX27 (Fig. 2H). Taken together, SARS-CoV-2 S inhibited the endocytic recycling of ACE2 mediated by SNX27.Fig. 2 SARS-CoV-2 S protein reduces the surface level of ACE2 by associating with SNX27. (A) SARS-CoV-2 S protein contains a SNX27 binding motif in the C-terminus. (B) Both SNX27 and its PDZ domain associate with SARS-CoV-2 S. Lysates from 293T cells transfected with the constructs expressing GFP-tagged SARS-CoV-2 S were pulled down by GST–SNX27, GST-SNX27 PDZ or GST. Input represents 2% of total cell lysates. (C) Both SNX27 and its PDZ domain associate with SARS-CoV-2 S but not T1238A mutant of SARS-CoV-2 S in GST pulldown experiments. Lysates from 293T cells transfected with the constructs expressing GFP-tagged SARS-CoV-2 S or T1238A mutant of SARS-CoV-2 S were pulled down by GST–SNX27 or GST-SNX27 PDZ. Input represents 2% of total cell lysates. (D) SARS-CoV-2 S inhibits the interaction between ACE2 and SNX27 PDZ domain in GST pulldown experiments. Lysates from 293T cells transfected with the constructs expressing HA-tagged ACE2 with GFP-tagged SARS-CoV-2 S or GFP were pulled down by GST–SNX27 PDZ or GST. Input represents 2% of total cell lysates. (E) SARS-CoV-2 S suppresses the interaction between ACE2 and SNX27 in GST pulldown experiments. Lysates from 293T cells transfected with the constructs expressing HA-tagged ACE2 with GFP-tagged SARS-CoV-2 S or GFP were pulled down by GST–SNX27 or GST. Input represents 2% of total cell lysates. (F) SARS-CoV-2 S but not T1238A mutant of SARS-CoV-2 S inhibits the interaction between ACE2 and SNX27 PDZ domain in GST pulldown experiments. Lysates from 293T cells transfected with the constructs expressing HA-tagged ACE2 with GFP-tagged SARS-CoV-2 S, T1238A mutant of SARS-CoV-2 S, or GFP were pulled down by GST–SNX27 PDZ or GST. Input represents 2% of total cell lysates. (G) SARS-CoV-2 S but not T1238A mutant of SARS-CoV-2 S suppresses the interaction between ACE2 and SNX27 in GST pulldown experiments. Lysates from 293T cells transfected with the constructs expressing HA-tagged ACE2 with GFP-tagged SARS-CoV-2 S, T1238A mutant of SARS-CoV-2 S, or GFP were pulled down by GST–SNX27 or GST. Input represents 2% of total cell lysates. (H) Compared with GFP, GFP-S but not GSP-S-T1238A mutant reduces the surface level of ACE2. HeLa cells transfected with the constructs expressing HA-tagged ACE2 with GFP-tagged SARS-CoV-2 S, T1238A mutant of SARS-CoV-2 S, or GFP were stained with HA antibody. ACE2-HA is in red. SARS-CoV-2 S, T1238A mutant of SARS-CoV-2 S, and GFP are in green. Nucleus stained with DAPI is in Blue. Scale bar: 10 μM. Relative ACE2 intensity was normalized by quantifying at least 20 cells through Image J. ***, p value < 0.001; ****, p value < 0.0001. (I) Model of how SARS-CoV-2 S blocks ACE2 transport mediated by SNX27. Endocytic recycling of ACE2 is mediated by SNX27. SARS-CoV-2 S could inhibit plasma membrane targeting of ACE2 by suppressing the association of SNX27 and ACE2, leading to the reduction of ACE2. Fig 2 Consistent with our study, recent studies found that ACE2 surface localization was mediated by SNX27 and SARS-CoV-2 S associated with SNX27.5, 6, 7, 8 However, those studies did not explore the role for SARS-CoV-2 S in the endocytic recycling of ACE2. Our finding advanced our understanding of many phenomena caused by ACE2-deficiency. In the last two years, long COVID concept has been gradually accepted.9 Upon SARS-CoV-2 infection, endocytic recycling of ACE2 mediated by SNX27 could be suppressed by SARS-CoV-2 S and surface level of ACE2 would be decreased (Fig. 2I), which could lead to many long COVID diseases due to the deficiency of ACE2. Meanwhile, some side effects of S-based SARS-CoV-2 vaccine may due to the reduction of ACE2 recycling by S. For instance, myocarditis and pericarditis appeared after administration of BNT162b2 from BioNTech and mRNA-1273 from Moderna.10 It is possible that ACE2-dificiency by SARS-CoV-2 S causes those side effects. Because T1238A mutant of SARS-CoV-2 S no longer suppresses endocytic recycling of ACE2, this mutant could be a better design for mRNA vaccine against SARS-CoV-2. In this study, we revealed the mechanism how SARS-CoV-2 S protein lowered the surface level of ACE2. SARS-CoV-2 S decreased the surface level of ACE2 by inhibiting endocytic recycling of ACE2 mediated by SNX27. ACE2 reduction by SARS-CoV-2 is considered as a critical driver for COVID-19 pathology, which could cause multiple diseases, such as lung injury, hypertension and abnormal coagulation. Our study provided new ideas for understanding some symptoms of SARS-CoV-2 infection, which could help the treatment of various diseases caused by SARS-CoV-2. Declaration of Competing Interest The authors declare that there are no conflicts of interest. Acknowledgments This work was supported by grants from 10.13039/501100001809 National Natural Science Foundation of China [82072270 and 81871663], and Academic promotion program of 10.13039/501100015507 Shandong First Medical University [2019LJ001]. ==== Refs References 1 Li R. Qiao S. Zhang G. Analysis of angiotensin-converting enzyme 2 (ACE2) from different species sheds some light on cross-species receptor usage of a novel coronavirus 2019-nCoV J Infect 80 2020 469 496 2 Angeli F. Zappa M. Reboldi G. Trapasso M. Cavallini C. Spanevello A. Verdecchia P. The pivotal link between ACE2 deficiency and SARS-CoV-2 infection: one year later Eur J Intern Med 93 2021 28 34 34588140 3 Lei Y. Zhang J. Schiavon C.R. He M. Chen L. Shen H. Zhang Y. Yin Q. Cho Y. Andrade L. Shadel G.S. Hepokoski M. Lei T. Wang H. Zhang J. Yuan J.X. Malhotra A. Manor U. Wang S. Yuan Z.Y. Shyy J.Y. SARS-CoV-2 spike protein impairs endothelial function via downregulation of ACE2 Circ Res 128 2021 1323 1326 33784827 4 Steinberg F. Gallon M. Winfield M. Thomas E.C. Bell A.J. Heesom K.J. Tavaré J.M. Cullen P.J. A global analysis of SNX27-retromer assembly and cargo specificity reveals a function in glucose and metal ion transport Nat Cell Biol 15 2013 461 471 23563491 5 Kliche J. Kuss H. Ali M. Ivarsson Y. Cytoplasmic short linear motifs in ACE2 and integrin β(3) link SARS-CoV-2 host cell receptors to mediators of endocytosis and autophagy Sci Signal 14 2021 eabf1117 33436498 6 Yang B. Jia Y. Meng Y. Xue Y. Liu K. Li Y. Liu S. Li X. Cui K. Shang L. Cheng T. Zhang Z. Hou Y. Yang X. Yan H. Duan L. Tong Z. Wu C. Liu Z. Gao S. Zhuo S. Huang W. Gao G.F. Qi J. Shang G. SNX27 suppresses SARS-CoV-2 infection by inhibiting viral lysosome/late endosome entry Proc Natl Acad Sci U S A 119 2022 e2117576119 7 Zhang Q. Gefter J. Sneddon W.B. Mamonova T. Friedman P.A. ACE2 interaction with cytoplasmic PDZ protein enhances SARS-CoV-2 invasion iScience 24 2021 102770 8 Zhao L. Zhong K. Zhao J. Yong X. Tong A. Jia D. SARS-CoV-2 spike protein harnesses SNX27-mediated endocytic recycling pathway MedComm (2020) 2 2021 798 809 34909756 9 Alwan N.A. The road to addressing long covid Science 373 2021 491 493 34326224 10 Patone M. Mei X.W. Handunnetthi L. Dixon S. Zaccardi F. Shankar-Hari M. Watkinson P. Khunti K. Harnden A. Coupland C.A.C. Channon K.M. Mills N.L. Sheikh A. Hippisley-Cox J. Risks of myocarditis, pericarditis, and cardiac arrhythmias associated with COVID-19 vaccination or SARS-CoV-2 infection Nat Med 28 2022 410 422 34907393
PMC009xxxxxx/PMC9001011.txt
==== Front Kidney Int Kidney Int Kidney International 0085-2538 1523-1755 International Society of Nephrology. Published by Elsevier Inc. S0085-2538(22)00282-4 10.1016/j.kint.2022.04.006 Letter to the Editor Humoral response after a fourth “booster” dose of a coronavirus disease 2019 vaccine following a 3-dose regimen of mRNA-based vaccination in dialysis patients Housset Pierre 1∗ Kubab Sabah 2 Hanafi Latifa 1 Pardon Agathe 1 Vittoz Nathalie 1 Bozman Dogan-Firat 1 Caudwell Valérie 1 Faucon Anne-Laure 13∗ 1 Department of Nephrology, Centre Hospitalier Sud-Francilien, Corbeil-Essonnes, France 2 Department of Microbiology, Centre Hospitalier Sud-Francilien, Corbeil-Essonnes, France 3 Institut National de la Santé et de la Recherche Médicale (INSERM) U1018, Clinical Epidemiology Unit, Centre for Research in Epidemiology and Population Health, Paris-Saclay University, Villejuif, France ∗ Correspondence: Pierre Housset or Anne-Laure Faucon, Centre Hospitalier Sud-Francilien, Service de Néphrologie, 40 avenue Serge Dassault, 91100 Corbeil-Essonnes, France. 12 4 2022 12 4 2022 © 2022 International Society of Nephrology. Published by Elsevier Inc. All rights reserved. 2022 International Society of Nephrology Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcTo the editor: Despite an initial satisfactory increase in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antispike antibody (Ab) titer after completing a 3-dose regimen in the first set of vaccination in dialysis patients,1 Ab titer substantially decreases at 6 months in this population.2 Other authors suggested that for the B.1.1.529 Omicron variant, 3 vaccine doses might be insufficient in in-center hemodialysis patients.3 We assessed the dynamics of the anti–SARS-CoV-2 spike protein S1 total Ig Ab (Roche Elecsys immunoassay4) of both hemodialysis (n = 17) and peritoneal dialysis (n = 28) patients who received a 3-dose regimen of the mRNA BNT162b2 (Pfizer–BioNTech), followed by a fourth “booster” dose of mRNA vaccine (BNT162b2, n = 43, or mRNA-1273 [Moderna], n = 2), after a median of 7.6 [interquartile range: 7.1; 7.8] months after the third dose (Supplementary Figure S1). Patients with a breakthrough infection (symptomatic or not) before the fourth dose were excluded (Supplementary Figure S2). In patients (57.8% men, median age 72 [56; 79] years), 15.6% had a history of immunosuppression (Supplementary Table S1). At 15 [14; 22] days after the fourth “booster” dose, antispike Ab titer significantly increased from 923 [369; 2019] to 21,883 [10,234; 42,870] AU/ml (Figure 1 ; Supplementary Figure S3), which corresponds to a 19-fold increase (median) in antispike Ab titer. Ab titer after the fourth dose was 3.4-fold higher (median) than the Ab peak reached after the third dose. Dose 4 appeared well-tolerated (Supplementary Figure S4), and no serious adverse event was observed. After the fourth dose, only 2 patients developed a breakthrough infection (vs. 7 cases of coronavirus disease 2019 after the third dose; Supplementary Table S2).Figure 1 Kinetics of antispike antibodies. The figure shows the antispike antibody (Ab) levels before, and 1 (M1), 3 (M3), and 6 (M6) months after the third dose of the mRNA BNT162b2 vaccine, and after the fourth vaccine dose (BNT162b2 Pfizer–BioNech or mRNA-1273 Moderna) in dialysis patients. Each point represents individual data. Antibody titers lower than 1 cannot be plotted in the graph because of the logarithm scale. The red points and vertical lines indicate the median with interquartile range. Conversion factor: 1 AU/ml = 1.0288 BAU/ml. To conclude, our finding shows that a 3-dose regimen of an mRNA-based vaccine with a fourth booster dose appears to produce an important antibody response in dialysis patients, with a significant increase in antispike Ab titer. Long-term follow-up studies are needed to assess if this vaccination strategy elicits a durable and robust protective immune response against SARS-CoV-2 in dialysis patients. Data Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Disclosure All the authors declared no competing interests. Supplementary Material Supplementary File (PDF) Acknowledgments The authors thank patients for their participation in the study. Author Contributions PH and A-LF researched the idea, created the study design, acquired the data, and analyzed and interpreted the data. A-LF provided statistical analyses. SK performed the antispike serology testing. Each author contributed important intellectual content during manuscript drafting or revision. Supplementary File (PDF) Supplementary questionnaire on vaccine reactions and global tolerance after the fourth vaccine dose. Table S1. Characteristics of the study population. Table S2. Characteristics of the patients with breakthrough coronavirus disease 2019 (COVID-19) after the third or the fourth vaccine dose during the fifth wave pandemic. Figure S1. Vaccination strategy in dialysis patients. Figure S2. Flowchart. Figure S3. Kinetics of the antispike antibodies. Figure S4. Self-reported tolerance. ==== Refs References 1 Bensouna I. Caudwell V. Kubab S. SARS-CoV-2 antibody response after a third dose of the BNT162b2 vaccine in patients receiving maintenance hemodialysis or peritoneal dialysis Am J Kidney Dis 79 2022 185 192.e1 34508833 2 Housset P. Kubab S. Pardon A. Waning but persistent humoral response 6 months after the third dose of the mRNA BNT162b2 vaccine in hemodialysis and peritoneal dialysis patients J Nephrol 35 2022 783 785 35192157 3 Carr E.J. Wu M. Harvey R. Omicron neutralising antibodies after COVID-19 vaccination in haemodialysis patients Lancet 399 2022 800 802 35065703 4 Muench P. Jochum S. Wenderoth V. Development and validation of the Elecsys anti-SARS-CoV-2 immunoassay as a highly specific tool for determining past exposure to SARS-CoV-2 J Clin Microbiol 58 2020 e01694-20
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==== Front Microb Risk Anal Microb Risk Anal Microbial Risk Analysis 2352-3522 2352-3530 Elsevier B.V. S2352-3522(22)00017-2 10.1016/j.mran.2022.100217 100217 Article Strain wars 3: Differences in infectivity and pathogenicity between Delta and Omicron strains of SARS-CoV-2 can be explained by thermodynamic and kinetic parameters of binding and growth Popovic Marko School of Life Sciences, Technical University of Munich, 85354 Freising, Germany 12 4 2022 12 2022 12 4 2022 22 100217100217 28 2 2022 10 4 2022 10 4 2022 © 2022 Elsevier B.V. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. In this paper, for the first time, empirical formulas have been reported of the Delta and Omicron strains of SARS-CoV-2. The empirical formula of the Delta strain entire virion was found to be CH1.6383O0.2844N0.2294P0.0064S0.0042, while its nucleocapsid has the formula CH1.5692O0.3431N0.3106P0.0060S0.0043. The empirical formula of the Omicron strain entire virion was found to be CH1.6404O0.2842N0.2299P0.0064S0.0038, while its nucleocapsid has the formula CH1.5734O0.3442N0.3122P0.0060S0.0033. Based on the empirical formulas, standard thermodynamic properties of formation and growth have been calculated and reported for the Delta and Omicron strains. Moreover, standard thermodynamic properties of binding have been reported for Wild type (Hu-1), Alpha, Beta, Gamma, Delta and Omicron strains. For all the strains, binding phenomenological coefficients and antigen-receptor (SGP-ACE2) binding rates have been determined and compared, which are proportional to infectivity. The results show that the binding rate of the Omicron strain is between 1.5 and 2.5 times greater than that of the Delta strain. The Omicron strain is characterized by a greater infectivity, based on the epidemiological data available in the literature. The increased infectivity was explained in this paper using Gibbs energy of binding. However, no indications exist for decreased pathogenicity of the Omicron strain. Pathogenicity is proportional to the virus multiplication rate, while Gibbs energies of multiplication are very similar for the Delta and Omicron strains. Thus, multiplication rate and pathogenicity are similar for the Delta and Omicron strains. The lower number of severe cases caused by the Omicron strain can be explained by increased number of immunized people. Immunization does not influence the possibility of occurrence of infection, but influences the rate of immune response, which is much more efficient in immunized people. This leads to prevention of more severe Omicron infection cases. Keywords Omicron stain Delta strain Gibbs energy of binding SGP-ACE2 binding rate SARS-CoV-2 variant ==== Body pmcNomenclature Symbols A Antigen (viral spike glycoprotein or SGP) [A] Concentration of the free antigen aJ Number of atoms of element J in its standard state form AR Antigen-receptor (SGP-ACE2) complex [AR] Concentration of the antigen receptor complex (Bio) Virus live matter E Number of electrons transferred to oxygen during complete combustion of live matter KB Binding equilibrium constant KD Dissociation equilibrium constant kon On-rate (attachment) constant koff Off-rate (dissociation) constant LB Binding phenomenological coefficient Lg Growth phenomenological coefficient nJ Number of atoms of element J in the empirical formula of live matter Q Binding reaction quotient R Host cell ACE2 receptor [R] Concentration of the free receptor Rg Universal gas constant rB Overall binding rate rg Growth reaction rate ron Attachment reaction rate roff Detachment reaction rate Sm0(J) Standard molar entropy of element J Sm0(bio) Standard molar entropy of live matter T Temperature [V]tot Total virion concentration in the organism ΔCH⁰(bio) Standard enthalpy of combustion of live matter ΔfG⁰(bio) Standard Gibbs energy of formation of live matter ΔfH⁰(X) Standard enthalpy of formation of compound X ΔfH⁰(bio) Standard enthalpy of formation of live matter ΔfS⁰(bio) Standard entropy of formation of live matter ΔBG Gibbs energy of binding ΔBG⁰ Standard Gibbs energy of binding ΔBH⁰ Standard enthalpy of binding ΔBS⁰ Standard entropy of binding ΔgG⁰ Standard Gibbs energy of growth ΔgH⁰ Standard enthalpy of growth ΔgS⁰ Standard entropy of growth ν Stoichiometric coefficient Superscripts and subscripts ⁰ Standard thermodynamic property eq Property at equilibrium B Property of binding g Property of growth 1 Introduction SARS-CoV-2 was first isolated in late-2019, as the cause of COVID-19 disease. SARS-CoV-2 has shown a great tendency to mutate (Wang et al., 2021a; Callaway, 2020). Thus, during the last 2 years, a great number of strains appeared, some of them classified as variants of concern (VOC) (WHO, 2021a), which compete and suppress earlier strains (Popovic and Minceva, 2021a; Popovic and Popovic, 2022). Every new strain has caused a new wave of the pandemic, all over the world. The last two waves were caused by the Delta and Omicron strains. The last wave that began in December 2021 was caused by the Omicron strain and exhibited a great increase in the number of infections, but a lower number of severe cases [Abdullah et al., F. 2021). A question is raised: is this a consequence of weakening (decrease in pathogenicity) of the Omicron strain or are there other reasons for this? To answer this question, viruses must be considered as open biothermodynamics systems (Popovic and Minceva, 2020a, M. 2020b; von Bertalanffy, 1950). Thus, it is necessary to chemically and thermodynamically characterize these two strains and compare their properties. The most important task is to find the binding rates of the Delta and Omicron strains to the host cell ACE2 receptor, as well as the virus multiplication rate inside the host cell. This became possible after publication of kinetic data that characterize the Delta and Omicron strains. Infectivity of viruses is related to antigen-receptor binding rate, while pathogenicity is related to the capability of a virus to reproduce inside a host and damage host tissues. Virus infectivity is defined as the capacity of viruses to enter the host cell and exploit its resources to replicate and produce progeny infectious viral particles (Rodríguez-Lázaro et al., 2013). An artificial intelligence model was used to compare Omicron, Wild type and Delta infectivity (Chen et al., 2022). It was found that the Omicron strain may be over 10 times more contagious than the Wild type or about 2.8 times as infectious as the Delta variant (Chen et al., 2022). Moreover, the Omicron strain was found to be able to avoid immune response, up to 88% (Chen et al., 2022). Pathogenicity is the potential disease-causing capacity of pathogens. The measure of pathogenicity is virulence (Pirofski and Casadevall, 2012). Organisms represent open thermodynamic systems with the property of growth (von Bertalanffy, 1950; von Stockar, 2013a; Popovic and Minceva, 2020a; Popovic, 2017). Thus, nonequilibrium thermodynamics needs to be used to analyze processes performed by organisms (Popovic, 2018; Popovic and Minceva, 2021c). Inside cells, viruses perform life processes. Two basic processes are important for the viral life cycle: binding and viral entrance into host cells, and viral multiplication inside host cells (Popovic and Minceva, 2021a; P. Gale, 2021, 2020, 2019, 2018; Riedel et al., 2019). Susceptibility and permissiveness are decisive factors for success of viral infections (Popovic and Minceva, 2021a). If at the same time, in the same place, two virus species or strains appear, they will compete for the host cell metabolic machinery and resources (Popovic and Minceva, 2021a). In that case, direct virus-host interactions occur, in parallel with indirect virus-virus-host interactions (Popovic and Minceva, 2021a). Interactions between viruses have their thermodynamic background (Lucia et al., 2021, 2020a, U. 2020b; Şimşek, 2021). Thermodynamic properties of the human host tissues have been reported in (Popovic and Minceva, 2020c). Various viral strains appear during mutations, replacement of nucleotides, leading to changes in information content of the mutated virus. Changes in information and entropy during self-assembly of open thermodynamic systems with the property of growth (to which viruses belong) have been reported in (Popovic, 2014; Skene, 2015; Hansen et al., 2018). Thermodynamic properties of SARS-CoV-2 strains have been reported in (Popovic and Popovic, 2022). Natural selection was found to be the dominating mechanism of SARS-CoV-2 evolution, which favors mutations that strengthen viral infectivity (Wang et al., 2021b). Viral infectivity is related to binding rate, which is proportional to Gibbs energy of binding (Popovic, 2022b). Indeed, time dependent evolution of SARS-CoV-2 caused by mutations has been reported (Popovic and Popovic, 2022). A decreasing trend has been reported in Gibbs energies of binding of SARS-CoV-2 strains through time (Popovic and Popovic, 2022). Mutation Y449S in the spike protein receptor-binding domain, which occurred with co-mutations Y449S and N501Y, was found to reduce infectivity compared to that of the Wild type, but can disrupt existing antibodies that neutralize the virus (Wang et al., 2021b). The property of infectivity is a complex phenomenon (Saragovi et al., 1999). There are several factors that result in the properties of infectivity and transmissibility. The first is thermodynamic Gibbs energy of binding and binding affinity (Gale, 2019, 2020, P. 2021; Popovic, 2022b). The second is kinetic – binding rate (Popovic, 2022b). The third is the infective reservoir size (HHS and CDC, 2012). The fourth is immune response (both quantitative and qualitative). The fifth is anti-epidemic measures (social distancing, wearing masks, lockdowns, isolation of infected, isolation of contacts etc.) (Hruda et al., 2021). All five factors need to be taken into account when discussing infectivity and transmissibility. The goal of this paper is to find what enabled the Omicron strain to dominate over the Delta and Wild type (Hu-1) strains. For this purpose, we will determine and compare the binding rates and multiplication rates of the Omicron, Delta and Wild type (Hu-1) strains. This can lead to a conclusion about whether the Omicron strain has, except for increasing its infectivity, decreased its pathogenicity. To achieve this, it is necessary to determine the elemental composition of the Delta and Omicron strains, their growth reactions, as well as thermodynamic properties of binding and growth. These properties include standard enthalpy of binding, standard entropy of binding, standard Gibbs energy of binding, binding phenomenological coefficient, binding rate, standard enthalpy of growth, standard entropy of growth and standard Gibbs energy of growth. 2 Methods 2.1 Data sources Dissociation constants for the SARS-CoV-2 Omicron strain have been reported by Wu et al. (L. 2022), Han et al. (P. 2022), Zhang et al. (2021) and Khan et al. (2022). Wu et al. (L. 2022) found the affinity (binding) constant to be 0.37 ⋅ 107 M − 1, at 37 °C, using the non-competitive ELISA approach (Beatty et al., 1987). This corresponds to a dissociation constant of 2.7 ⋅ 10−7 M. Han et al. (2022) measured the dissociation constant to be 3.14 ⋅ 10−8 M, at 25 °C, using surface plasmon resonance (Rusnati et al., 2015). Zhang et al. (2021) reported the dissociation constant of 8.85 ⋅ 10−9 M, using surface plasmon resonance (Rusnati et al., 2015). Khan et al. (2022) found the dissociation constant to be 1.80 ⋅ 10−10 M, using molecular docking simulations. Since the later two references (Zhang et al., 2022; Khan et al., 2022) reported no temperature, the analysis was made using the data from the first two references (L. Wu et al., 2022; Han et al., P. 2022). The considered binding constants of the Omicron strain are given in Table 1 .Table 1 Standard thermodynamic properties of binding of the SARS-CoV-2 Omicron strain. The dissociation equilibrium constant, KD, values at 37 °C and 25 °C were taken from (L. Wu et al., 2022) and (Han et al., P. 2022), respectively. The KD values were used to find standard enthalpy of binding, ΔBH⁰, standard entropy of binding, ΔBS⁰, and standard Gibbs energy of binding, ΔBG⁰, at the corresponding temperatures, as described in the Methods section. Table 1T ( °C) KD (M) Reference ΔBH (kJ/mol) ΔBS (J/mol K) ΔBG (kJ/mol) 37 2.7E-07 L. Wu et al., 2022 −143.5 −336.8 −39.0 25 3.14E-08 Han et al., 2022 −132.6 −301.0 −42.8 Dissociation constants for Hu-1 (Wild type), Alpha, Beta, Gamma, Delta and GD/1/2019-RBD strains were reported by Han et al. (P. 2022). The data were collected using surface plasmon resonance at 25 °C and are given in Table 2 . The association rate constants, kon, and dissociation rate constants, koff, for all the analyzed strains were generously provided by Mr. Linjie Li and Dr. Jianxun Qi from the Institute of Microbiology of the Chinese Academy of Sciences. They were collected on the research resulting in the publication (Han et al., P. 2022). The kon and koff data were collected using surface plasmon resonance at 25 °C and can be found in Table 2.Table 2 Thermodynamic and kinetic data for binding of SARS-CoV-2 strains to the ACE2 receptor. The association rate constant, kon, and dissociation rate constant, koff, data were generously provided by Linjie Li and Jianxun Qi from the Institute of Microbiology of the Chinese Academy of Sciences. The dissociation equilibrium constant, KD, data was taken from (Han et al., P. 2022). Based on these values, the binding phenomenological coefficient, LB, binding equilibrium constant, KB, and standard Gibbs energy of binding, ΔbG⁰, have been calculated, as described in the Methods section. All the data is at 25 °C. Table 2Strain kon (M−1s−1) koff (s−1) KD (M) LB (mol² K / J s dm³) KB (M−1) ΔbG⁰ (kJ/mol) Wild type 1.07E+05 2.67E-03 2.46E-08 5.51E-18 4.06E+07 −43.43 Alpha 7.87E+04 4.26E-04 5.40E-09 8.91E-19 1.85E+08 −47.19 Beta 9.26E+04 1.27E-03 1.38E-08 2.69E-18 7.22E+07 −44.85 Gamma 7.76E+04 8.52E-04 1.10E-08 1.79E-18 9.08E+07 −45.42 Delta 7.40E+04 1.88E-03 2.51E-08 3.89E-18 3.99E+07 −43.38 Omicron 8.68E+04 2.61E-03 3.14E-08 5.72E-18 3.18E+07 −42.82 GD/1/2019-RBD 1.07E+05 1.98E-03 1.91E-08 4.27E-18 5.24E+07 −44.06 Genetic and protein sequences of the Delta and Omicron strains were taken from the NCBI database (National Center for Biotechnology Information, 2022a). The data for the Omicron strain can be found with the following codes: genome (OL869974.1), nucleocapsid phosphoprotein (UGY75362.1), membrane glycoprotein (UFO69282.1), spike glycoprotein (UGY75354.1). The data for the Delta strain can be found with the following codes: genome (OM471068.1), nucleocapsid phosphoprotein (UIO52968.1), membrane glycoprotein (QUX81285.1), spike glycoprotein (GenBank: UKA47839.1). The protein copy numbers were taken from (Neuman and Buchmeier, 2016; Neuman et al., 2011, 2006). The virion size was taken from (Neuman and Buchmeier, 2016). 2.2 Binding reaction and rate constants The SARS-CoV-2 virus enters the host cell, in a process where the viral spike glycoprotein (SGP) binds to the host cell ACE2 receptor. Antigen-receptor binding can be described by the chemical reaction(1) A+R⇄AR Where A is the free virus antigen (SGP), R the host cell receptor (ACE2) and AR the antigen-receptor complex. Antigen-receptor binding is a reversible chemical process, consisting of a forward and a backward part. In the forward part the antigen and receptor bind to form the antigen receptor complex, in a second order reaction. The concentrations of the free antigen, [A], and free receptor, [R], determine the rate of the forward reaction, ron, which is described by the law of mass action(2) ron=kon[A][R] where kon is the forward rate constant, also known as the on-rate or association rate constant (Du et al., 2016). On the other hand, in the backward reaction, the antigen-receptor complex dissociates into the free antigen and receptor. The rate of the backward reaction, roff, depends only on the concentration of the antigen-receptor complex, [AR], and follows first order kinetics(3) roff=koff[AR] where koff is the first order rate constant for dissociation of the antigen-receptor complex, also known as the off-rate constant (Du et al., 2016). Therefore, the overall binding rate, rB, is the difference of the forward and backward rates.(4) rB=ron−roff The overall binding rate, rB, becomes zero at equilibrium, implying that the equilibrium forward, roneq, and backward, roffeq, rates are equal (Demirel, 2014).(5) roneq=roffeq This represents the kinetic perspective on antigen-receptor binding. A similar complementary perspective is given by nonequilibrium thermodynamics. 2.3 Thermodynamics of virus binding Binding of the spike protein to the host cell receptor represents a chemical reaction, similar to protein-ligand binding (Du et al., 2016). The rate of the antigen-receptor binding reaction, rB, is related to Gibbs energy of binding, ΔBG, by the phenomenological equation(6) rB=−LBTΔBG where LB is the binding phenomenological coefficient and T is temperature (Demirel, 2014; Popovic and Minceva, 2021a). Since all the analyzed strains of SARS-CoV-2 infect the same host, the temperature is the same for all the strains. However, every strain has its own LB and ΔBG. Thus, rB of the Delta and Omicron strains depends on their LB and ΔBG values. The ΔBG values vary between viruses, depending on mutations (and chemical change) in their SGP. The binding phenomenological coefficient depends on binding kinetic parameters. For chemical reactions the binding phenomenological coefficient is proportional the equilibrium forward reaction rate, roneq, divided by the universal gas constant Rg (Demirel, 2014).(7) LB=roneqRg Combining Eqs. (7) and (2) gives(8) LB=konKD[AR]eqRg The dissociation equilibrium constant is given by the equation (Du et al., 2016)(9) KD=[A]eq[R]eq[AR]eq Combining Eqs. (7) and (8) results in(10) LB=konKD[AR]eqRg The kon, koff, and KD values for the analyzed strains are shown in Tables 1 and 2. Since the reported KD values are very small, the equilibrium is shifted towards antigen-receptor binding. Thus, most virus particles in the body will be bound to host cells, implying that the equilibrium antigen-receptor complex concentration is approximately equal to the total virion concentration in the organism [AR]eq ≈ [V]tot. Thus, Eq. (10) becomes(11) LB=konKD[V]totRg The value of [V]tot was reported by Sender et al. (R. 2021), to be 1 · 107 RNA copies per gram of tissue. It seems reasonable to assume that one RNA copy corresponds to one virion. In that case, the concentration of virions is 1 · 107 per gram of tissue. The density of tissues is 1050 g/dm³ [IT'IS Foundation, R. 2021). Thus, the concentration of virions is 1.74· 10−14 M. 2.4 Standard thermodynamic properties of binding The dissociation process is the opposite of binding, meaning that dissociation equilibrium constants are reciprocal of binding equilibrium constants (Du et al., 2016). Thus, dissociation equilibrium constants were used to calculate binding equilibrium constants, KB, using the equation (Du et al., 2016)(12) KB=1KD The binding constants were used to find standard Gibbs energy of binding, ΔBG⁰, using the equation (Du et al., 2016)(13) ΔBG0=−RgTlnKB Temperature dependence of the binding constant was used to find standard enthalpy of binding, ΔBH⁰, using the Van 't Hoff equation (Atkins and de Paula, 2014, 2011)(14) ddTlnKB=ΔBH0RgT2 The calculated standard enthalpies of binding, ΔBH⁰, were combined with standard Gibbs energies of binding, ΔBG⁰, to find standard entropies of binding, ΔBS⁰, using the equation (Atkins and de Paula, 2014, 2011)(15) ΔBG=ΔBH−TΔBS 2.5 Virus binding rate Virus binding rates have been determined in three ways: kinetic, linear and exponential. The kinetic method uses the law of mass action. The overall binding rate is the difference of the forward and backward reaction rate and is given by Eq. (4). The forward and backward reaction rates are found by multiplying the appropriate reaction rate constants with concentrations of the reactants, using Eqs. (2) and (3), respectively. The rate constants, kon and koff, were taken from Table 2. The linear method uses linear nonequilibrium thermodynamics. Linear nonequilibrium thermodynamics states that the rate of a process is a linear function of its driving force, to which it is related by the linear phenomenological Eq. (6). Thus, the linear method determines binding rate through Eq. (6), using binding phenomenological coefficients and standard Gibbs energies of binding from Table 2. Standard Gibbs energies of binding, ΔBG⁰, are converted into Gibbs energies of binding, ΔBG, using the equation (Atkins and de Paula, 2011, 2014)(16) ΔBG=ΔBG0+RgTlnQ where Q is the quotient of reaction (1) defined as(17) Q=[AR][A][R] The exponential method uses a more general nonequilibrium thermodynamic equation, which is valid outside the linear region. In general, chemical reaction rate is proportional to the exponent of the driving force, according to the equation (Demirel, 2014)(18) rB=ron(1−eΔBG/RgT) This equation reduces into phenomenological Eq. (6) in case of small values of Gibbs energy (since then the exponent can be approximated as ex ≈ 1 + x, where x = ΔBG /RgT and ron/Rg = LB) (Demirel, 2014). The ron values were calculated using kon data from Table 2. 2.6 Elemental composition and growth reactions Elemental composition of virus particles has been determined using the atom counting method (Popovic, 2022a). The atom counting method calculates the number of atoms of each element in a virus particle, based on its genetic sequence, protein sequences, protein copy numbers and virus size (Popovic, 2022a). The atom counting method was applied, using a custom-made computer program. More details on the atom counting method can be found in (Popovic, 2022a). The genetic and protein sequences for the Delta strain were complete and were used as they were reported (National Center for Biotechnology Information, 2022a). However, for the Omicron strain, there were several gaps of unknown nucleotides and amino acids in the nucleic acid and protein sequences, respectively (National Center for Biotechnology Information, 2022a). In the Omicron strain genome (NCBI ID: OL869974.1), there were three gaps, 3 unknown nucleotides starting at position 11,107, 16 unknown nucleotides starting at 22,526 and 8 unknown nucleotides starting at 28,165. In the nucleocapsid phosphoprotein of the Omicron strain (NCBI ID: UGY75362.1), there was a 3 amino acid long gap starting at position 31. In the spike glycoprotein of the Omicron strain (NCBI ID: UGY75354.1), there was a 6 amino acid gap starting at position 382. The gaps were filled, using the corresponding genetic and protein sequences of the Delta strain. The corresponding sequences of the Omicron and Delta strain were first aligned using the Needleman-Wunsch algorithm (Needleman and Wunsch, 1970), implemented using NCBI BLAST (National Center for Biotechnology Information, 2022b). Since the sequences of the Delta and Omicron strains matched completely around the areas of the gaps, the gaps in the Omicron sequences were filled using the corresponding parts of the Delta sequences, obtained using alignment. Elemental composition of virus particles was used to construct growth reactions for the analyzed SARS-CoV-2 strains. Growth reactions are macrochemical equations that quantify growth of organisms, describing conversion of nutrients into new live matter and other metabolic products (von Stockar, 2013a, 2013b; Battley, 1998, 2013). Growth reactions have been used to study a wide range of organisms, including bacteria (Battley, 1992), fungi (Battley, 2013, 1998), algae (Wang et al., 2017), plants (Popovic and Minceva, 2021b) and viruses (Popovic and Minceva, 2020a, M. 2020b, 2021a). Growth reactions for the analyzed viruses have the general form (Popovic and Minceva, 2020a, M. 2020b, 2021a)(19) (Amino acid) + O2 + HPO42− + HCO3− → (Bio) + SO42− + H2O + H2CO3 Amino acids represent the carbon and energy source, and the nitrogen source (Popovic and Minceva, 2020a, M. 2020b; von Stockar, 2013b). Oxygen is the electron acceptor (Popovic and Minceva, 2020a, M. 2020b; von Stockar, 2013b). The hydrogenphosphate ion is the phosphorus source, while the hydrogencarbonate ion is a part of the bicarbonate buffer that takes the produced H + ions (Popovic and Minceva, 2020a, M. 2020b; von Stockar, 2013b). (Bio) denotes newly synthetized virus live matter (Popovic and Minceva, 2020a, M. 2020b; von Stockar, 2013b). The sulfate ion is takes excess sulfur, while H2CO3 takes oxidized carbon and excess H + ions, as a part of the bicarbonate buffer (Popovic and Minceva, 2020a, M. 2020b; von Stockar, 2013b). The stoichiometric coefficients of the growth reactions for the three analyzed strains of SARS-CoV-2 can be found in Table 5. 2.7 Thermodynamic properties of virus live matter and growth Elemental composition of virus live matter was used to find standard thermodynamic properties of the analyzed SARS-CoV-2 strains. Standard enthalpy of formation of virus live matter was calculated using the Patel-Erickson equation, also known as Thornton's rule. Elemental composition of live matter can be used to find the number of electrons transferred to oxygen during its complete combustion, E, using the equation(20) E=4nC+nH−2nO−0nN+5nP+6nS where nC, nH, nO, nN, nP and nS represent the number of carbon, hydrogen, oxygen, nitrogen, phosphorus and sulfur atoms in the empirical formula of live matter, respectively (Battley, 1998, 1992; Popovic, 2019). The number of electrons E can be used to calculate standard enthalpy of combustion of live matter, ΔCH⁰(bio), using the Patel-Erickson equation (Patel and Erickson, 1981; Battley, 1998, 1992; Popovic, 2019)(21) ΔCH0(bio)=−111.14kJC−mol·E ΔCH⁰(bio) is the enthalpy change of the combustion reaction of live matter(22) CnCHnHOnONnNPnPSnS + (nC + ¼ nH + 1¼ nP + 1½ nS - ½ nO) O2 → nC CO2 + ½ nH H2O + ½ nN N2 + ¼ nP P4O10 + nS SO3 Thus, Hess's law can be used to convert standard enthalpy of combustion of live matter, ΔCH⁰(bio), into standard enthalpy of formation of live matter, ΔfH⁰(bio) (Atkins and de Paula, 2011, 2014).(23) ΔfH0(bio)=nCΔfH0(CO2)+nH2ΔfH0(H2O)+nP4ΔfH0(P4O10)+nSΔfH0(SO3)−ΔCH0(bio) A similar procedure can be used to find standard molar entropy of virus live matter, using the Battley equation. The Battley equation relates elemental composition of live matter to its standard molar entropy, Sm0(bio),(24) Sm0(bio)=0.187∑JSm0(J)aJnJ where Sm0(J) is standard molar entropy of element J, aJ number of atoms of element J in its standard state form and nJ is the number of atoms of element J in the empirical formula of the virus (Battley, 1999; Popovic, 2019). The Battley equation can be modified to give standard entropy of formation of live matter, ΔfS⁰(bio). This is done by replacing the coefficient +0.187 with −0.813 (Battley, 1999)(25) ΔfS0(bio)=−0.813∑JSm0(J)aJnJ Finally, ΔfS⁰(bio) can be combined with ΔfH⁰(bio), to find standard Gibbs energy of formation of live matter, ΔfG⁰(bio), using the equation (Battley, 1998; Popovic, 2019)(26) ΔfG0(bio)=ΔfH0(bio)−TΔfS0(bio) Standard thermodynamic properties of virus live matter can be combined with growth reactions, to find standard thermodynamic properties of growth. Standard thermodynamic properties of growth are thermodynamic property changes accompanying growth reactions. They include standard enthalpy of growth, ΔgH⁰, standard entropy of growth, ΔgS⁰, and standard Gibbs energy of growth, ΔgG⁰. These properties can be found using the Hess's law(27) ΔgH0=∑productsνΔfH0−∑reactantsνΔfH0 (28) ΔgS0=∑productsνSmo−∑reactantsνSmo (29) ΔgG0=∑productsνΔfG0−∑reactantsνΔfG0 where ν represents a stoichiometric coefficient (Atkins and de Paula, 2011, 2014). Standard Gibbs energy of growth, ΔgG⁰, is of particular importance, since it represents the driving force of growth and is related to growth rate. 2.8 Growth rate and driving force Growth rate of microorganisms, rg, is proportional to their Gibbs energy of growth, ΔgG, according to the phenomenological equation(30) rg=−LgTΔgG where Lg is the growth phenomenological coefficient (different than the binding phenomenological coefficient) Westerhoff et al., 1982; Hellingwerf et al., 1982; von Stockar, 2013a; Demirel, 2014; Popovic and Minceva, 2020a, M. 2020b, 2021a). Notice that Eqs. (6) and ((30) have the same form, but apply to two different reactions. Both Eqs. (6) and (30) belong to the family of phenomenological equations (Demirel, 2014). However, they apply for different processes, namely binding and multiplication, respectively. Eq. (6) gives the rate of the antigen-receptor binding reaction (1). On the other hand, Eq. (30) applies to Eq. (19), giving growth (multiplication) rate. 3 Results Standard thermodynamic parameters of antigen-receptor binding have been calculated for the Omicron strain, using Eqs. (12) through (15). The results include standard enthalpy of binding, ΔBH, standard entropy of binding, ΔBS, and standard Gibbs energy of binding, ΔBG, at 25 °C and 37 °C. They are presented in Table 1. Standard Gibbs energies of binding are negative at both temperatures, implying a spontaneous process. This is in accordance with the observation that the Omicron strain binds to the ACE2 receptor. Standard enthalpies of binding are negative at both temperatures. Standard entropies of binding are also negative at both temperatures, implying that the binding process is driven by enthalpy. Table 2 shows binding phenomenological coefficients, LB, binding equilibrium constants, KB, and standard Gibbs energy of binding, ΔBG, for the Hu-1 (Wild type), Alpha, Beta, Gamma, Delta, Omicron and GD/1/2019-RBD strains of SARS-CoV-2. All the data in Table 2 are at 25 °C. Standard Gibbs energies of binding of all the analyzed strains are negative. The binding phenomenological coefficients are on the same order of magnitude for all the analyzed strains, but their values differ. The same trend can be observed for the binding equilibrium constants. Fig. 1 shows binding phenomenological coefficients and standard Gibbs energies of binding of the analyzed SARS-CoV-2 strains. The Omicron strain has a less negative standard Gibbs energy of binding (−42.82 kJ/mol), but a greater value of the binding phenomenological coefficient (5.72 × 1018 mol² K / J s dm³). The Delta strain has a more negative Gibbs energy of binding (−43.38 kJ/mol), but also lower binding phenomenological coefficient (3.89 × 1018 mol² K / J s dm³). This observation about LB and ΔBG⁰ applies to the other strains in Fig. 1. For example, the Wild type is characterized by the least negative standard Gibbs energy of binding, but the most negative binding phenomenological coefficient. The Alpha strain has the most negative standard Gibbs energy of binding, but the least negative binding phenomenological coefficient. It seems obvious that it is not enough to observe just the standard Gibbs energy of binding or binding affinity to determine the binding rate. Binding rates for the analyzed SARS-CoV-2 strains have been calculated for the first time and are given in Table 3 .Fig. 1 Phenomenological coefficients and Gibbs energies of binding of SARS-CoV-2 strains. Binding phenomenological coefficients, LB, are represented by the blue columns. Their values were multiplied by 1018, for better presentation. Gibbs energies of binding are represented by the orange columns. Their values were multiplied by −1 (made positive), for better presentation. Fig. 1 Table 3 Binding rates of SARS-CoV-2 strains calculated using the kinetic (rkin), linear (rlin) and exponential (rexp) methods. The kinetic method is based on chemical kinetics and uses Eqs. (2-4). The linear method uses linear nonequilibrium thermodynamics, and Eqs. (6), (16) and (17). The exponential method uses the general nonequilibrium thermodynamic Eq. (18). The results were calculated at Q = 0.91 KB. The binding rates are at 25 °C. Table 3Strain rkin (× 1020 M/s) rlin (× 1020 M/s) rexp (× 1020 M/s) Wild type 384.2 436.3 456.7 Alpha 72.1 70.6 73.9 Beta 252.5 213.1 223.1 Gamma 153.6 142.0 148.7 Delta 276.7 308.3 322.7 Omicron 683.2 453.3 474.5 GD/1/2019-RBD 451.8 338.6 354.4 Empirical formulas of Delta and Omicron SARS-CoV-2 strains have been calculated and are given in Table 4 . The empirical formula of the Delta strain entire virion was found to be CH1.6383O0.2844N0.2294P0.0064S0.0042, while its nucleocapsid has the formula CH1.5692O0.3431N0.3106P0.0060S0.0043. The empirical formula of the Omicron strain entire virion was found to be CH1.6404O0.2842N0.2299P0.0064S0.0038, while its nucleocapsid has the formula CH1.5734O0.3442N0.3122P0.0060S0.0033. It is interesting to compare these formulas to that of the Wild type (Hu-1) strain. The empirical formula of the Wild type entire virion was found to be CH1.6390O0.2851N0.2301P0.0065S0.0038, while its nucleocapsid has the formula CH1.5708O0.3452N0.3125P0.0060S0.0033(M. Popovic and Minceva, 2020b). Differences can be seen, which appeared due to mutations.Table 4 Elemental composition of entire virus particles and nucleocapsids for Wild type, Delta and Omicron strains of SARS-CoV-2. Table 4Strain C H O N P S Wild type (Hu-1) Entire virus 1 1.6390 0.2851 0.2301 0.0065 0.0038 Nucleocapsid 1 1.5708 0.3452 0.3125 0.0060 0.0033 Delta (B.1.617.2) Entire virus 1 1.6383 0.2844 0.2294 0.0064 0.0042 Nucleocapsid 1 1.5692 0.3431 0.3106 0.0060 0.0043 Omicron (B.1.1.529) Entire virus 1 1.6404 0.2842 0.2299 0.0064 0.0038 Nucleocapsid 1 1.5734 0.3442 0.3122 0.0060 0.0033 Based on the elemental composition of virus nucleocapsids, growth reactions were formulated. Growth reactions for the Wild type, Delta and Omicron SARS-CoV-2 strains can be found in Table 5 . They are necessary for calculating thermodynamic properties of growth of the virus strains. Based on elemental composition, standard thermodynamic properties of live matter of Delta and Omicron SARS-CoV-2 strains have been calculated. They can be found in Table 6 . These were combined with growth reactions to find standard thermodynamic properties of growth, given in Table 7 . For the Delta strain, standard enthalpy of growth was found to be −227 kJ/C-mol, standard entropy of growth is −37 J/C-mol K, while the standard Gibbs energy of growth was found to be −217 kJ/C-mol. For the Omicron strain, standard enthalpy of growth was found to be −232 kJ/C-mol, standard entropy of growth is −37 J/C-mol K, while the standard Gibbs energy of growth was found to be −221 kJ/C-mol. These properties can be compared to those of the Wild type (Hu-1) strain. For the Wild type, standard enthalpy of growth is −233 kJ/C-mol, standard entropy of growth is −38 J/C-mol K, while the standard Gibbs energy of growth is −222 kJ/C-mol (M. Popovic and Minceva, 2020b).Table 5 Stoichiometric coefficients for the growth reactions of Wild type, Delta and Omicron SARS-CoV-2 strains. The coefficients in the table correspond to the general growth reaction (Amino acid) + O2 + HPO42− + HCO3− → (Bio) + SO42− + H2O + H2CO3. The product (Bio) denotes virus live matter (new virions), described by the empirical formula from Table 4. The growth reactions are for virus nucleocapsids, which are synthetized by hijacking the host cell metabolic machinery [Popovic and Minceva, 2020a]. Table 5Strain Reactants Products Amino acid O2 HPO42− HCO3− → Bio SO42− H2O H2CO3 Wild type (Hu-1) 1.3905 0.4937 0.0060 0.0437 → 1 0.0279 0.0551 0.4342 Delta (B.1.617.2) 1.3820 0.4811 0.0060 0.0415 → 1 0.0268 0.0579 0.4235 Omicron (B.1.1.529) 1.3892 0.4910 0.0060 0.0438 → 1 0.0279 0.0539 0.4330 Table 6 Standard thermodynamic properties of formation of nucleocapsids of SARS-CoV-2 Wild type, Delta and Omicron strains. The properties include standard enthalpy of formation, ΔfH⁰bio, standard molar entropy, S⁰m,bio, and standard Gibbs energy of formation, ΔfG⁰bio. The data for the Wild type (Hu-1) strain was taken from [M. Popovic and Minceva, 2020b]. Table 6Strain ΔfH⁰bio (kJ/C-mol) S⁰m,bio (J/C-mol K) ΔfG⁰bio (kJ/C-mol) Wild type (Hu-1) −76 33 −34 Delta (B.1.617.2) −75 32 −33 Omicron (B.1.1.529) −76 33 −34 Table 7 Standard thermodynamic properties of growth of Wild type, Delta and Omicron strains of SARS-CoV-2 nucleocapsids. The properties include standard enthalpy of growth, ΔgH⁰, standard entropy of growth, ΔgS⁰, and standard Gibbs energy of growth, ΔgG⁰. The data for the Wild type (Hu-1) strain was taken from [M. Popovic and Minceva, 2020b]. Table 7Strain ΔgH⁰ (kJ/C-mol) ΔgS⁰ (J/C-mol K) ΔgG⁰ (kJ/C-mol) Wild type (Hu-1) −233 −38 −222 Delta (B.1.617.2) −227 −37 −217 Omicron (B.1.1.529) −232 −37 −221 4 Discussion The discussion will be divided into two parts. The first part is about chemical and thermodynamic characterization of antigen-receptor binding of SARS-CoV-2 strains. In the second part, thermodynamic characterization of growth (multiplication) of SARS-CoV-2 strains will be made. Finally, the consequences of differences in thermodynamic properties between the strains will be considered on infectivity and pathogenicity of SARS-CoV-2 strains. 4.1 Antigen-receptor binding and infectivity SARS-CoV-2 had appeared in late 2019 and has until now caused a pandemic with 498 million reported cases and 6.2 million casualties (WHO, 2022a; Worldometer, 2022). This great number of cases and casualties was caused by several waves of the pandemic, by different strains of SARS-CoV-2. Thus, SARS-CoV-2 has during the last two years showed a strong tendency to mutate (Wang et al., 2021a; Callaway, 2020). The result of this tendency is appearance of a great number of strains, during the period of two years. During the last wave of the pandemic, which started in December 2021, two strains were simultaneously detected in Europe: Delta and Omicron. Epidemiological data show that the previous wave of the pandemic, caused by the Delta strain, has occurred through a prolonged timespan, from late June to late October 2021, with a lower amplitude of the number of daily cases (WHO, 2022a; Worldometer, 2022). The pandemic wave caused by the Omicron strain was characterized by a greater amplitude of the number of daily cases and shorter duration (WHO, 2022a; Worldometer, 2022). The peak of the wave caused by the Delta strain occurred on August 19, 2021, with 747,110 new cases throughout the world (WHO, 2022a; Worldometer, 2022). On the other hand, the peak of the Omicron wave occurred on January 20, 2022, with 3779,169 new cases (WHO, 2022a; Worldometer, 2022). This increase in the number of new COVID-19 cases occurred despite the continuous increase in the number of vaccinated people (WHO, 2022a). Between 19 and 20 December 2021, 90% of cases in London and 76% of cases in England were Omicron cases (WHO, 2021b). The considered data indicate that infectivity of the Omicron strain, due to mutations, is greater than that of the Delta strain (Popovic, 2022b). Simultaneously, the number of severe cases, with bilateral pneumonia, has decreased (Abdullah et al., F. 2021). This led to a decrease in pressure on hospitals and lower number of death cases (Abdullah et al., F. 2021). A question is raised of whether the increase in infectivity is accompanied by a decrease in pathogenicity? If yes, how can this phenomenon be explained? The connection between infectivity, pathogenicity, and mutations in Delta and Omicron SARS-CoV-2 strains has been discussed by (Han et al., P. 2022). However, the exact reason has not yet been determined. Both Delta and Omicron strains use the same receptor, ACE2, for entering their host cells (Han et al., P. 2022; L. Wu et al., 2022). The host cells are the same, since both strains attack the same tissues that possess ACE2 receptors (e.g. primary infection in the upper respiratory system, secondary infection in the lower respiratory system etc.) (Liu et al., 2021). However, the Delta strain causes pulmonary infections much more often than the Omicron strain, even though both strains use the same receptor (Abdullah et al., F. 2021). The incubation period for infections caused by the Omicron strain is shorter (3 days), compared to the incubation period of the Delta strain (4 days) and Hu-1 strain (5 or more days) (Jansen et al., 2021). Thus, the rate of entry of the virus into host cells during primary infections with the Omicron strain should be greater. On the other hand, the Omicron strain attacks lower respiratory pathways less often than the Delta strain (Abdullah et al., F. 2021). This means that the infectivity of the Omicron strain is certainly greater than that of the Delta strain. Infectivity is related to susceptibility (Hu et al., S. 2021), while permissiveness is related to pathogenicity (Manjarrez-Zavala et al., 2013). However, a question remains open: is there a difference in pathogenicity between the Delta and Omicron strains? Binding strength of SARS-CoV-2 strains to host cell receptors has been a subject of intense research (Han et al., P. 2022; L. Wu et al., 2022; Chen et al., 2022; Wang et al., 2021b; P. Gale, 2021; M.I. Barton et al., 2021; Augusto et al., 2021; Laffeber et al., 2021). Omicron, Delta, and Wild type SARS-CoV-2 RBDs were found to have similar binding strength to the ACE2 receptor, according some reports (Han et al., P. 2022). However, the Omicron RBD has also been reported to show a weaker binding affinity than the Delta variant, to human ACE2, by other researchers (L. Wu et al., 2022). The binding affinity of the Omicron strain is lower than that of the Delta strain. However, the incubation period is shorter and rate of spreading is greater for the Omicron strain. Thus, the rate of entry of the Omicron strain into the host cell is expected to be greater than that of the Delta strain, despite its weaker binding affinity. This means that properties other than the binding affinity influence the entry rate into host cells. SARS-CoV-2 infections were found to have a thermodynamic physiological basis (Head et al., 2022). Gibbs energy of binding of the Omicron strain (B.1.1.529) is ΔBG⁰ = −42.8 kJ/mol, while its binding constant is KB = 3.18 · 10+7 M − 1 (Table 2). On the other hand, the Delta strain is characterized by ΔBG⁰ = −49.50 kJ/mol and KB = 2.17· 10+8 M − 1(Popovic and Popovic, 2022). The data indicate that the more negative Gibbs energy should make binding of the Delta strain more spontaneous. However, from these data, no conclusions can be drawn on entry rate of Omicron and Delta strains into host cells. This is a consequence of the fact that the virus entry rate into host cells depends, not only on binding affinity and Gibbs energy, but also on the binding phenomenological coefficient, according to Eq. (6). Binding phenomenological coefficients of various SARS-CoV-2 strains have been reported in Table 2. Another possible explanation exists for the shorter incubation period and faster spreading of the Omicron strain. It is related to potential faster multiplication of the Omicron strain, which would result in a greater virus reservoir. The Omicron strain has 45 mutations (Wie et al., C. 2021), of which 32 are on the SGP (Song and Masaki, 2021). This means that the remaining 13 mutations are located at regions other than SGP. Mutations in these regions could have an influence on Gibbs energy of growth, causing a change in virus multiplication rate (Popovic and Minceva, 2020a; U. 2020b). This hypothesis implies greater multiplication rate of the Omicron strain, which would cause greater damage on the attacked host tissues, accompanied by greater number of severe cases and casualties. However, epidemiological and clinical observations do not support this hypothesis (Abdullah et al., F. 2021). Thus, there are some indications that Omicron strain enters the host faster, but multiplies slower. This slower multiplication could appear as the result of mutation occurred in non-SGP region of virus nucleic acid. It has been already mentioned that infectivity is a complex process, influenced by several variables. Except for thermodynamic properties (change in enthalpy, entropy, standard Gibbs energy of binding, standard Gibbs energy of growth and binding constant), infectivity is influenced by kinetic properties (binding rate, kon and koff). Moreover, the quantity and quality of immune response cannot be neglected either. The infective reservoir size also influences infectivity. The infective reservoir size is a variable quantity. For example, in the moment of appearance of a new strain, its infective reservoir is relatively small compared to the reservoir of the dominant strain. If thermodynamic, kinetic and immunological factors are favorable for the new strain, then it will through competition increase the number of people infected with the new strain, suppressing the earlier variants. This can be interpreted as appearance of interference (Popovic and Minceva, 2021a). During analysis of mechanisms of infection by various virus strains, one should have in mind that the competition is between two or no more than three strains simultaneously. For example, currently in Germany, three strains are circulating: Delta, Omicron and BA.2. The strains characterized by lower infectivity will be suppressed, due to competition. Table 2 gives two parameters important for analysis of the phenomenon of infectivity. Standard Gibbs energy of binding, ΔBG⁰, and binding phenomenological coefficient, LB, change differently in various strains. Change in Gibbs energy in one strain can be compensated by change in binding phenomenological coefficient (Fig. 1), according to Eq. (6). Table 3 gives antigen-receptor binding rates. A comparison can be made of binding rates of the Omicron and Delta strains, which are currently dominant. The binding rate of the Omicron strain is between 1.5 and 2.5 times greater than that of the Delta strain (Table 3). This is in good agreement with the results found in the literature (Chen et al., 2022). Evolution of SARS-CoV-2 through mutations from Wild type to Delta strain has been described, from the thermodynamic perspective, in (Popovic and Popovic, 2022). Standard Gibbs energy of binding was found to be an important parameter in evolution of new virus strains (Popovic and Popovic, 2022). However, standard Gibbs energy of binding is not the only parameter that influences infectivity. According to the results of this research, if we consider only Gibbs energy of binding, then the order is:Alpha<Gamma<Beta<Widetype<Delta<Omicron This order does not explain the existence of Alpha, Gamma and Beta variants from the perspective of natural selection. However, as was mentioned, except for Gibbs energy of binding, other properties need to be taken into account when analyzing the infectivity order. For example, one should have in mind the changes in phenomenological coefficients and binding rates (Tables 2 and 3). Fig. 2 shows an increase in binding rate, during evolution of SARS-CoV-2 from Wild type to the Omicron strain. The trend of increase in binding rate is ascending, starting from Wild type to Omicron. If we use binding rate to assess infectivity, then the Fig. 2 shows the expected increase in antigen-receptor binding rate. The reason for this is that binding rate includes, except for change in Gibbs energy, change in binding phenomenological coefficient. A complete perspective should be available in the future, when other parameters become available required to evaluate infectivity of virus strains. For example, if Omicron has an 88% ability to avoid immune response (Chen et al., 2022), then certainly this quality contributes to the infectivity of the Omicron strain. This is in good agreement with the results of Barton et al. (M.I. 2021).Fig. 2 Binding rate and phenomenological coefficients of SARS-CoV-2 strains as a function of time. Every point represents a strain of SARS-CoV-2 including Wild type, Alpha, Beta, Gamma, Delta and Omicron. The X-axis represents time since first detection of the strain. (a) An increase in binding rate over time can be noticed. (b) Change in binding phenomenological coefficient during time. Change in binding phenomenological coefficient can be observed with mutations. Fig. 2 The rate of antigen-receptor binding for the Delta strain has been determined using three methods: kinetic, linear and exponential, as described in the Methods section. The results of the three methods are given in Table 3. For the Delta strain, the kinetic method gave a binding rate of 276.7 × 1020 M/s, the linear method gave 308.3 × 1020 M/s, while the exponential method gave 322.7 × 1020 M/s. The results obtained through the three methods are on the same order of magnitude. For the Omicron strain, the kinetic method gave a binding rate of 683.2 × 1020 M/s, the linear method gave 453.3 × 1020 M/s, while the exponential method gave 474.5 × 1020 M/s. Again, the three methods gave similar results. The binding rate for the Omicron stain is between 1.5 and 2.5 times greater than that of the Delta strain. This means that the Omicron strain enters the host cell faster and has an advantage in the competition for resources with the Delta strain. This also means that the incubation period in infections with the Omicron strain is shorter. This is in agreement with the clinical observations (Jansen et al., 2021). Moreover, greater binding rate of the Omicron strain leads to interference and suppression of the “slower” Delta strain. This is supported by the fact that in a very short period the Omicron strain suppressed the Delta strain in London and rest of the UK, representing 76 to 90% of new COVID-19 cases (WHO, 2021b). Thus, the greater binding rate of the Omicron strain to host cells gives it a better access to the multiplication machinery and other resources (i.e. nucleotides, amino acids etc.) during competition, enabling the domination of the Omicron strain and suppression of the Delta strain. On the other hand, standard Gibbs energy of binding of the Delta strain is −43.38 kJ/mol (Table 2), while that of the Omicron strain is −42.82 kJ/mol (Table 2). Thus, according to Eq. (6), rate of Delta strain binding would have been greater, if only Gibbs energy were the determining factor. More negative Gibbs energy should lead to faster binding of the Delta strain to host cells, shorter incubation period and faster propagation of the Delta strain. However, this is not the case. These facts clearly show that it is not possible to make conclusions on propagation rate and incubation period, based only on Gibbs energy, but it is also required to know the kinetic aspect, through the binding phenomenological coefficient. Knowing both standard Gibbs energy of binding and binding phenomenological coefficient can give information on antigen-receptor binding rate and virus entry into host cells. 4.2 Multiplication inside the host cell and pathogenicity In the section above, a mechanistic explanation of increased infectivity and faster transmission of the Omicron strain was presented. On the other hand, the Omicron strain has demonstrated a decrease in pathogenicity for lung tissue. The thermodynamic properties of various human tissues are available in the literature (Popovic and Minceva, 2020c). They are the same for infection of both Delta and Omicron strains. Thus, thermodynamic properties of lung tissue influence equally the permissiveness of Delta and Omicron strains. On the other hand, they do not influence the virus-virus interaction of the Delta and Omicron strains. Interaction between the Omicron and Delta strains, except for Gibbs energy of binding, depends on Gibbs energy of growth. Standard Gibbs energy of growth has been reported for the Wild type (Hu-1 strain) nucleocapsid to be −222.2 kJ/C-mol (M. Popovic and Minceva, 2020b). Using the atom counting method (Popovic, 2022a), elemental compositions have been determined for the Delta and Omicron strains of SARS-CoV-2. The elemental composition of the Wild type (Hu-1) strain has been taken from (M. Popovic and Minceva, 2020b). The elemental composition of the three strains can be found in Table 4. The empirical formula of the Delta strain nucleocapsid was found to be CH1.5692O0.3431N0.3106P0.0060S0.0043. The empirical formula of the Omicron strain nucleocapsid was found to be CH1.5734O0.3442N0.3122P0.0060S0.0033. The empirical formula of the Wild type nucleocapsid is CH1.5708O0.3452N0.3125P0.0060S0.0033(M. Popovic and Minceva, 2020b). Similar results were reported by Degueldre (C. 2021) and Şimşek et al. (B. 2021). Elemental composition of the Wild type, Delta and Omicron strains are different. The differences originate from mutations. Mutations lead to change in genetic sequence and amino acid chains of proteins. Thus, a difference exists in the number and type of amino acids and nucleotides in biopolymers comprising different strains, resulting in differences in elemental composition. Thermodynamic properties of formation of live matter of analyzed SARS-CoV-2 strains can be found in Table 6. Based on thermodynamic properties of formation and growth reactions (Table 5), thermodynamic properties of growth were calculated. The Wild type has the most negative Gibbs energy of growth (−222 kJ/C-mol), followed by the Omicron strain (−221 kJ/C-mol) and after it the Delta strain (−217 kJ/C-mol). The strain characterized by the most negative Gibbs energy of growth, the Wild type, should exhibit the greatest multiplication rate. The greatest growth (multiplication) rate should lead to greatest damage to host tissues and most severe clinical picture (Casadevall and Pirofski, 2000). Indeed, the Wild type has caused more severe clinical pictures than Omicron and Delta strains (WHO, 2022b). The calculated standard Gibbs energies of growth show that the Omicron strain should exhibit a greater multiplication rate than the Delta strain. In that case, damage to the host tissue caused by the Omicron strain should be greater than that caused by the Delta strain. This would lead to a more severe clinical picture. However, the last pandemic wave has been dominated by the Omicron strain and is characterized by less severe clinical pictures than the wave caused by the Delta strain. The explanation for this phenomenon seems to originate from increased number of vaccinated people. Even though all available vaccines were produced based on Hu-1 sequence, mass vaccination has led to excitation of immune system of the vaccinated people, which could have led to a more rapid immune response during infections and reinfections (for those who already had COVID-19 caused by an earlier strain) with the Omicron strain. In that case, Omicron infections could appear in both vaccinated people and people who recovered from COVID-19 caused by one of the previous strains, as well as in unvaccinated people. Having in mind the its greater entry rate, the Omicron strain has an advantage in causing primary infections. More severe clinical pictures and complications caused by Omicron are less common in vaccinated and recovered, due to excited immune system. However, unvaccinated people and those who haven't recovered from COVID-19 can have a more severe clinical picture, caused by Omicron. This is supported by the fact that the number of hospitalized vaccinated cases of Omicron strain is lower than that of unvaccinated cases (Veneti et al., 2022). Even though, no vaccine is available for the Omicron strain, it is obvious that vaccination with available vaccines is recommended, for avoiding more severe clinical pictures. Primary infection with the Omicron strain, due to mutations, occurs in vaccinated, recovered and unvaccinated. After that, the immune system of vaccinated and recovered should in a short time period be able to produce antibodies that would prevent the Omicron strain from causing lung infections. In unvaccinated people, the Omicron strain multiplies at the same rate as in vaccinated, but an unexcited immune system is not able to produce antibodies in a short time period. This enables a secondary infection on lungs. Fig. 2 shows rate of binding of various strains during the evolution of SARS-CoV-2 through time. A trend can be observed (dotted line) of increase in binding rate. From clinical and epidemiological data, it is known that the Omicron strain transmits faster (Jansen et al., 2021). The epidemiological data can be explained by the increase in binding rate of the Omicron strain (Fig. 2). From Fig. 2a, it can be seen that the reason for faster transmission is the increased rate of antigen-receptor binding, which appeared due to acquired mutations. However, the binding phenomenological coefficient, even though it varies between strains, does not show great changes over prolonged time intervals. This can be seen from Fig. 2b. 5 Conclusions Empirical formulas have been reported for the first time for the Delta and Omicron strains of SARS-CoV-2. They are different, due to mutations present in the two strains. Based on empirical formulas, growth reactions for the two strains have been formulated and reported. Moreover, standard thermodynamic properties of formation and growth have been reported for the two strains. These properties determine the virus multiplication rate inside the host cell. On the other hand, the virus entry rate into host cells depends, not only on binding affinity and Gibbs energy, but also on the binding phenomenological coefficient. Thus, knowing the antigen-receptor binding rate and the rate of virus entry into host cells requires knowledge of standard Gibbs energy of binding and the binding phenomenological coefficient. Binding phenomenological coefficients have been determined for Wild type, Alpha, Beta, Gamma, Delta and Omicron strains of SARS-CoV-2. The rate of binding of SGP to the ACE2 receptor have been calculated using kinetic and nonequilibrium thermodynamic approaches. The calculated values are on the same order of magnitude. The entry rates of SARS-CoV-2 strains have been quantified by their binding rates. The greater binding rate of the Omicron strain indicates a greater infectivity. Similar values of Gibbs energy of growth of the Delta and Omicron strains indicate that the multiplication rates of both strains are similar. The fact that the Omicron strain causes less severe clinical pictures can be explained by the phenomenon of immune system alertness. Therefore, mechanistic analysis of infectivity and pathogenicity requires knowledge of not only Gibbs energies of binding and growth, but also of the corresponding phenomenological coefficients. CRediT authorship contribution statement Marko Popovic: Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. Declaration of Competing Interest The author declares no conflict of interest. Acknowledgments The author would like to thank Mr. Linjie Li, Dr. Jianxun Qi and Dr. George F. Gao from the Institute of Microbiology of the Chinese Academy of Sciences, for providing kinetic data for SARS-CoV-2 strains. The author would also like to thank Dr. Zhijian Xu from the Shanghai Institute of Materia Medica of the Chinese Academy of Sciences, for providing information about the non-competitive ELISA approach. ==== Refs References Atkins P.W. de Paula J. 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==== Front Ann Hematol Ann Hematol Annals of Hematology 0939-5555 1432-0584 Springer Berlin Heidelberg Berlin/Heidelberg 4799 10.1007/s00277-022-04799-7 Letter to the Editor COVID-19: impact of vaccination in myeloma patients http://orcid.org/0000-0002-6629-1766 Hoornaert E. Ellen.hoornaert@uclouvain.be 1 Dachy F. 2 Hansenne A. 2 Bailly S. 2 van Maanen A. 3 Gruson D. 4 Vekemans M-C. 2 1 grid.48769.34 0000 0004 0461 6320 Department of Internal Medicine and Infectious Diseases, Cliniques universitaires Saint Luc, 10 avenue Hippocrate, 1200 Woluwe Saint Lambert, Brussels, Belgium 2 grid.48769.34 0000 0004 0461 6320 Department of Hematology, Cliniques universitaires Saint Luc, 1200 Brussels, Belgium 3 grid.48769.34 0000 0004 0461 6320 Statistical Support Unit, King Albert II Institute, Cliniques Universitaires Saint Luc, 1200 Brussels, Belgium 4 grid.48769.34 0000 0004 0461 6320 Department of Laboratory Medicine, Cliniques universitaires Saint Luc, 1200 Brussels, Belgium 11 4 2022 12 13 2 2022 14 2 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. ==== Body pmcDear Editor, The worldwide COVID-19 pandemic represents an unprecedented crisis that affects the entire medical community and appears to be a devastating infection in patients with hematological disorders, including myeloma (MM) [1–3]. Vaccination is therefore crucial in this population [4]. Seroconversion after COVID-19 has been shown to be lower in MM patients compared to the general population. The same is expected after vaccination, as different studies have already reported a lower antibody response after anti-SARS-CoV-2 vaccination in this group [5–8]. We investigated the impact of anti-SARS-CoV-2 vaccination in patients with MM or related disorders, excluding MGUS. Immunization was assessed after two shots of either a mRNA (Pfizer®/Moderna®) or a viral vector (Astra Zeneca®) vaccine, using the Elecsys® immunoassay performed on cobas®8000 (Roche Diagnostics®) that measures anti-SARS-CoV-2 antibodies including IgG. From March 2020 to September 2021, we determined the serological status at day 30 (median 36, range 1–148), of the first 164 patients with plasma cell dyscrasias that completed vaccination. Among them, 114 were affected by MM, 26 by asymptomatic MM, 8 by MGRS, 16 by AL amyloidosis. The characteristics of our cohort were as followed: median age of 69 (range 35–89), median IgG level of 700 mg/dL (range 80–1840), median CD4/CD8 levels of 530/μL (range 58–1668), and 398/μL (range 33–4556), respectively (Fig. 1).Fig. 1 Patients characteristics and results One hundred fifty patients (92%) developed regular antibodies confirmed by the presence of the receptor-binding domain of the spike protein (RBD) antibodies, while 23 (14%) presented nucleocapsid protein (N) antibodies, suggesting a previous contact with the virus. Among these, 12 had a history of a positive RT-PCR nasopharyngeal swab and 11 were fortuitously found to be positive in the absence of any clinical manifestation (Fig. 1). Thirteen patients failed to develop any immunization; all had received immunosuppressive therapies (renal transplantation in 1, long-term corticosteroids in 2, cyclophosphamide in 4, anti-CD20 monoclonal antibodies in 4, both in 2) or multiple lines of therapies in 1. We failed to identify any link with immunoparesis or CD4/CD8 levels. As well, there was no correlation between RBD antibody and CD4 levels. All patients tested after the third dose develop immunization except those exposed to anti-CD20 therapies in the previous 12 months. Only one patient underwent an interferon-gamma-release assay testing that was negative. Nowadays, with an 8-month median follow up after vaccination, if only 5 patients experienced a mild form of COVID-19 during the Delta-variant wave, more patients (n = 12) are tested positive with the emergence of the omicron variant, but there were no significant clinical manifestations, hospitalizations, or deaths. In conclusion, SARS-CoV-2 vaccination provided an adequate coverage in our MM population during the delta wave since only five patients developed a mild infection after vaccination. Seroconversion was however clearly affected by the anti-MM treatment. With the appearance of the omicron variant, we observed an upsurge of cases, even of benign evolution, which questions this protection. Whether non-responding patients will eventually develop T cell protection against COVID-19 remains also to be answered, as well as the positivity cutoff that measures neutralizing antibodies, the optimal vaccination schedule, particularly in the context of immunodeficiency, and diverse anti-MM therapies. Author contribution E.H., F.D., A.V.M., and M.C.V. analyzed the data. E.H., F.D., and M.C.V. wrote the manuscript. F.D., A.H., S.B., and M.C.V. collected the data and obtained the consent of each patient. All authors approved the final manuscript. Declarations Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Consent to participate Informed consent was obtained from all induvial patients included in the study. Competing interests The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Dufour I Impact of COVID-19 on myeloma patients Ann Hematol 2020 99 1947 1949 10.1007/s00277-020-04147-7 32577846 2. Cook G Real-world assessment of the clinical impact of symptomatic infection with severe acute respiratory syndrome coronavirus (COVID-19 disease) in patients with multiple myeloma receiving systemic anti-cancer therapy J Haematol 2020 190 83 86 10.1111/bjh.16874 3. Hultcranz M COVID-19 infections and clinical outcomes in patients with multiple myeloma in New York City: a cohort study from five academic centers Blood Cancer Discovery 2020 1 234 2343 10.1158/2643-3230.BCD-20-0102 34651141 4. Ludwig H Recommendations for vaccination in multiple myeloma: a consensus of the European Myeloma Network Leukemia 2021 35 31 44 10.1038/s41375-020-01016-0 32814840 5. Bird S Response to first vaccination against SARS-CoV-2 patients with multiple myeloma Lancet Haematol 2021 6 389 392 10.1016/S2352-3026(21)00110-1 6. Pimpinelli F Fifth-week immunogenicity and safety of anti-SARS-CoV-2 BNT162b2 vaccine in patients with multiple myeloma and myeloproliferative malignancies on active treatment: preliminary data from a single institution J Haematol Oncol 2020 14 81 10.1186/s13045-021-01090-6 7. Oekelen V Highly variable SARS-CoV-2 spike antibody responses to two doses of COVID-19 RNA vaccination in patients with multiple myeloma Cancer Cell 2021 39 1028 1030 10.1016/j.ccell.2021.06.014 34242572 8. Terpos E The neutralizing antibody response post COVID-19 vaccination in patients with myeloma is highly dependent on the type of anti-myeloma treatment Blood Cancer J 2021 11 8 138 10.1038/s41408-021-00530-3 34341335
PMC009xxxxxx/PMC9001029.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00151-8 10.1016/j.jpainsymman.2022.02.066 Article “I Had Never Felt More Alone in My Life as I Did Those Eight Days.” Impact of COVID-19 Visitation Restrictions on Patients and Caregivers Stevens Sandra MD Maine Medical Center Bickford Jaime DO St. Peter's Healthcare Fenton Anny PhD Dana Farber Cancer Institute Hutchinson Rebecca MD MPH Maine Medical Center 12 4 2022 5 2022 12 4 2022 63 5 872873 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Determine the impact of visitation restriction on caregivers and patients 2. Describe ways to improve communication and caregiver involvement to families during times of caregiver separation Original Research Background The impact of restricting hospital visitation during the COVID-19 pandemic on patients and caregivers has not been described. Research Objectives We explored how hospital visitation restrictions affected the health and experience of hospitalized patients and their caregivers. Methods We conducted a multimethod cohort study, matching adult patients (N = 100) hospitalized before the pandemic with 100 patients hospitalized after the pandemic. Matching was based on age, gender, and primary diagnosis. Based on chart abstractions, we conducted t tests estimating whether patient outcomes and medical teams’ communication with caregiver varied by status of visitor restrictions. We then conducted and analyzed semistructured interviews with a subset of patients hospitalized under visitor restrictions and their caregivers (N = 13) to understand the impact of visitation restrictions on patient and caregiver experience. Results Our chart abstraction revealed that caregivers of patients hospitalized during visitation restriction were more likely to receive no contact from medical teams (36.1% vs 16.5%; P < 0.001) and less likely to receive discharge counseling compared to those hospitalized before visitation restriction (36.5% vs 51.6%; P = 0.04). There were no significant differences in emergency department visits, rehospitalization, or death. Our qualitative analysis revealed that caregivers and patients experienced negative emotional consequences of the separation, such as anxiety, confusion, fear, and conflict with the medical team. Caregivers struggled with a lack of information about their loved ones’ overall psychological state. Although video visits were helpful, many caregivers either were not offered this option or did not have the technological literacy necessary to benefit. Conclusion Visitation restrictions during COVID were associated with lack of communication with caregivers but no significant differences in hospitalizations or ER visits. Interviews indicate that patients’ and caregivers’ unmet information needs due to lack of communication caused negative emotional consequences. Implications for Research, Policy, or Practice Future research should explore how to mediate the negative emotional sequelae of caregiver physical separation. ==== Body pmc
