Bromet et al. BMC Medicine 2011, 9:90
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RESEARCH ARTICLE

Open Access

Cross-national epidemiology of DSM-IV major
depressive episode
Evelyn Bromet1*, Laura Helena Andrade2, Irving Hwang3, Nancy A Sampson3, Jordi Alonso4,
Giovanni de Girolamo5, Ron de Graaf6, Koen Demyttenaere7, Chiyi Hu8, Noboru Iwata9, Aimee N Karam10,
Jagdish Kaur11, Stanislav Kostyuchenko12, Jean-Pierre Lépine13, Daphna Levinson14, Herbert Matschinger15,
Maria Elena Medina Mora16, Mark Oakley Browne17, Jose Posada-Villa18, Maria Carmen Viana19, David R Williams20
and Ronald C Kessler3

Abstract
Background: Major depression is one of the leading causes of disability worldwide, yet epidemiologic data are notavailable for many countries, particularly low- to middle-income countries. In this paper, we present data on theprevalence, impairment and demographic correlates of depression from 18 high and low- to middle-income
countries in the World Mental Health Survey Initiative.
Methods: Major depressive episodes (MDE) as defined by the Diagnostic and Statistical Manual of Mental Disorders,fourth edition (DMS-IV) were evaluated in face-to-face interviews using the World Health Organization Composite
International Diagnostic Interview (CIDI). Data from 18 countries were analyzed in this report (n = 89,037). All
countries surveyed representative, population-based samples of adults.
Results: The average lifetime and 12-month prevalence estimates of DSM-IV MDE were 14.6% and 5.5% in the ten
high-income and 11.1% and 5.9% in the eight low- to middle-income countries. The average age of onset
ascertained retrospectively was 25.7 in the high-income and 24.0 in low- to middle-income countries. Functional
impairment was associated with recency of MDE. The female: male ratio was about 2:1. In high-income countries,
younger age was associated with higher 12-month prevalence; by contrast, in several low- to middle-income
countries, older age was associated with greater likelihood of MDE. The strongest demographic correlate in highincome countries was being separated from a partner, and in low- to middle-income countries, was being
divorced or widowed.
Conclusions: MDE is a significant public-health concern across all regions of the world and is strongly linked tosocial conditions. Future research is needed to investigate the combination of demographic risk factors that are
most strongly associated with MDE in the specific countries included in the WMH.

Background
Major depression is a serious, recurrent disorder linked
to diminished role functioning and quality of life, medical morbidity, and mortality [1,2]. The World HealthOrganization ranks depression as the fourth leading
cause of disability worldwide [3], and projects that by
2020, it will be the second leading cause [4]. Although
direct information on the prevalence of depression does
* Correspondence: ebromet@notes.cc.sunysb.edu
1
Department of Psychiatry, State University of New York at Stony Brook,
Putnam Hall - South Campus, Stony Brook, NY 11794-8790, NY, USA
Full list of author information is available at the end of the article

not exist for most countries, the available data indicate
wide variability in the prevalence rates. Weissman et al.
[5] published the first cross-national comparison ofmajor depression as defined by the Diagnostic and Statistical Manual of Mental Disorders, third edition(DSM-III) from 10 population-based surveys that usedthe Diagnostic Interview Schedule (DIS) [6]. The lifetime prevalence ranged from 1.5% (Taiwan) to 19.0%(Beirut), with the midpoints at 9.2% (West Germany)and 9.6% (Edmonton, Canada). The 12-month prevalence ranged from 0.8% (Taiwan) to 5.8% (Christchurch,
New Zealand), with the midpoints at 3.0% (US) and© 2011 Bromet et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.

