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Cross-national associations between gender and mental

disorders in the WHO World Mental Health Surveys

Soraya Seedat, PhD1, Kate Margaret Scott, PhD2, Matthias C. Angermeyer, PhD3, Patricia

Berglund, MBA4, Evelyn J. Bromet, Ph.D.5, Traolach S. Brugha, MD (NUI), FRCPsych6, Koen

Demyttenaere, MD, PhD7, Giovanni de Girolamo, MD8, Josep Maria Haro, MD, MPH, PhD9,

Robert Jin, MA10, Elie G. Karam, MD11, Viviane Kovess-Masfety, MD, PhD12, Daphna

Levinson, PhD13, Maria Elena Medina Mora, PhD14, Yutaka Ono, MD, PhD15, Johan Ormel,

PhD16, Beth-Ellen Pennell, MA4, Jose Posada-Villa, MD17, Nancy A. Sampson, BA10, David

Williams, PhD, MPH18, and Ronald C. Kessler, PhD

10

1 MRC Research Unit on Anxiety and Stress Disorder, Cape Town, South Africa 2 Department of

Psychological Medicine, Wellington School of Medicine, Otago University, Wellington, New Zealand

3 Center for Public Mental Health, Austria 4 Institute for Social Research, University of Michigan,

Ann Arbor, MI 5 SUNY Stony Brook, Stony Brook, NY 6 Department of Health Sciences, University

of Leicester, United Kingdom 7 University Hospital, Gasthuisberg, Leuven, Belgium 8 Health Care

Research Agency, Bologna, Italy 9 Sant Joan de Deu-SSM, CIBERSAM, Instituto de Salud Carlos

III, Barcelona, Spain 10 Department of Health Care Policy, Harvard Medical School, Boston, MA 11

Institute for Development, Research, Advocacy, and Applied Care (IDRAAC) St., George Hospital

University Medical Center, Lebanon 12 University of Paris Descartes, EA 4069; MGEN Foundation

for Public Health, Paris, France 13 Ministry of Health, Mental Health Services, Israel 14 Department

of Epidemiology, National Institute of Psychiatry, Mexico City, Mexico 15 Keio University School of

Medicine, Tokyo, Japan 16 University Medical Center Groningen, University of Groningen,

Groningen, The Netherlands 17 Colegio Mayor de Cundinamarca University, Bogata, Colombia 18

Harvard University School of Public Health, Boston, MA

Corresponding author: Ronald C. Kessler, PhD., Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue,

Boston, MA 02115, Tel. 617-432-3587, Fax 617-432-3588, Kessler@hcp.med.harvard.edu.

Author Contributions: All authors had access to data from their own country, but only Berglund, Jin, Kessler, and Sampson had full

access to all of the data in the study. Kessler takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Angermeyer, Berglund, Bromet, Brugha, Demyttenaere, de Girolamo, Haro, Jin, Karam, Kessler, Kovess-

Masfety, Levinson, Medina Mora, Ono, Ormel, Pennell, Posada-Villa, Scott, Seedat, Williams.

Acquisition of data: Angermeyer, Berglund, Bromet, Demyttenaere, de Girolamo, Haro, Jin, Karam, Kessler, Kovess-Masfety,

Levinson, Medina Mora, Ono, Pennell, Posada-Villa, Scott, Seedat, Williams.

Analysis and interpretation of data: Kessler, Berglund, Sampson, Scott, Seedat.

Drafting of the manuscript: Seedat, Kessler, and Scott.

Critical revision of the manuscript for important intellectual content:All of the authors took part in critical revision of the manuscript.

Kessler organized responses from all coauthors to the suggestions of reviewers and made final revisions.

Statistical analysis: Kessler, Berglund, and Jin.

Obtained funding: Angermeyer, Bromet, Demyttenaere, de Girolamo, Haro, Jin, Karam, Kessler, Kovess-Masfety, Levinson, Ono,

Ormel, Posada-Villa, Seedat, Brugha, Williams.

Administrative, technical, or material support: Angermeyer, Berglund, Bromet, Brugha, de Girolamo, Haro, Jin, Karam, Kessler,

Kovess-Masfety, Levinson, Medina Mora, Ono, Pennell, Posada-Villa, Sampson, Scott, Seedat, Williams.

Study supervision: Kessler, Pennell, Sampson, Scott.

Financial Disclosures: Dr. Kessler has been a consultant for GlaxoSmithKline Inc., Kaiser Permanente, Pfizer Inc., Sanofi-Aventis,

Shire Pharmaceuticals, and Wyeth-Ayerst; has served on advisory boards for Eli Lilly & Company and Wyeth-Ayerst; and has had

research support for his epidemiological studies from Bristol-Myers Squibb, Eli Lilly & Company, GlaxoSmithKline, Johnson & Johnson

Pharmaceuticals, Ortho-McNeil Pharmaceuticals Inc., Pfizer Inc., and Sanofi-Aventis. No other authors have any financial disclosures.

NIH Public Access

Author Manuscript

Arch Gen Psychiatry. Author manuscript; available in PMC 2010 January 22.

Published in final edited form as:

Arch Gen Psychiatry. 2009 July ; 66(7): 785–795. doi:10.1001/archgenpsychiatry.2009.36.

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Abstract

Context—Gender differences in mental disorders, including more anxiety-mood disorders among

women and more externalizing disorders among men, are found consistently in epidemiological

surveys. The “gender roles” hypothesis suggests that these differences should narrow as the roles of

women and men become more equal.

Objective—To study time-space (i.e., cohort-country) variation in gender differences in lifetime

DSM-IV mental disorders across cohorts in 15 countries in the WHO World Mental Health (WMH)

Survey Initiative and determine if this variation is significantly related to time-space variation in

female gender role traditionality (GRT) as measured by aggregate patterns of female education,

employment, marital timing, and use of birth control.

Design/Setting and Participants—Face-to face household surveys of 72,933 community-

dwelling adults in Africa, the Americas, Asia, Europe, the Middle East, and the Pacific.

Main Outcomes—The WHO Composite International Diagnostic Interview (CIDI) assessed

lifetime prevalence and age-of-onset of 18 DSM-IV anxiety, mood, externalizing, and substance

disorders. Survival analyses estimated time-space variation in Female:Male (F:M) odds-ratios (ORs)

of these disorders across cohorts defined by age ranges 18–34, 35–49, 50–64, and 65+. Structural

equation analysis examined predictive effects of variation in GRT on these ORs.

