Three years down the line, 29.2% of those women considered the use of cryopreserved oocytes less likely than at the freezing time, due to an overoptimistic assessment on the chances of pregnancy through natural conception during advanced maternal age.

28 In the survey of Hodes-Wertz et al 14 , only 11 women of the 183 responders (6%) eventually used their oocytes, with a range age of 33-41 years at the time of vitrification and with 6-20 re- trieved oocytes.

Of these, only three women successfully conceived a child.

14 Another survey of 23 women indicated that, at the time of the interview, only two women had used their frozen eggs and one reportedly achieved a live birth.


Interesting data from these surveys include the willingness to donate unwanted oocytes to research or to other women for use in infertility treatment.

14,15 It is important to consider that that was the position at the moment of their oocyte cryopreservation, but attitudes to donation may change over time.


Hammarberg and coworkers investigated the reasons for an el- evated rate of non-used stored oocytes.

The most common reasons included (1) not wanting to be a single parent, (2) preferring to con- ceive naturally and (3) not wanting to use a sperm donor.

27 In a retrospective observational study, Cobo et al 22 showed that only 9.3% of women returned to use their oocytes (95% CI 7.8-10.8) at a mean age of 39.2 years, waiting a mean of 2.1 years to return.

Recently, a large multicenter retrospective study from the same re- search group found that the usage rate has become 12.1%, with a mean age at vitrification of 37.6 ± 3.5 and a return of 39.9 ± .7, con- firming a mean storage time of 2.1, as previously described.

43 The likelihood of cryopreserved oocytes to remain in the bank without further use represents one of the aspects to consider when assess- ing cost-effectiveness.


The evaluation of cost-effectiveness in a healthcare environment represents a crucial aspect to define procedure-related benefits.

One of the advantages of fertility preservation for non-medical in- dications is based on the possibility to improve the chances of child- birth using own gametes.

The process could potentially reduce costs associated with infertility treatments by hypothetically raising pro- cedure success rate (by freezing gametes at an early reproductive age).

The main controversy surrounding this hypothesis is the lack of confidence in estimating effective usage of egg banking in future years.


Based on theoretical models, four cost-effectiveness studies have tried to define efficacy of egg freezing in terms of costs sim- ulating several scenarios considering different age and number of treatments.

Van Loendersloot et al 44 compared the cost of IVF cycle between three strategies.

The first strategy included women with oocyte freezing at the age of 35 who used them in an IVF cycle at the age of 40.

The second strategy included IVF cycles of women at an age of 40 years using fresh oocytes.

The third strat- egy considered women at the age of 40 attempting to conceive without treatment.

Oocyte cryopreservation seemed to be more cost-effective only if at least 61% of women who cryopreserved oocytes at the age of 35 returned for IVF and if there was a will- ingness to pay €19,560 extra per additional live birth.

44 Another study showed that oocyte freezing was most cost-effective if per- formed within 37 years of age at a cost of $28,759 per additional live birth. 25 Conversely, Devine et al 45 concluded that oocyte freezing was more cost-effective if the cryopreservation occurred before the age of 38 and if more than 49% of those women, who did not achieve a spontaneous pregnancy, returned to use their cryopreserved oocytes.


Finally, a model by Hirschfeld-Cytron et al 46 found that oocyte cryopreservation at the age of 25 and 16000412, 2019, 5, Downloaded from https://obgyn.onlinelibrary.wiley.com/doi/10.1111/aogs.13573 by Mcgill University, Wiley Online Library on [19/05/2024].

See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License achieving a pregnancy at the age of 40 should be less cost-effec- tive compared with a "no action" strategy.


It is well known that an inherent limitation of theoretical models is represented by the assumptions used to build them.

Regarding egg freezing, several limitations are related to a wide range of IVF costs worldwide and the realistic usage rate of cryopreserved oocytes.


Since the costs of IVF differ between countries, the costs should be compared as multiples of the cost of single IVF cycle for each country.

47 Moreover, as described before, it is critical to know the re- alistic usage rate in order to have a more accurate prediction.

Indeed, Ben-Rafael used data from a Danish register-based study 48 deduced whether the threshold of approximately 50% as maximal usage rate, defined by Devine et al, 45 was realistic and achievable.


Analyzing different age-groups and corresponding cumulative delivery rate achieved within 5 years for each group, Ben-Rafael calculated the maximal possible usage rate expected if an entire age- group froze their oocytes and opted to use the banked eggs after treatment failure.

47 Considering women older than 40, characterized by the highest infertility rate (60%), only 27% were expected to be successful (16.2%), with 43.8% remaining infertile.

From this result, the theoretical rate to be cost-effective is not achieved even in the last age-group.

47 The lack of real indications for egg banking, which are the basis of social freezing, is probably the huge limitation for cost-effectiveness, reflected in a high societal cost.


As previously discussed, the use of terms such as "social" describes the social aspect linked to the medical strategy.

7 To contrast declin- ing fertility and giving hope to families in the future, egg freezing reflects the fulfillment of a natural reproductive instinct.

In recogni- tion of this accomplishment, technology companies such as Apple, Facebook and Google decided to offer female employees social freezing as a benefit, sparking an intense social debate.

Supporters of this initiative consider social freezing an opportunity to im- prove gender equality by giving women reproductive autonomy.

49 Moreover, the strategy represents an emancipation from biological reproduction restrictions.

Indeed, oocyte cryopreservation could guarantee a biological child in the future, reducing negative feel- ings of uncertainty such as anxiety and stress, and providing women with options to choose and increasing a sense of freedom, where any decision they make remains suitable.

29 For many women, oocyte cryopreservation is perceived as an option for a need that is not in the present but could be in the future, 50 after they have established a stable household with a reliable, trustworthy partner.

51 Critics, by contrast, argue that this kind of policy encourages women to prior- itize career over family.


Both of these positions reveal a different relational approach to autonomy, in one case by supporting social egg freezing as an ap- propriate response to the wider social context, and in the other by hinting at a loss of decisional power.

In other terms, the discussion is related to relational autonomy in the aspect of decision-making.

Human beings are relational beings, therefore never fully indepen- dent, which is interpreted by critics as a loss of individual decisional power.

The social context, in broad political and cultural sense, can steer choices in direction or the other.


It is evident that most of the countries in the Organization for Economic Co-operation and Development (OECD), in the period from 1970 to 2015 saw an increase of 2-5 years in the average age of women at childbirth, which is now around 30 years.

52 The socioeco- nomic evolution is identified as the main reason behind this trend.

53 Whereas social politics should work toward a framework that is fi- nancially and structurally supportive of young families, they seem, in this case to press women to defer and delay childbearing.

Egg freez- ing in this context could be seen as a technological fix for what is a biological and sociological problem, but the debate is still open.


Nowadays, there is no agreement on the terminology to define oo- cyte cryopreservation for women willing to delay childbearing.

It is necessary to take into account that there is an age-related effect on the number of mature oocytes available for banking and it is impor- tant to explain to women that in order to obtain an adequate number of oocytes, multiple cycles of controlled ovarian stimulation might be needed.

The procedure seems to be safe in terms of reduction of OHSS due to new patient-friendly protocols.

It is also reassuring that so far no relevant obstetric or perinatal risks have been found in pregnancies achieved with frozen oocytes.

However, it is known that the likelihood of cryopreserved oocytes remaining in the bank and not being used is quite high, and requires consideration when assessing cost-effectiveness.

The lack of real indications for egg banking, which constitute the basis of social freezing, is probably the huge limitations in terms of cost-effectiveness.

On the other hand, the assessment of cost-effectiveness should take into account not only the economic point of view but also the psychological and so- ciological aspects, because freedom of choice has an intrinsic value that it is hard to estimate from a monetary standpoint.


In this context, alternative strategies should be considered.

One possibility could be to offer free individual counseling to women and men, in order to improve the knowledge on fertility status and repro- ductive lifespan and to provide pro-fertility advices.

54 Other pro-fer- tility activities can be focused on improving the fertility awareness as shown by the study by Stern et al 55 addressing the reproductive life-plan-based information in contraceptive counseling that has suc- ceeded in increasing knowledge of the reproduction process among young women.


The risk of the technological improvement might be to reduce human responsibility for the reproductive event.

These pro-fertility activities, much more than oocyte freezing, should avoid this risk.

The increased possibilities provided by technology should rather be perceived as an increased moral responsibility.

56 Nevertheless, the advantages of elective egg freezing without medical indications re- main debatable.


Sir, in women who seek non‐medical fertility preservation, as age has a direct impact on the probability of favorable fertility.

It is a shared belief that women considering fertility preservation should be advised of the exponential decline in their ovarian reserve coupled with a diminished response to ovarian stimulation as they age.


We read the review by Alteri et al. [ 1] with great interest.

The authors stretch out the current knowledge on fertility preservation for non‐medical indications in women willing to postpone motherhood.

While the authors are to be congratulated for their comprehensive overview, a distinct point regarding the optimal timing to freeze oocyte should be clarified.


In their review, the authors state that there is a lack of agreement regarding the optimal timing of oocyte cryopreservation in light of the data they reviewed, as most studies they cited advise cryopreservation of oocytes before the age of 36‐38 years in contrast to one study demonstrating the age of 37 years as the optimal cutoff [2].

Their conclusion might leave the readers uneasy, as the largest study by Cobo et al. [ 3] demonstrated a continuous decline in live birth rate as women age.

Furthermore, a meta‐analysis has shown a substantial decline in live birth rates after the age of 36 years [4].


Interestingly enough, the study mentioning the age of 38 as the cutoff [5], based this value on receiver operating characteristic analysis, although the predicted probability of live birth rate declined with age after 30‐34 years.

It is important to underline that the aforementioned study stating the age of 37 years as the optimal cutoff [2] is based on a hypothetical decision analysis model and therefore its clinical relevance is uncertain.