PMC009xxxxxx/PMC9001030.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00372-4 10.1016/j.jpainsymman.2022.02.287 Article Seeding a Revolution Post-COVID: Equipping Staff in Long-Term Care to Integrate Palliative Care (FR267) Silvers Allison MBA Center to Advance Palliative Care Matti-Orozco Brenda MD FACP Morristown Medical Center 12 4 2022 5 2022 12 4 2022 63 5 825826 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes  1. Recognize opportunities in existing long-term care workflows to assess for palliative care or hospice needs and trigger palliative care consultation  2. Describe four key palliative care services that long-term care professionals can be taught and incorporate into their own practice, including timely referral to palliative care or hospice  3. Devise strategies to improve collaborations with long-term care providers in their own communities It is estimated that the majority of people receiving long-term care services have unmet palliative care needs, and for those near the end of life, hospice care remains underused. These disparities were highlighted during the COVID-19 pandemic, and now many long-term care programs and facilities are eager to address the palliative care needs of their patients or residents and avoid unnecessary hospital transfers, but they need to understand this in a way that aligns with their own priorities and operations. Two recent initiatives have provided education, resources, and peer learning to groups of long-term care providers, to expedite the integration of palliative care in long-term care settings. One learning collaborative was led by a local health system aiming to improve outcomes for patients in their community, and one was led by a national organization to improve end-of-life experiences in Programs of All-inclusive Care for the Elderly (PACE) nationwide. Common to both efforts was targeted education, focused on proactively identifying people in need of palliative care and how to incorporate this identification into existing workflows; holding effective goals of care and advance care planning conversations; managing physical and psychosocial symptoms; and facilitating referral to palliative care specialty teams or hospice, as appropriate. Note that creating a strong identification process can be instrumental in ensuring equitable access to palliative care for all populations, especially Black, Indigenous, and other people of color. This session shares the lessons learned from these two initiatives, providing insights for both hospital-based and hospice-based palliative care programs to bolster capabilities as well as timely referrals from the long-term care organizations in their own communities. The session ends with an exercise to help attendees devise their own efforts. ==== Body pmc
PMC009xxxxxx/PMC9001031.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00339-6 10.1016/j.jpainsymman.2022.02.254 Article Right There with You: Challenges for Inpatient Palliative Chaplains and Interdisciplinary Team Members During the Onset of COVID-19 (FR222) Galchutt Paul MPH MDiv M Health Fairview Labuschagne Dirk MDIV MPH Rush University Medical Center Usset Timothy MPH MDiv School of Public Health, University of Minnesota 12 4 2022 5 2022 12 4 2022 63 5 809809 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes  1. Identify ways inpatient palliative care chaplains triaged care at the onset of COVID, changed practices to adapt to evolving circumstances, and addressed professional pressures evident in this study's results  2. Identify salient coding and representative quotes to highlight data of when and where chaplains perceived encountering dimensions of burnout, components of moral distress, or aspects of moral injury while working alongside their interdisciplinary team members  3. Discuss the applicable roles and functions of inpatient care chaplains in consideration of these data in responding to the pandemic and how chaplains seek to mitigate the stressful effects of burnout, moral distress, and moral injury The National Consensus Project Clinical Practice Guidelines for Quality Palliative Care Guidelines (4th edition) assert the chaplain to be among the core interdisciplinary team members. However, not much is known about roles and functions of the palliative chaplain, especially amid the stressors, rigors, and chaos of this pandemic's early days. Even less is known about the professional pressures experienced by inpatient palliative chaplains and how they changed practice, including the specific ways they sought to be attentive to and help address interdisciplinary staff support. Although there are some recent data concerning healthcare chaplains globally and within the profession overall during the pandemic, these findings are not explicitly focused on the specialization of palliative chaplaincy. Through the results of our qualitative research investigation, this education will highlight how inpatient palliative chaplains based in the United States changed practice and bore witness to the uncertain and harrowing conditions conducive to the negative effects of overwhelming stressors. Through this concurrent session chaplains experienced in clinical palliative care and research will draw data from their study by using semistructured interviews with 10 inpatient palliative care chaplain research participants. Data collection for this project occurred during the onset (late April to early May 2020) of COVID-19 in the United States. How palliative chaplains triaged, changed practice to adapt to an evolving context, and addressed their unique professional pressures will be described. When and where chaplains perceived their palliative team members experiencing dimensions of burnout, components of moral distress, or aspects of moral injury will also be shared through representative quotes. Last, the shifting roles and functions of inpatient palliative care chaplains due to the pandemic will lead to a discussion of this changed practice, including how it influenced chaplains helping support interdisciplinary team members. ==== Body pmc
PMC009xxxxxx/PMC9001032.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00365-7 10.1016/j.jpainsymman.2022.02.280 Article “Tears over Zoom”: Leveraging Technology Across Cultures to Enhance Resiliency in Response to the COVID-19 Pandemic (FR257) Daubman Bethany Rose MD Massachusetts General Hospital LaPlante JR JD Avera Health, American Indian Health Initiative Spence Dingle MD Hope Institute Hospital Alvarez Catalina PsyD Pontificia Universidad Católica de Chile Stoltenberg Mark MD MPH MA Harvard Medical School 12 4 2022 5 2022 12 4 2022 63 5 822822 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes  1. Describe challenges of burnout and resiliency that unique patient and provider populations have experienced throughout the COVID-19 pandemic  2. Engage in self-reflection on experiences with resiliency and burnout through the lens of one's own culture, patient population, and personal experiences  3. Compare and contrast advantages and challenges of leveraging technology for resiliency enhancement, communal grieving, and bidirectional learning across cultures During the global COVID-19 pandemic, palliative care providers internationally have experienced varying levels of distress and burnout in response to this crisis. When compared to the United States overall, providers working globally and in American Indian tribal nations may have experienced increased personal and professional suffering and distress due to the pandemic. The pandemic has also shone a light on inequity in healthcare and revealed how minority patients have been affected disproportionately. In response to travel and in-person gathering limitations, technology can be leveraged to enhance resiliency, decrease isolation, and mitigate burnout in providers, as well as provide a place to discuss these inequities and engage in communal grieving. These necessary adaptations during the global pandemic provided valuable lessons we can take forward in our practices. Our team leveraged Zoom and Project ECHO groups across countries and cultures, resulting in enhanced resiliency and bidirectional learning. We present our experiences and best-practices for leveraging technology to enhance resiliency across cultures using case studies of 3 different settings: Lakota Nation, the Caribbean, and Latin America. ==== Body pmc
PMC009xxxxxx/PMC9001033.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00122-1 10.1016/j.jpainsymman.2022.02.037 Article Necessity Is the Mother of Implementation: Patient Satisfaction with Telemedicine for Palliative Care During the COVID-19 Pandemic Baksh Adrienne MD Memorial Hermann Medical Group, Houston Martin Aira BS John P. and Katherine G, McGovern Medical School at UTHealth Pacheco Soraira DO MS John P. and Katherine G. McGovern Medical School at UTHealth 12 4 2022 5 2022 12 4 2022 63 5 855856 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Assess patient satisfaction with telemedicine for outpatient palliative care 2. Quantify time and resources saved by telemedicine 3. Examine patient demographic data and how they correlate to patient satisfaction with telehealth for outpatient palliative care Background Current literature on telemedicine use for palliative medicine focuses primarily on the accessibility of virtual health platforms. Information about patients’ attitudes toward using this medium is limited. Telemedicine expansion during the COVID-19 pandemic provided an opportunity to fill this knowledge gap. Aim Statement The aim of this article is to appraise the value of telemedicine for palliative outpatient care and help guide policy regarding telehealth implementation and expansion. Methods Data are presented from a cross-sectional qualitative survey conducted via telephone of 51 patients who participated in 199 telemedicine visits (mean 3.9 visits per patient) from March through December 2020 during the COVID-19 pandemic. Appointments were for established patients at both a large academic palliative clinic and a safety-net, palliative clinic. Questions measured patient satisfaction with healthcare delivery modes, barriers to care, and technological preparedness. Results Primary: All patients (100%) were either “extremely” or “somewhat” satisfied with their symptom management conducted via telemedicine. A majority of patients (65%) preferred a hybrid model with both telemedicine and in-person visits, and 14% preferred all follow-up via telehealth. Secondary: Telemedicine appointments required less time, travel, and family resources. Combined wait and appointment time for virtual visits was less than 30 minutes for 74% of patients, compared with 65% of patients spending 1 hour or more on in-person clinic days. Over half (56%) of support persons missed work to attend visits. No difference in satisfaction was detected when data were stratified by English language proficiency, internet access, and education level. Conclusions and Implications Patient satisfaction with telemedicine for palliative symptom management was similar to that for in-person clinic visits. Study was limited by selection bias; 28.42% of patients had died, 40.53% were unreachable. A larger study is needed. Telemedicine is advantageous, and preliminary data support its use. ==== Body pmc
PMC009xxxxxx/PMC9001034.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00264-0 10.1016/j.jpainsymman.2022.02.179 Article “I Don't Want Him to Be Scared and Alone”: A Mixed-Methods Examination of the Death Experience of Homebound Patients During New York City's Spring 2020 COVID-19 Surge (S556) Franzosa Emily PhD Icahn School of Medicine at Mount Sinai Reckrey Jennifer MD Icahn School of Medicine at Mount Sinai Ornstein Katherine PhD MPH Icahn School of Medicine at Mount Sinai 12 4 2022 5 2022 12 4 2022 63 5 937937 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Characterize patients in a home-based medical care (HBMC) practice who died during the spring 2020 COVID-19 surge in New York City 2. Identify health service and caregiving disruptions that occurred during the COVID-19 surge 3. Identify home-based primary and palliative care practice adaptations to maintain care for homebound patients during the COVID-19 surge Original Research Background Much COVID-19 research focuses on care in institutional settings, but less is known about the care experiences of homebound patients receiving home-based primary and palliative care in the community. Before COVID-19, these patients faced high symptom burden, multiple comorbidities, and high mortality. Research Objectives To describe characteristics and care experiences of patients in a home-based medical care (HBMC) practice who died during New York City's (NYC) spring COVID-19 surge through a mixed-methods, retrospective electronic medical record (EMR) review. Methods We analyzed EMRs for all HBMC patients who died in the initial COVID-19 wave in NYC (March 1-June 30, 2020). We developed an abstraction tool to collect service-related measures (eg, phone calls, televisits), household and clinical characteristics (eg, dementia status), and clinical notes on key disruptions including those related to family caregiving, paid caregiving, medical supplies, and hospice. Results During the study period 112 patients died, twice the practice's usual monthly deaths. Thirty percent died from confirmed or suspected COVID-19, 73% died at home, and 46% were enrolled in hospice. Medical and service disruptions included medication shortages, delayed hospice enrollment, and suspension of nursing and hospice visits due to personal protective equipment and staff shortages. Caregivers experienced difficulties with long-distance care and frustration over service disruptions. HBMC providers adapted by conducting outreach via phone and telehealth and leveraging relationships with partners such as hospice and medical equipment vendors. Hospital restrictions and fear of infection increased caregivers’ and providers’ commitment to keeping patients at home at the end of life. Conclusion Disruptions in family caregiving and health services such as hospice during COVID-19 complicated the dying experience. HBMC providers were an important bridge between caregivers and supportive community and medical resources. Implications for Research, Policy or Practice The COVID-19 pandemic exemplified how home-based primary and palliative care can be an important resource at the end of life. Targeted resources are needed to support families in managing end-of-life care. ==== Body pmc
PMC009xxxxxx/PMC9001035.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00323-2 10.1016/j.jpainsymman.2022.02.238 Article Compassion Fatigue and Secondary Trauma During the COVID-19 Pandemic: Leading the Movement to Refashion Compassion (FR201) Ketterer Briana MD MS Oregon Health & Science University Callahan Mary MD MS Columbia University Irving Medical Center Gunturi Nivedita MD Rush University Medical Center Tapper Corey MD MS Johns Hopkins University School of Medicine Wills Ashley DNP 12 4 2022 5 2022 12 4 2022 63 5 800801 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes  1. Define and differentiate between the components of professional quality of life, including compassion satisfaction, compassion fatigue, secondary trauma, moral injury, and burnout  2. Recognize risk factors and signs of compassion fatigue in yourself and your interprofessional colleagues  3. Identify three specific strategies that can be implemented to improve compassion satisfaction in yourself and your interprofessional team The COVID-19 pandemic has changed the world and created a shared trauma. In this context, palliative care has been called upon in unprecedented ways to provide care and to support our colleagues in spite of the evolving risks of our work environment. The uncertainty, fear, and exhaustion during the pandemic are immense and not without life-altering consequences. Compassion fatigue is a sense of emotional exhaustion that leads to decreased ability to feel compassion for others. It is sometimes referred to as secondary traumatic stress. As palliative care clinicians, we are especially prone to compassion fatigue as we bear witness to the suffering of our patients and colleagues. Symptoms of compassion fatigue mimic those of chronic stress, including social isolation, apathy, poor self-care, emotional lability, and substance use. While compassion fatigue is a pre-existing phenomenon, the events of 2020-2021 have produced a considerable impact on clinicians’ practice. Palliative care clinicians are in a prime position to support one another and colleagues through the anticipated post-COVID recovery. Preliminary research in the field indicates that resiliency programs may increase compassion satisfaction and decrease burnout. Although more interdisciplinary research is necessary, the existing data identify potential risk factors and interventions. In this session, an interprofessional team of providers will use brief didactics, case-based examples, and small group discussion to present and define the terminology relevant to compassion fatigue, including professional quality of life, compassion satisfaction, secondary trauma, moral injury, and burnout. The session will equip palliative care clinicians with the tools necessary to identify compassion fatigue while providing a framework within which providers can work to address and manage its complex sequelae. In addition, the session will allow participants to learn from each other, with a forum for sharing strategies that have been successful in their own practice. ==== Body pmc
PMC009xxxxxx/PMC9001036.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00202-0 10.1016/j.jpainsymman.2022.02.117 Article Enhancing Virtual Communication Skills Among Medical Learners: A COVID-19 Telemedicine Goals of Care Standardized Encounter (QI430) Cooney Alissa DO University of Texas Health Science Center San Antonio Walker Megan MD South Texas Veterans Healthcare System Sanchez-Reilly Sandra MD FAAHPM University of Texas Health Science Center and South Texas Veterans Health Care System Ross Jeanette MD AGSF FAAHPM University of Texas 12 4 2022 5 2022 12 4 2022 63 5 901902 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Evaluate the effectiveness of a virtual communication framework to conduct goals-of-care conversations 2. Emphasize the importance of adapting a complex communication curriculum to current telemedicine needs Background The COVID-19 pandemic has forced adaptations in medicine, causing rapid growth in the use of telemedicine to continue caring for patients. Although undergraduate medical education has also been forced to adapt curriculum to a distance learning or virtual model, many providers have no formal telemedicine or COVID-19-specific goals-of-care (GOC) training. Thus, a COVID-19 GOC telemedicine curriculum was developed to provide undergraduate medical learners (UMLs) the skills necessary to facilitate advance care planning (ACP) via a virtual platform. Aim Statement Develop an effective COVID-19 GOC telemedicine curriculum. Methods UMLs were given a 2-hour virtual training session using P-T-SPIKEES, a framework for conducting difficult discussions with patients via telemedicine. Training objectives included gaining an understanding of telemedicine, ACP, and risk factors for worsening COVID-19 disease to facilitate appropriate COVID-19 GOC conversations. UMLs underwent pre/post training surveys in addition to a GOC Telemedicine Objective Structured Clinical Examination (TeleOSCE). Learner pre/post training survey responses were compared to their GOC TeleOSCE performance evaluation to gauge effectiveness of this pilot program. Results N = 83 UMLs. UMLs who experienced systemic technical problems or had incomplete data lacking either a pre, post, or TeleOSCE evaluation survey were excluded from the study. 10% of UMLs had prior telemedicine training, 50% had prior GOC training, and 0% had previously participated in a TeleOSCE. After completing the curriculum, 68% received scores of excellent or above average on their TeleOSCE performance. UML self-evaluated GOC competency increased from 33% to 89%, a 2.7-fold increase, and TeleOSCE competency increased from 22% to 90%, a 4-fold increase. Conclusions and Implications A COVID-19 GOC telemedicine curriculum using the P-T-SPIKEES framework can effectively teach UMLs the skills necessary to facilitate COVID-19-focused GOC discussions via a virtual platform. Further studies should examine the use of the P-T-SPIKEES framework throughout different institutions and in graduate medical education. ==== Body pmc
PMC009xxxxxx/PMC9001037.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00246-9 10.1016/j.jpainsymman.2022.02.161 Article The Role of Palliative Care During the COVID-19 Pandemic: Perceptions and Experiences Among Critical Care Clinicians, Hospital Leaders, and Spiritual Care Providers (S538) Vesel Tamara MD FAAHPM Tufts Medical Center Ernst Emma BS Tufts University School of Medicine Vesel Linda PhD MPH Ariadne Labs, Harvard T.H. Chan School of Public Health McGowan Kayla MA Rhode Island Department of Health, on behalf of HCH Enterprises Stopka Thomas PhD MHS Tufts University School of Medicine 12 4 2022 5 2022 12 4 2022 63 5 926927 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Describe ways that palliative care supported patients, families, and care providers during the COVID-19 pandemic, as identified by participants in this study 2. Identify roles that palliative care could play when it comes to responding to future public health emergencies, as described by participants in this study Original Research Background Palliative care offers a unique skill set in response to challenges posed by the COVID-19 pandemic, with expertise in advance care planning, symptom management, family communication, end-of-life care, and bereavement. However, few studies have explored palliative care's role during the pandemic and changes in perceptions and utilization of the specialty among health and spiritual care providers and hospital leaders. Research Objectives To explore the evolving utilization, perceptions, and understanding of palliative care among critical care clinicians, hospital leaders, and spiritual care providers during the pandemic. Methods We conducted a qualitative study employing in-depth interviews at a tertiary academic medical center in Boston, Massachusetts. Between August and October 2020, we interviewed 25 participants from three key informant groups: critical care physicians, hospital leaders, and spiritual care providers. Results Respondents recognized that palliative care's role increased in importance during the pandemic. Palliative care served as a bridge between providers, patients, and families, supported provider well-being, and contributed to hospital efficiency. The pandemic reinforced participants’ positive perceptions of palliative care, increased their understanding of the scope of the specialty's practice, and inspired physicians to engage more with palliative care. Respondents indicated the need for more palliative care providers and advocated for their role in bereavement support and future pandemic response. Conclusion Findings highlight rapidly evolving and increased utilization and understanding of palliative care during the COVID-19 pandemic. Implications for Research, Policy, or Practice Results suggest a need for greater investment in palliative care programs and for palliative care involvement in public health emergency preparedness and response. ==== Body pmc
PMC009xxxxxx/PMC9001038.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00283-4 10.1016/j.jpainsymman.2022.02.198 Article Mass Production of Compassionate Communication in the Era of COVID-19: A How-To Guide from Parkland Hospital's COVID ICU Team (TH105) Reddy Padmaja MD UT Southwestern Medical Center Tomlinson Anna DNP APRN Parkland Health & Hospital System Leveno Matthew MD UT Southwestern and Parkland Chen Catherine MD UT Southwestern Medical Center Arteaga Daniel MD MBA UT Southwestern Medical Center 12 4 2022 5 2022 12 4 2022 63 5 780781 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes  1. Describe and analyze a blueprint for rapid upscaling of inpatient palliative care services in the ICU, both in the context of normal operations and in the context of mass casualties, natural disasters, pandemics, and other events  2. Describe the natural history, prognosis, morbidity, and mortality associated with acute respiratory distress syndrome due to COVID-19  3. Review and simulate use of novel communication tools and scripts for communication with families of critically ill patients In March 2020, Parkland Memorial Hospital, Dallas County's safety net hospital and one of the busiest hospitals in the nation, opened its Tactical Care Unit, a surgical space converted into a 100-bed unit for patients suffering from the novel and rapidly spreading COVID-19. At the outset of the pandemic, the team committed to expanding access to specialty-level palliative care and maintaining a pipeline of high-quality daily communication for all families of critically ill patients admitted to the COVID ICU. In this session, members of the multispecialty and multidisciplinary Parkland COVID ICU team will present a blueprint for the novel care model that allowed them to meet these goals, even in the midst of massive surges in the summer and winter. The following components of this care model will be reviewed in detail: a clearly defined structure for efficient co-management and co-rounding between palliative care and critical care specialists; the use of volunteer communication extenders; detailed data analysis regarding natural history, prognosis, morbidity, and mortality associated with acute respiratory distress syndrome due to COVID-19; and the generation of standardized, data-driven communication tools and scripts for daily conversations with families of critically ill patients. Attendees will receive copies of said communication tools and scripts, and we will conduct case-based simulations in small groups. Afterwards, we will review lessons learned and outcomes, answer questions, and review the ways in which the strategies and tools described above are being applied in our hospital outside the context of the COVID-19 pandemic. ==== Body pmc
PMC009xxxxxx/PMC9001039.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00335-9 10.1016/j.jpainsymman.2022.02.250 Article Project ECHO in Hospice and Palliative Care: Interdisciplinary Virtual Meeting to Address Needs and Critical Gaps in Our Field (FR218) Burpee Elizabeth MD Four Seasons Cassel J. Brian PhD Virginia Commonwealth University Leff Vickie MSW LCSW APHSW-C Advanced Palliative & Hospice Social Work Certification Program Mikus Michelle PharmD Delta Care Rx VandeKieft Gregg MD MA FAAHPM Providence Institute for Human Caring 12 4 2022 5 2022 12 4 2022 63 5 807807 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes  1. Compare and contrast the Project ECHO model with other forms of education, mentoring, and support for practitioners in our field  2. Assess practice needs and gaps in your organization or in our field and develop interdisciplinary techniques and curricula to address those concerns As a response to the COVID-19 pandemic we created and executed an 18-month nationwide, interdisciplinary, virtual meeting project for hospice and palliative medicine team members. Through team expertise and collaboration, experience with the use of Project ECHO, and close monitoring of the impact the pandemic was disproportionately having on underserved communities and communities of color, we decided upon our focus areas for the project. We chose areas we thought were critical to the overall support of providers, patients, and our larger hospice and palliative medicine community. Areas to be addressed included knowledge gaps in treating people with COVID-19; advanced communication skillset needs with the rapid pivot to telehealth; and equity, diversity, inclusion, and racism in our field. Additionally, we wanted to build in techniques and methods we could use to foster strong interdisciplinary interactions toward building resilience and community in the face of the many unknowns of treating patients and caring for ourselves through the pandemic. We chose the model offered by Project ECHO, at the University of New Mexico School of Medicine, as the structure upon which we would build our project and to meet our objectives. The ECHO model uses virtual gathering to support mentorship, welcomes all levels of training with an “all teach, all learn” focus, and offers patient case–based learning in a way so as to democratize specialty knowledge. The virtual setting allows for participation regardless of location. We have gathered post-session survey data, participant data on moral distress, and data on provider views of their ability to affect equity, diversity, inclusion, and racism in our field. Our project is ongoing. Presenters are members of our project's interdisciplinary expert team, including MDs, an MSW/LCSW, a PharmD, and a PhD palliative care researcher. We will present data gathered thus far and will use interactive methods to teach and to elicit audience participation. ==== Body pmc
PMC009xxxxxx/PMC9001040.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00227-5 10.1016/j.jpainsymman.2022.02.142 Article Code Status Decisions: Did the Pandemic Change Patients’ and Families’ Preferences? (S519) Nagpal Vandana MD FACP University of Massachusetts Medical School Reidy Jennifer MD FAAHPM University of Massachusetts Medical School 12 4 2022 5 2022 12 4 2022 63 5 915915 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Understand influence of a public health emergency on goal-concordant hospital care based on pre-admission treatment preferences 2. Recognize the critical role of palliative care in navigating goal-concordant care for seriously ill patients hospitalized during the pandemic Original Research Background One measure of goal-concordant care is whether patients’ informed decisions on code status are honored. Public health emergencies, such as the COVID-19 pandemic, may create an environment of fear and confusion that could influence prior treatment decisions upon hospitalization. Research Objectives Evaluate whether code status decisions on preadmission Massachusetts Orders on Life Sustaining Treatment (MOLST) orders matched the admission orders to acute care units during first surge of the COVID-19 pandemic. Methods We did a retrospective chart review of patients admitted with severe COVID-19 infection and seen by the palliative care (PC) team between March and May 2020 at a tertiary care center in Massachusetts. The charts were evaluated for presence or absence of MOLST forms before admission, code status decisions on these MOLST forms and initial admission orders, and changes to code status after PC consult. Results The PC team had 92 patient encounters during the span of 2 months. 52 patient charts (57%) had a pre-admission MOLST form; among these, 24 patients had elected DNR/DNI, 2 elected DNR only, and 25 elected full code. Of note, 4/24 patients with DNR/DNI preference also had “do not hospitalize” orders. On admission, most prior DNR/DNI decisions carried forward except for 5 patients (3 changed to DNR only and 2 to full code). Most prior decisions for full code carried forward except for 7 patients, as limitations were added after goals-of-care discussions between families and frontline clinicians. After PC consult, 7/92 encounters had limitations of DNI/DNR or DNR only. Conclusion Our study demonstrated that most clinicians, patients, and families honored prior code status decisions, made by seriously ill patients and their surrogates, during the pandemic. This study sheds light on the critical role of MOLST/POLST in frontline goals-of-care conversations and the indispensable role of PC specialists during a public health emergency. Implications for Research, Policy, or Practice This study may inform further acceptance of MOLST/POLST to document patient preferences. ==== Body pmc
PMC009xxxxxx/PMC9001041.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00180-4 10.1016/j.jpainsymman.2022.02.095 Article Virtual Palliative Care Is Inclusive Care (QI408) Damiano Sara LMSW Ascension Bloise Rafael MD MA MBA Ascension Living Elliott Tania MD Ascension Hendrix John MD HMDC FAAHPM FACP Ascension St John Medical Center Ratz Teri APRN CNP Ascension St. John 12 4 2022 5 2022 12 4 2022 63 5 889889 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Describe a national virtual palliative care approach to increasing access to services for patients living with serious illness during the COVID-19 pandemic 2. Examine virtual palliative care utilization patterns in the inpatient and outpatient setting as well as the adoption of telephonic or video-based platforms 3. Recognize palliative care clinician perspectives and willingness to adopt virtual visits across the care continuum Background The number of Americans who are living with serious illness without adequate access to palliative care services is growing. Virtual palliative care offers an inclusive solution that enhances the patients’ quality of life and addresses complex patient centric needs. Aim Statement This project was developed during the COVID-19 pandemic to investigate telehealth utilization and engagement as well as clinician perceptions and experiences using the virtual platform. Methods We evaluated the use of telephone and video visits in 7 states from May 2020 to April 2021. Of the 84 palliative care clinicians (physicians and APPs) practicing in those states, 35 clinicians (42%) conducted virtual visits. A total of 1,816 virtual visits were completed based on claims analysis. We conducted a clinician experience survey that was completed via Google forms by 15 physicians (44% response rate), which provided qualitative feedback. Results Of the 1,816 visits completed, 332 were telephonic and 1,484 video. 30% of the visits were from the inpatient setting, 19% nursing facility, and 51% outpatient. Top diagnoses were respiratory failure, neuro/Parkinson's, dementia, CHF, and cancer. 37% of the patients lived in disadvantaged zip codes. 93% of the clinicians who responded to the survey were open to having a video visit with their patients with reported benefits such as improved access to care, more efficient use of time, and enhanced clinician-patient relationship. The barriers reported by clinicians included patients having limited availability to a device with a camera and inadequate internet bandwidth. Conclusions and Implications Clinicians find virtual palliative care to be beneficial to patients for goals-of-care conversations, frequent symptom assessment, and engaging multiple family members. Despite a perception that video visits have low adoption in older populations, more than 68% of the virtual visits were with patients 65 years+. These findings have strong implications for clinical practice transformation and further study in the field of virtual palliative care. ==== Body pmc
PMC009xxxxxx/PMC9001042.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00238-X 10.1016/j.jpainsymman.2022.02.153 Article The Quality of Telehealth-Delivered Palliative Care During the Initial COVID-19 Pandemic Surge (S530) Soliman Ann MD Yale New Haven Hospital Akgün Kathleen MD MS VA-Connecticut Healthcare System and Yale Medicine Coffee Jane MSW MSN APRN AGPCNP-BC CHPN Yale New Haven Health System Kapo Jennifer MD Yale School of Medicine Morrison Laura MD FAAHPM Yale New Haven Hospital Blatt Leslie MS MSN RN APRN Yale New Haven Hospital Schulman-Green Dena PhD New York University Rory Meyers College of Nursing Feder Shelli PhD FNP ACHPN Yale University 12 4 2022 5 2022 12 4 2022 63 5 921922 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Describe the use of telehealth-delivered palliative care at one of the largest hospitals in New England during peak COVID-19 infection rates 2. Compare differences in care quality provided by in-person and telehealth-delivered palliative care Original Research Background In March 2020, in response to rapidly increasing COVID-19 infection rates, the palliative care (PC) service at one of the largest hospitals in New England quickly shifted from in-person to telehealth-delivered PC (TPC). Research Objectives We compared the quality of TPC relative to in-person PC during peak COVID-19 rates in the setting of high clinical demands for PC, requiring rapid implementation of TPC. Methods We reviewed electronic health records of TPC and in-person consultation modalities of patients hospitalized between 3/2020 and 6/2020. We assessed established quality measures, including time from admission to inpatient PC consultation, interdisciplinary care, documented assessment at initial consultation of patient and family understanding of serious illness, and discussion of goals of care. Descriptive and bivariate statistics were used to describe differences by modality. Results Among 272 patients, mean age was 69.3 years (standard deviation = 18.3); 53% were male, 65% white, and 24% Black; 33% had primary cancer diagnoses; and 39% had COVID-19. Eighty percent of patients received TPC, and 20% received in-person PC. Median time from admission to PC consultation was 4.5 days (interquartile range 2-11). There were no differences between modalities by race, sex, or time from admission to PC consultation. Patients who received TPC were less likely to have cancer (25% vs 69%; p < 0.01). Patients who received TPC were slightly less likely to encounter more than 1 interdisciplinary PC team member (56% vs 61%) or to have a documented assessment of patient and family understanding of serious illness (60% vs 73%) or discussion of goals of care (71% vs 82%), though not statistically significant (p > 0.05). Conclusion Although PC quality measures varied by modality, the PC service demonstrated the ability to provide high-quality TPC, even under significant strain during the early COVID-19 pandemic. Implications for Research, Policy, or Practice Future work will evaluate opportunities to increase the quality of TPC beyond the initial pandemic surge and for sustained provision of TPC. ==== Body pmc
PMC009xxxxxx/PMC9001043.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00201-9 10.1016/j.jpainsymman.2022.02.116 Article COVID-19 and Advance Care Planning: A Unique Opportunity (QI429) Gessling Aliya MD University of California at Davis Tran Quy MD University of California at Davis Health Langston Jessica MD MPH Northern California VA Sacramento, CA Soloway Ann NP Department of Veterans Affairs, Northern California Health Care System Larson Deborah RN Department of Veterans Affairs NCHCS 12 4 2022 5 2022 12 4 2022 63 5 901901 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Apply process for completing advance care planning 2. Evaluate process for efficacy of document completion Background Advance care planning (ACP) is a process to document patient preferences for future healthcare. Conversations between healthcare providers, patients, and loved ones are needed to reflect a patient's values, goals, and choices for life-sustaining treatments (LSTs). The COVID-19 pandemic highlighted the critical importance of these discussions and the need for improved patient engagement. Aim Statement To improve ACP documentation for patients at high risk for COVID-19 complications and death. Methods As COVID-19 surged, the VA Northern California Health Care System Hospice and Palliative Care Section (HPCS)partnered with patient aligned care teams to expand outreach to high-risk patients needing LST documentation. High risk was defined as age >80, COPD or asthma, or Care Assessment Need Score >80 (which models risk of hospitalization or death within 1 year). An experienced HPCS nurse practitioner contacted these identified patients to provide COVID-19 education, conduct a high-quality goals-of-care conversation, and complete LST documentation and other ACP needs. A representative cohort was followed up to evaluate concordance of treatment with documented preferences. Results Between March and September 2020, 910 patients were identified as high risk, of which 294 agreed to participate in the telehealth visit and complete LST documentation. Importantly, 108 (37%) patients chose DNR and other LST limitations. Additionally, 142 (48%) patients created POLST documentation and 128 (43%) completed advance directives. Over 70% of patients hospitalized received care concordant with the documented LST preferences. A follow-up survey found the outreach impactful, with LST preferences documented correctly. Conclusions and Implications Prior studies have demonstrated success at training primary providers to conduct ACP discussions, but given the limitations imposed by COVID-19 restrictions, this novel and highly cost-effective process of coupling a highly trained HPCS NP with multiple primary care teams to perform ACP was piloted with success. ==== Body pmc
PMC009xxxxxx/PMC9001044.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00230-5 10.1016/j.jpainsymman.2022.02.145 Article Prevalence, Predictors and Outcomes of Documented DNR and/or DNI Orders in COVID-19 Patients (S522) Comer Amber PhD JD Indiana University Fettig Lyle MD Indiana University School of Medicine Bartlett Stephanie PT MS Indiana University–Purdue University Indianapolis Schmidt Amanda MD Community Health System Endris Katelyn BSN student IUPUI Zepeda Isabel BS The Chicago School of Professional Psychology Waite Carly BS Indiana University Slaven James MA MS Indiana University Torke Alexia MD MS Indiana University 12 4 2022 5 2022 12 4 2022 63 5 917917 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Understand the prevalence, predictors, and outcomes associated with DNR and DNI orders for hospitalized patients with COVID-19 throughout the pandemic 2. Understand the reasons for differences in code status order utilization for hospitalized patients with COVID-19 throughout the pandemic Original Research Background The COVID-19 pandemic created complex challenges regarding timing and appropriateness of do not resuscitate (DNR) and do not intubate (DNI) orders. Research Objectives This study sought to determine the prevalence, predictors, timing, and outcomes associated with having a documented DNR or DNI order for hospitalized patients with COVID-19. Methods A retrospective multisite chart review of hospitalized patients with COVID-19 was performed to determine characteristics, medical treatments received, and outcomes associated with having a documented DNR or DNI order. Patients were divided into two cohorts (early and late) by timing of hospitalization during the pandemic. Results Among 1,358 hospitalized patients with COVID-19, 19% (n = 259) had a documented DNR or DNI order. In multivariate analysis, age (older) (p < .01, OR 1.13), race (White) (p = .01, OR 0.55), and hospitalization during the early half of the pandemic (p = .02, OR 1.8) were associated with having a DNR or DNI order. Palliative care consultation occurred more often in the early cohort (p < .01). Medical treatments, including ICU (p = .31) and level of ventilator support (p = .32) did not differ between cohorts. Hospital mortality was similar between the early and late cohorts (p = .27); however, among hospital decedents median hospital day from DNR or DNI order to death differed between cohorts (p < .01) (6 days from order to death in early vs 2 days in the late cohort). Conclusion More frequent use of DNR orders and orders written farther from death in decedents characterized the early pandemic phase. White patients were more likely to have DNR or DNI orders, consistent with prior research. Implications for Research, Policy, or Practice Uncertainty in prognosis may have played a role in the frequency and timing of DNR and DNI orders early in the pandemic. Additional factors, such as fear of resource shortage and transmission of COVID-19 to healthcare workers, may have also played a role. ==== Body pmc
PMC009xxxxxx/PMC9001045.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00211-1 10.1016/j.jpainsymman.2022.02.126 Article Health Professionals’ Perceptions of the Contributions of Palliative Care Consultation for Patients with COVID-19 (S503) Samala Renato MD MHPE Cleveland Clinic Range Patrick MA MSW Cleveland Clinic Hoeksema Laura MD MPH FAAHPM Cleveland Clinic Fong Kimberlee DO Cleveland Clinic Shoemaker Laura DO MS FAAHPM Cleveland Clinic 12 4 2022 5 2022 12 4 2022 63 5 906906 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Discuss health professionals’ perceptions of the contributions of palliative care consultation in COVID-19 inpatient care 2. Recognize the effect of palliative care consultation on health professionals’ self-reported psychological distress Original Research Background Palliative care (PC) programs worldwide became immediately involved in caring for patients with COVID-19 together with other health professionals. Research Objectives The study aimed to determine health professionals’ perceptions of the contributions and helpfulness of PC consultation for COVID-19 care and describe its effect on their own psychological distress. Methods This was a survey-based cross-sectional study of physicians, nurses, advance practice providers, and case managers at two acute care hospitals in a large hospital system in the midwestern United States. Eligible participants completed a 17-item questionnaire on demographic and work-related information, contributions and helpfulness of PC consultation, self-reported psychological distress, and likelihood of working with PC in the future. Results Of 427 health professionals invited to participate, 76 responded (18%). Among 64 eligible respondents, 72% were female, 56% were under the age 40, 40.6% were nurses, 28.1% were physicians, and 66% worked in the intensive care unit. The PC team was perceived as helpful in managing pain and other symptoms, coordinating care between providers, discussing end-of-life preferences, communicating with patients and families, and supporting the care team. Median self-reported psychological distress was 7 (range, 2-10). Twenty-five (39%) participants agreed that PC eased distress by communicating with patients, families and other professionals, providing guidance in difficult conversations, and offering companionship. Among respondents, 84% would probably work with PC in the future. Conclusion During the COVID-19 pandemic, health professionals perceived working with the PC team as helpful in caring for patients and families and in easing their own psychological distress. Implications for Research, Policy, or Practice During the COVID-19 pandemic, health professionals perceived working with the PC team as helpful in caring for patients and families and in easing their own psychological distress. As the pandemic persists and professionals from various fields continue to care for patients, PC appears to be a valuable resource that they can call on. ==== Body pmc
PMC009xxxxxx/PMC9001046.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00183-X 10.1016/j.jpainsymman.2022.02.098 Article Advance Care Planning for Emergency Department Patients with COVID-19 Infection: An Assessment of a Physician Training Program (QI411) Markwalter Daniel MD University of North Carolina at Chapel Hill Casey Martin MD MPH University of North Carolina at Chapel Hill Price Laiken MS University of North Carolina–Chapel Hill School of Medicine Bohrmann Thomas PhD Analytical Partners Consulting LLC Tsujimoto Tamy MS University of North Carolina at Chapel Hill Lavin Kyle MD MPH University of North Carolina at Chapel Hill Hanson Laura MD MPH FAAHPM University of North Carolina at Chapel Hill Lin Feng-Chang PhD University of North Carolina at Chapel Hill Platts-Mills Timothy MD MSc Quantworks, Inc. 12 4 2022 5 2022 12 4 2022 63 5 891891 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Describe the components of an educational toolkit to improve advance care planning in the emergency department for patients with COVID-19 2. Appraise the effectiveness of an educational toolkit to improve advance care planning in the emergency department for patients with COVID-19 Background The sudden emergence of coronavirus disease 2019 (COVID-19) heightened the importance of advance care planning (ACP) conversations, particularly in the emergency department (ED). The objective of this quality improvement project was to determine the effect of an educational training program (“toolkit”) for emergency providers on ACP conversations in the ED during the COVID-19 pandemic. Aim Statement We sought to examine the efficacy of an emergency provider–facing educational intervention, led by palliative care physicians, on the initiation of ED-based ACP for patients with COVID-19. Methods This was an evaluation of a quality improvement project at an academic ED using observational pre-/post-interventional data. Palliative care physicians delivered a 60-minute ACP educational intervention for emergency medicine providers in conjunction with release of reference documents as part of an educational toolkit. Initial training occurred on April 1, 2020. Measured outcomes for each patient included identification of a healthcare decision maker (HCDM), an order for a code status, or a documented goals-of-care (GOC) conversation. Results In total, 143 charts of patients with confirmed COVID-19 presenting for ED care between March 26 and May 25, 2020 were reviewed. There was exceptional representation in gender, race, and ethnicity, with 58% of participants being female, 29% Black, and 49% Hispanic/Latino. There was a 25.4% (95% CI, 7.0-43.9) increase in ED-based ACP, as measured by documentation of at least an HCDM, code status, or GOC conversation. Even after adjustment for patient demographics, a trend toward increased ACP activity was observed (OR = 2.71, 95% CI, 0.93-8.64). Conclusions and Implications In response to a pandemic threat, we found that a rapid and simple provider-facing educational toolkit was associated with increased ED-based ACP activities for patients with COVID-19. ==== Body pmc
PMC009xxxxxx/PMC9001047.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00212-3 10.1016/j.jpainsymman.2022.02.127 Article We Are Not Heroes; Elevating the Discourse of Burnout in Hospice and Palliative Care Nurses (HPCNs) in the Pre/Post COVID Era: A Scoping Review (S504) Frechman Erica PhD(c) AGPCNP-BC ACHPN Vanderbilt University School of Nursing Wright Patricia PhD CRNP University of Scranton 12 4 2022 5 2022 12 4 2022 63 5 907907 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Describe results from a scoping review related to personal factors, organizational and workplace factors, and nursing professional development factors related to hospice and palliative care nurses 2. Recognize characteristics of hospice and palliative care nurses (HPCNs) that are associated with burnout and strategies to mitigate burnout Background and Objective Burnout among HPCNs has been rising throughout the COVID-19 pandemic and threatens patient safety and quality of care. The extant literature provides insight into burnout within the interdisciplinary hospice and palliative care team, but little attention has been given among HPCNs specifically. We conducted a scoping review to examine burnout among HPCNs and unify disparate findings. Study Identification Using the Arksey and O'Malley framework, we systematically searched 8 major databases from 2015 to 2020. Studies were included if they focused on HPCN experiences of burnout, were published in English within the last 5 years with the exception of seminal works, and were discoverable in electronic databases. Exclusion factors included articles that were not focused solely on hospice and palliative nursing, specifically focused on burnout (ie, depression, compassion fatigue, workplace environment), or research articles. Data Extraction and Synthesis Two authors extracted data from the full-text inclusion studies. Results were presented in tabular summary and descriptive summary of quantitative findings. Results Among 1,893 studies, 8 met inclusion criteria. All studies were quantitative, classified as level IV within the rating system for hierarchy of evidence for literature, and spanned across 6 countries. HPCNs working across settings such as inpatient, outpatient, community, and inpatient hospice were represented. Results of studies were coalesced into 3 overarching categories: personal factors, organizational or workplace factors, and nursing professional development factors. Each of these categories was then divided into three cross-cutting subcategories: contributory and noncontributory factors, mitigating factors, and workplace issues. Conclusions and Implications for Practice, Policy, and Research Burnout among HPCNs may not be entirely preventable, but the recognition of contributory and mitigating factors should be taken into consideration by professional nurses and organizations. Additional research is needed to test workplace interventions suggested in the literature, including whether resilience or self-care measures affect burnout in HPCNs. Qualitative research is needed to capture HPCN experiences of burnout, especially in a post-COVID-19 era. ==== Body pmc
PMC009xxxxxx/PMC9001048.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00249-4 10.1016/j.jpainsymman.2022.02.164 Article Utilization of Palliative Care for Patients with Acute Kidney Injury and COVID-19 (S541) Scherer Jennifer MD New York University School of Medicine Rau Megan MD MPH FACP New York University School of Medicine Qian Yingzhi MA New York University Soomro Qandeel MD New York University School of Medicine Sullivan Ryan MPH NYU Langone Health Zhong Hua PhD New York University School of Medicine Linton Janelle MPH NYU Langone Health Chodosh Joshua MD New York University Grossman School of Medicine Charytan David MD MSc New York University Grossman School of Medicine 12 4 2022 5 2022 12 4 2022 63 5 928928 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Understand the historical use of palliative care for patients with acute kidney injury (AKI) 2. Describe the use of palliative care for patients with AKI and COVID-19 during the surge at our institution 3. Describe the associations of palliative care with subsequent health care utilization such as hospice use, ICU time, and mechanical ventilation Original Research Background Acute kidney injury (AKI) is a common morbidity seen in patients with COVID-19 and is associated with high mortality. Palliative care is valuable for these patients yet is historically underused in AKI. Research Objectives To describe the use of palliative care and subsequent health care utilization by COVID-19 patients with AKI. Methods A retrospective analysis of NYU's electronic health data of COVID-19 hospitalizations between March 2, 2020 and August 25, 2020. AKI was defined by the AKI Network creatinine criteria. Regression models examined characteristics associated with a receiving palliative care and discharge to hospice versus death in the hospital. Results Patientswith COVID-19 and AKI were more likely than those without AKI to receive palliative care (42% vs 7%, p < 0.001); however, consults came significantly later (10 days from admission vs 5 days, p < 0.001). 66% of patients initiated on renal replacement therapy (RRT) received palliative care versus 37% (p < 0.001) of those with AKI not on RRT, also later in timing (12 days from admission vs 9 days, p = 0.002). Patients with AKI had a significantly longer stay, more ICU admissions, use of mechanical ventilation, discharges to hospice (6% vs 3%), and changes in code status (34% vs 7%, p < 0.001) than those without AKI. Among those who received palliative care, AKI both without RRT (adjusted odds ratio [aOR] 0.51, 95% confidence interval [CI] 0.27-0.95) and with RRT (aOR 0.18, 95% CI 0.04-0.67) was associated with a lower likelihood of discharge to hospice versus hospital death compared to those without AKI. Conclusion Palliative care was used more for patients with AKI and COVID-19 than historically reported, yet this consultation came later in the hospital course and did not avoid invasive interventions despite high mortality. Implications for Research, Policy, or Practice These data can lead to further exploration of earlier timing of palliative care consultation in AKI. ==== Body pmc
PMC009xxxxxx/PMC9001049.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00245-7 10.1016/j.jpainsymman.2022.02.160 Article Goals of Care Rapid Response Team at a Comprehensive Cancer Center: Feasibility and Preliminary Outcomes (S537) Zhukovsky Donna MD FACP FAAHPM University of Texas MD Anderson Cancer Center Enriquez Parema MSN APRN FNPc ACHPN University of Texas MD Anderson Cancer Center Nortje Nico PhD MA MPhil HEC-C University of Texas MD Anderson Cancer Center Heung Yvonne MD MS University of Texas MD Anderson Cancer Center Itzep Nelda MD MD Anderson Children's Cancer Hospital Wong Angelique MD University of Texas MD Anderson Cancer Center Lu Zhanni DrPH University of Texas MD Anderson Cancer Center Stanton Penny CCRP University of Texas MD Anderson Cancer Center Bruera Eduardo MD FAAHPM University of Texas MD Anderson Cancer Center 12 4 2022 5 2022 12 4 2022 63 5 926926 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Describe an approach to supporting goal-concordant care for critically ill hospitalized patients with cancer 2. Identify areas for improvement in the Goals of Care Rapid Response Team process 3. Discuss potential implications for use in other settings Original Research Background The COVID-19 pandemic placed the issue of resource utilization front and center. Our comprehensive cancer center developed a Goals of Care Rapid Response Team (GOCRRT) to optimize resource utilization with goal-concordant patient care. Research Objectives 1. Evaluate feasibility of GOCRRT by number of consultations that occurred for referred patients. 2. Describe adherence to GOCRRT processes: core team member participation (clinical ethics, medical oncology, supportive care, and social work) and advance care planning (ACP) template use for easily retrievable documentation. 3. Explore preliminary efficacy of GOCRRT consultations in limiting goal-concordant care escalation (change of resuscitation status to DNR, location change from ICU to regular nursing unit, or withdrawal of life-sustaining treatment). Methods We conducted a retrospective chart review of patients referred to GOCRRT from 3/23/2020 to 9/30/2020. Analysis was descriptive. Categorical variables were compared with Fisher's exact or chi-square tests and continuous variables with Mann-Whitney U tests. Results Eighty-nine patients were referred. 76 (85%) underwent a total of 95 consultations. Mean (SD) patient age was 60 (14) years, 54% male, 19% Hispanic, 48% White, 72% married, and 66% of Christian faith traditions. There were slightly more hematologic malignancies than solid (53% vs 47%). The majority (77%) had metastatic disease or relapsed leukemia. 7% had confirmed COVID-19 at referral. 69% expired during the index hospitalization. There was no statistically significant difference in demographic or clinical characteristics between groups (no consultation, 1 consultation, >1 consultation). All core team members were present at 64% of consultations. Consultations were documented in ACP templates in 33%. Care de-escalation occurred in 45% of patients. Conclusion GOCRRT consultations are feasible and associated with care de-escalation. Adherence to core team participation was good, but documentation in ACP templates was uncommon. Implications for Research, Policy, or Practice Research to confirm efficacy and components critical to success and to evaluate outcomes in different patient populations and care settings is needed. ==== Body pmc
PMC009xxxxxx/PMC9001050.txt
==== Front J Pain Symptom Manage J Pain Symptom Manage Journal of Pain and Symptom Management 0885-3924 1873-6513 Published by Elsevier Inc. S0885-3924(22)00234-2 10.1016/j.jpainsymman.2022.02.149 Article “Shoot from the Hip”: What Patients with Cancer Want from Communication About Serious Illness During COVID-19 (S526) Singh Nainwant MD VA Giannitrapani Karleen PhD VA Health Services Research & Development and Stanford University Gamboa Raziel MA VA Palo Alto Healthcare System Walling Anne MD FAAHPM UCLA Lindvall Charlotta MD PhD FAAHPM Dana-Farber Cancer Institute Lorenz Karl MD MSHS VA Palo Alto 12 4 2022 5 2022 12 4 2022 63 5 919920 Copyright © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Outcomes 1. Describe what patients with cancer and caregivers value in communication about serious illness 2. Examine strategies that model these values Original Research Background When asked to share recommendations for providers and health systems to foster high-quality care during COVID-19, patients with cancer and their caregivers recommended providers to “communicate proactively and effectively” about serious illness. Research Objective In this secondary analysis of participant responses, we aimed to identify patient and caregiver perspectives on what it means to “communicate proactively and effectively” about serious illness. Methods Content analysis of communication-related output from 15 semistructured interviews of diverse patients with cancer and caregivers of patients with serious illness. Results Theme 1: Transparency: Clinicians share the medical rationale for recommendations: “If what's explained to me is that my chance of recovery … is minimal, and I'm just going to increase my suffering, well, then that feels like a chance for acceptance.” Theme 2: Proactivity: Clinicians facilitate conversations about care preferences in advance: “Right now, you guys have this incredible opportunity to have these conversations. To enable—you know, oncologists to have these conversations with their patients while they're as an outpatient, before they get COVID?” Theme 3: Coordination: Clinicians integrate with the interdisciplinary palliative care team to communicate serious news: “I would ask that [what] be done a little bit better is the integration of the social worker with the doctor, especially in the palliative and hospice care. We know that not every doctor has got a good bedside manner… it's hard to tell someone you're going to die.” Theme 4: Respect for autonomy: Patients and caregivers feel empowered by clinicians to make informed decisions: “You're still in control of your decision-making, given the parameters, even though you're not in control of the parameters.” Conclusion Patients with serious illness and caregivers of patients with serious illness value transparent, proactive, and coordinated communication that respects their autonomy. Implications for Research, Policy, or Practice Efforts to make serious illness communication more patient-centered during COVID-19 will target these areas that align with established patient-centered communication theories. ==== Body pmc
PMC009xxxxxx/PMC9001051.txt
==== Front spthr THR Tourism and Hospitality Research 1467-3584 1742-9692 SAGE Publications Sage UK: London, England 10.1177_14673584221085217 10.1177/14673584221085217 Professional Perspectives How are tourism businesses adapting to COVID-19? Perspectives from the fright tourism industry https://orcid.org/0000-0001-6542-5454 Weidmann Susan Department of Recreation Management and Physical Education, 1801 Appalachian State University , Boone, NC, USA Filep Sebastian School of Hotel and Tourism Management, 26680 Hong Kong Polytechnic University , Hong Kong, China Lovelock Brent Department of Tourism, 2495 University of Otago , Dunedin, New Zealand Susan Weidmann, Department of Recreation Management and Physical Education, Appalachian State University, 287 Rivers St, Boone, NC 28608-2026, USA. Email: weidmannsg@appstate.edu 6 4 2022 6 4 2022 14673584221085217© The Author(s) 2022 2022 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. The COVID-19 pandemic has seriously impacted the global tourism industry, effecting the livelihoods of millions of tourism workers and disrupting host communities. Current research in tourism management has focused on understanding the economic, social and political impacts of the pandemic. This professional perspective aims to examine operational adaptations that businesses in the fright tourism industry have adopted under the COVID-19 pandemic circumstances. The study collated industry association press releases, undertaking content analysis to examine the changes businesses employed to adapt during the pandemic. Findings suggest that businesses made a variety of operational changes, such as changing queueing, diversification of props and changes to make-up hygiene, allowing these businesses to survive pandemic imperatives. edited-statecorrected-proof typesetterts10 ==== Body pmcIntroduction There is no doubt that the COVID-19 pandemic has had a profound effect on the global tourism industry, impacting the livelihoods of millions of tourism workers with flow-on effects for host communities (Gössling et al., 2021). Current research in tourism management has focused on understanding the economic, social and political impacts of the pandemic (Hall et al., 2020) and there are already numerous studies addressing post-pandemic tourism (Brouder, 2020). However, less is known about how small and medium sized tourism businesses are adapting to the pandemic (Kristiana et al., 2021). Adaptation, based on Adaptation Level Theory (Helson, 1948), is a key process by which people manage perceived risks and projected changes (Füssel, 2007). In a dynamic tourism environment, it is assumed that businesses employ adaptation strategies (Putra, 2010; Kristiana et al., 2021), or patterns of behaviour to resolve problems faced by the business. The paper examines operational adaptations that small and medium sized fright tourism businesses have adopted in response to the challenges posed by the COVID-19 pandemic. Fright tourism is a form of dark tourism that combines macabre-themed fantasy elements, Gothic narratives, and industry-created props to create interactive and engaging tourist experiences (McEvoy, 2016). Prior to the pandemic, fright tourism was a $7 billion US dollar a year tourism sector (Kirchner, 2020), but as the COVID-19 pandemic gripped the world from early 2020, the fright tourism industry experienced a dramatic economic loss with no clear adaptation strategies apparent. Many independent fright tourism attractions in the United States have closed permanently (Kirchner, 2020), while others are trying to adapt to a new pandemic environment. Understanding fright tourism Fright tourism is often considered a subset of dark tourism (Bristow and Newman, 2004) which encompasses visiting sites (real or fabricated) of death, disaster and the macabre (Stone, 2006). In fright tourism, tourists are often attracted by the thrills of simulation, rather than the reality of visiting the sites (Bristow and Newman, 2004). A level of fantasy (staged experiences that include elements of the supernatural), and a Gothic aesthetic (McEvoy, 2016) are typical features of fright tourism attractions. Research focussing on this form of tourism includes examinations of ghost tourism experiences (Dancausa et al., 2020; Gentry, 2007); Dracula tourism (Reijnders, 2011; Light, 2017); marketing of fright tourism attractions (Brown et al., 2012; Weidmann, 2016); and the liminal experiences in simulated settings (Bristow, 2020). Tourists typically visit fright tourism attractions to experience the thrill of fear in an environment that is safe and simulated (De Visser-Amundson et al., 2016; Kerr, 2015). The attraction to fear challenges the dominant conceptions of fear as a negative emotion (Goyal and Verma, 2021). Fright tourism businesses employ mechanisms designed to create enjoyable experiences that induce feelings of shock, horror and thrills in tourists. Animatronics, odours, music and other effects are commonly used to elicit these scary experiences. Due to the interactive nature of the fright tourism industry (high adrenaline experiences of tourists running and encountering psychological and physical challenges), risk management techniques have long been employed by the fright tourism sector (Stark, 2015). These techniques include activities such as placement and handling of illumination and electrical appliances for fire prevention (Moore, 2021), eliminating the use of extension cords to reduce instances of tripping hazards (HAA, 2021) and regular maintenance of air compressors to reduce potential water spills resulting in slip hazards, to mitigate potential accidents and medical emergencies (Stark, 2015). However, beyond risk management, the new pandemic imperatives (especially social distancing and enhanced hygiene protocols) are forcing the fright tourism sector to adapt across multiple operational and financial aspects of their businesses. COVID-19 and business adaptation Since March 2020, when the COVID-19 pandemic took hold of most of the world, many tourism businesses were forced to operate in a limited capacity in order to control the spread of the virus (Kock et al., 2020; Zenker and Kock, 2020; Li et al., 2021). Research has examined the economic impacts of COVID-19 (Verma & Gustafson, 2020), changes in tourist behaviours related to contact between tourists and service providers (Kock et al., 2020), and the role of organisational resilience in the ability of tourism businesses to recover from the pandemic (Bhaskara and Filimonau, 2021) but the wider adaptive measures of tourism businesses have yet to be fully addressed. Richards and Morrill (2021) studied the youth travel sector and indicated the biggest adjustment was in the modification of cancellation policies, after the pandemic emerged. Alonso et al. (2021), in investigating coping strategies of small and medium sized enterprises, found that many businesses reduced employment hours and rotated staff, while others discontinued some of their services. Other businesses applied for governmental relief funds and discontinued operations all together (Alonso et al., 2021; Rogerson, 2021). Rogerson (2021) examined operational changes made in South African tourism businesses to maintain operations during the pandemic, concluding that some of the most significant business changes included product diversification, reduction of prices, reduced staffing, changed marketing and greater inter-enterprise cooperation. A review of the literature however did not reveal any studies addressing the operational adjustments taken by small and medium sized tourism businesses in fright tourism or related sectors. In order to address this gap, we undertook a content analysis of secondary data to determine how businesses in this sector employed adaptive measures as a result of the pandemic. Methodology Primary data collection was not feasible due to the restrictions on movement and lack of access to electronic email databases, so data collection utilizing secondary sources was necessary. Our study, which was exploratory in nature, was conducted in April 2021 in the United States (US). An online search of press release documents of two major industry associations was conducted, in order to obtain information provided by fright tourism attractions to their associations, with the information disseminated to other association members to share best practises. Search phrases: COVID-19 and business adaptation; responses to COVID-19, and similar words and phrases, were entered in search engines of the websites of the two major fright tourism related associations. The two associations are the Haunted Attraction Association (HAA) – a US-based industry association of fright tourism businesses, and the International Association of Amusement Parks and Attractions (IAAPA) – the largest global amusement park association. The IAAPA contains information for large scale amusement parks, many of which have Halloween-themed fright attractions during the months of September and October and has over 6300 members (2019). These parks include, for example, Halloween Horror Nights at Universal Studios Orlando, Scare-o-winds at Carowinds in Charlotte, North Carolina and Knott’s Scary Farm at Knott’s Berry Farm in California. The HAA is the only official global association for the haunted attraction industry, has approximately 600 members (B Hayes 2022; personal communication, January 20) and member organisations represent the fright tourism industry, including haunted attraction owners and operators, designers, vendors, artists and other suppliers. The association offers the Certified Haunted Operator Seminar (CHAOS) safety programme, public relations, legal consultation, annual tradeshows and conferences and industry education for members. Membership numbers for the association were not available at the time of research. The online search resulted in 13 press releases in total. The press release documents, typically pdf or Word files of up to 500 words in length, documented the views of business owners and/or general managers. These views were based on informal, anonymous, surveys conducted by the two associations with business owners. Content analysis, defined by Holsti (1969) as ‘any technique for making inferences by objectively and systematically identifying specified characteristics of messages’ (p. 14) was then applied to review the press release documents. This method of analysis was chosen in order to classify the themes and determine their meanings by three researchers. Emergent coding was utilised, broadly following processes outlined in Haney et al. (1998) and used in other tourism studies (Halpern and Regmi, 2013; Wilson and Látková, 2016; Kim and Madhuri, 2019). First, the three researchers individually examined each press release and determined some general patterns (broad themes that related to the study aim). The researchers then compared the notes and reconciled any differences in the general patterns. Following this step, the press release documents were again independently and more closely read, this time generating additional, more specific, themes. A final set of operational adaptations (representing each theme) was then generated following a group discussion. Approximately 95% agreement was reached on the final set of the themes, following this iterative process. In addition to business-to-business industry press releases, a search of Nexis Uni (an academic search engine specializing in news, business and legal sources) for the period of September and October of 2021 was conducted to see if any COVID-19 related business protocols for fright tourism business were communicated via press to the general public. The scope of search was limited to the entries for the state of North Carolina in the US as we desired a state with a moderate number of well-distributed fright tourism businesses. As such, we felt the state is representative of the fright tourism industry in the US. The Nexis Uni search included such phrases as: COVID-19, Haunted Attractions and Halloween in order to be as inclusive as possible. The search revealed 86 results with the combinations of search phrases. Of these articles, only one referred to COVID-19 protocols, in which the article stated face coverings were recommended at an attraction. Other results were on such topics as what places to visit during Halloween, which date is best to celebrate Halloween, and Halloween events in various cities. Results and discussion Four specific business adaptation strategies were identified in the data obtained from the press releases, with the fourth strategy involving eight elements. These are detailed in the following section. The one Nexis Uni article referenced a specific haunted attraction, and that information was also included in the attraction’s Web site, so is included with the fourteen COVID-19 Web site statements discovered. Relocating tourists to safe zones The findings from the documents demonstrate innovative ways of adapting to new social distancing requirements. Haunted attractions, often called haunted houses, are purpose-built attractions in fright tourism that follow a storyline and require revealing spaces in a certain order to tourists (Clasen et al., 2019). The structure of these haunted house attractions often requires patrons to travel through in a particular order in tight spaces, which is a challenge for social distancing. Although changing the design of the house would be costly, the findings show that changing the flow and locations of patrons is possible. This would involve relocating visitors to so-called safe zone areas where groups of patrons can be separated from one another. Moving operations outdoors to the extent possible may assist with this challenge due to the flow of air and ability to create moveable mazes. It was revealed some fright tourism attractions could also modify their experiences to offer a drive-through service, which keeps patrons separated from actors as well as one another, avoiding direct contact. Examples of this include a haunted car wash, the Tunnel of Terror Haunted Car Wash in Belmont, North Carolina, United States (Deier, 2020), and a drive-through haunted trail, the Haunted Hills Terror Drive in Pittsboro, also in North Carolina (Mace, 2020). Changing queueing processes Queueing for entry into a haunted attraction is an important part of the fright tourism experience, as patrons build camaraderie with other guests, and anticipate what the experience will offer (Bristow and Keenan, 2018). The press release documents show that changes in direction for queueing for entry is one way to tackle social distancing requirements. Often, queues wrap in a back-and-forth fashion, and to avoid direct face to face interactions with other customers, queues would need to be adjusted. The findings show they can become one directional, take up more space so there can be greater distances between customer groups, and offer a timed ticket approach (i.e. offer visitors a specific time to enter the haunted house). These adaptation strategies would reduce the number of people waiting in line at any given time. Re-aligning promotional activities The third strategy revealed in the data was modified promotion. Given the very real fears people were experiencing during the global pandemic, businesses needed to re-think how they marketed the message of ‘fear as fun’ in haunted fright tourism attractions. Fright tourism attractions use different themes each year in order to promote the experience as new and to encourage repeat visitation. However, considering the health crisis, and given the fears people were experiencing, the findings reveal that businesses were focussing on fantasy elements to manage customer expectations and to reinforce the idea of fear as fun. The results show that depictions of medical themes in promotional materials of fright tourism businesses have become inappropriate, considering the global medical emergency that the pandemic has generated. Subsequently images at websites and in business brochures were being re-designed with new non-medical elements. Changing experience design elements at attractions Lastly, in order to provide a safe and memorable fright tourism experience, and to ensure that experiences continue to incorporate appropriate and relevant design, the documents show that businesses have adopted changes to tangible elements at their attractions. These included:• Incorporation of plexiglass partitions in areas that offer high traffic and/or close contact. Businesses should be thoughtful about how they use plexiglass and find a way to blend it in with the attraction’s storyline. • Adoption of mirrors in attractions to make scare actors appear closer to patrons than they actually are in order to maintain social distancing. • Updating sound technology in order to make actor voices seem louder. • Reducing the level of props and gear on set that customers typically come into contact with, such as having to push a door open to move from one area of the attraction to another. • Offering fewer touch points and/or confined spaces. • Introducing games to outdoor waiting spaces to offer the opportunity to keep patrons socially distant while waiting to enter the haunted house. • Incorporating personal protective equipment (PPE) into costume design for scare actors. This provides an opportunity to re-think the design of character costumes, and how they can be created in a way that includes comfortable, storyline-appropriate, face masks and gloves. • Investing in animatronics. This strategy further reduces the points of contact between customers and staff and allows scare actors to be responsible for operating an animatronic character to interact with the guests from a safe distance. Conclusion Overall, the study and its findings extend the literature on business adaptation in tourism management (Kristiana et al., 2021) and provide fresh perspectives from the fright tourism sector in the wake of the COVID-19 crisis. At a practical level, the paper identifies a set of new strategies that may be useful to small and medium enterprises. Due to the interactive experience of fright tourism attractions, which is created by storytelling through set design, props, and oftentimes confined spaces, the adaptations taken by this industry in order to ensure a safe and fun experience may be unique and necessary for such attractions to survive and thrive during and following the pandemic. However, some of the adaptations are more generic in character (e.g. changing queueing processes) so they may be useful to industry sectors well beyond fright tourism. In contrast to the study undertaken by Rogerson (2021) which highlighted management issues in relation to the pandemic, such as changes to staff management, the current study reveals physical operational changes (e.g. changing queueing, diversification of props and make-up hygiene), and is the first study to do so. There are however limitations in the reported research project. The study was exploratory and based on secondary data, using online information; in depth research with fright tourism operator-participants may shed further light on the effectiveness and longevity of these adaptations. The study was based in the United States, and it would be beneficial to examine fright tourism operational adaptations in other regions of the world where COVID-19 may have manifested differently, in terms of its medical impact and also regulatory responses. Many fright tourism businesses entirely changed the manner in which they operated during 2020, and there is a likelihood that the adaptations employed may be more permanent-the success of these adaptation measures could be explored through longitudinal studies. Finally, research on customers’ perceptions of these strategies may prove to be a useful avenue for future research. ORCID iDs Susan Weidmann https://orcid.org/0000-0001-6542-5454 Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. ==== Refs References Alonso AD Bressan A Kok SK , et al. (2021) Facing and responding to the COVID-19 threat–an empirical examination of MSMEs. Experimental Brain Research 33 (5 ): 775–796. Bhaskara GI Filimonau V (2021) The COVID-19 pandemic and organisational learning for disaster planning and management: a perspective of tourism businesses from a destination prone to consecutive disasters. Journal of Hospitality and Tourism Management 46 : 364–375. Bristow R (2020) Communitas in fright tourism. Tourism Geographies 22 (2 ): 319–337. Bristow R Keenan D (2018) A study of fright tourism at Halloween. 