Results—Women had more anxiety-mood disorders than men and men more externalizing-

substance disorders than women in all cohorts and countries. Although gender differences were

generally consistent across cohorts, significant narrowing was found in recent cohorts for major

depressive disorder (MDD) and substance disorders. This narrowing was significantly related to

temporal (MDD) and spatial (substance disorders) variation in GRT.

Conclusion—While gender differences in most lifetime mental disorders were fairly stable over

the time-space units studied, substantial inter-cohort narrowing of differences in major depression

was found related to changes in the traditionality of female gender roles. Further research is needed

to understand why this temporal narrowing was confined to major depression.

Epidemiological surveys have consistently documented significantly higher rates of anxiety

and mood disorders among women than men1, 2 and significantly higher rates of externalizing

and substance use disorders among men than women.3–5 Although a number of biological,

psychosocial, and biopsychosocial hypotheses have been proposed to account for these

patterns,6–8 evidence that gender differences in depression9, 10 and substance use11–13 have

narrowed in a number of countries has led to a special interest in the “gender roles” hypothesis.

The latter asserts that gender differences in the prevalence of mental disorders are due to

differences in the typical stressors, coping resources, and opportunity structures for expressing

psychological distress made available differentially to women and men in different countries

at different points in history.14, 15 Consistent with this hypothesis, evidence of decreasing

gender differences in depression and substance use has been found largely in countries where

the roles of women have improved in terms of opportunities for employment, access to birth

control, and other indicators of increasing gender role equality, while trend studies in countries

where gender roles have been more static11, 16 or over periods of historical time when gender

role changes have been small17 have failed to document a reduction in gender differences in

depression or substance use.

Most research aimed at investigating the gender roles hypothesis has focused on individual-

level variation in roles in a single country at a single point in time.18–20 This approach is

limited in three ways. First, selection bias into roles due to pre-existing mental illness (e.g.,

women with agoraphobia having a higher probability than other women of becoming

homemakers rather than seeking employment outside the home) confounds attempts to evaluate

the causal effects of gender roles. Second, gender differences are largely confined to differences

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in lifetime risk, with much less evidence for gender differences in recent prevalence among

lifetime cases.21 This means that investigation of the determinants of gender difference should

focus on lifetime first onset rather than on the recent prevalence that has been the focus of most

studies. Third, as the gender roles hypothesis is a hypothesis about the effects of social context,

a rigorous test of the hypothesis requires an analysis of societal-level time-space variation

rather than analysis of the individual-level variation that has been the focus of most studies.

A small number of cross-national comparative studies have examined spatial variation in

gender differences in depression22 and alcohol abuse13 at a point in time or, more rarely, at

two points in time.11 Although these studies raised the possibility that gender roles might be

associated with variation in the magnitude of gender differences in these outcomes, they were

unable to test this hypothesis due to the small number of cross-sectional country-level

observations included in the analyses. The current report provides a more direct test of the

gender roles hypothesis by analyzing community epidemiological data collected from

respondents surveyed in 15 countries as part of the World Health Organization (WHO) World

Mental Health (WMH) Survey Initiative.21 Previous cross-national comparisons of gender

differences in mental illness focused on cross-sectional differences. We, in comparison, use

retrospective reports obtained in the WMH surveys about lifetime occurrence and age-of-onset

of mental disorders in different birth cohorts to study time-space variation in lifetime risk.

Specifically, we examine both variation across cohorts within a single country (i.e., temporal

variation) and variation across countries within a single cohort (i.e., special variation) in

lifetime risk of mental disorders as a function of time-space variation in the traditionality of

gender roles. Lifetime risk is the focus rather than recent prevalence even though accuracy of

reporting is doubtlessly better for recent episodes than lifetime occurrence in order to address

the fact that gender differences in lifetime risk are much more robust than gender differences

in current prevalence among lifetime cases

METHODS

Sample

WMH surveys were carried out in samples of adults (ages 18+) in five countries classified by

the World Bank23 as developing (Colombia, Lebanon, Mexico, South Africa, Ukraine) and ten

classified as developed (Belgium, France, Germany, Israel, Italy, Japan, Netherlands, New

Zealand, Spain, and United States of America). The total sample size was 72,933. Individual

country samples ranged from 2,372 (Netherlands) to 12,790 (New Zealand). (Table 1) The

weighted average response rate was 71.2%. Country-specific response rates ranged from 45.9%

(France) to 87.7% (Colombia). All surveys were based on probability household samples either

regionally representative (Colombia, Japan, and Mexico) or nationally representative (other

countries). Survey sample characteristics are described in more detail elsewhere.24

All interviews were conducted face-to-face by trained lay interviewers. Each interview had

two parts. All respondents completed Part I, which contained assessments of core mental

disorders, while all Part I respondents who met criteria for any core disorder plus a probability

sub-sample of approximately 25% of other Part I respondents were administered Part II. Part

II assessed correlates, service use, and disorders of secondary interest. The Part II data, used

in the current report, were weighted to adjust for over-sampling Part I respondents with mental

disorders, for differential probabilities of selection within households (due to only one

household member, and in some cases two, being surveyed in each household no matter the

number of adults residing in the household), and to match sample distributions to population

socio-demographic distributions. Standardized interviewer training procedures, translation and

back-translation procedures, and quality control procedures were applied across all WMH

countries to ensure comparability. Informed consent was obtained in all countries. These

procedures are described in more detail elsewhere.24, 25

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DSM-IV disorders

Mental disorders were assessed with Version 3.0 of the WHO Composite International

Diagnostic Interview (CIDI),26 a fully-structured diagnostic interview. Translation, back-

translation, and harmonization of the interview in local languages with the original English

language version of CIDI 3.0 were carried out in each WMH country using WHO guidelines.

27 Disorders were assessed using the definitions of the American Psychiatric Association

Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV).28 Disorders

assessed included mood disorders (major depressive disorder, dysthymic disorder, bipolar

disorder), anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia without

panic disorder, social phobia, specific phobia, separation anxiety disorder, post-traumatic stress

disorder), externalizing disorders (attention-deficit/hyperactivity disorder, conduct disorder,

intermittent explosive disorder, oppositional-defiant disorder), and substance disorders

(alcohol and illicit drug abuse with or without dependence). DSM-IV organic exclusion rules

were used to make diagnoses.

Methodological evidence collected in clinical reappraisal studies show that diagnoses of

anxiety, mood, and substance disorders based on CIDI 3.0 have generally good concordance

(25th–75th percentiles of area under the receiver operating characteristic curve equal to .71–.