Furthermore, the method used for asserting the age carrying the “largest benefit” for cryopreservation was the difference in probability of live birth between theoretical women utilizing cryopreservation of oocyte and those who do not.

Moreover, one should not overlook an important finding of that study, that the highest probability of live birth was seen when oocyte cryopreservation was performed at <34 years of age.

Therefore, we continue to emphasize that oocyte cryopreservation should be carried out as early as possible (the sooner the better) elective egg freezing.


We read with interest the correspondence commenting on our review on fertility preservation without medical indications [1] by Levin and Rottenstreich [2].

The purpose of our manuscript was to summarize the highlights of social freezing regarding chances of success, safety, costs, and socio/ethical issues.

The authors claimed limits of studies that deal with the issue on optimal timing of oocyte cryopreservation, concluding that egg freezing should be performed as early as possible [2].

We agree with them that the maternal age has a straight influence on the reproductive chances of women.

It has been known that the “time” factor is crucial not only to cryopreserve oocytes to postpone childbearing but also in general for in vitro fertilization treatment.

Nevertheless, some issues should be considered.


First, data from surveys showed that women cryopreserve their oocytes at around 36‐38 years [3][4].

This indicates that these women are undergoing fertility preservation at a later‐than‐intended time but many of them could obtain acceptable successes in terms of clinical outcomes within this age range.

In addition, in the reproductive age range 25‐30 years, the chances of spontaneous conception in the following years are higher, so little benefit is obtained for oocyte cryopreservation compared with no action.


Second, as already mentioned by Jones et al [5], there are countries, for example, the UK, where the storage of oocytes is permitted for a specific period.

Hence, storing oocytes earlier could lead to the need to discard them.


Finally, rather than cryopreserving oocytes at age 25 years, it is more cost‐effective for women who want to conceive at the age of 40 to attempt spontaneous conception and, if unsuccessful, to use assisted reproductive technology with fresh oocytes [6].

In conclusion, women need precise counseling considering the physiological, psychological, economic, and social aspects of egg freezing to make a fully informed decision.


The principal biases pertain to the use of random digit dialing and to selecting controls "nonconcurrently," i.e., after the cases were diagnosed (9).

Both studies in this issue (1,2) used random digit dialing.

The problem with this control-selection method is primarily one of analytic control of variables such as parental income, education, and occupational class, since control groups selected in this way are suspected to be of spuriously high socioeconomic level (9-11).

The magnitude and even the direction of any uncontrolled bias might vary from one study to another (9).

Review of studies using random digit dialing should include careful assessment of the adequacy with which indicators of socioeconomic status are measured and controlled.


Bias due to nonconcurrent control selection is a somewhat more complicated matter.

The Los Angeles study (1) was partially nonconcurrent; the cases were diagnosed in 1984-1991, but enrollment of controls did not begin until 1989.

To the authors, this design feature explains the elevated relative risk estimate in the underground wire code category.

Unfortunately, the report of the Washington State study (2) does not provide the basic information on the time lag, if any, between case diagnosis and control selection.


Two clarifying points may be helpful.

The first is that bias would arise from nonconcurrent control selection only because potential controls in one or more wire code categories, e.g., the underground category in the Los Angeles study (1), died or moved away from the study area in greater proportions than did those in the other categories between the time of case diagnosis and the time of control selection.

In-migration, e.g., of Latinos into Los Angeles County (1), during this interval is not of concern if children arriving after the diagnosis date are properly excluded.


The second point is that this hypothetical bias is empirically testable.

Far from being an all-or-none phenomenon, nonconcurrent control selection has degrees.

The more time that elapses between case diagnosis and control selection, the greater the bias will be.

Thus, for the partially nonconcurrent Los Angeles study (1), the authors' bias hypothesis predicts a specific shape for the time trend in relative risk estimates for the underground wire code category: a distinct ramp downward in 1984-1988 to the vicinity of the null value, followed by a relatively flat surface in 1989-1991.

Flexible trend analysis methods (12) can be used, even with a limited number of subjects, to see how closely the results conform to this prediction.

A simpler but similar analysis (13) firmly refuted, in my opinion, the hypothesis (7, 9, 14) that nonconcurrent control selection in a previous study (7) produced an upward gradient of leukemia relative risk estimates that closely tracked the median magnetic flux densities in the wire code categories (8).


Because of their efficiencies of cost and time, random digit dialing and nonconcurrent control selection are very popular methods in modern case-control research.

With creditable candor, Preston-Martin et al. relate their original view that nonconcurrent control selection would be "justified in order to include a sufficient number of cases" (1, p. 111).

The investigators' subsequent experience should make others think twice about purchasing data quantity at the price of nettlesome concerns about data quality.


Not long ago, a consensus seemed to be forming that, among children, brain cancer was the most likely cancer to be related to magnetic field exposure.

In 1992, a panel called the epidemiologic evidence "less weak for brain cancer than for leukaemia" (15, p. 130).

In the same year, another reviewer concluded that "[a] single finding in this extensive group of studies points to a possible causal association, namely, the increase in risk of central nervous system cancer in childhood...The leukemias may not be a fruitful area of study" (16, p. A-21).


Times have changed.

The two studies in this issue (1,2) and, in the aggregate (17), three recent Nordic studies (18-20) are liable to be pigeonholed as "negative" for brain cancer.

With appeal to the ancestral criterion of specificity of effect, some observers might see the literature as beginning to point more specifically toward leukemia.

Thus, by the peculiar logic of this causal criterion, "negative" evidence for brain cancer may become transmuted into "positive" evidence for leukemia.


The problem with this interpretation is that two key studies, by Wertheimer and Leeper (21) and by Savitz et al. ( 7), without which the case for a leukemia effect would not be much of a case at all, were "positive" for leukemia and for brain cancer.

If one were to accept the null hypothesis for brain cancer on the basis of the more recent results, one would be obliged to conclude that both of these earlier studies were "false positives" for brain cancer but "true positives" for leukemia.

Many possible explanations for "false-positive" results, e.g., certain biases or random control-sampling error, would have been nonspecific and thus would have affected the results for both diseases.

As a consequence, without a tenable hypothesis of upward bias specific to brain cancer in the This fact of scientific life is not peculiar to research on childhood cancers and magnetic fields, nor even to epidemiology.

Unfortunately, like other facts of life, it tends to be denied.


Who treats publication as the end of peer review?

The usual suspects include journalists (22), of course, and their symbiotically codependent flacks at journals, funding sources, and universities (23), who all conspire to bestow Warhol's predicted 15 minutes of fame onto newly published reports.

The antijunk science movement (24) may be partly to blame for promoting peer-review publication as an imprimatur of quality, entitling one to defend scientific work not only on its merits but on its mere appearance in such a forum (25, 26).


As authors, however, we must shoulder much of the responsibility ourselves.

Standing up to take part in the critical discussion of our published work is seldom pleasurable and seems like inefficient careerism.

We all have new proposals to write, new data to collect, new papers to publish.

Moreover, many of us seem to have a great deal of difficulty just saying "no comment" or something ambivalent to the sensationalizing press, when "no comment" or something ambivalent would be the most constructive or accurate thing to say.

Thus, we too often fail to anticipate or resist the varied pressures toward taking a defensive stance about our published work.


In reality, the postpublication phase is the most important phase of peer review.

Publication merely signifies that one's work is deemed worthy of widespread critical scrutiny.

Society would be far better served if it were persuaded to view publication not as a claim to knowledge, but simply as an open invitation to inspect one's results and to consider one's preliminary interpretation.

Funding sources would do a great service by providing financial support for investigators to participate fully in the postpublication peer review of their own work.


The process is aided greatly when conscientious and self-critical authors like Preston-Martin et al. ( 1) leave relatively few important questions about methods and results unanswered in crafting their initial published reports.

Nevertheless, the history of science, distant and near, reveals the wisdom in Bartley's wistful plea that "it could only do good if every published manuscript were prominently marked 'Damaged Goods'" (27, p. 450).


Until today, only the authors themselves and a small group of privileged observers have had the opportunity to develop preliminary ideas about the ultimate contributions these two newest studies (1,2) will make to the epidemiology of magnetic fields and brain cancer in children.

Today, the number of peer reviewers increases by orders of magnitude.

As the most crucial phase of peer review gets under way, we shall learn more about, and from, these important investigations.


The initial reports in this issue of two new studies of childhood brain cancer and 60-Hz magnetic fields [1,2] illustrate a few general observations on evolving research in this area.


Epidemiologic assessment of magnetic field exposures is still primitive.

Magnetic field exposures come from many different sources.

We should not be proud that we still study them one by one.

Appliance-specific relative risks (one for blow dryers, one for curling irons, etc.) are the most glaring example.


Time-activity data and magnetic flux density measurements are needed to quantify exposures from individual appliances.

At a minimum, this information could be used to stabilize ensembles of appliance-specific relative risk estimates [3-6].

Ideally, appliance exposures would be consolidated and combined with measures of exposure from power lines and other sources.


Developing a program of combined-source magnetic field exposure assessment tailored for use in epidemiologic studies would be a challenging, but technically feasible, task.

No methodological need is greater in this area of research.


Responding to a private exhortation to work toward a multiappliance exposure measure, an epidemiologist once expressed to me a reluctance to "combine apples and oranges." The simile was ironic.

Combining fruits is exactly what is done in the more mature field of nutritional epidemiology to measure consumption of fructose and other nutrients.


For power-line exposures, it has become clear that "wire codes," i.e., magnetic field exposure measures based on characteristics of lines and their distances from residences, have substantial advantages over short-term flux density measurements in homes.

The measurements, often made years after the case or control actually lived in the residence, are obviously limited by their temporal variability.

Independently severe is the problem of missing data.