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==== Front Am Surg Am Surg spasu ASU The American Surgeon 0003-1348 1555-9823 SAGE Publications Sage CA: Los Angeles, CA 35393863 10.1177_00031348221086804 10.1177/00031348221086804 SESC Podium Papers Emotional Intelligence, Burnout, and Wellbeing Among Residents as a Result of the COVID-19 Pandemic Kirkpatrick Heather PhD 1 Wasfie Tarik MD, FACS 2 https://orcid.org/0000-0002-0179-1882 Laykova Alexandra BS 3 Barber Kimberly PhD 4 Hella Jennifer MPH 4 Vogel Mark PhD 1 1 Department of Psychology, 3577 Ascension Genesys Hospital , Grand Blanc, MI, USA 2 Department of General Surgery, 3577 Ascension Genesys Hospital , Grand Blanc, MI, USA 3 3078 Michigan State University College of Osteopathic Medicine , East Lansing, MI, USA 4 Department of Academic Research, 3577 Ascension Genesys Hospital , Grand Blanc, MI, USA Tarik Wasfie, MD, FACS, Trauma Surgery, Ascension Genesys Hospital, 1 Genesys Pkwy, Grand Blanc, MI 48439 1477, USA. Email: tarik.wasfie@ascension.org 8 2022 8 2022 8 2022 88 8 18561860 © The Author(s) 2022 2022 Southeastern Surgical Congress This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Background We previously reported the correlation between emotional intelligence (EI) with burnout/wellbeing in our PGY-1 residents, finding that EI moderated the development of burnout in the PGY-1 year. When COVID-19 arrived in early 2020, we were already collecting EI and burnout data for the 2019-2020 year. We elected to follow those residents throughout the year and compare them to the subsequent cohort to study the effect of the pandemic on their burnout and wellbeing and the influence of EI on this pattern. Materials and Methods All residents entering the training program (PGY-1) 2019-2020 (SURGE) & 2020-2021 (POST-SURGE) were administered the emotional intelligence questionnaire short form (TEIQue-SF), the Maslach burnout inventory, and the physician’s wellness inventory. The questionnaires were completed quarterly. Statistical analysis included ANOVA. Institutional Review Board approval was obtained prior to the study. Results The overall combined PGY-1 residents year (n = 73) mean EI was 3.9 with no differences between academic year groups. The domains of burnout and physician wellbeing were examined across four different time points during the resident’s first year. Domain scores changed over the four time periods during the first year. There was a relative decrease in achievement by 3.4 points, decrease in career purpose by 1.8 points, decrease in cognitive flexibility by .6 points and increase in distress by 4.1 points. Emotional exhaustion increased significantly more for the SURGE 2019-2020 group compared to the POST-SURGE 2020-2021 group (a relative 77% change). Emotional intelligence was independently assessed within each domain at baseline and for changes over time. Discussion Patterns of burnout and wellbeing were different with the COVID-19 SURGE group compared to the COVID-19 POST-SURGE group, perhaps because of differing expectations of the PGY-1 year participants but also perhaps due to the destabilizing effect of the first COVID-19 surge. emotional intelligence resident burnout COVID-19 typesetterts10 ==== Body pmcKey Takeaways Emotional intelligence is partially protective against resident’s burnout and wellbeing during their residency. The COVID-19 pandemic initial surge appeared to negatively alter the protective effect of residents’ emotional intelligence on their burnout and wellbeing in our community hospital. Introduction When the COVID-19 pandemic began the health professionals were asked to provide needed care at a time of crisis, not fully knowing the effect this would have on their personal wellbeing. It was unknown at that time how the pandemic would affect everyday human behavior and wellbeing and the medical establishment proved no exception to the disruption. For our medical residents, as with many others across the country, educational activities and rotations were altered to meet the surge demand of treating patients with COVID-19 in our hospital. Our hospital declared emergency status with the ACGME for 8 weeks (March 25-May 18, 2020) which allowed for this disruption in normal education processes. In unpublished data we described patterns of burnout and wellbeing in our PGY-1 residents at our community hospital (classes 2017-18 and 2018-19). We demonstrated how burnout and wellbeing were related to previously assessed emotional intelligence at the beginning of their residency (emotional intelligence as measured by trait emotional intelligence questionnaire remains mostly unchanged). Specifically, a resident physician’s emotional intelligence appeared to moderate the normal development of burnout and reduction of perceived wellness. In the current study, we sought to examine the burnout and wellness in those residents who experienced greater disruptions in their training in the initial COVID-19 surge as well as the subsequent class which experienced a lesser degree of disruption post-COVID-19 surge. We elected to analyze this data to detect if there is a different response to burnout and wellness in the time of COVID-19. Methods Eighty-one PGY-1 residents entering the following programs in 2019-20 and 2020-21, general surgery, orthopedic surgery, obstetrics and gynecology, internal medicine, family medicine, emergency medicine and podiatry were surveyed at four different time periods (June, October, February, May) during their first year. The questionnaires were the trait emotional intelligence questionnaire—short form (TEIQue-SF),1 the Maslach burnout inventory (MBI),2 and the physician wellness inventory (PWI).3 The TEIQue-SF is a 30-item validated instrument based on the conceptual framework of emotional intelligence (resulting in a score of emotional quotient—EQ). The MBI is a 17-item validated measure of burnout and the three subscales of emotional exhaustion (EE), depersonalization (DP), and personal achievement (PA). The PWI Is a 14-item validated measure of wellness with three subscales which include career purpose (CP), distress (D), and cognitive flexibility (CF), and was constructed with a physician normative group. Scores were analyzed by EQ quartiles calculated from respondent score distributions. EQ score cutoffs based on the sample’s calculated quartiles included: lower <3.7, middle >3.71 to <4.0, and higher >4.1 (See Table 1.) For each questionnaire, where applicable, global and construct scores were tabulated for each resident and baseline measurements were used for identifying significant associations for the analysis. For burnout domain scoring interpretations of EE and DP, higher scores equate to increased exhaustion and depersonalization. For PA, higher scores equate with increased achievement. For wellness domain scoring interpretations of CP and CF, higher scores equate with increased purpose/meaning and greater mental flexibility. For D, higher scores equate with increased distress. All residents entering the training program (PGY-1) during the 2019-2020 year were classified as the SURGE group and those entering training during the 2020-2021 program year were classified as the POST-SURGE group.Table 1. Mean Change in Physician Burnout Index Levels and Differences between Groups. Groups Baseline mean (SD) Year end mean (SD) Differencea P-value EE (N = 53) SURGE 2019 17.1 (10.9) 23.4 (13.3) +6.3 .07 POST COVID 2020 22.3 (10.2) 20.3 (11.1) −2.0 .50 P-value between groups <.001 DP (N = 48) SURGE 2019 7.4 (5.0) 8.2 (5.3) +0.8 .59 POST COVID 2020 7.6 (4.4) 8.9 (5.5) +1.3 .37 P-value between groups .71 PA (N = 54) SURGE 2019 39.4 (6.8) 37.7 (7.8) −1.7 .40 POST COVID 2020 32.4 (6.7) 32.3 (10.1) −0.1 .97 P-value between groups <.001 aA positive equates to an increase and a negative equates to a decrease in scores at the post measure. Change in burnout and wellness scores was calculated by the difference between the year end (May) and early in the year (October) which was chosen as June measurements indicated no burnout was present for residents beginning the academic year. Pearson’s r correlation coefficients were calculated to determine significant associations at P < .05. Multiple group score means were compared using one-way and two-way ANOVA. Ethics approval was obtained by the Institutional Review Board prior to the study. Results A total of 81 PGY-1 residents were invited to participate. Response rate to consent for the study was 100%. The sample was slightly more male 44/81, than female 37/81. No data was collected on ethnicity or age. In the comparative time analyses, the total numbers differ due to missing data at various time points (decreasing our n from 81 to 73.) The overall (combined PGY-1 residency years, n = 73) emotional quotient (EQ) global trait score was a mean of 3.4 (SD: .59) and ranged from 3.3 to 6.87. There was a group effect observed. For the SURGE (2019) group, EI subgroups responded differently in their burnout and wellness responses. This was not the case in the POST-SURGE 2020 group (Tables 2 and 3). For the SURGE group, EE increased in the low EI, decreased in the middle EI, and decreased in the high EI (P = .06). DP increased for the low EI and decreased for the other two subgroups (P = .07). PA had increased for the middle EI but little change was observed for the other two, and the three means did not significantly differ (P = .66). For CP the low EI decreased, no change in the middle EI, and a significant increase for the high EI (P = .02). D changed little for the low and middle EI but decreased for the high EI but these means did not differ significantly (P = .58). Likewise, for CF there was little change for the low and middle EI but a larger increase for the high EI which approached being significant (P = .07).Table 2. Mean Change in Physician Wellness Levels and Differences between Groups. Groups Baseline mean (SD) Year end mean (SD) Differencea P-value CP (N = 55) SURGE 2019 21.6 (2.5) 21.2 (2.9) −0.4 .59 POST COVID 2020 19.9 (3.2) 19.4 (3.2) −0.5 .52 P-value between groups .94 D (N = 55) SURGE 2019 13.6 (5.2) 14.9 (4.4) +1.3 .32 POST COVID 2020 14.5 (4.0) 15.1 (3.6) +0.6 .55 P-value between groups .59 CF (N = 55) SURGE 2019 17.9 (1.9) 17.6 (1.4) −0.3 .66 POST COVID 2020 16.9 (1.9) 16.4 (2.1) −0.5 .35 P-value between groups .52 aA positive equates to an increase and a negative equates to a decrease in scores at the post measure. Table 3. Burnout Scores Overtime by Domain by EQ Quartile for Post Covid Group. Domain Time 1 (n = 30) Time 2 (n = 32) Time 3 (n = 31) Time 4 (n = 33) Mean change (T4 - T2) Sig A. Emotional exhaustion All EQ* 12.8 21.9 23.6 21.2 1.4 .24 Low EQ 10.6 17.0 22.8 13.0 −3.8 .40 Med EQ 10.1 20.4 21.8 21.2 4.0 High EQ 21.3 26.2 30.8 31.6 8.4 B. Depersonalization All EQ* 5.4 7.5 8.5 8.1 2.2 .18 Low EQ 5.0 6.1 8.0 4.3 −1.2 .48 Med EQ 4.4 6.9 8.6 7.7 2.9 High EQ 7.7 11.0 11.8 14.8 5.4 C. Personal achievement All EQ* 38.0 30.8 31.1 32.7 2.7 .94 Low EQ 39.0 34.0 31.3 38.7 1.7 .18 Med EQ 39.0 34.0 31.3 38.7 4.3 High EQ 34.0 26.7 30.8 28.0 5.0 For the POST-SURGE group (2020), none of the domain factors showed a statistically significant difference across the EI subgroups: EE (P = .41), DP (P = .39), PA (P = .88), CP (P = .38), D (P = .43), and DF (P = .48). The mean change in each burnout factor was compared across the EI subgroups for both the SURGE and the POST-SURGE groups. For EE and DP, high EI appears to be protective. Their EE decreased (mean 9.1) while the low EI had increased (mean 1.7) (P = .04). The middle EI group fell in between. They experience less EE at Time 4 but not as much as the high EI group (mean 5.7). The same pattern was seen for DP (P = .03). For PA there was similar change for each subgroup (P = .97) (Table 4).Table 4. Wellness Scores Overtime by Domain by EQ Quartile for Post Covid Group. Domain Time 1 (n = 30) Time 2 (n = 32) Time 3 (n = 31) Time 4 (n = 33) Mean change (T4 - T1) Sig A. Career purpose All EQ* .32 Low EQ 21.9 19.6 19.8 20.2 −0.8 .18 Med EQ 21.5 19.3 18.6 18.7 −0.6 High EQ 23.0 20.8 20.2 20.8 −1.0 B. Distress All EQ* .47 Low EQ 10.1 14.1 13.3 15.7 1.8 Med EQ 10.3 13.8 15.5 14.9 0.8 High EQ 12.0 13.3 14.2 15.8 −0.3 C. Cognitive flexibility All EQ* .08 Low EQ 18.0 16.4 16.5 16.5 −1.0 Med EQ 17.9 16.7 16.7 16.5 −0.3 High EQ 18.4 17.2 18.0 16.8 −1.5 The mean change in each wellness factor was compared across the EI subgroups for both the SURGE and the POST-SURGE groups. For CP and CF, high EI was not protective. Their CP decreased (mean −2.35) while the low EI had a slight increased CP (mean .41) (P = .01). The middle group had no change. For CF also, the high EI had a greater decline (mean −1.35) than the middle group (mean −.35) or the low group (.12) but this did not reach statistical significance (P = .12). The EI subgroups did not differ in their change in D (P = .99) (Table 4). Discussion Emotional intelligence (EI) is defined as an ability to perceive emotions in one’s self and others and one’s ability to manage emotions of self and others.2 While there is no shortage of stressors during residency, the impact of sudden unexpected changes in the curriculum and structure of residency during the COVID-19 pandemic is of high magnitude with expected serious impact on physicians, residents, attendings, and allied healthcare workers.4-8 The relationship of individual EI and its relation to changes in resident burnout and physical wellbeing during residency has shown a protective and positive impact at least partially in our residents during the academic years 2017-2018 and 2018-2019 (unpublished data). We expected this relationship to maintain during the pandemic. However, the question remains as to what extent and whether external support is not only needed but vital to maintain this relationship. The EI construct in this study had a resilience factor within its measures and resilience has been proven overtime to be one of the factors that support individual response to stressors.9 We previously identified that residents with high EI had better wellness and reduced burnout and higher EI was shown to be undoubtedly a protective measure to a certain extent. The question raised with our residents was how much of this relationship remained and was maintained throughout an especially difficult time (surge of cases from the COVID-19 pandemic). Our institution was positioned well for this study as we were conducting the study of EI relationships with burnout and physician wellbeing and the opportunity to further this project was presented when the pandemic affected the healthcare industry and brought sudden unexpected changes to the 2019-2020 academic year. Our data indicates that the initial shock to the healthcare system appears to be replaced by accommodation in expectations and training practices post initial COVID-19 surge demands. One notable difference to our previous study was the SURGE group of residents scoring at pre-pandemic baseline with an average overall EQ score of 3.9 as compared to 5.8 from the previous study (unpublished data). However, the EI relationship and its protective effects on burnout and wellbeing remained the same. It continued to show that those with the higher EI index had a better response to stressors. Limitation Demographic characteristics (ie, age, gender, race, income, and marital status) were not considered in the analysis. The sample size is relatively small for subgroup analysis which limits representation. A few study responses were missing during the peak of the COVID-19 surge which may limit generalizability. Conclusion Emotional intelligence continues to partially protect our residents’ burnout and wellbeing. However, a health crisis like the COVID-19 pandemic might alter that relationship. Future studies with larger study populations are needed to confirm these findings. ORCID iD Alexandra Laykova https://orcid.org/0000-0002-0179-1882 Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. ==== Refs References 1 Petrides KV Furnham A . Trait emotional intelligence questionnaire (TEIQue). Available at: http://www.eiconsortium.org/measures/teique.html. Accessed January 31, 2022. 2 Maslach C Jackson S Leiter M . Maslach burnout inventory manual . 3rd ed. Palo Alto, CA: Consulting Psychologists Press; 1996. 3 Eckleberry-Hunt J Kirkpatrick H Taku K Hunt R . Self report study of predictors of physician wellness, burnout, and quality of patient care. South Med J. 2017;110 (4 ):244-248.28376519 4 Dyrbye LN West CP Satele D , et al. Burnout among U.S. medical students, residents, and early career physicians relative to the general U.S. population. Acad Med. 2014;89 (3 ):443-451. doi:10.1097/ACM.0000000000000134.24448053 5 Walton M Murray E Christian MD . Mental health care for medical staff and affiliated healthcare workers during the COVID-19 pandemic. Eur Heart J Acute Cardiovasc Care. 2020;9 (3 ):241-247. doi:10.1177/2048872620922795.32342698 6 Columbus AB Breen EM Abelson JS , et al. “What just happened to my residency?” The effect of the early coronavirus disease 2019 pandemic on colorectal surgical training. Dis Colon Rectum. 2021;64 (5 ):504-507. doi:10.1097/DCR.0000000000001981.33939385 7 Ellison EC Spanknebel K Stain SC , et al. Impact of the COVID-19 pandemic on surgical training and learner well-being: report of a survey of general surgery and other surgical specialty educators. J Am Coll Surg. 2020;231 (6 ):613-626. doi:10.1016/j.jamcollsurg.2020.08.766.32931914 8 Aziz H James T Remulla D , et al. Effect of COVID-19 on surgical training across the United States: a national survey of general surgery residents. J Surg Educ. 2021;78 (2 ):431-439. doi:10.1016/j.jsurg.2020.07.037.32798154 9 Arslan HN Karabekiroglu A Terzi O Dundar C . The effects of the COVID-19 outbreak on physicians’ psychological resilience levels. Postgrad Med. 2021;133 (2 ):223-230. doi:10.1080/00325481.2021.1874166.33412973
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==== Front Am J Health Promot Am J Health Promot spahp AHP American Journal of Health Promotion 0890-1171 2168-6602 SAGE Publications Sage CA: Los Angeles, CA 35388708 10.1177_08901171221086962 10.1177/08901171221086962 Quantitative Research Americans’ Attitudes Toward COVID-19 Preventive and Mitigation Behaviors and Implications for Public Health Communication https://orcid.org/0000-0002-9224-3709 Thompson Jessica PhD 1 Squiers Linda PhD 1 Frasier Alicia M. MPH 1 Bann Carla M. PHD 1 Bevc Christine A. PhD 1 MacDonald Pia D. M. PhD, MPH, CPH 12 McCormack Lauren A. PhD, MSPH 13 1 6856 RTI International, Research Triangle Park, NC, USA 2 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , Chapel Hill, NC, USA 3 Department of Health Policy & Administration, Gillings School of Global Public Health, University of North Carolina , Chapel Hill, NC, USA Jessica Thompson, PhD, RTI International, 3040 E Cornwallis Rd, Research Triangle Park, NC 27709, USA. Email: jessicathompson@rti.org 7 2022 7 2022 7 2022 36 6 987995 © The Author(s) 2022 2022 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Purpose Identifying drivers of behavior is essential to develop effective messaging around COVID-19 prevention and mitigation. Our study assessed for behavioral antecedents of social distancing, wearing face coverings, and sheltering in place during the onset of the COVID-19 pandemic. Although ours is an early assessment, understanding motivation for behavior will remain critical as U.S. vaccination uptake has stalled and variants continue to pose a health threat. Design Cross-sectional survey; Setting: Online assessments in April 10–13 and 17–20, 2020; Subjects: 2,279 U.S. adults identified through a national, probability-based web panel (34% response rate). Measures: self-reported behavior, perceived effectiveness and risk, worry, social norms, and knowledge. Analysis Multivariable regression analyses Results Most Americans reported social distancing (91%) and sheltering in place (86%). Just over half reported wearing face coverings (51%), whereas more (77%) said they intended to do so. Perceived effectiveness of the behavior was consistently associated with each outcome (OR = 2.34, 1.40, 2.11, respectively; all P < .01). Perceptions about the extent to which others should comply with behavior (social norms) were strongly associated with intentions to wear a face covering only (OR = 6.30, 95% CI 4.34-9.15; P < .001) and worry about getting COVID-19 was associated with sheltering in place and social distancing (OR = 2.63, 95% CI 1.15–5.00; 4.91, 95% CI 1.66, 14.50, respectively; all P < .05). Conclusion Behavioral constructs were strongly associated with COVID-19 preventive and mitigation behaviors and have implications for communication. COVID-19 behavior public health communication RTI International https://doi.org/10.13039/100008199 typesetterts10 ==== Body pmcPurpose Emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated disease (COVID-19) rapidly escalated to become an unprecedented worldwide pandemic. On March 13, 2020, the White House declared a national emergency.1 The lack of a vaccine and effective treatment for COVID-19 at that time required that the public adopt non-pharmaceutical prevention and community mitigation strategies to slow the spread of disease and “flatten the curve.2” Many of these behavioral strategies and recommendations, such as wearing face coverings, practicing social distancing, or following mandates to shelter in place, were novel or difficult for some individuals to implement. Early reports suggested that adoption of many of the recommended prevention and mitigation behaviors was high,3-5 although estimates varied across samples and timeframes during this swiftly evolving public health crisis. Other evidence suggested that some vulnerable subgroups, such as black adults, those with comorbid conditions or low health literacy, had certain knowledge gaps about COVID-19 or questioned the value of preventive behaviors.3-6 These groups may also have disparate risk factors that affect their exposure to COVID-19, such as close living or working conditions. Of more recent concern is the possibility for “behavioral fatigue,” where people become less motivated or less capable of following recommendations over sustained periods or when behavior recommendations are intermittently relaxed and then re-enforced.7 Motivation to engage in preventive behaviors further may be impaired when people are presented with conflicting or inconsistent messaging, and introduction of an effective vaccine may also have this undesired effect on preventive behavior.8 Because managing the COVID-19 crisis continues to require large-scale behavior change, particularly wearing face coverings in many public settings, insights from social and behavioral science can be instrumental in guiding the development of effective intervention and communication efforts.9 Previous studies have shown that behavioral constructs have a strong influence on COVID-19-related behaviors, including beliefs about the effectiveness of a prevention strategy10 and beliefs about the social norms around the behavior, that is, what they perceive or observe that others are doing.11,12 Emotion and worry may also motivate preventive behaviors around COVID-19, but at the expense of increased anxiety.13 The primary objective of our study was to identify behavioral antecedents of COVID prevention and control behaviors, namely social distancing, sheltering in place and wearing face coverings. We also explored sociodemographic determinants of these behaviors to identify subgroups who may be less likely to engage in these behaviors. Our study comes from a national survey of U.S. households on attitudes, knowledge, and behaviors related to COVID-19. We conducted our survey in April 2020, when most U.S. states had recently issued shelter-in-place orders. On April 3, 2020, just prior to fielding our survey, the White House Coronavirus Task Force and U.S. Centers for Disease Control and Prevention (CDC) announced a new recommendation to wear face coverings when in public in attempt to help slow the spread of the disease.14 Prior to this announcement, the guidance on who should wear face coverings was evolving. Although our assessment provides insight to public behavior at one early timepoint during the pandemic’s trajectory, identifying the underlying influences of these behaviors and the communication recommendations that arise from our findings remains highly relevant, as adherence to preventive and mitigative behaviors will remain critical during dissemination of the COVID-19 vaccine and potentially beyond, as variants continue to pose a health threat. Methods Design We conducted an online, cross-sectional survey of U.S. households from April 10–13 and 17–20, 2020. The RTI International Institutional Review Board reviewed the study protocol and determined it to be exempt from human subjects review. Sample We used a pre-recruited, address-based web panel consisting of 55,000 members to identify study participants. The panel is based on a probability sampling of the U.S. population. Households received a computer and/or internet access if needed to be part of the panel. The resulting panel includes households with listed and unlisted telephone numbers, telephone and non-telephone households, cell phone-only households, and households with and without internet access. Study participants’ provided consent to participate in the online survey upon receiving the emailed invitation. From a random sample of 6710 panel members meeting initial eligibility criteria (adults aged 18 and over), a total of 2279 respondents completed the survey, yielding a 34% stage completion rate.15 To prevent the possibility for misinformation, we provided all respondents with the link to the CDC website at the end of the survey. Measures We developed measures based on key behavioral theories, including the protection motivation theory16,17and the health belief model,18 and the emerging COVID-19 scientific evidence from authoritative sources such as the CDC and the World Health Organization. We selected constructs that were potential drivers of behavior in the current COVID-19 context, based on theory and empirical evidence.3,5,9-13 Due to survey length constraints, we assessed most behavioral constructs with one or two items, with the exception of COVID-19 related knowledge, which used a 16-item index. Behavior We assessed behavior with one item: “Which of the following actions, if any, are you currently taking to protect yourself from the coronavirus?” Respondents could check all behaviors that applied. Our analysis focused on three behaviors: (1) sheltering in place referring to orders to stay at home, (2) practicing social distancing, and (3) wearing a cloth face covering while in public. Because guidelines recommending face coverings coincided with the timing of our data collection, we also assessed intentions to engage in the behavior by asking respondents their level of agreement with the statement I plan to wear a cloth face covering in public settings on a 4-point Likert scale (from 1 = strongly disagree to 4 = strongly agree). Perceptions and Attitudes We measured perceptions and attitudes using well-known behavioral constructs. Responses were on 4-or 5-point Likert scales with higher values reflecting higher agreement with or level of the outcome being assessed. We assessed the following constructs with one item each: perceived susceptibility (I am likely to get the coronavirus); perceived severity (If I get the coronavirus, chances are I will recover; item reverse coded), worry (I am worried about getting the coronavirus), self-efficacy to prevent COVID-19 (I feel confident I can prevent myself and my family from becoming infected with the coronavirus), and negative outcome expectations (What I do on a day-to-day basis will not affect how many people in my community get the coronavirus). We assessed perceived effectiveness of each of the three behaviors with a single item each (How effective do you think each of the following will be in protecting you and your family from getting the coronavirus?). We assessed social norms with two items (alpha=.67) (e.g., Everyone, including people who do not have symptoms, should wear a cloth face covering if they leave their home to prevent possible transmission of the coronavirus). We also asked respondents their level of agreement with the statement I would rather risk getting the coronavirus than lose my job on a 4-point Likert scale. Respondents also reported their level of household resistance to social distancing through three items (e.g., it is hard to get people in my household to stay home). Knowledge We developed a 16-item index assessing knowledge around COVID-19 across four domains: transmission (e.g., The coronavirus is spread through coughing and sneezing), susceptibility (e.g., People of all ages can become infected with the coronavirus), treatment or prevention (e.g., Antibiotics can be used to prevent infection from the coronavirus), and morbidity and mortality (e.g., Most people who are infected with the coronavirus die from it). Item responses were true/false/don’t know. Higher scores on the index reflected greater knowledge about COVID-19. The knowledge index had a Cronbach alpha of .85, indicating high internal consistency.6 Socio-Demographic and Medical Characteristics We collected sociodemographic information from all respondents as covariates: age, sex, education, annual household income, region of country, and race/ethnicity. The panel’s dataset also included responses (within a year old) on political leaning, employment status, and health insurance status. We assessed respondents’ potential risk for severe disease through a series of self-reported items on the presence or absence of conditions and comorbidities associated with higher morbidity from COVID-19, per CDC’s website at the time of data collection. Analysis We used a post-stratification process to adjust for survey nonresponse and for any noncoverage, undersampling, or oversampling resulting from the study-specific sample design based on the Current Population Survey19 and weighted all respondents to these distributions. We conducted analyses in SAS version 9.3 and incorporated the survey weights to extrapolate to the U.S. population. We used multivariable logistic regression analyses on the weighted sample to examine associations between behaviors and our constructs. Results of the multivariable analysis report the weighted, adjusted odds ratios (ORs) and 95% confidence intervals (CIs). The multivariable model included the following sociodemographic variables: age, sex, education, income, region of country, race/ethnicity, risk for severe disease, political leaning, employment status, and health insurance status. Results Characteristics of the weighted and unweighted survey analytic sample (n = 2279) are shown in Table 1. Respondents (unweighted) were about one-half male, 65% White, Non-Hispanic, 38% college educated, 23% were aged 65 or over, and 66% were employed. All four U.S. geographic regions were represented, with 35% coming from the South. Almost half (47%) reported the presence of one or more comorbid conditions that put them at risk for severe COVID-19 disease.Table 1. Sample Demographic Characteristics (n = 2279). Characteristic Unweighted Weighted N % N % Sex  Male 1176 51.6 1103 48.4  Female 1103 48.4 1176 51.6 Age  18–24 192 8.4 237 10.4  25–34 358 15.7 399 17.5  35–49 543 23.8 548 24.1  50–64 672 29.5 693 26.0  65+ 514 22.6 502 22.0 Race/ethnicity  White, Non-Hispanic 1489 65.3 1440 63.2  Black, Non-Hispanic 174 7.6 269 11.8  Hispanic 453 19.9 374 16.4  Other, Non-Hispanic 163 7.2 196 8.6 Education  High school or less 785 34.4 886 38.9  Some college  621 27.3 634 27.8  Bachelor’s degree or higher  873 38.3 759 33.3 Income  <$25,000 261 11.5 308 13.5  $25,000–$49,999 419 18.4 415 18.2  $50,000–$99,999 736 32.3 707 31.0  $100,000–$149,999 398 17.5 375 16.5  ≥$150,000 465 20.4 474 20.8 Employed  Yes 1508 66.2 1480 64.9  No 771 33.8 799 35.1 Geographic region  Midwest 481 21.1 474 20.8  Northeast 399 17.5 399 17.5  South 787 34.5 864 37.9  West 612 26.9 542 23.8 Have one or more chronic conditions  Yes 1031 47.1 1038 47.5  No 1156 52.9 1148 52.5 Figure 1 shows the proportion of respondents who reported engaging in the recommended behaviors. Most respondents reported following social distancing (91%) and sheltering in place (86%) measures. When asked about intentions to wear face coverings in public, 77% said they agreed or strongly agreed with this statement; only 51% were currently wearing face coverings in public at the time of this survey. Table 2 shows multivariable results and adjusted odds ratios for association between behavioral constructs and recommended behaviors separately, controlling for sociodemographic variables. We report findings related to respondents’ intentions to wear face coverings rather than their reported behavior; the latter was a less useful outcome for study due to the recency of recommendations about face coverings at the time of data collection.Figure 1. Proportion of Americans Engaging in Protective and Mitigation Behaviors, April 10-13 and 17-20, 2020. Table 2. Multivariable Analyses of Behavioral and Sociodemographic Constructs. Variable Sheltering in place Social distancing Face covering Adjusted OR (95% CI) P-value  Adjusted OR (95% CI) P-value  Adjusted OR (95% CI) P-value  Behavioral Constructs Perceived susceptibility  Strongly agree .49 (.18, 1.33) .164 .31 (.10, .93) .037 3.54 (.88, 14.27) .075  Agree .91 (.45, 1.86) .800 .67 (.27, 1.62) .372 1.06 (.55, 2.05) .864  Disagree .91 (.48, 1.74) .781 1.18 (.51, 2.72) .692 1.01 (.54, 1.88) .973  Strongly disagree REF REF REF Perceived severitya  Strongly agree .40 (.014, 1.19) .098 .12 (.04, .33) <.001 .70 (.21, 2.27) .551  Agree .33 (.12, .95) .040 .10 (.03, .27) <.001 .55 (.17, 1.79) .323  Disagree .38 (.12, 1.20) .102 .18 (.06, .57) .004 .37 (.10, 1.40) .146  Strongly disagree REF REF REF  Perceived effectiveness 1.40 (1.08, 1.82) .012 2.34 (1.77, 3.09) <.001 2.11 (1.66, 2.70) <.001 Negative outcome expectations  Strongly agree 1.39 (.63, 3.07) .415 .82 (.31, 2.13) .682 .46 (.26, .80) .006  Agree .53 (.26, 1.05) .067 .65 (.27, 1.55) .333 1.03 (.62, 1.72) .903  Disagree .69 (.36, 1.34) .274 .88 (.37, 2.06) .764 1.43 (.92, 2.24) .113  Strongly disagree REF REF REF Self-efficacy  Strongly agree 2.11 (.61, 7.30) .237 2.75 (.66, 11.37) .163 1.67 (.42, 6.64) .464  Agree 2.23 (.68, 7.32) .186 1.21 (.34, 4.35) .765 1.67 (.44, 6.40) .452  Disagree 2.06 (.62, 6.89) .240 2.18 (.55, 8.62) .266 1.47 (.39, 5.61) .568  Strongly disagree REF REF REF Social norms (scale) 1.28 (.94, 1.75) .122 1.26 (.87, 1.83) .221 6.30 (4.34, 9.15) <.001 Worry about getting coronavirus  Strongly agree 2.63 (1.15, 6.00) .022 4.91 (1.66, 14.50) .004 2.16 (.91, 5.13) .081  Agree 3.06 (1.50, 6.24) .002 4.36 (1.66, 11.48) .003 1.61 (.73, 3.56) .240  Disagree 2.59 (1.32, 5.10) .006 2.40 (.96, 6.00) .062 1.38 (.63, 3.00) .416  Strongly disagree REF REF REF Employment Risk Appraisal  Strongly agree .28 (.14, .56) <.001 .54 (.20, 1.49) .236 .70 (.33, 1.51) .366  Agree .33 (.20, .56) <.001 .43 (.21, .88) .020 1.00 (.63, 1.60) .988  Disagree .94 (.59, 1.52) .815 .54 (.27, 1.07) .077 1.33 (.89, 1.98) .169  Strongly disagree REF REF REF Household resistance to social distancing (scale) .49 (.37, .66) <.001 .53 (.37, .77) .001 .85 (.65, 1.11) .234 Knowledge (index) 1.02 (1.01, 1.03) <.001 1.02 (1.00, 1.03) .011 .99 (.98, 1.01) .405 Medical/Demographics Sex  Male  .62 (.43, .90) .011 .82 (.53, 1.28) .382 .67 (.49, .93) .015  Female  REF REF REF Age  18–24 1.05 (.43, 2.58) .919 1.11 (.47, 2.64) .816 .36 (.17, .76) .008  25–34 .89 (.41, 1.89) .755 .55 (.27, 1.14) .110 .28 (.16, .50) <.001  35–49 .78 (.38, 1.60) .501 .88 (.43, 1.81) .735 .26 (.15, .44) <.001  50–64 .80 (.41, 1.58) .524 1.78 (.84, 3.77) .130 .56 (.33, .95) .032  65+ REF REF REF Race/ethnicity  White, non-Hispanic REF REF REF  Black, non-Hispanic .98 (.47, 2.02) .950 .98 (.42, 2.30) .965 .99 (.52, 1.89) .980  Hispanic 1.34 (.84, 2.15) .223 .58 (.32, 1.06) .078 1.10 (.69, 1.75) .699  Other 1.06 (.53, 2.13) .871 .89 (.34, 2.34) .809 1.21 (.63, 2.31) .567 Education  High school or less .29 (.17, .49) <.001 .78 (.39, 1.54) .472 .55 (.36, .84) .006  Some college .47 (.28, .78) .003 .64 (.34, 1.23) .181 .62 (.41, .94) .023  Bachelor’s degree or higher REF REF REF Income  <$25,000 1.10 (.50, 2.41) .812 .98 (.40, 2.41) .968 1.42 (.72, 2.84) .314  $25,000–$49,999 .92 (.47, 1.78) .795 .72 (.31, 1.67) .444 .87 (.51, 1.48) .595  $50,000–$99,999 .93 (.51, 1.70) .816 1.27 (.59, 2.77) .540 1.18 (.74, 1.87) .494  $100,000–$149,999 .72 (.40, 1.31) .285 .96 (.43, 2.17) .928 1.56 (.95, 2.56) .081  ≥$150,000 REF REF REF Employed  Yes .37 (.23, .61) <.001 1.53 (.92, 2.54) .101 .90 (.60, 1.35) .626  No REF REF REF Region  Northeast 1.31 (.72, 2.36) .374 .77 (.38, 1.54) .459 1.35 (.81, 2.25) .252  Midwest 1.20 (.69, 2.07) .519 .70 (.37, 1.34) .280 .46 (.30, .71) <.001  South .76 (.48, 1.20) .242 .66 (.36, 1.18) .162 .62 (.42, .93) .021  West REF REF REF Have one or more chronic conditions  Yes 1.70 (1.17, 2.47) .005 1.22 (.76, 1.96) .414 .87 (.64, 1.18) .367  No REF REF REF Political leanings  Liberal/extremely liberal REF REF REF  Slightly liberal 1.09 (.54, 2.19) .808 1.30 (.59, 2.85) .510 .60 (.33, 1.08) .086  Moderate/middle of the road 1.16 (.67, 1.98) .599 1.67 (.87, 3.19) .122 .81 (.50, 1.31) .389  Slightly conservative 1.88 (.91, 3.89) .088 2.97 (1.27, 6.95) .012 .67 (.37, 1.23) .197  Conservative/extremely conservative 1.50 (.85, 2.66) .165 1.85 (.94, 3.65) .075 .58 (.35, .97) .038 aitem was reverse coded such that higher perceived severity reflects lower perceptions of recovering from COVID should one contract the disease. Sheltering in Place Behavioral factors associated with a greater likelihood to report sheltering in place were higher degree of worry (OR = 2.63, 95% CI 1.15–6.00; P = .022) and stronger perceived effectiveness of the behavior (OR = 1.40, 95% CI 1.08–1.82; P = .012). Greater knowledge about COVID-19 was moderately associated with this behavior (OR = 1.02, 95% CI 1.01–1.03 P < .001). Those who strongly agreed (OR = .28, 95% CI .14–.56; P < .001) or agreed (OR = .33, 95% CI .20–.56; P < .001) compared to strongly disagreed that they would rather risk getting the disease than lose their job were less likely to shelter in place. Those who agreed compared to strongly disagreed that COVID-19 was severe were also less likely to report the behavior (OR = .33, 95% CI .12–.95; P = .040). Respondents who reported more resistance to social distancing among members of their household were less likely to report sheltering in place (OR = .49, 95% CI .37–.66, P < .001). Measured behavioral variables not associated with sheltering in place were perceptions of susceptibility to COVID-19, negative outcome expectations, self-efficacy to prevent disease, and social norms (all P > .05). Men were less likely than women (OR = .62, 95% CI .43–.90; P = .011) to report sheltering in place, as were those with less than a college education (high school or less: OR = .29, 95% CI .17–.49; P < .001; some college: OR = .47, 95% CI .28–.78; P = .003) compared with those with a college education or more; and those who were employed (OR = .37, 95% CI .23–.61; P < .001) compared with those who were unemployed. Those at higher risk for serious disease were more likely (OR = 1.70, 95% CI 1.17–2.47; P = .005) than those who were not at higher risk to report sheltering in place. Social Distancing Stronger perceived effectiveness (OR = 2.34, 95% CI 1.77–3.09; P = <.001) and higher levels of worry (OR = 4.91, 95% CI 1.66–14.50; P = .004) were strongly associated with social distancing behavior. Greater knowledge about the disease was moderately associated with social distancing behavior (OR = 1.02, 95% CI 1.00-1.03; P = .011). Those who perceived COVID-19 as more severe were much less likely to report social distancing (OR = .12, 95% CI .04–.33; P < .001), as were those who strongly agreed compared to strongly disagreed with the statement about perceived susceptibility to COVID (OR = .31, 95% CI .10–.93, P = .037). Respondents who agreed compared to strongly disagreed with the statement I would rather risk getting the coronavirus than lose my job were less likely to engage in the behavior (OR = .43, 95% CI .21–.88, P = .020) as were those who reported more resistance to social distancing among members of their household (OR = .53, 95% CI .37–.77, P = .001). Measured behavioral variables not associated with social distancing were negative outcome expectations, self-efficacy to prevent disease, and social norms (all P > .05). Medical and sociodemographic variables were not associated with social distancing (all P > .05), with one exception. Those who reported slightly more conservative political beliefs were more likely to report social distancing compared with those with very liberal beliefs (OR = 2.97, 95% CI 1.27–6.95, P = .012); this association was not present when compared with those with very conservative beliefs (P > .05). Plans to Wear Face Covering Higher social norms around mitigation behaviors were strongly associated with intentions to wear a face covering (OR = 6.30, 95% CI 4.34–9.15; P < .001), as were stronger perceptions of the behavior’s effectiveness (OR = 2.11, 95% CI 1.66–2.70; P < .001). Respondents that strongly agreed compared to strongly disagreed that behavior does not affect the larger community had lower intentions to wear face coverings (OR = .46, 95% CI .26–.80; P = .006). Measured behavioral variables not associated with intentions to wear a face covering were perceptions of susceptibility, severity, self-efficacy, worry, employment risk appraisal, household resistance to social distancing, and knowledge. Male sex (OR = .67, 95% CI .49–.93; P = .015), lower education (high school or less: OR = .55, 95% CI .36–.84; P = .006; some college: OR = .62, 95% CI .41–.94; P = .023), younger age (age 18–24: OR = .36, 95% CI .17–.76, P = .008; age 25–34: OR = .28, 95% CI = .16–.50, P < .001; age 35–49: OR = .26, 95% CI .15–.44, P < .001; age 50–64: OR = .56, 95% CI .33-.95, P = .032), living in the Midwest (OR = .46, 95% CI .30–.71; P < .001) or South regions (OR = .62, 95% CI .42–.93; P = .021), and reporting highly conservative political beliefs (OR = .58 95% CI .35–.97; P = .038) were associated with lower intentions to wear a face covering. Discussion Our survey provides insight into Americans’ self-reported behaviors during the early period of the COVID-19 pandemic. We fielded the survey in mid-April 2020, one week after CDC and the White House Coronavirus Task Force issued a new recommendation to wear face coverings in public14 and when most U.S. states had recently issued shelter-in-place orders. Our findings showed that half of respondents reported wearing face coverings and more (77%) said that they intended to adopt this behavior. Most, but not all participants reported sheltering in place (86%) and social distancing (91%). Lower intentions and actual adherence to wearing face covering may have been partly due to the recency of the recommendation and because it was reversal of previous guidance by public health leaders. Our study suggests that behavioral constructs strongly influenced COVID-19 behaviors, above and beyond the influence of standard sociodemographic measures. Social norms, which refers to respondents’ perceptions about the extent to which other people should be expected to perform a behavior, were very strongly associated with intentions to wear face coverings in our study and is consistent with recent COVID-19 studies.11,12 Other behavioral factors associated with greater intentions to perform this behavior were the belief that the behavior is effective for disease prevention or mitigation and the belief that a person’s day-to-day behaviors around COVID-19 can affect the larger community. Public health messaging that emphasizes these key points is more likely to promote the desired behaviors around face coverings. Stronger belief about the effectiveness of the recommended behaviors was the only measured variable that was consistently associated with each of our outcomes, and the influence of this factor on COVID-19 behavior has been shown in other research.10,11 This finding emphasizes the importance of clearly communicating that these behaviors are scientifically proven to prevent and mitigate the spread of COVID-19 in attempt to motivate behavior. Respondents who reported more worry about contracting COVID-19 were much more likely to engage in social distancing and sheltering in place, but greater perceptions of risk were not consistently associated with these behaviors. This finding suggests that worry about disease, which evokes a more emotional response, is potentially more motivating than a person’s perceptions of their vulnerability to disease. This finding is consistent with research suggesting that fear and emotion are primary motivators of protective behavior in the context of the current pandemic.13 Stronger perceptions about the potential severity of COVID-19 should one get the disease was inversely related to engaging in these same two behaviors. This somewhat counterintuitive finding may suggest a fatalistic attitude toward disease for some. More likely, this inverse finding may be an example of reverse causation due to the cross-sectional nature of the data, such that people who were not able to shelter in place or socially distance due to work or family household considerations held more fatalistic attitudes towards the disease. Consistent with other research,13 self-efficacy for preventing COVID-19 was not associated with any of the measured behaviors, suggesting that instilling confidence in one’s ability to perform the recommended behaviors is unlikely to influence outcomes. Knowledge about COVID-19 prevention and transmission had only a moderate influence on behavior, suggesting that knowledge is an important but not sufficient catalyst for behavior change. Male gender, younger age, and lower education were associated with less engagement in COVID-19 protective behaviors, particularly intentions to wear face coverings. Political leaning was not a reliable predictor of behavior in our study once controlling for other factors. Similarly, likelihood of engaging in preventive behaviors was not influenced by race and ethnicity, once controlling for other factors. This finding is encouraging, as it may suggest that messaging around COVID-19 behaviors may be reaching some vulnerable populations. However, it is plausible that messaging may still be problematic for other vulnerable populations, including those with low health literacy.3 Participants in our study who reported having comorbid conditions that put them at risk for severe disease were no more likely than healthy individuals to engage in preventive behaviors, suggesting that greater effort may be needed to mobilize behavior for those who may benefit the most.5 We note some limitations to our findings. Our study assessed the influence of selected behavioral constructs that were actionable from a communication perspective. Therefore, other factors not measured in our survey may have influenced behavior, including low health literacy, living in multigenerational households, and close-contact working conditions. The cross-sectional design precludes us from determining the directionality of the findings, for example, inability to perform a behavior due to living or work conditions may have in turn influenced attitudes towards the behavior. Social desirability could have inflated self-report of the recommended behaviors. Although survey response was moderate, we used a population-based sample and weighted the analysis to ensure generalizability. In conclusion, our findings suggest that behavioral constructs, including perceptions of the effectiveness of a given behavior, the social norms around behavior, worry about COVID, and the belief that individual action has little effect on the health of the community, influence the public’s likelihood of engaging in COVID-19 prevention and mitigation behaviors, above and beyond the influence of medical and socio-demographic factors. Communication strategies should also consider targeting male gender, younger age groups, and those with lower educations. Additionally, those who are at highest risk for severe COVID-19 disease due to comorbid conditions may require more careful approaches to motivate protective behavior.SO WHAT What is already known on this topic? Strategies for communicating COVID-19 prevention and mitigation behaviors are needed, as vaccination efforts have stalled in many areas of the US, and variants continue to pose a health threat. What does the article add? Behavioral constructs were strongly associated with COVID-19 prevention and mitigation behaviors, above and beyond medical and sociodemographic factors, and have implication for communication. What are the implications for health promotion practice or research? Messaging to support continued practice of COVID-19 prevention and mitigation behaviors should focus on strengthening perceptions around the effectiveness of the recommended behaviors. Addressing social norms around face coverings should also be prioritized, as well as emphasizing how individual’s day-to-day behaviors can have an influence on the larger community. Those with comorbid health conditions that put them at risk for severe COVID-19 disease may require more effort to motivate protective behavior. Acknowledgements The authors thank Ana Saravia for her analysis support and Ashley Wheeler for her research assistance. Author Contributions: Thompson-analysis and interpretation; drafted manuscript; approval of final version. Squires–concept/design, data acquisition, analysis and interpretation, drafted manuscript; approved final version. Frasier-concept/design, data acquisition, critically revised manuscript, approved final version. Bann–concept/design, analysis, critically revised manuscript, approved final version. Bevc-concept/design, data acquisition, critically revised manuscript, approved final version; MacDonald-concept/design, interpretation, critically revised manuscript, approved final version; McCormack-concept/design, interpretation, critically revised manuscript, approved final version Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by RTI International. Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. IRB Review: The RTI International Institutional Review Board determined this study to be exempt IRB review. Study participants provided their consent to participate in the survey. ORCID iD: Jessica Thompson https://orcid.org/0000-0002-9224-3709 ==== Refs References 1 Federal Register. Proclmation 9994. Declaring a national emergency concerning the novel coronavirus disease (COVID-19) outbreak. Washington, DC: Federal Register; 2020. 2 Centers for Disease Control and Prevention. Interim pre-pandemic planning guidance: community strategy for pandemic influenza mitigation in the United States: early, targeted, layered use of nonpharmaceutical interventions. Atlanta, GA: Centers for Disease Control and Prevention; 2007. 3 Bailey SC Serper M Opsasnick L , et al. Changes in COVID-19 knowledge, beliefs, behaviors, and preparedness among high-risk adults from the onset to the acceleration phase of the US outbreak. J Gen Intern Med. 2020;35 (11 ):3285-3292.32875509 4 Masters NB Shih SF Bukoff A , et al. Social distancing in response to the novel coronavirus (COVID-19) in the United States. PloS One. 2020;15 (9 ):e0239025.32915884 5 Wolf MS Serper M Opsasnick L , et al. 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==== Front J Pharm Pract J Pharm Pract spjpp JPP Journal of Pharmacy Practice 0897-1900 1531-1937 SAGE Publications Sage CA: Los Angeles, CA 35387511 10.1177_08971900221087116 10.1177/08971900221087116 Original Research Article Clinical Advanced Pharmacy Practice Experience Rotations During COVID-19: Evaluation of a Transition to Virtual Learning https://orcid.org/0000-0003-2654-8843 May Casey C. PharmD 1 Atyia Sara A. PharmD 1 Hafford Amanda J. PharmD, MS 1 Smetana Keaton S. PharmD 1 1 12306 The Ohio State University Wexner Medical Center , Department of Pharmacy, Columbus, OH, USA Casey C. May, BCCCP, Department of Pharmacy, The Ohio State University Wexner Medical Center 410 W 10th Ave, Doan Hall Room 368, Columbus, OH 43210, USA. Email: casey.may@osumc.edu 6 4 2022 6 4 2022 08971900221087116© The Author(s) 2022 2022 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Background All Advanced Pharmacy Practice Experience (APPE) pharmacy rotations at a large academic medical center were converted to virtual experiences during the beginning of the coronavirus disease 2019 (COVID-19) pandemic. Objective This study aimed to describe information obtained through pre- and post-rotation surveys, implemented to improve experiences for future students who may be required to complete virtual APPE pharmacy rotations. Methods A single-center, descriptive study was conducted at a 1382-bed academic medical center. A pre- and post-rotation survey was sent to 32 students, and a post-rotation survey was sent to 38 preceptors via email to assess newly implemented virtual rotations. Results Students’ response rate for pre- and post-rotation surveys was 59% and 41%, respectively, and the preceptors’ response rate for the post-rotation survey was 37%. A statistically significant improvement in videoconferencing abilities after the rotation was found for students but no differences in other skills were noted. In the post-rotation survey, students rated all of the following areas as being “effective”: rotation as a whole, virtual topic and patient discussions; but were “neutral” regarding the utility of the introductory training guide. In the post-rotation survey, preceptors rated all of the following areas as being “effective”: rotation as a whole, virtual topic and patient discussions. Conclusion Abrupt shifts to virtual pharmacy clinical rotations due to COVID-19 have led to many challenges. Both students and preceptors felt that virtual rotations were an effective alternative to in-person experiences; however, further studies are warranted to evaluate actual performance compared to perceived effectiveness. pharmacy education advanced pharmacy practice experience rotation virtual edited-statecorrected-proof typesetterts10 ==== Body pmcIntroduction Coronavirus disease 2019 (COVID-19) is a respiratory illness caused by a novel coronavirus (SARS-CoV-2) which placed a significant strain on the healthcare system.1 This strain reduced access to personal protective equipment and raised concern for exposure to patients, providers, and students. Didactic and clinical education of medical, pharmacy, nursing, and all allied health professional students were severely impacted. Colleges across the nation closed their physical doors and moved all classes online, which led to significant challenges for those providing healthcare education. Professors who were still providing didactic education to students in the classroom had to quickly ramp up virtual education by pre-recording lectures, assigning homework, altering project work, and learning how to have beneficial discussions through virtual meeting platforms. During this time, many students were in the practical or clinical portion of their healthcare education. The first change most institutions made when COVID-19 became present in their community was to start limiting visitors and all non-essential personnel from the hospital. This included preventing all healthcare learners from attending their clinical rotations and participating in direct patient care. Many of these students were about to begin the last rotation of their academic career, a required rotation many of them needed to graduate with their healthcare degree. It can be estimated that in pharmacy education alone this impacted a quarter of the 60 594 pharmacy students enrolled.2 All healthcare education accreditation organizations charged colleges to evaluate each student’s specific requirements for graduation and to only have those students who required clerkship or clinical hours to continue clinical rotations.3 To maintain the safety of all students and preceptors, while ensuring that pharmacy students could graduate on time, the institution decided to allow those students who required clinical hours to graduate to maintain their rotation experience. Due to the requirement of social distancing and the fact that all learners were removed from the hospital on March 17th, 2020, all clinical experiences for the foreseeable future became “virtual” or “remote” rotations. Given the lack of data available to help direct these experiences and that there was no end in sight at that time, students were surveyed before and after each virtual rotation and preceptors were surveyed after each virtual rotation to gain information so that experience and learning opportunities could be improved for future pharmacy students. The purpose of this quality initiative was to describe information obtained through pre- and post-rotation surveys, with the intention to obtain feedback to improve the experience for students who may be required to complete virtual Advanced Pharmacy Practice Experience (APPE) pharmacy rotations in the future. Methods During the months of April and May 2020, all Advanced Pharmacy Practice Experience clinical pharmacy rotations at this practice site were virtual. The April students were on their last APPE rotation, and the May students were on their first APPE rotation. On average, this practice site (1382-bed academic medical center) takes approximately 15–20 students on rotation per month. A wide variety of rotations are available for students including the following practice areas: academia, acute care surgery, ambulatory care, cardiology, community retail pharmacy, critical care, emergency medicine, drug information, hematology, infectious disease, internal medicine, medication safety, nuclear medicine, oncology, pain and palliative care, pharmacy administration, psychiatric care, and solid organ transplant. Currently, this practice site only takes students from one College of Pharmacy which is a state school enrolling approximately 130 students per academic year and is one of seven health science colleges on campus. Prior to April 2020, the institution had never offered virtual rotations; therefore, there was a learning curve for preceptors and learners alike. To prepare for the month, all students were given remote access to the electronic medical records (EMRs) so they could review patients from home. An introductory training guide and a “Virtual Rotation Student Checklist” (Figure 1) was created to teach the student how to access the EMR remotely, how to utilize the various virtual meeting platforms available at the institution, and offer advice on how to be successful during the virtual clinical rotation. Tips on how to maximize the virtual meeting platforms, rules and regulations around sharing patient information on these platforms, maintaining Health Insurance Portability and Accountability Act compliance, and basic expectations were shared with the preceptors. Outside of the completely remote nature of the rotation, the expectations and day-to-day patient care that the student provided were no different than if they were on-site with the exception that they were unable to participate in patient care rounds. Preceptors were encouraged to schedule meeting times with the students every day to have in-depth discussions on the patients they were providing care for. As an attempt to improve efficiency across the health system, a team teaching calendar was developed for topic discussions. Each preceptor was encouraged to schedule their virtual topic discussions on the team teaching calendar and to share every topic discussion with their students, despite the area of focus for the rotation.Figure 1. Virtual rotation student checklist. In an effort to assess the quick flip to virtual APPE rotations and to obtain feedback as a quality initiative from students and preceptors, a pre- and post-rotation Qualtrics® survey was developed and sent out to students and a post-rotation survey was sent out to preceptors. The pre-rotation survey was sent out via email to all students on rotation at the medical center one week prior to the rotation start date, with a reminder 2 days prior to the start date. The pre-rotation survey closed the day the rotation started. The post-rotation survey was sent out via email to both the students mentioned above and all preceptors who had those students on rotation on the last day of the rotation; a reminder was sent 1 week later, and the survey closed 2 weeks after the last day of the rotation. The surveys were voluntary, and there were no incentives for participation. In the pre-rotation survey, students were asked to describe their previous rotation experiences, their practice setting post-graduation if known, videoconferencing abilities, and how they rated themselves in various areas (i.e., written communication skills, verbal communication skills, public speaking, interpersonal skills, and follow through with assignments). Additionally, specific questions were added to the post-rotation survey to evaluate how effective they rated the following: the rotation as a whole for their learning, virtual topic discussions, virtual patient discussions, and the introductory training guide. All preceptors received a post-rotation survey, and were asked to describe their practice setting and questions to evaluate how effective they rated the following: the rotation as a whole for them to teach, virtual topic discussions, and virtual patient discussions. Additionally, both students and preceptors had an option to leave open-ended comments at the end of the post-rotation survey. The results were anonymously compiled and aggregated data were assessed. When the data were pulled, each respondent was assigned a number. Those respondents that completed both the pre- and post-survey were included in the analysis to compare their skills in the various areas mentioned above. Three Likert scales were utilized dependent upon the question: Likert scale 1 was a 7-point scale to assess ability (0—far below average, 1—moderately below average, 2—slightly below average, 3—average, 4—slightly above average, 5—moderately above average, and 6—far above average), and Likert scale 2 was a 5-point scale on effectiveness (0—very ineffective, 1—ineffective, 2—neutral, 3—effective, and 4—very effective). The primary outcome of this quality initiative was to describe information obtained through pre- and post-rotation surveys, with the intention to obtain feedback to improve the experience for future students who may be required to complete virtual APPE pharmacy rotations. Continuous data are reported as median (25–75% interquartile range (IQR)) or a percentage for descriptive data, as appropriate. Before and after Likert scale responses were analyzed using the related-samples, Wilcoxon signed rank tests and data are presented as median (interquartile range (IQR)).4 All analysis was performed using IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp. Results Students An email to complete a pre- and post-rotation survey was sent out to a total of 32 unique students. The response rate for completion of these surveys was 59% (n = 19) and 41% (n = 13), respectively. Out of the 19 students who responded to the pre-rotation survey, 72.2% previously had an acute care rotation, 13.6% had an academia rotation, 50% had an ambulatory care rotation, 68.2% had a community/retail rotation, and 54.5% had an “other” type of rotation which was noted to be a home infusion, industry, managed care, drug information, and/or administration rotation. The practice setting that students would be entering upon graduation was the following: postgraduate year-one pharmacy residency (47%, n = 9), undetermined (26%, n = 5), acute care (11%, n = 2), community/retail (5.3%, n = 1), industry (5.3%, n = 1), and health-system pharmacy administration (5.3%, n = 1). Ten students completed both the pre- and post-rotation survey assessing how they rated the various skills including written communication, verbal communication, public speaking, interpersonal skills, follow through on assignments, and videoconferencing abilities. No differences were found in median response rates of skills assessed except in videoconferencing abilities where a statistically significant improvement was observed after the rotation (3 (IQR, 1–4.25) vs 5 (IQR, 3–5.25); P = .049) (Table 1). The increase in median score is representative of a response of “average” pre-rotation to “moderately above average” post-rotation based on Likert scale 1.Table 1. Student Self Rated Ability Assessment, n = 10. Area assessed Pre-virtual rotation Post-virtual rotation P value Written communication skills 3 (1–5.25) 5 (3.75–5.25) .072 Verbal communication skills 3.5 (2.5–4.5) 4 (3–5) .491 Public speaking 3 (2.75–3.25) 4 (3–4) .096 Interpersonal skills 4 (1–5.25) 5 (4–5.25) .121 Follow through with assignments 5.5 (2.5–6) 5 (4–6) .279 Videoconferencing abilities 3 (1–4.25) 5 (3–5.25) .049 Data are presented as median (interquartile range). Likert scale to assess ability: 0—far below average, 1—moderately below average, 2—slightly below average, 3—average, 4—slightly above average, 5—moderately above average, and 6—far above average. Students’ responses on the post-rotation survey to the questions on the effectiveness of the rotation, topic and patient discussions, and introductory training guide can be found in Table 2. There were a total of 13 responses, and the median response to all question was “effective” on Likert scale 2, with an exception that they were “neutral” with the introductory training guide being helpful to prepare them for the virtual rotation. There were no constructive comments provided by the students in the open-ended comment box at the end of the survey as the majority of the comment boxes were blank or the comments were simply stating that utilization of a web camera helped simulate in-person interactions.Table 2. Student Evaluation of Virtual Rotation, n = 13. Question Very effective, n (%) Effective, n (%) Neutral, n (%) Ineffective, n (%) Very ineffective, n (%) How effective would you rate the rotation as a whole for your learning? 6 (46%) 6 (46%) 0 1 (8%) 0 How effective were virtual topic discussions? 7 (54%) 4 (31%) 2 (15%) 0 0 How effective were virtual patient discussions? 6 (46%) 5 (38%) 1 (8%) 1 (8%) 0 How effective was the introductory training guide at preparing you for this virtual rotation? 0 3 (23%) 9 (69%) 1 (8%) 0 Preceptors An email to complete post-rotation survey was sent out to a total of 38 preceptors and 14 responded, which was a response rate of 37%. Of those who responded (n = 14), a majority (71%) practiced in the acute care setting, 14% community/retail setting, 7% in ambulatory care, and 7% in a medication assistance program. Preceptors’ responses to the questions on the effectiveness of the rotation, topic, and patient discussions can be found in Table 3. The median response for all questions was “effective” on Likert scale 2. There were no constructive comments provided by the preceptors in the open-ended comment box at the end of the survey as the majority of the comment boxes were blank or the comments were simply stating that virtual rotations were an acceptable short-term solution and that utilization of videoconferencing with a web camera helped simulate in-person interactions.Table 3. Preceptor Evaluation of Virtual Rotation, n = 14. Question Very effective, n (%) Effective, n (%) Neutral,n (%) Ineffective, n (%) Very ineffective, n (%) How effective would you rate the rotation as a whole for you to teach? 1 (7%) 8 (57%) 4 (29%) 1 (7%) 0 How effective were virtual topic discussions? 1 (7%) 10 (72%) 3 (21%) 0 0 How effective were virtual patient discussions? 3 (21%) 5 (36%) 5 (36%) 1 (7%) 0 Discussion With the abrupt removal of learners from the clinical setting as the COVID-19 pandemic progressed, preceptors and students had to quickly adapt to virtual patient care, teaching, precepting, and learning. Concerns were anticipated from preceptors and students on the ability to conduct an effective clinical APPE rotation through videoconferencing and this quality initiative sought to assess perceptions and gather feedback for improvement as this uncharted territory was explored. This report suggests that virtual pharmacy clinical rotation were perceived, by both students and preceptors, to be an effective learning platform. Students and preceptors alike felt that both patient discussions and topic discussions were effective. One factor thought to have had a strong impact on the perceived effectiveness was that students were given remote access to the EMR. Without such access, real-time patient review would not have been possible and, thus, make a patient care rotation nearly impossible. To our knowledge, this is the first report of a virtual clinical APPE rotation where students were given access to the EMR and were expected to review and work up patients from home. A recent publication by Johnston and colleagues discuss how their critical care faculty quickly created a virtual APPE rotation during the COVID-19 pandemic.5 The rotation they initiated included topic discussions, journal clubs, case presentations, online learning modules, and formal writing assignments. The students only had access to an educational EMR where they worked through practice or pre-built patient cases. During the virtual rotations carried out in our report, as mentioned above, students had access to the actual EMR and reviewed patients admitted to the preceptors’ service daily. The day-to-day patient care that the student provided was no different than if they were on-site with the exception that they had limited interactions with the interdisciplinary team, and they were unable to participate in patient care rounds. The results of this survey demonstrated significant improvement in videoconferencing abilities for APPE students. Utilization of videoconferencing allowed for frequent face-to-face interactions and screen sharing to give synchronous access to the EMR and facilitate patient care. From the comments that were provided in the post-rotation survey, both students and preceptors felt that utilization of videoconferencing helped simulate in-person interactions and positively impacted learning. The majority of preceptors met with students via videoconferencing twice daily, typically in the morning for patient discussions and in the afternoon for topic discussions. In addition to videoconferencing, preceptors and students communicated via secure messaging in the EMR for patient-related items and instant messaging for non-patient-related items. Students were also able to communicate with the interdisciplinary team via the secure messaging in the EMR facilitating some degree of interaction with other healthcare providers. Currently, there is limited literature on the use of videoconferencing for synchronous clinical rotations. Given the students’ perceived improvement in videoconferencing skills during the virtual APPE rotation and feeling that the virtual rotation was effective for their learning, this may be the first report to support videoconferencing with off-site EMR access as a method to offer clinical rotations. This finding matches those highlighted by Eiland, which demonstrated that students who participated in an off-site academic APPE felt that communicating primarily through videoconferencing did not impede learning.6 The COVID-19 pandemic has pushed students and preceptors to think outside of the box and try methods of teaching and learning that had previously not been attempted. The ability to offer virtual rotations could open up opportunities for students to gain experiences that they would have previously not had due to location, timing, cost, or other travel-related restrictions. Additionally, the use of videoconferencing in pharmacy education could also help develop preceptor communities, both locally and nationally, as more information on the direction of the pandemic and how virtual rotations could mold the APPE clinical rotation experiences moving forward. Conducting a virtual clinical APPE did have several limitations. Students noted that a virtual rotation took away from some of their learning experiences, especially their ability to interact with other members of the healthcare team. The primary driver for this concern was the lack of interdisciplinary interaction with the patient care team and inability to be physically on patient care rounds. Prior to the start of the rotation, preceptors attempted numerous methods to be virtually present on rounds with the team (e.g., virtual teleconferencing or phone call), but were deemed not to be feasible given the current rounding structure. Issues that arose primarily were due to communication difficulties. For example, it was difficult to hear team members during patient presentations, to interject with pharmacy interventions, and to understand when a question was being posed to the pharmacist when only present via virtual teleconference. Depending on how patient rounds are conducted, a possible method to circumvent these last 2 issues would be to have a dedicated pharmacy time out during each patient or to implement “sit down” rounds in a room with videoconferencing. Having students reach out to patient care team members through the “Secure Chat” feature in the EMR and attaching the preceptor was a useful method to bridge this educational gap. This allowed for appropriate oversight from the preceptor while the student was able to learn necessary skills in alternative methods of communication to recommend therapeutic modifications. These limitations surrounding interprofessional skills are consistent with another hospital’s experience when conducting virtual inpatient APPE rotations utilizing de-identified or made-up patient cases during the COVID-19 pandemic.7 If APPE rotations were required to be virtual again or if this opportunity was offered for another reason (extended student leave for health reasons or distant virtual clinical rotation), the problem of solving the communication difficulties for rounding to ensure students had the ability to engage with the interprofessional team in a more real-time manner would be paramount. Another limitation identified by the students was the need for improved communication and planning. Students’ median response to the effectiveness of the introductory training guide was “neutral.” When planning the virtual rotations, the introductory training guide and “Virtual Rotation Student Checklist” (Figure 1) was created in an effort to help prepare students up front and in hopes that the rotation can be started in a positive direction. Additionally, the guidance was for each preceptor to establish additional expectations with their student and preceptors were encouraged to place their planned topic discussion on a shared teaching calendar on the Microsoft Outlook® platform. The preceptor was then responsible for sharing all available topics with their student. After receiving feedback from the students, it was recommended that preceptors create a detailed calendar prior to the start of rotation to set consistent times and methods for patient care discussions. In the future, it would be beneficial to make the real-time shared teaching calendar for topic discussions more readily available to students on a more accessible platform. This would help if lecturers needed to move the date or time of the topic discussion due to patient care responsibilities. When the lecturer was not the student’s primary preceptor, this caused confusion for the student and could often result in missing the topic discussion. To assist with some of these issues, it is very important that the shared teaching calendar include specific details for topic discussions including but not limited to the date, time, virtual location, lecturer name, and contact information. Overall, preceptors appreciated the shared teaching calendar. This provided a division of the workload and a sense of community, similar to that demonstrated by Ackman and Romanick when evaluating virtual communities and networks for pharmacy preceptor development.8 This quality improvement survey project was conducted to evaluate the urgent need to send both students and preceptors off-site during the height of the first phase of the pandemic. Given this information, the results of this study should be interpreted with caution due to the limitations of the survey design and quick rollout. First, this was a small snapshot in time (April and May 2020) when students were first required to be off-site. The goal was to capture a limited amount of information to improve survey compliance and to capture specific information to ensure that virtual rotations were an effective venue for students to learn in; however, this was all self-reported information, and clinical performance was not assessed. This report included a small number of preceptors and students surveyed at one academic medical center. It is possible that the perceived effectiveness would be reduced if multiple centers were involved due to a lack of cohesive approach to training and systems utilized. It must also be recognized that there was a low response rate from preceptors. This was a time of high stress and obligation for all preceptors and clinical staff as many working remotely, creating COVID-19 treatment guidelines, and trying to maintain their own safety in such difficult times. It is believed that the low response rate from the preceptor group was multifactorial. While grades were provided to students for their performance on the virtual rotation, the students’ actual grades were not obtained and thus were not compared to their perceived effectiveness of the rotation. If given optimal time to design a similar study, actual grades students were awarded would have been compared to their perceived effectiveness and to grades awarded for a similar rotation that was not virtual. Additionally, students could have also been surveyed again after entering their job or residency position to evaluate how prepared they felt. This type of information would give a better understanding of the actual effectiveness of a virtual clinical rotation. Finally, it is important to note that when virtual rotations were initiated, the Accreditation Council for Pharmacy Education (ACPE) had not announced a stance on how virtual rotation would impact the students’ ability to collect the experiential hours needed to graduate. After implementation of virtual rotations, in August 2020, ACPE provided communication on recommendations for participation in experiential education activities during a national, regional, or local crisis. In this communication, they state that students may make up hours for the experiential education either online or through a virtual experience.9 The ACPE’s stance along with reports like this one will continue to mold the future of APPE rotations for pharmacy students moving forward. Conclusion The COVID-19 pandemic disrupted the education of students at all levels of learning across the nation. For pharmacy students, the abrupt change from the usual in-person to virtual clinical APPE rotation format appeared to be an effective format for their learning; however, further studies are warranted to evaluate the actual performance compared to the perceived effectiveness. Acknowledgment We would like to thank Jessica Martz for her assistance with survey management. ORCID iD Casey C. May https://orcid.org/0000-0003-2654-8843 Author’s Note: Dr May contributed to the design of the study, survey development, analysis of the data, and drafting the article. Drs Atyia and Hafford contributed to the design of the study and revising the article for intellectual content. Dr Smetana contributed to the design of the study, survey development, analysis of the data, and revising the article for intellectual content. Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. ==== Refs References 1 Ji Y Ma Z Peppelenbosch MP Pan Q . Potential association between COVID-19 mortality and health-care resource availability. Lancet Glob Health. 2020;8 (4 ):e480. doi:10.1016/S2214-109X(20)30068-1.32109372 2 ACCP. Academic Pharmacy’s Vital Statistics. Accessed March 30, 2021. https://www.aacp.org/article/academic-pharmacys-vital-statistics. 3 Barzansky B Catanese V . LCME Update on Medical Students, Patients, and COVID-19: Approaches to the Clinical Curriculum Published. Liaison Committee on Medical Education. 2020. Accessed March 30, 2021 https://lcme.org/wp-content/uploads/filebase/March-20-2020-LCME-Approaches-to-Clinical-Curriculum.pdf. 4 Sullivan GM Artino AR . Analyzing and interpreting data from likert-type scales. J Grad Med Educ. 2013;5 (4 ):541-542. doi:10.4300/JGME-5-4-18.24454995 5 Johnston JP Andrews LB Adams CD , et al. Implementation and evaluation of a virtual learning advanced pharmacy practice experience. Curr Pharm Teach Learn. 2021. Published online April. doi:10.1016/j.cptl.2021.03.011. 6 Eiland LS Staton AG Stevenson TL . Providing an academic APPE elective via videoconference between off-campus faculty and students. Am J Pharmaceut Educ. 2018;82 (8 ):6645. doi:10.5688/ajpe6645. 7 Badreldin HA Alshaya O Saleh KB Alshaya AI Alaqeel Y . Restructuring the inpatient advanced pharmacy practice experience to reduce the risk of contracting coronavirus disease 2019: Lessons from Saudi Arabia. J Am Coll Clin Pharm. 2020;3 :771-777. Published online April 13. doi:10.1002/jac5.1237. 8 Ackman ML Romanick M . Developing preceptors through virtual communities and networks: Experiences from a pilot project. Can J Hosp Pharm. 2011;64 (6 ):405-411. doi:10.4212/cjhp.v64i6.1081.22479095 9 Thompson M Kanmaz T Clarke C , et al. Recommendations for participation in experiential education activities during a national, regional or local crisis. Published. 2020. Accessed March 30, 2021. https://www.acpe-accredit.org/wp-content/uploads/Experiential-Education-ACPE-Proposal-Response-Final.pdf.