81) with diagnoses based on blinded clinical reappraisal interviews.29 No evaluations were

made of test-retest reliability. The evidence regarding good concordance with clinical

diagnoses is based on surveys carried out in only a small number of countries. It is not clear

that the translations of the instrument in all countries yield data that would have the same good

concordance with blinded clinical reappraisal interviews. In addition, the externalizing disorder

diagnoses were not validated in the CIDI 3.0 clinical reappraisal studies because the clinical

interview used as the gold standard in these studies did not assess externalizing disorders.

However, a subsequent independent clinical calibration study documented good concordance

between diagnoses of adult ADHD based on CIDI 3.0 and those based on blinded clinical

reappraisal interviews.30 Another problem exists with the diagnoses of substance dependence,

which were assessed only among respondents who had a history of abuse. This means that

cases of dependence without abuse are excluded. However, empirical studies in the US have

shown that the number of cases of dependence without a history of abuse is small and that their

exclusion does not have a substantively meaningful effect on the estimated associations of

predictors with the outcomes.31–33 Nonetheless, because of this exclusion we focus here on

abuse rather than dependence. Retrospective age-of-onset (AOO) reports were obtained in the

CIDI using a series of questions designed to avoid the implausible response patterns obtained

in response to a simple question asking for recall of age of first episode of a focal disorder.34

Gender role traditionality

Each respondent was classified as being in one of four birth cohorts defined by age at interview

(18–34, 35–49, 50–64, 65+) to distinguish broad life course stages (early adulthood, early

midlife, late midlife, and old age). Four within-cohort indicators of female gender role

traditionality were calculated in each of the 58 resulting time-space subsamples (i.e., four

cohorts in each of 15 countries, minus the two oldest cohorts in Colombia and Mexico due to

an upper age limit of 64 in those two surveys). The four were as follows: (i) the ratio of the

proportion of women to men in the cohort who had labor force experience before age 35

(extrapolated using survival analysis in the 18–34 cohort and calculated directly in the older

cohorts); (ii) the ratio of the proportion of women to men in the cohort who achieved the median

level of education found among workers in the upper quartile of the income distribution in the

cohort; (iii) the ratio of the median ages of marriage of women versus men in the cohort; and

(iv) the proportion of women in the cohort who used birth control pills or other medical forms

of contraception before age 25 (restricted to women ages 25–34 in the 18–34 cohort).

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We make no claim that these indicators form an exhaustive set of the defining characteristics

of gender role traditionality or that the cut-points used to construct the measures (e.g., labor

force participation by age 34 rather than by some other age that we might have selected) are

optimal. Rather, the indicators were constructed from the WMH survey data on an ad hoc basis

to operationalize aspects of gender roles that we considered importanct based on our reading

of the demographic literature on gender roles.35–37

Confirmatory factor analysis carried out at the level of the time-space unit (n = 58) showed

that our initial thinking in selecting the four indicators was correct in the sense that a strong

single-factor structure was found among these four indicators, with factor loadings in the range .

59–.91. (Detailed results showing the values of each GRT indicator for each time-space unit,

the correlation matrix among the indicators, and factor loadings are available on request.) This

finding confirmed that the indicators are, in fact, strongly related and can be used to construct

a composite measure that we interpret as a measure of gender role traditionality (GRT). This

is the key predictor variable in the analysis described below.

Rather than use the ad hoc GRT measure described above, it would have been preferable to

obtain objective administrative data on country-level trends in indicators of GRT. Our attempts

to obtain such data, though, were unsuccessful because of sparse historical data on these

indicators in most countries. The Global Economic Forum (GEF) collected country-level data

of this sort to assess the social-economic-political positions of women in 58 countries in the

year 2000,38 but they, like us, were unable to obtain retrospective trend data. The goal of the

GEF undertaking was to create a baseline measure that could be used to track the UN

Millennium Development Goals of gender equality in social, economic, and political

functioning (www.un.org/milleniumgoals). The GEF report constructed a country-level

Gender Empowerment Measure (GEM) for this purpose that summarized objective data on the

economic opportunity and participation of women in each country along with data on female

political empowerment, educational attainment, life expectancy, and access to health care

(legal birth control and legal abortion). The GEM measure could be developed for only 58

countries because of missing data in the other countries of the world. GEM scores are

unavailable for two WMH countries (Lebanon and Ukraine). As the GEM measure was

developed only for the year 2000, we could not use it to study within-country changes in gender

equality over time. However, we were able to compare scores on the composite WMH GRT

measure with GEM scores for the 13 WMH countries where GEM scores were available in an

effort for validate our survey-based measure against the gold standard GEM measure. The

Pearson correlation between the two measures was found to be .78. This high correlation

strongly suggests that our GRT measure validly assesses the traditionality of the roles of women

in the WMH countries.

Statistical Analyses

Gender differences in lifetime risk of each disorder were examined using discrete-time survival

analysis with person-year as the unit of analysis.39 This is a method that takes into consideration

age of onset of the disorder in examining predictors, making it possible to study the predictors

of lifetime occurrence of the disorder among respondents who vary in age. Each year in the

life of each respondent up to and including the age of onset of the focal disorder (or, in the case

of respondents who never had the disorder, up to their age at interview) was treated as a separate

observational record in this analysis, with the year of first onset coded 1 on a dichotomous

outcome variable and earlier years coded 0. Years after first onset were excluded from the data

file. Logistic regression analysis was used to analyze these data, with gender (coded 1 for

women, 0 for men), cohort (coded into the four categories noted above), and person-year (age

at the time of the person-year observational record) included as predictors of first onset of the

disorder. The logistic regression coefficients and their standard errors were exponentiated to

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create odds-ratios (ORs) and 95% confidence intervals for ease of interpretation. Female:Male

(F:M) ORs are the main focus of attention.

A separate model was estimated for each DSM-IV/CIDI disorder separately in each country.

These models were also estimated in a data file that combined observations across all countries.

The cross-national models included 14 dummy predictor variables to distinguish among the

15 countries in addition to the other predictors. The basic models were elaborated to consider

possible non-linear effects of cohort and person-year (using polynomials and dummy variables

to define ranges on these continuous variables) and to assess whether the gender difference in

lifetime risks of the disorders varied by cohort, life course phase, or country. Gender differences

were for the most part consistent across the life course, so these results are not reported here

but are available on request. The models were then estimated a final time in the sub-sample of

person-years in the age range 1–34 (i.e., up to the oldest age in the youngest grouped cohort

sub-sample) so as to remove the association between cohort and age in the person-year data

file.