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4.5% (Paris). A subsequent cross-national comparison[7] included 10 population-based studies that used theWorld Health Organization (WHO) Composite International Diagnostic Interview (CIDI) for the revised third
edition and the fourth edition of the DSM (DSM-III-R)
and (DSM-IV) [8]. Consistent with the earlier report [5],the lifetime rates ranged from 1.0% (Czech Republic) to
16.9% (US), with midpoints at 8.3% (Canada) and 9.0%
(Chile). The 12-month prevalence ranged from 0.3%
(Czech Republic) to 10% (USA), with midpoints at 4.5%
(Mexico) and 5.2% (West Germany). Most recently,
Moussavi et al. [9] summarized data on depressive episodes as defined by the International Classification of
Diseases, 10th revision (ICD-10) in participants in the
WHO World Health Survey used in 60 countries, notingthat the 1-year prevalence was 3.2% in participants without comorbid physical disease, and 9.3% to 23.0% in
participants with chronic conditions.
The wide variability in lifetime and 12-month prevalence estimates of major depression is presumably due
to a combination of substantive (genetic vulnerability
and environmental risk factors) and measurement (cultural differences in the acceptance and meaning ofitems, and the psychometric properties of the instruments) factors. Differences in study design might also be
involved. That is, apart from administering a common
interview schedule, the surveys were not designed as
replications with a standard protocol for translation,interviewer training, sampling and quality control. Morerecently, the WHO World Mental Health (WMH) Survey Initiative conducted a coordinated series of studies
using a common protocol and a common instrument,
the WHO CIDI, version 3.0 [10], to assess a set of
DSM-IV disorders in countries from every continent[11]. The 12-month prevalence of DSM-IV major
depressive episode (MDE) in 18 countries ranged from
2.2% (Japan) to 10.4% (Brazil) [12]. The mid-point
across all countries was similar to that in previous surveys (5%), as was the weighted average 12-month prevalence for the ten high-income (5.5%) and eight low- to
middle-income (5.9%) countries.
Almost all studies find that gender, age and marital status are associated with depression. Women have a twofold
increased risk of MDE compared with men [13], people
who are separated or divorced have significantly higher
rates of depression than the currently married [5,7], andthe rate of depression generally goes down with age [5,7].
This evidence, however, comes primarily from studies
conducted in western countries. The sparse data available
from low- to middle-income countries suggest that theage pattern is either not monotonic or that the associationis reversed, with depression increasing with age [12,14].
Other socioeconomic factors have less consistent relationships with depression in different countries [7].

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The current report presents data on the prevalence,
age of onset and sociodemographic correlates of MDE
in 18 countries participating in the WHO WMH Survey
Initiative. As noted earlier, each of the WMH surveys
used the CIDI for DSM-IV. The CIDI includes a seriesof diagnostic stem questions to determine which diagnoses are assessed. Unlike previous reports from theWMH or previous surveys, our study used the screening
information for MDE in responses to these diagnostic
stem questions to conduct an examination of thescreen-positive percentages, and of the conditional lifetime and 12-month prevalence of MDE in respondents
who endorsed the diagnostic stem questions. This was
carried out to investigate the possibility that crossnational differences in prevalence estimates of MDE aredue, at least in part, to differences across countries in
the optimal threshold of CIDI symptom scores for
detecting clinical cases. If such variation exists, wewould expect much smaller cross-national differences inendorsement of diagnostic stem questions (which merely
ask respondents if they had episodes of several days of
being sad or depressed or losing interest in usual activities), than in diagnoses. If this were the case, we would
expect the largest cross-national differences in conditional prevalence estimates of MDE to occur in screened
positives. If differential variation of this sort exists, itwould provide more reason than currently exists to suspect that cross-national differences in optimal diagnostic
thresholds of the CIDI symptom scale lead to biased
estimates of cross-national differences in prevalence in
the WMH data.
A justification for this line of thinking comes from anearlier cross-national WHO study of major depression
in primary-care patients, which found strong similarity
in the latent structure of depressive symptoms across 14
different countries in different parts of the world, butalso found that countries with the highest prevalence
estimates generally reported the lowest impairment
associated with depression [15]. The authors concluded
from these results that although cross-national differences in the estimated prevalence of depression cannot
be attributed to differences in the nature or validity of
the concept of a depressive episode, it is possible that
DSM criteria may define different levels of depression
severity in different countries. Our cross-national comparison of responses to diagnostic stem questions,described in the previous paragraph, was designed toshed some light on this possibility. In addition, we carried out a parallel analysis of cross-national differencesin impairment associated with MDE.
Results are organized by distinguishing between countries classified by the World Bank [16] as low- or middle-income versus higher-income countries. Thisdistinction was made based on patterns both in theBromet et al. BMC Medicine 2011, 9:90
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WMH surveys [10] and in other cross-national epidemiologic surveys [7,9], which raise concerns that MDE
prevalence estimates might be artificially lower in lowto middle- than higher-income countries due to methodological differences of the types considered here.