PMC009xxxxxx/PMC9001056.txt
==== Front Am Surg Am Surg spasu ASU The American Surgeon 0003-1348 1555-9823 SAGE Publications Sage CA: Los Angeles, CA 35387524 10.1177_00031348221086821 10.1177/00031348221086821 SESC Poster Papers Influence of Covid-19 Restrictions on Urban Violence Lalchandani Priti MD 1 Strong Bethany L. MD 1 https://orcid.org/0000-0001-9555-5698 Harfouche Melike N. MD 2 Diaz Jose J. MD 2 Scalea Thomas M. MD 1 1 137889 R Adams Cowley Shock Trauma Center , Baltimore, MA, USA 2 12264 University of Maryland Medical Center , Baltimore, MA, USA Priti Lalchandani, MD, R Adams Cowley Shock Trauma Center, 22S Greene Street Baltimore, MA 21201, USA. Email: priti.lalchandani@som.umaryland.edu 8 2022 8 2022 8 2022 88 8 19281930 © The Author(s) 2022 2022 Southeastern Surgical Congress This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. We investigated whether the COVID-19 pandemic affected rates of interpersonal violence (IV). A retrospective study was performed using city-wide crime data and the trauma registry at one high-volume trauma center pre-pandemic [PP] (March-October 2019) and during the pandemic [PA] (March-October 2020). The proportion of trauma admissions attributable to IV remained unchanged from PP to PA, but IV increased as a proportion of overall crime (34% to 41%, p<0.001). Assaults decreased, but there was a proportionate increase in penetrating trauma which was mostly attributable to firearms. Despite a reduction in admissions due to IV in the first 4 months of the pandemic, the rates of violence subsequently exceeded that of the same months in 2019. The cause of the observed increase of IV is multi-factorial. Future studies aimed at identifying the root causes are essential to mitigate violence during this ongoing health crisis. COVID-19 interpersonal violence trauma typesetterts10 ==== Body pmcShortly after the initial COVID-19 cases were reported in the United States in March 2020, stay-at-home and social distancing orders were enacted to mitigate spread of the deadly virus. These restrictions led to unintended outcomes, such as exacerbating mental illness and increasing strain on interpersonal relationships.1 There is limited data on how COVID-19 affected urban violence and, interpersonal violence (IV), in particular.2 We hypothesized that rates of IV would be higher during the COVID-19 pandemic than in the preceding time period. A retrospective review of the trauma registry at the R Adams Cowley Shock Trauma Center (STC) was performed for the periods of March to October 2019 (PP = pre-pandemic) and March to October 2020 (PA = pandemic). The STC is a level I trauma center located in Baltimore that also serves as a regional referral center for the state of Maryland. Approximately 7000 trauma patients are treated yearly. The trauma registry contains prospectively collected demographic and clinical variables. The time periods allowed us to compare overall violence, and violence as a percentage of all injuries, over similar periods. City-wide crime data was obtained from a publicly available, online dataset hosted by the city of Baltimore. The dataset contains information on the date, location, and type of crime. IV was defined as any act of harm committed toward another individual. This was defined as an assault, stab, or firearm injury in the trauma registry and an assault, homicide, rape, or shooting in the city-wide database. Self-inflicted injuries were excluded. The primary outcome of interest was change in incidence of IV between the PP and PA periods. Additional analyses were performed for changes in demographic characteristic and proportion of injuries attributable to firearms by period, and trends in incidence of IV by month. STATA/BE 17.0 was used for all statistical analyses, and significance was set at P < .05. There were 2894 patients in the PP group and 2657 patients in the PA group. The proportion of trauma admissions suffering IV remained unchanged from PP to PA (22.3% and 22.2%, respectively) with a decrease in IV from 645 to 591 patients. Mean age was 34.6 years, 86% of victims were male, 78.2% were of black race, and mean Injury Severity Score was 10.5 among those experiencing IV. There were no differences in demographic or clinical characteristics between the PP and PA groups. While IV did not change during the study periods, there was a reduction in IV admissions from 20.6% to 6.7% in the first 4 months of the pandemic. Starting in August 2020, however, the rates of violence exceeded that of the same months in 2019. During the entire study period, 48.7% of IV injuries were attributable to firearms, followed by an equal percentage of stab wounds and assaults (25.6%). The mechanism of violence did not vary between the PP and PA periods (Table 1).Table 1. Incidence and Subtypes of IV Before and During the Pandemic*. Trauma Registry City-wide Crime Data PP PA p PP PA P IV 645 (22) 591 (22) .97 11053 (34) 9821 (41) <.001 IV subtype  Assault 172 (27) 145 (25) .39 8358 (76) 7089 (72) .32  Stab 172 (27) 145 (25) .39 808 (7) 807 (8) .01  Firearm 301 (47) 301 (51) .13 1887 (17) 1925 (20) <.001 *Listed as count (percent). IV = interpersonal violence; PP = pre-pandemic; PA = pandemic. Out of 56,555 acts of crime which occurred in Baltimore City during the study period, 32,535 (57.5%) were during the PP and 24,020 (42.5%) were during the PA period. Although cases of IV decreased from the PP to PA period (11,053 vs 9821), the proportion of overall crimes attributable IV increased from 34.0% to 40.9% (P < .001). Odds of a crime being associated with IV increased by 1.34 from the PP to PA period (P < .001). There was an increase in the use of firearms during acts of IV from the PP to PA period (17.1% to 19.6%, P < .001). A similar trend was seen in the proportion of stab wounds but not assaults (Table 1). Despite an overall decrease in city-wide crime during PA, there was a proportionate increase in IV. A similar overall trend in trauma center admissions due to IV was not observed in this study but has been reported in other studies.3,4 Although there were fewer admissions attributable to IV during the first half of the pandemic compared the pre-pandemic period, interpersonal violence increased after COVID-19 restrictions were in place for more than 4 months. It seems reasonable to think that citizens of Baltimore took the regulations seriously and violence decreased initially. When the consequences of social isolation became intolerable, the violence returned surpassing previous levels. Baltimore has been one of the US cities that has not seen a decrease in violence over the past few years. The fact that pandemic levels of violence exceeded even the impressive rates seen pre-pandemic is extremely concerning, underscoring the severe negative effects seen with isolation. This study represents the experience of a single urban trauma center and may not reflect overall trauma admissions in Baltimore. It is feasible that other local trauma centers experienced a proportionate increase in admissions due to IV during the pandemic. Given that restrictions and social distancing rules are ongoing, a longer period of analysis may result in trauma center admissions that more closely parallel city-wide crime data. This study does not explore the root causes of increase in crime due to interpersonal violence during the pandemic. Future studies must explore contributing factors to greater firearm use during periods of social isolation. Increase in rates of intimate partner violence may have contributed to the proportionate rise in IV. Our data cannot address this, and future studies are needed. Findings of this study do underscore the need for greater resources to address the burden of interpersonal violence in urban centers during pandemics or other periods of forced social isolation, including increased access to hospital-based violence intervention programs and domestic abuse protection services. Limitations in social interaction have likely also reduced the availability of counseling and peer support groups in the community, posssibly exacerbating the conditions that lead to interpersonal violence. ORCID iD Melike Harfouche https://orcid.org/0000-0001-9555-5698 Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. ==== Refs References 1 Pfefferbaum B North CS . Mental health and the Covid-19 pandemic. N Engl J Med. 2020;383 (6 ):510-512.32283003 2 Chodos M Sarani B Sparks A , et al. Impact of COVID-19 pandemic on injury prevalence and pattern in the Washington, DC Metropolitan region: a multicenter study by the American College of surgeons committee on Trauma, Washington, DC. Trauma Surg Acute Care Open. 2021;6 (1 ):e000659.34192164 3 Abdallah HO Zhao C Kaufman E , et al. Increased firearm injury during the COVID-19 pandemic: a hidden urban burden. J Am Coll Surg. 2021;232 (2 ):159-168.e3.33166665 4 Olding J Zisman S Olding C Fan K . Penetrating trauma during a global pandemic: changing patterns in interpersonal violence, self-harm and domestic violence in the Covid-19 outbreak. Surgeon. 2021;19 (1 ):e9-13.32826157
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==== Front ICS spics International Journal of Cultural Studies 1367-8779 1460-356X SAGE Publications Sage UK: London, England 10.1177/13678779221091293 10.1177_13678779221091293 Special Issue: COVID-19 Conjunctions of resilience and the Covid-19 crisis of the creative cultural industries Frosh Paul Georgiou Myria https://orcid.org/0000-0002-1043-4270 Yue Audrey 37580 National University of Singapore Audrey Yue, Communications and New Media, National University of Singapore, 11 Computing Drive, AS6-03-14, Singapore 117416, Singapore. Email: audrey.yue@nus.edu.sg 7 2022 7 2022 7 2022 25 3-4 Special Issue: COVID-19: The Cultural Constructions of a Global Crisis 349368 © The Author(s) 2022 2022 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. This article compares the conjunctions of emergency resilience and ecological resilience that underpin the creative cultural industry (CCI) crisis. It first introduces three characteristics that socially construct the CCI crisis and its hegemonic practice of emergency resilience (time, disaster discourse, and the adaptation of aesthetic digitalization) and exposes multiple discourses – from the technologies of cultural statistics to corporate financial modelling – that construct an ideology of ‘resilience-as-deficit’. In contrast to this approach, the article develops three characteristics of ecological resilience: a focus on transition and the long term; resilience as a decentred strategy and networked resource; and aesthetic digitization as a radical praxis of adaptability. Examining arts impact and cultural policy reports, drawing on ecological, feminist and cultural resilience studies, and analysing a digital cultural event in Asia (the Singapore LGBT cultural festival, Pink Dot), the article argues that ecological resilience offers new capacities towards a cultural ecology that can nurture fair work, artistic innovation, economic growth and cultural vitality. CCI crisis creative cultural industry (CCI) cultural crisis ecological resilience emergency resilience LGBT Pink Dot Singapore typesetterts19 ==== Body pmcIntroduction In early 2020 just before the Covid-19 (C-19) pandemic, the creative cultural industries (CCI) contributed US$2250 billion to the global economy and accounted for 26.5 million jobs worldwide (UNESCO, 2020a). A year later, more than 10 million jobs and US$750 billion in CCI goods and services were lost globally (UNESCO, 2021). Part of entertainment and recreation, including venue and visitor-based sub-sectors such as performing arts, museums, cinemas and heritage, the CCI sector relies on footfall to thrive. Health protocols such as travel bans, lockdowns and social distancing have caused in-person attendance to plummet, leading to the mass cancellation of exhibitions, tours and events, the closure of cultural institutions and programmes, and loss of jobs for artists, freelancers and contractors. It was the first sector to close and will be the last to fully reopen (AFA, 2020a). The Covid-19 public health crisis has exposed the crisis of the CCI sector. A crisis is not simply a disaster to be managed. It is socially constructed by ideological, political and juridical forms that create the site through which it is articulated (Hall, 1978: 219). This site is the vector of the conjunction that brings together multiple fields, discourses, knowledge and institutions as the problem space through which the crisis is produced (Grossberg, 2019). Understood in this way as social construction and conjunction, crises ‘are moments of simplification of social relations and clarification of reality. They reveal the hidden which we usually do not see’ (Antentas, 2020: 316). This article examines the social construction of the CCI crisis and, by contrasting its ‘reopening’ practices of emergency and ecological resilience, uncovers the conjunctions of resilience that shape the current CCI ecology. It draws on critical theories in resilience, queer and digital placemaking studies, reviews global cultural policy and arts impact reports, and analyses case studies from selected digital cultural events in Asia. The first section introduces the social construction of the CCI crisis through the hegemonic ideology of emergency resilience characterized by crisis time, disaster discourse and short-term adaptation. It shows how the CCI crisis has exposed the sector's endemic vulnerabilities, such as precarious cultural labour, and, through its corporatized financial modelling towards ‘recovery’, the economic reductionism of digital globalization. The second section contrasts these top-down practices of emergency resilience with ecological resilience. Where emergency resilience highlights short-term adaptation through mitigation, ecological resilience emphasizes long-term adaptability through creative iteration and transformation. It examines alternative sector discourses of ‘reopening’ that focus on transition and empirical studies of informal CCI. In particular, it discusses the case study of Pink Dot, an annual LGBT festival in Singapore, and its ecological impact. Where emergency resilience constructs resilience as a lack (resilience-as-deficit), ecological resilience draws on ecological, feminist and cultural studies of resilience as a decentred strategy and resource for capacity-building (resilience-as-dividend). In elucidating these conjunctions of resilience, this article argues that a robust post-pandemic CCI ecology must embrace a long-term commitment towards sector transition, one that supports fair work, embeds place and engages community. The CCI crisis and emergency resilience This section examines how the CCI crisis is socially constructed by the ideology of emergency resilience through the temporal mode of crisis, the discourse of disaster and the adaptation practice of mitigation. It evaluates arts impact and cultural policy reports from UNESCO, the UK, the US, Australia and Singapore to situate different scales and geographical regions, and exposes how, despite their differences, these organizations and countries have developed similar universal emergency measures to respond to common sector problems. Time The CCI crisis is socially constructed by time. A crisis is usually referred to as a short-term disaster, connoted through a condensed temporality and experienced as immediate, urgent and abrupt.1 The CCI crisis began around early March 2020, escalated over the following few weeks and continued over the next few months until summer that year when some countries gradually reopened. CCI crisis time was precipitous and intense. During this period, advocacy associations initiated urgent impact surveys to ascertain the extent of immediate economic loss and hardships for organizations and individuals. This was not easy as official cultural statistics have always been unevenly collected and non-comparable, internationally and even within nation-states (Throsby, 2010). Additionally, the market failure rationale of the arts, following the microeconomic cost disease theory (Baumol and Bowen, 1966), has meant that the external benefits and financial deficits of public arts, for example, cannot be easily quantified. The arts, as cultural policy theorists have long extolled, produce intrinsic cultural values that transcend box office profits and neoclassical economics (Crossick and Kaszynska, 2014). Nonetheless, arts advocacy organizations across the world – such as the Americans for the Arts (AFA, 2020a, 2020b), The Artist Trust (2020), European Creative Business Network (2020a), a-n The Artists Information Company (2020) and the Australia Council (2020a, 2020b) (see also Crosby and McKenzie, 2021) – quickly documented the direct impact on the sector. CCI crisis time is evident in the speed by which these rapid assessment surveys had suddenly appeared and circulated across the short weeks between March and April. Data enumerated and materialized the experiences of hundreds of thousands of cultural workers directly affected, and the problems faced by the sector. While economic impact studies are limited and instrumentalist, these reports nevertheless provided some baseline evidence to track the ravaged sector, so much so that as early as 23 March, less one month into the first lockdown, Culture Action Europe (2020) was already calling the European Commission to earmark €25 billion in emergency package. Although this temporal mode parallels the public health crisis, its cultural statistics made visible the severity of job loss, the lack of job relief support, and the diminished capacity of organizations to maintain operations. These aggregations legitimate the disaster discourse. Disaster discourse The disaster discourse, as the second characteristic of the social construction of the CCI crisis, was becoming evident by the end of summer 2020 when it was clear the pandemic had severely crippled the sector with sudden and prolonged closures. This discourse is first established by referring to the CCI crisis as a disaster. Terms such as ‘culture shock’ (OECD, 2020), ‘culture in crisis’ (UNESCO, 2020b), ‘cultural catastrophe’ (Creative Industries Federation, 2020) and ‘lost art’ (Brookings Institute, see Florida and Seman, 2020), headlined in reports of leading cultural organizations, convey this fallout. They connote the CCI crisis as a disaster that will damage the sector temporarily and a seismic blow that will collapse it permanently. This discourse is next anchored through the signifier of ‘lack’, such as in these reports’ frequent use of terms ‘vulnerable’ (European Parliament Think Tank, 2020; UNESCO, 2020a) and ‘weaknesses’ (UNESCO, 2020b). It is further entrenched by foregrounding the narrative of ‘unpreparedness’. For example, the opening of the OECD’s (2020) Culture Shock report highlights the sector's lack of readiness and its trauma by pointing to ‘jobs at risk’ and the ‘structural fragility’ of cultural production (2). The same structure also informs UNESCO’s (2020b) Culture in Crisis report of how jobs have been ‘profoundly affected,’ the ‘precarious nature of their work’ (2020b: 1) and the need to ‘[strengthen] the resilience of the sector’ (2020b: 4). Even the term ‘disaster preparedness’ is used as a matter of fact in the title of the Americans for the Arts’ (AFA, 2020a) report. Through signifiers of disaster and lack, and the narrative of unpreparedness, this discourse socially constructs the CCI crisis as one that is created by the sector's lack of readiness because it could not withstand the economic shocks caused by the Covid-19 crisis, and how insecure cultural work has made creative professionals unable to protect their jobs. Underpinning this discourse is emergency resilience. The concept of emergency resilience derives from disaster management studies to refer to systems and their ability to ‘maintain or rapidly return to desired functions in the face of a disturbance, to adapt to change, and to quickly transform systems [for] future adaptive capacity’ (Meerow et al., 2016: 45). It promotes normative understandings of resilience centred on the trait-orientation of lack and adopts a hegemonic discourse of emergency preparedness. The disaster discourse anchors this dominant ideology of resilience-as-deficit (Yue, 2020), which has genealogies in engineering resilience (Folke, 2006; Holling, 1996) and social resilience studies (Adger, 2000; Adger et al., 2005; Keck and Sakdapolrak, 2013; Welsh, 2014). The former highlights the ability of systems to withstand sudden shock, quickly adapt and return to normal, while the latter highlights the skills needed by groups and individuals to successfully integrate into society. Shaped by deficit, this ideology gives rise to the short-term adaption practice of mitigation. Adaptation The adaptation of mitigation foregrounds the third characteristic that undergirds the social construction of the CCI crisis. As the term ‘mitigation’ suggests, adaptation measures focus on reacting to change and ameliorating short-term stress so systems can rapidly rebound and regain normality. This is evident in two ways: the first is structural, through the provision of job relief support; the second is aesthetic, through the production of new digital genres. One reveals the precarity of cultural labour while the other unproblematically celebrates the platform capitalism of digital globalization. Adaptation: precarity The most significant emergency measure is job relief support for creative professionals. Recommended in Culture Shock (OECD, 2020) and Culture in Crisis (UNESCO, 2020b), and mirrored in local measures globally, these unfurled in crisis time in March (e.g. Arts Council England, 2020; Australia Council, 2020b; European Commission, 2020; US CARES Act in Loane, 2020) – so much so that by the end of March, about 54 countries had provided emergency support to the arts (Bailey, 2020). In addition to income loss compensation, other mitigation measures focus on business support, education and training. The Singapore National Arts Council, for example, launched the SG$55 million Arts and Culture Resilience Package (MCCY, 2020a) that included a Capability Development Scheme to provide training programmes on digital technology to remediate art forms; digital learning for the arts; creating videos for social media; writing, planning and budgeting for the arts; audience engagement arts education; professional skill improvement in play-writing, filmmaking, dancing, set management, and artist self-care (NAC, 2020a). While these measures slightly offset wage loss and provide basic digital skills for artists to quickly pivot online, they ignore non-traditional forms of cultural work, and the role and needs of cultural workers. Cultural workers are considered the original ‘gig’ economy workers. An Australian study estimated that most creative professionals (81%) work as freelancers or are self-employed in their art form (Throsby and Petetskaya, 2017). CCI job losses were mainly caused by arts and recreation businesses ceasing trading; as venues closed and events were cancelled, organizations had no alternative income and could not pay rent and overheads, including utilities, staff, contractors or themselves, and including those from the ancillary services industry. Most could not qualify for support due to their freelance and non-standard forms of work, leaving them to fall through the cracks in terms of public support (OECD, 2020). Indirect impacts were also immensely felt in the downstream industries, including tourism, hospitality and SMEs (small, medium enterprises), and on the broader economy and community. In a country like Singapore, with no social welfare unemployment benefits, many freelancers fell through the safety net. Cultural impact data reveals there are about 26,300 full-time creative professionals (MCCY, 2020b: 14) and 12,361 freelancers (47%) (NAC, 2019) and in an emergency impact survey (NAC, 2020) conducted with the latter, 91% had reported project cancellations, 83% project postponements, 54% had lost more than half of their income, and expected their income to decrease by 70% over the year, and more than 50% were under the age of 35. The precarity of cultural work has long characterized labour in the CCI sector. With non-standard employment in 1970s that introduced contingent work, the increased pressures of flexibility in the new economy of the 1980s, and the rise of the cool economy in the 1990s that glamorized creative work and encouraged young professionals to internalize the risks associated with entrepreneurial labour (Neff et al., 2005), post-Fordist structural transformations of production have led to precarity as a norm of capitalism for increasing numbers of workers and a defining feature of cultural work. While there are debates regarding creative labour, which is viewed as part of the romantic general human condition, and cultural work, which is specifically related to meaning making, identity construction and pleasurable consumption (e.g. McGuigan, 2010), the definition of cultural work as the ‘symbolic, aesthetic or creative labour in the arts, media and other creative or cultural industries’ (Banks et al., 2013: 4) is most useful for this article. Cultural work is experienced as immaterial labour, where labour is that which produces the informational and cultural content, and value is extracted from these cognitive, affective, informational and creative activities in commodity capitalism (Lazzarato, 2003). Such work has become increasingly individualized in the current risk society (Beck, 1992 [1986]) and is sustained in an environment where jobs are frequently short-term, poorly paid, insecure, uncertain and non-unionized (McRobbie, 1998). Usually informal, with long working hours that blur the boundary between work and play, cultural work requires distinct forms of socialization and networking to keep up with new trends, projects and contracts (Ross, 2009). These jobs often attract workers who blend work with identity and creativity, and are more willing to undertake voluntary self-exploitation to pursue what they perceive as their passion (Gill and Pratt, 2008). These features underpin precarity as both ‘ontological experience and labour condition’ (Neilson and Rossiter, 2008: 54) associated with vulnerability and susceptibility to injury (Butler, 2004). Bourdieu (1979) uses the French term précarité to refer to these conditions where there were no work timetables or fixed places of work, and where the search for work was constant, and ‘the whole of life [was] lived under the sign of the provisional’ (1979: 66; see also Standing, 2011). In the CCI sector, precarious cultural work is evident in this shift from open-ended full-time employment to part-time, self-employed and serial portfolio work, with no or little access to health, unemployment, pension and other benefits (McDowell and Christopherson, 2009). The CCI crisis in general, and the precarity of cultural work in particular, have exposed these unequal and unsustainable labour practices. Small organizations and freelancers are most at risk (Betzler et al., 2020). In the UK, 90% of grassroots venues face permanent closure, and 25% of freelancers have stopped working (UNESCO, 2021: 13). A global museum report from 107 countries notes the situation facing freelancers as ‘alarming’ and ‘fragile’, with 56% suspending salary, 39% downsizing firms and 54% considering the future of their firms at risk (ICOM, 2020: 2). Rather than adapt to emergency short-term stop-gap measures such as quick-fix digital training, urgent reform is needed to labour policy, reform that recognizes the ‘flexicurity’ of cultural work (Murray and Gollmitzer, 2012). Adaptation: aesthetic digitalization Mitigation is also evident in the promotion of aesthetic digitalization through digital cultural transformation and platform adoption. This comprises digitization as a technical process for creating new genres in order to facilitate digitalization as an economic process for establishing new business models. Towards the end of the first lockdown in the West, the CCI sector began to develop best organizational practices on how to safely reopen cultural venues, and align with audience sentiments towards online arts reception and their readiness to resume attendance at live cultural events. Governments, non-profit foundations and sector peak organizations commissioned consultancy companies such as A Different View and Panelbase (2020), Patternmakers and Wolf Brown (2020), AEA Consulting (2020), Slover Linett (2020), and TRG Arts (2021) to track audience participation and artistic trends. Common to these reports is the sector's accelerated digital transformation and increased cultural consumption. China's successful and immediate digital pivot furnishes the scope to introduce these new strategies of digital transformation. A few days after the Wuhan lockdown (on 23 January 2020), the National Administration of Cultural Heritage encouraged state-owned and private museums to share their exhibitions online (Mughal and Thomas, 2020). Netizen's online exhibitions on WeChat also concurrently appeared, initiated by the community including interest-based societies, clubs and communities, and showcased people's responses to the pandemic via participatory arts in poetry, calligraphy and craft (Feng, 2020). By February, eight museums had opened to online visitors via live streaming (Yang, 2020). The National Museum of China exhibited ‘Ancient China’ and ‘Dream of Red Mansions’ online, and initiated interactive lecture and audio tours of ancient cultural treasures. Virtual tours of museums in the West were also livestreamed on short video platforms such as Kuaishou. A 90-minute livestream of the British Museum, for example, attracted more than 2 million viewers one Saturday (Bi, 2020). In March, live performances had also begun to go virtual. China's four most popular performance groups, including the comedy cross-talk Beijing Deyun Club, launched programmes on the video platforms Xigua, Toutiao and Douyin (Cai, 2020). While its film industry was the hardest hit, it was also a digital innovator. January coincided with the Chinese New Year Spring Festival holiday season, which is usually the biggest movie-going month of the year and has traditionally seen the release of blockbuster films. To offset this box office loss (which saw the shares of some of its largest exhibition chains such as Wanda Film, Imax China and China Film Company plummet by an average of 25% [Davis, 2020]), the sector launched premium-video-on-demand (PVOD), a new television-on-demand (TVOD) business model that allows customers to access new releases of movies that are also concurrently showing in the cinemas by paying a higher subscription fee. By early February, the market rebounded and popular film-viewing soared on video streaming platforms such as Alibaba's Youku, iQiYI's IQ and Ten Holding's Tencent Video. PVOD quickly reinvented the cinema's ecosystem, stimulated new consumption patterns and, in the following few months, became a dominant global trend. Aesthetic digitalization is evident in these practices of digitizing analogue collections of official and grassroots archives, and sharing them online; curating interactive digital arts tours via livestreaming; virtual live performances; and the introduction of PVOD. Although enabled within the state-sponsored platformatization of the internet in China, with its specific technological affordances, market demands and Party political logic (Yang, 2021), they nevertheless demonstrate how mitigation measures can be quickly and successfully implemented across all CCI funding models, from formal to informal, grassroots-initiated to government-sponsored and commercially facilitated. As new media technology is swiftly harnessed to generate new genres and delivery formats, cultural organizations and workers have also expediently returned to work with little or no delay. These early practices were lauded in a follow-up British Council (2020) report that reviewed similar practices in the Asia-Pacific: for example, in Korea, the livestreaming of dance and theatre performances was conducted via Naver and Kakao digital platforms; in Australia, the Adelaide Symphony Orchestra broadcast new performances online via their ‘Virtual Concert Hall’, and the Biennale of Sydney (the country's biggest festival of contemporary arts) delivered live content; and, in Jakarta's Museum of Modern and Contemporary Art in Nusantara, fintech payment portals like GoPay and OVO were implemented for contactless digital ticketing. In describing these developments as ‘creative’ and pioneered from ‘resilience through innovation’ (British Council, 2020: np), this report unproblematically celebrates creativity as a technical practice of platform adoption. Key to aesthetic digitalization is adaptation as a short-term practice of digital skills acquisition to shore up preparedness. This process, a moebius loop-like movement from origin to disaster (change) and back to the equilibrium of the origin, has become a dominant sector discourse. In the West, connotative terms such as ‘rebound’, ‘restart’ and ‘recovery’ are commonly used by the OECD (2020), UNESCO (2020b) and the European Creative Business Network (2020b) and WTO (Nurse, 2020). In Asia, in Deloitte Singapore’s (2020) impact-readiness report, words such as ‘recover’ and ‘thrive’ are used to develop a ‘readiness frontier’ framework to ‘position for a potential rebound,’ ‘ensure success in the post-COVID-19 world’ and plan towards ‘readiness for the future’ (2020: 3, 7). This framework identifies a ready industry with a gap of ‘0’, and highlights a gap of −22 for adaptability and −12 for resilience for the CCI sector. Supporting the CCI sector's deficiencies using economic indicators such as revenue, operational and supply chain vulnerabilities, this framework underscores the resilience-as-deficit discourse that promotes ‘readiness’ through the hegemonic ‘preparedness for change’ language of technology adoption. Implicit here is also the promotion of platform adoption for sector innovation. In these reports, the ideology of emergency resilience constructs aesthetic digitalization as a techno-centric practice where advanced digital skills are used to furnish cultural production with innovation that will expand value chains through new forms of public–private partnerships such as with big-tech collaboration. Not surprisingly, when China's CCI sector was announced as the world's biggest importer and exporter of creative cultural goods in 2020 (Cui, 2020), its impact was measured through intellectual property earnings derived predominantly from new media platforms. Resonating with the theoretical roots of creativity such as creative destruction (Schumpeter, 1942) and new arts contracts (Caves, 2000), emergency resilience cloaks creativity with a veneer of digital aesthetics that champions the economic reductionism of digital globalization. Digital globalization – by deepening and broadening the connections between people, countries and businesses that have arisen from the expansion and integration of digitization and globalization – presents opportunities and challenges. As a process of ‘rapid increase in both the size and the value of cross-border data flows’, including soft and hardware, services and support systems, it has opened up multiple-directional flows that challenge the Western domination of media and creative cultural industries (Thussu, 2018: 55). Despite accelerated access to and diffusion of knowledge, as well as decentred audience networks and democratized information, crucial here is the role of digital infrastructure as foundational to the economy and quality of life for nation states and their citizens on the one hand, and as central to the agility and user experience of global businesses on the other (Luo, 2021). Arts impact reports, by celebrating platform adoption and economic indicators, have inadvertently prioritized the latter and championed a new financial model based on platform capitalism (Srnicek, 2017). To address the precarity of cultural work and harness the radical praxis of new digital cultural forms, it is necessary to support long-term sector development. This commitment is already emerging in a recent World Trade Organization (WTO) publication that highlights how digital globalization has led to the rise of the digital creative economy and is creating economic value in developing nations (Nurse, 2020). Despite drawing on economic indicators such as trade, copyright data and the fast-rising valuation of Facebook, Apple, Netflix, Google, Spotify and Tencent stocks to support its assertions, it nevertheless calls for a robust governance framework to improve the participation of creative digital entrepreneurship, including the development of a whole-system support structure – from education, skills training and enterprise development to intellectual property rights – that links start-ups, incubators and clusters to end-to-end market entry and business solutions. Although this report does not use the term ‘long-term’ to describe the timescale of this structure, implicit here is a long-term commitment to the development of a CCI sector that nurtures ecological resilience. Ecological resilience: adaptability This section contrasts the cultural sector's practice, policy and ideology of emergency resilience with ecological resilience. It does so by examining alternative cultural policy reports that focus on ‘transition’ and through a sustained case study analysis of the community-based CCI festival, Pink Dot. Drawing on ecological, digital placemaking, and cultural and feminist resilience studies, it highlights the following characteristics of ecological resilience: the timescale of the long-term; a decentred strategy and networked resources; and aesthetic digitization as a practice of adaptability that exposes (in)equality, embeds place and produces community. Instead of the quick mitigation of emergency resilience, ecological creative resilience adopts a long-term view where adaptation is better understood in evolutionary terms (Caputo et al., 2015) and expressed through creative capacities such as learning (preparedness), robustness (persistence), innovation (transformability) and flexibility (adaptability) (Davoudi et al., 2013). It updates common ‘emergency’ understandings of resilience (vulnerability and lack) to focus on ‘emergent’ qualities of benefits that arise from the flexible and creative ability of individuals, groups, communities and systems to persist and transform through change. Characterized by resilience-as-dividend rather than resilience-as-deficit (Yue, 2020), this approach draws from the change mechanism in the ecological resilient system to highlight the threshold as the point where, if the system changes too much and begins to behave in a different way with different feedback that changes the component part and structure, it is said to have undergone a regime shift (Walker and Salt, 2006). Where emergency adaptation is short-term focused, and refers to highly specialized and specific skills, emergent adaptability is enduring, and refers to general flexibility and inherent capacities created through the continuing relationship between individuals and the environment. Resilience-as-dividend demonstrates resilient capacities as creative (rather than reactive) in ways they open up new practices and spaces of change. Ecological resilience is emerging as an alternative discourse in the sector's reopening plans. In IDEA Consult's framework for the European Parliament (European Parliament Think Tank, 2021), the term ‘transition’ is used rather than ‘recovery’ to refer to the rebuilding and repair of the sector (2021: 77). It also uses the term ‘crisis resistance’ instead of ‘crisis recovery’ to highlight transition as a sustainable process of foundational and continual change to structures and practices (2021: 78). This focus on transition supports ecological resilience's change mechanism as the threshold point where the emergence of new structures and practices is also intertwined with the dynamics of co-evolution and self-organization. Where recovery is ‘back to normal’, transition extends beyond a recovery approach to consider ‘repair and prepare’ (2021: 81). Drawing inspiration from the UN 2030 Agenda for Sustainable Development (2015) and UNESCO's (2018) roadmap for remapping cultural policies, transition is a long-term process where CCIs are drivers and enablers of sustainable development through creative and artistic innovation, economic growth and employment, social inclusion, equity and environmental justice. In such an ecology, technological adaptability through digital business innovation intersects with other domains as public health, and business models must also respond to digital exclusion, and individual and community wellbeing. Ecological resilience has always been an enduring trait of the CCI sector. Examining how the UK arts survived and thrived under economic austerity, Pratt (2017) highlights resilience as a socially engaged and change-focused relational strategy located in the composition of the sector, the context of its change economy, and the new kinds of connections forged. He shows how the CCI proliferated not through large organizations or corporations but informal rhizomatic circuits of small firms, businesses and self-employed freelancers. Examining artists’ and organizations’ continual adaptation and transformation of work practices, he stresses they are ‘born resilient’ (Pratt, 2017: 136) rather than having internalized the governance of resilience. Drawing from Pratt's thesis that approaches resilience as a strategy of local embeddedness through decentralized networks of people and organizations, ecological resilience has the capacity to generate new resources (dividend) that can reorganize and contest culture. It recalls feminist theorizations on vulnerability (Butler et al., 2016) and resilience (McRobbie, 2020) that challenge the pastoral patriarchy model of resilience as lacking agency and powerless, and draws on the embodiment of performativity to expose how precarious subjects and contexts are being acted on and acting in relation to their dependency and interdependency on infrastructures, networks and support. The resilience of Singapore's Pink Dot and its digital iteration in 2020–21 evince this. Case study: Pink Dot Pink Dot is Singapore's annual LGBT pride festival-day. The community-based festival is named after the pink colour of the Singapore identity card and its small dot size in the world map. In a country where homosexuality is criminalized and organized public gathering is only permitted (with a licence) at the city-based Hong Lim Park, the festival provides a common public space once a year for LGBTs to come out and express their freedom to love (see Figure 1). It consists of community stalls in the garden, with speeches and pop singers on the stage. The main activity is the evening light-up, when everyone in the park is corralled to gather inside a large circle formation to shine their pink torch lights (provided by the organizer) to the sky for an aerial group photo (see Figure 2). During the light-up, the lit pink dot formation materializes the place, event, people and message. Figure 1. Pink Dot 2010 with LGBTs wearing pink awaiting the light-up. Source: https://pinkdot.sg/2011/06/over-10000-supporters-of-the-freedom-to-love-turn-hong-lim-park-pink-for-pink-dot-2011/, 15 May 2010. Figure 2. Pink Dot 2019 during the light-up with a call to repeal section 377A of the penal code. Source: Mediacorp, https://www.channelnewsasia.com/singapore/pink-dot-calls-acceptance-and-equality-lgbtq-community-1329326, 29 June. Like Pratt's CCI practitioners, Singapore's LGBTs are also ‘born resilient’ having to fight institutional and everyday discrimination, and yet have survived with a visible and enviable queer Asian culture (Yue and Zubillaga-Pow, 2012). Pink Dot has even created a new cultural form that has been exported to other cities such as Toronto, London, New York, Montreal, Penang, Taipei, Utah and Okinawa. Its reverse globalization and disjunctive queer modernity challenges the post-Stonewall rights-based model of emancipation that undergirds Western pride festivals. Over the years its growing popularity has attracted state recrimination that would introduce control mechanisms to diminish its influence, such as the banning of foreign residents from attending, and foreign companies like Apple, Microsoft and Facebook from sponsoring the event. Conservative public discourse has also emboldened religious groups to counter-protest at the event, such as in the mass turnout of Christians and Muslims under the Wear White Movement in the mid 2010s. Ecological resilience is evident in Pink Dot's longevity: its persistence to endure, survive and thrive despite the illegality of homosexuality. It started with 2500 people attending in 2009, rose to 26,000 in 2014, and even reached 20,000 in 2017 when foreign participation and sponsorship were banned. It highlights the long-term commitment in its fight for LGBT recognition. In the face of escalating state suppression, the festival has not once been cancelled, and has even continued to evolve. Pink Dot's adaptability relies on a decentred strategy and a network of resources: community leaders and volunteers, paid and pro-bono artist-performers (some of whom are successful mainstream practitioners like award-winning filmmaker Boo Junfeng, the resident photographer of the iconic ‘pink dot’ press photo), private donations from individuals and local companies, and an audience-base of resident-citizen LGBTs and their allies. In a country where participatory and community arts are predominantly state-sponsored, Pink Dot evinces a novel approach to producing and organizing culture. Significantly, it is also embodied and embedded in its practice, with the mass wearing of pink attire, mass gathering and mass light-up, and the coming together at Hong Lim Park to celebrate the park's gay history of cruising and to subvert its current branding as an eco-hotel tourist precinct (see also Yue and Leung, 2017). Pink Dot exemplifies placemaking through these subjugated histories and communities, and exposes the politics of place, people and identity with its carnivalesque ritual. Through its transnational and sub-national commercial and community partnerships, and via its socially engaged mode of participatory arts, Pink Dot demonstrates resilience-as-dividend. Decoupled from governance and neoliberal public management that marks hegemonic emergency resilience, it shows how ecological resilient capacities can generate a resource pool of social and cultural capital to build and maintain an enduring queer cultural institution that has evolved with its decentralized networks of sponsors, organizers and patrons. Its performativity makes explicit the vulnerabilities that accompany continued minority subjugation and suppression. Its radical praxis of aesthetic digitization is evident in 2020–21 when Pink Dot, like all cultural events, took place online. While songs and speeches are pre-recorded and livestreamed, the most distinct is the digital light-up. During the two-hour plus event, audiences who wish to participate in the digital light-up are invited to log in, enter their postcode, and write a support message. Instead of a still photo that captures the spatial enclosure of LGBTs squeezed into Hong Lim Park, the digital map is a map of Singapore linked to its postcodes. Rather than a pink dot composed of people shining their pink-lit torches in the park, the digital pink dot is a Singapore map of pink-lit geo-tagged postcodes. Postcodes in Singapore are classified according to the street address of apartment blocks, and in a country where 95% of the population live in high-rise blocks (DSS, 2021), each postcode is singular and unique to one block. In the digital map, any blocks with a registered participant will be lit up. The post light-up map even allows interactive close-ups into any districts or blocks to read individual support messages tagged to participants’ postcodes. With the digital iteration's ‘Love Lives Here’ theme, the digital Pink Dot map shows participants coming from all over the island (see Figure 3). Figure 3. Digital Pink Dot 2021 where more than 15,000 pink dots lit up across Singapore. Source: https://pinkdot.sg/2021/06/pink-dot-13-over-15000-pink-dots-light-up-across-singapore/, 12 June. Where Hong Lim Park encloses, exceptionalizes and minoritizes LGBTs in a ghetto, digital Pink Dot enlarges, naturalizes and materializes the aphorism that ‘LGBTs are everywhere’. Where the former is physical, abject and minuscule (the park is only 9400 square metres in size), the latter is virtual, expansive and nation-wide. It is not the actual–virtual divide that reveals the conditions of subjugation; rather, it is the virtuality of the event that makes visible its infrastructural relations that construct the hegemonic conditions of subjugation. This is evident in the event's digital placemaking practice. Digital placemaking does not simply embrace technological adoption as an aesthetic veneer to buttress the actual event; it uses digital technology to impact the urban experiences of place, identity and belonging (Toland et al., 2020). While the digital light-up's interactivity makes the event embodied and participatory, it is in its cultural somatics, a process of suturing technology, body, place and community together with the virtual information on the screen (digital Pink Dot) and the past actual information from the park (past Pink Dot), that produces the virtuality of the event and its experience of identity, place and belonging. This is evident when its post-lit map celebrates the real spaces (postcodes) in the country where LGBTs live, and exposes at the same time how relational infrastructures (including laws, parks and high-rise blocks) are governed to exclude LGBTs from recognition, access and rights. In enacting and performing resilience, the post-lit map also reveals how these infrastructures (apartments, neighbourhoods and parks) are everyday intimate tactical spaces of habitation, dwelling, cruising and love. Its aesthetic digitization demonstrates ecological resilience as an iterative process of creative transformation that reveals entrenched heteronormative subjugation, social exclusion as well as an embodied practice of local embeddedness and networked belonging. Conclusion This article has critically examined how the Covid-19 public health crisis is also a CCI crisis by introducing three characteristics that socially construct the CCI crisis and its hegemonic ideology of emergency resilience. First, CCI crisis time has generated arts impact reports to enumerate a large segment of globally unemployed cultural workers. Second, connotations of disaster and lack, and a narrative of unpreparedness, have framed the CCI crisis discourse to reveal the sector's unsustainable model of work. Third, the CCI crisis is mitigated by short-term adaptation: through business relief support that exposes the precarity of cultural labour, and aesthetic digitalization that favours the platform capitalism of digital globalization. Multiple discourses and institutions – from the technologies of disaster management and cultural statistics to corporate financial modelling, and from global cultural organizations to transnational commercial consultancies – construct this conjunction of resilience-as-deficit, and give warning that a post-pandemic cultural policy must incorporate the securities of labour and social policy, and foster an enduring commitment to cultural economic recovery. This article further proposed rethinking emergency resilience by critically developing three characteristics of ecological resilience. First, a focus on the timescale of the long-term as a process of transition; second, as a decentred strategy and networked resource decoupled from neoliberal governance; and, third, through aesthetic digitization as a radical praxis of adaptability. Drawing on ecological, feminist and cultural studies approaches to resilience and demonstrating this with Pink Dot, it argued these characteristics support resilience as emergent (not emergency), iterative (not rebound) and transformative (not back to normal), and furnish new resilience-as-dividend practices for building the capacity of a wholistic cultural ecology that can nurture fair work, artistic innovation, economic growth and cultural vitality. In Asia, where cultural digitalization is accelerated, and in Singapore, where marginal groups like LGBTs confront continued persecution, ecological resilience further presents opportunities for formal and informal CCIs to thrive sustainably and inclusively. Author Biography Audrey Yue is Professor of Media, Culture and Critical Theory, and Head of the Department of Communications and New Media at the National University of Singapore. Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. ORCID iD: Audrey Yue https://orcid.org/0000-0002-1043-4270 1. 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==== Front Maastrich J Eur Comp Law Maastrich J Eur Comp Law MAA spmaa Maastricht Journal of European and Comparative Law 1023-263X 2399-5548 SAGE Publications Sage UK: London, England 10.1177/1023263X221077006 10.1177_1023263X221077006 Article Extremely urgent public procurement under Directive 2014/24/EU and the COVID-19 pandemic https://orcid.org/0000-0002-0666-6351 Telles Pedro * * 4300 Copenhagen Business School , Frederiksberg, Denmark Pedro Telles, Copenhagen Business School, Frederiksberg, Denmark. Email: pt.law@cbs.dk 7 4 2022 7 4 2022 1023263X221077006© The Author(s) 2022 2022 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. The COVID-19 pandemic swept throughout the European Union swiftly and led to significant changes in how we live and operate. Some of those changes occurred in public procurement as well, with Member States struggling to react to the dissemination of the virus. The purpose of this paper is to assess what scope the EU's public procurement legal framework provides to deal with a crisis, and how the rules should be interpreted. This paper will show how the EU public procurement legal framework deals with extreme urgency situations and how it has been intentionally designed to allow Member States flexibility within very clearly defined boundaries. This means that the path to award contracts without competition on the grounds of extreme urgency is narrow due to Article 32(2)(c) of Directive 2014/24/EU1 and the case law from the CJEU. The narrowness of this path is due to the exceptional nature of procedure and the obligation for the contracting authority to discharge the tight grounds for use in full for every contract. Therefore, this paper concludes that the view exposed by the European Commission on its guidance from April 2020 that the pandemic is a single unforeseeable event amounts to an incorrect reading on how the grounds for the use of Article 32(2)(c) operate. If such interpretation was already too broad in April 2020, it certainly is no longer in line with the transition from an unfolding crisis into a new and more permanent equilibrium. In the context of COVID-19, particularly the need for the crisis to be unforeseeable and the extreme urgency not being attributable to the contracting authority raise significant difficulties for some contracting authorities to discharge the grounds for use of the negotiated procedure without prior notice. This is particularly the case in those situations where governments centralized pandemic-related procurement. As such, the paper concludes that existing substantive rules for extremely urgent procurement are adequate and, albeit sufficient to respond to crisis situations, that does not entail that the wholesale use of the negotiated procedure without prior notice is necessarily legal. COVID-19 EU public procurement emergency procurement edited-statecorrected-proof typesetterts19 ==== Body pmc1. Introduction Within the EU, how public bodies from Member States enter into public contracts is subject to specific rules which have to be complied with under the penalty of a contract being annulled by the courts. These legal obligations originate from EU primary law, namely principles from the Treaty on the Functioning of the European Union, such as equal treatment, non-discrimination, mutual recognition and transparency,2 as a means to achieve ever deeper integration and the single market. These have then been densified in secondary legislation via successive rounds of Directives since the late 1960s.3 The EU's secondary legislation on public procurement aims to ensure free and equal access to economic operators irrespective of where they are based in the Union, so that they can compete for contracts without being discriminated against. Therefore, they aim to help complete the EU's internal market and therefore EU public procurement rules establish that public contracts are to be awarded in accordance with principles such as transparency, competition4 and equal treatment. As such, the rules impose certain restraints on contracting authorities’ behaviour and restrict their margin of discretion5 by means of defining procedures and obligations to be followed before a contract is awarded and entered into. For example, contract opportunities need to be published in advance so that they are transparent and visible to economic operators, thus potentially generating more competition and guaranteeing equal opportunities for any economic operator that may be interested in taking part. The EU's current substantive public procurement legal framework6 is divided into three different Directives that cover different types of public contracts and contracting authorities. Directive 2014/24/EU7 is the main public sector Directive regulating how public bodies are to deal with works, goods and services contracts. Directive 2014/23/EU8 applies to concessions and Directive 2014/25/EU9 to the utilities sector.10 For this paper's purpose, the analysis will be focused on Directive 2014/24/EU since it covers the purchase of works, goods and services most affected by the COVID-19 crisis, that is, in the healthcare sector. 2. The general procurement rules of Directive 2014/24/EU Within Directive 2014/24/EU we can find two main legal regimes directly relevant for the healthcare sector. First, the general rules of Title II (Articles 25–73) and then the special rules for social and other services of Title III, Chapter I (Articles 74–77). Overall, the former crystallizes key principles such as transparency, competition and equal treatment, whereas the latter (partially) derogates from said principles under specific circumstances. Furthermore, the special regime only applies to services contracts and not to works or goods, which appear to be the most common areas of procurement connected with COVID-19 where general rules have not been observed. As such, the subsequent analysis will be focused on the general rules and in particular on the extreme urgency exception of Article 32(2)(c). Directive 2014/24/EU establishes the open and restricted procedures11 as the default procedures to be used for the award of contracts and it is no surprise that they have been branded as the ‘gold standard’ for procurement. A trade-off with the level of protection for the principles they afford is their long lead in times that may span multiple months between start and contract award. Therefore, the Directive recognizes that there may be justifiable situations where the contracting authority needs to depart from them, either because some special procedures could be used instead, or time pressures call for quicker turn arounds. For these situations, the Directive rightly includes a suite of cascading options to be adopted depending mostly on the grounds leading to the urgency of the situation. The more urgent the situation, the more derogation can be achieved from the general rules and procedures but at the expense of compromising the main features of the system and introducing ever more risks to the principles as we move further away from the general procurement procedures.12 The first step in this cascade is to adopt accelerated versions of the open and restricted procedures,13 which the CJEU has considered multiple times as the legal solution if the timescales allow for their use.14 These require urgency and said urgency needs to be duly substantiated by the contracting authority, effectively burdening it with justifying the choice. As the only change here are shortened timescales, these procedures represent a minor restriction to the principles established by the Directive. It is thus logical that the bar to clear by the contracting authority is set reasonably low. This is not the case when the need justifies more significant exceptions to the general rules and procedures, namely the recourse to awarding contracts directly via a negotiated procedure without prior publication, which is the focus of the present paper. 3. Negotiated procedure without prior publication This procedure is the antithesis of legal procurement framework described above and can only be used in the specific cases and circumstances set forth in Article 32 of Directive 2014/24/EU. Whereas the other procedures strive to protect transparency, equal treatment, non-discrimination and competition, the negotiated procedure without prior publication of Article 32 does the opposite by providing significant discretion to the contracting authority to award contracts without being constrained by such principles. Since the whole procedure is carried out privately until the contract award information is published, there is no transparency in the sense of a contract opportunity and its rules being made public in advance. There are no guarantees of equal treatment between participants or potential participants. As for competition, since the contract can be awarded directly to an economic operator, competition is also not observed. It is no surprise then that this procedure appears at the end of the cascading mechanism of exceptions to the general procurement rules and their standard procedures. While open and restricted procedures are the standard procedures that can be used by a contracting authority in any circumstance, the negotiated procedure without prior publication is an exceptional procedure.15 This classification is not without consequence, since the exceptional nature carries with it an obligation to narrowly interpret its grounds16 to use and with the burden of proof resting in the party invoking them, as recognised by the CJEU in case C-250/07 Commission v Greece.17 Article 32(2) of Directive 2014/24/EU18 includes three sets of reasons for the adoption of a negotiated procedure without prior publication. It can be used when one of the standard or special procedures was attempted and failed,19 when only a single economic operator can supply the works, goods or services,20 or when extreme urgency so requires.21 For the purposes of the current paper and how to deal with the COVID-19 crisis, it is this latter set of grounds of use that is relevant. 4. Negotiated procedure without prior publication due to extreme urgency (Article 32(2)(c)) The grounds included on paragraph 32(2)(c) are usually described as ‘extreme urgency’ grounds and have been – correctly – described as a ‘get out of jail’ card for the general procurement rules,22 but that is not to say this card is free or not subject to stringent conditions to be exercised. The paragraph is dense and composed of multiple layers of requirements that need to be met in full so that the negotiated procedure without prior notice may be used. In addition, these grounds for use need to be met for every single contract awarded by this procedure. As such, even in the context of an unfolding crisis like the response to COVID-19,23 it cannot be considered that the requirements for use of the procedure are automatically complied with simply because there is an overarching crisis to respond to. This is a crucial point that will be addressed when discussing the Commission's Guidance from April 202024 in section 5. The three grounds for use of the negotiated procedure without prior notice are (i) strict necessity, (ii) unforeseeable extreme urgency and (iii) extreme urgency not being attributable to the contracting authority. As mentioned above, it is up to the contracting authority as ‘beneficiary’ of the procedure to discharge the burden of evidence associated with all the requirements. These grounds are to be assessed ex-ante, that is, based on the information the contracting had (or should have had) considered at the time the decision was taken and not with information which was made available afterwards or with the benefit of hindsight. This is necessary to set the correct threshold for the burden of evidence the contracting must discharge for each requirement. A. Strict necessity The first requirement is that the intervention, in this case the adoption of a negotiated procedure without prior publication, passes a strict necessity test.25 To do so it must be the least damaging way to achieve the necessary outcome. This means that only the smallest exception necessary to solve the problem at hand will meet the strict necessity test. This is a logical requirement due to the exceptional nature of this procedure and the possibility of using alternatives less damaging for the principles of transparency, equal treatment and competition. It is posited that this strict necessity requirement applies not only to the moment when the decision to use the negotiated procedure itself is made, but also subsequent decisions on how to configure it in practice. Therefore, even if it is legal to use the negotiated procedure in theory, the actual application of it is still subject to a strict necessity test. Therefore, if the need of the contracting authority can be met in time by consulting three economic operators instead of directly awarding the contract to an economic operator without competition, only the first will comply with the strict necessity test. It is debatable if such strict necessity makes sense in the context of an emergency since it can generate legal uncertainty and reduce the flexibility available to contracting authorities,26 but the fact of the matter is that it is the law in force. Furthermore, if one looks at the strict necessity requirement from a systemic perspective approach of its place in the procurement legal framework then it makes perfect sense since it is a complete departure from what said legal framework is trying to achieve. As such, it works as an inbuilt safety mechanism to ensure contracting authorities are not using the flexibility afforded by the exception for situations where it is not needed. And in fact, looking at the bulk of the CJEU's case law on the use of this procedure indicates a clear care in restricting the role of this procedure.27 B. Unforeseeable extreme urgency The second requirement is one of extreme urgency brought about by events unforeseeable by the contracting authority.28 This requirement is to be divided into two sub-requirements: the extreme urgency in and of itself and the events being unforeseeable. Taken together, these two sub-requirements constitute the core of the reasoning to justify the use of the negotiated procedure without prior publication by a contracting authority. As for the extreme urgency, the contracting authority needs to be in a situation where the issue requires an immediate solution which would make the use of other procedures such as the accelerated open or restricted procedures unsuitable. For the use of the negotiated procedure on the grounds of Article 32(2)(c), a solution is needed immediately and not sometime in the future.29 It is this immediateness that justifies the use of the negotiated procedure because any other option will not be able to solve the contracting authority's issue in time.30 As such, it is not possible to use the negotiated procedure to either solve a future problem or to solve a current one in the future in case any other procedure less damaging to the general principles is suitable. The negotiated procedure is instead to be used to serve a need now, when time is of the essence to solve an existing problem. In consequence, using an ex-ante analysis it is not possible to use this procedure in situations whereby the product being procured will not be available in time to answer to the emergency. In that scenario the correct procedure would either be one of the accelerated procedures (if the requirements are met), or one of the general ones if they are not. This leads to the view that immediate procurements undertaken to safeguard uncertain future needs31 does not fit within the legal definition of extreme urgency of Article 32(2)(c). The second sub-requirement is that of the urgency being unforeseeable by the contracting authority, thus leading to the question of how compliance with this requirement should be assessed. To reach a conclusion, one must consider if the unforeseeable nature of the urgency is subject to the reasonably diligent test set forth by the FastWeb judgment.32 Although FastWeb is a case about Article 31(1)(b) of Directive 2004/18 and therefore the use of negotiated procedure without prior notice due to exclusive rights, the reasonably diligent test it applies to establish if the grounds for use of the procedure are met is relevant in this instance as well. It is fair, however, to say that the threshold for what constitutes a diligent behaviour in a situation of extreme urgency needs to be lower from that of a situation where the other grounds for the negotiated procedure without prior notice is being used. Having said that, if the contracting authority acts diligently, it can avail itself of the procedure, otherwise it cannot do so, and any resulting contract is ineffective. In addition, the Court held in FastWeb that the contracting authority must set out in the justification of choice of the procedure ‘must disclose clearly and unequivocally the reasons that moved the contracting authority to consider it legitimate to award the contract without prior publication of a contract notice’,33 effectively meaning that it is crucial for the justification to be provided and in full in the contract award notice so that interested parties may have recourse to the appropriate remedies. Even in a situation of urgency, it does not make sense to obviate the need for proper justification of the reasons that led to the use of the negotiated procedure without prior notice. In practice, what that means is that how the contracting authority acts in the run-up to a decision to use a negotiated procedure without prior publication has an impact in the legality of such decision.34 That is not to say that there is no scope to use it, only that the contracting authority must provide evidence it acted in a reasonably diligent way. This requirement naturally goes beyond simply claiming an extreme urgency exists and that it was unforeseeable by the contracting authority, therefore rendering moot the idea that simply because of the unfolding COVID-19 crisis it would immediately justify the use of this procedure. It has been argued that Member States enjoy a margin of discretion with regards to policy decisions where risk is involved, therefore the risk/harm threshold should not be unreasonably high.35 Whereas it is indeed the case that Member States do enjoy a degree of discretion when undertaking a risk analysis, the second element is a step too far in this regard. First, the unforeseeable extreme urgency is to be assessed on a case-by-case basis, thus implying that for each contract (and each contracting authority) a specific assessment must be carried out. Second, whereas the Member State may have such discretion at a policy-making level, it is not relevant when public bodies (with or without such policy-making powers such as ministries) are operating as a contracting authority awarding contracts. In this context they are constrained instead by the requirements of Article 32(2)(c). The above shows that the unforeseeable nature of the extreme urgency consists of an objective-subjective test to be discharged and not a purely objective one. It is objective because the cause of urgency must be in general terms be passable as being unforeseeable. But it is subjective as well since it depends on the behaviour of the contracting authority and whether it has acted diligently or not. Therefore, it is conceivable that the same situation may be ‘unforeseeable’ for one contracting authority but not for another, depending on the information available to and behaviour of each contracting authority. C. Extreme urgency not attributable to the contracting authority The next requirement set forth on Article 32(2)(c) relates to the attribution for the situation of extreme urgency. The logic behind this requirement is self-evident and is a defence against actions (or inactions) from the contracting authority which bring about the situation of extreme urgency. It thus implies defining how attribution is to be assessed and which party bears the burden of proof for it. As established above, the burden of proof needs to be discharged by the contracting authority benefiting from the negotiated procedure, and this obligation applies here as well. This sub-criterion is distinct from the previous one as while the other focus on the underlying situation is unforeseeable, this one applies instead to the behaviour of the authority creating or not creating the situation of extreme urgency in itself. Therefore, it is possible for an event to be unforeseeable under the previous criterion and still fail in the urgency being attributable to the contracting authority. For example, an unexpected storm hits a city, damaging a bridge which is not assessed by the local authority. The local authority only assesses the bridge 6 months later in accordance with its regular maintenance schedule, by which time the bridge is now in immediate risk of collapse and needs to have urgent repairs. In this scenario the damaging event is unforeseeable (the storm) but the extreme urgency for the repairs is attributable to the contracting authority since it did not act in due time immediately after the storm. Article 32(2)(c) is silent regarding how the attribution requirement is to be assessed, that is to say, what test is needed to consider for compliance. It is posited here that this test can be laid down and discharged via two different mechanisms. The first is based on non-contractual or tortious liability, that is by looking at intention and negligence. In this regard, there is no reference in Article 32(2)(c) for the need for intent by the contracting authority in the creation of the extreme urgency situation. Bearing in mind the objective of the provision, the logical conclusion here is that the attribution requirement does not depend on it but simply upon mere negligence by the contracting authority bringing about the urgency. In addition, this is the only possible interpretation compatible with the obligation of a restrictive interpretation as required for the use of an exceptional rule. Otherwise, the exception is not as narrow as it could be while still achieving its goals. At first sight, this seems like an obvious solution to determine the test, but it is arguable that liability is assessed after the grounds for it have checked and in this way one would pre-judge liability, just as in the case where the grounds have not been met. The other option is to look into Article 32(2)(c) and in particular the requirement analysed in the previous section, that is, the unforeseeable nature, specifically the diligence test. In both cases what is important is the behaviour of the contracting authority in the lead-up to initiating a procedure and as such they share similarities. Whereas in the unforeseeable nature we assess the behaviour vis-à-vis the event that gives origin to the urgency, in this one we look instead into the behaviour in the run-up to initiating the procedure, irrespective of the existence of an event that brought about the extreme urgency and whether it was foreseeable. Therefore, it is posited here that the diligence test from FastWeb could be applicable here. The use of a diligence test in this regard allows us to assess, for example, how the risk arising from an event has been considered and whether the contracting authority's behaviour has discharged its duty in this regard. There is a concern, however, that the contracting authority would be put in the position of proving a negative in the context of the extreme urgency not being attributable to it since it carries the burden of proof.36 In reality, the test is not negative but positive, as the contracting authority's threshold is to prove it acted as a reasonably diligent contracting authority,37 which means looking into the actions it took. In consequence, the logical conclusion is that a contracting authority which cannot prove it was reasonably diligent will be considered to have failed the attribution test. The attribution test is relevant only within the confines of a single contracting authority as the text of Article 32(2)(c) defines it. It would make no sense for a contracting authority to be deprived of the use of an exceptional tool for the negligence of another contracting authority. In normal times, the behaviour of individual contracting authorities is easily firewalled by the operation of Article 32(2)(c) and the list of Annex II to Directive 2014/24/EU. Every body contained there is an individual contracting authority for the purposes of determining the attribution of extreme urgency. Usually, public procurement of medical equipment or supplies is done either centrally by the Health Ministry (national or regional) or locally in hospitals or commissioning groups, depending on how each Member State organizes its healthcare sector. Therefore, defining the contracting authority for the purposes of the attribution test in normal times tends to be easy, and to map out its competencies and how they were used seems unproblematic as well. During an unfolding health crisis like COVID-19, however, we have seen procurement being done very differently, with a rush to centralise procurement activities38 and, crucially, contract award decisions. Spain re-centralised the health sector which was hitherto a devolved competency and Italy moved at least some local purchasing responsibility to some regional and national bodies.39 Others, like Ireland40 and Germany,41 appeared to have centralised at least some key decisions at government level based on direct dealings between government members and their equivalents in countries which could sell them the equipment needed.42 The implications of this centralisation for the use of the negotiated procedure by governments are significant, since all decisions taken by the contracting authority need to be taken into account to assess whether the extreme urgency is attributable to it or not. This means that even decisions taken outside the context of a procurement procedure are relevant, thus including political or wider public health decisions. This is the logical reading of the requirement since it does not limit the scope of decisions to be assessed to any given nature. In addition, this is also the only reading compatible with a narrow interpretation of the grounds for use as required by the exceptional nature of the procedure. In consequence, policy decisions related to allocating resources even when taken under the guise of a risk analysis43 are subject to the restraints imposed by the grounds of the procedure. It has been argued, however, that political decisions cannot be syndicated in the context of public procurement and, in consequence, cannot be used to determine the attribution requirement.44 Such view can be set aside based on multiple arguments. First, what is being syndicated in this context of the attribution is the procurement decision and not the political considerations behind it. The latter continue to produce their effects, but it is possible that for the purposes of the legality of the use of the negotiated procedure they may not assist in meeting the attribution criterion. Second, political decisions in themselves are syndicated in terms of compliance and they do not obviate existing legal obligations. In fact, there is no discussion that even political decisions at legislative or administrative levels are also subject to judicial review, and that is a key tenet of the rule of law. Third, while a decision may be relevant to assess if the attribution criterion is met or not, trying to purge such decision from the analysis seems artificial and contrary to the letter and spirit of Article 32(2)(c) since the article includes no such limitation. Finally, this is an exceptional procedure and one which requires a narrow interpretation of its grounds. If one were to accept that political decisions would be set aside from scrutiny for the purposes of determining the attribution requirement, then potentially the scope of the interpretation (and application) of the procedure would not be as narrow as it could otherwise be. In consequence, such interpretation would be illegal. 