In cases where the survival analysis documented significant time-space variation in the gender

difference for a particular outcome, structural equation models (SEMs) using the 58 time-space

sub-samples as the unit of analysis estimated the extent to which a latent measure of gender

role traditionality (defined in terms of the four indicators described above) could account for

this variation. The best-fitting model was determined as the one with the lowest value on the

Bayesian Information Criterion (BIC),40 a standard measure of model fit. SEMs are regression

models that estimate coefficients simultaneously across a series of equations, some of which

can include presumed latent (i.e., not directly measured) variables that are assumed to have a

pre-specified relationship to measured variables, in an effort to maximize the fit between

predicted and observed matrices of covariation among the observed variables. In our case, the

SEMs assumed that time-space variation in a latent measure of gender role traditionality, which

was indicated by the four observed measures described above, predicted time-space variation

in the F:M ORs of disorders that were found to have significant time-space variation.

Survival coefficients and their standard errors were estimated using the Taylor series

linearization method41 implemented in the SUDAAN software system.42 Multivariate

significance tests of the significance of interactions involving gender with person-year, cohort,

and country were made with Wald χ2 tests using Taylor series design-based coefficient

variance-covariance matrices. In the case of cross-national models, a single variable was

created that assigned a unique value to each sampling stratum in each country, while a second

variable was created that distinguished sampling-error calculation units (SECUs) within each

stratum. These two variables were used as the input to SUDAAN to calculate design-based

estimates. SEMs were calculated using the Mplus software system www.statmodel.com).43

Significance tests of regression coefficients in the SEMs were estimated using the standard

errors generated by Mplus, which assumed that the 58 time-space observations represent a

simple random sample from a larger universe of such units. All significance tests were

evaluated at the .05 level with two-sided tests.

RESULTS

Gender differences in lifetime risk

Results are highly consistent across countries in showing that women have a significantly

higher lifetime risk than men of most mood disorders (major depressive disorder and dysthymic

disorder) and all anxiety disorders (Table 2). The pooled F:M ORs for these disorders are all

statistically significant and in the range 1.3–2.6. Within-country ORs for these disorders are

also consistently greater than 1.0. The one exception to this general pattern is bipolar disorder,

where the pooled OR is not statistically significant (0.9). Results are the opposite for most

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externalizing disorders (ADHD, conduct disorder, intermittent explosive disorder) and all

substance disorders. The pooled F:M ORs for these disorders are all statistically significant

and less than 1.0 (in the range 0.3–0.7), indicating significantly higher risk among men than

women. Within-country ORs for these disorders are also consistently (100%) less than 1.0.

Despite their consistent direction, the magnitudes of the ORs vary significantly across countries

for many of the disorders studied.

Inter-cohort variation

Analysis of interactions between gender and cohort show that gender differences in lifetime

risk for most disorders do not differ significantly across cohorts. (Table 3) However, there are

three notable exceptions. The first involves major depressive disorder (MDD), where the

pooled OR for the gender-by-cohort interaction across countries is 0.88 (0.82–0.95). This

means that the higher odds among women than men are less pronounced in more recent than

older cohorts. This general pattern is found in 11 of the 15 countries. The second involves

intermittent explosive disorder (IED), where the pooled gender-by-cohort OR across countries

is 1.26 (1.07–1.48). This means that the higher odds among men than women are less

pronounced in more recent than older cohorts. This general pattern is found in 5 of the 6

countries that assessed IED. The third involves substance disorders, largely driven by alcohol

disorders. The pooled gender-by-cohort OR across countries in predicting any substance

disorder is 1.45 (1.27–1.66). It is important to remember that “any” substance disorder is

equivalent to either alcohol or drug abuse because, as noted above in the section on measures,

dependence was assessed only among respondents with a history of abuse. This means that the

higher odds of a lifetime substance abuse among men than women are less pronounced in more

recent than older cohorts. This general pattern is found in 12 of the 14 countries where substance

abuse was assessed.

Time-space variation in gender role traditionality and F:M ORs

The remaining analyses focused on MDD and substance disorders, the disorders associated

with significant inter-cohort variation in the F:M ORs across the majority of countries. (IED

was excluded because it was assessed in only six WMH countries, yielding too few time-space

units for stable analysis). Before turning to the results, it is instructive to examine the raw data

on time-space variation in the composite measure of GRT created by averaging standardized

scores on the four indicators. (Table 4) A generally monotonic decrease in traditionality can

be seen across successively more recent cohorts in each country. All but two countries (New

Zealand and Ukraine) were above the mean in GRT at the time respondents in the oldest cohorts

were in early adulthood. All but two countries were below the mean and the other two (Italy

and Lebanon) very close to the mean GRT, in comparison, by the time respondents in the most

recent cohorts entered early adulthood. Lebanon had by far the highest traditionality score in

each cohort, but the other countries with high GRT scores in the oldest cohorts were all

developed countries either in Southern Europe (Italy and Spain) or Asia (Japan). Three of these

four countries (Japan, Lebanon, and Spain) had the most dramatic decreases in GRT over time,

along with Belgium and the Netherlands.

A number of structural equation models were fit to examine the associations of this time-space

variation in GRT with the F:M ORs in MDD and substance disorder. (Detailed results are

available on request.) The final model (Figure 1) defines GRT as a standardized (to a mean of

0 and variance of 1) latent variable indicated by our four observed measures, where GRT is a

predictor of the F:M ORs of MDD and any substance disorder. The four measured indicators

are assumed to have effects on these outcomes only through GRT. A high GRT score is defined

as high gender role traditionality, which is significantly associated with an increase of .38

standard deviations (SD) in the F:M OR for MDD (i.e., the female excess in MDD decreases

as female gender roles become less traditional) and with a decrease of .46 SD in the F:M OR

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for any substance disorder (i.e., women begin to “catch up” to men in their rates of substance

disorder as female gender roles become less traditional).

As all coefficients in the model are standardized (i.e., the measures are transformed to have a

mean of 0 and a variance of 1), it is necessary to consider the substantive meaning of a standard

deviation on each measure in order to put the results into meaningful terms. Beginning with

the GRT indicators, a one SD decrease in GRT would be equivalent to changing the F:M ratio

of labor force participation from the sample-wide mean of .85 (i.e., women about 15% less

likely than men to be in the labor force) to 1.0 (i.e., women and men equally likely to be in the

labor force), changing the F:M ratio of high educational attainment from the mean of .84 (i.e.,

women 16% less likely than men to obtain high education) to 1.03 (i.e., rough gender equality),

changing the older age at marriage of men than women from the mean of 3.2 years to 2.2 years,

and changing the proportion of young women using birth control from the mean of 37.5% to

69.4%.