Methods
EthicsProcedures for human subject protection were approved
and monitored for compliance by the institutional
review boards of each local organization coordinating
the survey. Informed consent was obtained before beginning interviews in all countries.
Sampling and procedure

The WMH surveys are a series of community-based studies conducted throughout the world [11]. This paper
included data on MDE from ten high-income countries(Belgium, France, Germany, Israel, Italy, Japan, Netherlands, New Zealand, Spain, United States) and eight
low- to middle-income countries (Brazil (São Paulo),
Colombia, India (Pondicherry), China (Shenzhen), Lebanon, Mexico, South Africa, Ukraine) based on WorldBank development criteria [16]. As noted in the introduction, we distinguished results from low- to middleincome versus higher-income countries based on thesuspicion that optimal thresholds for defining clinicallysignificant depression might be lower than the CIDI
thresholds in the former countries, resulting in underestimation of the prevalence of MDE in the CIDI in thosecountries. The surveys involved either national household samples or representative samples of urban areas(Table 1). Weights were used to adjust for differential
probabilities of selection into the study, and to matchthe sample sociodemographic distributions with the
population distributions within each country. Sample
sizes ranged from 2,372 (the Netherlands) to 12,790(New Zealand), giving a total of 89,037. The average
weighted response rate was 71.7% (Table 1).
The WMH interviews were administered face-to-face bytrained lay interviewers. To reduce respondent burden, theinterview was divided into two parts. All respondents
completed part I, which assessed a set of core mental disorders, including MDE. Part II assessed additional disorders and correlates, and was administered to all part I
respondents who met criteria for a part I disorder, plus aprobability subsample of other part I respondents. Part II
responses were weighted by the inverse of their probabilityof selection into part II to adjust for differential selection.
Details about WMH survey methods and weighting procedures are presented elsewhere [11,17].
Standardized procedures for interviewer training,translation of study materials and quality control wereconsistently used in each country [11].

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Measures
MDE

Near the start of the interview, the CIDI includes three
screening questions about sadness/depressed mood, feelings of discouragement, and loss of interest lasting several days or longer. Respondents endorsing one or moreof these questions (screen-positives) were given theremainder of the MDE module. DSM-IV MDE requires
the presence of five of nine cardinal symptoms that persist for 2 weeks or longer, are present for most of the
day nearly every day, and cause significant distress or
impairment. These symptoms are depressed mood andmarkedly diminished interest or pleasure (one of thesemust be present to meet the criteria for diagnosis), andclinically significant weight gain/loss or appetite disturbance, insomnia or hypersomnia, psychomotor agitation
or retardation, fatigue or loss of energy, feelings ofworthlessness or excessive guilt, diminished ability toconcentrate or think clearly, and recurrent thoughts ofdeath or suicide. MDE was defined for purposes of thepresent report without organic exclusions and without
diagnostic hierarchy rules [12]. Clinical reappraisal studies conducted in several countries found good agreement between diagnoses of MDE based on the CIDI
and independent diagnoses based on blinded reappraisal
interviews carried out by a clinician [18].
It is noteworthy that the CIDI interview translation,
back-translation and harmonization protocol required
culturally competent bilingual clinicians in the participating countries to review, modify and approve the key
phrases used to describe symptoms of all disorders
assessed in the survey [19]. That meant that the terms
used to describe core symptoms of depression (that is,sadness, depression, loss of interest) were customized
when the original CIDI wording did not match theterms used in the local settings. However, no attempt
was made to go beyond the DSM-IV criteria to develop
distinct criteria for depression-equivalents that might beunique to specific countries. It is conceivable that thelatter kind of expansion would have led to a reduction
in cross-national variation in prevalence estimates. However, as noted in the introduction, previous research has
shown that the latent structure of the symptoms ofmajor depression is consistent across countries [15],providing a principled basis for focusing on this criterion set in our analysis.
Global impairment

A modified version of the WHO Disability Assessment
Schedule-II (WHO-DAS-II) was used to assess frequency and intensity of restrictions in performing normal activities during the 30 days prior to the interview[20]. The activity areas included the number of days the
person was unable to carry out their normal daily activities because of problems with physical or emotional

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Table 1 World Mental Health (WMH) Survey sample characteristics
Sample size
Country

SurveyaSample characteristicsb

Field
dates

Age
Part I Part
range
II

Part II and Responseage ≤ 44
ratec
yearsdI. High-income
Belgium

ESEMeD

Stratified multistage clustered probability sample of individuals
residing in households from the national register of Belgium
residents. NR

2001-218+2419

1043 486

50.6FranceESEMeD

Stratified multistage clustered sample of working telephone
numbers merged with a reverse directory (for listed numbers).
Initial recruitment was by telephone, with supplemental inperson recruitment in households with listed numbers. NR

2001-218+2894

1436 727

45.9

Germany

ESEMeD

Stratified multistage clustered probability sample of individuals
from community resident registries. NR