5. The Commission's guidance on COVID-19 related procurement The European Commission published in early April 2020 a guidance document on procurement related to COVID-19.45 In this document the Commission attempted to explain what different procurement options were available to contracting authorities reacting to the COVID-19 crisis. For the most part, the Commission stayed close to the letter of the law in relation to Directive 2014/24/EU and to (some) case law as well. The guidance sets out how contracting authorities should choose between the different procurement mechanisms available to them, starting from the general procedures and moving on firstly to the accelerated procedures and then, only if these are not suitable, to the negotiated procedure without prior notice. In this context, the Commission correctly mentions that each negotiated procedure needs to be justified via an individual report.46 It is thus surprising that the Commission did not take the idea of justifying each and every use of the procedure to its logical conclusion, stating instead that ‘[p]recisely for a situation such as the current COVID-19 crisis which presents an extreme and unforeseeable urgency, the EU directives do not contain procedural constraints’, thus effectively negating the application and verification of requirements needed to establish the extreme and unforeseeable urgency. Taken as a single sentence, perhaps the correct interpretation might be different, but further ahead the Commission exposes its views in more detail:47These events and especially their specific development has to be considered unforeseeable for any contracting authority. The specific needs for hospitals, and other health institutions to provide treatment, personal protection equipment, ventilators, additional beds, and additional intensive care and hospital infrastructure, including all the technical equipment could, certainly, not be foreseen and planned in advance, and thus constitute an unforeseeable event for the contracting authorities. [emphasis by the author] Herein lies the crux of the matter with the guidance since it is evident that the Commission is arguing that reacting to the pandemic could not be planned in advance and that it constitutes an unforeseeable event. This is a misguided view of how the rules of Article 32(2)(c) operate and at least as regards what concerns governments, that could not be farther for the truth for four reasons. The Commission's view is misguided because the COVID-19 pandemic cannot be subsumed into a single event that would lead to any procurement decision connected with it being considered as unforeseeable. However, all elements of Article 32(2)(c) must be individually assessed and present for every single contract awarded under this exceptional regime since they depend on objective and subjective elements as mentioned above. There is no provision in Article 32(2)(c) for a blanket approach such as that professed by the Commission and others.48 Every single procurement decision taken in the context of the COVID-19 is a reaction to the specificity of each contracting authority at that moment in time and needs to be assessed as such. This also means that our analysis of the unforeseeable nature and the non-contribution to the emergency will depend on the nature of the contracting authority and the moment the grounds of Article 32(2)(c) are to be assessed. As mentioned above, in general, the answer for procurement decisions taken by governments will naturally be different from those taken elsewhere in any given health sector. As for the remaining reasons why one cannot assume that governments could not foresee the pandemic, first, countries do plan for pandemics and, in fact, for example, Lithuania had since 2010 a State Emergency Plan covering pandemics49 and the UK carried out multiple pandemic exercises in 2016, one of them based on the Mers virus50 and another on a simulated variant of influenza.51 The question here is really what we consider either to be a negligent or diligent behaviour by contracting authorities depending on what information they have had access to or should have had access to, particularly as regards what concerns governments. Therefore, at least for these as contracting authorities it is obvious that a pandemic is predictable and should be planned for in advance. That does not mean of course that all eventualities can be planned in detail, and for those the procedure of Article 32(2)(c) is still available if all grounds are met. That takes the negotiated procedure without prior notice to its correct realm of application, that is, the exceptions that do not fit in the pre-existing pandemic plans. Not having planned at all or not having acted on the existing plans is thus a contributory fact by governments to create the situation of urgency when they act as contracting authorities, irrespective of the adoption of the negligent or diligent behaviour test for attribution. Second, in response to the H1N1 pandemic influenza of 2009 the Commission set up in 2014 a Joint Procurement Agreement (JPA) for medical countermeasures to which most (though not all) Member States were party at the beginning of 2020.52 This, in fact, constitutes a degree of planning for future medical emergencies, therefore amounting to a diligent measure to handle a situation such as that of the COVID-19 pandemic. However, not all Member States joined the JPA in 2014, with some only deciding to do so already in 2020, indicating a difference in diligence amongst the Member States.53 In February 2020, some Member States were already asking the European Commission to activate the JPA for the purchase of medical supplies,54 thus indicating they were aware of the risks for public health arising from the novel coronavirus and foreseeing the need for medical supplies. Again, this can be interpreted as a measure of diligence by the Member States involved and, in consequence, lack of diligence by those who did not do as such. The third is the actual delay between the news arising from China in the beginning of the year and the arrival of the COVID-19 pandemic in Europe a couple of months later. The signs were there to see and be acted upon in good time to avoid making matters worse. That included acting regarding the procurement of medical equipment. However, the difference in behaviour between Taiwan, which was exposed to the SARS epidemic a decade ago, and most EU Member States is striking. As far as could be established for this paper, for the purchasing of personal protective equipment (PPE) only Ireland reacted quickly, by centralizing the purchasing of PPE in March and bought directly from China for immediate delivery of 13× the annual spend in those items.55 As such, it is logical to conclude that Ireland acted diligently (and swiftly) to react at least in part to serve its immediate needs for medical supplies. As for contracting authorities other than the Health Ministries in Governments, such as hospitals, the answer is indeed more complex and turns on the actual circumstances of each authority. Did they take part in pandemic planning? Were they under the obligation to have pandemic response plans? What were their resource levels in the run-up to the first pandemic wave? It is impossible to be as prescriptive as with Governments which have competences in the field of public health. For these and barring any specific information stating otherwise vis-à-vis the information they had access to, the Commission's guidance was indeed helpful at the time it was issued. But for governments acting as contracting authorities who deployed the negotiated procedure without notice based on the guidance, it helpfully provided a degree of legal cover to the Member States who could point out to it as a justification for poor or illegal procurement practices in the context of COVID-19 procurement. In conclusion, each pandemic will always be different from the previous ones and require some decisions at governmental level that will necessarily be different as well, with some fulfilling the requirements of Article 32(2)(c) on a case-by-case basis. However, that is different from taking a blanket approach that any procurement decision taken in the context of COVID-19 will be excused from the checks required by such article. 6. Conclusion This paper has shown how the EU public procurement legal framework, while allowing Member States flexibility on how to react to a crisis such as that of COVID-19, does so with very clearly defined boundaries. In the context of the negotiated procedure without prior notice on grounds of extreme urgency, Article 32(2)(c) and the case law from the CJEU provide a narrow path to its use. This path is narrow due to the exceptional nature of procedure and the grounds for use which are to be discharged in full by the contracting authority. In the context of COVID-19, particularly the need for the crisis to be unforeseeable and the extreme urgency not being attributable to the contracting authority raise significant difficulties for some contracting authorities, namely governmental departments with public health responsibilities. The Commission issued guidance on the topic in April 2020, but it is posited that the interpretation of the extreme urgency grounds provided there is incorrect since it creates a general presumption of compliance with the requirements which does not find support in the actual text of the Directive. In addition, even if a generous interpretation of the grounds could make sense in early 2020, it certainly does not do so at the time of writing (December 2021) and it is time the guidance was either revoked or amended. As of late 2021 the COVID-19 pandemic has been running for almost two years, effectively meaning that we are no longer in a situation of crisis but instead on a new permanent equilibrium where our lives changed due to the challenges imposed by the SARS-COV2 virus. Since the requirements for use of the negotiated procedure without prior notice for urgency in Article 32(2)(c) depend on foreseeability and the contracting authority not contributing to the urgency, it is relevant to enquire how the grounds for extreme urgency are to be assessed today. Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) received no financial support for the research, authorship and/or publication of this article. ORCID iD: Pedro Telles https://orcid.org/0000-0002-0666-6351 1 Directive 2014/24/EU of the European Parliament and of the Council of 26 February 2014 on public procurement and repealing Directive 2004/18/EC, [2004] OJ L 94. 2 On the principle of transparency see I. Georgieva, Using Transparency Against Corruption in Public Procurement: A Comparative Analysis of the Transparency Rules and their Failure to Combat Corruption (Springer, 2017) and K. Halonen, R. Caranta and A. Sanchez-Graells, Transparency in EU Procurements: Disclosure Within Public Procurement and During Contract Execution (Edward Elgar, 2019). 3 On these early Directives see, P. Trepte, Regulating Procurement: Understanding the Ends and Means of Public Procurement Regulation (Oxford University Press, 2004), p. 342 and P. Telles, ‘Procurement Financial Thresholds in the EU: the Hidden Relationship with the GPA’, 3 European Procurement and Public Private Partnerships Law Review (2016), p. 205. 4 On the principle of competition see, A. Sanchez Graells, Public Procurement and the EU Competition Rules (2nd edition, Hart, 2015). 5 On discretion in public procurement see S. Bogojevic, X. Groussot and J. Hettne (eds), Discretion in EU Public Procurement Law (Bloomsbury, 2020). 6 For a general overview of EU public procurement rules see, A. Semple, A Practical Guide to Public Procurement (Oxford University Press, 2015) and C. Bovis, The Law of EU Procurement (2nd edition, Oxford University Press, 2015). 7 On this Directive see, M. Steinicke and P. Vesterdorf (eds), Brussels Commentary on EU Public Procurement Law (Bloomsbury, 2018); R. Caranta and A. Sanchez-Graells, European Public Procurement: Commentary on Directive 2014/24/EU (Edward Elgar, 2021) and S. Treumer and M. Comba (eds), Modernising Public Procurement: the Approach of the Member States (Edward Elgar, 2018). 8 Directive 2014/23/EU of the European Parliament and of the Council of 26 February 2014 on the award of concession contracts, [2004] OJ L 94/1. For an overview of the Concessions Directive see P. Bogdanowicz, R. Caranta and P. Telles (eds), Public-Private Partnerships and Concessions in the EU (Edward Elgar, 2020). 9 Directive 2014/25/EU of the European Parliament and of the Council of 26 February 2014 on procurement by entities operating in the water, energy, transport and postal services sectors and repealing Directive 2004/17/EC, [2004] OJ L 94/243. 10 In addition, Directive 2009/82/EC of the European Parliament and of the Council of 13 July 2009 on the coordination of procedures for the award of certain works contracts, supply contracts and service contracts by contracting authorities or entities in the fields of defence and security, and amending Directives 2004/17/EC and 2004/18/EC, [2009] OJ L 1216/76, applies to the defence sector. On this Directive, B. Heuninckx, The Law of Collaborative Defence Procurement in the European Union (Cambridge University Press, 2016) and M. Trybus, Buying Defence and Security in Europe: the EU Defence and Security Procurement Directive in Context (Cambridge University Press, 2014). 11 Articles 26–28 of Directive 2014/24/EU. 12 On the integrity risks posed by urgent procurement in the context of COVID-19, see OECD, Policy measures to avoid corruption and bribery in the COVID-19 response and recovery (May 2020) and OECD, Public integrity for an effective COVID-19 response and recovery (April 2020). 13 Articles 27(3) and 28(6) of Directive 2014/24/EU. On these see T. Kotsonis, ‘EU Procurement Legislation in the Time of COVID-19: Fit for Purpose?’, 4 Public Procurement Law Review (2020), p. 199. 14 Case C-24/91, Commission v Spain, EU:C:1992:134, para. 15, Case C-126/03, Commission v Germany, EU:C:2004:728, para. 23 and Case C-337/05, Commission v Italy, EU:C:2008:23, para. 75. 15 Case C-292/07 Commission v Belgium, EU:C:2009:246, para. 106. For a more complete taxonomy, P. Telles and L. Butler, in F. Lichere, R. Caranta and S. Treumer (eds), Novelties in the 2014 Directive on Public Procurement, p. 131. 16 Opinion of Advocate General Kokott in Case C-385/02 Commission v Italy, EU:C:2004:276, para. 30. 17 Case C-250/07 Commission v Greece, EU:C:2009:338, para. 34-39. 18 Paragraphs 3 through 5 add other grounds too, but they are not particularly useful in the context of responding to COVID-19 and as such will not be covered in this paper. 19 Article 32(2)(a) of Directive 2014/24/EU. 20 Ibid., Article 32(2)(b). 21 Ibid., Article 32(2)(c). 22 A. Sanchez-Graells, ‘Procurement in Time of COVID’, 71 Northern Ireland Legal Quarterly (2020), p. 83. 23 On what kinds of purchases would prima facie fit in this context, see L. Valadares Tavares and P Arruda, ‘Public Polices for Procurement under COVID19’, European Journal of Public Procurement Markets (July, 2021), p. 14. 24 Guidance from the European Commission on using the public procurement framework in the emergency situation related to the COVID-19 crisis (2020/C 108 I/01). This was argued as well in the literature, T. Kotsonis, 4 PPLR (2020), p. 201. 25 The wording adopted by Article 32(2)(c) implies only the necessity test is required and not full application of the principle of proportionality. With a similar view, P. Bogdanowicz, ‘Article 32’, in R. Caranta and A. Sanchez-Graells (eds.) European Public Procurement: Commentary on Directive 2014/24/EU (Edward Elgar, 2021), para. 32.21. 26 T. Kotsonis, 4 PPLR (2020), p. 203. 27 For example, Case C-318/94 Commission v Germany, EU:C:1996:149 at para. 18 and Case C-394/02 Commission v Greece, EU:C:2005:336, para. 42. 28 On unforeseeable events see Opinion of Advocate General Jacobs in Case 525/03 Commission v. Italy, EU:C:2005:343, para. 61. 29 This is the implication of the CJEU's reading on Case C-250/07 Commission v. Greece and also Case C-385/02 Commission v. Italy, EU:C:2004:522, para. 27. 30 On this issue, albeit specifically about the purchase of personal protective equipment in England, A. Sanchez-Graells, ‘COVID-19 PPE Extremely Urgent Procurement in England’, in D. Cowan and A. Mumford (eds.), Pandemic Legalities (Bristol University Press, 2021). 31 With this view, S. Arrowsmith, ‘Recommendations for Urgent Procurement in the EU Directives and GPA: COVID-19 and Beyond’, in S. Arrowsmith et al., Public Procurement Regulation in (a) Crisis? Global Lessons from the COVID-19 Pandemic (Hart, 2021), p. 78. 32 Case C-19/13 Fastweb EU:C:2014:2194, para. 50. 33 Case C-19/13 Fastweb, para. 48 and Case C-275/08, Commission v Germany, EU:C:2009:632, para 72. 34 With a similar view while focusing on the means of the contracting authority, nature and characteristics of the specific project and good practice in that particular area, P. Bogdanowicz, in R. Caranta and A. Sanchez-Graells (eds.), European Public Procurement: Commentary on Directive 2014/24/EU, para. 32.22. 35 S. Arrowsmith, in S. Arrowsmith et al., Public Procurement Regulation in (a) Crisis? Global Lessons from the COVID-19 Pandemic, p. 78. 36 A. Sanchez-Graells, ‘Drilling Down on the Statutory Interpretation of Extreme Urgency Procurement Exemption in the Context of COVID-19’, www.howtocrackanut.com/blog/2020/4/16/drilling-down-on-the-statutory- interpretation-of-the-extreme-urgency-procurement-exemption-in-the-context-of-covid-19. 37 Stating the need to apply this test, A. Sanchez-Graells, ‘More on COVID-19 Procurement in the UK and Implications for Statutory Interpretation’, www.howtocrackanut.com/blog/2020/4/6/more-on-covid-19-procurement-in-the-uk-and-implications-for-statutory-interpretation. 38 Sixty-eight per cent of OECD countries centralised public procurement in the immediate response to COVID-19, OECD, OECD Policy Responses to Coronavirus (COVID-19), Public procurement and infrastructure governance: Initial policy responses to the coronavirus (Covid-19) crisis, July 2020. 39 V. Vecchi, N. Cusumano and, E.J. Boyer, ‘Medical Supply Acquisition in Italy and the United States in the Era of COVID-19: The Case for Strategic Procurement and Public–Private Partnerships’, 50 The American Review of Public Administration (2020), p. 643. On Italy's response in general, G. L. Albano and A. La Chimia, ‘Emergency Procurement and Responses to COVID-19: The Case of Italy’, in S. Arrowsmith et al., Public Procurement Regulation in (a) Crisis? Global Lessons from the COVID-19 Pandemic (Hart, 2021), p. 350. 40 Leading to a large purchase of PPE, which started arriving in late March 2020, RTE, ‘Shipment of PPE Supplies Arrives in Ireland from China’, 29 March 2020, www.rte.ie/news/2020/0329/1127076-ppe-equipment-china/, with a Chinese ambassador interview providing background info on how the deal was brokered; Embassy of the People’s Republic of China in Ireland, ‘Coronavirus: China Working with Ireland to Help Combat Virus’, 25 March 2020, http://ie.china-embassy.org/eng/sgxw/t1761158.htm, and Embassy of the People’s Republic of China in Ireland, ‘China and Ireland Work Together to Keep PPE Supply Running Smoothly’, 5 April 2020, http://ie.china-embassy.org/eng/sgxw/t1766380.htm. 41 Angela Merkel's speech of 23 April 2020, available at www.lengoo.de/blog/angela-merkel-we-are-walking-on-thin-ice/. 42 On how countries managed their trade policy in connection with COVID-19 procurement, B. Hoekman et al., ‘COVID-19, Public Procurement Regimes and Trade Policy’, The World Economy (2021), p. 1. 43 S. Arrowsmith, in S. Arrowsmith et al., Public Procurement Regulation in (a) Crisis? Global Lessons from the COVID-19 Pandemic, p. 82. 44 A. Sanchez-Graells, ‘More on COVID-19 Procurement in the UK and Implications for Statutory Interpretation, www.howtocrackanut.com/blog/2020/4/6/more-on-covid-19-procurement-in-the-uk-and-implications-for-statutory-interpretation. 45 Communication from the Commission, Guidance from the European Commission on using the public procurement framework in the emergency situation related to the COVID-19 crisis (2020/C 108 I/01). 46 Ibid, p. 4. 47 Ibid, p. 4. 48 T. Kotsonis, 4 PPLR (2020), p. 203. 49 J. Dvorak, ‘Lithuanian COVID-19 Lessons for Public Governance’, in P. Joyce, F. Maron and P. S. Reddy (eds.), Good Public Governance in a Global Pandemic (The International Institute of Administrative Sciences, 2020), p. 330. 50 The Guardian, ‘Secret Planning Exercise in 2016 Modelled Impact of Mers Outbreak in the UK’, 10 June 2021, www.theguardian.com/society/2021/jun/10/secret-planning-exercise-in-2016-modelled-impact-of-mers-outbreak-in-uk. 51 Department of Health and Social Care, ‘Annex B: Exercise Cygnus Report (accessible)’, (2020). 52 On this mechanism, E. McEvoy and D. Ferri, ‘The Role of the Joint Procurement Agreement during the COVID-19 Pandemic: Assessing Its Usefulness and Discussing Its Potential to Support a European Health Union’, 11 European Journal of Risk Regulation (2020), p. 851. 53 Sweden (February), Poland (March) and Finland (March) only joined in 2020, European Commission, Signing Ceremonies for Joint Procurement Agreement, https://ec.europa.eu/health/preparedness_response/joint_procurement/jpa_signature_en. This was based on the Decision 1082/2013/EU on serious cross-border threats to health which is now under review as part of a new health security framework, Proposal for a Regulation of the European Parliament and of the Council on serious cross-border threats to health and repealing Decision No 1082/2013/EU COM(2020) 727 final. 54 Euobserver, ‘Will Coronavirus Lead to Medicine Shortage in EU?, 14 February 2020, https://euobserver.com/coronavirus/147449. 55 BBC, ‘Coronavirus: Historic Ireland-China PPE Flights Lands in Dublin’, 29 March 2020, www.bbc.co.uk/news/world-europe-52085363.
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==== Front Eur J Womens Stud Eur J Womens Stud EJW spejw European Journal of Women's Studies 1350-5068 1461-7420 SAGE Publications Sage UK: London, England 10.1177/13505068221085847 10.1177_13505068221085847 Special Issue The gender and sexual politics of the COVID-19 pandemic Mehrabi Tara Tainio Luca Karlstad University, Sweden Tara Mehrabi, Karlstad University, Universitetsgatan 2, Karlstad, 651 88, Sweden. Email: tara.mehrabi@kau.se 7 4 2022 7 4 2022 13505068221085847© The Author(s) 2022 2022 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. edited-statecorrected-proof typesetterts19 ==== Body pmcWhatever it is, coronavirus has made the mighty kneel and brought the world to a halt like nothing else could. Our minds are still racing back and forth, longing for a return to “normality,” trying to stitch our future to our past and refusing to acknowledge the rupture. But the rupture exists. And in the midst of this terrible despair, it offers us a chance to rethink the doomsday machine we have built for ourselves. Nothing could be worse than a return to normality. —Arundhati Roy, 3 April 2020 In 2019 the world witnessed the beginning of yet another global health crisis, this time COVID-19. The World Health Organization (WHO) has reported over 265 million cases of COVID-19 and over 5.2 million related deaths globally, as of 6 December 2021. As Arundhati Roy beautifully describes, COVID brought about a rupture, tearing into the temporality, spatiality, and sociality of everyday-ness. As many countries (mostly in the global north) are now trying to move ahead of the crisis by means of vaccination, the rupture remains, with many parts of the global south still living and dying with the virus. As different narratives of the pandemic filled social media, news, and public discussions following the outbreak, many lost hope. With the ever-present possibility of a dystopian future, they gave in to grief over the loss of human life and humanity as the death toll continued to increase, jobs were lost, and people were displaced from their homes. It was as if the last remnants of a fragile security and normalcy, already fractured by neoliberalism and (neo)colonial capitalism, had vanished into thin air in the blink of an eye. Others maintained a utopian excitement about the potentiality brought about by the pandemic, arguing that COVID-19 might be the wakeup call humans needed to rethink power relations, community, mass consumption, and climate change. They felt that, perhaps, COVID would enable environmental restoration as it brought the global capitalist machinery to a halt. What matters, whether looking through the lens of utopian optimism or dystopian pessimism, COVID has made it clear once again that health and illness are matters of social justice, structural vulnerabilities, and unequal power relations upon which the world as we know it is built. COVID urges us to rethink our sense of “normalcy” and our ways of living; the underlying distribution of vulnerabilities and privilege; and the interconnectedness of the world beyond borders and boundaries. Perhaps, as Arundhati Roy argues, “nothing could be worse than a return to normality.” COVID is not merely a question for medical sciences, natural sciences, epidemiology, and public health, nor is it simply a bodily/medical/public health crisis. While the scientific community, pharmaceutical industries, epidemiologists, and policymakers are trying to make sense of the virus, its origins and effects, and to find proper modes of handling the pandemic to minimize casualties and to avoid a complete economic breakdown, there is even more at stake. Living through the pandemic is a matter of governance, biopolitics, and necropolitics, and as such begs an intersectional lens (Ajana, 2021; de Kloet et al., 2020; Manderson et al., 2021; Milan et al., 2021; Sandset, 2021). Value is placed on lives differentially as action plans are discussed and drafted, often deeming the precarious lives disposable. As most of these political interventions are coming from and situated within the neoliberal, patriarchal, nationalist, cis-heteronormative, and colonial system, we see yet again how the processes of naming and marking the sick “deviant” bodies matter. On the one hand, socially marginalized groups are among those most affected by COVID, suffering the greatest death toll due to lack of access to proper healthcare while also navigating other social injustices. On the other hand, these groups are yet again marked as “deviant” bodies less worthy of care. While we see the emergence of a “certain ‘Covid-elite’, that is holders of so-called ‘immunity passports,’” especially in the global north, immigrant others are often referred to as the “unruly,” the global south as “irresponsible,” the queer and the sex worker as “contagious,” and the disabled and the poor as lives too “costly” to be saved (Ajana, 2021, 24; Butler and Yancy, 2020). It is for this reason that social scientists and researchers within the humanities stress that the pandemic is not solely a medical/health crisis but also a social one. It is crucial to understand the pandemic in its full complexity, namely its effects on medical as well as social infrastructures individuals’/communities’ modes of exposure to, and experience of, the pandemic. However, as it is shown by different studies on the global scale, social aspects such as gender has not been included successfully in policy making and management of COVID (Azcona et al., 2021; de Paz et al., 2020; Galasso et al., 2020; Oertelt-Prigione, 2020; Ruxton and Burrell, 2020). At its worst, the pandemic has been used as a cover for discrimination against marginalized groups. Turkish president Erdogan blaming the gay community for the pandemic (Yackley Ayla, 2020), or former president Trump publicly making demeaning remarks about Asian people (Reja, 2021), are examples of how marginalized groups have been scapegoated and become targets of homophobic or racist attacks (see also Wojnickás, Parmanand´s and Altaýs articles in this issue). Another example is the formulation of quarantine guidelines that relied on a narrow definition of a (nuclear) family, or failed to account for the economic and embodied vulnerabilities of marginalized groups and their precarity, such as sex workers (Platero & Saez; Probst & Schnepf in this issue). Contributing to the discussion on the importance of intersectional perspective for understanding politics around COVID-19, the Centre for Gender Studies at Karlstad University in Sweden organized a two-day online conference in September and October 2020, issuing a call for papers to scholars within the humanities and social sciences who research gender and intersectionality in relation to the pandemic. We received a number of important contributions to the conference connecting to the global scholarship on COVID, such as the lived realities and experiences of COVID within marginalized groups, the structural intersection of power and unequal distribution of vulnerability across socially constructed categories of gender, sexuality, ethnicity, class, disability, and more. This special issue contains a selection of the papers presented during this two-day event. The papers were selected in order to present a diverse range of discussions, methods, and theoretical approaches, as well as geopolitical positionings, which can highlight how gender and its intersections with other social relations of power matter in times of crisis. With this special issue we want to put a spotlight on the importance of gender studies knowledge and feminist scholarship for understanding, managing, and countering the effects of the global COVID-19 pandemic. Moving away from understanding gender as a single or the most significant dimension, the special issue asks, what are the gender, sexual, racial, and class-bound politics of the COVID-19 pandemic? Attending to this question, the different contributions chart out how the social politics of the current pandemic play out in an international context. While policy responses to the pandemic have in most cases been limited by national boundaries, the effects of the pandemic are anything but confined to specific nation states. It is also worth mentioning that these global effects are not lived in a universal way by individuals and communities. Wanting to understand the politics of the COVID-19 pandemic properly, thus also requires an international vantage point, combining a transnational and intersectional feminist perspective. This special issue brings together a collection of empirical contributions that span the globe, tackling the transnational and intersectional dimensions of the pandemic in a variety of socio-cultural contexts. Analytically, the special issue will contribute through a reworking of the concepts of gender by combining transnational (Conway, 2017; Mendoza, 2002) and intersectional feminism (Cho et al., 2013; Crenshaw, 1991; Davis and Zarkov, 2017; Salem, 2018) and a gender- and sexuality-governance perspective (Mohr, 2019; Repo, 2015; Whitmarsh and Jones, 2010). The aim is to provide a wide range of conceptual-analytical tools enabling feminists and gender studies scholars to engage with and capture the complexities of the pandemic. Mapping out the contributions to the special issue During 2020 and 2021 we witnessed a surge in research publications on COVID-19 within disciplinary and interdisciplinary scholarship in the humanities and social sciences as researchers try to examine such an unusual socio-material phenomenon and its complexities from a variety of perspectives and in relation to different questions. Articles, special issues, and book volumes have been dedicated to understanding the pandemic as a structural, institutional, individual, environmental, political, and ethical question that needs multidisciplinary engagement (e.g. Drabble and Eliason, 2021; Manderson et al., 2021; Milan et al., 2021). Transnational scholarship has been dedicated to presenting local specificities as well as transnational relations, and comparative studies, in order to better our understanding of a phenomenon that is at best described as glocal: globally shared yet locally specific. With this special issue we contribute to this scholarship as we discuss it in the following order. COVID and geographical/cultural contexts Situatedness and paying attention to context, local practices, and cultures have always been central to gender studies (Haraway, 1988; Rich, 1984). Gender studies has a long history of approaching matters of care, body, and health/illness in relation to class, gender, sexuality, and race, and as matters that are transnationally in conversation yet locally specific. In moments of crisis, paying attention to such cultural and geopolitical diversity is crucial for knowledge-making and analyses of social structures. For example, the significance of geographical context in how the pandemic affects local communities and how the experiences and the burdens of living through the pandemic differ as an effect of such geopolitical differences. How transnational politics interacts with that of national agendas in processes of handling the pandemic and how such practices are situated within the matrix of power and matters of race, class, sexuality, and gender. How and why measures such as social distancing, staying at home, and wearing masks are not a possibility for everyone everywhere, because of their situatedness within structures of power in different contexts. How the distribution of vaccines follows the distribution of wealth and power. Which modes of survival - and for whom – are made im/possible through the contemporary global, political, and structural inequalities and the consequences of capitalism. This special issue contributes to analysing the effects of the pandemic, modes of response to the pandemic, and intersecting structures of power and their effects on the individual and collective lives of those at the margins, in a variety of contexts in Europe and beyond. Taking into consideration the cultural understanding of gender, masculinity, and femininity, Sharmila Parmanand's article, “Macho Populist versus COVID,” shows how responses to the pandemic in countries such as the USA, Turkey, the Philippines, and Brazil are marked by the macho culture and male leaders’ toxic cis-hetero-masculinity. In her article, “Virus among the Vegetables,” Rebecca Irons shows how a gender segregation approach in the Peruvian context failed because politicians did not take into consideration the overlapping of matters of class, ethnicity, and colonial legacies entangled with gender within the context of vender market culture. Focusing on the Indian context, Sreya Banerjea in her article “Regulating Intimaté Labour and Unrulý Citizens: The Plight of India's Sex Workers in the Pandemic and Beyond” analyses the effect of the pandemic; and the national response to it; on immigrant sex workers and the precariousness of their situation, which has been amplified by lockdowns and border control. Other geopolitical contexts discussed in in this special issue include Germany and Spain, in articles discussed below. In addition to the locally specific, the significance of online spaces reaching across geographical and cultural borders is also addressed in some of the articles in this special issue. While physical spaces have been lost or become topics of debate and restrictions, some have moved to seek safety and a sense of community in online spaces. As Tunay Altay notes in his article “The pink line across digital publics: Political homophobia and the queer strategies of everyday life during COVID-19 in Turkey” on Turkish online LGBTQ + spaces, digital space can offer gender and sexual minorities a much-needed break from everyday violent realities. The importance of online platforms for sex work, when physical space is not a possibility, has also been noted by Ursula Probst and Max Schnepf in their article “Moral Exposures, Public Appearances: In/visible Sex and Emerging Normativities in Pandemic Berlin”. While discussing the relevance of technology as a means of surviving the pandemic, maintaining social contact or mental health, as a contemporary place of work, or as a community, however, it is also important to consider the in-built inequalities. Internet access is, of course, also a question of privilege, geographical location, and availability of socio-economic resources. These questions are addressed through considerations of online activism, data collecting, and knowledge production during the “first pandemic of a datafied society,” brought into the conversation through Noora Oertel's review of COVID-19 from the Margins: Pandemic Invisibilities, Policies and Resistance in the Datafied Society. Lisa Lindqvist's book review of Digital Health and Technological Promise: A Sociological Inquiry by Alan Petersen also touches on the role of the digital sphere in the context of digital health. Intersectional structures of power and reinforcing patterns of injustice While the pandemic enacts new kinds of challenges, it also amplifies the already-existing global health challenges and social inequalities. In fact, COVID has magnified different forms of injustice, processes of marginalization, and uneven distribution of precarity globally and locally. It has drawn attention, more than ever, to the global and local operation of power and how necropolitics/biopolitics are always situated within the interlocking axes of power that function along the lines of gender, sexuality, class, race, and more. It has shown how the interaction between bodies, politics, health/illness, and social structures of power shape populations, collectives, as well as individual modes of living (well) in a pandemic. Katarzyna Wojnickás article ” What's masculinity got to do with it? The COVID-19 pandemic, men and care” as well as Sharmila Parmanand´s article “Macho populists versus COVID: Comparing political masculinities” make explicit the entanglement of whiteness and cis-hetero-masculinity within the discourses and practices of COVID management in several contexts. As Lucas Platero and Miguel Ángel Lopez-Sáez point out in their article on Spanish LGBTQ youth forced into lockdown in possibly violent family homes, the policies focused on, and privileging of, the normative nuclear family can be harmful to those who fall outside of that category. While the home and the (nuclear) family have generally been represented as “safe,” and many restriction guidelines formulated to specifically consider cis-heteronormative family units, for some this can be a place of double confinement, restricting both movement and self-expression. Being isolated from one's community creates an added layer of vulnerability for those youth who are experiencing violence from their families or in their neighbourhoods more widely. Whether it is in relation to LGBTQ youth, or the precarious living conditions of immigrant sex workers in India analysed by Banerjea, gender comes to matter in connection with other axes of power such as sexuality, age, class, and ethnicity. Mari Pieri's study of disability from a crip-queer theoretical perspective reflects on the neoliberal ableism that has been reinforced during COVID times, exposing those living with disabilities to further vulnerability. Altay's research on the rise of the LGBTQ + online spaces in Turkey, especially during the pandemic, highlights the intersectional politics of gender, sexuality, and local politics and how technology can mediate a space of resistance that emerges from a crisis situation. Last but not least, Ursula Probst and Max Schnepf address the pandemic biopolitics of sex through discussing the effects of the pandemic on visibility and public responses to non-normative sex in the Berlin cityscape, echoing Platero's and Lopez-Sáez's observations on the norm-enforcing nature of some of the political responses to the pandemic. As such, many of the contributors to the special issue show not only that gender is always already intersectional, but also how such intersecting matrices of power matter in a pandemic. (Health)care, gender, and intersectionality Matters of gender, health, body, and medicine have always been part of gender studies and feminist activism, whether in the early movements of the Boston Women's collective (as outlined in the collective's seminal publication, Our Bodies, Ourselves in 1970), or the second-wave feminist movement on reproductive rights, IVF technologies, and digital technologies. Gender has always been at the centre of discussions on health and illness, particularly with regard to demographics and prevalence. Our very understanding of the healthy body, dis/ability and illness, medical treatment, even the healthcare professions is imbued with gender norms and gender ideals, and deeply situated within the binary sex/gender model (e.g. Connell, 2012; Moore, 2008; Stephenson and Zeldes, 2008; Twigg, 2006). As feminist, crip, and trans critiques of medicalization have pointed out, bodies that are not white, able, and cis male become targets of not only medical but also social control (Kafer, 2003; Terry and Urla, 1995). This has also been illuminated by recent debates on trans-specific healthcare, and especially the so-called gender critical, or trans exclusionary, voices gaining space in those debates. Matters of health and illness are entangled with social matters, such as gender, race, ethnicity, class, and sexuality, and thus such social categorizations influence how we construct notions of health and illness. For example, the ways in which we live and generally experience our bodies, as well as during illness, are connected to our positioning within matrices of power. Some bodies are pathologized while others are taken to be healthy and viewed as the norm. On one hand, health and illness are matters of accessibility, stratification, and gatekeeping of healthcare possibilities, quality of life, environment, and lifestyle. On the other hand, matters of health and illness are used as a biopolitical tool to make nations and bodies, and to manage lives. As such, social change and justice are often overlooked in relation to techno-medical promises, even as social scientists have repeatedly proven that social change, education, empowerment, and justice have to be combined with medical and technological solutions to be effective. As Platero and Lopez-Sáez point out in this issue, the mental health effects of not only the pandemic, but also the measures taken to control it, can be harmful to those already marginalized and in a precarious position. Further contributing to the discussion on the gendered nature of the pandemic, Katarzyna Wojnicka notes in her article “What's masculinity got to do with it? The COVID-19 pandemic, men and care”, citing multiple previous studies regarding men, masculinities, and health, that there is a gendered difference in willingness to consult medical professionals among those identifying as cismen. Similarly, the same group seems more reluctant to follow COVID health guidelines and recommendations, such as hand washing, social distancing or wearing a mask in public, measures suggested by healthcare experts to be crucial in maintaining the health of individuals as well as the population in general. As matters of health/illness and biopolitics are connected to matters of care (care for the sick, for family, for the nation), it is no wonder that feminist research has been busy analysing and theorizing care and gendered care practices for decades (e.g. Ehrenreich and English, 2005). The pandemic has, yet again, made visible the unequal burden of care work, as the medical professionals working at or over their limits are predominantly women. Then again, as Wojnicka's and Parmanand's articles demonstrate, the relationship between masculinity/ies and care is not a simple one either, and deserves a more nuanced intersectional approach to be better understood. As both highlight, in many ways, the pandemic has both challenged and reinforced hegemonic or toxic models of masculinity, whether we are looking at the victims of the pandemic or those making political decisions concerning national guidelines to manage the situation. Discussing care from a different perspective, Irons focuses on the feminized notion of care and how it escalated during the pandemic in Peru. In her analysis, she highlights how care becomes a matter of gendered practices, places, and relations that are always entangled with economic situations and ethnicity. Finally, the third book review, by Paju Kettunen, offers both theoretical and practical tools for organizing collective care in times of crisis. Mutual Aid: Building Solidarity during this Crisis (and the Next) by Dean Spade, focuses on solidarity, community mobilization, and social activism as ways of surviving the pandemic, especially for those who are not looking forward to a return to normality. To summarize, the collection of articles in this special issue aim to demonstrate how an intersectional approach to COVID can provide better understanding of such health crisis not merely as a medical matter but also a social and political one in needs of a gendered lens. The collection of articles hence show how matters of gender, class, ethnicity, and more interact with COVID creating modes of precarity, both in terms of personal experiences of the crisis and modes of political response to it, urging one not to separate matters of health/illness, politics and power relations. Declaration of conflicting interests: The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author received no financial support for the research, authorship, and/or publication of this article. ==== Refs References Ajana B (2021) Immunitarianism: Defence and sacrifice in the politics of COVID-19. History and Philosophy of the Life Sciences 43 (1 ): 1–31.33400025 Azcona G Bhatt A Davies S , et al. (2021) Spotlight on gender, COVID-19 and the SDGS: will the pandemic derail hard-won progress on gender equality? 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==== Front Mobile Netw Appl Mobile Networks and Applications 1383-469X 1572-8153 Springer US New York 1965 10.1007/s11036-022-01965-z Article A Proposed Framework for Evaluating the Academic-failure Prediction in Distance Learning http://orcid.org/0000-0003-4777-9683 Takaki Patrícia patricia.takaki@unimontes.br 12 https://orcid.org/0000-0003-1000-5553 Dutra Moisés Lima 1 https://orcid.org/0000-0003-0572-6997 de Araújo Gustavo 1 https://orcid.org/0000-0003-3510-8882 Júnior Eugênio Monteiro da Silva 1 1 grid.411237.2 0000 0001 2188 7235 Postgraduate Program in Information Science (PGCIn), Federal University of Santa Catarina (UFSC), Florianópolis, Brazil 2 grid.412322.4 0000 0004 0384 3767 Department of Computer Science, State University of Montes Claros, Montes Claros, Brazil 12 4 2022 2022 27 5 19581966 15 11 2021 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Academic failure is a crucial problem that affects not only students but also institutions and countries. Lack of success in the educational process can lead to health and social disorders and economic losses. Consequently, predicting in advance the occurrence of this event is a good prevention and mitigation strategy. This work proposes a framework to evaluate machine learning-based predictive models of academic failure, to facilitate early pedagogical interventions. We took a Brazilian undergraduate course in the distance learning modality as a case study. We run seven classification models on normalized datasets, which comprised grades for three weeks of classes for a total of six weeks. Since it is an imbalanced-data context, adopting a single metric to identify the best predictive model of student failure would not be efficient. Therefore, the proposed framework considers 11 metrics generated by the classifiers run and the application of exclusion and ordering criteria to produce a list of best predictors. Finally, we discussed and presented some possible applications for minimizing the students’ failure. Keywords Machine learning Educational data mining Failure prediction Distance learning issue-copyright-statement© Springer Science+Business Media, LLC, part of Springer Nature 2022 ==== Body pmcIntroduction Student failure in undergraduate courses is an old issue for which no definitive solution has yet been found. This event occurs in different ways and depends on several factors; it is an educational issue involving different actors [2]. The failure of a single student can result in this person’s demotivation, loss of his/her expected learning flow, waste of financial resources, negative impacts on the institution’s indicators, decreased funding granted to the institution, besides strongly contributing to increasing the course and institution dropout rate. According to [4, 18], dropout in higher education represents a problem for all nations, causing social, scientific, and economic losses. Sometimes these events represent public resources that do not provide an effective return on investment; other times, they are significant losses of revenues in the private sector. When students abandon their opportunity for training and intellectual growth, they are also excluded from an entire educational process that prepares them for the job market and citizenship. Educational Data Mining (EDM) represents a multidisciplinary research field dedicated to developing methods to explore data from educational environments [3, 16]. It is possible to understand students more effectively and appropriately through an EDM process, i.e., how they learn, the importance of the context in which the learning occurs, and other factors influencing the learning process [6]. For [14], the main idea behind EDM is developing and using methods to analyze and interpret the ‘big data’ from computer-based learning systems and school, college, or university administration and management systems. This work applied prediction models to identify undergraduate students at risk of subject failing, with about 50% of the total course time yet to elapse. As a case study to evaluate this proposal, we analyzed the students’ performance in an undergraduate course offered at a distance by a Brazilian public university part of the Open University of Brazil (UAB) system. UAB offers distance learning courses intermittently. That means that there are no annual or semester offers like for traditional courses. Therefore, if a student fails a course, he/she may not have the opportunity to enroll to retake it in the future, as there is no guarantee that there will be a new offer of this subject until the end of his/her undergraduate course. Students who fail in a subject in this context, even if there are pedagogical strategies to recover their learning and passing, will have a greater chance of dropping out of their courses. Such a situation can negatively impact their training, professional future, institutional promotion, and even the social and economic development of the location where these students are inserted, besides other potential losses. At best, they will choose to continue their course at another higher educational institution. Based on this application scenario, this work sought to provide those involved in the teaching-learning process at UAB (students, teachers, and managers) with a predictive model composed of information about individual risks of academic failure in specific subjects. Managers (directors, coordinators of both courses and remote centers), teachers (in-person and distance tutors), and students can benefit from qualified information to support the teaching-learning process. We believe that this initiative has the potential to minimize this type of event. With the support of subject-failure predictive-analysis-based alerts, they can act to mitigate the risk of academic failures. This work carried out experimental tests with different classification models to evaluate the best results for predicting students’ failure in a given distance higher education course. For this purpose, we analyzed and compared eleven metrics captured from each of the seven classification models run. Specifically, we intended: To collect and wrangle student-grade reports available in the UAB’s Moodle Virtual Learning Environment; To perform data pre-processing, including anonymizations, data insertions and deletions, data normalization, conversion of categorical data into numeric data, and splitting training and test datasets; To predict the academic failure by applying seven different predictive models, by training the data with and without balancing options and with a dataset consisting of three weeks of class, for a total of six weeks; To gather and compare eleven different evaluation metrics for each model: true positive, true negative, false positive, false negative, accuracy, precision, recall, specificity, kappa index, geometric mean (g-means), and harmonic measure (f-measure); To analyze and discuss the results obtained. To propose a framework to assess the academic-failure prediction in distance education. The remainder of this paper is organized as follows. Section 2 describes the materials and methods used. Section 3 presents and discusses the results obtained. Section 4 presents the proposed framework. Finally, in Section 5, we draw some conclusions and give the proposal’s limitations and suggestions for future works. Materials and methods This is an exploratory work organized in three stages, based on [11]: pre-processing, machine learning, and post-processing. During pre-processing, we collected, cleansed, and prepared the data gathered in the Moodle environment. This data corresponds to the students’ grades in 21 subjects taken by approximately 250 students enrolled in the analyzed course. In the machine learning stage, we run four classification algorithms written in Python and evaluated them: Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine. We used the RBF kernel and polynomial kernel of degrees 2, 3, and 4. We tested the algorithms with and without data balancing. During the unbalanced processing, we used the SMOTE and Near Miss methods. In the post-processing stage, we carried out different analyses, isolated or combined, of the evaluation metrics obtained by the classification models previously tested: true positive, true negative, false positive, false negative, accuracy, precision, recall, specificity, g-means, f-measure, and kappa index. Subsequently, we proposed, implemented and evaluated a framework to identify the best academic-failure prediction models. As supporting tools, in addition to the Moodle Learning Management System, we worked with the WEKA software, the GitHub platform, and the Google Collaboratory environment. Related works EDM techniques to predict students’ academic failures have been used in several studies reported in the literature [1–3, 5, 7, 8, 10, 12, 13, 15]. Each of them uses different data, collected at different times in the course offering, applies different predictive models and compares results with different metrics. Some of these works are worth mentioning. The work by [5] presents a methodology to automatically detect students “at-risk” of failing a module of computer programming courses and support adaptive feedback simultaneously. They used six classifiers: K-Neighbors, Decision tree, Random forest, Logistic regression, Linear SVM, and Gaussian SVM, without data balancing techniques. The K-Neighbors (k = 12) was the classifier selected, which showed high performance for F1, precision, and recall metrics compared with other classifiers. A comparative study on the effectiveness of four EDM techniques (Decision Tree, Support Vector Machine, Neural Network, and Naïve Bayes) for early identification of students likely to fail in introductory programming courses is presented by [8]. They used two data sources from a Brazilian university: one from distance education and the other from face-to-face learning. They performed data pre-processing and fine-tuning tasks, adopting only the f-measure evaluation. The fine-tuned SVM techniques identified at least 92% and 83% of effectiveness the students are likely to fail when they have performed at least 50% and 25% of the distance education and on-campus courses, respectively. The work by [13] proposed a learning analytics approach using data mining and machine learning to predict the grades of four primary assignments in a Hellenic Open University’s annual module. They used Moodle data, ran the classifiers Random Forest, Linear Regression, Neural Network, AdaBoost, SVM, and k-NN, and measured the Mean Absolute Error (MAE) to identify the accuracy of classifiers’ predictions. The Random Forest algorithm provided the best results for the four assignments. Our literature review could not identify studies that used the same methodology we proposed, i.e., that possessed a preliminary stage for evaluating and selecting the best predictors. Consequently, to the best of our knowledge, this fact supports the originality of our work. Results and discussion Step 1: Data pre-processing Data collection and pre-processing produced 4,396 instances of performance data for 250 UAB distance students. We collected student grades for 21 subjects, all with the same amount and score of evaluative activities. The data collected corresponds to students’ grades in subjects completed by March 2020. At that point, the 4th semester of classes was in progress, and the UAB’s methodology for evaluating its students was being modified due to the unexpected coronavirus pandemic outbreak. The selected course possesses the largest number of students among all courses of the distance learning modality at the analyzed university. It is offered in five on-site remote centers (cities) in the Brazilian state of Minas Gerais and provides subjects of 30h, 60h, 90h, and 120h. For this case study, we chose to work with the 60h subjects because they represent 84% (21 out of 25) of the subjects completed by the time of the data collection. There are 15 points for each of the two Collaborative Activities (CA1 and CA2, whose points are distributed in 7 points for the presentation in class-based meetings, and 8 points for the work delivery in the virtual room); 3 points for each of the four Discussion Forums (DF1, DF2, DF3, and DF4); 9 points for each of the two Individual Activities (IA1 and IA2); and 40 points for the face-to-face assessment (FA), on the last day of class. The 60h courses have a duration of six weeks. We gathered 105 files of grade reports (electronic spreadsheets) from the virtual rooms of Moodle used by the chosen course. We performed several initial data cleaning transformations on the spreadsheets coming from the standard Moodle “Grade Report” to consolidate them in a single format for all spreadsheets. With the spreadsheets assembled, we selected the columns for grades obtained in CAI and CA2 (split into presentations and deliveries), grades received in DF1 and DF2, and grades received in IA1. Next, we inserted two columns: Status (“Passing/Failing”), based on the final grade, and Remote Center (city number). The grades from individual activity 2, discussion forums 3 and 4, and the face-to-face assessment have been removed because they occur in the last three weeks of class. That is, grades IA2 and FA are only known at the end of the course. It is important to point out that the objective of this work is to predict subject failure halfway through. The data were anonymized, the numerical values were converted to the American standard (decimal points), and the data file was converted to the CSV format. In WEKA’s Preprocess tab, the numerical data of the grades (from 3 to 9 points) were normalized by using the filter called Normalize, as explained by [19]. The resulting spreadsheet totaled 4396 data instances. It was possible to verify that 3736 students (85%) passed the subjects and 660 students (15%) failed. This dataset was divided so that the scores of the first 16 completed subjects were used to train the models (3348 instances, 76%) and the last five subjects for tests (1048 instances, 24%). An initial exploratory analysis of the collected data is presented in Table 1. Table 1 Data Overview: division between training/test and passing/failing DATASET Amount Passing Failing Training 3348 (76%) 2814 (84%) 534 (16%) Test 1048 (24%) 922 (88%) 126 (12%) TOTAL 4396 (100%) 3736 (85%) 660 (15%) The division between the training (76%) and testing (24%) datasets1 is consistent with the recommendations and practices found in the literature [17]. The data are imbalanced between the Passing (85%) and Failing (15%) categories, which suggests that the evaluation of predictive models should consider using strategies to deal with this imbalanced data and verify its effectiveness. Step 2: Machine learning To develop the predictive models, we used the following Python libraries: numpy, math, matplotlib, pandas, seaborn, scikit-learn, and imblearn. The imblearn library provides the SMOTE (oversampling) and Near Miss (undersampling) APIs for data balancing. After creating the training and testing dataframes, we converted the categorical variables Remote Center (RC) and Status into numeric variables. Next, we separated the dependent and independent variables and created the X_train, y_train, X_test and y_test dataframes. A training data correlation matrix for viewing selected features is shown in Fig. 1. Fig. 1 Correlation matrix of features As expected, the highest correlations between student features and the predictive variable Status_Failing refer to students’ performances in different activities performed up to the 3rd week of class. Individual Activity 1 (IA1) presented the highest correlation with the predictive variable, while remote educational centers had the lowest correlations, slightly different for the City4 remote center. It is important to emphasize that these and other variable analyses are limited to correlations, and under no circumstances can they be interpreted as causes of the event being studied. Subsequently, we compared the values obtained after running the classification algorithms with the test dataset’s known outputs. Due to the unbalance of the data in the predictive class, we compared the results of the classifiers with the training data with and without balancing options. The balanced options were executed via oversampling (SMOTE method, imblearn over_sampling API) and undersampling (Near Miss method, imblearn under_sampling API). While the SMOTE method creates synthetic data in the minority class to have the number of instances of the majority class, the Near Miss method removes data from the majority class so that it has the number of instances of the minority class. Thus, we carried out 21 prediction tests and collected 11 evaluation metrics for each classifier run. Table 2 shows the analyzed metrics. Table 2 Analyzed metrics METRIC MEANING True positives (tp) failing classified as failing True negatives (tn) passing classified as passing False positives (fp) passing classified as failing False negatives (fn) failing classified as passing Accuracy (accu) total of correct predictions in relation to the total of instances Precision (prec) total of failing correctly classified in relation to all classified as failing Recall (reca) total of failing correctly classified in relation to the truly failing Specificity (spec) total of passing correctly classified in relation to the truly passing G-means (gmeans) geometric mean between recall and specificity F-measure (fmeasu) harmonic mean between precision and recall Kappa index (kappa) accuracy of the model in relation to a random classification Figure 2 presents the confusion matrices for the predictive models tested, and Fig. 3 shows the seven metrics generated from the confusion matrices. When considering each metric in isolation, the best results are highlighted in green and the worst in red. Fig. 2 Confusion matrix metrics for predictive models Fig. 3 Results of the other 7 metrics of the predictions Step 3: Discussion With the set of 11 metrics applied for each predictive model run, the analysis and discussion of these results sought to identify the best results to predict a students’ failure in advance. We aimed to identify a predictive model that makes it possible to generate alerts at the beginning of the 2nd half of each subject so that everyone (students, tutors, teachers, and coordinators) receives information to carry out pedagogical interventions to reverse this possible failure. Accuracy and recall metrics analysis This work initially sought to consider the mathematical-computational context for comparing the performance of the classifiers, where the accuracy metric has a prominent position in identifying the best prediction made. The following formula gives its calculation: (tp + tn)/(tp + tn + fp + fn), indicating how many classifications were made correctly. The results ordered in decreasing order of accuracy are shown in Fig. 4(a). Fig. 4 Accuracy (a) and Recall (b) of classifiers The predictive model with the SVM-RBF-based kernel obtained the best accuracy, 94.37%. Most models that did not use the data balancing methods SMOTE and Near Miss achieved the best results (above 90%), except for Decision Tree, which underperformed other models that used the SMOTE method. However, the isolated analysis of the accuracy metric can limit the context in situ, where the data is unbalanced. It is necessary to analyze the classifiers’ metrics combined so that the performance of these models from other and more specific viewpoints is taken into account. For this reason, the present work used other metrics to evaluate the performance of the implemented models. The recall metric is an indispensable tool to indicate how well the model is getting right in predicting the minority class. The following formula gives its calculation: tp/(tp + fn), showing how many of those who failed were classified as failing. This metric allows knowing if the model is classified as failing the most significant possible number of students that will really fail. The results ordered in decreasing order of recall are shown in Fig. 4(b). Searching for the best results for recall meets the objective of identifying the model that ideally does not classify as passing a student who will fail. This error will cause this student to be out of reach for the scope of actions and initiatives to reverse or minimize such a negative outcome. The NearMiss with SVM-RBF-based kernel model obtained the best recall (84.92%), but this result should also not be used in isolation. When analyzing the accuracy metric, now combined with the recall, it is possible to notice that the model with the best accuracy (SVM-RBF-based kernel, 94.36%) obtained the 2nd worst recall (60.31%). That is, it misses a lot in the minority class (failing). This situation means that 50 out of the 126 failed students were classified as passing, demonstrating the significant damage to mitigating the failure if one had only adopted the accuracy metric. Precision and specificity metrics analysis Precision measures how well the model is getting it right when classifying a student as failing. The following formula gives its calculation: tp/(tp + fp), indicating how many of those classified as failing were actually failed. Analyzing the recall metric, now combined with the precision (Fig. 5(a)), we observe that the model with the best recall (NearMiss with SVM-RBF-based kernel) presented a very low precision (27.65%), the 6th worst. In comparison, Logistic Regression obtained 94.37% of precision. Fig. 5 Precision and Specificity of classifiers Figure 5(a) shows the results ordered by precision. These results demonstrate that the NearMiss with SVM-RBF-based kernel model obtained the best recall (84.92%) and the 6th worst precision (27.65%). It incorrectly classified 280 passing students as failing, accumulating 387 failing alerts that needed only 126. In this hypothetical scenario, tutors, teachers, and coordinators are being unnecessarily mobilized by alerts and pedagogical guidelines to contact students who should not be reached at this time. Results like this could generate an overload of work for the teachers and a lack of motivation for students, which clearly can harm the teaching-learning process in several ways. It is also worth mentioning that the Logistic Regression model, without balancing options, obtained the best precision (94.36%) while presenting the worst result for recall (53.17%). The specificity metric assesses the accuracy of the prediction in the majority class (passing). The following formula gives its calculation: tn/(tn + fp), indicating how many of those who passed were classified as passing. Figure 5(b) shows the results ordered by specificity. At first, an incipient analysis could conclude that low specificities would not have much relevance in predicting failure. If a model makes a mistake classifying a successful student as a failing one, the consequence of this error for the student could be to study and try harder, which seems to be quite acceptable. However, as the previous analysis demonstrated with the precision metric, classifying many students who passed as failing can cause different types of loss, from financial through motivational, to logistical, among others. As the specificity metric analyzes the majority class (passing), any error is proportionally impactful in the general context. Regarding specificity, the Logistic Regression model achieved the highest score (99.566%). Analysis of the other metrics In addition to the metrics analyzed above (accuracy, precision, recall, and specificity), g-means, f-measure, and kappa index metrics add more information that helps to assess the classifiers’ performance. They have the power to combine the previous ones in different ways and can be analyzed in an isolated or combined way, preferably the latter. When taking these three metrics into account allows attributing greater or lesser relevance to the results obtained by each classifier. The g-means metric corresponds to the square root of the product between specificity and recall spec∗reca. This metric varies from 0 to 1 and considers the model’s correctness rates in the majority and minority classes. The f-measure metric corresponds to the value of twice the precision times recall divided by the sum of the precision with the recall 2∗prec∗recaprec+reca. F-measure varies from 0 to 1 and considers the precision and recall, combining two crucial metrics related to the minority class (failing). Both g-means and f-measure are metrics whose values vary between 0 and 1, with the highest values being the most relevant. The Cohen’s kappa index corresponds to a statistical coefficient that compares the accuracy expected by a random classification with the general accuracy of the evaluated model. The following formula gives its calculation: kappa=pra-pre1-pre, where pra=tp+tntp+tn+fp+fn and pre=tp+fntp+tn+fp+fn∗tp+fptp+tn+fp+fn+fp+tntp+tn+fp+fn∗fn+tntp+tn+fp+fn Values below 0.3 suggest that the model has a low capacity to make bold predictions, which indicates a possibly lower quality of its results in comparison to a random prediction. All the analyses presented here proved essential for understanding the need to interpret the metrics for predicting students’ failure. The complexity inherent to teaching-learning process goes beyond the objectivity proposed by the performance metrics and must also be taken into consideration. Proposal Assessing predictive models for students’ failure requires in-depth studies to consider the specific institutional and methodological context in which those students are inserted. The proposed framework consists of (i) applying exclusion criteria on the evaluation metrics obtained to produce a smaller set of viable models; and (ii) applying an ordering criterion to the resulting models. At first, we must exclude models whose accuracy, precision, recall, specificity, g-means, and f-measure are less than 50%, and models whose kappa indexes are less than 0.3. These criteria eliminate models whose applications in real contexts are not feasible, given the low ability to correctly classify students. The application of the kappa index eliminates models whose metrics are of low quality, considering a random classification. The application of the exclusion criteria removed nine prediction models, leaving 12 for the subsequent stage. Next, we must sort these remaining models in descending recall order to generate a list with the best results of the models filtered in the previous step. As a result, models with the highest recalls will have the lowest false negatives, prioritizing models that less frequently classify students who fail as having passed. Figure 6 shows the result obtained after applying the proposed framework. The rows in the table highlighted in yellow represent the top three student-failure prediction models. As we can see, the SVM classifier with RBF kernel using the SMOTE method is the best option among the 21 analyzed models. This method minimized the number of failing students incorrectly classified as passing (only 24). Furthermore, it obtained the highest values of g-means and f-measure among all tested models. The metrics presented in Fig. 6 provide a more detailed inspection of the performance of the filtered and ordered models. For example, we can observe that all models based on the undersampling balancing method were excluded after applying the proposed exclusion criterion, suggesting that this balancing strategy did not facilitate the failure prediction in the analyzed scenario. Moreover, it is possible to notice that the 12 remaining models have 8 of the 11 best metrics evaluated (highlighted in green). These observations corroborate the quality of the proposed framework. Fig. 6 Ranking of the best classifiers and their metrics Closing remarks and future work The contribution of this work is the proposition, application, and analysis of a framework for evaluating the prediction of school failure in distance learning, which can be used as a strategy to facilitate early pedagogical interventions. Predicting students’ failure in advance can be an effective strategy to reverse this negative outcome and favor the teaching-learning process in different ways. This work implemented and tested seven different predictive models, with and without balancing options, and collected 11 evaluation metrics. The results show the feasibility of achieving good performances in predicting the student failure with 50% of the total course time elapsed (accuracy: 94.37%; precision: 94.37%; recall: 84.93%; specificity: 99.57%; g-means: 87.65%; f-measure: 74.18%; and kappa index: 0.705). The challenge to be overcome in this work was to choose the best predictive model to be adopted in a student failure risk warning system. Therefore, we proposed a framework to incorporate both the subjectivity inherent to the educational context and the objectivity of the classification metrics. The results of this framework proved to be suitable to be adopted in decision-making processes that aim to mitigate and eventually eliminate the risk of students failing. Promoting data-driven education that takes advantage of the growing availability of digital data and the spread of machine learning techniques is an increasingly clear educational agenda. Many educational contexts lack new specific theoretical and practical contributions to face their shortcomings and strengthen their virtues. Therefore, different knowledge areas need to interact to propose updated technological solutions and produce practical actions that are, above all, ethical. It is worth mentioning that this work possesses some limitations. First, since the analyzed case study represents a specific scenario of a Brazilian university, the results obtained cannot be generalized. Furthermore, although we have compared 11 different evaluation metrics, one could evaluate other metrics, such as the ROC-area metric, or even combine weights from other metrics, as pointed out in [9] and [15]. Finally, all algorithms we implemented used the standard parameterization, so potential adjustments in these parameters could significantly impact the results. As future studies, we intend to collect and evaluate the opinion of educational experts (notably distance-learning coordinators, teachers, and tutors) about the proposed framework. Based on this data collected, we intend to propose a new evaluation metric that assigns penalties to the false positives and false negatives of tested models, to improve the identification of the best models. We also intend to evaluate the predictive results by using demographic data, together with data collected from Moodle and specific questionnaires. Finally, we hope to implement strategies to incorporate automatic failure prediction alerts to the LMS used by university, considering the selected prediction model. The authors declare that they do not have any conflict of interest. 1 The datasets generated and analysed during the current study are available in the GitHub repository, https://github.com/ptakaki/ML. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Adejo OW Connolly T Predicting student academic performance using multi-model heterogeneous ensemble approach J Appl Res High Educ 2018 10 1 61 75 10.1108/JARHE-09-2017-0113 2. Aguiar E Ambrose GAA Chawla NV Goodrich V Brockman J Engagement vs performance: Using electronic portfolios to predict first semester engineering student persistence J Learn Anal 2014 1 3 7 33 10.18608/jla.2014.13.3 3. 