According to the model, a decrease of one standard deviation in the GRT indicators is

associated with a decrease in the F:M OR of MDD of .38 SD and an increase in the F:M OR

of any substance disorder of .46 SD. We need to know the means and SDs of the outcomes in

the un-weighted sample of 58 time-space units to make substance sense of these effect size

estimates. These values are 2.61 (Mean) and 1.81 (SD) for MDD and 0.17 (Mean) and 0.14

(SD) for any substance disorder. Therefore, changes of one SD in the GRT indicators are

associated with a reduction in the F:M OR for MDD from the mean of 2.6 to 1.9 (a reduction

of nearly 45% in the elevated F:M OR) and with an increase in the F:M OR of any substance

disorder from the mean of 0.17 to 0.25 (a reduction of nearly 30% in the elevated M:F OR).

Although variations due to time and space were combined in the structural equation analysis,

it is possible to separate the two components by introducing dummy variable controls in the

model either for cohort (time) or for country (space). (Table 5) When this is done, we see that

the association between GRT and the F:M OR for MDD is entirely due to between-cohort

variation within countries, while the association between GRT and the F:M OR for any

substance disorder is largely due to within-cohort variation across countries. It is consequently

only the association involving MDD that involves inter-cohort changes within countries. We

also investigated the possibility that the results could be sensitive to extreme values in a small

number of countries by replicating the results in Table 5 15 times, each time deleting the data

from one country. (Detailed results are available on request.) The only case in which a

meaningful change in the model coefficients occurred was when we deleted Lebanon, but even

in this case the coefficients remained statistically significant and strong in substantive terms.

This result demonstrates that the overall study results are not highly sensitive to individual

outlier countries.

COMMENT

Several methodological limitations need to be noted in interpreting the WMH results. First,

the response rates were lower in developed than developing countries and might have been

related to gender role traditionality, possibly introducing bias into results. We weighted the

data in each country for differential non-response by census socio-demographic variables, but

there is no guarantee that this corrected for biases introduced by incomplete response. Second,

the surveys also differed across countries in other ways, such as the language in which the

survey was administered and the extent to which the country had a tradition of independent

public opinion research that allowed respondents to see the survey as a normative undertaking,

that might have affected results. Third, diagnoses were based on fully-structured interviews

administered by lay interviewers rather than on clinician-administered semi-structured

interviews. This limitation is somewhat reduced by the fact that WMH clinical reappraisal

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studies documented generally good concordance between the diagnoses based on the CIDI

interviews and diagnoses based on blinded semi-structured clinical reappraisal interviews.29

As noted in the section on measures, though, the diagnoses of externalizing disorders were not

validated and might be less accurate than the diagnoses of other disorders. The substance

dependence diagnoses had the additional problem of excluding cases of dependence without

a history of abuse. The results concerning the broadly-defined measures of substance disorders

should consequently be interpreted as applying to abuse rather than to dependence. As noted

in the section on measures, though, empirical studies have shown that the number of cases of

substance dependence without a history of abuse is small in the US and that their exclusion

does not meaningfully affect the size of the coefficients between predictors and measures of

substance disorders. Fourth, lifetime prevalence and age of onset were assessed with

retrospective reports, which could be systematically biased.44 We used an innovative probing

method designed to minimize recall bias,34 but it might still be that bias was introduced by

age-related gender differences either in memory failure, mental health awareness, or

willingness to admit emotional problems to an interviewer. Fifth, the analysis of gender

differences in lifetime risk across cohorts could be biased if the increasing attrition with age

in older cohorts is differentially related to history of mental disorders among women versus

men. Sixth, the indicators of female gender role traditionality were few in number and might

not have captured all the dimensions of female gender roles that are important for explaining

secular trends in the F:M ORs. The indicators we used, furthermore, might be related to

constructs other than gender role traditionality. These concerns are reduced, though, by the

fact that our composite GRT measure correlates very strongly with an independent measure of

Gender Empowerment based on objective administrative data assembled by the Global

Economic Forum.

Within the context of the above limitations, the current report is the first to present the results

of a quantitative examination of time-space variation in the association between female gender

role traditionality and gender differences in mental illness. We found that the frequently

observed gender differences in anxiety, mood, externalizing, and substance disorders have

remained relatively stable over the more than half century separating respondents in the

youngest and oldest WMH cohorts despite the fact that unprecedented changes occurred over

this time period in female gender roles. Furthermore, we found that aggregate F:M ORs are

relatively consistent across countries despite substantial between-country variation in female

gender role traditionality. These patterns argue against the claim that changes in gender roles

play an important role in bringing about reductions in gender differences in the lifetime risk

of most mental disorders.

The only notable exceptions to this general pattern concern major depression, intermittent

explosive disorder, and substance disorders, where gender differences were found to be

significantly smaller in more recent than earlier cohorts. As noted in the introduction, evidence

consistent with this narrowing has been found in several within-country studies of gender

differences in MDD,9, 10, 45 substance use,11 and substance disorders,46–48 while other studies

have documented cross-national variation in gender differences at a point in time.13, 22 No

previous study, though, combined the two types of comparisons to study time-space variation

while linking independent measures of gender role traditionality with data on variation in

gender differences over time or space.

In the case of MDD, the gender roles hypothesis would interpret our findings as meaning that

increases in female opportunities in the domains of employment, birth control, and other

indicators of increasing gender role equality promote improvements in female mental health

by reducing exposure to stressors that can led to depression and by increasing access to effective

stress-buffering resources.15, 49 It is important to acknowledge, though, that we did not directly

evaluate the validity of this hypothesis. We documented that gender differences in MDD onset

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risk are significantly narrower at times and places when the roles of women are more equal to

those of men, but we did not measure time-space variation in stress exposure or stress reactivity

to see if the latter mediate the predictive effects of gender role traditionality. These more fine-

grained analyses go beyond the limits of the WMH data, but should be the subject of future

studies.

In the case of substance disorders, the gender roles hypothesis takes a somewhat different form

in arguing that opportunities for female substance use and attitudes about the appropriateness

of female substance use both change as female roles become more similar to male roles,

resulting in an increase in female substance use.14, 50, 51 Consistent with this hypothesis, cross-

national research has documented that current female drinking behavior is more similar to male

drinking behavior in countries where female and male gender roles are more equal,52, 53

although there results have not been entirely consistent.54 These previous studies did not

examine gender differences in lifetime risk of substance disorders, though, which means that

our results can be seen as building on the findings of these earlier studies.

It is unclear why the WMH findings showed much stronger evidence for temporal than spatial

predictive associations for MDD and for spatial than temporal associations for substance

disorders. It is also unclear why the narrowing of gender differences in recent cohorts was

confined to MDD, IED, and substance disorders. It is conceivable that the much earlier ages

of onset of most other disorders, especially the anxiety disorders and externalizing disorders,

than MDD or substance disorders makes the former disorders less susceptible than the latter

disorders to the influences of changes in adult gender roles. That argument does not extend to

generalized anxiety disorder, though, which has a similar age of onset distribution to MDD.