Whether by design [1] or because of nonresponse [7], it is not uncommon for magnetic field measurements to be obtained for fewer than 50 percent of cases and controls.

Relative risk estimates based on such small numbers of subjects are of highly limited value, whether viewed in isolation or in contrast to estimates based on wire codes.


The in-home measurements should not be abandoned entirely, however. They have considerable value when used in conjunction with wire codes rather than as alternatives to them.

Specifically, the measurements can provide exposure scores for wire code categories in dose-response analyses [8].

Conducting such an analysis forces one to confront the fact that not all wire code categories appear to differ in exposure.

If any principle of dose-response analysis is beyond dispute, it is that response should not vary when dose does not vary.

It is therefore necessary to combine some of the wire code categories.

The categories to combine may change from locale to locale and over time within a locale.


In the report of their Los Angeles County study, Preston-Martin et al. [ 1] laudably focus on this issue.

The authors present the case and control counts in each of the five wire code categories, but combine the second and third categories ("very low" and "ordinary low") for data analysis.

Only an anomalistically elevated relative risk estimate in the lowest ("underground") category prevented them from combining the first three categories.

Alternative analytic approaches could be defended, however.

In the extreme, some of the more exposure-relevant measurements, e.g., median 24-hour child's bedroom values of 0.5, 0.6, 0.4, 0.6, and 1.1 mG, might have supported a decision to combine all categories but the highest.


Unfortunately, the paper by Gurney et al. [ 2] does not provide quantitative information on magnetic flux density levels within wire code categories in western Washington State. Without such information, one cannot tell how strongly to interpret the resolutely null results the authors report for childhood brain cancer across the five wire code categories in that study.


Control-selection biases are still potentially important, but are still poorly understood.

The principal biases pertain to the use of random digit dialing and to selecting controls "nonconcurrently," i.e., after the cases were diagnosed [9].

Both studies in this issue [1,2] used random digit dialing.

The problem with this control-selection method is primarily one of analytic control of variables such as parental income, education, and occupational class, since control groups selected in this way are suspected to be of spuriously high socioeconomic level [9-11].

The magnitude and even the direction of any uncontrolled bias might vary from one study to another [9].

Review of studies using random digit dialing should include careful assessment of the adequacy with which indicators of socioeconomic status are measured and controlled.


Bias due to nonconcurrent control selection is a somewhat more complicated matter.

The Los Angeles study [1] was partially nonconcurrent; the cases were diagnosed in 1984-1991, but enrollment of controls did not begin until 1989.

To the authors, this design feature explains the elevated relative risk estimate in the underground wire code category.

Unfortunately, the report of the Washington State study [2] does not provide the basic information on the time lag, if any, between case diagnosis and control selection.


Two clarifying points may be helpful.

The first is that bias would arise from nonconcurrent control selection only because potential controls in one or more wire code categories, e.g., the underground category in the Los Angeles study [1], died or moved away from the study area in greater proportions than did those in the other categories between the time of case diagnosis and the time of control selection.

In-migration, e.g., of Latinos into Los Angeles County [1], during this interval is not of concern if children arriving after the diagnosis date are properly excluded.


The second point is that this hypothetical bias is empirically testable.

Far from being an all-or-none phenomenon, nonconcurrent control selection has degrees.

The more time that elapses between case diagnosis and control selection, the greater the bias will be.

Thus, for the partially nonconcurrent Los Angeles study [1], the authors' bias hypothesis predicts a specific shape for the time trend in relative risk estimates for the underground wire code category: a distinct ramp downward in 1984-1988 to the vicinity of the null value, followed by a relatively flat surface in 1989-1991.

Flexible trend analysis methods [12] can be used, even with a limited number of subjects, to see how closely the results conform to this prediction.

A simpler but similar analysis [13] firmly refuted, in my opinion, the hypothesis [7, 9, 14] that nonconcurrent control selection in a previous study [7] produced an upward gradient of leukemia relative risk estimates that closely tracked the median magnetic flux densities in the wire code categories [8].


Because of their efficiencies of cost and time, random digit dialing and nonconcurrent control selection are very popular methods in modern case-control research.

With creditable candor, Preston-Martin et al. relate their original view that nonconcurrent control selection would be "justified in order to include a sufficient number of cases" [1, p. 111].

The investigators' subsequent experience should make others think twice about purchasing data quantity at the price of nettlesome concerns about data quality.


Acceptance of the null hypothesis for magnetic fields and childhood brain cancer would not strengthen the causal hypothesis for childhood leukemia and might weaken it.


Not long ago, a consensus seemed to be forming that, among children, brain cancer was the most likely cancer to be related to magnetic field exposure.

In 1992, a panel called the epidemiologic evidence "less weak for brain cancer than for leukaemia" [15, p. 130].

In the same year, another reviewer concluded that "[a] single finding in this extensive group of studies points to a possible causal association, namely, the increase in risk of central nervous system cancer in childhood...The leukemias may not be a fruitful area of study" [16, p. A-21].


Times have changed.

The two studies in this issue [1,2] and, in the aggregate [17], three recent Nordic studies [18-20] are liable to be pigeonholed as "negative" for brain cancer.

With appeal to the ancestral criterion of specificity of effect, some observers might see the literature as beginning to point more specifically toward leukemia.

Thus, by the peculiar logic of this causal criterion, "negative" evidence for brain cancer may become transmuted into "positive" evidence for leukemia.


The problem with this interpretation is that two key studies, by Wertheimer and Leeper [21] and by Savitz et al. [ 7], without which the case for a leukemia effect would not be much of a case at all, were "positive" for leukemia and for brain cancer.

If one were to accept the null hypothesis for brain cancer on the basis of the more recent results, one would be obliged to conclude that both of these earlier studies were "false positives" for brain cancer but "true positives" for leukemia.

Many possible explanations for "false-positive" results, e.g., certain biases or random control-sampling error, would have been nonspecific and thus would have affected the results for both diseases.

As a consequence, without a tenable hypothesis of upward bias specific to brain cancer in the earlier studies, the recent "negative" results for brain cancer actually weaken the overall case for a leukemia effect.


Publication lies much closer to the beginning than to the end of peer review.

This fact of scientific life is not peculiar to research on childhood cancers and magnetic fields, nor even to epidemiology.

Unfortunately, like other facts of life, it tends to be denied.


Who treats publication as the end of peer review?

The usual suspects include journalists [22], of course, and their symbiotically codependent flacks at journals, funding sources, and universities [23], who all conspire to bestow Warhol's predicted 15 minutes of fame onto newly published reports.

The antijunk science movement [24] may be partly to blame for promoting peer-review publication as an imprimatur of quality, entitling one to defend scientific work not only on its merits but on its mere appearance in such a forum [25, 26].


As authors, however, we must shoulder much of the responsibility ourselves.

Standing up to take part in the critical discussion of our published work is seldom pleasurable and seems like inefficient careerism.

We all have new proposals to write, new data to collect, new papers to publish.

Moreover, many of us seem to have a great deal of difficulty just saying "no comment" or something ambivalent to the sensationalizing press, when "no comment" or something ambivalent would be the most constructive or accurate thing to say.

Thus, we too often fail to anticipate or resist the varied pressures toward taking a defensive stance about our published work.


In reality, the postpublication phase is the most important phase of peer review.

Publication merely signifies that one's work is deemed worthy of widespread critical scrutiny.

Society would be far better served if it were persuaded to view publication not as a claim to knowledge, but simply as an open invitation to inspect one's results and to consider one's preliminary interpretation.

Funding sources would do a great service by providing financial support for investigators to participate fully in the postpublication peer review of their own work.


The process is aided greatly when conscientious and self-critical authors like Preston-Martin et al. [ 1] leave relatively few important questions about methods and results unanswered in crafting their initial published reports.

Nevertheless, the history of science, distant and near, reveals the wisdom in Bartley's wistful plea that "it could only do good if every published manuscript were prominently marked 'Damaged Goods'" [27, p. 450].

Until today, only the authors themselves and a small group of privileged observers have had the opportunity to develop preliminary ideas about the ultimate contributions these two newest studies [1,2] will make to the epidemiology of magnetic fields and brain cancer in children.

Today, the number of peer reviewers increases by orders of magnitude.

As the most crucial phase of peer review gets under way, we shall learn more about, and from, these important investigations.


Dr. Poole has made a number of useful points about the interpretation of studies of the association of childhood cancers with extremely low frequency magnetic fields [1].

We wish to comment on only two of his points.


First, Dr. Poole takes issue with the appliance-specific relative risks that were reported in the two papers in this issue [2, 3] and several earlier ones, including another Los Angeles study [4].

We agree with his desire for an exposure assessment that consolidates appliances with other sources of magnetic fields, including power lines.

However, numerous difficulties preclude doing so at this time.

Too little is known about the fields generated by each appliance at the target organs, variations between brands, duration and frequency of use, and other modifying influences needed for a sensible summary index.

More important is the uncertainty over the appropriate "exposure metric," a single number that represents a subject's magnetic field environment, including its frequency spectrum, time variation, polarization, spatial orientation, and high-frequency transients.

Since fields from appliances and power lines differ in many of these qualities, weighting exposures from various electromagnetic field sources requires an exposure metric that is hypothetically carcinogenic. " Combining apples and oranges" makes sense in dietary epidemiology only because fructose has been identified as an etiologically relevant nutrient!

Although summary electromagnetic field exposure indices would be useful, their development clearly poses formidable challenges to epidemiologists, exposure assessors, and biologists.

During the present state of ignorance, however, we feel that appliance-specific risk estimates may provide potential clues about etiologically important components of magnetic fields (if any) and may provide some preliminary guidance to individuals and officials in making decisions about their safety.


Second, we agree that the best use of household magnetic field measurements is not as a direct cancer risk factor, but in building exposure models from wiring configuration data.