2002-318+3555

1323 621

57.8

IsraelNHSStratified multistage clustered area probability sample ofindividuals from a national resident register. NR

2002-421+4859

4859 –

72.6

Italy

ESEMeD

Stratified multistage clustered probability sample of individuals
from municipality resident registries. NR

2001-218+

4712

1779 853

71.3

Japan

WMHJ
20022006

Unclustered two-stage probability sample of individuals residing 2002-6
in households in nine metropolitan areas (Fukiage, Higashi-ichiki,
Ichiki, Kushikino, Nagasaki, Okayama, Sano, Tamano, Tendo
Tochigi)

20+

3416

1305 425

59.2

2002-318+2372

1094 516

56.4

2004-518+12790 7312 4119

73.3

Netherlands ESEMeD

Stratified multistage clustered probability sample of individuals
residing in households that are listed in municipal postal
registries. NR
Stratified multistage clustered area probability sample ofhousehold residents. NR

NewZealande
Spain

NZMHS
ESEMeD

Stratified multistage clustered area probability sample ofhousehold residents. NR

2001-218+5473

2121 960

78.6

UnitedStatesNCS-R

Stratified multistage clustered area probability sample ofhousehold residents. NR

2002-318+9282

5692 3197

70.9

II. Low- to middle-income
BrazilSãoPaulomegacity

Stratified multistage clustered area probability sample ofhousehold residents in the São Paulo metropolitan area, Brazil

2004-718+5037

2942 –

77.7

Colombia

NSMH

Stratified multistage clustered area probability sample ofhousehold residents in all urban areas of the country(approximately 73% of the total national population)

2003

18-65

4426

2381 1731

87.7

India

WMHI

Stratified multistage clustered area probability sample ofhousehold residents in Pondicherry region, India. NR

2003-518+2992

1373 642

98.6

LebanonLEBANON Stratified multistage clustered area probability sample ofhousehold residents. NR

2002-318+2857

1031 595

70.0

MexicoM-NCS

Stratified multistage clustered area probability sample ofhousehold 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
AfricaSASH

Stratified multistage clustered area probability sample ofhousehold residents. NR

2003-418+4315

4315 –

87.1

Ukraine

CMDPSD

Stratified multistage clustered area probability sample ofhousehold residents. NR

2002

18+

4724

1719 540

78.3

PRC

Shenzhen Stratified multistage clustered area probability sample ofhousehold residents and temporary residents in the Shenzhen
area, China

2006-718+7132

2475 1994

80.0

a
ESEMeD (The European Study Of The Epidemiology Of Mental Disorders); NHS (Israel National Health Survey); WMHJ 2002-2006 (World Mental Health Japan
Survey); NZMHS (New Zealand Mental Health Survey); NCS-R (The USA National Comorbidity Survey Replication); NSMH (The Colombian National Study of Mental
Health); WMHI (World Mental Health India); LEBANON (Lebanese Evaluation of the Burden of Ailments and Needs of the Nation); M-NCS (The Mexico National
Comorbidity Survey); SASH (South Africa Stress and Health Study); CMDPSD (Comorbid Mental Disorders during Periods of Social Disruption)b
Most WMH surveys are based on stratified multistage clustered area probability household samples in which samples of areas equivalent to counties ormunicipalities in the USA were selected in the first stage, followed by one or more subsequent stages of geographic sampling (for example,, towns withincounties, 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Bromet et al. BMC Medicine 2011, 9:90
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one or two people were selected from this listing to be interviewed. No substitution was allowed when the originally sampled household resident could not beinterviewed. These household samples were selected from Census area data in all countries other than France (for which telephone directories were used to
select households) and the Netherlands (for which postal registries were used to select households). Several WMH surveys (Belgium, Germany, Italy) usedmunicipal resident registries to select respondents without listing households. The Japanese sample is the only totally unclustered sample, with households
randomly selected in each of the four sample areas, and one random respondent selected in each sample household. Fourteen surveys are based on nationally
representative (NR) household samples, and two others (Colombia, Mexico) were based on nationally representative household samples in urbanized areas. The
Israeli survey is a representative sample of individuals.
c
The response rate was calculated as the ratio of the number of households in which an interview was completed to the number of households originallysampled, excluding from the denominator households known not to be eligible either because they were vacant at the time of initial contact or because theresidents were unable to speak the designated languages of the survey. The weighted average response rate for all countries included was 71.7%.
dBrazil, Israel and South Africa did not have an age-restricted part II sample. All other countries, with the exception of India and Ukraine (which were agerestricted to ≤ 39 years) were age-restricted to ≤ 44 tears.
e
The New Zealand response rate was calculated on the entire survey sample size which was of respondents age 16+ years, giving a total of 12,992. For purposesof this analysis we only used respondents aged 18+ years.