55 Why narrowing occurred for MDD but not GAD, then, is especially puzzling. Increased

understanding of these specifications should be the subject of future theorizing and empirical

investigation.

Acknowledgments

Funding/Support: The surveys included in this report were carried out in conjunction with the World Health

Organization World Mental Health (WMH) Survey Initiative. We thank the WMH staff for assistance with

instrumentation, fieldwork, and data analysis. These activities were supported by the United States National Institute

of Mental Health (R01MH070884), the John D. and Catherine T. MacArthur Foundation, the Pfizer Foundation, the

US Public Health Service (R13-MH066849, R01-MH069864, and R01 DA016558), the Fogarty International Center

(FIRCA R03-TW006481), the Pan American Health Organization, the Eli Lilly & Company Foundation, Ortho-

McNeil Pharmaceutical, Inc., GlaxoSmithKline, and Bristol-Myers Squibb. A complete list of WMH publications can

be found at http://www.hcp.med.harvard.edu/wmh/. The Mexican National Comorbidity Survey (MNCS) is supported

by The National Institute of Psychiatry Ramon de la Fuente (INPRFMDIES 4280) and by the National Council on

Science and Technology (CONACyT-G30544-H), with supplemental support from the PanAmerican Health

Organization (PAHO). The Lebanese survey is supported by the Lebanese Ministry of Public Health, the WHO

(Lebanon) and unrestricted grants from Janssen Cilag, Eli Lilly, GlaxoSmithKline, Roche, Novartis, Fogerty (R03

TW0006481) and anonymous donations. The ESEMeD project is funded by the European Commission (Contracts

QLG5-1999-01042; SANCO 2004123), the Piedmont Region (Italy), Fondo de Investigacion Sanitaria, Instituto de

Salud Carlos III, Spain (FIS 00/0028), Ministerio de Ciencia y Tecnologia, Spain (SAF 2000-158-CE), Departament

de Salut, Generalitat de Catalunya, Spain, Instituto de Salud Carlos III (CIBER CB06/02/0046, RETICS RD06/0011

REM-TAP), and other local agencies and by an unrestricted educational grant from GlaxoSmithKline. The Colombian

National Study of Mental Health (NSMH) is supported by the Ministry of Social Protection. The World Mental Health

Japan (WMHJ) Survey is supported by the Grant for Research on Psychiatric and Neurological Diseases and Mental

Health (H13-SHOGAI-023, H14-TOKUBETSU-026, H16-KOKORO-013) from the Japan Ministry of Health,

Labour and Welfare. The New Zealand Mental Health Survey (NZMHS) is supported by the New Zealand Ministry

of Health, Alcohol Advisory Council, and the Health Research Council. The South Africa Stress and Health Study

(SASH) is supported by the US National Institute of Mental Health (R01-MH059575) and National Institute of Drug

Abuse with supplemental funding from the South African Department of Health and the University of Michigan

(National Institutes of Mental Health HHSN271200700030C). The Ukraine Comorbid Mental Disorders during

Periods of Social Disruption (CMDPSD) study is funded by the US National Institute of Mental Health (RO1-

MH61905). The US National Comorbidity Survey Replication (NCS-R) is supported by the National Institute of

Mental Health (NIMH; U01-MH60220) with supplemental support from the National Institute of Drug Abuse (NIDA),

Seedat et al.

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the Substance Abuse and Mental Health Services Administration (SAMHSA), the Robert Wood Johnson Foundation

(RWJF; Grant 044780), and the John W. Alden Trust.

Role of the Sponsors: The study’s funders had no role in the design or conduct of the study, in the collection, analysis,

interpretation of the data, or in the preparation, review, or approval of the manuscript.

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Figure 1.

Standardized1 parameter estimates of the association between time-space variation in gender

role traditionality (GRT) and the female-male odds-ratios of lifetime DSM-IV major depressive

disorder (MDD) and substance abuse-dependence (SAD) in the 58 WMH cohort-by-country

time-space subsamples

1Observed variables and GRT are all standardized to a mean of 0.0 and a standardized deviation

of 1.0. See the text for a discussion of the transformations of model parameters into

substantively meaningful matrices.

2 Lf = Female to male percentage in labor force by age 35.

Ed= Female to male percentage reaching the median education level of the top quartile of

earners.

Ma= Female to male median age at first marriage.

Bc= Percentage of women using birth control before the age of 25.

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Table 1

WMH Sample Characteristics

Country

Survey1

Sample Characteristics2

Field Dates

Age Range

Sample Size

Response Rate

Part I

Part II

Part II and

Age ≤ 444

I. Developed countries

Belgium 4.30

ESEMeD

Stratified multistage clustered

probability sample of

individuals residing in

households from the national

register of Belgium residents.

Nationally representative (NR)

2001–2

18+

(2419)

(1043)

(486)

50.6

France

ESEMeD

Stratified multistage clustered

sample of working telephone

numbers merged with a reverse

directory (for listed numbers).

Initial recruitment was by

telephone, with supplemental

in-person recruitment in

households with listed

numbers. NR

2001–2

18+

(2894)

(1436)

(727)

45.9

Germany

ESEMeD

Stratified multistage clustered

probability sample of

individuals from community

resident registries. NR

2002–3

18+

(3555)

(1323)

(621)

57.8

Israel

NHS

Stratified multistage clustered

area probability sample of

individuals from a national

resident register

2002–4

21+

(4859)

--

--

72.6

Italy

ESEMeD

Stratified multistage clustered

probability sample of

individuals from municipality

resident registries. NR

2001–2

18+

(4712)

(1779)

(853)

71.3

Japan

WMHJ2002-2003

Un-clustered two-stage

probability sample of

individuals residing in

households in four

metropolitan areas (Fukiage,

Kushikino, Nagasaki,

Okayama)

2002–3

20+

(2436)

(887)

(282)

56.4

Netherlands

ESEMeD

Stratified multistage clustered

probability sample of

individuals residing in

households that are listed in

municipal postal registries. NR

2002–3

18+

(2372)

(1094)

(516)

56.4

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Country

Survey1

Sample Characteristics2

Field Dates

Age Range

Sample Size

Response Rate

Part I

Part II

Part II and

Age ≤ 444

New Zealand

NZMHS

Stratified multistage clustered

area probability sample of

household residents. NR

2004–5

18+

(12790)

(7312)

(4119)

73.3

Spain

ESEMeD

Stratified multistage clustered

area probability sample of

household residents. NR

2001–2

18+

(5473)

(2121)

(960)

78.6

United States

NCS-R

Stratified multistage clustered

area probability sample of

household residents. NR

2002–3

18+

(9282)

(5692)

(3197)

70.9

II. Developing countries

Colombia

NSMH

Stratified multistage clustered

area probability sample of

household residents in all

urban areas of the country

(approximately 73% of the

total national population)

2003

18–65

(4426)

(2381)

(1731)

87.7

Lebanon

LEBANON

Stratified multistage clustered

area probability sample of

household residents. NR

2002–3

18+

(2857)

(1031)

(595)

70.0

Mexico

M-NCS

Stratified multistage clustered

area probability sample of

household residents in all

urban areas of the country

(approximately 75% of the

total national population).