Where Dr. Poole uses the measured fields simply to combine categories of the Wertheimer-Leeper wire code [5], we feel that a more powerful application is predicting long-term residential exposures to a variety of magnetic field metrics.

For example, we recently developed a regression model for predicting the mean magnetic field from measurements, wire configurations, and physical principles [6].

When applied to the data from the Los Angeles childhood leukemia study [3], this model predicted household magnetic fields far better than did the Wertheimer-Leeper code and was significantly associated with leukemia risk, providing new evidence for magnetic field carcinogenicity.

However, the predicted fields did not improve upon the Wertheimer-Leeper code in a multivariate risk model, suggesting that the mean magnetic field may not be the appropriate exposure metric [6].

Such wire configuration models could be used to evaluate other plausible exposure metrics.

Metrics correlated with the Wertheimer-Leeper categories would more likely be associated with cancer.

We have identified two such candidates: the field's autocorrelation over time [7] and frequency components that create ionic resonances with the earth's magnetic field [8].

We are looking forward to the postpublication phase of the peer-review process that Dr. Poole describes.

We hope Dr. Poole's commentary [1] and the responses by Gurney et al. [ 9] and ourselves to this debate will stimulate further thinking about some of these methodological issues, as well as our substantive conclusions.


Insomnia is the most common sleep disorder and is associated with significant morbidity and mortality.

1,2 However, its pathophysiology remains poorly understood, and most com- monly available treatments for this disorder are associated with good outcomes in self-reported sleep quality but have no effect in objective sleep duration.

3 Previous research has attempted to define subgroups within insomnia based on eti- ology (ie, primary vs secondary), age of onset (ie, childhood vs adult), and objective sleep findings.

4 However, these subtypes have shown poor reliability and validity and have not shown utility for guiding insomnia treatment decisions.

5 Therefore, current diagnoses of insomnia are based solely on self- reported complaints.

4 Previous research on the association of insomnia with objective short sleep duration (ISS) with the stress system, 6-8 the autonomic system, 9 and medical morbidity 10-13 and mortality 14 led the authors to suggest 2 phenotypes of chronic insomnia.


The first phenotype, ISS, is associated with physiological hyperarousal (ie, short sleep duration and activation of the stress system 6-8 ) and significant medical sequelae (eg, hypertension, 12 type 2 diabetes, 13 cardiovascular disease, 15,16 increased mortality, 14 and a persistent natural course 17 ).

The second phenotype, in- somnia with normal sleep duration (INS), is not associated with physiological hyperarousal (ie, normal sleep duration, normal activity of the stress system, 8 and lack of significant medical sequelae 10-14 ) but instead with sleep misperception, an anxious- ruminative profile with poor coping skills, 18 and an intermittent natural course.

17 Based on these findings, we have previously proposed that the ISS phenotype may respond better to treat- ments that primarily aim at decreasing physiological hyper- arousal (eg, hypercortisolemia) and increasing sleep duration, such as medication or other biological treatments, 17 and the INS phenotype may respond better to treatments that primarily aim at decreasing cognitive-emotional hyperarousal (eg, rumination) and altering sleep misperception, such as psychological treatment (ie, cognitive behavioral therapy for insomnia [CBT-I]).


Because the ISS phenotype is associated with hypothalamic- pituitary-adrenal (HPA) axis activation, the use of medication that downregulates the HPA axis may be a promising tool for the pharmacological approach.

In support of this consideration, researchers have shown that a small dose of a sedative anti- depressant (doxepin) was effective in improving sleep and normalizing plasma cortisol secretion indicated with primary insomnia.

7 Other studies have also shown that doxepin is ef- fective in increasing objective sleep duration in chronic insomnia.

7,19,20 Although trazodone does not have a U.S. Food and Drug Administration indication for insomnia, it has been the second-most prescribed pharmacological agent for insomnia management over the past few decades at doses ranging between 25 mg and 100 mg.

21 However, its use has not been supported by systematic studies including efficacy, tolerance, and adverse effects.

In addition, trazodone is known to downregulate HPA arousal and may be specifically suited for the treatment of patients with ISS.

22-28 Other common medications such as the benzodiazepines and benzodiazepine receptor agonists do not have this effect and therefore would not target the HPA arousal that has been identified in patients with ISS.

29-31 Hence, we have chosen trazodone to compare to CBT-I, which is the "gold standard" of care for patients with insomnia disorder.


CBT-I is recommended as first-line treatment, with insomnia remission rates of 50%-60% and significant symptom reduction in 75%-80% of patients.

32-34 We have hypothesized that pa- tients with the INS phenotype, which is associated with normal activity of the HPA axis, and lack of significant medical morbidity may respond better to psychological treatment.

In support of this hypothesis, several studies on CBT-I effec- tiveness have included patients with insomnia with an average objective sleep duration of 6 hours or more and have reported improved sleep efficiency but no significant lengthening of sleep duration.

33,35,36 Furthermore, recent investigators have shown that patients with the ISS phenotype have a blunted treatment response to CBT-I relative to those with the INS phenotype.

10,37,38 Whether the ISS phenotype responds better to pharmaco- logical treatment than to psychological treatment has not yet been examined.

Therefore, the goal of the current preliminary, open-label, randomized study was to assess the effect of tra- zodone compared to CBT-I in patients with the ISS phenotype on the primary outcome, objective total sleep time, and on the secondary outcomes of salivary cortisol levels and self-reported insomnia severity.


A total of 24 patients were recruited through advertisements in the local community, were screened according to research protocols by the Sleep Research and Treatment Center at Penn State Milton S. Hershey Medical Center (Hershey, PA), and were randomized to either the CBT-I or the trazodone group.

Of the 24 patients who were randomized, 3 discontinued partici- pation after randomization had occurred but before initiation of treatment because of the time commitment required for the study, unwillingness to discontinue morphine use, and disin- terest in taking trazodone.

The remaining patients' diagnoses of insomnia and other sleep disorders were established with a clinical sleep history and semistructured interview conducted by a clinical psychologist who practices sleep medicine.

Chronic insomnia disorder was defined as difficulties initiating and maintaining sleep, waking up too early, and being unable to return to sleep despite adequate sleep opportunity, concurrent with daytime impairment, all of which had persisted for the duration of at least 1 year.

Other inclusion criteria included an age range of 30-60 years.

Patients with a current diagnosis of a major mental disorder (ie, schizophrenia, major depression), sleep disorder (ie, OSA, periodic limb movement disorder, narcolepsy, circadian rhythm disorders), morbid obesity (body mass index > 39), current use of hypnotics or sleep-inducing sedative antidepressants, a diagnosis of substance abuse or de- pendence, treatment for diabetes, chronic renal failure, hepatic in- sufficiency, or chronic heart failure were excluded from the study.

During the pretreatment phase, an AHI ≥ 5 events/h was used to define the presence of OSA and a periodic limb movement index ≥ 15 was used to define the presence of periodic limb movement disorder, so none of the participants that entered the trial had either elevated AHI or elevated periodic limb movement.

A total of 21 patients completed the clinical trial (Figure 1).


Objective "short sleep" was defined based on the closest clinically meaningful cutoff (total sleep time [TST] < 7 hours) to the median value (TST < 6.8 hours) measured with actigraphy at pretreatment (mean value for 2 consecutive weeks).

Previous research has used a 6-hour criterion to identify the ISS phe- notype measured with polysomnography (PSG), for which 6 hours was the median of the previously studied samples (ie, primarily a general random population sample).

8,12,17 In the current study, the primary objective method to assess TST was actigraphy.

Given that the median value for TST in the current sample of patients measured with actigraphy was 6.8 hours, we chose a TST cutoff of 7 hours to define clinically meaningful objective short sleep duration.

It is to be expected that the median value of TST will differ based on the method used (ie, actigraphy vs PSG), population studied (ie, general random population sample vs clinical or volunteer sample), and demographics (ie, age range of the sample).

Based on this criterion, 15 out of the 21 patients were categorized as having the ISS phenotype.

Patients were randomly assigned to either a trazodone or a CBT-I group by a statistician so that the investigators remained blind to the randomization process.

Data were collected at the following phases of the study: pretreatment (before initiation of treatment), posttreatment (3 months after initiation of treat- ment), and follow-up (6 months after termination of treatment).

Patients were compensated upon completion of the study.

The study and all procedures were approved by the Penn State College of Medicine institutional review board (IRB 35933) and registered with ClinicalTrials.gov (ClinicalTrials.gov identifier: NCT01348542).


stimulus control, sleep restriction, and cognitive therapy.

39-41 The CBT-I protocol was implemented by a clinical psychologist who practices behavioral sleep medicine (coauthor JFM) for a total of 9 months, consisting of 3 months of weekly/biweekly sessions (total of 9 consultation/therapy sessions each lasting approximately 50 minutes) plus 6 months of self-implementation of learned CBT-I techniques.

Sessions 1 through 4 were held on a weekly basis, sessions 5 through 8 were held on a biweekly basis, and session 9 was held 6 months after the initiation of treatment.

The treatment program utilized in the current study was based on validated protocols according to specific parameters.

39-41 Stimulus control was implemented based on standard instructions using the 15-minute rule for time awake in bed.

39-41 Sleep restriction was implemented based on sleep diary data provided by par- ticipants at each session, as follows 39 : (1) in the case of positive clinical gains (sleep efficiency ≥ 90% or sleep efficiency 85%- 89% with increased sleepiness), upwardly titrate sleep oppor- tunity by 15 minutes; (2) in the case of marginal gains (sleep efficiency 85%-89% without increased sleepiness), maintain sleep opportunity at prescribed times; and (3) in the case of negative gains (sleep efficiency < 85%), downwardly titrate sleep opportunity.