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==== Front J Pastoral Care Counsel J Pastoral Care Counsel PCC sppcc The Journal of Pastoral Care & Counseling 1542-3050 2167-776X SAGE Publications Sage UK: London, England 35388734 10.1177/15423050221090860 10.1177_15423050221090860 Articles The Perceptions and Lived Experiences of African-American Pastors at the Onslaught of the COVID-19 Pandemic in Mississippi Funchess Tanya 5104 University of Southern Mississippi , School of Health Professions (Department of Public Health), Hattiesburg, Mississippi, USA https://orcid.org/0000-0002-2447-6148 Hayes Traci 5104 University of Southern Mississippi , School of Health Professions (Department of Public Health), Hattiesburg, Mississippi, USA Lowe Samaria 5104 University of Southern Mississippi , School of Health Professions (Department of Public Health), Hattiesburg, Mississippi, USA Mayfield-Johnson Susan 5104 University of Southern Mississippi , School of Health Professions (Department of Public Health), Hattiesburg, Mississippi, USA https://orcid.org/0000-0001-9047-6341 Baskin LaWanda 5104 University of Southern Mississippi , School of Leadership and Advanced Nursing Practice (Department of Nursing), Hattiesburg, Mississippi, USA Traci Hayes, University of Southern Mississippi, School of Health Professions (Department of Public Health), Hattiesburg 39406, Mississippi, USA. Email: Traci.Hayes@usm.edu 6 2022 6 2022 6 2022 76 2 8996 © The Author(s) 2022 2022 Journal of Pastoral Care Publications Inc. This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. The purpose of this phenomenological study was to explore perceptions and lived experiences of African-American pastors addressing the onslaught of COVID-19 with their congregation. Thirty-seven pastors representing various denominations from across Mississippi participated in semi-structured, in-depth interviews. From the data, five themes emerged including (1) Pastors’ relentlessness, (2) Pastors’ adoption of new technology, (3) Maximized social capital, (4) Unintended consequences resulting from COVID-19, and (5) Unintended benefits resulting from COVID-19. COVID-19 faith-based organizations and administration community engagement African-American clergy typesetterts19 ==== Body pmcIntroduction African-American churches are a trusted source in the Black community (Brewer & Williams, 2019; Ecklund & Coleman, 2020; Frazier, 1996; Williams & Cousin, 2021). The Black church serves as a refuge where its members can receive spiritual, emotional, physical, and monetary support particularly in times of crisis (Baruth et al., 2014; Blake, 2021; Hankerson et al., 2018; Matthews et al., 2002; PBS, 2020). Henry Louis Gates Jr. stated “The Black church is the oldest, most continuous and the most important institutional structure created by African Americans.” The Black church is the birthplace and nurturing ground for major social, cultural, educational, economic, and political institutions that define Black America (PBS, 2021). However, in Mississippi on March 14, 2020, at the onslaught of the state’s first cases of COVID-19, Governor Tate Reeves asked churches to suspend worship services to abate the spread of the virus with implications that further guidance would be provided (Skinner & Ganucheau, 2020). Due to the COVID-19 guidelines, several services and programs that are embedded in the fabric of the Black community’s culture and relegated as core responsibilities of the church were restricted or stopped altogether. Pastors found themselves treading unfamiliar terrain. Services such as visiting the sick in hospitals, officiating congregants’ funerals, and supporting them through the loss of a loved one, hosting face-to-face worship, and physically interacting with their congregants were either suspended, limited, or stopped all together (State of Mississippi Office of the Governor, 2020a; 2020b; 2021). A summary of the initial Mississippi Executive Orders is listed in Table 1. Table 1. Executive orders timeline in Mississippi. Issue Date Executive Order Order Details March 14, 2020 EO No. 1463 Restricts nonessential gatherings of 10 people or more April 3, 2020 EO No. 1466 Statewide shelter-in-place order to slow the spread of COVID-19, which goes into effective from Friday, April 3, 2020 to Monday, April 20, 2020. April 21, 2020 EO No. 1477 Establishes a statewide Safer at Home order laying out a measured and strategic plan to reopen Mississippi while continuing to flatten the curve and conserve health care resources. Table 2. Type of service delivery approach. Service Type North South Central Online Facebook/Zoom/You Tube 7 10 9 Radio 0 0 1 Conference Call 3 0 2 Church Application 0 0 1 Transferred Outside (i.e., parking lot services) 0 2 0 Church Inside/Online 1 0 1 No Church Services 0 0 1 As ongoing changes to guidelines and protocols continued, pastors had to be nimble and respond accordingly (Johnson, Eagle, Headley, & Holleman, 2021). The few quantitative studies conducted did not capture how religious leaders have responded to the altering of traditional religious activities (Lifeway Research, 2021; O’Brien, 2020). Johnson, Eagle, Headley, Holleman (2021) conducted a qualitative study to learn how pastors and congregations responded to COVID-19 restrictions and guidelines. However, their study focused on one denomination, the United Methodist. Additionally, their study sample consisted of 88% White pastors. Due to the duality of the Black church in Black communities, it is crucial to explore how these pastors addressed COVID-19 guidelines and met the needs of their congregants during the pandemic. Therefore, in-depth, semi-structured, interviews were conducted with African-American pastors across Mississippi during the months of July–December 1, 2020. The purpose of the interviews was to take a deeper dive into African-American pastors’ perceptions and lived experiences in addressing COVID-19 with their congregants. Methodology To explore African-American pastors’ perceptions and lived experiences of addressing the onslaught of COVID-19 with their congregation, a phenomenological study design was applied to the study. Health care professionals and scholars have used a qualitative study design to learn of the lived experiences of others (Neubauer et al., 2019; Peart et al., 2020). According to Glesne (2006, p. 4), qualitative research methods are used to understand a phenomenon from the perspective of those involved. A purposive snowball sampling frame was used to recruit pastors across Mississippi. According to Morgan (2008) to seek as much diversity in the sample as possible, it is best to start the sample with seeds that are as diverse as possible. To this end, gatekeepers in the local communities aided in the recruitment of African-American pastors from churches of all denominations and membership sizes and from rural and urban areas. According to the Mississippi State Department of Health (2015), rural is defined as any Mississippi county that has a population of less than 50,000 individuals; this equates to 80% of the 82 counties in Mississippi. Each interview session was attended by one or more of the co-investigators and/or the research assistant for the purpose of observation and note taking. Pastors were assigned a pseudocode and a number to equate with the part of the state in which they resided. Each interview was recorded and transcribed verbatim. Interview Guide A semi-structured interview questionnaire was used to guide the in-depth conversation with each pastor. In-depth interview was the method used to collect data for this study. Polit and Beck (2006), define an interview as a method in which one person asks questions of another person. This method was appropriate to explore the study research question. As noted by DeJonckheere and Vaughn (2019), a semi-structured protocol allows the researcher to collect open-ended data, to dive deeper into sensitive matters, and to explore the participant’s thoughts, feelings, and beliefs about a particular topic. Topics included sources for COVID-19 information, financial challenges, consequences and unintended benefits of COVID-19, technology use, and how COVID-19 had impacted their life professionally and personally. For an in-depth look at the interview guide view “Engaging African Americans in COVID-19 Research: Lessons Learned” (Hayes et al., 2021). Data Analysis Data were analyzed using the Ritchie and Lewis’s (2003) Framework Approach. Pastors meeting the inclusion criteria (at least 18 or older, African-American/Black, serving in the position of pastor or associate pastor, and residing in Mississippi) were recruited to participate. The interview process continued until data saturation occurred. The researchers followed closely Ritchie and Lewis’s Framework Approach (2003). The Framework Method is a popular approach to analyzing qualitative data and is widely used in the health care field. According to Gale et al. (2013), the Framework Method is most used for the thematic analysis of semi-structured interviews. The Framework Method follows seven steps: (1) The audio recordings were transcribed verbatim using a denaturalized approach, (2) Transcripts were read and reread so the investigators could become familiar with the data allowing open coding to take place, (3) All researchers coded the first two transcripts independently to allow for different perspectives to reduce the likelihood of one perspective becoming dominant, (4) Researchers than met and compared the labels they applied, and agreed on a set of codes thus forming a working analytical framework, (5) The analytical framework was then applied to subsequent transcripts using identified categories and codes, (6) Researchers used NVivo software to help simplify the raw data into frequencies, patterns, and categories, and 7() Data were interpreted to describe or explain aspects of the data. Results The researchers recruited 44 pastors to participate in the study. Thirty-nine completed the consent forms, and 37 pastors participated in the in-depth interviews. Eighty percent of the churches’ congregants ranged from the ages of 18 to 65 years; 17.95% had an age group that ranged from 65 and over. Eighty-two percent of the pastors were married, 10.26% were divorced, and 5.13% had never married. The majority of the pastors had a graduate degree (40%), 27% had a bachelor’s degree, 5% had completed community college, and 5% had completed high school. A majority of the pastors had secular jobs, just 31% pastored only. Most of the pastors were bi-vocational (48.22%), meaning they pastored and worked an external job in addition to pastoring, and 20.5% pastored and performed outside the ministry part-time. In the study, 8% of the pastors had been infected with COVID-19 themselves. From the data, five themes emerged including (1) Pastors’ relentlessness, (2) Pastors’ adoption of new technology, (3) Social capital helped sustain during challenging times, (4) Unintended consequences resulting from COVID-19, and (5) Unintended benefits resulting from COVID-19. Theme 1: Pastors’ Relentlessness Pastors first learned of COVID-19 from the news (50%), work (21%), or from other sources (35%). For example, many pastors learned of COVID-19 from families and friends who lived in states already impacted. All pastors discussed-making quick, fast, and informed decisions, as congregants trusted them for guidance. They reported notifying their members of the initial changes for worship service in countless ways, at church one Sunday in March or April, by word of mouth, emails, text messages, individual phone calls, robocalls, or video messages through Facebook and YouTube. They also used flyers and Twitter to announce the changes in worship services to their congregants. Ninety-seven percent of the pastors reported stopping in-person worship services in March 2020 immediately following the state guidelines; this caused a minor break in service for some as they sought alternate ways to continue worship services. While the remainder (8%) reported, they never stopped in-person worship services. Theme one entails two subthemes, approach to guidelines adherence and giving. Approach to Guidelines Adherence: Pastors used several approaches to follow the COVID-19 guidelines related to in-person service. One pastor (S1), transitioned to online services, but was adamant about not opening the church back up until a vaccine became available. One pastor (C1) stopped church altogether and did not hold in person or virtual services. He shared with us how challenging technology can be in rural areas, C1 indicated, “That makes it challenging to have services online, challenging to sometime to use Cash Apps or whatever. Another thing you have to have is people need the expertise to get those things done. And not every community has that or easy access to it.” Most pastors (76%) talked with their church leadership, Boards of Bishops, their health team, or their congregants who were medical/health care experts and decided to transition worship services online in March 2020 or shortly after (S2, S3, S5, S6, S7, S8, S9, S10, S11, C1, C2, C3, C4, C8, C9, C10, C13, C12, C14, C15, C17, N1, N2, N4, N6, N7, N8, N9). The majority of the pastors (98%) reported little to no resistance from congregants about worship services. N1 shared, “And when the pandemic struck, the pastors who I know of and the ones that I pastor, it was like someone dropped a bomb at one time. It was just like somebody shooting at a herd of buffalo because everyone became frightened. Everybody wanted to run, and the question I asked the pastors, if the shepherds are running, who’s attending to the sheep?” S10 met with his deacons and decided to still have worship service as usual but would modify how attendees partook in Communion. For example, they had planned to use the small peel off cups, to make certain attendees sanitize, and would not shake hands. However, when the fourth Sunday in March came and no congregants were in church S10 revealed, “The people stopped coming. The people’s voices have been heard.” Eight percent of the pastors in this study never stopped having in-person service; they either transitioned to outside worship services (S4) or delegated essential personnel to live stream the worship services for the broader audience (N3). Some pastors opted to have the congregants in the church closely follow COVID-19 guidelines and protocols (S15, C7). C7 stated, “We never shut down; because the mandate I have from the Lord would not allow me to shut down.” Giving: Giving, including tithes and offerings, varied depending on the church in this study. Oddly enough, most of the pastors (46%) reported that their finances increased; some spoke of a higher increase than before COVID-19. Pastors (29%) said tithes and offerings were sustained. Fourteen percent of the pastors reported struggling financially and reported tithes and offerings had decreased significantly. S11 conveyed, “As I said, everybody still got their job, but the charitable gifts to the church have been affected, really, bad.” While S2 noted, “That the giving was slow but had not affected their ability to operate.”C10 reported, “As far as having the financial support prior to the pandemic, we don’t’ have it now. We’re at ground zero; we are going back to service.” Theme 2: Adaption and Adoption of Technology Pastors in this study discussed using limited technology to support church services prior to COVID-19. N3 stated, “There was not much technology protocol going on, not at that point anyway. We were going to do it eventually, but COVID-19 pushed us in what we were actually going to do.” Over 70% of the pastors at the time of this study had started or heightened their use of Facebook Live, Zoom, YouTube, and Application to hold church worship services on Sundays. Several of the pastors used these systems to conduct bible study on Wednesday nights. Fourteen percent of the pastors used conference call lines to communicate and deliver their Sunday messages. Pastors also used YouTube, conference calls, emails, and text messages to stay connected with their members between worship services. S10 stated, “I send 80–125 text message of encouragement to members of both churches. I send the same message to all of them, you know ‘keep the faith’, ‘don’t let fear set in’, ‘keep trusting God’.” To sustain the financial support of the church, pastors used specific applications such as Givelify, Cash App, and Pay Pal to receive tithes, offerings, and other donations during the pandemic. Table 2 provides a listing of the types of service delivery approaches. Theme 3: Maximized Social Capital Social capital is the relationship, assets, and reputation that exist among people, its customers, and partners that make the organization runs effectively and efficiently (Mask, 2019). Several churches (10%) had an already established health care team or relied on members of their congregation who were medical experts such as doctors, nurses, pharmacists, or those who worked in other related fields. Pastors trusted these teams or individuals to implement COVID-19 guidelines, to educate the congregation, to modify information to assure it was culturally and linguistically appropriate for the assembly and the community at large, and to discuss the impact of COVID-19 on vulnerable populations such as the elders and those with underlining chronic health conditions. In addition, they helped to sift through erroneous information while dispelling myths and clarifying facts. S5 stated “What I did was, I moved myself out of the way. All my decisions were made with Dr. Blank, who is in that field.” C3 stated, “We have a medical ministry. On the medical ministry we do have nurse practitioners, nurses, pharmacy; so, we really allow our team to function in that aspect and give us council and direction.” Pastors used their connections with other pastors within and external to their denominations. C14 described his work with other pastors, “I did some teaching and training through Zoom to actually help other pastors to get some of these programs going for their congregation.” Theme 4: Unintended Consequences Unintended consequences were denoted as those negative factors that happened because of COVID-19 that were unexpected. Several of the pastors (8%) described never expecting to be infected themselves with COVID-19. More than 25% of the pastors in this study mentioned not expecting the pandemic to last as long. Pastors listed unintended consequences as loneliness, depression, and grief. They mentioned not being able to see, touch, or observe congregants as an unintended consequence. S5 specified, “We couldn’t hug and fellowship.” Pastors described not being able to take care of their pastoral duties like visiting the sick, officiating funerals, or being able to comfort members through the loss of a loved one. S9 described it as, “My biggest burden, it has affected me, my ministry is one of visitation.” S5 stated, “We are in a situation now where we go straight to the graveside and bury our loved ones.” Pastors expressed being heavily burdened by the inability to “worship inside the building.” S4 stated, “Moving outside the building was the hardest thing for us.” Pastors were overwhelmed by the unexpected death tolls. S12 stated, “The death rate bothers me. I work for a funeral home part-time, directing funerals. The death rate, it’s real! It’s real as it can be! Again, our president didn’t take it as such. He took it lightly, but it’s definitely serious.” Pastors described the church closing as something they never expected. Additionally, pastors also mentioned an increase in domestic violence and couples on the verge of divorce seeking counseling. C7 reported, “One thing that I have seen creep up is domestic violence between husband and wife or live-in boyfriend or girlfriend. Even people at the verge of divorce. People angry with one another. If a man went to work, he spent 9–10 h away from home; he could tolerate the wife/children whatever. But when things shut down and people must tolerate each other 24/7; it began to show a lot of things that people didn’t know about each other. It causes a lot of tension to flow.” Theme 5: Unintended Benefits Forty-nine percent (49%) of the pastors in the study mentioned tithes and offerings not decreasing as a benefit. N9 stated, “People felt the need to give more to the poor people’s fund/the benevolent fund just in case we had a situation where a family might need additional help during the pandemic.” Pastors listed the adoption and adaptability to technology as a benefit. C4 shared, “It’s a rural area and people want to stay in their ways. Now we’re forced to get out of our way and comfort zone. Even though it’s a lot of things on the internet that is bad, we can use what’s good.”C3 reported “To see, a 70-year-old senior have a screen up that tickles me to death; to see them press their way through and to see that we are going to stay connected. That pushes your real heart button, to see people push their way through the learning curve, have been the best benefit.” Eight percent (8%) of the pastors in this study emphasized the resources from government programs as a benefit during this time. More specifically, the pastors applauded government programs such as the stimulus checks and the additional unemployment funds as valuable benefits for their congregants. C9 stated, “Government wise; from federal to state, instead of you having to find agencies, they came and found us. The usual paperwork and red tape, the majority of that was eliminated. That was the best benefit for me.” Pastors also mentioned people relying on God more, being able to spend more quality time with family, helping each other more, and an increase in the number of people receiving their Sunday messages through technology as unintended benefits.” N3 denoted “Being in a small rural town like we are, we are getting 500–600 views or whatever the case may be. When you are getting views like that, you’re reaching more than you were sitting in the pews. God works in mysterious ways.” Discussion At the onslaught of COVID-19, pastors found themselves treading unfamiliar territory due to federal and state restrictions forcing them to rethink how worship services are implemented and how they intermingled with their congregants and the community. Data revealed how pastors diffused information with their congregants, what sources they utilized and trusted, and the opportunities and benefits derived as consequences of COVID-19. This study has implications that may be useful to support health care professionals, decision-makers, and pastors alike to develop projects, programs, services, and interventions in collaborating with religious leaders across the state as we continue to address the pandemic now and in the future. All pastors in this study self-reported following the recommended COVID-19 guidelines; they were determined to protect their congregants with a particular focus on seniors and those with an underlining condition. This meant adapting, modifying, and creating new strategies and practices to conduct culturally traditional worship services and pastoral care. Their decision-making process was based on where, who, and how they obtained their COVID-19 information. For example, bi-vocational pastors received firsthand information from their jobs, and thereby they had time to process the information before disseminating it and acting upon it with their congregants. Pastors who had churches with established health care ministries were confident in letting those teams handle the coordination, organization, and dissemination of COVID-19 information and prevention activities. In a short period, they came up with new innovative ways to continue worship services that were not in the “building.” The “building” symbolizes a sacred space for African Americans to gather for not only worship services but also a space for refuge, support, resources, political, and economic activism. Therefore, closing the church or congregants being unable to gather in the “building,” left pastors disconcerted. S4 described the experience as, “Church is all I know since I was 10–11 years old. To move outside the building, that was hard for me, and I don’t know when we will be able to go back inside.” The Pastors’ approaches may have differed, but they all desired to mimic worship services that closely aligned with the cultural traditions seen in the Black church; singing, playing musical instruments, and taking communion while simultaneously striving to stay connected to the congregants. They remained connected to congregants through virtual services, phone calls, conference lines, and delivery of foods baskets, sanitizers, masks, and other personal protective equipment. They also provided counseling when needed. These findings aligned closely with Johnston et al. (2021). These researchers used Swiler’s Framework to analyze their data. Their study sought to maintain preexisting practices such as singing, worship, and connection to pastoral care. These researchers referred to Swiler’s mention of “unsettled times” as being characterized by making a conscious effort to match new practices and strategies of actions with preexisting ideas, values, and resources while being responsive to the changing context. The relentlessness of pastors to protect their congregants appeared to be part of their core values. Pastors relied profoundly on guidelines from the Centers for Disease Control and Prevention and the Mississippi State Department of Health as their sources for information on COVID-19 and, in many instances, received this information on an individual basis. Therefore, implementing a system statewide where all pastors in Mississippi could receive education on COVID-19, updated guidelines and protocols as they are rolled out, and offering them a space to provide input, to ask questions, and to receive feedback is recommended. Technology tools used to help enhance programs and services were now flaunted as essential. Pastors in the study learned new skills and discovered new talents unbeknown to them. Pastors who were more technological savvy taught other pastors to use the virtual platforms that included how to set up their services, record their messages, and send and make available their recordings to congregants. Since the pastors were working with limited personnel to assist with services, they relied on their young congregants to help the elderly members stay connected. These identified congregants pulled double duty, enabling them to develop new talents and skills. The new areas pastors ventured into gave them newfound confidence. S1 shared that “having to address a lot of the key issues myself, I now believe in myself more.” Pastors experienced a paradigm shift, and although they will hold on to some traditions, some decided to adopt these new practices and strategies long-term. Shifting from face-to-face to an online platform caused pastors to study more. C10 discussed, “it made me study more, made me dissect God’s word more, and I had to be a professional at all times.” During a Follow-up Results Session organized for the participating pastors at the end of 2021, they discussed returning to face-to-face services. However, they had a demand from some congregants to continue online services. Therefore, a sustainable training program to assist pastors in developing their technical skills and knowledge is recommended, and the adoption of policies where concretive progress is made to decrease the digital divide in rural areas and reduce the technological inequalities. Putnam discusses social capital in terms of internal bonding and bridging. Putnam (2000) states that internal bonding is needed “when specific reciprocity and mobilizing solidarity is necessary for ‘getting by’ in oppressive situations.” Trust is a foundational element of social capital. At the onslaught of the pandemic, pastors continued to lean on their members connected in the community through larger groups such as civic organizations, occupational associations, minority nonprofit organizations, and church groups to assist in addressing COVID-19 with their congregants. Pastors were relieved to have health care teams already established within the church; the health care teams had networked with external agencies and could link their leadership and congregants to immediate resources and services. As social capital is embedded in the networks of the Black church and is an essential construct built into its foundation, two pastors in this study specifically spoke out about their perception and experiences of the diminishing social bonds between the pastors. One pastor noted, “You don't see pastors gather together like they used to; it's more competition. Decision-makers would call one person, and all the pastors would assimilate themselves together. Decision-makers don't hear us because we don't come in numbers anymore.” Moreover, external to the church is what Putnam (2000) described as bridging social capital. Putnam (2000) defines bridging social capital as building various social groups to find common ground and cause. Putnam states, “Bridging networks are good for linking to external assets and for information diffusion for “getting ahead“ of the status quo.” Pastors bridged social capital by working with diverse social and community groups. For example, during the study and still in progress today, Mayor Tolby Barker of Hattiesburg, Mississippi, formed a diverse COVID-19 working group, consisting of representatives from various sectors as well as local pastors. The purpose of this workgroup is to protect the city’s most vulnerable populations and prevent an overrun of the health care system by slowing the spread of the coronavirus. During weekly meetings, updates are provided. Pastors in the Forrest County area receive direct and indirect information from this group. S3 discussed how he and several pastors met with the mayor early on where they received information about COVID-19, and they were forewarned about the closing of the churches to abate the continual rise of the coronavirus cases. Internal bonding appeared strong among the pastors; however, it is recommended that the government agencies and health care organizations facilitate social bridging to increase the pastors’ linkage to resources and services during a crisis. Pastors noted unintended consequences and benefits from COVID-19. Pastors have carried the responsibility on their shoulders to visit the sick, officiate funerals, and check on their congregants to offer support and resources. Being a pastor is who they are, and they grieved not being able to carry out these duties. Their core purpose was being challenged. C9 expressed, “In the African American church, we don’t bury our loved ones, and it’s a two- or three-day thing. So, there will have to be some counseling.” Having congregants receive stimulus checks and extra funds for unemployment was seen as beneficial to pastors. In addition, pastors experienced a moment to regroup, reflect, and reprioritize. Pastors saw this refocusing as a silver lining during the pandemic. A recommendation is for mental health agencies, crisis management organizations, health care professionals to partner with pastors to provide them and their congregant's services post-COVID-19. Limitations The purpose of this study was to explore the perceptions and lived experiences of African-American pastors at the onslaught of COVID-19 and how they addressed it with their congregants. This study was conducted with a small sample of African-American pastors in the northern, southern, and central regions of Mississippi who met the study’s criteria. Therefore, data cannot be generalized for all African-American churches in Mississippi. More pastors from different areas of the state may have different perceptions and experiences and, therefore, should be recruited from areas where there was limited to no representation. Future Studies This study looked at pastors’ perceptions and lived experiences as they addressed COVID-9 at the onslaught of the pandemic. Future studies should explore the pastors’ responses in the later stage of the pandemic and extend the analysis to focus on other races and ethnicities. In Mississippi, 97.8% had broadband width in urban areas compared to 63.4% in rural areas (Congressional Research Services, 2021). Future studies may focus on how pastors will manage the two worship services, online and face to face with the same resources and personnel. Pastors mentioned an uptick in domestic violence and divorce rates, having COVID-19 themselves and experiencing depression. Future studies may address mental health among congregants and pastors and assess the available mental health resources. Additionally, pastors’ engagement with agencies and organizations outside the reach of their members was not explored, so future studies should address social bridging among pastors across the state. Conclusion and Implications The Black church has and still is considered a hub in the Black community. African Americans depend on the church for refuge, a safe place to gather, and one where congregants and the community come to collect or learn about resources. However, during the onslaught of COVID-19, pastors were being asked to suspend worship services and later reduce the number of attendees. This study provides an in-depth look at how African-American pastors in Mississippi responded to the COVID-19 guidelines and restrictions with their congregants. Pastors in this study learned new skills, gained newfound confidence, and instituted alternative strategies and practices for hosting worship services virtually. Pastors witnessed growth in the number of people listening to their messages and in tithes and offerings. Not all pastors were able to adopt the new innovative technological strategies. The broadband width in some rural areas is still low. Other factors that prohibit technology adoption and use were aging congregants and people not having internet services because of affordability. The pastors in our study spoke about the loss of loved ones, the inability to have closure at funerals, domestic violence, and an uptick in people needing marital counseling as focus areas after COVID-19. Thus, there are implications for agencies and nonprofit organizations to collaborate with pastors to create programs and services to address the issues. Research is already underway looking at how Black churches can become more involved in managing mental health. Acknowledgments The research team would like to acknowledge all the pastors representing North, Central, and Southern regions of Mississippi who participated in this study. Author’s Contribution: Conceptualization (TF, TH, LB, SMJ); Methodology: TF, TH, LB, SMJ; Formal analysis and investigation: TF, TH, LB, SMJ, SL; Writing: TF, TH, LB, SMJ, SL; Reviewing and editing: TF, TH, LB, SMJ, SL. Availability of Data and Material: Not applicable. Code Availability: Not applicable. Consent to Participate: Informed consent was obtained from all individual participants included in the study. Consent for Publication: Not applicable. Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Ethics Approval: The study was approved by the Institutional Review Board at the University of Southern Mississippi (#IRB-20-201). Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. ORCID iDs: Traci Hayes https://orcid.org/0000-0002-2447-6148 Baskin https://orcid.org/0000-0001-9047-6341 ==== Refs References Baruth M. Wilcox S. Evans R. (2014). The health and health behaviors of a sample of African American pastors. Journal of Health Care for the Poor and Underserved, 25 (1 ), 229–241. 10.1353/hpu.2014.0041 24509023 Blake J. B. (2021). The Black church is having a moment. CNN. https://www.cnn.com/2021/02/16/us/black-church-pew-pbs-moment/index.html Brewer L. C. Williams D. R. (2019). We've come this far by faith: The role of the Black church in public health. American Journal of Public Health, 109 (3 ), 385–386. 10.2105/AJPH.2018.304939 30726121 Congressional Research Services (2021, March 9). 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==== Front Ir J Med Sci Ir J Med Sci Irish Journal of Medical Science 0021-1265 1863-4362 Springer International Publishing Cham 35411487 2999 10.1007/s11845-022-02999-8 Original Article Patient knowledge, personal experience, and impact of the first wave of the COVD-19 pandemic in an Irish oncology cohort http://orcid.org/0000-0002-9749-3651 Kieran Ruth kieranr@tcd.ie 1 Moloney Carolyn 1 Alken Scheryll 1 Corrigan Lynda 1 Gallagher David 1 Grant Cliona 1 Kelleher Fergal 1 Kennedy M. John 12 Lowery Maeve A. 12 McCarthy Michael 1 O’Donnell Dearbhaile M. 1 Sukor Sue 1 Cuffe Sinead 1 1 grid.416409.e 0000 0004 0617 8280 Department of Medical Oncology, St. James’s Hospital, Dublin, Ireland 2 The Trinity St James’s Cancer Institute, Dublin, Ireland 12 4 2022 2023 192 2 533540 11 10 2021 30 3 2022 © The Author(s), under exclusive licence to Royal Academy of Medicine in Ireland 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Background Oncology patients have had to make many changes to minimise their exposure to COVID-19, causing stress. Despite education, some patients still do not recognise potential COVID symptoms. Aims We assessed patient knowledge of COVID, and its impact on their behaviours, concerns, and healthcare experience. Methods A 16-page questionnaire was distributed to 120 oncology patients attending the day unit of a tertiary Irish cancer centre for systemic anti-cancer therapy (May/June 2020). The Irish 7-day COVID incidence during this period ranged from 2 to 11 cases/100,000 people. Results One hundred and one responses were received, 1% had tested positive for COVID, and 31% had undergone testing. Participant insight into their knowledge about COVID and their own behaviour was limited in some cases. Seventy-five percent reported total compliance with restrictions, but many were not fully compliant. Self-reported confidence in knowledge was high, but did not predict demonstrated knowledge. Sixty percent did not recognise two or more symptoms; 40% did not self-identify as high-risk. Patients reported more health-related worry (72%), loneliness (51%), and lower mood (42%) since the pandemic began. Financial toxicity worsened, with increased financial worry (78%), reductions in household income (40%), and increased costs due to lockdown (62%). Use of facemasks introduced new communications barriers for 67% of those with hearing loss. Conclusions Despite self-reported confidence in knowledge, some patient’s recognition of COVID symptoms and the preventative strategies they should use are not optimal, highlighting the need for further education in this regard. COVID has been a significant stressor for patients and more practical, financial, and psychological supports are needed. Supplementary information The online version contains supplementary material available at 10.1007/s11845-022-02999-8. Keywords COVID-19 Financial toxicity Oncology Patient education Patient knowledge Social support issue-copyright-statement© The Author(s), under exclusive licence to Royal Academy of Medicine in Ireland 2023 ==== Body pmcBackground The COVID pandemic has had a substantial impact on cancer care provision and on patients’ lives. Those with cancer are much more vulnerable to COVID—in China, patients with cancer had a case fatality rate of 5.6% compared to 2.3% in the general population. Liang et al. [1] showed that cancer was associated with higher risk of severe events, especially in those who had had recent chemotherapy or surgery. In Wuhan, patients with cancer were more than twice as likely to contract COVID [2]. The most prevalent signs of COVID-19 infection are fever, dyspnoea, fatigue, cough, myalgias, anosmia, and ageusia [3, 4]. Laboratory features include leukopenia/leukocytosis, lymphopenia, elevated liver enzymes, lactate dehydrogenase, C-reactive protein, ferritin, or d-dimer [3, 5]. All of these occur commonly in unwell oncology patients, making it difficult to distinguish between cancer-related, treatment-related, and COVID symptoms. In Ireland, general community restrictions began on March 15th 2020, 2 weeks after the first Irish case was reported. By March 24th, non-essential businesses were closed and a ‘stay at home’ order was introduced, with exceptions for essential work, food shopping, and exercise within 2 km of the home only (see Fig. 1). High-risk patients were told to ‘cocoon’ and to avoid leaving home entirely [6]. Case numbers fell from a maximum 7-day incidence of 132 cases/100,000 people (April 15th) to 11 cases/100,000 at the beginning of study recruitment (May 24th), and 2 cases/100,000 by the end (June 12th, see Fig. 1) [7]. The most severe restrictions began to be relaxed in May, including an extension of the travel radius to 5 km and groups of 2–3 being allowed to meet outdoors, but most businesses remained closed and advice did not change for our ‘cocooners’ (‘Phase 1’ of the ‘Roadmap to Reopening’ [8] ).Fig. 1 Local COVID timeline In Wuhan, 41% of COVID cases in cancer patients were nosocomial [2], so additional measures were put in place in our oncology service (Fig. 1) to minimise nosocomial transmission. Where appropriate, patients who were stable on maintenance therapy were offered treatment breaks or were switched to oral therapy. Some routine clinic reviews were moved to telephone reviews. Previously, unwell patients had been assessed directly by a member of the oncology team. Instead, these patients were admitted under general medicine (‘COVID pathway’ in Fig. 1). Inpatients were not allowed to have visitors, with exceptions on compassionate grounds for those who were receiving end of life care. The Irish government provided clear messaging on restrictions and on recognition of COVID symptoms. Despite this, we noticed that many patients did not recognise some symptoms as potentially representing COVID or reported ‘risk behaviour’, e.g. having guests in their homes. Furthermore, it was increasingly recognised that a ‘COVID denier’ narrative has become popular on social media. In studies in other countries, the vast majority of cancer patients trusted government (98%) and doctors (94%) for reliable information, with a minority more trusting of information obtained via WhatsApp, Twitter, Instagram, and Facebook [9]. The attitudes of Irish patients with cancer surrounding information about COVID have never previously been explored to our knowledge. We assessed patient knowledge of COVID, and the impact of the pandemic on their behaviours, beliefs, concerns, and healthcare experience, to identify any further education/quality improvement needs. Methods A 16-page investigator-created, self-reported anonymous qualitative survey (supplementary data) was distributed to 120 oncology patients attending the day unit of a tertiary Irish cancer centre for systemic anti-cancer therapy (May/June 2020). Patients were excluded if they had an ECOG performance status of > 2, if they did not speak fluent English and needed a translator during medical consultations, or were too unwell to complete the questionnaire. The Flesch-Kincaid grade level of the questionnaire was 4.9. Questions that had a high (> 25%) non-response rate were excluded from analysis. Data were analysed with the Statistical Package for the Social Sciences (SPSS) version 27 (IBM Corporation, Armonk, NY). Descriptive statistics for sample characteristics and study measures were calculated and reported as means and standard deviations for quantitative variables or percentages and frequencies for categorical variables. Differences between groups were evaluated using independent sample t-tests, chi-square analyses, and ANOVAs. A p value of < 0.05 was considered statistically significant. The study was conducted under the supervision of the Tallaght University Hospital/St. James’s Hospital Joint Research Ethics Committee. Results Patient demographics One hundred and one responses were received from 120 surveys distributed. Demographics are described in Table 1. Participant ages ranged from 22 to 87 years.Table 1 Participant demographics n Gender Male 40 Female 60 Non-response 1 Age  < 30 1 30–40 11 40–50 19 50–60 28 60–70 31 70–80 5 80 +  1 Non-response 5 Treatment intent Palliative 53 Curative 41 Non-response 7 Cancer type Breast 19 Upper GI 19 Lung 14 Head and neck 11 Lower GI 10 Skin 8 Gynae 7 Lymphoma 6 Prostate 4 Non-response 3 Employment status in January 2020 Active employment 47 Sick leave 22 Retired 20 Homemaker 7 Unemployed 2 Non-response 3 Housing House 88 Apartment 11 Residential care 1 Non-response 1 Cohabitation* Living alone 16 With 1 other person 32 With 3 + other people 29 With an essential worker 7 With people not taking COVID precautions 4 Home internet access Adequate 80 Limited 13 None 6 Non-response 2 *Possible to select more than 1 category Percentage values not displayed—as 101 responses were received, these are almost identical to numerical values. COVID knowledge, screening, and symptoms Most were aware of government restrictions and reported good understanding of and compliance with these (Fig. 2), with 75% (n = 72 of 96 respondents, 5 non-responses) reporting complete compliance with cocooning. Despite this, of these 72 ‘cocooning’ patients, 36% (n = 24 of 66, 6 non-responses) continued to shop in-store, of whom 42% (n = 10 of 24) went as/more often than before, while 28% (n = 16 of 57, 15 non-responses) received visitors to their homes.Fig. 2 Perceptions of restrictions Including all respondents (including those not cocooning), 43 regularly went shopping, of whom many were not using risk-reduction strategies (Fig. 3). Most shoppers (60%, n = 25 of 42, 1 non-response) had alternatives to shopping in-store but chose to keep going out, most often (n = 19 of 34*, 56% each) because they enjoyed it or wanted to maintain independence, and only 18% (n = 6 of 34*) thought there was no risk in going out. A minority (24%, n = 23 of 95, 6 non-responses) now never went shopping. *n = 34 due to addition of 9 patients who earlier indicated that they no longer shopped for themselves but gave valid responses to this section.Fig. 3 Protective strategies used (%) Many patients (46%, n = 39 of 84, 17 non-responses) were somewhat/very fearful of COVID, but this did not strongly predict either protective (e.g. mask-wearing: OR 1.1, 95% CI 0.3–4.8, p = 0.9), or risky behaviours (e.g. shopping frequently: OR 0.5, 95% CI 0.1–1.4, p = 0.2). We offered a list of 10 symptoms identified in HSE (Irish health service executive) patient information campaigns as potentially representing COVID infection (see Fig. 4). Patients were asked how many of these could potentially be COVID-related, and if they had experienced any of them as symptoms of their cancer/side effect of treatment (Fig. 4). Almost all respondents (95%, n = 93 of 98, 3 non-responses) reported feeling confident/very confident in recognising COVID symptoms, but 60% (n = 60) did not recognise two or more out of 6 major symptoms (indicated with asterisks in Fig. 4), most frequently fatigue (55%, n = 56), aches/pains (58%, n = 59), altered smell/taste (33%, n = 33), and dyspnoea (14%, n = 14). The mean number of symptoms recognised overall was 5.3 ± 1.9; the mean number personally experienced (excluding vomiting/diarrhoea) was 1.2 ± 1.5 symptoms (range 0–7 out of a possible 8). Most patients (59%, n = 60) had experienced at least one symptom.Fig. 4 Recognition and personal experience of COVID-type symptoms (%) The number of symptoms recognised did not correlate with confidence (p = 0.9) or desire for more information about COVID (p = 0.9) but did correlate weakly to the number of COVID-type symptoms personally experienced (r = 0.25, p < 0.01). Patients who had been tested for COVID recognised more symptoms than those who had not (5.9 ± 1.9 vs 5.2 ± 1.7, t(97) = 2, p < 0.05). Age, gender, level of fear around COVID, and being on curative or palliative-intent treatment did not predict number recognised or personally experienced. Many respondents (40%, n = 40) did not feel they were at higher risk of contracting COVID, while 15% (n = 15) thought they were no more likely to be very sick than an average person if infected. Many did not know that chemotherapy, radiation, and immunotherapy can impact morbidity/mortality in COVID (31% (n = 31), 44% (n = 44), and 49% (n = 48), respectively). One patient had tested positive for COVID; two had been contacts. Overall, 31% (n = 31) of patients had been tested, of whom most found testing stressful (53%, n = 15 of 28, 3 non-responses), but were glad to have been tested (92%, n = 24 of 26, 5 non-responses). The majority (94%, n = 91 of 97, 4 non-responses) knew COVID could be spread by an asymptomatic carrier, and many thought oncology staff should be screened for COVID routinely (82%, n = 75 of 92, 9 non-responses), even if well, while some thought that patients should also be routinely screened (37%, n = 33 of 89, 12 non-responses). Most patients had obtained their knowledge about COVID from the television news (87%, n = 85 of 98, 3 non-responses), or from government messaging (80%, n = 78 of 98, 3 non-responses). A minority relied on information from family/friends or social media (33%, n = 32 of 98, 3 non-responses each), though reliance on social media was numerically more common in those under 50 (45%, n = 14 of 31) than older patients (25%, n = 16 of 65, X2 (1, N = 93) = 3.7, p = 0.053). While only 14% (n = 14 of 98, 3 non-responses) wanted more general information about COVID, 66% (n = 65 of 98, 3 non-responses) would have liked more cancer specific information. These 65 patients particularly wanted more information about prevention (46%, n = 30 of 65) and symptoms (37%, n = 24 of 65), with a preference for written (75%, n = 46 of 61, 4 non-responses) over verbal, video, formal educational session, or helpline-based formats. Emotional and social impact Interestingly, 76% (n = 66 of 87, 14 non-responses) had thought more about their mortality as a result of COVID, and while some patients (19%, n = 16 of 82) already had an advance directive in place, another 39% (n = 32 of 82, 19 non-responses each) were considering one as a result of COVID, and of those who had not already made wills, 38% (n = 18 of 47) had made/were making one because of COVID. Many patients reported more health-related worry (72%, n = 63 of 88) or lower mood (42%, n = 37 of 88, 13 non-responses each) since the pandemic began. Even though most reported the amount of social support they received was unchanged (58%, n = 47 of 81), or had actually increased (22%, n = 18 of 81, 20 non-responses each), many respondents felt lonely more often (51%, n = 40 of 78, 23 non-responses). Of the 39 patients with a cough related to their cancer, 49% felt socially stigmatised (n = 19 of 39), while 30% (n = 28 of 94, 7 non-responses) of all patients felt stigmatised by the need to cocoon. A minority (18%, n = 17 of 93, 8 non-responses) felt they needed more support during the pandemic, of whom most (94%, n = 16 of 17) wanted more emotional support, and many (47%, n = 8 of 17) wanted more financial support. Thirty patients reported some baseline hearing loss, of whom 67% (n = 20 of 30) found communication more difficult while others were wearing masks. Health-related behaviours There was a significant decrease in reported exercise, with 64% (n = 59 of 92, 9 non-responses) reporting doing less exercise, most commonly (69%, n = 41 of 59) due to decreased motivation, but also because of travel restrictions (59%, n = 35 of 59), closures of gyms/swimming pools (39%, n = 23 of 59), and fear of meeting other people (22%, n = 13 of 59). Forty-one percent (n = 24 of 59) were worried this would impact their long-term health. While some patients gained weight (n = 27 of 81, 33%), others lost weight (n = 15 of 81, 19%, 20 non-responses each). Overall 15% (n = 13 of 88) felt they were eating less healthily, and 25% (n = 22 of 88, 13 non-responses each) worried their dietary changes would impact their long-term health. Only 39% (n = 35 of 90, 11 non-responses) drank alcohol, many of whom had increased their intake: 17% (n = 6 of 35) by a little, 23% (n = 8 of 35) significantly. Of the 14 patients with increased alcohol intake, boredom was the most common reason where one was given (57%, n = 4 of 7, 7 non-responses). A minority of patients (8%, n = 7 of 93, 8 non-responses) felt they drank excessively. Financial toxicity Many respondents faced increased financial stress and reported increased worry about money (78%, n = 67 of 86, 15 non-responses), reductions in household income (40%, n = 32 of 80, 21 non-responses), and increased costs due to lockdown (62%, n = 53 of 85, 16 non-responses), most commonly grocery (n = 32) and heating (n = 25) costs. Ten patients had lost their jobs as a result of COVID, and 7 had been put on reduced working hours (19% of respondents), with 34% of households (n = 28 of 82, 19 non-responses) in receipt of the pandemic unemployment payment. Of the 15% struggling to pay bills (n = 13 of 87, 14 non-responses), 38% (n = 5 of 13) had not been struggling prior to COVID. Financial worry was not predicted by age, gender, or palliative/curative treatment intent. Health service adaptations Patients found several aspects of changes to the oncology service difficult (see Fig. 5), particularly attending appointments on their own, not only because they found the waiting room more boring without company (44%, n = 43 of 97), but also because they had a poor memory of the visit outcome afterwards (41%, n = 40 of 97) or had been given ‘bad news’ on their own (27%, n = 26 of 97, 4 non-responses for all). At the same time, many worried that they would contract COVID from another patient/visitor (56%, n = 54 of 97, 4 non-responses).Fig. 5 Most disliked aspects of changes to the oncology services due to COVID COVID adaptations caused difficulty in accessing some form of healthcare in 34% (n = 26 of 77, 24 non-responses), most commonly (n = 19) dental services. At least one patient was not able to access fertility preservation before their chemotherapy began. Despite this, only 17% (n = 14 of 84) were less satisfied with the healthcare system overall compared with pre-pandemic, while 29% (n = 24 of 84, 17 non-responses each) reported greater satisfaction. A vast majority of patients (84%, n = 70 of 83, 18 non-responses) reported a lot more respect for healthcare workers since the pandemic began, and 7 patients (8%) a little more. Of the 6 patients who reported no change, 4 hand-wrote addendums explaining that this was because their respect was already very high. No patient reported less respect. Discussion Even by mid-2020, most Irish adults had received many hours of government messaging about COVID, including education about common symptoms, and guidelines on infection control measures for vulnerable people, such as those with cancer. Despite this, our study conducted during the first wave of the COVID pandemic has shown that there remained some gaps in knowledge regarding COVID among Irish cancer patients, in particular with respect to symptom recognition and risky behaviours, with resultant clinical implications. Of concern, 60% of patients failed to recognise 2 or more classic symptoms (cough, fever, fatigue, ‘shortness of breath’, ‘aches and pains’, or altered smell/taste) as possibly suggesting infection with COVID. This has important clinical implications given that patients may delay presentation with symptomatic COVID infection, while also increasing the risk of potentially exposing others to infection. Moreover, we observed that many respondents had low insight into their knowledge gaps, with poor correlation between demonstrated knowledge of COVID symptoms and self-reported confidence in knowledge or desire for more information. The majority of our patients self-reported complete compliance with social distancing and cocooning measures, yet many of those were engaging in at least some risky behaviours which were clearly in contradiction to government guidelines. For example, 33% of all patients accepted visitors in the home, while 28% continued to shop even when they had alternative options. A significant proportion of cancer patients also underestimated the risks posed by COVID to their own health. Many felt they were no more likely to contract COVID than someone without cancer and/or underestimated their risk of developing severe illness if infected. It is unclear whether this reflects complacency due to high self-perceived compliance with safety measures or simply a lack of knowledge. Nonetheless, it highlights the ongoing need to regularly reinforce public health advice in the clinical setting, while also assessing patients understanding in this regard. One potential consequence of these issues is that patients who feel they are already ‘doing everything right’ may be at risk of complacency regarding their risks. Such patients may be harder to target and engage in further education, yet at the same time may continue to expose themselves, their families, staff, and other patients to increased risks of transmission, and may not seek appropriate care for themselves if they have potential COVID symptoms that they do not recognise. We have anecdotally noted episodes where patients have been attending the hospital for a routine visit, passed through multiple symptom-screening checkpoints, and yet only in the clinic room report a new cough or pyrexia, for which they had not isolated or sought care or testing. Such instances may provide an opportune moment for patient re-education, and particular care should be taken that patients who are self-isolating with symptoms/confirmed infection understand exactly what this means, as extrapolating from our study many of those who believe they are self-isolating correctly may not actually be taking all necessary measures. It is possible that the deficits in knowledge regarding COVID and/or the lack of compliance with safety measures which we observed among some oncology patients reflect active absorption of misinformation, rather than patients simply being uninformed. Fortunately, the majority of our patients reported that they derived most their information from reliable sources such as government messaging, with only 33% receiving information about COVID from social media. This is similar to data from the Middle East [9], but different to US data, where almost 80% rely on the internet as a main source [10]. Though not inherently harmful, reliance on social media has previously been shown to increase the risk of being exposed to potentially damaging/dangerous misinformation, which predict lack of compliance with public health advice and vaccine hesitancy [11]. As the use of social media information was slightly more common in younger oncology patients, there is an opportunity to target this population with reliable information from a trusted source (e.g. through use of HSE or hospital social media applications). As expected given the COVID/cancer symptom overlap, the proportion of our patients who had ever had a COVID test (31 tests/100 people) was far in excess of the rate in the general population (7.8 tests/100 people) [7]. At the time of our survey, no mass population or ‘pop-up’ testing had taken place in the Irish population. Patients were only tested if they had symptoms, were a known close contact of a case, or were admitted to hospital. COVID knowledge was better in those who had actually been tested or who had reported more symptoms. It is unclear if this reflects cognitive re-framing as a result of having been tested, or if those with better knowledge reported their symptoms differently or were more likely to seek testing. COVID has placed a very significant burden on our patients, particularly in terms of mood, anxiety, financial stress, and changes to their treatment. We know that a cancer diagnosis has always put a large financial burden on patients, with a mean loss of income of almost €20,000 a year and additional costs of €756 per month in 2019 [12]. Given that 40% of our patients experienced a loss of household income due to COVID, and that even in a study conducted in the summer months 25% noted significant extra heating costs, it is not surprising that a majority of our patients reported increased financial concerns due to the pandemic. Moreover, practical supports (e.g. the volunteer driver service) were greatly reduced during this time period, often requiring patients to fund alternative arrangements privately, thereby significantly adding to costs. Our study once again highlights the considerable financial strains faced by cancer patients which have been further significantly impacted due to the pandemic. Additional financial supports such as expanding access to the winter fuel allowance should be considered to help cancer patients offset increased expenses. Of concern, we detected significant levels of distress among our oncology patients, with more than 40% reporting lower mood due to COVID, a finding replicated across other studies [13]. Moreover, 72% reported worrying more about their health as a result of COVID, while 76% reflected more on their own mortality. Unfortunately, the increased levels of distress reported by our patients early in the first wave of the COVID pandemic also coincided with a time when many of the usual support services were unavoidably significantly curtailed. For example, many support groups had to cease in-person meetings, while virtual meetings were not yet well established. Furthermore, access to in-hospital support services such as psycho-oncology and medical social work departments were also somewhat curtailed due to the pandemic. The increased uptake of virtual services later in the pandemic helped to substitute some of these supports, but demands on the services are high, e.g. the Irish Cancer Society Cancer Support line has reported a 63% rise in calls [14]. The high levels of distress detected among cancer patients in our study highlight the need for ongoing resourcing of both in person and virtual support services to address the increased psychological and social needs of such patients due to the pandemic. Many of our new approaches to providing healthcare in a pandemic, such as universal mask-wearing or the use of telephone clinics, have inadvertently introduced barriers to care for some patients. This is a particular issue for patients with hearing impairment. Of note, 30% of our patients reported some degree of hearing loss, of whom 67% reported that they found communication more difficult during the pandemic due to mask wearing. While Irish sign language interpreters and smart devices are available for those with profound hearing loss, many patients with milder hearing loss, who previously managed without additional supports, face new communication barriers due to the requirement for mask wearing. Clinicians should be mindful of this and follow the guidelines for communication while wearing PPE, such as that issued by the National Healthcare Communication Programme [15]. The COVID pandemic was associated with some maladaptive behavioural changes among our study population. While the numbers of patients who regularly drank alcohol was somewhat lower than expected at just 39%, many (40%) of those that did drink reported that they had increased levels of consumption of alcohol as a result of the pandemic, with 23% reporting a significant increase. As alcohol consumption is often underreported by Irish drinkers [16], and abuse is associated with negative outcomes and cancer-related mortality [17], this has important clinical implications for our patient cohort. As such, clinicians should be mindful of the potential impact of COVID on alcohol consumption, and alcohol abuse should be screened for in clinic. Our study also noted a reduction in levels of activity due to COVID, with 64% exercising less than usual. This is of particular concern for cancer patients, given that physical activity is associated with better outcomes in survivorship research [18], and in some studies was associated with better tolerance of treatment [19] and overall survival [19, 20]. As such, there may be scope for more hospital-led interventions to overcome this issue, such as online-based exercise programmes [21]. Subsequent to our study, advice for ‘cocooners’ has changed, and current advice is that ‘The risk of catching COVID-19 is low if you go for a walk … and you keep away from other people’. This contrasts with initial advice that people should not leave their own properties except to attend medical appointments or similar. Patients should be made aware of this change in public health advice and there is an onus on clinicians to reassure patients and encourage moderate exercise with appropriate safety precautions in place. In the time since this survey was conducted, we have seen 7-day COVID-19 incidences ranging from 1 case/100,000 people in July 2020 to an estimated 3000/100,000 in January 2022, along with the introduction of vaccinations, mask mandates, and novel variants. With fluctuating incidences, public health guidance has changed, and restrictions have eased and been re-introduced multiple times. These factors may substantially alter how patients would respond to these questions today. Most (80%) of our patients frequently wore masks pre-mandate, it is likely that this would be now almost-universal, in keeping with the 98% of adults who reported wearing a mask while shopping in 2021 [22, 23]. In a subsequent study conducted in our hospital in February–April 2021 with a similar participant profile and similar methodology [24], 6% of patients had contracted COVID (compared to 1% in our study), and despite very low vaccine hesitancy, 5% still believed that ‘the pandemic is not serious’, and 20% disagreed/were unsure if they were more vulnerable to severe illness because of their cancer/treatment, suggesting that a small subset of patients still have not been reached by public health messaging. The failure of patients to recognise ‘classic’ COVID-19 symptoms has become less important with the dominance of novel variants, and indeed patients who were very attentive to education regarding almost-pathognomonic symptoms of the original strain (e.g. anosmia/dyspnoea) might now be disadvantaged if they remain fixed on these. There are some limitations to our study. As the sample population was gathered from those attending for treatment in the dayward, patients on treatment breaks or oral therapy were not represented. The questionnaire was at an appropriate level for 10–11 year olds; however, our hospital is in a deprived inner city area. While we did not formally assess educational level, some patients may have found some questions challenging. We noticed that more complex questions had higher non-response rates, such as an item querying what treatments patients had previously received, posed in a multi-stem question/answer format. This item was excluded from analysis due to a 26% (n = 26) non-response rate. Any future work should avoid multi-stem questions and should include ‘I don’t know’ as an option for all responses. Some patients reported fatigue towards the end of the survey, and the non-response rate rose from a median of 3% for items on pages 1–2 (demographics, see supplementary data), to 12% by page 12 (changes in shopping practice) and 17–18% by pages 15–16 (social and emotional impact/access to services/respect for healthcare workers). Results from later sections of the survey may under-represent views from some groups, particularly those with lower performance scores, and future surveys should be shorter and more focused to reduce effects of participant fatigue. Conclusions Despite self-reported confidence in knowledge about COVID, patient’s self-assessments of their own knowledge, their risk category, and the preventative strategies they should use are not optimal. Increased education about risk, symptom recognition, and public health guidelines is necessary, both for patient safety and to minimise staff and community exposure. COVID has been a significant stressor for patients and more practical, financial, and psychological supports are needed. Supplementary Information Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 442 KB) Availability of data and material On request. Code availability NA. Declarations Ethics approval Tallaght University Hospital/St. James’s Hospital Joint Research Ethics Committee. Consent to participate All patients gave written consent. Consent for publication All patients gave written consent. Conflict of interest The authors declare no competing interests. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. 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Accessed: 10/4/2022. https://www.hse.ie/eng/services/news/media/pressrel/hse-outlines-main-cocooning-measures.html 7. National Public Health Emergency Team (2020) Epidemiology of COVID-19 in Ireland - HPSC daily reports, 25/5/20 to 12/6/20 8. Department of Health (Irish government) (2020) Phase 1 - roadmap for reopening society and business 9. Baabdullah M Zeeneldin A Alabdulwahab A Optimizing the communication with cancer patients during the COVID-19 pandemic: patient perspectives Patient Prefer Adherence 2020 14 1205 1212 10.2147/PPA.S263022 32764893 10. Miaskowski C Paul SM Snowberg K Oncology patients’ perceptions of and experiences with COVID-19 Support Care Cancer 2020 29 1941 1950 10.1007/s00520-020-05684-7 32809060 11. Roozenbeek J, Schneider CR, Dryhurst S et al (2020) Susceptibility to misinformation about COVID-19 around the world: susceptibility to COVID misinformation. R Soc Open Sci 7. 10.1098/rsos.201199 12. Kantar (2019) The real cost of cancer. 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Available at: https://www.hse.ie/eng/about/our-health-service/healthcare-communication/nhcp-communication-skills-for-staff-wearing-personal-protective-equipment-ppe.pdf 16. Mongan D Millar SR O'Dwyer C Drinking in denial: a cross-sectional analysis of national survey data in Ireland to measure drinkers’ awareness of their alcohol use BMJ Open 2020 10 e034520 10.1136/bmjopen-2019-034520 32699125 17. LoConte N Brewster AM Kaur JS Alcohol and cancer: a statement of the American Society of Clinical Oncology J Clin Oncol 2018 36 83 93 10.1200/JCO.2017.76.1155 29112463 18. Courneya K Exercise in cancer survivors: an overview of research Med Sci Sports Exerc 2003 35 1846 1852 10.1249/01.MSS.0000093622.41587.B6 14600549 19. Stout N Baima J Swisher AK A systematic review of exercise systematic reviews in the cancer literature (2005–2017) PM&R 2017 9 S347 S384 10.1016/j.pmrj.2017.07.074 28942909 20. Hayes S, Steele M, Spence R et al (2017) Can exercise influence survival following breast cancer: results from a randomised, controlled trial. J Clin Oncol 35 21. Bland KA Bigaran A Campbell KL Exercising in isolation? The role of telehealth in exercise oncology during the COVID-19 pandemic and beyond Phys Ther 2020 100 1713 1716 10.1093/ptj/pzaa141 32737965 22. Hendrick L (2021) ECDC update on using face masks in the community. Dublin: Office of the CMO, Department of Health. Accessed 10/4/22. Available at: https://assets.gov.ie/137936/b3678de4-d008-4dee-a19a-5a1f8c8bc210.pdf 23. European Centre for Disease Prevention and Control. Using face masks in the community: first update. 15 February 2021. ECDC: Stockholm; 2021. Available at: https://www.ecdc.europa.eu/sites/default/files/documents/covid-19-face-masks-community-first-update.pdf 24. 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==== Front Spinal Cord Spinal Cord Spinal Cord 1362-4393 1476-5624 Nature Publishing Group UK London 35414697 800 10.1038/s41393-022-00800-2 Article Translation, cross-cultural adaptation, and analysis of the measurement properties of the Brazilian Portuguese version of the spinal cord injury pain instrument Cacere Marcela 12 http://orcid.org/0000-0002-3983-5342 Pontes-Silva André 3 Fidelis-de-Paula-Gomes Cid André 4 Bassi-Dibai Daniela 5 Dibai-Filho Almir Vieira almir.dibai@ufma.br 13 1 grid.411204.2 0000 0001 2165 7632 Postgraduate Program in Physical Education, Universidade Federal do Maranhão, São Luís, MA Brazil 2 Hospital Sarah, São Luís, MA Brazil 3 grid.411204.2 0000 0001 2165 7632 Postgraduate Program in Adult Health, Universidade Federal do Maranhão, São Luís, MA Brazil 4 grid.412295.9 0000 0004 0414 8221 Postgraduate Program in Rehabilitation Sciences, Universidade Nove de Julho, São Paulo, SP Brazil 5 grid.442152.4 0000 0004 0414 7982 Postgraduate Program in Programs Management and Health Services, Universidade Ceuma, São Luís, MA Brazil 12 4 2022 2022 60 9 820825 28 10 2021 31 3 2022 1 4 2022 © The Author(s), under exclusive licence to International Spinal Cord Society 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Study design A questionnaire validity study. Objectives To perform the translation, cross-cultural adaptation, and analysis of the measurement properties of the Brazilian Portuguese version of the Spinal Cord Injury Pain Instrument (SCIPI) for the screening of neuropathic pain in spinal cord injury. Setting Neurorehabilitation hospital in north-eastern Brazil. Methods We performed the translation and cross-cultural adaptation of the SCIPI. The pre-final version was applied in 10 patients with spinal cord injury sequelae and pain report. The final version of the SCIPI was applied to 100 patients. The measurement properties evaluated were structural validity, test-retest reliability, internal consistency, construct validity, and diagnostic accuracy. Results None of the items in the pre-final version of the SCIPI had any comprehension problems. The one-dimensional structure of the final version of the SCIPI was adequate. There were significant correlations between the SCIPI and the Douleur Neuropathique 4 (rho = 0.546), as well as adequate test-retest reliability (intraclass correlation coefficient = 0.89, kappa ≥ 0.79), internal consistency (Cronbach’s alpha ≥ 0.76), and diagnostic accuracy (area under the curve = 0.860). Conclusion The Brazilian version of the SCIPI presents measurement properties that are suitable for measuring neuropathic pain related to spinal cord injury. Subject terms Neurological disorders Trauma Neuropathic pain https://doi.org/10.13039/501100002322 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education) 001 Dibai-Filho Almir Vieira issue-copyright-statement© International Spinal Cord Society 2022 ==== Body pmcIntroduction Spinal cord injury generates important impacts in terms of healthcare, disabilities, economic level, and quality of life [1]. Pain is a frequent problem in this population, with 70% presenting chronic pain symptoms [2] and approximately 57.6% of the pains having neuropathic characteristics [1]. The association between nociceptive pain and neuropathic pain is common in people with spinal cord injury sequelae [3]. The correct and accurate diagnosis of pain, taking into account the different pathophysiological mechanisms (nociceptive, neuropathic, and nociplastic), is of great importance for successful treatment, considering that conduction of the treatment of neuropathic pain is different from that of a nociceptive pain [4, 5]. Considering the current lack of a gold standard method to diagnose neuropathic pain, there are several instruments to perform this screening [6]. In Brazil, adapted and validated versions of the Douleur Neuropathique 4 (DN4), Leeds Assessment of Neuropathic Pain Questionnaire (LANSS), Neuropathic Pain Symptom Inventory (NPSI), and Pain Quality Assessment Scale (PQAS) are available [7]. These more generic neuropathic pain tracking instruments, which are widely used for peripheral causes of pain, have lower measurement properties when applied to homogeneous samples and from central causes of pain (as in the case of pain related to spinal cord injury), reaching indices of diagnostic accuracy from 55 to 88% depending on the instrument applied [8]. Another aspect to be considered is that these instruments are based on physical examination, in addition to the verbal descriptors of pain, as spinal cord injury has peculiarities inherent to the injury, such as changes in skin sensitivity, in which the items of these generic instruments cannot be sensitive, thus interfering with accuracy [8, 9]. The Spinal Cord Injury Pain Instrument (SCIPI) is a rapid, standardised and valid (for English and German) screening instrument to detect neuropathic pain manifestations in a population with spinal cord injury sequelae [8, 9]. Its differential is based on an updated review of the definition of neuropathic pain and updated guidelines for detecting this condition. It is administered in the form of an interview or self-report, does not require clinical physical examination, and can be applied when the patient is unavailable in person. In addition, the SCIPI has an item aimed at the changes in skin sensitivity caused by spinal cord injury [8, 9]. Thus, the aim of this study was to perform the translation, cross-cultural adaptation, and analysis of the measurement properties of the Brazilian Portuguese version of the SCIPI for the screening of neuropathic pain in spinal cord injury. Methods Study design This is a questionnaire validation study carried out according to the Guideline for the Process of Cross-cultural Adaptation of Self-Report Measures [10] and the Consensus-based Standards for the Selection of Health Measurement Instruments [11]. The authorisation to carry out the translation and adaptation of the SCIPI into Brazilian Portuguese was granted via email by one of the authors of the questionnaire (Dr. Thomas N. Bryce). We carried out this research at the Sarah Network of Rehabilitation Hospitals (São Luís, MA, Brazil); all study procedures were previously approved by the institution’s research ethics committee (opinion number 3.714.777). Participants All participants included in the research were treated under the Hospital’s Spinal Cord Injury Program. Patients of both sex, aged over 18 years, and with painful complaints arising after spinal cord injury were included. We consider the following exclusion criteria: Patients diagnosed with severe cognitive and/or psychiatric disorders (by the team of doctors and psychologists of the hospital during routine assessments); pain above the level of spinal cord injury (since pain above the level of spinal cord injury may be related to another cause, e.g., musculoskeletal disorders); and patients who did not speak Brazilian Portuguese as their native language. A minimum sample size of 100 patients was used for validity analyses [11]. For reliability analyses, we used a sub-sample composed of 50 patients; the questionnaire was applied at two timepoints by one of the physiotherapists from the spinal cord injury program team, with an interval of 4 to 7 days between assessments [11]. Spinal Cord Injury Pain Instrument (SCIPI) The SCIPI is a 4-item instrument that investigates the pain characteristics of patients with spinal cord injury. For each item, a dichotomous answer is possible (yes = 1, no = 0). The total score ranges from 0 to 4; a score ≥ 2 indicates the presence of neuropathic pain. Translation and cross-cultural adaptation The SCIPI translation and cross-cultural adaptation process into Brazilian Portuguese followed the criteria of Beaton et al.[10]; however, the pre-final version test was applied to 10 patients [9, 12]. The stages for the translation and cross-cultural adaptation of the SCIPI are described below.Translation: Two independent translators (both with Brazilian Portuguese as their mother tongue and who were fluent in English) translated the original version of the SCIPI into Brazilian Portuguese. Synthesis of translations: After discussions and revisions, the two translators (under observation by one of the researchers) synthesized the two versions of the questionnaire translated independently and produced a single version of the SCIPI in a consensual manner. Back-translation: Two independent translators (without technical knowledge of subjects in the health area), both with English as their mother tongue and who was fluent in Portuguese, translated the Brazilian Portuguese version of the SCIPI back into English, with no prior knowledge of the original version of the questionnaire. Analysis by the expert committee: Two specialists in the field of pain and rehabilitation, together with the four translators involved in the adaptation process, defined the pre-final version of SCIPI in a manner agreed upon by all members of the committee. Pre-final version test: Considering the characteristics of the population with spinal cord injury sequelae, we applied the pre-final version of the SCIPI in a sample size of 10 individuals [9, 12], according to the same inclusion criteria established here. Participants answered the SCIPI questions in the form of an interview (carried out by the interviewer without receiving any kind of influence); at the end of the interview, answered “yes” or “no” about the understanding of each question in the questionnaire. Questions that were not understood by more than 20% of the participants would be reformulated and tested again in a new sample of 10 participants until the desired level of understanding was reached [13], thus establishing the final version of the SCIPI in Brazilian Portuguese. Test of the final version: In order to verify the measurement properties of the instrument, the cross-culturally adapted final version of the SCIPI was applied to 100 patients. Other clinical measurements In addition to the final version of the SCIPI, we applied the instruments DN4, LANSS, Numerical Pain Scale (NPS), and the anamnesis form containing information on sociodemographic, clinical, and spinal cord injury characteristics. The DN4 was used to validate the construct, and the LANSS, and NPS were used to characterize the pain of the sample; the assessments were performed by a physiotherapist from the neurorehabilitation team in spinal cord injury. Statistical analysis In descriptive analysis, we used mean, standard deviation (SD), frequency and percentage. These data were processed using the Excel program (Microsoft, Redmond, WA, USA). Internal consistency was obtained using Cronbach’s alpha, with an acceptable value above 0.70 [14]. Test-retest reliability was assessed via intraclass correlation coefficient (ICC) with a 2-way random-effects model and acceptable values above 0.75 [15]. Also, the standard error of measurement (SEM) was calculated [16]. For each item of the SCIPI, the kappa value was calculated, with acceptable values above 0.60 [17]. For the correlations between the questionnaires, the normality of the data was initially verified using the Kolmogorov-Smirnov test. For the construct validity, Spearman’s correlation coefficient (rho) was used to determine the magnitude of correlation between the SCIPI and the DN4. Our hypothesis is that the SCIPI demonstrates a good correlation, with a correlation magnitude greater than 0.50 with DN4, as it presents similar constructs [11, 18]. The internal structure of the SCIPI was evaluated through confirmatory factor analysis (CFA), with tetrachoric correlations and the robust diagonally weighted least squares (RDWLS) extraction method [19]. CFA processing was performed in R Studio (version 1.1.453, Boston, MA, USA), using the lavaan and semPlot packages. The Model fit had the following classification: values greater than 0.90 were considered adequate for comparative fit index (CFI) and Tucker-Lewis index (TLI), while values less than 0.08 were considered adequate for root mean square error of approximation (RMSEA) and standardised root mean square residual (SRMR). Values below 3.00 were considered adequate in the interpretation of the chi-square/degree of freedom (DF). In the CFA, factor loadings equal to or greater than 0.40 were considered adequate for the domain [14, 20]. We used the ROC curve to determine the diagnostic accuracy, with the following interpretation of the area under the curve (AUC):[21, 22] 0.5 (due to chance); > 0.5 to 0.7 (low degree of precision), > 0.7 to 0.9 (moderate degree of precision); > 0.9 and < 1.0 (high degree of accuracy); and 1.0 (perfect test). Determination of the best cut-off point was based on the lowest value obtained from the equation (1 – sensitivity)2 + (1 – specificity)2 [22]. In addition, positive and negative predictive values and positive and negative likelihood ratios were calculated [23]. Data were processed by SPSS (version 17, Chicago, IL, USA). Results Sample characteristics The sample consisted of 100 patients, 69% male, with a mean age of 41 years (SD = 12.16); in addition, 53% were married, 45% had completed/incomplete secondary education, and 81% had no occupational activity. Details of the characteristics of spinal cord injury and pain are described in Table 1.Table 1 Characteristics of spinal cord injury and pain of patients (n = 100). Characteristics n Non-traumatic cause of spinal cord injury Myelopathy or Myelitis 20 Spastic paraparesis 9 Arnold Chiari I 2 Neoplasm 2 Traumatic cause of spinal cord injury Motorcycle accident 23 Firearm projectile 20 High drop 10 Car accident 7 Dropped object on the back 3 Shallow water dive 3 Knife Injury 1 Spinal cord injury level C2 to T1 33 T2 to T12 58 L1 to L5 9 ASIA Impairment Scale (AIS) A 42 B 13 C 26 D 19 Spinal cord injury time (years) Up to 2 35 3 to 10 49 11 to 20 8 21 or more 8 Presence of pain At the level of spinal cord injury 60 Below the level of spinal cord injury 40 Pain site Lower limb 20 Low back 14 Dorsal region 14 Hip 10 Knee 8 Leg 7 Shoulder 6 Gluteal region 5 Hand 3 Foot 3 Upper limb 3 Abdomen 3 Thigh 2 Perineum 2 Pain time (months) Up to 3 2 4–6 6 7–12 17 13–24 10 25 or more 65 Regarding the characteristics of spinal cord injury, 67% had a cause of injury of traumatic origin. In total, 58% of patients presented a neurological level of thoracic spinal cord injury and 58% of spinal cord injuries were incomplete (ASIA-AIS Deficiency Scale classified as B, C, or D). The mean duration of spinal cord injury was 7 years (SD = 8.55); pain complaints at the neurological level of spinal cord injury were reported in 60% of patients, with mean pain duration of 60 months (SD = 74.32). The number of medications used on average was 4.24 (SD = 2.25), including several categories of medications for the management of secondary dysfunctions (caused by spinal cord injury or previous comorbidities). Regarding to the questionnaires used in the study, we observed the following mean scores: 2.47 (SD = 1.11) for SCIPI, 5.48 (SD = 2.34) for DN4, 11.28 (SD = 5.95) for LANSS and 6.16 (SD = 2.40) for the NPS. Cross-cultural adaptation In the stage of translation and cross-cultural adaptation, there was no need to adapt any specific term. The pre-final version of the SCIPI was applied to 10 patients with spinal cord injury sequelae and who had pain complaints; no item in the questionnaire reached a misunderstanding rate greater than 20%. Thus, the final version of the SCIPI was established in the Brazilian Portuguese language (Supplement 1). Structural validity Based on the structure presented in the study by Franz et al. [9], we performed CFA considering the 4 items and one-dimensional structure. Thus, the fit indices with adequate values were found: Chi-square/DF = 0.808, CFI = 1.000, TLI = 1.000, RMSEA (90% CI) = 0.000 (0.000–0.187), and SRMR = 0.061. Regarding the factor loading, as shown in Fig. 1, only item 4 presented a value lower than 0.40; however, considering the good fit indices, we chose to keep the original SCIPI structure.Fig. 1 Path diagram of the Spinal Cord Injury Pain Instrument. NP Neuropathic pain, i1 Pain sensation like electric shock, i2 Pain sensation like pins and needles, i3 Sensation of thermal pain, i4 Pain sensation in an area without sensitivity. Construct validity The SCIPI score was correlated with the DN4 score. Significant correlation was found with correlation magnitude greater than 0.50 (rho = 0.546, p-value < 0.001). Reliability and internal consistency Table 2 shows the appropriate values for test-retest reliability and internal consistency, with ICC = 0.89 (p-value < 0.001), kappa ≥ 0.79 (p-value < 0.001), and Cronbach’s alpha ≥ 0.76.Table 2 Reliability and internal consistency of the Spinal Cord Injury Pain Instrument (SCIPI) (n = 50). Measure Value Mean (standard deviation) Test 2.54 (1.21) Retest 2.60 (1.22) ICC 0.89 CI 95% 0.82 to 0.94 SEM (score) 0.40 SEM (%) 15.68 Kappa Item 1 0.80 Item 2 0.82 Item 3 0.80 Item 4 0.79 Cronbach’s alpha 0.78 Cronbach’s alpha if item deleted Item 1 0.76 Item 2 0.76 Item 3 0.78 Item 4 0.76 ICC Intraclass correlation coefficient, CI Confidence interval, SEM Standard error of measurement. Accuracy For this analysis, we considered DN4 to be a reference instrument to classify patients into two categories (with or without neuropathic pain) due to its higher correlation magnitude. The distribution of volunteers with and without neuropathic pain according to the DN4 and SCPI instruments is shown in Table 3. The SCIPI had moderate diagnostic accuracy, as described in Fig. 2 and Table 4, with an AUC value of 0.86, sensitivity of 93.8%, and specificity of 65%.Table 3 Cross-tabulation on the presence and absence of neuropathic pain according to the Douleur Neuropathique 4 (DN4) and Spinal Cord Injury Pain Instrument (SCIPI). SCIPI DN4 Neuropathic pain No neuropathic pain Neuropathic pain 76 6 No neuropathic pain 5 13 Fig. 2 ROC curve of the Spinal Cord Injury Pain Instrument (SCIPI). Table 4 Area under the ROC curve, best cut-off point, sensitivity, specificity, predictive values and likelihood ratios of the Spinal Cord Injury Pain Instrument (SICPI). Area (95% CI) Cut-off point Sensitivity (%) Specificity (%) PPV (%) NPV (%) PLR NLR Accuracy 0.860 (0.763, 0.956) 1.5 93.8 65.0 93 72 2.97 0.09 89% CI Confidence interval, PPV Positive predictive value, NPV Negative predictive value, PLR Positive likelihood ratio, NLR Negative likelihood ratio. Discussion We observed that the Brazilian version of the SCIPI has a reliable, one-dimensional structure, and a valid construct. We used the CFA to verify the internal structure of SCIPI, which allowed the theoretical factor structure of the observed data to be tested [24] and demonstrated that the instrument has a one-dimensional structure with observed variables is related to the latent variable. Only item 4 of the SCIPI in the Brazilian version had a factor loading of less than 0.40 (this item investigates whether the pain occurs in a region of the skin that is not sensitive). Our hypothesis for this aspect is that regions with partial changes in sensitivity were not considered, such as hypoesthesia, a frequent condition in incomplete spinal cord injury or spinal cord injuries from non-traumatic causes. Our study demonstrated that the SCIPI has a good correlation with the DN4 (rho = 0.546). As there is no gold standard tool/method for neuropathic pain assessment, construct validity was verified by correlating the SCIPI score with the DN4 score. We chose to use DN4 for two reasons: it is an instrument recommended by the Neuropathic Pain Special Interest Group for neuropathic pain screening [6], and it has suitable measurement properties into the Brazilian Portuguese language [25, 26]. The DN4 was translated, adapted, and validated for 11 languages, including Brazilian Portuguese. All adaptations demonstrated low levels of cross-cultural validity, as shown in the systematic review conducted by Mathieson et al. [6], although this review emphasises that the Brazilian Portuguese version has more satisfactory evidence among all other versions. In the validation of the Brazilian Portuguese version carried out by Santos et al. [24], the DN4 obtained a Cronbach’s alpha of 0.713 (considered reasonable), ICC of 0.62 (considered moderate), and factor loadings between 0.502 and 0.817 in the factor analysis. Regarding diagnostic accuracy, the Brazilian version of the SCIPI had a sensitivity value of 84%, similar to the German version (86%), and higher than the original version (72%). The Brazilian version of the SCIPI proved to be less specific (65%) when compared to the original (78%) and the German (84%) versions [8, 9]. The assessment of the diagnostic accuracy of the original and German version SCIPI used the diagnosis issued by a committee of medical experts as the gold standard, while our study used the DN4 as a reference. We also verified other forms of accuracy, such as predictive values and likelihood ratio, but these data were not presented in other SCIPI validation studies. Although the DN4 has adequate measurement properties, SCIPI was developed based on the clinical peculiarities of a person with spinal cord injury, validated in its original version according to revised definitions and guidelines for the clinical detection of neuropathic pain. This study has limitations that must be described. The diagnostic evaluation during the performance of the accuracy was not performed by physicians’ team specializing in neuropathic pain. Inter-examiner reliability was not tested, as we considered that assessments performed by two examiners in sequence using a self-report instrument with only 4 items would likely generate equal responses due to the patient’s memory. Finally, considering the COVID-19 pandemic period, we performed the pre-final test phase in only 10 patients, although there was 100% understanding for all items. Conclusion The Brazilian version of the SCIPI presents measurement properties suitable for measuring neuropathic pain related to spinal cord injury. Supplementary information Supplement 1 Supplement 2 Supplementary information The online version contains supplementary material available at 10.1038/s41393-022-00800-2. Acknowledgements We thank all patients. Author contributions AVDF designed the study; MC and collected the data; MC, AVDF and CAFPG analyzed and interpreted the data; All authors read and approved the final manuscript. Funding This work was partially supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), finance code 001. Data availability The database of the present study is available in Supplement 2. Competing interests The authors declare no competing interests. Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards (opinion number 3.714.777). Consent for publication The consent for publication was obtained from all individual participants included in the study. Consent to participate Informed consent was obtained from all individual participants included in the study. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Teixeira MJ Paiva WS Assis MS Fonoff ET Bor-Seng-Shu E Cecon AD Neuropathic pain in patients with spinal cord injury: Report of 213 patients Arq Neuropsiquiatr 2013 71 600 3 10.1590/0004-282X20130103 24141439 2. 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==== Front Environ Dev Sustain Environ Dev Sustain Environment, Development and Sustainability 1387-585X 1573-2975 Springer Netherlands Dordrecht 35431618 2301 10.1007/s10668-022-02301-x S.I.: Fuzzy optimization models Performance measurement of construction suppliers under localization, agility, and digitalization criteria: Fuzzy Ordinal Priority Approach http://orcid.org/0000-0001-7091-5044 Mahmoudi Amin pmp.mahmoudi@gmail.com http://orcid.org/0000-0002-2159-7518 Sadeghi Mahsa mahsadeghi.pmp@gmail.com http://orcid.org/0000-0002-2987-505X Deng Xiaopeng dxp@seu.edu.cn grid.263826.b 0000 0004 1761 0489 Department of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing, 210096 People’s Republic of China 12 4 2022 126 29 4 2021 16 3 2022 © The Author(s), under exclusive licence to Springer Nature B.V. 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The suppliers’ performance plays a vital role, with a domino effect, in project success, organizational competitiveness, protecting supply chain and construction industry from disruptions and PESTEL risks (political, economic, social, technological, environmental, and legal). Therefore, measuring the performance of the construction suppliers has become the primary focus of project-oriented organizations and the core of business decision-making, especially during global megatrends. The question that may arise here is, “How can the performance of the construction suppliers be determined under uncertainties considering the post-COVID-19 era?” Organizations need eligible suppliers for the rapid recovery of the supply chain and construction sector at this critical stage. Given the importance of the issue, this study aims to propose a novel approach for measuring the performance of construction suppliers using the fuzzy ordinal priority approach (OPA-F). OPA-F is a recent development in multiple criteria decision-making (MCDM) that can determine the criteria weights for performance measurement using fuzzy linguistic variables. We do not always have access to a complete data set in real-world situations and business environments. Nevertheless, OPA-F can handle this dilemma, even with incomplete input data. This research intends to consider three main aspects of the construction suppliers, known as (L-A-D) capabilities, including localization, agility, and digitalization. In this regard, we bring up a case study from the construction industry to demonstrate the application of the proposed framework. The findings show that the most critical criterion is “digitalization” for the case study. This criterion covers “supply chain automation” and “virtualization and dematerialization” of services/products. The proposed approach is practical and straightforward, particularly for academicians and decision-makers; it can also incorporate uncertainties. Keywords Performance measurement Supply chain disruptions Localization Agility Digitalization Fuzzy ordinal priority approach http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 71771052 Deng Xiaopeng ==== Body pmcIntroduction In an increasingly unpredictable and volatile world, the construction industry and supply chain (SC) are evolving rapidly and experiencing various external risks, uncertain events, and transformational changes. Leading organizations and pioneering companies grasp these opportunities to get ahead of emerging trends and keep pace with technological advancements, shaping the next generations of supply chain management (SCM). To achieve this goal, they usually outsource part of external tasks to qualified suppliers to stay up-to-date and ensure the success of their projects, businesses, and entire SCM. Over the last decades, sustainable supply chain management (SSCM) has received significant attention among market players and organizations. Sustainability pillars usually drive organizations to increase their sustainability efforts, including selecting and engaging sustainable suppliers. In this regard, the triple-bottom-line (TBL) approach can be employed to measure suppliers’ performance in 3Ps of social responsibilities (people), environmental protection (plant), and economic growth (profit) (Sarkis & Dhavale, 2015). Circular closed-loop supply chain management (CSCM) is another newly developed concept that questions the traditional linear supply chain and emphasizes the environmentally friendly economy. Proper evaluation of suppliers is an essential step toward a circular economy (CE). This requires measuring CE suppliers’ performance based on the ReSOLVE framework: Regenerate ecosystems, Share assets, Optimize processes, Looping materials, Virtualize and dematerialize products/services, and Exchange old with advanced equipment (EMF, 2015; Kouhizadeh et al., 2020; McKinsey, 2016a). Environmental concerns have given rise to a new school of thought in SCM that leads to green supply chain management (GSCM). Successful performance of green suppliers can help organizations eliminate the negative environmental impacts of their activities and achieve green supply chain goals (Abu Seman et al., 2019; Fallahpour et al., 2020; Keshavarz Ghorabaee et al., 2016). In uncertain circumstances and abrupt disruptions, the greenly resilient supply chain has become organizations’ focal point to take proactive strategies for quick recovery. Therefore, measuring suppliers’ Gresilience performance, considering green and resilience dimensions, allows organizations to regain the original status promptly (Mohammed, 2020). This list does not end here. By today’s world’s growing complexity, construction industries will take new approaches to be ahead of changes and unexpected transformations to meet global needs. In the early 2020s, we have witnessed widespread COVID-19 outbreaks and long-term lockdowns significantly impacting projects and organizations. It caused global economic shock, supply chain disruptions, logistics shutdowns, and shortages of high-demand materials (Chowdhury et al., 2021; Guan et al., 2020). Now with strict control of the COVID-19 epidemic and massive vaccination programs, it seems that industries are entering into the post-COVID-19 era. Lessons learned from this megatrend show that organizations need to rethink business as usual, develop capacities, and gain the ability to withstand any disturbance; this helps them return to normal conditions and become more resilient and agile (Prashara, 2021). Kermani et al. (2012) believed that selecting qualified suppliers can determine the long-term viability of an organization. Otherwise, it may expose a project-based organization to risks of cost overruns, improper quality, and delivery delays. According to the conditions governing the post-COVID-19 era, suppliers’ localization, agility, digitalization can play a pivotal role in the construction ecosystem. At the time of writing, no research considers these three criteria simultaneously for measuring suppliers’ performance as a corporate strategy. In this regard, the current study proposes a practical and novel approach for estimating the construction supplier’s performance based on L-A-D capabilities. It is an emergent concept that brings impressive results not only for organizations but also for entire supply chain resilience and its rapid recovery while facing unforeseen market volatility (Nandi et al., 2020; Sarkis, 2020). L-A-D capabilities is a holistic approach including several sub-criteria. To gauge suppliers’ performance in each localization, agility, digitalization criterion, we employed the OPA extension under the fuzzy environment, known as fuzzy OPA (hereinafter OPA-F) proposed by Mahmoudi, Abbasi, et al. (2021). OPA-F can calculate the weight of criteria under uncertain conditions, which is essential for performance measurement. To clarify the scope of the study, we try to answer three main Research Questions (RQs) throughout the current paper:RQ1 What sub-criteria needs to be considered for measuring suppliers’ performance to address disruptions in supply chain management and construction industry? RQ2 How can a multi-criteria-decision-making (MCDM) tool be employed to evaluate suppliers and measure their performance under uncertainties? RQ3 How can OPA-F be applied in real-world business and help organizational decision-makers measure suppliers’ performance in each criterion and sub-criterion? Based on the information above, the contributions of the current study can be summarized below:We present an integrated framework to measure the performance of the construction suppliers based on localization, agility, and digitalization in the fuzzy environment. For the first time, we employ the OPA-F, a new method in the MCDM context, for performance measurement with a new use-case. The rest of the current study is organized as follows: After Introduction, the literature review is provided in Sect. 2 to identify the influential criteria and sub-criteria. Sect. 3 is devoted to preliminaries. Section 4 outlines research Methodology; and a novel approach based on OPA-F is elaborated in more detail. After that, the proposed approach is employed in an actual case study in the construction industry. Related results and discussion are presented in Sect. 5. Finally, the conclusion is provided in Sect. 6. Literature review Performance measurement in supply chain One of the popular areas in the supply chain has been evaluating suppliers, selection, and measuring their performance. Exiting literature (mainly 2015 afterward) shows that scholars and practitioners attempted to measure the performance of the suppliers in various types of problems and approaches. In this regard, we reviewed 14 studies from two aspects: (i) criteria used for performance appraisal and (ii) methods used for calculating the criteria weight and suppliers’ score in each criterion. The fuzzy set theory is appropriate for handling uncertainties in all selected studies. Furthermore, fuzzy decision-making tools are popular since they can choose optimal probable choices (Mardani et al., 2015). Govindan et al. (2013) explored the initiatives of SSCM based on the TBL approach. Then, the authors performed qualitative performance evaluation for criteria weighting and then they used fuzzy TOPSIS for suppliers ranking. Kayhan and Cebi (2014) determined the importance of reliability criteria and used fuzzy DEMATEL to analyze the interaction among the factors. This possibility allows firms to prioritize suppliers based on their reliability in the construction industry. Akman (2015) used fuzzy c-means and VIKOR methods to prioritize suppliers based on their environmental/green performance. Zhou et al. (2016) employed Fuzzy type-2 multi-objective DEA for supplier evaluation in the field of SSCM. Chen et al. (2019) focused on careful supplier selection in cooperative and specialized industrial chains. The authors used six sigma indicators to develop a fuzzy supplier selection based on 4R green concepts (Reduce, Reuse, Recycle, and Recovery). Alikhani et al. (2019) employed interval type-2 fuzzy DEA for strategic supplier selection considering sustainability and risk criteria. Rashidi and Cullinane (2019) used Fuzzy TOPSIS and fuzzy DEA to assess service quality commitments and price for selecting potential sustainable suppliers. Alegoz and Yapicioglu (2019) proposed a hybrid approach based on goal programming, trapezoidal type-2 fuzzy AHP, and fuzzy TOPSIS. The authors used this model to determine the appropriate supplier(s). Tong et al. (2020) developed the extended fuzzy PROMETHEE II for supplier performance evaluation in highly polluting and high-risk industries. The authors consider three main dimensions: processing, sustainability, and other basic criteria (time, cost, and quality of service). Tavassoli et al. (2020) employed Stochastic-fuzzy DEA for evaluating suppliers in the field of SSCM. Mina et al. (2021) used fuzzy AHP to determine the weight of sub-criteria and employed a fuzzy inference system to rank CE supplies’ in the CSCM. Liou et al. (2021) proposed a novel hybrid MCDM tool comprised of fuzzy TOPSIS and fuzzy best-worst method (FBWM) for selecting proper suppliers in GSCM. As we can see, the scholars have widely used various MCDM methods such as AHP, TOPSIS, and PROMETHEE, along with DEMATEL and DEA. Tirkolaee et al. (2020) employed a new hybrid approach based on fuzzy logic, including fuzzy ANP and fuzzy DEMATEL, to determine criteria weights and fuzzy TOPSIS to select sustainable-reliable suppliers. To the best of our knowledge, no one has employed the OPA-F for performance measurement. Nevertheless, its use-cases in other areas are remarkable, and its application will be discussed in Sect. 4. In this regard, the current study makes a pioneering attempt to measure the performance of the construction suppliers through the OPA-F. Furthermore, the literature shows that a wide range of criteria is being used for evaluation, selection, and performance measurement of suppliers, considering supply chain needs and conditions of the business environment. Given the current megatrends and delicate state of the post-COVID-19 era, this study focuses on suppliers’ performance associated with supply chain localization (Sub-Sect. 2.2), supply chain agility (Sub-Sect. 2.3), and supply chain digitalization (Sub-Sect. 2.4) for recovery of construction industry and supply chain disruptions. Figure 1 is illustrated to show integration among L-A-D capabilities.Fig. 1 Integration among L-A-D capabilities In this regard, the following sub-sections are elaborated to answer “RQ1. What sub-criteria needs to be considered for measuring suppliers’ performance to address disruptions in supply chain management and construction industry?” Supply chain localization Supply chain localization (hereinafter SC localization) has always been the focal point of scholars and practitioners. Bateman (1998) outlined the SC localization and its strategic role in the development of Kazakhstan during the 1990s. The author stated that accelerating the localization can be a way for shoring up the situations of the key companies, reconstruction of the supply chain, and national economic recovery of Kazakhstan. According to Albino et al. (2002), the supply chain is a network of processes and flows that can be localized within or outside an area. The authors proposed input-output models to understand relationships among the environment and the processes. This paves the way for managing the global and local supply chain regarding the sustainability of a local community. Martin and Rickard (2012) identified and ranked the external factors affecting companies’ decisions and strategies for localizing the supply chain. The authors believed that SC localization could help Swedish companies stay competitive and sustainable in future markets. Wu and Jia (2018) investigated the relationship between corporations’ strategies and localized supply chain operations within western multinational enterprises (MNEs) in China. According to findings, SC localization is an institutional process that causes cognitive and normative changes in MNEs and improves economic, environmental, and social performance. Reza-Gharehbagh et al. (2020) evaluated the role of the government in enhancing supply chain localization. Government policy and support can directly increase the profit of the local supply chains. Today, SC localization is still a hotly debated topic. In a report of Prashara (2021) published by Project Management Institute (PMI), it is said that working with local suppliers, partners, non-governmental organizations, and service providers contributes to local communities in terms of building trust, market sustainability, and societal benefits. Local and regional patterners will eliminate the risks posed by megatrends in the 2020s, including COVID-19, climate crisis, social movements, globalization, artificial intelligence, and so forth. For managing supply chain disruptions and risks in the post-COVID-19 era, more retailers, suppliers, and SC participants are interested in localization strategies in their respective regions (Sakthivel et al., 2021). All of the above studies emphasized the importance of SC localization in the economy and development. Therefore, this sub-section considered localization as one of the main criteria for performance measurement that covers the following definition and four sub-criteria. Localization (Christopher, 2011; Martin & Rickard, 2012; Nandi et al., 2021): Localization is defined as the placement of the organization, physical assets, and supply chain functions. From the industrial perspective, local presence is essential to meet market demands for faster, more cost-efficient, reliable, and flexible product and service delivery. Localization is a strategic solution to secure suppliers from supply chain disruptions and external risk factors. It minimizes distance-related issues, transportation costs, international currency fluctuations, global market changes, geopolitical risks, and so forth.C1.1. Localized processes: This criterion determines the suppliers’ efforts in regionalizing processes associated with planning, research and development (R&D), purchasing, sourcing, manufacturing, procurement, distributing, and recycling activities (Albino et al., 2002; Martin & Rickard, 2012; Nandi et al., 2021). According to Sadeghi et al. (2022b), emerging technologies enable suppliers to benefit from localized processes with minimum time, cost, and transportation with maximum quality. C1.3. Developing local capabilities: This criterion indicates suppliers’ engagement in making capacity for communities by running local businesses and accessing local skills, knowledge, financial resources, technologies, equipment, and facilities (Nandi et al., 2021; Reza-Gharehbagha et al., 2020). According to Sadeghi et al. (2022b), suppliers can use emerging technologies for local economic prosperity and the growth of nearby businesses for improving living standards. C1.4. Local waste management and value recovery: The traditional linear model of “take-make-waste” does not work in the supply chain and economy anymore. This field demands sustainable approaches, like the circular economy, to minimize raw material usage, design-out waste, and improve value recovery (EMF, 2015). This criterion indicates suppliers’ efforts to keep products as long as possible within local closed-loops and make new and by-products out of local wastes (Albino et al., 2002; Nandi et al., 2021; Sillanpää & Ncibi, 2019). Supply chain agility Some scholars discussed supply chain agility (hereinafter SC agility) and its importance in responding to unprecedented changes. White et al. (2005) addressed the position of emergent information technologies in improving the agility of SC and suppliers. The authors measured the SC agility based on the time taken for suppliers to address a customer’s order and meet their needs. Lin et al. (2006) presented a framework for evaluating enterprises based upon business processes, people, technologies, information systems, and facilities. An enterprise can accomplish agile capabilities in terms of responsiveness, competency, flexibility, and quickness. Braunscheidel and Suresh (2009) extended a theatrical framework to enhance the supplier’s agility and address the effect of two cultural antecedents, such as learning orientation and market orientation, on SC agility. They also investigated the impact of three organizational practices, including (i) internal and external integration with stakeholders and suppliers, and (ii) external flexibility, on SC agility. Kumar Sharma and Bhat (2014) employed interpretive structural modeling (ISM) to provide a hierarchy-based framework and examine the relationship among the drivers of an agile SC. Lee et al. (2015) evaluated the suppliers based on agility criteria using the MCDM approach. In this study, fuzzy AHP calculates the weights of the criteria, and Fuzzy TOPSIS obtains suppliers’ rank for each defined criterion. Today, SC agility has attracted a lot of attention. Russell and Swanson (2019) addressed the gap between SC agility and information processing theory. At the organizational level, bridging trending technologies to agile practices can lead to information processing, demand sensing, and SC agility. Ivanov (2020) presented a new notation named viable supply chain, a combination of agility, sustainability, and resilience. He mentioned that the viable supply chain could help the firms redesign and recover their business from a global pandemic like COVID-19. Al Humdan et al. (2020) provided a systematic review on the SC agility context, including enablers, definitions, and performance implications. Raji et al. (2021) explored the Industry 4.0 technologies for agile and lean supply chain strategies to promote companies’ performance. All of the above studies emphasized the benefits of SC agility. Therefore, this sub-section considered agility as one of the main criteria for performance measurement that covers the following definition and four sub-criteria.C2. Agility (Al Humdan et al., 2020; Lee et al., 2015; Nandi et al., 2021; Tallon & Pinsonneault, 2011): Agility is defined as firms’ capability to meet time and speed requirements within product and service delivery. Agility indicates suppliers’ performance in identifying and responding to environmental opportunities and threats.C2.1. Stakeholder agility: This sub-criterion is about detecting and capturing market opportunities for competitive advantages and innovation. Stakeholder agility is achievable through (i) Gathering customers’ needs and their preferences as a source of innovative ideas, (ii) Understanding market requirements and predicting prospective changes, (iii) Developing novel products, services, and creating value (iv) Receiving stakeholders’ feedbacks for continual improvement, and finally, (v) Accelerating responsiveness based on delivery time and speed requirements (Kohli & Jaworski, 1990; Lee et al., 2015; Sambamurthy et al., 2003). C2.2. Partnering agility: This criterion indicates strategic cooperation, alliances, and forming joint ventures with partners to grasp market opportunities, create synergy and gain competitive advantages. In this regard, suppliers benefit from partnering agility in the following ways: (i) Utilizing partners’ knowledge, competencies, resources, and their value proposition, (ii) Involving agile partners and sub-suppliers at upstream and downstream, and (iii) Collaborating with key and powerful suppliers (Al Humdan et al., 2020; Nandi et al., 2021; Sambamurthy et al., 2003). C2.3. Operational agility: Operational agility is at the core of the business model and implies suppliers’ operations/practices/processes for product and service delivery in a cost-efficient, accurate, and rapid manner. This criterion illustrates firms’ capability to adapt or respond to uncertainties and turbulent markets to gain competitive advantage by (i) Redesigning existing processes or designing new processes, (ii) Swiftness in decision-making and solution providing, and (iii) Flexibility in the organizational and business processes (Al Humdan et al., 2020; Braunscheidel & Suresh, 2009; Lee et al., 2015; Nandi et al., 2021; Raji et al., 2021; Sambamurthy et al., 2003). C2.4. Marketing agility: Marketing agility is suppliers’ market sensitivity, alertness, responsiveness, and adaptability to market changes promptly. This criterion helps firms to have reactive/ proactive responses for changing demand patterns (Al Humdan et al., 2020). Supply chain digitalization Supply chain digitalization (hereinafter SC digitalization) has been a popular topic in recent years. Büyüközkan and Göçer (2018) provided a review on the digital supply chain and its challenges both in industry and academic. The authors tried to discover how digitalization could be integrated with the supply chain. Frederico et al. (2019) defined the term Supply Chain 4.0 by considering the industry 4.0 in the supply chain context. Supply Chain 4.0 can be the reorganization of the supply chain using frontiers technologies that could significantly improve conventional supply chain management. Indeed, Supply Chain 4.0 is the new generation of the digital supply chain that makes the supply chain participants faster and more flexible than before. Srai and Lorentz (2019) extended a framework for SC digitalization. This framework allows practitioners to employ digital technologies and artificial objects in the firms. Schniederjans et al. (2020) explored the future trend of research about the role of knowledge management in supply chain digitization. The authors used textual analysis large-scale literature using data from 2010 to 2018. Their results could contribute to the readers exploring the human dimension for optimizing SC digitalization. They also discussed the niche areas in the digital supply chain where scholars could focus in future. Agrawal et al. (2020) stated that Industry 4.0 has a significant impact on the construction supply chain; however, it faces several challenges which should be identified and addressed carefully. They used the AHP method for prioritizing the enablers, in which E-supply achieved the highest rank. After that, digitization, tracking, and localization come at the other top positions. Gupta et al. (2020) identified and ranked the critical drivers of the digital supply chain. Based on the results, “big data and data science skills” and “tracking and localization of products” achieved the first and second positions, respectively, as the most critical factors in the digital supply chain. Based on the studies above, digitalization is an essential aspect of the construction suppliers. Therefore, this sub-section considered digitalization, as a crucial pillar of L-A-D capabilities, covering the below definitions and three sub-criteria.C3. Digitalization (Nandi et al., 2021): Digitalization deals with embedding technology into processes, practices, activities, assets, and services, which disrupts the traditional supply chain. With the arrival of industry 4.0, a new form of trust, transparency, connectivity, security, traceability, and automation emerges in projects, businesses, supply chain, and the entire construction industry (Sadeghi et al., 2022a).C3.1. Value exchange using IT infrastructures: Three flows go parallel within the supply chain: goods/services flow, information flow, and financial flow. Suppliers can use comprehensive technology stacks (software, hardware, and infrastructure) for value exchange with other supply chain participants (Hofmann & Belin, 2011; Nandi et al., 2021). C3.2. Virtualization and dematerialization: Technological innovations can replace physical products/services with digital products/services. Such dematerialization results in remote service delivery, resource-saving, and less dependence on resources. Virtualization has also revolutionized collaboration and communication mechanisms. Suppliers use technological platforms to reduce unnecessary travel, time-consuming transportation, and face-to-face meetings (Al Humdan et al., 2020; Büyüközkan & Göçer, 2018; Lee et al., 2015; McKinsey, 2016b; Mussomeli et al., 2015; Nandi et al., 2021). C3.3. Supply chain automation: This criterion refers to leveraging computing, physical, and digital technologies (i.e., Internet of things (IoT), artificial intelligence (AI), robotics, distributed ledger technologies, and so forth) for value proposition in a real-time and cost-efficient manner. These technologies provide suppliers with self-executed contracts, automated processes and operations, and intelligent information systems. The use of technology improves information transparency, immutability, reliability, and generate traceable data for real-time SCM (Agrawal et al., 2020; Büyüközkan & Göçer, 2018; Nandi et al., 2021). Preliminaries One of the most well-known approaches for dealing with uncertainty is the “fuzzy set theory” proposed by Zadeh (1965). Scholars have widely employed and developed fuzzy set theory since then. There is a wide range of fuzzy numbers, distinguished by the type of membership function. The proposed framework used triangular fuzzy numbers (TFNs) during the performance measurement of supplier construction. Referring to Pedrycz (1994), obvious motivation for TFN utilization lies in popularity and simplicity compared to other types of fuzzy numbers. In addition, TFN has desirable achievements to deal with uncertainty and imprecision. Definition 1 (Pedrycz, 1994) The TFN can be presented as q~=l,m,u while l≤m≤u and its membership function can be defined by Eq. (1).1 μq~x=0,x<lx-lm-l,l≤x<mu-xu-m,m≤x≤u0,x>u The membership function associated with TFN is depicted in Figure 2.Fig. 2 Membership function of TFN Definition 2 Giachetti and Young (1997) For two positive fuzzy numbers A~=l,m,u and B~=l′,m′,u′, the primary operations are as Eq. (2) to Eq. (5).2 A~+B~=l+l′,m+m′,u+u′ 3 A~-B~=l-u′,m-m′,u-l′ 4 A~×B~=l.l′,m.m′,u.u′ 5 A~/B~=l/u′,m/m′,u/l′ Definition 3 Kumar et al., (2011) Graded Mean Integration Representation (GMIR) for the fuzzy number q~=li,mi,ui is calculated as Eq. (6).6 Rq~=li+2mi+ui4 Definition 4 Mahmoudi, Javed, et al. (2021) Fuzzy linguistic variables are scaled terms convertible to fuzzy numbers. Tables 1 and 2 illustrate the fuzzy linguistic variables employed in this study. Table 1 Transformation of fuzzy linguistic variables for criteria importance as input data Linguistic Variables TFN for Criteria Rank (r) Very Low (VL) (0.9, 1, 1) 7 Low (L) (0.7, 0.9, 1) 6 Medium Low (ML) (0.5, 0.7, 0.9) 5 Medium (M) (0.3, 0.5, 0.7) 4 Medium–High (MH) (0.1, 0.3, 0.5) 3 High (H) (0, 0.1, 0.3) 2 Very High (VH) (0, 0, 0.1) 1 Table 2 Transformation of fuzzy linguistic variables for performance-rating as input data Linguistic Variables TFN for Performance-rating Worst (W) (0, 0.5, 1.5) Very Poor (VP) (1, 2, 3) Poor (P) (2, 3.5, 5) Fair (F) (3, 5, 7) Good (G) (5, 6.5, 8) Very Good (VG) (7, 8, 9) Excellent (E) (8.5, 9.5, 10) In Table 1, the numbers close to zero should be considered "0.01" instead of absolute zero while transforming linguistic variables to fuzzy numbers Definition 5 Kaur and Kumar (2016) The fuzzy linear programming model with TFNs can be defined as Model (7). 7 Maximizeor Minimize∑j=1npj,qj,rj⊗xj,yj,zjSubject to∑j=1naij,bij,cij⊗xj,yj,zj<=>bi,gi,hi∀i=1,2,…,m.xj,yj,zjandaij,bij,cijare non-negative fuzzy numbersxj≤yj≤zj Note: To solve Model (7), it should be converted into a conventional linear programming model. In this regard, Model (7) should be converted into Model (8). Then, it can be solved using the simplex algorithm (Mahmoudi, Javed, et al., 2021).8 Maximizeor MinimizeR∑j=1npj,qj,rj⊗xj,yj,zjSubject to∑j=1naijxj+si=bi∀i=1,2,…,m.∑j=1nbijyj+ti=gi∀i=1,2,…,m.∑j=1ncijzj+fi=hi∀i=1,2,…,m.xj,yj,zj,pj,qj,rjandaij,bij,cijare non-negative fuzzy numbersxj≤yj≤zjsi≤ti≤fi Methodology This section addresses the second research question, “RQ2. How can a multi-criteria-decision-making (MCDM) tool be employed to evaluate suppliers and measure their performance under uncertainties?” In practice, supplier evaluation and selection based on criteria is a type of MCDM problem. Typically, an MCDM tool (i) aggregates the multiple experts’ viewpoint about criteria and alternatives, (ii) determines the importance of each criterion, and (iii) ranks alternatives based on criteria. The current study needs a novel approach for measuring performance. One of the recent state-of-the-art MADM tools is the Ordinal Priority Approach (OPA) proposed by Ataei et al. (2020). OPA calculates the weights of criteria, the rank of alternatives, and experts’ weights in parallel. Several strengths make it superior to other MCDM tools. It is independent of normalization, pairwise comparison, complete data set, positive/ negative ideal solutions, and averaging methods for accumulating experts’ opinions. Furthermore, OPA enables decision-makers to leave some items unanswered, for cases they do not have adequate knowledge or experience around the subject, to improve the accuracy and reliability of output data. Mahmoudi, Javed et al. (2021) asserted that OPA needs some improvements for (i) handling linguistic information by embedding fuzzy set theory, and (ii) subjective evaluation of values considering uncertainties of real-world situations. Given the above sore points, the authors extended OPA to the Fuzzy OPA (also called OPA-F) with the following features:The convergence of fuzzy linear programming and fuzzy MCDM, The conversion of linguistic variables to fuzzy numbers; OPA extensions have received significant attention among researchers and scholars in a short time. Mahmoudi et al. (2022) proposed Robust OPA (also called OPA-R) in MCDM. Authors employed this novel method for optimal project portfolio selection considering the resilience strategies of project-based organizations. They also proposed a new concept which was a combination of profitability and resilience (Presilient) for portfolio selection problems. The first OPA use-case is a study about “blockchain technology in the construction sector” conducted by Sadeghi et al. (2022b). The authors applied OPA to evaluate barriers to blockchain and distributed ledger adoption in the construction industry based on sustainability dimensions. Mahmoudi and Javed (2021) proposed a novel relative performance index to measure the performance of the sub-contractors of construction projects using the OPA. Moreover, Mahmoudi, Abbasi et al. (2021) combined the DEA with the OPA to provide a hybrid approach for the performance measurement of the suppliers. In context of blockchain technology in construction, Sadeghi, Mahmoudi, and Deng (2022b), used trapezoidal OPA-F to develop a risk assessment model. Here, the current study aims to develop a framework based on Lin et al. (2006) for measuring performance index using OPA-F. To illuminate the concept behind OPA-F, the steps of the proposed framework are presented. Before anything, it is required to provide information for utilized variables, parameters, and sets. Sets I Set of experts ∀ i ∈ I J Set of criteria ∀ j ∈ J K Set of suppliers ∀k∈K Indexes i Index of the experts (1, 2, …, p) j Index of preference of the criteria (1, 2, …, j′,j″,j…, n) k Index of the suppliers 1,…,m Parameters a~ij Fuzzy linguistic variable for criterion j by expert i r The rank of the linguistic variable x~jk Average of Fuzzy linguistic variables for supplier k in criterion j by all experts Variables Z~ Objective function W~ij Fuzzy weight of criterion j by expert i W~j Fuzzy weight of criterion j FLPIk Fuzzy performance index of the supplier k in terms of Localization FAPIk Fuzzy performance index of the supplier k in terms of Agility FDPIk Fuzzy performance index of the supplier k in terms of Digitalization FPIk Overall fuzzy performance index of the supplier k The steps of the proposed framework are as follows:Step 1 Specifying the expert(s) who has enough knowledge about the suppliers and their background. Step 2 Specifying the criteria/sub-criteria which are essential for the business objectives and play a vital role in project success, organizational competitiveness, and in the long-term, supply chain recovery through localization, agility, and digitalization. Step 3 Collecting the experts’ opinions regarding the criteria importance using fuzzy linguistic variables in Table 1. Step 4 Collecting the experts’ opinions associated with the performance of each supplier in each criterion using fuzzy linguistic variables in Table 2 and calculating the average value. Step 5 Constructing and solving Model (9) using the collected data in Step 3 and Model (8). 9 MaxZ~S.t:a~ij′rW~ij′r-W~ij″r+1≥Z~∀iandra~ijnW~ijn≥Z~∀iandr=n∑i=1p∑j=1nW~ij=0.8,1,1.2l~ijw≤m~ijw≤u~ijw∀iandjl~ijw≥0∀iandj The weight of the criteria can be calculated using Eq. (10).10 W~j=∑i=1pW~ij∀j Step 6 Calculating the fuzzy performance of the suppliers using Eq. (11). It should be noted that the authors could not use the formula of Lin et al. (2006) for calculating the performance index because the authors of the current study could not confirm the fuzzy division in their performance index formula. Therefore, the authors revised and extended the formula of Lin et al. (2006) as follows:11 FPIk=(Localizationindex,Agilityindex,Digitalizationindex) where:11.1 Localizationindex(FLPIk)=∑j=1n∈C1(RW~j∗x~jk)/∑j=1n∈C1RW~j 11.2 Agilityindex(FAPIk)=∑j=1n∈C2(RW~j∗x~jk)/∑j=1n∈C2RW~j 11.3 Digitalizationindex(FDPIk)=∑j=1n∈C3(RW~j∗x~jk)/∑j=1n∈C3RW~j Step 7 The distance of FLPIk, FAPIk, and FDPIk, with the labels in Table 3, should be calculated using Eqs. (12.1) to (12.3). The minimum distances show the performance of suppliers in terms of localization, agility, and digitalization, respectively. 12.1 DFLPIk,LLt=∑x∈vFLPIkx-LLtx21/2v⊂0,10 12.2 DFAPIk,ALt=∑x∈vFAPIkx-ALtx21/2v⊂0,10 12.3 DFDPIk,DLt=∑x∈vFDPIkx-DLtx21/2v⊂0,10 Table 3 Transformation of fuzzy performance of labels for L-A-D capabilities Labeling for TFN for Labels Localization Level Agility Level Digitalization Level Slowly Local Slowly Agile Slowly Digital (0, 1.5, 3) Fairly Local Fairly Agile Fairly Digital (1.5, 3, 4.5) Local Agile Digital (3.5, 5, 6.5) Very Local Very Agile Very Digital (5.5, 7, 8.5) Extremely Local Extremely Agile Extremely Digital (7, 8.5, 10) where LLt is t th linguistic variables of localization level, ALt is t th linguistic variables of agility level, and DLt is t th linguistic variables of digitalization level. Case study, results, and discussion In this section, we bring an actual case study to answer “RQ3. How can OPA-F be applied in real-world business and help organizational decision-makers measure suppliers’ performance in each criterion and sub-criterion?” We employ the proposed framework in a project-oriented organization from the construction sector to answer this question. This organization requires to evaluate the performance of the switch and socket suppliers. For a better conception, we illustrated the structure of the MCDM problem in Fig. 3.Fig. 3 The structure of the MCDM problem, including the objective, criteria, and sub-criteria To demonstrate the applicability of the proposed framework, we followed the procedure of the Methodology (Sect. 4). For this, we selected four eligible experts from the organization under study (Step 1). After finalizing the criteria (Step 2), we collected experts’ opinions on the criteria and sub-criteria (Step 3), presented in Table 4.Table 4 Experts’ opinions associated with the criteria and sub-criteria Criteria Sub-criteria Expert 1 Expert 2 Expert 3 Expert 4 C1. Localization C1.1 M MH L M C1.2 M M L ML C1.3 VH H H MH C1.4 H MH MH M C2. Agility C2.1 MH H M MH C2.2 M MH L M C2.3 H H MH MH C2.4 MH H M MH C3. Digitalization C3.1 MH M M ML C3.2 VH VH H H C3.3 VH H VH VH Abbreviations of each fuzzy linguistic variable can be found in Table 1 In the case study, the performance of four suppliers will be evaluated and measured. In Step 4, we gathered experts’ opinions about the suppliers in each sub-criterion, presented in Table 5.Table 5 Experts’ opinions associated with the suppliers Experts Suppliers C1.1 C1.2 C1.3 C1.4 C2.1 C2.2 C2.3 C2.4 C3.1 C3.2 C3.3 Expert 1 Supplier 1 G F F G P F VP G VG G F Supplier 2 VG E G VG F P F VP F P VP Supplier 3 F P VP F VG E VG G VG E VG Supplier 4 G VG G F F G G F G VG F Expert 2 Supplier 1 F G F F P F P G E G F Supplier 2 G VG F VG F P P P F P P Supplier 3 G VP P G E G E F VG E VG Supplier 4 VG F F G VG G VG F G VG F Expert 3 Supplier 1 VG F G P F F F G E VG VG Supplier 2 F VG VG G F P F VP G F VG Supplier 3 VG VP P VG E G G F F E G Supplier 4 E VG VG F VG G G F F VG VG Expert 4 Supplier 1 E F G G F F F F E E VG Supplier 2 F E G VG P P F W F F P Supplier 3 G VP P G G G E F VG E VG Supplier 4 G F G F G G VG F G VG F Abbreviations of each fuzzy linguistic variable can be found in Table 2 After constructing and solving the model (Step 5), we obtained the fuzzy weights of the criteria and sub-criteria, which are listed in Table 6.Table 6 The fuzzy weights of criteria and sub-criteria Criteria Weight Sub-criteria Weight Defuzzification values (Eq. 6) l m u l m u Localization 0.26895 0.29474 0.35602 W11 0.05443 0.05443 0.06400 0.056824 W12 0.05199 0.05199 0.05596 0.052979 W13 0.09762 0.12341 0.13844 0.120721 W14 0.06492 0.06492 0.09762 0.073092 Agility 0.25538 0.25722 0.33580 W21 0.06400 0.06492 0.08238 0.069053 W22 0.05443 0.05443 0.06400 0.056824 W23 0.07295 0.07295 0.10704 0.081477 W24 0.06400 0.06492 0.08238 0.069053 Digitalization 0.27566 0.44804 0.50818 W31 0.05351 0.05443 0.06492 0.056824 W32 0.10704 0.17387 0.21792 0.168175 W33 0.11510 0.21974 0.22534 0.194981 It is worthwhile to mention that we also calculated the defuzzification value of sub-criteria using Eq. (6), which are illustrated in Fig. 4.Fig. 4 The defuzzification value of the sub-criteria As can be seen from Fig. 4, the top-ranked weights are W33, W32, and W13, respectively. In other words, “C33: supply chain automation” with a weight of 0.194981 achieved the first position; “C32: virtualization and dematerialization” with a weight of 0.168175 is in the second position; And, “C13: developing local capabilities” reached the third rank, in this case study. The aggregated fuzzy weight of each L-A-D criterion is also depicted in Fig. 5. As we can see, the “C3: Digitalization” criterion achieved the highest value among the group of criteria. Afterward, the “C1: Localization” and “C2: Agility” placed the second and third positions, respectively, with a slight difference.Fig. 5 The fuzzy weight of the criteria The findings prove that experts reached a consensus on the importance of digitalization in suppliers’ performance. From their perspective, the ideal suppliers need to employ big data technology, the internet of things (IoTs), advanced robotics, distributed ledgers, and other emerging technologies for proactive strategies and predictive analytics. Furthermore, digitalization allows suppliers to expand value networks everywhere, automate every process for operational effectiveness, customer satisfaction, at the highest level, rapid recovery of supply chain disruptions. McKinsey (2016b) verifies that digitization can bring about (i) Agility and on-time delivery time, (ii) Flexibility and responding to changing demands by new business models, (iii) Granularity and managing customers in granular clusters and providing them with diverse suited products and services, (iv) Accuracy and proving real-time and transparent data throughout the supply chain, and (v) Efficiency both in physical tasks and planning activities. In Step 6 of the proposed approach, we calculated the fuzzy performance of the suppliers in terms of localization, agility, and digitalization, summarized in Table 7.Table 7 The fuzzy performance of the suppliers Suppliers Localization Agility Digitalization l m u l m u l m u Supplier 1 4.2035 5.8750 7.5232 3.0287 4.7621 6.4954 5.9734 7.3057 8.5371 Supplier 2 5.7474 7.0933 8.3955 2.1586 3.7379 5.3485 2.8674 4.4022 5.9370 Supplier 3 3.0534 4.4383 5.8231 6.0305 7.3943 8.5962 7.2332 8.3251 9.2167 Supplier 4 4.8962 6.4397 7.9598 4.9200 6.4400 7.9600 5.2690 6.7017 8.1345 As shown in Fig. 6, the fuzzy performance of the suppliers in terms of localization is based on the following relation: Supplier 2 > Supplier 4 > Supplier 1 > Supplier 3. The result implies that Supplier 2 reached the first position with the highest performance in the localization criterion.Fig. 6 The fuzzy performance of the suppliers in terms of localization It is worth mentioning that the performance of the suppliers in terms of Agility is illustrated in Fig. 7, with the following order: Supplier 3 > Supplier 4 > Supplier 1 > Supplier 2. As can be seen, Supplier 3 reached the first position with the highest performance in agility criterion.Fig. 7 The fuzzy performance of the suppliers in terms of agility It should be noted that the performance of the suppliers in terms of digitalization is illustrated in Fig. 8. As can be seen, the suppliers’ performance is based on Supplier 3 > Supplier 1 > Supplier 4 > Supplier 2. Supplier 3 reached the first position with the highest performance in digitalization.Fig. 8 The fuzzy performance of the suppliers in terms of digitalization By taking Step 7 of the proposed approach, the performance of the suppliers can be presented based on linguistic variables. As shown in Table 8, final results become more tangible for the decision-makers.Table 8 Performance of the suppliers based on linguistic variables Suppliers Localization Agility Digitalization Supplier 1 Local Agile Very Digital Supplier 2 Very Local Fairly Agile Digital Supplier 3 Local Very Agile Extremely Digital Supplier 4 Very Local Very Agile Very Digital More information about the labeling mechanism of L-A-D capabilities is provided in Table 3 According to Table 8, Supplier 4 has a good situation in all three criteria and can be a good option for our case study. However, the decision-makers should decide based on business objectives and organization strategies. This approach is flexible, which lets them check the performance of the suppliers in different aspects and makes the optimal decision based on the needs. For example, Suppliers 3 can be the optimal option for a decision-maker who pays more attention to digitalization, while Supplier 4 can be the optimal option for another decision-maker who pays more attention to three criteria simultaneously. In the end, key points are extracted from the case study and discussion, as listed below:Digitalization is an essential dimension of L-A-D capabilities. Therefore, suppliers’ effort in “C3.3. Supply chain automation,” “C3.2. Virtualization and dematerialization,” and “C3.1. Value exchange using IT infrastructures” are solutions to supply chain disruption and PESTELL risks. From now on, we will witness a wide range of disruptive transformations and various megatrends overshadowing the construction industry. To be on the safe side, supply chain participants should put their digitalization and localization strategies at high priority. The proposed framework shows high flexibility in performance measurement under uncertainty. Conclusion In the sensitive circumstance of the post-COVID-19 era, construction organizations are interested in collaborating with qualified suppliers for the success of projects, advancing organizational goals, and quick recovery of supply chain disruptions. Given the importance of this topic, a comprehensive approach is highly needed to evaluate suppliers, measure their performance, and prioritize them. This study’s contributions are mainly rooted in the application of OPA-F to develop a framework for measuring suppliers’ performance. The proposed framework considers three essential aspects of localization, agility, and digitalization in the construction suppliers’ performance. The reasons why the L-A-D capabilities of construction suppliers are critical in the post-crisis era that were reviewed in the literate thoroughly. Furthermore, the current study employed the fuzzy set theory to handle the input and output uncertainty. Indeed, we defined the performance measurement of the suppliers as a fuzzy MCDM problem. The proposed approach is easy to employ and presents the performance of the suppliers in both fuzzy numbers and fuzzy linguistic variables, which is more perceivable for the decision-makers and scholars. This paper also put the proposed framework into practice to illustrate its applicability in real-world conditions. In this regard, we targeted a construction company, and we got four experts involved in evaluating their suppliers’ performance based on 11 sub-criteria from localization, agility, and digitalization aspects. According to the results, “C3.3: supply chain automation” is a high-ranked criterion that needs consideration in the supplier evaluation process. Leveraging digital technologies in supply chain practices enables suppliers to reduce operational costs and time for an efficient product and service delivery. The findings also show that “C3.2: virtualization and dematerialization’ is the second high-ranked criterion. From the experts’ perspective, suppliers can use information and communication technologies in the business process to substitute or eliminate materials, energy, and waste in the supply chain, where it is possible. According to the findings, the least important criterion is “C1.2: insourcing process and activities.” It can be perceived that suppliers are basically selected according to their internal ability, capacity, and knowledge to undertake ongoing functions and execute projects, then outsourcing to a third party. In other words, the low weight of criterion does not undermine its importance but makes it less challenging. While conducting current research, we encountered some limitations. One of the sore points of the proposed approach is the lack of consideration of the experts’ reliability during the performance measurement. The experts with different working experiences and academic degrees can play a different role in determining the performance. Hence, extending the proposed approach to Z-numbers in future is suggested, which can present more reliable results based on the experts’ competencies. We hope that our research will serve as a base for future studies in this context. Scholars can expand the framework for a wide variety of criteria in performance appraisal and answer this question: what other capabilities should suppliers have for supply chain resilience and protecting the supply chain and construction industry from risks and unexpected disruptions? Finally, it is strongly suggested to utilize OPA-F to make the optimal decision in the research projects and real-world cases to take wise response actions. Acknowledgements The authors thank the editor and the anonymous reviewers for their important suggestions. More information regarding the Ordinal Priority Approach (OPA) can be found at www.ordinalpriorityapproach.com Funding This study was supported by the National Natural Science Foundation of China under Grant Nos. [72171048 and 71771052]. Data availability statement The data that support the findings of this study are available on request from the corresponding author. Declaration Conflict of interest No potential conflict of interest was reported by the authors. 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==== Front Trans Indian Natl Acad Eng Trans Indian Natl Acad Eng Transactions of the Indian National Academy of Engineering 2662-5415 2662-5423 Springer Nature Singapore Singapore 35836616 328 10.1007/s41403-022-00328-0 Original Article RP3MES: A Key to Minimize Infection Spreading Ghosh Mahasweta mahasweta94g@gmail.com http://orcid.org/0000-0002-1996-3822 Barman Mandal Soma sbrpe@caluniv.ac.in grid.59056.3f 0000 0001 0664 9773 Institute of Radio Physics and Electronics, University of Calcutta, 92, APC Road, Kolkata, West Bengal 700009 India 12 4 2022 2022 7 3 809821 12 10 2021 16 3 2022 © Indian National Academy of Engineering 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Healthcare facilities, especially in highly populated countries like India where patient to doctor ratio is very high, are under a huge burden. Thus, Remote Patient Physiological Parameter Monitoring using Embedded System (RP3MES) becomes essential to monitor a large number of people admitted in hospitals and also patients afflicted with infectious diseases. The design for RP3MES addresses the key issues of portability, cost-effectiveness, low power consumption, user-friendliness, high accuracy and remote communication to facilitate vital parameter(s), like heart rate and body temperature, measurements and emergency notification, keeping in mind, the health of the caregiver(s). ARM Cortex M3 embedded processor and low-cost sensors are used to achieve the cost-effectiveness and low power consumption. Alarming unit intimidates a remote caregiver regarding their patient’s health condition. The accuracy of the system measured data is 99.4% compared with the gold standard, which has been verified using Lin’s Concordance Correlation Coefficient and Bland–Altman analysis. A comparison of our system with other commercially available ones is also presented here. The proposed system has wireless connectivity which minimizes infection transmission among family members and caregivers of the patients. It may also reduce the burden on healthcare staffs in hospitals. Keywords ARM Cortex M3 Correlation Micro-controllers Public healthcare Remote monitoring Zigbee issue-copyright-statement© Indian National Academy of Engineering 2022 ==== Body pmcIntroduction Keeping in mind India’s current healthcare infrastructure of around 1.4 hospital beds per 1000 people of its population, which is unevenly distributed in the country, where existing bed capacity is mostly saturated in government hospitals (Kapoor et al. 2020). According to census 2001, there are 61.3 nurses and midwives per lakh population, out of which only 9.9%, i.e., 62,592 nurses have a medical qualification (Anand and Fan 2016), which indicates a huge shortage of trained healthcare workforce. Thus, there is a lot of pressure on these trained healthcare staff to monitor all the in-hospital patients regularly. There are also chances of the nursing staffs or caregivers getting infected from any patient suffering from communicable diseases, rendering them incapable of providing further services. The current pandemic has taught us that personal protection is utmost important for the caregiver(s) of any patient suffering from Anthroponoses (human-to-human transmitted diseases). This dire need has driven us to aim for remote patient physiological parameter monitoring using embedded system (RP3MES) where we have designed, implemented and analyzed a low power, accurate, cost-effective and user-friendly remote health monitoring system. In this paper, we describe the design of an ARM Cortex M3 embedded processor based system which will be beneficial for remotely monitoring the physiological parameters of patients, mainly in-hospital and home-based patients or elderly persons living alone, and analyze the performance of this proposed system using Bland–Altman curve and Concordance Correlation Coefficient. S. Das et al. have developed a robust photoplethysmography (PPG) based real-time heart rate measurement technique (Das et al. 2016) using an Arduino Uno board. However, this system can handle a limited amount of data because of the low memory of an Arduino Uno micro-controller and may crash when simultaneously multiple programs are run or large data is handled. A. B. Hertzman in his paper has proved the utility of PPG measured from the fingers and toes in man (Hertzman 1937). Its application in clinical physiological measurement has further been established by Allen (2007). It has also been verified that PPG-based heart rate monitoring has many advantages over traditional Electrocardiogram (ECG) methods (Zhang 2015). Motion artifacts (MA) are major contributors to noise in a PPG signal. They arise due to physical activities of the person whose PPG signal is being measured and can adversely affect the heartbeat count. State-of-art technologies are used to minimize MA in PPG signals (Puranik and Morales 2019; Chen et al. 2018; Tseng et al. 2015; Ram et al. 2012). However, we are interested in measuring the resting heart rate of a person, so, MA is not a concern in our sensor design. Considering the pros and cons of PPG sensors over traditional ECG-based sensors for heart rate monitoring, we have chosen PPG-based sensors for measuring the resting heart rate of a patient, which makes hardware implementation of the system simple, cost-effective and requiring lesser power. A. Leone et al. proposed a prototype Temp100 sensor-based vital sign monitoring system using near field communication (NFC) to ensure security, low power consumption and cost-effectiveness (Leone et al. 2015). In their work, NFC has up to 10 cm range which defeats our purpose of keeping at least 1 m physical distance with the care-giving family member. L. Yu et al. have designed a Hong Kong-based health monitoring system that caters to the needs of elderly people (Yu et al. 2018). This sophisticated health monitoring system is expensive and is not affordable by an average Indian family for their home-based patient. An online real-time health monitoring system dedicated to constantly monitor and send data to a server via General Packet Radio Service (GPRS) (Patil and Hogade 2012) is not ideal as a medical staff must continuously monitor these uploaded data for any abnormalities. The system is not capable of interpreting an emergency based on these measured data and hence fail to generate an alarm. The authors in this paper have addressed the two most important key issues of the healthcare system—cost and accuracy. We have considered the issues of cost because it imposes a financial burden to the family, hospitals and ultimately the government to install expensive and high-end devices to monitor vital parameters and accuracy which is crucial for the patient’s safety and the proper amount of medical attention received by him/her. So, instead of using market available sensors, we have designed the sensor modules of our system which reduces the overall system cost. The system performance has been judged using Bland–Altman analysis and Lin’s Concordance Correlation Coefficient and achieved 99.4% accuracy. The proposed system has provisions of raising an alarm and also notifying the immediate caregiver, situated within 100 m indoors, wirelessly using Zigbee protocol. The significant features of our work lie in the following aspects of the proposed system: Self-designed low-cost sensors, having low power consumption, with 99.4% accuracy are used to measure the vital parameters. Local LCD help the doctors to monitor the patient’s vital parameters during every visit. Alarm Unit helps to indicate any abnormality in the patient’s vital parameters. To avoid the risk of infection transmission to other family members, a Zigbee remote communication module helps to send the patient’s vital signs data to the immediate caregiver monitoring with a central Zigbee receiver module/hub up to a range of 75–100 m (indoors). Up to 6500 Zigbee nodes can be integrated into a central hub making it possible for even a single person to monitor a huge number of patients simultaneously from a safe physical distance. Thus, the main novelty of our proposed system is that it can mitigate the problem of healthcare staff shortage that India faces under this pandemic situation and also minimizes the spreading of infection. The rest of the paper is organized as follows: Section “System Architecture” presents the system architecture of our proposed remote patient physiological parameter monitoring system. The methodology of our system with the experimental arrangement, collection and statistical analysis of the data is described in Section “Methodology”. Section “Results and Discussions” displays the results of the measured vital signs of the volunteers compared with the gold standard measurement and the performance analysis of the system is manifested in tables and plots. Finally, the paper is concluded in Section “Conclusion”. System Architecture The proposed system can measure two vital parameters— heart rate [in beats per minute (bpm)] and body temperature [in ∘C and ∘F]. It comprises four sections— Input Section, Embedded Processor Section, Output Section and Remote Communication Section. Figure 1 represents the overall layout of our system.Fig. 1 The overall layout for Remote Patient Physiological Parameter Monitoring using Embedded System (RP3MES) Input Section The input section of our system is designed for acquiring and conditioning the patient physiological parameters (PPP) like heart rate and surface body temperature. Heart Rate Sensor We have designed our heart rate sensor based on the principle of reflective photoplethysmography (rPPG), which gives better reading than transmission PPG (tPPG) (Nijoboer et al. 1981). The clinical acceptance and feasibility of PPG signals have already been established in the biomedical field by Allen (2007) and Hu et al. (2008). The measurements have been taken from the fingertip non-invasively. The proposed heart rate sensor module consists of three units: Optical sensor unit, Signal conditioning unit and Comparator unit. The detailed circuit description is elaborately discussed in one of our previous works (Ghosh et al. 2020). A non-coherent infrared (IR) signal (from a low-cost optical sensor with low power consumption, placed below the fingertip) penetrates the skin and enters the bloodstream flowing through the arteries. During the ventricular systolic phase of the heart cycle, the oxygenated blood rushes to the arteries which absorbs a large amount of IR signal. This results in a sharp fall in the amount of reflected IR signal received by the phototransistor. Thus, a weak pulsatile ac is obtained at the output of the optical sensor superposed on a large dc signal. Each such pulse corresponds to one heart cycle and represents one heartbeat. The rPPG signal sensed by the optical sensor is weak and noisy, so it is passed through a signal conditioning circuit comprising a two-stage active band-pass filter (BPF) and a voltage comparator circuit. The BPF is used to eliminate the dc component of the sensed rPPG signal (due to ambient light, IR reflected from the skin instead of the blood, etc.) and to amplify the ac component (due to arterial pulsation). The voltage comparator circuit converts the rPPG pulses to a square waveform of 0–3.3 V, which is the maximum input voltage for the Analog-to-Digital Converter (ADC) of the embedded processor ARM Cortex M3. Thus, each square pulse represents one heartbeat. The two stages of the BPF are identical, and it comprises a passive high pass filter (HPF) and an active low pass filter (LPF), shown in Fig. 2. The sensor is capable of reading the heartbeats of 43–140 bpm corresponding to a frequency range of 0.72–2.34 Hz.Fig. 2 The circuit diagram of a single stage of BPF of the proposed heart rate sensor The cascaded filter circuit is simulated in Tina-TI software to check its gain—frequency ac characteristic analysis curve. The simulation results are summarized in Table 1 and the graphs are shown in Fig. 3.Table 1 Filter simulation data Characteristic curve parameters Values Upper Stop-band Rejection Limit 2.60 Lower Stop-band Rejection Limit 0.65 Bandwidth Δf (in Hz) 1.95 Peak Gain G (in dB) 75.51 Resonance Frequency f0 (in Hz) 1.32 Q-factor 0.68 Fig. 3 Amplitude Response and Phase Response curves for the ac characteristics analysis plot for the filter circuit of our system Body Temperature Sensor For measuring skin temperature of a person using our proposed system, we designed the surface body temperature sensor module using a low cost, low power temperature sensor. These circuit specifications can also be found in our earlier works (Ghosh et al. 2020). The measurements have been taken from the fingertip for our experimentation but can be wired into a probe-like arrangement to measure body temperature from the oral cavity, tympanic cavity or any other medically accepted body part (McCallum and Higgins 2012). Embedded Processor Section The embedded processor section comprises the different hardware and software tools (depicted in Fig. 4) which takes the physiological parameters in raw form (electrical signals) from the input section and processes it to give the same parameters in a readable format to the output section.Fig. 4 The embedded processing tools used in our proposed system Hardware Processing Tools We have used the Educational Practice Board EPBLPC1768 (Edutech Learning Solutions Pvt. Ltd 2013), which uses an ARM Cortex M3 architecture based LPC1768 chip (Nxp Semiconductors 2016), as the hardware processor. The rationale for choosing the ARM Cortex M3 architecture are as follows: It is one of the most cost-effective micro-controller architecture which provides faster operation compared to its other contemporaries. It has a very good performance efficiency which allows it to do work without increasing power or frequency requirements. It has very low power consumption which allows it to be a very good choice for making portable systems. Programming can be done in a user friendly way in it. Due to the properties of parallel processing and good memory capacity, the system does not crash even when a large number of peripherals are integrated with it. A detailed comparison of the ARM architecture as compared to other contemporary micro-controllers is given below in Table 2.Table 2 Comparison between AVR, ARM, 8051 and PIC Micro-controllers ElProCus (2021) 8051 PICa AVRb ARMc Bus width 8-bit 8/16/32-bit 8/32-bit 32-bit/64-bit Communication UART, USART, SPI, PIC, UART, USART, UART, USART, SPI, UART, USART, LIN, I2C, Protocols I2C LIN, CAN, Ethernet, I2C SPI, CAN, USB, Ethernet, SPI, I2S I2S, DSP, SAI, IrDA Speed 12 Clock/cycle 4 Clock/cycle 1 clock/cycle 1 clock/cycle Memory ROM, SRAM, Flash SRAM, FLASH Flash, SRAM, EEPROM Flash, SDRAM, EEPROM ISAd CLSC Some feature of RISC RISC RISC Memory Harvard Von Neumann Modified Modified Harvard Architecture Architecture Architecture Architecture Power Consumption Average Low Low Low Cost (as compared Very Low Average Average Low to features provide) Other Feature(s) Standard Cheap Cheap, effective High speed operation a Peripheral Interface Controller b Advanced Virtual RISC (Reduced Instruction Set Computer) c Advanced RISC Machines d Instruction Set Architecture Software Processing Tools Eclipse Kepler 4.3 integrated development environment (IDE) handles the codes written in Embedded C programming language to produce a .hex file. This .hex file can be downloaded to the EPBLPC1768 board using Flash Magic so that the system can act as a stand-alone entity in future. Output Section The output section is used for displaying the measured health parameters in a readable format simultaneously on a 16×2 local Liquid Crystal Display (LCD) and a Hyperterminal console display. To ensure alarming the caregiver in all cases of abnormal vital signs, an indicator circuit comprising two different alarms are used—optical (red LED) and acoustic (piezo-buzzer). Since the proposed system turns ON the alarm for only 2 s after each abnormal reading, it is not prone to alarming fatigue. Remote Communication Section Two Zigbee modules in the remote communication section are used to communicate the measured data to the remote caregiver for constant monitoring while maintaining physical distancing and notifying the caregivers in case of any abnormality in measured vital parameters. One Zigbee module attached to the patient’s monitoring system acts as the transmitter while the other module with the remote caregiver acts as the receiver. In our proposed system, we have used the Tarang-P20 modules by Melange Systems (Melange Systems Pvt. Ltd 2011). These modules work in the ISM 2.4 GHz frequency band. The module requires 3.3–3.6 V dc power to run and has transmit power output and receiver sensitivity of 19 dBm and – 105 dBm respectively which makes it sufficient for our proposed system. The indoors communication range is 75–100 m (Zigbee Alliance 2020). Around 6500 Zigbee nodes can be simultaneously connected to a central hub making it possible to monitor a large number of patients by a single person. So, our system is a very good choice for patient monitoring in hospitals while handling a huge patient load with a limited number of trained healthcare staff. The caregiver(s) can immediately inform a doctor regarding a patient’s deteriorating health. Thus, the patient can get immediate medical attention that will be helpful in reducing the mortality rates in patients. The LPC1768 micro-controller has up to 512 kB on-chip flash memory usable for code and data storage. The EPBLPC1768 also has provisions for a micro-SD (Secure Digital) card and a USB (Universal Serial Bus) device/OTG (On-The-Go). However, the proposed system displays and communicates data in real-time without storing them. In case the caregiver or hospital staff want to store them, they can easily collect these data from the Zigbee receiver hub/node and store in any desired server. Methodology Experimental Set-up & Workflow of the Proposed System The experimental set-up for our proposed system is displayed in Fig. 5. The Zigbee receiver module at the remote caregiver end used for remotely monitoring the patient is shown in Fig. 6. The process steps for the entire workflow of our proposed system is given below in Algorithm 1: Fig. 5 Experimental set-up of our proposed system Fig. 6 Zigbee Receiver module at the remote caregiver end of the proposed remotely monitoring system Data Collection The heart rate and body temperature of 65 volunteers are tested by using our designed RP3MES. The demographics of the volunteers are listed in Table 3.Table 3 Demographics of volunteers participating in the Vital Sign Measurement using our system Heart rate measurement Body temperature measurement Sex Male Female Male Female Numbera 35 (63.64%) 20 (36.36%) 7 (70%) 3 (30%) Age  Mean 28.71 years 26.25 years 30.57 years 33.67 years  SD 8.91 years 5.23 years 10.10 years 10.60 years  Skewness 1.42 2.83 1.08 0.69  Minimum 20 years 23 years 20 years 24 years  Maximum 52 years 45 years 48 years 45 years aThe data has been collected on different dates but under similar environmental conditions, during the same month of the year. All the volunteers involved here are the students or professors or technical and non-technical staff of the Department of Radio Physics and Electronics, University of Calcutta. Body temperature analysis was done only in 10 patients whereas heart rate was done for 50 subjects. Body temperature measurement is a simple process and even with age and sex variations, it was showing a stable reading for all the volunteers. So, lesser number of samples were taken. However, the heart rate varied for each and every person and was also found to be affected by various factors, like, the heart health, age, sex, etc. of the volunteer. So, comparatively a lot more volunteers were considered to get an unbiased analysis of our heart rate monitor. Hence, different groups and numbers of volunteers participated for the data collection for vital signs measurement using our proposed system All the measured data are simultaneously validated by a registered medical practitioner, which was taken as the gold standard (GS). Manual readings of pulses from the radial artery are taken for heart rate validation, while the body temperature data is validated using a digital thermometer (pressed between the fingertips of forefinger and thumb) (Hicks 2018). The digital thermometer data is taken as the GS as it is clinically acceptable (Hicks 2018; Gerensea and Murugan 2016). Figures 7 and 8 shows the data collection process of a male volunteer both using our proposed RP3MES and by following the GS measurement procedure respectively.Fig. 7 HR measurement of a male volunteer using the proposed RP3MES Fig. 8 HR measurement of a male volunteer using the GS procedure, for verification purpose The system is capable of measuring the resting heart rate, i.e., either in lying position or calmly sitting position, without any vigorous physical activities in the last 10–15 min. For our data collection, we have measured the heart rate of the volunteers in resting condition (Mishra and Rath 2011), which implies that they are in a calm condition and sitting position (after 10 min rest). During the entire data collection, the ambient temperature varied within 32–34 °C. Statistical Analysis To validate the acceptance of the proposed system, the data collected from both the heart rate sensor and the body temperature sensor are compared with our GS (manual measurement by registered medical practitioners). This comparison is done by plotting Bland–Altman Agreement Curves (Bland and Altman 1986) and by calculating Lin’s Concordance Correlation Coefficient (CCC) (Lin 1989). The utility of these analyses to estimate the agreement between two methods of measurement is already proved in clinical and statistical measurements (Bland and Altman 1986; Lin 1989; Lin et al. 2002). Bland–Altman Analysis Bland-Atman (BA) analysis has been done for all the data collected by our system to study the clinical acceptability of such data. The horizontal lines in the BA plot denotes the mean of the differences d¯ (further called as mean or bias) and upper and lower Limits of Agreement (LoA). Both the LoAs represent the range around the mean error value, within which, the difference between the measurements by both the methods for 95% pairs of future measured data lies (Bland and Altman 2007). All the analysis data are calculated with a 95% Confidence Interval (CI), i.e., 95% of the future measurements’ data will lie within this interval for any specific parameter like mean or upper or lower LoAs. These are calculated using the mean, standard deviation of the differences (SD), t-value of the distribution (t) [assuming 2-tailed normal distribution] and sample size (n) as follows Bland and Altman (1986); Carkeet (2015):1 Meand¯=1nΣ(ProposedSystemData-GSData) 2 LowerLoA=d¯-1.96×SD 3 UpperLoA=d¯+1.96×SD 4 95%CIofMean=d¯±tSDn 5 95%CIofLowerLoA=LowerLoA±t2.92SDn 6 95%CIofUpperLoA=UpperLoA±t2.92SDn. Lin’s Concordance Correlation Coefficient The Pearson’s correlation coefficient (ρ) measures only a linear relationship between the collected data and the GS (gold standard/reference) data rather than any deviation from the 45° line. However, in a graph of collected data versus the GS data, the perfect correlation is depicted by not only a linear relationship but also by the slope/inclination of the said straight line. When the inclination of the straight line (which shows a correlation between the GS and the collected data) with the horizontal becomes 45°, the collected data perfectly agree with the GS data without a bias towards either of them. In mathematical terms, the correlation coefficient becomes a perfect 1.00. Lin’s Concordance Correlation Coefficient (CCC) (ρc) can measure the agreement between the data collected and the GS data by measuring the deviation from the Concordance Line, i.e., the 45° line passing through the origin (Lin 1989). Thus, CCC is used, in this work, as a measurement metrics to judge the performance of our proposed sensors. The accuracy and precision for measurement can be given in terms of the Pearson’s correlation coefficient (ρ) and the Bias Correction Factor (Cb) by the Eqs. (7) and (8) respectively (Lin 1989; Lin et al. 2002). Thus, ρ gives a measure of the closeness of all the data points to the best-fit line while Cb depicts the deviation of the best-fit line from the Concordance Line (Lin 1992).7 MeasureofPrecision=ρ 8 MeasureofAccuracyCb=ρcρ. Results and Discussions Heart Rate Measurement Analysis The Heart Rate (HR) data measured by our system is plotted in a Bland–Altman plot (Fig. 9), where the Y-axis represent the difference of the GS (manual) HR from our system’s HR and the X-axis represent the mean of these two HR data. Figure 10 shows the deviation of the regression line for the HR data, plotted for our system measured data with the GS collected data, from the 45° line. The statistical analysis parameters and their values calculated using (1)–(8) is tabulated in Tables 4 and 5.Fig. 9 Bland–Altman plot for the HR data, with the solid blue line representing the zero difference level, the finely dashed red line representing the mean d¯, the boldly dashed red lines representing the upper and lower LoAs and their corresponding whiskers representing their respective 95% CI limits Fig. 10 The comparative plot of the HR data measured from our system versus the GS (manual) data, with the dotted line representing the regression line in comparison to the solid 45° Concordance Line Table 4 Statistical parameters for the Bland–Altman Analysis Parameters Heart rate (in bpm) Body temperature (in °C) Value 95% CI Value 95% CI Mean d¯ 1.29 0.45–2.13 – 0.22 – 0.39 to – 0.04 SD 3.10 – 0.24 – t-value 2.00 – 2.26 – Lower LoA – 4.78 – 6.20 to – 3.34 – 0.70 – 1.00 to – 0.40 Upper LoA 7.36 5.93–8.79 0.26 – 0.04 to 0.56 Table 5 Comparison of performance of our proposed system with the Gold Standard Parameters Heart rate monitoring system Body temperature monitoring system CCC (ρc) 0.964 0.956 Precision (ρ) 0.970 0.962 Accuracy (Cb) 0.994 0.994 Adjusted R2 0.944 0.916 Regression Line y=1.047x-2.519 y=0.993x+0.023 Body Temperature Measurement Analysis The Bland–Altman difference versus mean plot for the body temperature (BT) data between our system’s measured data and the manually measured digital thermometer data (GS) is shown in Fig. 11. The deviation of our system measured BT data from the GS data is depicted in Fig. 12. Table 4 and Table 5 contain the statistical analysis results for the BT measurement (calculated using (1)–(8)) using our proposed system.Fig. 11 Bland–Altman plot for the BT data, with the solid blue line representing the zero difference level, the finely dashed red line representing the mean d¯, the boldly dashed red lines representing the upper and lower LoAs and their corresponding whiskers representing their respective 95% CI limits Fig. 12 The comparative plot of the BT data measured from our system versus the GS (manual) data, with the dotted line representing the regression line in comparison to the solid 45° Concordance Line Comparison of the Proposed Model The American National Standard on “Cardiac Monitors, Heart Rate Meters and Alarms” by AAMI (Association for the Advancement of Medical Instrumentation) states that the allowable readout error while measuring heart rate should not be greater than ± 10% of the input rate or ± 5 bpm whichever is greater (Institute 2002). The proposed system measures accurately both the heart rate and body temperature of a person which is clinically acceptable accuracy. Table 6 shows the comparison of our proposed system with commercially available sensing systems whose performance (at rest) have been analyzed in previous works of literature.Table 6 Comparison of performance of our proposed system with Commercially Available Sensors as Analyzed by Previous Works of Literature Sensors Mean LoA range (Limits) CCC Cost Proposed System < $65a HR Measurement 1.29 bpm 12.14 bpm (– 4.78 to 7.36 bpm) 0.964 BT Measurement – 0.22 °C 0.96 °C (– 0.70 to 0.26 °C) 0.956 SensiumVitalsb (Downey et al. 2019) ≈$44 HR Measurement 1.85 bpm 44.14 bpm (– 23.92 to 20.22 bpm) – BT Measurement 0.82 °C 3.91 °C (– 1.13 to 2.78 °C) – Basis Peakb (Cadmus-Bertram et al. 2017) 2.8 bpm 39.7 bpm (– 17.1 to 22.6 bpm) – ≈$200 Fitbit Chargeb (Cadmus-Bertram et al. 2017) – 0.7 bpm 19.7 bpm (-10.5 to 9.26 bpm) – ≈$91 Fitbit Surgeb (Cadmus-Bertram et al. 2017) – 0.3 bpm 9.6 bpm (– 5.1 to 4.5 bpm) – ≈$230 Mio Fuseb (Cadmus-Bertram et al. 2017) 1.0 bpm 17.7 bpm (– 7.8 to 9.9 bpm) – ≈$197 Fitbit Charge HRb (Kroll et al. 2016) – 4.7 bpm 52 bpm (– 31 to 21 bpm) – ≈$105 Apple Watch 3b (Nelson and Allen 2019) – 2.47 bpm 28.95 bpm (– 16.94 to 12.01 bpm) 0.453 > $280 Fitbit Charge 2b (Nelson and Allen 2019) – 4.69 bpm 19.2 bpm (– 14.29 to 4.91 bpm) 0.561 ≈$155 SensiumVitalsb (Breteler et al. 2020) 1.0 bpm 31.3 bpm (– 14.6 to 16.7 bpm) – ≈$44 HealthPatchb (Breteler et al. 2020) 1.3 bpm 11 bpm (– 4.1 to 6.9 bpm) – N/A Non-Contact IR Thermometerd (Chen et al. 2020) – 0.96 °C 3.47 °C (– 2.70 to 0.77 °C) – ≈$10 aThe estimated cost of our proposed system will be highly reduced on mass production bSystem can also measure Respiration Rate (RR) whose parameters are not considered cSystem cannot measure Body Temperature (BT) dThe work in this paper involves only Body Temperature analysis data, measurements being done from the wrist region Conclusion This paper presents a low cost, low power remote patient physiological parameter monitoring system which measures the physiological vital parameters accurately. The overall development cost of our proposed system is estimated to be less than $ 65. However, on batch processing for mass production, the cost will be highly reduced and the system will become cost-effective. The processor of our system has low power consumption and can be run by a 9 V supply. The system is an improvement over many commercially available sensor systems (Table 6) at a reasonable cost. Our proposed system will be highly beneficial for in-hospital or home-based patients (like COVID-19 or TB1 patients), caregivers of home-based patients, medical staff in hospitals and also any person requiring constant vital sign monitoring (like elderly people). It can also be used regularly to monitor the PPPs and even warn the caregivers when their vital signs or PPPs indicate an unhealthy physical condition. These data are constantly communicated to a remote caregiver or medical staff via Zigbee communication for further medical help. The results of the BA and scatter plots clearly show that all the measured data points closely agree with the gold standard data. CCC value establishes that our system can monitor both heart rate and body temperature with 99.4% accuracy (compared to the GS) and has more than 96% measurement precision. In our study we collected the vital signs data from healthy adults within the age group of 20–52 years, it can be equally effective for children, senior citizens or any other chronically infected patient. This is because the proposed system is not an illness/disease predictive system. The main aim of our system design is to measure the vital signs of a person and alert the caregiver whenever such measured value(s) is/are out of typical medically approved ranges. As our system does not require age and sex as inputs, our proposed system is independent of these demographics. The work can further be extended by incorporating more low-cost sensors in system to monitor other vital signs like peripheral capillary oxygen saturation (SpO2), ECG, Respiration Rate (RR) etc. for a complete hospital or home-based remote vital sign monitoring set-up. Integration of all modules in a single package of smaller size will make it easily portable. This work has potential application for communicable diseases like COVID-19, TB, influenza, etc. because it can minimize infection spreading by maintaining physical distance. The work has a scope to further extend in the field of a complete home-based remote health care management system by incorporating telemedicine and tele-ambulance services in this system. Acknowledgements The authors would like to thank UGC UPE II project “Modern Biology: Signal Processing Group”, Calcutta University for providing research facility and SMDP-C2SD project, Calcutta University for technical support. Author Contributions All authors equally contributed to the study conception and design. Material preparation, data collection and analysis were performed by the scholar, Mahasweta Ghosh. The first draft of the manuscript was written by Mahasweta Ghosh and her supervisor, Soma Barman (Mandal) commented on and revised the previous versions of the manuscript. All authors read and approved the final manuscript. Declarations Conflict of Interest The authors have no conflicts of interest to declare that are relevant to the content of this article. Funding The authors have no relevant financial or non-financial interests to disclose. No funding was received for conducting this study. Ethics approval An Institutional Ethical Clearance Certificate has been issued by the Institutional Ethical Committee for Bio Medical and Health Research involving Human Participants, University of Calcutta for conducting this project. Consent for publication The authors grant the Publisher an exclusive licence to publish the article, once accepted by the journal. Availability of data and materials Real-time data is collected by our system which will be made available on request when the entire project is complete. Code availability On request after the project is complete. 