2001–2

18–65

(5782)

(2362)

(1736)

76.6

South Africa

SASH

Stratified multistage clustered

area probability sample of

household residents. NR

2003–4

18+

(4351)

--

--

87.1

Ukraine

CMDPSD

Stratified multistage clustered

area probability sample of

household residents. NR

2002

18+

(4725)

(1720)

(541)

78.3

1

ESEMeD (The European Study Of The Epidemiology Of Mental Disorders); NHS (Israel National Health Survey); WMHJ2002–2003 (World Mental Health Japan Survey); NZMHS (New Zealand Mental

Health Survey); NCS-R (The US National Comorbidity Survey Replication).NSMH (The Colombian National Study of Mental Health); LEBANON (Lebanese Evaluation of the Burden of Ailments and Needs

of the Nation); M-NCS (The Mexico National Comorbidity Survey); SASH (South Africa Health Survey); CMDPSD (Comorbid Mental Disorders during Periods of Social Disruption);

2

Most WMH surveys are based on stratified multistage clustered area probability household samples in which samples of areas equivalent to counties or municipalities in the US were selected in the first stage

followed by one or more subsequent stages of geographic sampling (e.g., towns within counties, blocks within towns, households within blocks) to arrive at a sample of households, in each of which a listing

of household members was created and one or two people were selected from this listing to be interviewed. No substitution was allowed when the originally sampled household resident could not be interviewed.

These household samples were selected from Census area data in all countries other than France (where telephone directories were used to select households) and the Netherlands (where postal registries were

used to select households). Several WMH surveys (Belgium, Germany, Italy) used municipal resident registries to select respondents without listing households. The Japanese sample is the only totally un-

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clustered sample, with households randomly selected in each of the four sample areas and one random respondent selected in each sample household. 12 of the 15 surveys are based on nationally representative

(NR) household samples, while two others are based on nationally representative household samples in urbanized areas (Colombia, Mexico).

3

The response rate is calculated as the ratio of the number of households in which an interview was completed to the number of households originally sampled, excluding from the denominator households

known not to be eligible either because of being vacant at the time of initial contact or because the residents were unable to speak the designated languages of the survey. The weighted average response rate is

71.2%

4

All countries with the exception of Ukraine (which was age restricted to ≤ 39) were age restricted to ≤ 44.

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Table 2

Associations (odds-ratios) of gender with lifetime risk of DSM-IV mental disorders in the WMH surveys (n = 73,099)1

All countries combined

Within-country OR variation

Range

GxC interaction2

F:M OR (95% CI)

Min

Max

% in dominant

direction

% p ≤ .05 in

dominant

direction

χ2

(n)3

I. Mood disorders

 Major depressive disorder

1.9* (1.8–2.0)

1.6

2.4

100.0

100.0

29.3*

(15)

 Dysthymic disorder

1.9* (1.6–2.2)

1.3

3.8

100.0

70.0

13.0

(10)

 Bipolar disorder

0.9 (0.8–1.0)

0.6

1.1

83.3

20.0

8.1

(6)

 Any mood disorder

1.8* (1.7–1.8)

1.5

2.5

100.0

100.0

47.7*

(15)

II. Anxiety disorders

 Panic disorder

1.9* (1.7–2.2)

1.2

3.4

100.0

66.7

15.8

(12)

 Generalized anxiety disorder

1.7* (1.5–1.9)

0.7

2.7

86.7

76.9

23.7*

(15)

 Agoraphobia

2.0* (1.7–2.3)

1.4

4.6

100.0

62.5

18.5

(8)

 Social phobia

1.3* (1.2–1.4)

1.1

2.0

100.0

46.2

13.8

(13)

 Specific phobia

2.0* (1.9–2.2)

1.3

3.1

100.0

100.0

39.3*

(12)

 Separation anxiety disorder

1.6* (1.4–1.8)

1.4

2.0

100.0

75.0

3.8

(4)

 Post-traumatic stress disorder

2.6* (2.2–2.9)

1.3

6.4

100.0

78.6

30.6*

(14)

 Any anxiety disorder

1.7* (1.6–1.8)

1.2

3.2

100.0

86.7

41.2*

(15)

III. Externalizing disorders

 Attention-deficit/hyperactivity disorder

0.6* (0.5–0.8)

0.3

0.6

100.0

20.0

6.9

(5)

 Conduct disorder

0.5* (0.4–0.7)

0.3

0.6

100.0

100.0

29.5*

(3)

 Intermittent explosive disorder

0.7* (0.6–0.8)

0.4

0.8

100.0

33.3

5.3

(6)

 Oppositional-defiant disorder

0.8* (0.6–1.0)

0.5

0.8

100.0

33.3

18.2*

(3)

 Any externalizing disorder

0.7* (0.6–0.8)

0.3

1.4

83.3

40.0

7.2

(12)

IV. Substance disorders

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All countries combined

Within-country OR variation

Range

GxC interaction2

F:M OR (95% CI)

Min

Max

% in dominant

direction

% p ≤ .05 in

dominant

direction

χ2

(n)3

 Alcohol abuse

0.2* (0.2–0.3)

0.1

0.4

100.0

100.0

128.6*

(15)

 Alcohol dependence

0.3* (0.3–0.4)

0.1

0.4

100.0

100.0

95.6*

(11)

 Drug abuse or dependence

0.4* (0.3–0.4)

0.1

0.4

100.0

100.0

35.3*

(5)

 Any substance disorder

0.3* (0.2–0.3)

0.1

0.4

100.0

100.0

141.6*

(14)

V. Any disorder

1.1* (1.1–1.2)

0.7

2.2

66.7

70.0

111.5*

(15)

*

Significant at the .05 level, two-sided test

1

Based on discrete-time survival models that used respondent cohort (age at interview), sex, and person-year to predict first onset of each disorder both separately in each country and pooled across all countries.