Cognitive therapy was implemented in the fifth session until the end of treatment and included decatastrophizing, 39 constructive worry, 40 and cognitive restructuring 40,41 techniques.

Participants in the CBT-I group were recommended to continue following the recommendations made at their last face-to-face ses- sion, through posttreatment and all follow-up data collection.


The trazodone group received a standard handout on sleep hygiene instructions and had a total of 9 sessions to check treatment effectiveness, adherence, and possible adverse effects and review sleep hygiene practices (eg, avoiding naps, trying to keep a consistent wake time, cutting down on all caffeine products, ensuring a comfortable and quiet bedroom).


All patients in the trazodone group received a standard dose of trazodone (50 mg) in their first session and took trazodone for a total of 9 months, including 3 months of weekly/biweekly sessions (total of 9 sessions) plus 6 months of follow-up.


Participants were followed up with 2 additional biweekly sessions (during sessions 2 through 3).

During these biweekly sessions, dosages were adjusted according to drug effectiveness and possible adverse effects.


The maximum dose used was 100 mg of trazodone, which was taken by 2 participants; 3 participants increased their dosages to 75 mg.


Sessions 4 through 5 were scheduled monthly and focused on assessing treatment effectiveness, adherence, possible adverse effects, and the need to adjust medication doses.


Session 6 was held 6 months after the initiation of treatment.


Participants in the tra- zodone group were recommended to continue taking trazodone as needed until the final follow-up actigraphy was returned.


All patients continued to take trazodone until the end of the final actigraphy assessment.


Participants were monitored until their last pill remained and then were recommended to follow-up with their primary care provider for any further care.


Each participant completed a medical history and physical examination using a semi-structured format and a battery of clinical tests.

Blood pressure was measured in the morning and in the evening using a pneumoelectric microprocessor- controlled instrument.

The recorded blood pressure was the average of 3 consecutive readings during a 5-minute period after 10 minutes of rest in the supine position.

Anthropometric measures included height, weight, neck size, and waist and hip measurements according to standard procedures.

Specifi- cally, neck size was measured at the superior border of the cricothyroid membrane with the patient in the upright posi- tion.

The waist was measured at or 1 cm above the umbilical midline, and the hip was measured at the widest area around the buttocks.


Each patient was monitored continuously for 8 hours for 3 consecutive nights (1 adaptation night and 2 consecutive nights) according to standard techniques at each phase of the study (ie, pretreatment, posttreatment, and follow-up).

For analytic purposes, we excluded the adaption night and used the mean of TST for the 2 consecutive nights.

Respiration was moni- tored throughout the night by thermocouples at the nose and mouth (Salter Labs, Lake Forest, IL), nasal cannula/pressure, and thoracic strain gauges.


An actigraphy monitor (ActiGraph GT3×; ActiGraph, Pensa- cola FL) was placed on the wrist of patients' nondominant hand during the second night in the laboratory and worn for a 2-week period at each time point (pretreatment, posttreatment, and follow-up).

Patients were asked to keep an actigraphy log for a 2-week period in which they noted daily time to bed, time out of bed, and times when the device was removed (eg, taking a bath, swimming).

ActiLife Software (ActiGraph, Pensacola, FL) was used to calculate the estimated sleep variables (ie, TST, time in bed [TIB], sleep efficiency).

Actigraphy logs were used to enter participant-reported bedtimes and wake-up times.

Average TST values were calculated from the 2-week recording period for each phase of the study (pretreatment, posttreatment, and fol- low-up).

TST measured with actigraphy was chosen as the main outcome in the current analyses because the ad libitum actig- raphy design allowed patients in the CBT-I group to practice sleep restriction (as per recommendations from the psycholo- gist) if necessary, compared to the fixed TIB protocol during the PSG recording, for which patients were not able to practice their sleep restriction techniques.


I read with interest the article by Vgontzas et al, entitled “Effects of trazodone versus cognitive behavioral therapy in the insomnia with short sleep duration phenotype: a preliminary study” [1].

However, suggesting that “trazodone, but not CBT-I (Cognitive Behavior Therapy-Internet), significantly improves objective sleep duration and reduces hypothalamic-pituitary-adrenal axis activation” based on the small sample size of 7 patients on trazodone may not be appropriate.


It is surprising that the authors state that they wanted to study trazodone despite American Academy of Sleep Medicine recommendations not to use trazodone in the guidelines for treatment of insomnia disorder.

Despite the popularity of trazodone, American Academy of Sleep Medicine guidelines state that “We suggest that clinicians not use trazodone as a treatment for sleep onset or sleep maintenance insomnia (versus no treatment) in adults.”


This is based on the investigations on efficacy of trazodone 50 mg vs zolpidem 10 mg and placebo.

The final sample for the trazodone and placebo groups included 187 adults with sleep-onset insomnia.

Participants were administered either trazodone or placebo in a double-blind fashion for 14 consecutive nights.


In their study, the trazodone group experienced significantly more side effects than the placebo group.

Chief among these were headache (trazodone, 30%; placebo, 19%) and somnolence (trazodone, 23%; placebo, 8%).

In all, 75% of trazodone participants reported adverse events [2].


Although trazodone was more effective at improving self-reported sleep quality, there were no differences in sleep-onset latency, total sleep time, or wake after sleep onset and was the reason the US Department of Veterans Affairs and the US Department of Defense Clinical Practice Guidelines also do not recommend “use of trazodone for treatment of chronic insomnia disorder” [3].


The authors have used “short sleep” based on the closest clinically meaningful cutoff (total sleep time < 7 hours) to the median value (total sleep time < 6.8 hours) measured with actigraphy at pretreatment (mean value for 2 consecutive weeks).

This seems to be too liberal in terms of enrollment criteria.

The authors had used the short sleep duration definition of less than 5 hours in their earlier studies, as this was associated with risk of hypertension and type 2 diabetes.

Mortality risk was significantly increased only in people with insomnia who slept fewer than 6 hours (odds ratio = 4.33; 95% confidence interval: 1.25–15.04) in their earlier study [4].

The stricter definition of total sleep time of less than 5 hours might be more meaningful to evaluate the differential effect of an intervention.


We thank Dr. Hunasikatti for his commentary [1] on our study titled “Effects of trazodone versus cognitive behavioral therapy in the insomnia with short sleep duration phenotype: a preliminary study.” [ 2] Trazodone, a heterocyclic antidepressant in low doses of 25 to 150 mg, has been the second most widely prescribed medication for sleep for the last 20 years in the United States [3].

In fact, a recent study showed that trazodone has been prescribed in increasing trends in the last 10 years [4].

Only in 2018, there were 24,000,000 prescriptions for low-dose trazodone in U.S. adults [4].

Hence, the following question arises: Why do so many physicians and patients use this medication although it is not approved by the U.S. Food & Drug Administration, not recommended by scientific organizations, and not promoted by industry?

A critical issue is that trazodone, despite its widespread use by the U.S. public as prescribed off-label by their physicians, has remained grossly understudied in randomized clinical trials (RCTs) for insomnia; thus, trazodone lacks evidence from RCTs [5].

This concern was one of our motives to initiate this small pilot study.


The lack of evidence and guideline indication for trazodone did not stop Morin and colleagues [6] from investigating its therapeutic effect as a second-stage agent in a sequential RCT with hundreds of patients.

Interestingly, their large RCT showed that cognitive-behavioral therapy for insomnia was most effective in reducing sleep latency and wake time after sleep onset and in increasing sleep efficiency, whereas medications had their strongest effect on increasing total sleep time, particularly when patients were switched from zolpidem to trazodone [6].

The investigators concluded that “[G]iven the emerging literature on insomnia phenotypes and the higher risk for cardiovascular morbidity among individuals with insomnia and short sleep duration, such findings could guide the development of personalized therapies for insomnia management.

” [ 6]


In summary, we would agree with Dr. Hunasikatti’s concern [1] about the use of trazodone in our small pilot study if our sample had comprised at-risk individuals for whom trazodone contraindications existed; with this not being the case, “lack of evidence” arising from multiple reasons and sources, including physicians’ preferences or industry interest, should never stop scientific inquiry.

Leaving trazodone underinvestigated, despite promising results, and grossly prescribed should be a matter of public health concern for the National Institutes of Health and the U.S. Food & Drug Administration.


Insomnia with objective short sleep duration was proposed by our group as a novel phenotype associated with activation of the stress system, particularly the hypothalamic-pituitary-adrenal axis, significant cardiometabolic and neurocognitive morbidity, and, possibly, better response to biologic treatments [7].

Comparing the effectiveness of a pharmacologic agent that seemed to decrease the activation of the hypothalamic-pituitary-adrenal axis and increase total sleep time with that of cognitive-behavioral therapy for insomnia, the first-line recommended treatment, was one of the goals of our preliminary study.

Our results are interesting but far from conclusive, as indicated in the title of our report.

Notably, 4 studies consisting of retrospective secondary analyses of previously published RCTs have found lower insomnia remission rates after cognitive-behavioral therapy for insomnia in the insomnia with objective short sleep duration phenotype than in the insomnia with normal sleep duration phenotype [8-11], and 3 other studies have found equivalent insomnia remission rates in these 2 phenotypes [12-14].

Similarly, despite this preliminary evidence, the issue of the relative efficacy of cognitive-behavioral therapy for insomnia in our proposed insomnia phenotypes remains inconclusive but should fuel scientific inquiry for well-designed, adequately powered, prospective RCTs using a personalized medicine, phenotype-matching approach.


Dr. Hunasikatti [1] also noted our choice of measure and cutoff point for total sleep time when classifying the insomnia with objective short sleep duration phenotype.

The answer is clearly indicated in the “Methods” section of the article and discussed in the following manner:


In this study, we used the cut-off of less than 7 hours via actigraphy, which was the closest meaningful cut-off to the median of 6.8 hours.