1 Tuberculosis (TB), mainly the Multi Drug Resistant variety (MDR-TB), is known to affect hospital staffs, caregivers and family members Parmar et al. (2015). Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Allen J Photoplethysmography and its application in clinical physiological measurement Physiol Meas 2007 10.1088/0967-3334/28/3/R01 American Heart Association (2015) All About Heart Rate (Pulse). https://www.heart.org/en/health-topics/high-blood-pressure/the-facts-about-high-blood-pressure/all-about-heart-rate-pulse. 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==== Front Soc Sci Med Soc Sci Med Social Science & Medicine (1982) 0277-9536 1873-5347 Elsevier Ltd. S0277-9536(22)00279-9 10.1016/j.socscimed.2022.114973 114973 Article The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19 Galetsi Panagiota a Katsaliaki Korina a Kumar Sameer b∗ a School of Humanities, Social Sciences and Economics, International Hellenic University, 14th Km Thessaloniki-N.Moudania, Thessaloniki, 57001, Greece b Opus College of Business, University of St. Thomas Minneapolis Campus 1000 LaSalle Ave, Schulze Hall 333, Minneapolis, MN, 55403, USA ∗ Corresponding author. 12 4 2022 5 2022 12 4 2022 301 114973114973 19 8 2021 21 2 2022 8 4 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. With Covid-19 impacting communities in different ways, research has increasingly turned to big data analytics (BDA) and artificial intelligence (AI) tools to track and monitor the virus's spread and its effect on humanity and the global economy. The purpose of this study is to conduct an in-depth literature review to identify how BDA and AI were involved in the management of Covid-19 (while considering diversity, equity, and inclusion (DEI)). The rigorous search resulted in a portfolio of 607 articles, retrieved from the Web of Science database, where content analysis has been conducted. This study identifies the BDA and AI applications developed to deal with the initial Covid-19 outbreak and the containment of the pandemic, along with their benefits for the social good. Moreover, this study reveals the DEI challenges related to these applications, ways to mitigate the concerns, and how to develop viable techniques to deal with similar crises in the future. The article pool recognized the high presence of machine learning (ML) and the role of mobile technology, social media and telemedicine in the use of BDA and AI during Covid-19. This study offers a collective insight into many of the key issues and underlying complexities affecting public health and society from Covid-19, and the solutions offered from information systems and technological perspectives. Keywords Big data analytics Artificial intelligence Social medicine Diversity equity and inclusion Covid-19 pandemic Public health Prediction models PRISMA ==== Body pmc1 Introduction Pandemics are a worldwide challenge, mainly for healthcare but also for other industries. Coronavirus (Covid-19) is an infectious disease that was first reported December 2019 in Wuhan, China, and has since spread all over the world with varying speed and intensity (World Health Organization, 2020). The prevalence of this global pandemic has emphasized the need to investigate health disturbances and prepare response strategies more quickly than the pace of the infectious disease (Atkinson et al., 2020). The introduction of big data analytics (BDA) in healthcare makes it possible to study the effects of the crisis, facilitate pandemic strategies, and discover vaccines and treatments (Lv et al., 2021). BDA refers to the techniques, technologies, systems, practices, methodologies, and applications for analysing the vast amount of data to understand organizations and society better (Galetsi et al., 2019). BDA techniques include forecasting, optimization, simulation, and others that assist in decision-making (Doumpos and Zopounidis, 2016). One of the most emerging analytic techniques in healthcare is machine learning (ML), which automates the execution of rules through algorithms with many successful artificial intelligence (AI) applications in the healthcare sector (Davenport and Kalakota, 2019; Galetsi et al., 2020). AI with its ML algorithms makes BDA simpler by automating and enhancing data preparation, data visualization, predictive modelling, and other complex analytical tasks. AI's algorithms use big datasets to train themselves to perform a specific task, such as identifying a lesion in a radiographic image (Kulkarni et al., 2020b) and therefore make raw data meaningful for decision-making purposes. Covid-19 applications can use BDA to deal with increasingly large amounts of data. Over time, availability of larger data help refining algorithms for applications and increase their output accuracy. This was not feasible a few decades ago because of data storage and computing power limitations. Nowadays, we can handle any data sizes with cloud servers and the current computing power. For these reasons, ML/AI applications development has gained prominence. The advancement of cloud computing has given rise to instant data retrieval from servers around the world. Decision support-systems are being built with access to large repositories of data residing in cloud servers linked to various data sources stemming from individuals or organizations. Consequently, IT professionals constantly develop new infrastructure and new applications with big data capabilities to help stakeholders making informed decisions (Wang et al., 2018). Specifically, during Covid-19, the proliferation of diverse big datasets (e.g., data from public records, social media and sensor data from smartphones), was a critical driver of empirically based problem-solving. Therefore, available digital technologies and big data on Covid-19 facilitated researchers, practitioners and policymakers in developing numerous BDA/AI Covid-19 applications in short time hoping to successfully direct strategies and responses to the pandemic and its unforeseen ‘black swan’ events (Sheng et al., 2020). Nonetheless, developing these applications requires huge investments, by organizations and the government, in human and technological resources, and capital. This can result in diverting resources from other pressing needs for the betterment of the society. Additionally, the extent of using new information technologies for gathering and analyzing big data is profoundly influenced by the country's ability to acquire, absorb, and use new innovations for social good (Mehraeen et al., 2020). Most of these AI-driven tools are reinforced and practiced in high-income countries (Naseem et al., 2020). Less developed countries, due to capital shortage, lack of technological expertise and poor infrastructure may face barriers in implementing such applications, which impedes effective management and control of the pandemic. Some examples include challenges in identifying Covid-19 cases, rapidly re-organizing limited hospital resources for the infected and regular patients and understanding population behaviour against the restriction measures (Ozsahin et al., 2020; Belciug et al., 2020; Imran et al., 2020). The World Health Organization (WHO) has envisioned securing the health and well-being of people around the world with the concept of “Health For All” (HFA/2000). Because of this, healthcare organizations worldwide have shown a growing responsibility to improve diversity, equity, and inclusion (DEI) efforts to better serve patients and their families. This planned effort is attempted by understanding peoples’ backgrounds including culture, gender, religious beliefs, and socioeconomic status (diversity); by ensuring populations effectively benefit from best practices in treatment (equity); and by providing high-quality care and treatment experience for all social groups (inclusion) (Piggott and Cariaga-Lo, 2019). However, despite the growth in public policy research and government interest in fostering socio-economic determinants and equity in health policies, “unhealthy” public policies are still implemented among certain segments of the population (Embrett and Randall, 2014). As an example, public hospitals in Saudi Arabia require male guardian permission to allow an adult woman to be admitted or receive care of any kind, even an urgent medical procedure (Beckerle, 2016). In the case of Covid-19, the literature shows that different political beliefs, ideologies, and attitudes create a split in societal perceptions on public health issues such as vaccination and medical protocols (Ward et al., 2020). An aspect that should not be overlooked about effective implementation of Covid-19 BDA applications based on DEI principles is lack of common understanding of such principles in different parts of the world. This impedes the adoption of a global mindset for a common DEI approach. Initiatives must be localized to avoid the appearance of not relevant or not culturally tailored diversity mandates (Goodman, 2013). In the implementation of any localised strategy; local laws, regulations, and societal norms need to be acknowledged and the systems and processes established must suit the way things get done locally (Goodman, 2013). The current focus of DEI is based on North American social and political context (Majmudar and Kymal, 2020), which takes away the attention from the rest of the world where DEI might be of greater importance but with a different focus. A search in the international literature for reviews related to BDA, AI, and Covid-19 revealed a lack of articles that analyse both the medical and social impact of BDA/AI applications for managing the pandemic. There are few informative papers that profile research in BDA discussing various aspects of modern technology used to tackle Covid-19, including medical image processing, disease tracking and prediction outcomes, computational biology, and medicines (Jia et al., 2020). There is also a number of opinion papers on similar issues (Dwivedi et al., 2020; Kulkarni et al., 2020a; Sheng et al., 2020); reviews and profiling papers relevant to a specific BDA method, such as AI (Pham et al., 2020; Chiroma et al., 2020) or to a certain aspect of Covid-19 diagnosis, such as chest X-rays and CT scan imaging analysis (Ozsahin et al., 2020) or reviews and overviews of mHealth (Islam et al., 2020), telemedicine (Battineni et al., 2020) and social media analysis (Alhumoud, 2020). However, none of these investigate the medical and social aspects of the use of BDA and AI for Covid-19. Therefore, this study explores the usefulness of BDA for tackling Covid-19 under a social and medical approach. It especially examines AI methods and new applications of big data analysis, their positive or negative effects on society and the pandemic and what more can be done. Through a DEI lens, this research examines whether the developed BDA and AI for Covid-19 applications are available to all individuals and communities. Specifically, it examines capacity requirements, for full participation of community in the provided medical services created by these advances (equity). Further, it looks at how to cater to the most disadvantaged to be able to use the provided information and technologies (inclusion) by capturing and supporting diversity based on individuals' and societies’ demographic characteristics, religion, culture, and so forth. Overall, the study addresses the following research questions.RQ1 What are the applications of BDA and AI for Covid-19 management? RQ2 What are the values of these applications from a medical and societal/DEI perspective? RQ3 What are the societal/DEI challenges created by these applications? RQ4 How can future research help mitigate these societal/DEI challenges and help create new applications that can bring additional solutions in combating pandemics and other large-scale health issues and crises? The main contribution of this research is to identify the significant applications that analyse big data in response to Covid-19 and discuss their direct and long-term benefits to healthcare and the society along with their limitations and challenges under the lens of DEI. This research also identifies to develop additional responsible BDA applications for similar cases. Identifying and recognizing the positive and negative effects of rapid advances in technology can assist policymakers, scientists, and technology developers to avoid malfunctions and provide diverse and equitable healthcare to all population segments. 2 Research methodology We followed the key principles of the PRISMA methodology to conduct the literature review. The methodology includes three stages to synthesize the themes of this research: 1) input, 2) processing and 3) output. The first stage, input, involves the identification of relevant articles in the Web of Science® (WoS), a database containing quality impact factor journals. Screening the literature revealed that important tools in battling the pandemic are artificial intelligence (AI) and machine learning (ML) analysis methods, and the broad use of smartphones and social media since the last outbreak (Bansal et al., 2020; Rao and Vazquez, 2020). Therefore, the search strategy for relevant articles concentrated on a list of keywords. The list includes general terms providing a wide dataset of BDA methods that scientists use to design and apply methods against Covid-19. It also includes terms that ensure we do not miss articles that use specific, popular BDA techniques, as identified from an initial literature screening. Our keyword list allowed us to maximize the number of articles in our dataset. The detailed search strategy is provided below. Studies published online in 2020 (including early publications of 2021) were retrieved from the WoS a year after the first appearance of Covid-19. The keywords “Big Data”, “Big Data Analytic”, “Artificial Intelligence”, “Machine Learning” and “Mobile app” were combined with the keywords “Pandemic”, “Epidemic”, “Coronavirus”, “Covid-19” and “SARS-Cov-2” on a one-to-one basis. Only journal research papers and review studies that were written in English and relevant to Covid-19 and BDA were included in the dataset. We included only articles and reviews to capture the full information of a study, which is usually better presented in a published journal article. The initial keyword search retrieved 985 records, but from an abstract scan, only 607 papers were finally included in our dataset after applying the inclusion-exclusion criteria. Content analysis of these papers was conducted January–July 2021. The second stage of the research process focuses on grouping and classifying the papers into selected topics by capturing the relevant texts with the use of the NVivo software after full-text review. The categories and their sub-categories, which act as the guide to the dataset content analysis, were inspired by recent literature in the health BDA field (Galetsi and Katsaliaki, 2020). After reviewing all 607 papers we established 15 sets of BDA/AI Covid-19 applications based on their contents and intentions. We assigned these papers into the 15 identified sets of applications and then allocated these sets into two topics based on their targeted entities: 1) public healthcare and 2) individuals and community. Within the pool of articles, we have identified around 30 papers that relate to DEI, Covid-19 and BDA/AI together, and we used them to drive our discussion of identifying the social challenges for each set of applications. We applied text retrieval methods to capture specific information from the papers. The relevant section that explained its link to a sub-dimension was recognized and coded by the NVivo software. The third stage of the methodology, output, presents the results of the classification process. First, a profile of the dataset's content is presented (e.g., publishing journals, institutions, data types used) and the highlights of citation, co-citation analysis from the use of VosViewer software. Because the collected sample of the published work is large, it can be characterized as representative and therefore a presentation of some proportional results of this dataset could shed some light on the research conducted thus far in this area. Secondly, the content analysis of the data pool answers to the main research questions by providing an overview of the developed BDA/AI applications for Covid-19 and an understanding of their impact, challenges, and future directions. The tables in the appendix include article frequencies per set of applications and indicative research examples. Fig. 1 outlines the research methodology strategy.Fig. 1 Research methodology approach. Fig. 1 3 Dataset profiling This section presents an overview of our dataset demographics, including country of origin and authors’ affiliation, publishing journals, subject areas, most cited and co-cited papers and authors. We provide statistics on generic paper classification, Covid-19 research in various disciplinary fields, BDA capabilities and techniques, and sources of data. We also identify various stakeholders of the BDA/AI applications. The dataset includes publications from 61 countries. USA is first with 175 published articles (counting the number of authors affiliated with that country), followed by China (114) and India (80). Overall, the 607 papers are published in 290 different journals, with IEEE Access and the Journal of Medical Internet Research having the highest number of publications. The four most popular journals have a short publishing history (launched after 2000). Most papers belong to the subject area of “Computer Science & Information Systems” followed by “Medical Informatics.” Harvard University stands at the top with 31 articles, followed by the University of California (20). These universities excel in many fields of science and are ranked in the top 10 universities worldwide (Times Higher Education: World University Rankings, 2010–2019). The most cited paper (Wang et al., 2020) out of the 607 papers (all published in 2020), is published in JAMA with 659 citations in WoS (as of Feb 16, 2022). The paper is about Taiwan's government response to Covid-19, making Taiwan an example of how a society can use infrastructure and BDA to respond quickly to a crisis and protect the interests of its citizens. All papers have many co-authors (>7), indicating the importance of group work for achieving quick academic output. The most frequently co-cited papers amongst our 607 papers are the first publications using data from patients in Wuhan, China's hospitals (Huang et al., 2020; Wang et al., 2020; Chen et al., 2020). Professor Duong (Radiology department, Montefiore Medical Center-Albert Einstein College of Medicine, NY,USA) and Dr Haifang Li (Radiology department, Renaissance Medicine School, Stony Brook University, NY,USA) are the researchers with the highest number of publications and are also the most co-cited authors. It is notable that the majority of the most publishing authors are junior researchers from China, where the pandemic began. The dataset comprised a range of paper types. Most of them were experimental papers (65.0%), followed by reviews (16.0%), opinion papers (9.2%), case studies (8.2%) and surveys (3.3%). In the fields that were investigated with the aid of BDA techniques, most papers were about public health (61%), followed by human behaviour (10.5%), social science (9.2%), business (7.7%), pharmacology (5.1%), environment (2.6%), tourism (2%), and other. Many papers were classified under more than one field. The developed methods utilized the evaluation: BDA capability by 36.7%, the prediction capability (29.5%), monitoring (23.4%), reporting (12.5%) and data mining (8.3%). The BDA techniques employed in mitigating the effects of the pandemic were usually AI methods, such as ML (Ardabili et al., 2020), deep-learning (Abdel-Basset et al., 2020), neural network analysis (Shrivastav and Jha, 2021), natural language processing (Leelavathy and Nithya, 2020) and incorporation of cloud computing (Ndiaye et al., 2020). For more information on these techniques, readers can refer to the references. In the investigated applications, the data used came from a variety of sources. From the sample of 607 papers, more than 55% mention the analysis of clinical patient data, such as EHR and medical images retrieved in most cases from healthcare centres. Another 21% of studies analyse patient behaviour data which are collected from wearable sensors and social sites. The studies also manipulate pharmaceutical data (6.5%), including drug therapeutic mechanisms retrieved from chemical laboratories of hospitals, research organizations and pharmaceuticals companies, and finally administrative and cost data (5%) mostly financial and operational in nature, retrieved from healthcare centres, health insurance and other commercial companies. Although many stakeholders were identified in the examined studies, the final beneficiaries of BDA Covid-19 research were mostly the society and the patients as they were offered diagnosis and ways to tackle their disease. Other stakeholders were IT specialists who, with their valuable contribution, designed the desired systems, physicians and health professionals who were provided with tools that helped them understand the disease, and policymakers who receive information from these applications about the current and future progress of the pandemic for taking appropriate measures. 4 Results: applications and impact This classification attempts to map the knowledge in the field and explains BDA and AI impact in tackling Covid-19. Therefore, in this section we offer a list of significant BDA/AI applications that were developed to deal with the pandemic. Tables A1, A2 and A3 in the appendix present the main set of applications based on their target-group: public healthcare, individuals and the community. Each table reports for each set of applications the indicative BDA/AI technique/method, the immediate healthcare benefit and enduring societal benefits derived from such techniques and methods, and their associated challenges faced by society The last column in these tables reports the frequency of the research studies associated with each set of applications (N), and the second column provides an indicative reference for each application category (Key Ref). This indicative reference is selected either based on popularity (number of citations) or ease of understanding its current use. 4.1 BDA/AI applications for public healthcare According to Table A1 (appendix), nine different types of models/applications that refer to public health and medicine were identified in the literature. The majority of BDA models were developed by using AI on clinical datasets for evaluation and prediction to make medical treatments more efficient. In particular, the first category of applications focuses on the identification of Covid-19 positive patients. Some of the 185 papers focus on detecting SARS-CoV-2 from chest CT scan images (Ardakani et al., 2020) or from chest x-ray images (Brunese et al., 2020). Novel AI models using chest images from coronavirus patients (initially retrieved from collaborating Chinese medical centres) were developed by researchers in university medical schools and biomedical engineering centres specialized in image processing and cardio-thoracic imaging. This chest imaging data trained the ML algorithms to identify whether the patient is covid positive. Such AI models/toolkits can easily be deployed worldwide to other hospitals’ radiology departments, either online or integrated into their systems. Because these models are highly sensitive and can diagnose unclear cases, they can provide a second opinion to radiologists and physicians. This pool of papers also focuses on detecting SARS-CoV-2 from blood tests, like real-time polymerase chain reaction (RT-PCR) tests that detect the presence of the virus by amplifying the virus’ genetic material until it can be detected by scientists in a microbiologist lab, and also from a nasal swab or saliva rapid antigen tests (Brinati et al., 2020) that are now self-administered too. The latter tests work by detecting specific proteins-antigens, on the surface of SARS-CoV-2 particles by a convolutional neural network which classifies microscopy images of single intact particles of different viruses (Dey et al., 2020). Physicists and biomedical scientists have worked with clinical collaborators to make this discovery possible. The rapid antigen tests are now provided by pharmacists and can be done by individuals at home. All methods (CT, x-ray, blood tests) seem to bring results that are quite accurate, though not of the same accuracy. This is important when considering countries with different levels of medical technological resources. These Covid-19 detection methods are fast, widely available and do not impose substantial cost. Even the chest imaging AI models can be implemented in any radiology department providing the opportunity to also be implemented in deprived areas via telecommunication analysing the images remotely (Ardakani et al., 2020). These applications can gain society's trust because they can provide accurate results creating the sense of a widely available and accessible system that is not influenced by personal or human bias (Nouri, 2021). The majority of developed countries appear to embrace technological advances by providing social status rewards to innovation and holding more patents for inventions including patents related to Covid-19 (Frey et al., 2020). However, certain cultures may be more skeptical towards technological advances, which deprives them of participating in the testing of new diagnostic tools and therapies (Drissi et al., 2020). In a cyclic way, this skepticism may lead to exclusion of these societies because of their absence from the development phase which may also create reservations for the use of such Covid-19 detection models. In the second category, we identified systems that predict and monitor whether Covid-19 will spread in the population, forecasting the progress of the outbreak and the relevant policy decision scenarios such as “no action,” “lockdown,” and “new medicines” (Alanazi et al., 2020; Allam et al., 2020). Another study in this area proposes a “bioinspired metaheuristic” model, which simulates how Covid-19 spreads and infects healthy people from the primary infected individual (patient zero) using data such as reinfection probability, spreading rate, social distancing measures and traveling rate to simulate Covid-19 activity as accurately as possible (Martínez-Álvarez et al., 2020). Another novel application is a drone model, equipped with a thermal vision camera to detect human body temperature in order to monitor the spread of the disease in the population (Manigandan et al., 2020). We also observed the use of ML algorithms to identify possible Covid-19 cases more quickly using phone and web surveys (Rao and Vazquez, 2020). Sentiment data such as spatio-temporal data detecting human mobility have been used by researchers in various fields (e.g., computer scientists, statisticians, and epidemiologists) in order to capture population movement patterns and trajectories (Abdallah et al., 2020). The necessary data are collected by smartphones and transmitted for further mapping analysis e.g., call detail records (CDR) data from mobile network base stations and from a wide range of surveillance technologies, such as facial recognition and thermal cameras, biometric wearables, smart helmets, drones, smartphone GPS, QR codes, and Bluetooth functions (Kitchin, 2020). Specifically, GPS aids in crowd mapping for tracking the spread of Covid-19. The Bluetooth smartphone function detects other devices retained for a certain time within a specific range of distance and notifies the smartphones that have been sufficiently in contact with the infected individual's device, assuming that the infected individual has reported the infection to the app. The QR codes scanning method with the physical temperature testing equipment or thermal imaging cameras track through the smartphone app the individuals' movement on public transport (Li and Guo, 2020). Such use of GPS, QR codes and Bluetooth tracking data have been implemented by governments of several countries, such as Australia and Singapore, through the use of a centralized governmental mobile app, to trace whether infected patients are staying at home as part of quarantine measures or have come in contact with infected individuals (Li and Guo, 2020). Based on the predictions of these novel models, policymakers make decisions about population movement restriction measures. The variety of these applications provide outcomes based on the real situation, helping governments issue lockdown policies only in specific places, instead for the whole country, to balance the human rights of free movement against the health risks. However, pursuing such applications comes with challenges such as personal rights violation because restriction measures such as quarantines, canceling mass gatherings, isolation may conflict with ethical and religious principles. Moreover, tracing and tracking the movement of infected people (like they do in Singapore) also violates people's privacy, so people might not comply with these measures (Nguyen et al., 2020). Furthermore, these prediction models use real-time, regional data, which assumes that data collection takes place locally and repeatedly, but not all regions or even countries have the technological and financial resources or the expertise to develop prediction models for continuous Covid-19 monitoring. The third category of applications refers to models predicting mortality risk. Some of the models forecast the mortality rate in specific countries (An et al., 2020). Many studies focus on defining the right parameters for predicting mortality from Covid-19. These parameters are gathered from healthcare settings and include the number of positive Covid-19 cases per day in the population, hospitalized Covid-19 patients, respiratory rate, arterial oxygen partial pressure, chest x-ray images (Assaf et al., 2020) and more. There are also novel applications that can predict the risk of patients with other chronic diseases, such as cardiovascular diseases (Brown et al., 2020) or glaucoma (Bommakanti et al., 2020). In this category, we also identified ML models using the Covid-19 patients’ geographical, travel, health and demographic data to predict the severity of the cases and the possible outcomes (recovery or death) in nationwide cohorts (Iwendi et al., 2020). In these studies, researchers and medical professionals across the world have used population demographic and clinical data from national healthcare centres and laboratories to provide safe results at the national level (Budd et al., 2020). In the same category, there are also mobile applications and wearable sensors for analyzing and reporting the status of health, fitness and recovery or risk probability of Covid-19 patients by identifying pain levels and variations in heart-rate (Josephine et al., 2020). Using the predictive national mortality rates and other demographic indicators such as education, migrants background, religion and employment, policymakers can closely monitor the populations and communities most affected and avoid health inequities (Tai et al., 2021). However, because socioeconomically disadvantaged people from low income and education levels face barriers to obtaining, processing, and understanding basic health information and following instructions, they may not be able to participate in national level studies and health interventions (Stormacq et al., 2020), such as the studies that require mobile apps and wearable sensors for their analysis. As a result, social minorities may be under-represented in such population observations and therefore may not obtain the appropriate treatment when needed (Kirby, 2020). Studies have shown that widely used algorithms to identify high-risk patients were significantly biased against race groups, revealing systemic inequalities that led to poor access to care for such groups (Röösli et al., 2021). The fourth category is optimizing Covid-19 patient management in healthcare centres. The articles in our review discuss several applications, such as systems that outline the route of Covid-19 patients in the intensive care unit (ICU) considering the queueing parameters, while optimizing necessary resources such as beds and associated hospital costs (Belciug et al., 2020). This category also includes the development of the CPAS, the Covid-19 ML-based hospital capacity planning and analysis system in the UK. This system was deployed in coordination with NHS Digital (Qian et al., 2021). In this category we also find studies that use ML modelling to predict the length of Covid-19 patients' hospital stays based on clinical data (Kasilingam et al., 2021). In all these applications, medical staff and administration within hospitals and among hospital networks have used various data to facilitate the management of COVID-19 patients: hospital admissions, bed occupancy and electronic health records (EHR) from the hospital information systems (HIS), drug prescriptions from the e-prescription system, and medical images from the cloud-based medical image sharing systems of healthcare centres (Bae et al., 2020). Improved patient management in emergency departments decreases delays in treatment for all patients and increases social distancing and safety in healthcare centres. It is true though that not all healthcare systems worldwide can support these efforts of fast re-arrangement of resources or appropriate resources’ acquisition (e.g., extra ICU beds) to react to urgent situations. Therefore, this might lead to better treatment of civilians in high-income countries compared to developing countries, creating social inequalities (Alamo et al., 2020). Moreover, heavy reliance on AI models for optimal allocation of limited resources for tackling Covid-19, such as ventilators, ICU beds, lead to delicate decisions that may provide a false sense of objectivity and equity while diverting scarce resources from regular healthcare services (Laudanski et al., 2020). In the fifth category, we included models that intend to create warning systems for society by using data from several sources to look for Covid-19 pre-symptoms. We also found ML models that combine disease estimates from digital traces, such as official health reports, Covid-19-related internet searches, and news media activity to forecast Covid-19 activity in certain areas in real-time (Liu et al., 2020). Also, there are digital approaches such as contact tracing apps to manage population mobility and to provide the public with dynamic and credible updates on the Covid-19 pandemic (Nakamoto et al., 2020). Patient behaviour data are gathered from mobile apps, such as from GPS and from beacons or QR codes that may reveal the spatial location of the users. Based on these data, the models reveal congested population areas so that health policymakers and governments can release restriction warnings (Simmhan et al., 2020). Similarly, in this category we also include studies using electronic wearable devices for immediate sensing of Covid-19 clinical symptoms, such as fast heart rate (Pépin and Bruno, 2020). However, ethical concerns have been raised about the broad use of contact tracing technology and therefore app developers are prompted to build more trustworthy platforms for collaborative use of raw individual-level data which will ensure that private information is not used against certain social groups (Chatfield and Schroeder, 2020). Documentation, validation and explainability of these platforms is a first step regarding the transparent use of such intellectual property (Luengo-Oroz et al., 2020). Transparency is necessary to understand intended predictions, target populations, hidden biases, class imbalance problems including the ability to generalize emerging technologies across hospital settings and populations (Röösli et al., 2021). In the sixth category of applications, we identified models that focus on drug repurposing and investigational therapies such as IDentif.AI (Abdulla et al., 2020), an AI-based platform, which can interrogate a 12-drug candidate therapy set, representing around half a million drug combinations against the SARS-CoV-2 virus collected from a patient sample. Such platforms identify drug interactions and optimize infectious disease combination therapy that inhibits lung cell infection with clinically relevant dosages (Abdulla et al., 2020). Another application is an AI-based approach that integrates known information about the functional interaction of proteins with experimental and patient data available in public repositories, with a focus on clinical translation. Simultaneously, a web server uses an alternative method to corroborate the drug combination (Artigas et al., 2020). Overall, these applications, developed by bioinformatics researchers and medical laboratory staff, use pharmaceutical data translated in drug regimens and doses. By testing them and comparing clinical outcomes from laboratories, such as cell density, allow physicians and drug manufacturers to mix or create certain drug substances for (to-be) infected patients to face the virus (Abdulla et al., 2020). These platforms accelerate the process of identifying Covid-19 treatment, ensuring populations benefit from best practices and enhancing the value of equity when such drug combinations are easily available in all countries’ healthcare settings. However, if drug patents, manufacturers, and expensive or scarce substances are involved, then equity is usually lost, and exclusion plays out especially for low-income people in developed countries and the population in developing countries. Moreover, there are different opinions and ethical considerations about whether specific clinical trials due to the urgent situation have correctly followed the existing medical protocols in terms of recommended processes and time allowed for realizing side-effects (Agoro, 2020). Furthermore, in most cases, the clinical trials are held in developed countries and the patient sample may not be appropriately diversified. Until recently, the majority of such samples consisted of young white men (Murthy et al., 2004). Clinical research should ensure diversity and inclusion by creating mechanisms that better engage with underrepresented communities and include patient samples from diversified race, ethnicity, age, and sex. A high-quality dataset must be collected at first in order to mitigate bias in the data being fed into the AI application (European Union Agency for Fundamental Rights, 2019). In the seventh category, we identified studies about applications for detecting probability of false negative or false positive Covid-19 cases. These applications use ML approaches to identify the top performing analysis methods and determine the accuracy of SARS-CoV-2 detection in nasal swab samples of rapid antigen tests (Nachtigall et al., 2020). In addition, they determine the accuracy of frameworks that collect real-time symptoms data from Covid-19 cases (such as fever, cough, shortness of breath) from biosensors and wearable devices and physicians monitoring suspected cases using cloud infrastructure and propose algorithms avoiding false positive or negative results (Otoom et al., 2020). Best practices in medicine, and more accurate disease diagnoses are considered social goods for all populations. However, the complex nature of AI solutions may also lead to biased output because of the unpredictable or unexpected occurrences in the internal data analysis process that inform about false alarms and emergent measures in society (Sipior, 2020). Due to possible bias, these systems denote their accuracy percentage for their prediction, however, this percentage might also be miscalculated (e.g., rapid test results accuracy). It is known that actuarial risk algorithms in the US health insurance industry affect millions of patients and may exhibit significant racial and other biases at a given risk score. Specifically, in cases of false test results, the algorithms may result in decisions causing unequal access to care as health systems rely on such prediction algorithms to classify patients with complex health needs (Obermeyer et al., 2019). In the eighth category, we find ML algorithms that are used as vaccinology tools to investigate the entire SARS-CoV-2 proteome, which is crucial to viral adherence and host invasion, in order to induce high protective antigenicity (Ong et al., 2020; Zame et al., 2020). Pharmaceutical data such as proteomes, proteins genomics, etc. are analyzed by such applications in pharmaceutical companies and laboratories to introduce vaccines to the populations (Ong et al., 2020). These applications lead to faster invention and introduction of vaccines which can offer protection to large populations and especially to groups of people most at risk. Nevertheless, because AI/ML algorithms are often developed on non-representative samples and evaluated based on narrow metrics, algorithmic bias and disparity may exist (Zou and Schiebinger, 2021). Moreover, the Covid-19 immunity ethics has started a big debate among societies (Allam et al., 2020) about the criteria applied for the acquisition of these vaccines from drug manufacturers (usually the main criterion is based on monetary values meaning that rich nations are the first to get them) and the distribution rules within a nation's population as for the prioritization of the groups of people who are first vaccinated (Chagla and Pai, 2021). The last category includes bioinformatic models that attempt to understand the nucleotide sequences of diseases, analyzing the viral genome sequences using approaches such as data stream, digital signal processing, and ML techniques (Batra et al., 2020). The methods invented to analyse the DNA sequences of virus are the beginning of analysis of many other diseases by geneticists. These methods lead to a better understanding of the viruses and other diseases’ behaviour, and eventually to the discovery of better therapies and treatments. A major social concern is the extended use of automated applications to manipulate genetic data which are stored in electronic databases without clear rules for their management, who uses them and for what purpose (Capps et al., 2019). 4.2 BDA/AI applications for individual and community Continuing with the next set of applications pioneered during the Covid-19 pandemic that are targeted to individuals or communities, we come across models that report populations’ mental health impacted from Covid-19. An example of this is the design of a sophisticated AI chatbot, on a smartphone application, that can diagnose and recommend immediate measures to psychologically distressed patients who have been exposed to the virus (Battineni et al., 2020). Such an application is especially useful for people living in remote areas. Other applications combine ML methods and AI using data collected from Twitter—about the healthcare environment, emotional support, business economy, social change, and psychological stress—to analyse reactions and sentiments of citizens from different cultures and to advise on subsequent actions taken by different countries (Imran et al., 2020). Another indicative study developed a digital platform to detect the needs and pressing situations of frontline healthcare workers through a self-assessment test and provide support resources for their well-being (Mira et al., 2020). Data sources of these applications are mainly people behaviour data retrieved from questionnaires, social media networks and platforms, or mobile applications. The analysis of these data by the IT specialists provides information to psychologists, physicians, and policymakers in order to detect new mental health statuses and other needs in certain segments of the population (e.g. children with prolonged home confinement due to school closures and social distancing) and offer support. Inclusion and protection of vulnerable social groups (such as the children, domestic violence victims, unemployed, healthcare workers, people with mental health disorders, and the elderly) is attempted by customizing these applications to cater for the aforementioned subpopulations (Balcombe and De-Leo, 2020). However, data gathered from social media platforms spark data privacy concerns, especially when it involves sharing confidential healthcare information. Moreover, the inability of many elderly and disabled people to use technology, such as mobile apps through which these innovations are offered, may finally exclude them (Sufian et al., 2020). Therefore, the developed applications might not reach the populations most in need of support. The second set of applications under this category includes chatbots through mobile apps invented to track and directly communicate individuals' vital signs to a registered doctor to be factored into a treatment decision (Ros and Neuwirth, 2020). This category also includes the design of mobile apps such as “Check Your Mask,” which validates the correct way to wear a protection mask by taking a selfie and suggests improvements to limit the spread of Covid-19 and protect people from infection through personalized measures (Hammoudi et al., 2020). Moreover, it includes mobile-friendly applications and sensors that incorporate algorithms that consider epidemiological factors like fever, breath sounds and other symptoms to help patients decide when to seek medical care (Heo et al., 2020). This kind of tool provides immediate healthcare to people who have difficulty moving, such as the elderly or disabled, making it possible to be included in medical treatment. However, as said before, these groups of people may not own or be acquainted with the use of such technology. Moreover, this more automated diagnosis process may affect the doctor–patient relationship and undermine doctors’ diagnostic authority in the long-term (Lupton and Jutel, 2015). The third set of applications uses ML-based frameworks to measure the spread and tension of misinformation to verify the credibility of data from social media (Al-Rakhami and Al-Amri, 2020). Attempts such as CovidSens, a social sensing-based risk alert system, analyses social data to infer the state of Covid-19 propagation, keep the general public informed about the spread of Covid-19, and identify risk-prone areas by predicting future propagation patterns (Rashid and Wang, 2021). These models are mainly created by computer scientists who run sentiment analysis on data of a certain time period gathered from social sites and platforms (e.g., tweets). Therefore, the results identify and track untrusted sources of Covid-19 information that derive from certain religious or political groups, and measure the “traffic” that this information has created by the number of followers, likes, etc. This knowledge is valuable to cyber fraud policymakers for protecting societies from disorientation and conspiracy theories. However, not distinguishing between intentional and unintentional spread of false information probably driven by different ideologies may result in unethical criticism of opinions of certain social groups (Brennen et al., 2021). The fourth set of applications is relevant to lockdown measures introduced by several governments, which affect personal life and the global economy. Flexible modelling frameworks have been developed that use available epidemiological data to show the results of lockdown measures in slowing down the disease transmission in specific countries (Aguiar et al., 2020). To locate problems and mitigate consequences, ML classification models and complex algorithm models evaluate the effectiveness of lockdown regulations by investigating whether a correlation exists between lockdown procedures and infection rate (Al Zobbi et al., 2020). Epidemiological data and healthcare information systems data (such as ICU admissions) used in these applications are provided by healthcare centres and are continually collected (Aguiar et al., 2020). Another approach uses ML models fed by meteorological and urban data to quantify the health implications of the reduced air pollution in the presence and absence of the lockdown measures (Saravanan, 2020). The information about the positive or negative outcomes from a lockdown policy in society and in the environment is valuable feedback for the policymakers to better protect certain regions and communities. However, the results of these metrics could encourage authorities to continue strict measures, increasing individuals’ psychological problems and creating social frustration by limiting social interaction (Ardabili et al., 2020). Extended lockdown policies may lead to society division and social confrontation and conflict. The fifth set of applications is models for investigating different communities' reactions to the same news about the Covid-19 outbreak. An indicative study investigated reactions by analyzing the text of readers' comments on news of lockdown measures and social distancing norms across India, such as masks in public spaces and on transportation (Debnath and Bardhan, 2020). Other applications used the same method to compare different populations and countries (Imran et al., 2020). In these applications, computer scientists, usually from IT consulting companies or universities, use data mining techniques to retrieve user comments from social media and press articles when a new policy regarding Covid-19 is released. Then, they apply text analytics and sentiment analysis techniques to capture public opinion. The results are discussed with policymakers, psychologists, and communication specialists to evaluate the impact of the policy. In case of massive public reaction, policymakers will either drop the policy, or based on the reported arguments, will attempt to persuade the public to abide by the regulation. The benefit of this type of application is the ability to recognize different social groups’ reactions and inform policymakers for improved healthcare policies. On the contrary, there are issues to ascertain credibility hidden behind anonymity (e.g. internet trolls) that is intended mostly to manipulate public opinions and therefore it is important that the affected parties should verify the sources of such information (Al-Rakhami and Al-Amri, 2020). Lastly, we observe frameworks and models, developed by computer scientists and pedagogists, which use AI and educational data analysis tools in learning management systems. Such systems help teachers generate better learning methodologies for online classes and use data mining algorithms in educational databases to identify students' knowledge deficiencies. AI techniques learn from users' interactions by analyzing students' data (e.g., times of microphone-camera on/off, speaking time, chatting time, online quiz scores-performance), and a virtual assistant can be developed to manage the information of each student and offer automatic and personalized monitoring for improving communication and students’ learning quality at home (Lv et al., 2021; Villegas-Ch et al., 2020). These efforts lead to a better and more effectively learning experience for socially distanced individuals. However, this effort may exclude people in poverty, who cannot afford the digital infrastructure for distance learning (Budd et al., 2020). 5 Challenges and future agenda This section reports the findings of concerns as stated by researchers in their studies included in the dataset and their suggestions for overcoming them. BDA and AI provided fast and efficient solutions to face the outbreak but also created numerous challenges. The previous section presented evidence that scientific efforts to find technological solutions to limit the effects of the pandemic effected society positively and negatively, especially in terms of DEI. Table A3 (appendix) summarizes six main categories of concerns, the main approaches that these studies propose for mitigating them, an indicative reference from our dataset, and the number of studies that refer to each concern. The most recognized challenge, appearing in 154 papers, concerns the ethical issues of privacy, the use of personal data to limit the pandemic spread, and the need for security to protect data from being overused by technology. Digital technologies could be abused by benign users, malicious attackers, public authorities, and other powerful players in social media, compromising integrity and confidentiality and creating financial loss and social upsets (Wang et al., 2020). For example, genetic information and viruses’ DNA sequences are included in electronic databases without being regulated for their management and maintenance, who is allowed to use their information and for what purpose. It is fundamental to create ethical frameworks for bioinformatics and new paradigms to safeguard and oversee data collection and storage (Capps et al., 2019). Researchers call for collective efforts from multiple parties, including governments, health agencies, practitioners, service providers and users, with the common objective to build security and privacy defense lines that cover both technical and social aspects. Researchers highlight the need for strong legislative protection, such as the General Data Protection Regulation (GDPR), the e-Privacy Directive, and the European Charter of Human Rights to safeguard the right to privacy and data protection (Gasser et al., 2020). They also advocate for even more specific protocols, such as the Pan-European Privacy-Preserving Proximity Tracing (PEPP-PT) for development of apps that monitor the spread of the disease and alert people if they have come into contact with a Covid-19 positive case. Despite these guidelines and laws, the consensus amongst the technical community is that some of these frameworks are too academic for practical development (Li and Guo, 2020). The right direction is to develop apps with decentralized architecture, wherein the personal data is enclosed and controlled by individuals on personal devices, instead of the centralized architecture in which personal data collected through the app is controlled by government authority, which is currently the case of most such apps (Li and Guo, 2020). Even though health data governing, and new legislative proposals increasingly focus on privacy by limiting or controlling access to health-related data, implementation of more inclusive strategies is necessary for protecting such data. These strategies must go beyond a pure privacy focus and extend to preventing or penalizing uses that could harm individuals (McGraw and Mandl, 2021). Another possible challenge of BDA/AI applications is the biased outputs that may result from hastily monitoring the pandemic to offer solutions—the fast collection of not so “clean” data and circumventing some validation model checks. AI systems are built on learning from data, and if the data is skewed, it can have major consequences. Therefore, outcomes from analytics systems may be biased and perform poorly (Kiener, 2020). The teams developing AI Covid-19 applications in organizations may not be diverse enough to build inclusive applications that reflect the diversity of the general population (Nouri, 2021). If these AI Covid-19 applications are not appropriate because of the aforementioned reasons, then it is a case of wasted scarce resources which could have been used for more pressing societal needs. In the previous section, we argued that AI solutions are essential in reflecting the changed circumstances of life imposed by Covid-19; however, because of the complexity, the degree of confidence in AI results or datasets must always be examined (Sipior, 2020). Since biases may exist in all BDA phases - from how the model is designed, developed and deployed to the quality, integrity and representativeness of the underlying data sources - developers must consider (or be required by national or global authority directives) addressing these biases, and physicians should recommend policies while considering the biases of parameters due to the need for fast solutions (Sipior, 2020). In the case of an international emergency, the dissemination of factual and timely information is a crucial part of the collective response. Research of previous epidemics show that social media has been effective in presenting the public with both factual and false information (O’brien et al., 2020). As observed, 90% of Internet users seek online health information as one of the first tasks after experiencing a health concern, so this pandemic exposed societal concerns about online misinformation that can exacerbate the pandemic (Choudrie et al., 2021). For instance, newsfeed algorithms may amplify misinformation and existing approaches are difficult to scale manipulation and bias in human consumption of information or exacerbate polarization among populations with different political or religious perceptions (Bhadani et al., 2022). For this reason, scientists insist that new methods for testing credibility be developed. They also insist that reputable organizations, such as government bodies and health agencies (e.g., World Health Organization) open links to society and establish a presence on social media as an integral part of their communication strategy with frequent updates and reports in the national and international levels, so that populations can rely on them for credible information (Naeem et al., 2021). Additionally, the content analysis of our article pool revealed social inequalities. For example, less developed countries have been excluded from health solutions because modern digital health works well in developed countries that have a fully equipped health system using AI and various mobile-based applications (Kaushik et al., 2020). For this reason, scientists raise concerns about social inequalities due to the unavailability of digital tools and services. They also point out the absence of regulatory authorities avoiding malpractices and, therefore, the need for an appropriate body to consider solutions for patients' data ownership and acceptance of digital health. This goal is to minimize inequity and inequality, and also to raise money to build appropriate smart infrastructure worldwide as the spread of the pandemic does not follow country boundaries (Ndiaye et al., 2020). There are also concerns about the so-called “immunity ethics” and the societal consequences of Covid-19 immunity passports and the obligation to get vaccinated. Vaccines are particularly urgent because globalization and immigration play a major role in the spread of Covid-19. The control of vaccination through new cutting-edge technologies also creates challenges (O’brien et al., 2020). Many legal issues ensue about whether immunity counts as a beneficial act for the individual and society or whether these immunity passports incentivize discrimination (Allam et al., 2020). Since a person infected with or vaccinated against Covid-19 can still get the infection again and spread it, there are doubts about whether immunity is achieved. Countries using immunity passports should consider the societal consequences of this decision as it might lead to division in society and raise conflict with uncertain medical evidence to support the logic behind the measure. Immunity passports would impose an artificial restriction on who can resume social and economic activities which might perversely incentivize individuals to seek out infection, especially economically deprived people who cannot afford a period of workforce exclusion (Phelan, 2020). Therefore, governments should be as inclusive as possible in offering access to activities to all people who can safely prove they are Covid-19 negative (e.g., through test, vaccination, or natural immunity) (Drury et al., 2021). Lastly, three studies emphasized the inequalities in various regions of education and training on how to apply BDA/AI solutions to combat the pandemic. These studies informed policymakers about the risks of certain regions not following best practices. These inequalities may exist in training healthcare workforce in BDA and in how to use the new applications to benefit their population. The global nature of the pandemic requires global solutions, but there is also a need to adapt in certain areas because of their characteristics, such as in pastoral areas, refugee camps or conflict zones to consider different socioeconomic, or cultural and structural variables (Luengo-Oroz et al., 2020). Therefore, policymakers should consider worldwide education strategies in BDA in terms of each country's workforce (Paudel, 2021). An analysis of our dataset also reveals a future agenda of BDA/AI opportunities that offer solutions to Covid-19 or other crises. This pandemic experience has shown societies that future living will need to be adaptable. Digital technology can provide opportunities to respond to many future societal challenges and scientists should create future smart ecosystems for collecting, analysing, and sharing real-time information and performance benchmarks to be used by health service providers and policymakers (Marston et al., 2020). New ML and soft computing models should be invented to predict outbreaks and the complex variations in their behaviour across nations by providing benchmarks (Ardabili et al., 2020). Moreover, biobanks should be integrated into healthcare systems, which can preserve the biological material and host prospective cohorts and material related to clinical trials so that research infrastructure can offer access to materials for future medical crises (Holub et al., 2020). Future applications must also focus on more complex frameworks that can instantly analyse high volumes of emerging clinical trials on therapies, such as those for Covid-19. Also, these new applications, combined with emerging disciplinaries such as bioinformatics and cheminformatics, should target structure-based drug designs, network-based methods for prediction of drug-target interactions, and work with AI, ML and Phage techniques to provide alternative routes for discovering patent drugs (Omolo et al., 2020). Also, social media can further be exploited to offer novel insights. Since Covid-19 revealed that people can experience symptoms for many weeks as well as post-covid symptoms that may change over-time, data about patient experiences could be used to develop rapid assessments of large numbers of social media conversations to monitor public health (Picone et al., 2020). 6 Discussion Our study reviewed the publications on the Covid-19 pandemic that use BDA and AI algorithms. After providing a dataset profiling of relevant papers, we focused on the applications developed for public health and for the individuals/community, and we examined their impact on society and medicine. This review sheds light on the many benefits of BDA/AI Covid-19 applications and also on their challenges and limitations, especially in terms of DEI. From the literature review, future research prospects include ways to overcome some of these challenges and work towards developing more applications to combat Covid-19 and other crises. The most frequently used methods relevant to BDA are AI algorithms, specifically ML and deep learning. Researchers mainly from computers and medical informatics disciplines have developed the relevant applications that cover BDA capabilities, such as evaluation, prediction and monitoring. A lot of studies cite the first publications about the pandemic demographics from Wuhan, where Covid-19 started. Based on the results of our dataset, we identified numerous BDA applications for Covid-19 with novel ML algorithms focused on public health evaluation and prediction models for identifying Covid-19 positive patients, mapping the spread of the disease in the community (Allam et al., 2020), forecasting the severity classification of Covid-19 hospitalized patients and estimating Covid-19 patient admission and in-hospital mortality (Abdulla et al., 2020). These applications have aided decision-making for local or national lockdowns and other measures. This study also identified applications developed for individuals, including mobile applications (mHealth) that use behaviour data to support people facing psychological distress from social distancing, unemployment or overwork (Battineni et al., 2020). Applications also applied text analytics in social media platforms to monitor public opinions about Covid-19 and associated restriction policies (Debnath and Bardhan, 2020), and the sources of misinformation (Brennen et al., 2021) which inform policymakers. Without underestimating the benefits of these applications on the general population, it is important to stress the concerns that are raised. These concerns are related to equity and exclusions of certain population segments, usually the poorest ones, from participation in and using these new applications and their byproducts (e.g., more healthcare resources allocated to Covid-19 patients; and reducing infection with appropriate local policies, vaccines and new drugs), mainly due to lack of technological infrastructure, financial resources and research expertise (Kirby, 2020; Stormacq et al., 2020). Moreover, the relevant literature showed that the top challenges of using BDA during the pandemic are: “data privacy,” “bias of output” and “spread of false information”. The use of high volumes of personal data for public-health surveillance raises legal concerns about security and privacy, and interventions have allowed consensual adoption or have made the option of public consent for specific purposes explicit, which highlights the need for public trust and engagement (Budd et al., 2020). Furthermore, there is a lot of discussion on the use of ‘black box’ AI in medicine and the systematic bias between AI's implicit assumptions and an individual patient's background situation (Kiener, 2020). Therefore, physicians must be alert when incorporating patients' data and interpreting outcomes. Taking this a step further, it is possible that public authorities and other powerful players may abuse the technologies at the expense of privacy and human rights. Therefore, while the health emergency was depicted as a positive force driving the development and adoption of new digital technologies at scale and speed, their uninhibited implementation in some areas raise legal, ethical and privacy concerns intensifying risks for disadvantaged communities (Hantrais et al., 2021). Moreover, the development of m-RNA technology and fast vaccine production at a low cost is another benefit for public health. However, many countries' low vaccination percentages of certain minorities such as migrants, the disabled and people with low income have raised a lot of discussion about their inclusion in the vaccine programs and targeted campaigns (Njoku et al., 2021). A great debate has also started about some countries’ decisions to use compulsion rather than persuasion for their immunization programs and the possibility such decisions do more harm to the well-being of free people than good (Pennings and Symons, 2021). This is also relevant to the discussion of immunity passports which will allow individuals to return to their daily activities but raise immunity ethics concerns regarding pros and cons for the society. Overall, Covid-19, as a threat to world-wide well-being, has led to vast research into BDA for Covid-19 and the rapid “commercialization” of research. The need for urgent solutions has led researchers to shorten their models’ validation processes to produce fast treatment outcomes. It is hopeful, however, that this need has brought information disclosure related to research about vaccines and drugs formulas that prevent/treat the virus. However, pharmaceutical patents have restricted access to generic supplier companies to develop the vaccine (Siegel and Guerrero, 2021). The articles also identify the future direction of these applications, describing experimental models and systems that explain human-machine interactions and promote approaches for better data management in unpredictable situations (Iandolo et al., 2020); digital technology that create smart ecosystems to respond to possible health crisis by mitigating diversity, equity and inclusion challenges (Marston et al., 2020); and prediction models of outbreaks that incorporate variations in the behaviour across nations and biobanks (Holub et al., 2020). It is a great opportunity to learn from the pandemic and its accelerated technology advancements attained in a short time. We can learn how to use related BDA/AI technologies to deal with similar humanitarian disasters in the future. There is also a great need to address the unintended consequences of Covid-19 with BDA/AI technologies. For example, the models for optimizing Covid-19 patient management in healthcare centres (Table A1-group 4) that focus on appropriate hospital resources allocation can also be used to deal with the re-allocation of healthcare delivery resources (e.g., physicians, beds, surgical theatres). These models can address the unintended consequences of Covid-19 such as the prolonged postponement of elective surgeries and treatments which have surmounted during the pandemic making people's general health deteriorate. In addition, this pandemic has created many other economic and societal challenges due to social isolation and increased unemployment. These challenges include the increase in mental health cases among children and adult population, addiction to internet usage, agoraphobia, and poverty. Therefore, improving and increasing use of models for mitigating populations’ mental health impact from Covid-19 (Table A2-group 1) is very important to be able to identify psychological distress and addictions and provide these people healthcare resources to fight the problems. Such applications can be improved and used through a mobile app which will monitor patients by scheduled questions related to their health and a chatbot that can provide appropriate responses, through text classification and trained ML algorithms. In combination with behavioral data received by the smartphone (such as phone activity, step counter, sleep, and heart-rate monitor) and video-call capabilities, a doctor can monitor and manage the patients and intervene with telemedicine when necessary. The same applies to models for providing personalized telehealth (Table A2-group 2) as this may become the new reality of health services not only for teleconsultation but also for more healthcare tasks, such as measuring vital signs using a mobile app. The increasing use of such models can enhance patient management with patient support systems for automated messages, such as appointment reminders/bookings, clinical results release, and drug prescriptions through an authentication process. Such telehealth options will provide more efficient service and give access to more people, especially all vulnerable people or those leaving in rural areas. Additionally, with small adaptations in the collected data and in the spread patterns, models for predicting the spread of Covid-19 in the community (Table A1-group 2) can be used for other diseases in the future and for different regions. Models measuring the spread and tension of misinformation for Covid-19 (Table A2-group 2) can be used for other situations of breaking news to flag misinformation. Models such as those for the immediate identification of Covid-19 positive cases from CT chest images (Table A1-group 1) can be used for more purposes and be functional in mobile apps. For example, image recognition capabilities, such as Google lens, can be embedded in a mobile app and a trained algorithm can perform image matching and automatic differential diagnoses [e.g. for skin cancer (Zakhem et al., 2018) or drug effectiveness from a petri-dish image (Agarwal et al., 2019)]. Another BDA/AI health application for enhancing doctors' experience could retrieve similar patients EHRs by matching a patient's disease, and the doctor can evaluate possible disease progress, survival analysis, causal inference, etc. (Schuler et al., 2018). These new health applications could shape the future of healthcare and improve society's well-being. However, before such apps are released in the market, many issues must be resolved related to data governance and equity for their availability to all segments of world population. 7 Conclusions This article illustrates how the pandemic accelerated the adoption of digital technologies such as new applications for diagnosis, e-health online therapies, online working, learning and social interconnectedness. Along with the benefits came new challenges demanding government interventions to prevent harm and social exclusion associated with technological development that did not necessarily result in social progress (Hantrais et al., 2020). Commercializing science and AI algorithms into useable applications in day-to-day healthcare could bring certain benefits to society, medicine and business, and ethical practices could reduce the negative impact on society. The first step to minimize the risk and provide safe healthcare to humanity is recognizing and identifying the possible effects of using innovative technology when technology becomes the basic road of survival in case of a sudden attack, such as the recent pandemic. BDA applications created opportunities to identify who is getting sick and dying and who is in danger of being affected, helping governments to take social distancing measures and public health agencies to direct money and resources to the populations most in need (Lopez and Neely, 2021) (e.g., by creating more Covid-19 hospitals and ICUs). On the other hand, numerous scholars, doctors, and policy experts have argued different health inequalities including developing nations with limited health resources, minorities with limited access to healthcare settings and vaccination programs and restriction policies that people find hard to follow due to lack of resources, religious perceptions and ideologies concerning the suppression of human rights. Detailed guidelines and regulations are needed when developing such applications to ensure technology transparency and data privacy and protection. Moreover, healthcare professions and other segments of the workforce require training in BDA/AI techniques for better comprehension, use and correct interpretation of the results. Global policymakers should provide necessary resources to introduce the BDA/AI applications equally in the healthcare systems of all countries to equip them with appropriate tools to combat future challenges and to cater to public health and society's well-being. Moreover, they should transfer the generated knowledge of which policies have worked well and which have not for all segments of society in terms of exploiting the new technology for informed decision-making. This pandemic has introduced new policies and ideologies, raising awareness of the need to create a more caring society (Lopez and Neely, 2021). At the same time, as societies have learned to function remotely during the pandemic, advanced mHealth apps will become very relevant in the near future for accomplishing many health tasks such as diagnosis and health monitoring faster and from everywhere (Galetsi et al., 2021), making healthcare accessible to wider populations. The development of responsible technology must be the target of all health applications, which will help society solve the major problems that affect the well-being of human populations and come closer to the broader mythology of tech-fixes for social problems (Holzmeyer, 2021). Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.socscimed.2022.114973. ==== Refs References Abdel-Basset M. Chang V. Mohamed R. HSMA_WOA: a hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images Appl. Soft Comput. 95 2020 106642 32843887 Abdallah H.S. Khafagy M.H. Omara F.A. Case study: spark GPU-enabled framework to control covid-19 spread using cell-phone spatio-temporal data CMC-Computers Materials & Continua 65 2 2020 1303 1320 Abdulla A. Wang B. 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