2

GxC=gender-by-country; based on pooled cross-national survival model that included an interaction between gender and the dummy variables for country.

3

The number of countries differs across outcomes because not all disorders were assessed in all countries. The pooled results for any mood disorder, any anxiety disorder, any externalizing disorder, and any

substance disorder pooled whatever disorders in the relevant category existed in the country across countries even though the set sometimes differed across countries.

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Table 3

Interactions (odds-ratios) of gender with cohort in predicting lifetime risk of DSM-IV mental disorders in the WMH surveys (n = 73,099)1

All countries combined

Within-country OR variation

Range

OR (95% CI)

Min

Max

% in dominant

direction

% p ≤ .05 in

dominant direction

(n)2

I. Mood disorders

 Major depressive disorder

0.9* (0.8–1.0)

0.6

1.1

73.3

36.4

(15)

 Dysthymic disorder

0.9 (0.7–1.1)

0.2

1.5

50.0

25.0

(8)

 Bipolar disorder

1.1 (0.9–1.3)

0.9

2.1

66.7

0.0

(6)

 Any mood disorder

0.9* (0.8–0.9)

0.6

1.2

60.0

33.3

(15)

II. Anxiety disorders

 Panic disorder

1.0 (0.8–1.1)

0.3

3.5

58.3

0.0

(12)

 Generalized anxiety disorder

1.1 (1.0–1.2)

0.4

1.7

60.0

11.1

(15)

 Agoraphobia

1.0 (0.8–1.2)

0.7

1.6

50.0

0.0

(8)

 Social phobia

1.0 (0.9–1.1)

0.7

2.3

58.3

14.3

(12)

 Specific phobia

1.0 (0.9–1.1)

0.7

1.2

50.0

16.7

(12)

 Separation anxiety disorder

0.9 (0.7–1.1)

0.7

1.3

50.0

0.0

(4)

 Post-traumatic stress disorder

1.1 (1.0–1.3)

0.4

2.0

57.1

25.0

(14)

 Any anxiety disorder

1.0 (0.9–1.0)

0.7

1.2

53.3

0.0

(15)

III. Externalizing disorders

 Attention-deficit/hyperactivity disorder

1.0 (0.7–1.5)

0.1

5.9

80.0

0.0

(5)

 Conduct disorder

1.4 (0.8–2.2)

0.1

1.5

66.7

100.0

(3)

 Intermittent explosive disorder

1.3* (1.1–1.5)

0.4

1.8

83.3

40.0

(6)

 Oppositional-defiant disorder

0.9 (0.5–1.6)

0.7

0.9

100.0

0.0

(3)

 Any externalizing disorder

1.1 (0.8–1.4)

0.2

6.5

58.3

28.6

(12)

IV. Substance disorders

 Alcohol abuse

1.5* (1.3–1.7)

0.7

4.0

85.7

66.7

(14)

 Alcohol dependence

1.5* (1.2–1.8)

0.8

6.6

90.0

55.6

(10)

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All countries combined

Within-country OR variation

Range

OR (95% CI)

Min

Max

% in dominant

direction

% p ≤ .05 in

dominant direction

(n)2

 Drug abuse or dependence

1.2 (0.9–1.5)

0.6

1.3

66.7

50.0

(3)

 Any substance disorder

1.4* (1.3–1.7)

0.7

5.0

85.7

66.7

(14)

V. Any disorder

1.0 (0.9–1.0)

0.8

1.7

60.0

0.0

(15)

*

Significant at the .05 level, two-sided test

1

Based on discrete-time survival models that used respondent cohort (age at interview), sex, person-year, and the interaction between sex and cohort to predict first onset of each disorder both separately in

each country and pooled across all countries. Each respondent was classified as being in one of four cohorts defined based on age at interview (18–34, 35–49, 50–64, and 65+ years of age). A continuous 1–4

variable coded 1 for the earliest cohorts (i.e., ages 65+ at interview) and 4 for the most recent cohorts (i.e., 18–34 at interview) was used to create the interaction terms, where the 1–4 variable was multiplied

by a 0–1 variable for gender (coded 1 for females and 0 for males). An interaction term with an OR significantly greater than 1.0 means that the F:M OR is significantly higher (i.e., higher relative prevalence

among females than males) in more recent than older cohorts, while an OR less than 1.0 means that the F:M OR is significantly lower (ie., lower relative prevalence among females than males) nm more recent

than older cohorts.

2

The number of countries differs across outcomes because not all disorders were assessed in all countries. The pooled results for any mood disorder, any anxiety disorder, any externalizing disorder, and any

substance disorder pooled whatever disorders in the relevant category existed in the country across countries even though the set sometimes differed across countries.

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Table 4

The distribution of the standardized1 gender role traditionality composite measure across the WMH countries

and cohorts

Cohorts

18–34

35–49

50–64

65+

I. Developed countries

 Belgium

−3.7

−2.8

0.9

2.7

 France

−3.5

−2.5

−0.7

0.9

 Germany

−3.4

−3.4

0.9

1.6

 Israel

−2.4

−0.4

−0.3

0.7

 Italy

0.2

1.2

2.9

4.8

 Japan

−4.4

0.1

−1.2

3.1

 Netherlands

−5.1

−0.2

−1.9

1.7

 New Zealand

−2.2

−0.9

−1.3

−1.3

 Spain

−4.0

−0.7

3.3

3.4

 U.S.

−3.5

−3.1

−2.3

2.0

II. Developing countries

 Colombia

−0.5

1.2

2.0

 Lebanon

0.2

4.4

8.3

8.3

 Mexico

−0.6

2.0

3.1

.

 South Africa

−0.8

1.1

1.2

2.2

 Ukraine

−2.1

−4.8

0.2

−1.0

1

Each indicator was standardized to have a mean of 0 and a variance of 1.0 across the sample of 58 time-space units. Each time-space unit was assigned

equal weight in calculating the mean and variance. The composite was then created by summing the four standardized indicator scores and then

standardizing the sum do have a mean of 0 zero and a variance of 1.0 across the sample of 58 time-space units

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Table 5

Change in the associations of gender role traditionality with first onset of major depressive disorder and any

substance disorder in models that either do or do not control for temporal or spatial variation

Standardized regression coefficients linking gender role traditionality with

Major Depressive Disorder

Any Substance Disorder

Controls

Est

(se)

Est

(se)

None1

.38*

(.19)

−.46*

(.19)

For temporal variation

.06

(.20)

−.44*

(.21)

For spatial variation

.60*

(.24)

−.20

(.18)

*

Significant at the .05 level, two-sided test

1

The coefficients in this row are those presented in Figure 1

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