This is higher than the cut-off of 6 hours, which was the median PSG sleep duration in physiological studies and in large random general population samples.

It is to be expected that the median value of TST will differ based on the method used (i.e., actigraphy tends to overestimate TST when compared to PSG), population studied (i.e., general random sample versus clinical or volunteer), and age of the sample.

We have emphasized that the previously suggested cut-off of 6 hours for the ISS phenotype has been used as an internally valid marker of the severity of insomnia and not as a recommended optimal sleep duration for the general population [2].


These statements and approach should have resolved any concerns and is consistent with our early proposal that an important research agenda for insomnia phenotyping was to “replace the expensive, inconvenient, and impractical in-lab sleep measures with easy-to-use, inexpensive, home-based objective sleep measures.

” [ 7] Future studies should validate actigraphy and other wearable technology, not just to accurately estimate specific polysomnography parameters as has been traditionally done but also to provide reliability and validity for specific cutoff points needed to identify individuals who are short sleepers despite adequate opportunity for sleep and, thus, individuals with the insomnia with objective short sleep duration phenotype.


The results of our small pilot study are interesting, obviously stimulating, but far from conclusive.

We cannot agree more with the final statement by Wong and colleagues [4] in their Journal of the American Medical Association article that “[M]ore studies evaluating the efficacy and safety of trazodone for insomnia are warranted” and with the conclusive statements by Morin and colleagues [6] in their Journal of the American Medical Association Psychiatry article: “Additional studies are needed to validate best treatment algorithms for insomnia disorder, and rather than randomizing patients to treatment options, perhaps a more effective strategy would involve a personalized approach matching patients with their preferred treatment, while also taking into account their insomnia phenotypes (ie, presence of hyperarousal and TST).”


The sustainable development goals (SDGs) have helped retrain the spotlight on one of the most critical issues of our time - reducing inequality [1].

The call to 'leave no one behind' is an apt reminder of the cen- trality and importance of the task before us.

A number of SDGs, including Goals 1 (on poverty alleviation), 5 (on gender equality) and 10 (on reducing inequalities within and among countries), directly aim to tackle inequality, while the third goal on health, in placing emphasis on universal health coverage, builds a strong agenda for reducing inequalities in health and ensur- ing health and wellbeing for all [1,2].

In addition, SDG 17, dealing with strengthening the means of imple- mentation and revitalising global partnerships, makes specific mention of the importance of capacity build- ing to increase the availability of disaggregated data [1], which in turn enables continuous monitoring of health inequalities within countries.


As a key step in advancing these goals, the World Health Organisation (WHO) has placed emphasis on building capacity for measuring and monitoring health inequalities [3].

Capacity building activities have com- prised facilitating training workshops in WHO regions and member states as well as developing a number of resources on health inequality monitoring, including the Health Equity Assessment Toolkit (HEAT) [3,4].


HEAT is a software application that facilitates the assessment of within-country health inequalities using the built-in WHO Health Equity Monitor data- base [4,5].

Last updated in February 2018, HEAT allows users to explore and compare inequalities using disaggregated data and summary measures of inequality, visualised in a variety of customised tables and graphs.

The software was developed in tandem with capacity building efforts in countries and regions, and benefited from the feedback of workshop partici- pants and technical experts.

The process of developing HEAT is described in detail elsewhere [4].


While HEAT was very well received by countries and programmes, demand emerged for greater customisation of the data used in HEAT.

In addition to using the built- in database, users also expressed interest in using their own data, including national- and subnational-level data (e.g. province-, district- and municipality-level data).

In response to this demand, the Upload Database Edition of HEAT, HEAT Plus, was developed.

Launched in July 2017, HEAT Plus allows users to upload their own data- bases and assess inequalities at the global, national or subnational level for a range of (health) indicators and dimensions of inequality.

The purpose of this article is to introduce HEAT Plus and provide empirical examples of its application in different settings.


Like HEAT [4], HEAT Plus was developed using the free and open source statistical software R (https://www.r- project.org) and the R package shiny (https://cran.r- project.org/package=shiny).

It uses R packages that were used in the first version of HEAT (dplyr, ggplot2, grid, gridExtra and RColorBrewer [6-10]) as well as data.table, DT, readxl, and tidyr [11-14].

HEAT Plus source code has been published under the GNU General Public License version 2 or later (https://www.gnu.org/ licenses/gpl-2.0) and is freely available through GitHub (https://github.com/WHOequity/HEATPlus-1.0).


HEAT Plus is available as a standalone version for download from the WHO website (http://www. who.int/gho/health_equity/assessment_toolkit/en/).

Standalone packages can be downloaded as .zip files that also include portable versions of R and Mozilla Firefox, which are required to run HEAT Plus, but do not require any installation.

Separate packages are available for Windows and Macintosh operating systems.


Once setup, HEAT Plus can be used to assess health inequalities in a variety of different settings and for a range of health indicators and inequality dimensions - depending on the data uploaded.

Databases of disaggre- gated data have to be in a specified format and must be stored as comma-separated values (.csv) or Microsoft Excel (.xls or .xlsx) files in order to be uploaded.

The HEAT Plus template illustrates the required content and structure; the user manual provides detailed information on how to prepare disaggregated data according to the HEAT Plus template (both available at http://www.who. int/gho/health_equity/assessment_toolkit/en/).

Table 1 provides an overview of the variables included in the HEAT Plus template, including man- datory, recommended and optional variables.

Mandatory variables are essential for the full func- tionality of HEAT Plus; databases cannot be uploaded if information for these variables is missing for any observation in the dataset.

This includes basic information, such as the setting name, as well as additional variables required for the correct calcu- lation of summary measures, including: (1) whether the indicator is favourable or not (variable 'favoura- ble_indicator' in the template), (2) the indicator scale (variable 'indicator_scale'), (3) whether the inequal- ity dimension is ordered or not ('ordered_dimen- sion'), (4) the subgroup order of the inequality dimension ('subgroup_order'), and (5) the reference subgroup of the inequality dimension ('reference_- subgroup').

Recommended variables are required for certain calculations or features in HEAT Plus, but databases can be uploaded if information for these variables is missing for some or for all observations in the dataset.

For example, users are encouraged to enter the number of people affected or at risk within each population subgroup (variable 'population' in the template) as this information is required for the calculation of weighted summary measures.

However, if this information is not available to the user, then databases can still be uploaded, but HEAT Plus will only be able to calculate unweighted sum- mary measures.

Finally, optional variables may be included to provide additional information, but need not be included in order to upload databases to HEAT Plus.

For instance, users may want to enter the 95% confidence intervals (CIs) of subgroup esti- mates in order to display this information in tables and graphs, although this is not essential for the functionality of HEAT Plus.


Provided databases have been prepared in the correct format, HEAT Plus will be able to upload the disaggre- gated data and calculate up to 15 summary measures of inequality.

Table 2 provides an overview of which sum- mary measures can be calculated for which dimension of inequality, depending on the data included in the uploaded database.

Disaggregated data and summary measures will be stored in HEAT Plus under a user- specified name.

Stored databases can be opened when returning to HEAT Plus.


Like HEAT [4], HEAT Plus is organised around four main tabs: Home, Explore Inequality, Compare Inequality and About.

Home is the starting point for any assessment in HEAT Plus, where users can upload new databases or open existing databases (see Figure A1).

Under About, users can access further information about HEAT Plus, including the user manual and technical notes as well as software, version, licence and feedback information, and the acknowledgements.

The two key tabs, Explore Inequality and Compare Inequality, allow users to undertake health equity assessments using disaggregated data and summary measures (see Figures A2 and A3).

Both tabs comprise a number of subtabs, each displaying the data in a graph or table that can be customised using a selection menu on the left.

The subtabs are identical in HEAT and HEAT Plus; details of the subtabs included in HEAT version 1.0 have been described elsewhere [4].

However, a number of improvements have been made since the first release of HEAT, which are highlighted in the following section.


First, a new subtab has been added under Explore Inequality, named Disaggregated data (detailed bar graphs).

The tab features horizontal bar graphs showing data for population subgroups in a selected setting of interest and is the preferred option for assessing dimensions with a large num- ber of subgroups, such as data by districts.

Graph Options in the left panel allow users to modify the graphs: bars can be sorted by subgroup name or subgroup estimate (in ascending or descending order); the median and/or setting average can be shown as vertical lines in the graph; one or more subgroups may be highlighted in the graph; the graph height and width and the axis range can be modified; graph and axis titles can be added.

As in HEAT, the graph and the data represented in the graph may be downloaded as .pdf/.png/.jpg and .

csv/.tsv files, respectively.

Second, graph and table options in all subtabs now make use of accordion- style menus, allowing users to hide or show these options at their convenience.

Third, the order of subtabs under Explore Inequality has been chan- ged and graphs are now listed before tables (this was done in response to users' preference).


HEAT Plus can be used for monitoring health inequalities at global and national levels.

Preparing data for upload to HEAT Plus requires good data management skills and familiarity with Excel.

Once prepared and uploaded, data can be used for health inequality assessments in HEAT Plus by anyone with data interpretation competence, such as technical staff (for example, in ministries of health), public health professionals, policy makers, researchers, stu- dents and others.

The following examples illustrate prior applications of HEAT Plus.


At the global level, HEAT Plus has been used to assess the state of inequality in access to improved drinking water in WHO Member States.

Data disaggregated by place of residence (urban/rural) for 192 countries were taken from the WHO Global Health Observatory data repository [15], prepared according to the HEAT Plus template and uploaded to HEAT Plus.

The Explore Inequality tab was then used to explore the latest situa- tion of inequality and the change in inequality over time within a selected country of interest.

The Compare Inequality tab was used to compare the situation in that country with the situation in other countries from the same or different country income groups and/or WHO regions.

Looking at Indonesia, for example, ana- lyses in HEAT Plus showed that access in both urban and rural areas increased between 1990 and 2015 and that the absolute difference between the two areas was halved during this period.

However, a considerable gap between urban and rural areas remained in 2015 (see Figures 1 and 2).

When comparing Indonesia with other low- and middle-income countries from the WHO South-East Asia region, it was found that some countries, such as Bhutan and the Democratic People's Republic of Korea, achieved almost universal coverage, while other countries reported even larger inequalities than Indonesia (see Figure 3).


HEAT Plus has also been used to assess the state of health inequality at the national level in Indonesia, as part of efforts to build capacity for health inequality monitoring in the country [16].

Data from different national sources, including health facility surveys and household surveys, were used to assess inequalities in selected indicators by rele- vant dimensions of inequality.

For example, data from the 2015 National Socioeconomic Survey (SUSENAS) were used to assess inequalities in access to improved drinking water between districts [17].

Overall, it was found that there were large variations between districts within each pro- vince (see Figure 4).

Looking simultaneously at the province average and the level of absolute within- province inequality, Papua was identified as one of the provinces with the lowest overall access and the highest absolute inequality (Figure 5).

Looking closer at the situation in Papua, it was observed that, over- all, about half of the population in Papua had access to improved drinking water, yet in some districts, such as Jayapura, nearly every household had access, while in others, no one had access (Figure 6).


Using HEAT Plus as part of the capacity building process in Indonesia not only facilitated the assessment of the state of health inequality in the country, but also helped to improve the software.

For example, users expressed the need to be able to analyse subnational- level data in more detail, which led to the development of the Disaggregated data (detailed bar graphs) tab.

Moreover, feedback from users helped to improve the template as well as the warning messages that appear in HEAT Plus if data have not been entered correctly.

The results from the capacity building process have been presented in the national report State of health inequal- ity: Indonesia, published in November 2017, as well as in this special issue [17,18].


While many countries routinely monitor health at the national level, national averages may mask underlying inequalities and population subgroups being left out of health programmes and services.

Many countries have disaggregated data available, but lack the tools and capacity to routinely assess health inequalities.

HEAT Plus is one of several tools and resources WHO has developed to support global and national health inequality monitoring, such as the Health Equity Monitor theme page and data repository, the Handbook on health inequality monitoring: with a special focus on low- and middle-income countries, the Health inequality monitoring eLearning module, the National health inequality monitoring: a step-by-step manual and the WHO Innov8 approach for reviewing national health programmes [19-24].

The hope is that with HEAT Plus and related tools and resources, coun- tries will be able to incorporate health inequality monitoring as part of their routine health reporting and planning.


Indeed, HEAT Plus shows promise for the adapta- tion, expansion, and deepening of health inequality monitoring at various administrative levels within countries as the software is highly flexible and adap- table.

For instance, once data is prepared following the HEAT Plus template, over time, data may be added for additional periods of time, indicators, dimensions of inequality, sources of data, levels of programming and so on, allowing the analysis and reporting of equity to be richer, more nuanced and constructive for policymaking and/or programme design.

This in turn can help institutionalise consid- eration of equity in the process, which then must be addressed through programme redesign and refine- ment [24].


Flexibility and adaptability are perhaps the greatest strengths of this software.

Importantly, HEAT Plus allows users to upload and work with their own databases.

Health equity assessments can be done at different levels, e.g. at global, national or subnational levels, depending on the data uploaded to HEAT Plus.

Assessments can also be done for a variety of indicators, not just in the domain of health (e.g. HEAT Plus can work with health indicators preva- lence of a disease, coverage of treatment, services or human resources, as well as non-health indicators like adolescent school attendance, voter turnout, etc.).

Similarly, typical dimensions of inequality such as age, sex, education or economic status, but also country- or context-specific dimensions, such as eth- nicity, disability or migratory status can be used for the equity assessment.

Data used for an assessment in HEAT Plus may come from various types of data sources, such as household surveys, administrative/ facility data, census, or civil registration/vital statistics databases.


In fact, data from different sources may be linked using individual or small-area unique identi- fiers and analysed simultaneously [25].

Datasets may be saved securely on the user's machine and analyses generated and downloaded based on user conveni- ence.

Finally, once the software is downloaded, inter- net access is not required to run analyses, making this a useful tool for analyses 'in the field' where internet access may be scarce.


The software has incorporated features in response to the requests and requirements of users carrying out health inequality monitoring at the national level, particularly in Indonesia [16].

An email address is provided (heat@who.int), where users may provide feedback to improve the software.


HEAT Plus is in the process of incorporating addi- tional features to enhance its capabilities for equity assessments and to make the software more user friendly.

For instance, interactive features will be added to the graphs, allowing users to engage with the data and obtain additional information (e.g. through tooltips that appear when hovering over a data point).

A feature is also being added that, should shape files be available, will engage a map function, showing estimates using chloropleth maps.

Finally, to ensure wide use and application, HEAT Plus is envi- sioned to be translated into other languages, with an initial emphasis on languages of the United Nations.


HEAT Plus, the Upload Database Edition of the Health Equity Assessment Toolkit is a software appli- cation that allows users to undertake health equity assessments using their own data.

The ability of HEAT Plus to work with uploaded data makes it a suitable tool for both global and national inequality assessments.

In particular, the software has direct application for use at the subnational level, enabling customised equity assessment that may be of particu- lar use and relevance for local use and planning.

To conclude, HEAT Plus represents a concrete and tan- gible contribution to the realisation of the SDG vision of leaving no one behind.


The world is united on the path to achieving the United Nations’ Sustainable Development Goals (SDGs) – in which, specifically in the realm of health, Universal Health Coverage is the central strategy for ‘leaving no-one behind.’ Fulfilling this commitment requires vigilance and political will on the part of each country through health inequality monitoring [1].

As this Special Issue demonstrates, Indonesia is a model of how the health-related SDGs may be achieved by individual countries, in a truly collaborative fashion.

Technical support from the World Health Organization (WHO) – from its Geneva headquarters through its regional office to the country office in Jakarta – working in close partnership with the Indonesian Ministry of Health as well as critical stakeholders in the country, has led to this watershed exercise and serious reflection and action on health inequalities in Indonesia.

The process was mutually beneficial, in that Indonesia was able to carry out analyses related to inequality while supporting and implementing the development of health inequality monitoring tools under the aegis of WHO.

The path for other WHO Member States may look slightly different, but it is already paved by Indonesia’s commitment and example.


WHO’s Thirteenth General Programme of Work calls for strategically disaggregating data when they are collected, analysed and reported across data sources, identifying health inequalities and their drivers to improve programme delivery, and acknowledging the centrality of health information systems for monitoring health inequalities [2]. WHO is committed to extending such support to additional equity-oriented activities that Indonesia may wish to undertake as well as other nations on this path.

A range of tools and processes have been put in place in support of this.

This includes publications such as the Handbook on health inequality monitoring with a special focus on low- and middle-income countries [3], a detailed manual for national health inequality monitoring [4], and a variety of reports that outline the process and demonstrate the output of monitoring the state of inequality at the global and national levels, and in relation to specific domains [5–7].

In addition, tools and resources such as the Health Equity Assessment Toolkit [8,9] and the Health Equity Monitor [10] can provide further guidance and facilitate monitoring.


Achieving the SDGs requires repeated, iterative efforts.

This Special Issue demonstrates that health inequality monitoring can be a model for the content and process of such efforts by promoting continuous, mutual learning, and an explicit, operational commitment to equity.


Sekali merengkuh dayung, dua tiga pulau terlampaui.

With one stroke of the paddle, two – three islands have passed.

This is a common saying in Indonesia, suggesting that in one action, multiple goals may be achieved.

It is perhaps a fitting metaphor for those who see the sweeping breadth and scope of the Sustainable Development Goals (SDGs) not as a challenge, but as an opportunity.

Our experience shows that the SDGs are indivisible, intersectional and synergistic.

For instance, the health goal SDG 3 puts at its heart the notion of universal health coverage, which is premised on the idea of ensuring equity.

This is enabled by SDG Target 17.18 which calls for data disaggregation as a means of both monitoring and tackling inequality.

Further, SDG 10 on reducing inequalities is also served by the purposes of improved data systems, and efforts to achieve universal health coverage.


One such stroke of the paddle in Indonesia, as it were, began in 2016, resulting in the country’s first comprehensive assessment of health inequality, published in December 2017 [1].

Over 50 indicators across 11 health topics were disaggregated by dimensions of inequality, such as household economic status, education level, place of residence, age or sex.

Apart from this process of collaboration, capacity-building, analysis and advocacy at the national level, those involved with the process were also committed to dissemination in the academic community, manifest as nine distinct contributions in this special issue on health inequality monitoring in Indonesia.

Health inequality monitoring – in Indonesia or in any other setting (at global, regional, national or subnational level) – adds an equity dimension to standard health monitoring.

This is done by ensuring the availability and use of disaggregated data on population subgroups of interest.

The process of health inequality monitoring is a continuous cycle comprising five steps: the selection of health indicators and dimensions of inequality; the sourcing of this data; its analysis using both disaggregated data estimates and summary measures of inequality; reporting of results; and the implementation of policy and/or programme changes in light of findings.


The first paper in this special issue highlights the 20-month capacity-building process of health inequality monitoring in Indonesia, which led to the development of myriad products including a national state of health inequality report and several publications [2].

It outlines the capacities required for health inequality monitoring and then details the content and duration of the process in Indonesia, describing stakeholders (including the lead agency and contributors), outcomes, successes, challenges and lessons learned.

It also takes a view on the adaptation and implementation of analogous processes of health inequality monitoring in other countries.


A critical initial step towards creating a system for health inequality monitoring, which includes collecting, analysing and reporting health inequality data, is mapping available data sources.

A short communication outlines the process by which data source mapping was undertaken in Indonesia, presenting in detail the template used for this purpose, as well as various data sources identified through its use (e.g. the Census, the Basic Health Research Survey, and various topic- and disease-specific surveys, as well as the sample registration system and health facility data) [3].

Availability of disaggregated data on particular health topics was mapped across 16 of these sources in order to determine which source would be best suited for use in the creation of Indonesia’s national state of health inequality report.


Another essential ingredient for health inequality monitoring, one that allows customisation and visualisation of inequality analyses, is HEAT Plus.

HEAT Plus is a software application developed by the World Health Organization (WHO) to facilitate analysis and reporting of health inequality data [4].

HEAT Plus is the upload database edition of the Health Equity Assessment Toolkit [5]; it allows users to upload data on any indicator disaggregated by any dimension of inequality following a simple template.

In Indonesia, HEAT Plus was used for the analysis of inequality across 11 health topics, some of which are included in this special issue.

One of the papers employing HEAT Plus examines data from the National Socioeconomic Survey on access to improved drinking water and sanitation, disaggregated by district within Indonesia’s provinces using both absolute and relative inequality measures (i.e. mean difference from mean and weighted index of disparity) [6].

Large variation in inequality was observed within provinces with the same numbers of districts.

The province of Papua, for instance, stood out in that it had among the lowest levels of coverage in the country (only 28% of the population reporting access to improved sanitation), as well as the highest levels of inequality (a value of 92.3% for its weighted index of disparity as compared to 3.1% in DKI Jakarta), clearly indicating domains and locations of priority.


A unique contribution to this special issue is one describing analysis of subnational regional inequality in the form of Indonesia’s Public Health Development Index (PHDI), a composite of 30 indicators across several health topics with 7 sub-indices (for reproductive and maternal health, infectious diseases, newborn and child health, non-communicable diseases, environmental health, health risk behaviours and health service provision) [7].

This index was developed for use in priority-setting, planning and resource allocation across districts in the country.

For this special issue, both average levels of PHDI and within-province inequalities were reported.

Provinces in western Indonesia tended to report higher overall PHDI scores than those in the east.

Cases were also identified where within-province inequality in PHDI and its sub-indices were higher in provinces with the same average scores, especially in the case of the environmental health index, again suggesting domains of priority for addressing inequality.


In the course of using HEAT Plus, there was discussion about Indonesia’s high rates of adolescent tobacco use, relative to other countries.

This topic was delved into more deeply using logistic regression methods on data from the 2013 Basic Health Research Survey [8].

It was found that the odds of current smoking were higher among males and older adolescents, and among those in the poorest quintile.

Prevalence also tended to be significantly higher in the western provinces as compared to those in the east, controlling for individual socio-economic and demographic characteristics.

The article discusses how prevalence may be shaped by historical factors and the broader policy context related to tobacco, pointing towards areas for programme and policy action.


Moving from analysis to action, a methods article in this special issue lays out the process to increase the equity-orientation of health programme and policy workflows in Indonesia [9].

The WHO’s Innov8 approach comprised eight steps that sought to understand the design and programme theory of these plans, develop consensus around who is being left out by these plans and why, and develop a redesign proposal that foregrounds intersectoral action and social participation, while also ensuring monitoring and evaluation.

The first review of the Maternal Health Action plan led to the generation of various recommendations.

To bring focus, a training session for district health authorities was developed in order to inform their annual planning cycles in 10 provinces.

Concurrent demand for capacity to analyse, report and visualise data on inequalities was met with the introduction of HEAT Plus for health inequality monitoring, described at length in this special issue [2].


Many of the processes followed, as well as challenges and opportunities observed in Indonesia, are applicable to other countries and settings, even as they may be at different stages of development in terms of their national health information systems.

The debate article on health inequality monitoring highlights common challenges across countries in the domain of data collection, analysis, reporting and effective communication, as well as the development of strategies to address inequalities identified [10].

It also describes shared opportunities, including broad-ranging global initiatives that aim to standardise data collection, its analysis and/or also to improve the quality and level of customisation of communication, as well as guidance on how equity-oriented policymaking can occur.

The WHO in particular has developed tools and resources to support these elements of health inequality monitoring [4,11–15].

The second debate article showcases WHO’s broad strategy for Gender, Equity and Human Rights to which health inequality monitoring is a major contributor [16].

It presents definitions of key concepts and principles in this domain – equity, gender and the right to health.

It also briefly describes other components of WHO’s work, including: barrier analysis in Nigeria using qualitative approaches; human rights monitoring in coordination with the United Nations High Commissioner for Human Rights; guidance provided to Member States through WHO country offices under the National Health Policies, Strategies and Plans (NHPSPs), citing the case of Mongolia in particular; and programme reviews using the Innov8 approach, with application to Nepal’s Adolescent Health strategy.

The broader goal for WHO has been and will continue to be to ensure country adaptation, moving away from single-method, siloed and exclusively technocratic approaches that may have been relied upon in the past.


Findings across papers converged, perhaps indicative of the demographic and epidemiologic transition underway within Indonesia, requiring prioritisation of provinces and districts for particular health issues and dimensions of inequality.

The province of Papua, for instance, appears to have the greatest within-province inequalities in terms of sanitation as well as the PHDI, while adolescent smoking is not a priority area in this province.

Prevalence rates of smoking were significantly higher in western provinces, and among poorer adolescent males.

Thus, in making the effort to understand health inequalities in Indonesia, the country has used one paddle to pass many islands.

The process brought certain issues to the fore (like adolescent smoking), showcased unique analytics, and highlighted indicators related to health as well as its determinants (like water and sanitation).

More broadly, the process of capacity-building for health inequality monitoring in Indonesia documented here is a rough template for adaptation and customisation to improve the use and equity-orientation of other national health information systems.

This can be facilitated by a growing armamentarium of tools the WHO has developed to support health inequality monitoring at the global level and in countries.

There are clear synergies with policy-making and lessons for other members states wishing to make great strides – or in this case strokes! – on the path to leaving no one behind.


After obtaining approval from the research ethics commit- tee of Hospital Universitário Pedro Ernesto in Rio de Janeiro, Brazil, a prospective, randomized study was con- ducted on 34 women who underwent breast augmentation between June 2011 and March 2013.

Patients were included who were between 20 and 40 years of age.

Exclusion criteria were mammary hypoplasia; ptotic, asymmetric, or tuberous breasts; significant comorbidities; obesity or overweight (body mass index [BMI] >25 kg/m 2 ); a family history of breast cancer; prescribed medication other than birth control; previous breast interventions; and pregnancy or breastfeed- ing at time of study.

Patients received full disclosure regard- ing the study, including a description of the protocol, possible complications, and postoperative precautions.


Patients were randomly assigned to endoscopic or non- endoscopic surgical groups with Research Randomizer (version 4.0).

12 The endoscopic group (n = 17) underwent transaxillary augmentation mammaplasty with video-assisted endoscopy, whereas the nonendoscopic group (n = 17) underwent the same procedure without endoscopy.

All pa- tients received high-profile rounded and textured silicone im- plants (Silimed, Rio de Janeiro, Brazil) in the subglandular plane.

Implant sizes ranged from 205 to 305 cc.

All proce- dures were performed by the main author (A.C.W.R.).


With the patient seated or standing, preoperative markings of the submammary fold, anterior axillary line, midline in the anterior thoracic region, and proposed incision line were made (Figure 1A).

After induction of locoregional an- esthesia with sedation, the patient was positioned supine with the arms abducted 90 degrees and the back raised ap- proximately 15 degrees such that the sternal notch was hor- izontal to facilitate dissection and manipulation of the instruments.

A first-generation cephalosporin was adminis- tered intravenously as prophylaxis, and the breasts were in- filtrated with a solution of lidocaine and adrenaline (1:200.000).


A 3.5- to 4-cm incision was made in the anterior axillary fold immediately behind the pectoralis major (Figure 1B).

In patients without a well-delineated axillary fold, the inci- sion was made approximately 1 cm from the lateral crest of the pectoralis major, as high as possible on the apex of the axilla.

This ensured maximum concealment of the scar with the arm lowered.

Subcutaneous dissection then was performed to the lateral border of the pectoralis major as superficially as possible for maintenance of the lymphatic structures (Figure 1C).

A subcutaneous tunnel was made to enable access to the mammary pocket through the incision.

This tunnel was large enough to allow passage of the implant but was not overly dissected to avoid cranial migra- tion of the implant.

After the lateral crest of the pectoralis major was identified, undermining of the mammary pocket was performed in the suprafascial plane.


For the nonendoscopic surgical group, dissection was vi- sualized directly by means of an optical fiber and electrocau- tery forceps (a 20 cm forceps) (Figure 1D).

All instruments were sufficiently long for dissection of the lower internal and external quadrants below the nipple-areola complex (NAC).

These areas are distal to the incision and can be difficult to access.


For the surgical group in which video-assisted endosco- py was included, the monitor was positioned at the foot of the bed so the surgeon could move freely around the oper- ating table.

Instrumentation for the endoscopic procedure included a retractor, a 30-degree lens, an aspirator, and dis- sectors (Karl Storz, Tuttlingen, Germany).

Under endo- scopic visualization, sharp dissection and cautery were performed (Figure 1E).


The surgeon manually inserted the implant through the incision without a Keller Funnel (Keller Medical, Inc, Stuart, FL).

Povidone-iodine ointment was not applied.

After implant insertion, the subcutaneous tunnel was closed with 3-0 nylon sutures to prevent migration of the implant toward the surgical wound.

The surgical wound was closed with 3-0 nylon subcutaneous sutures and 4-0 monocryl subdermal and intradermal sutures.

No suction drains were inserted.

Postoperative dressings included gauze, sterile micropore tape, and adhesive skin closures.