health as well as various difficulties in role performance
during the days in role. WHO-DAS scores are coded in
the range 0 to 100, where 0 represents no impairment
and 100 total impairment. Reported levels of impairment were low in all countries, with means in the range
1.0 to 5.5 in high-income countries and 1.1 to 4.8 inlow- to middle-income countries.
Demographic factors

We examined gender, age (18 to 34, 35 to 49, 50 to 64,65+), current marital status (separated, divorced,
widowed, never married, currently married), living
arrangement (alone, with others but not spouse/partner,with spouse/partner), income (low, low average, highaverage, and high, which were based on country-specificquartiles of gross household earnings in the past 12
months [21]) and education (low, low average, highaverage or high, which were based on country-specificquartiles that take into consideration the fact that distributions of educational attainment vary widely between
countries [22]).
Statistical analysis

Cross-tabulations were used to estimate the absoluteand relative lifetime and 12-month prevalence of endorsing diagnostic stem questions and meeting DSM-IV/
CIDI criteria for a diagnosis of MDE. F-tests (linear
regression) were used to study differences in global
impairment by recency of MDE (past 30 days, pastmonth but not in the past 30 days, prior to the past
year, never). Logistic regression analysis was used to
examine sociodemographic correlates. Unadjusted odds
ratios and 95% confidence intervals are presented forthese associations. Because the data were weighted andclustered, the Taylor series linearization method [23]implemented in the SUDAAN software package [24]was used to estimate design-based standard errors. Statistical significance was consistently evaluated using
two-sided tests, with P < 0.05 considered significant.

Results
Prevalence of MDE

On average, about half of the respondents in both highincome (52.3%) and low- to middle-income (54.1%)

countries endorsed at least one depression diagnostic
stem question (screen-positive). (Table 2) However, thescreen-positive rate ranged widely, from < 30% in Japanand Pondicherry (India) to ≥ 60% in France, New Zealand, the USA, Brazil and Ukraine. The ratio of the highest to lowest screen-positive rates across countries was
3.3. On average, the estimated lifetime prevalence washigher in high-income (14.6%) than low- to middleincome (11.1%) countries (t = 5.7, P < 0.001). Indeed,
the four lowest lifetime prevalence estimates (< 10%)
were in low- to middle-income countries (India, Mexico,
China, South Africa). Conversely, with the exception ofBrazil, the highest rates (> 18%) were in four highincome countries (France, the Netherlands, New Zealand, the USA).
The percentage of the screen-positive respondents
who had lifetime MDE was also higher in high-income(28.1%) than in low- to middle-income (19.8%) countries, although both the lowest and the highest percentages were in low- to middle-income countries (12.0% in
China vs. 35.9% in India). The ratio of the highest to
lowest conditional prevalence scores in screened positives is 3.0. Among the high-income countries, these
conditional prevalence estimates were relatively low (<25%) in Germany, Italy, Israel and Japan, and higher (>30%) in the Netherlands and USA.
We previously reported that the pooled 12-month prevalence of MDE was similar in high-income (5.5%) andlow- to middle-income (5.9%) countries (t = 1.2, P =0.25), with the specific estimates varying from 2.2%
(Japan) to 8.3% (USA) in high-income countries, andfrom 3.8% (China) to 10.4% (Brazil) in low- to middleincome countries [11]. In the group of screen-positive
respondents, the percentage with 12-month MDE wasalso similar for high-income (10.6%) and low- to middle-income (10.5%) countries (Table 2). The lowest rate
was 6.7% (Italy) and the highest 18.0% (India). In 10
countries, these percentages were between 8 and 12%.
The ratio of the 12-month prevalence to lifetime prevalence is an indirect indicator of persistence. This ratio wassignificantly lower on average in surveys carried out inhigh-income (37.7%) than low- to middle-income (53.3%)

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Table 2 Prevalence (%) of DSM-IV/CIDI major depressive episodes in the 18 countries participating in the WMH
surveys a
Screenpositive, mean
± SE

Lifetime
Lifetime/prevalence, mean Screen positive,± SE± mean ± SE

12-monthprevalence, mean
± SE

12-month/
Screenpositive, mean
± SE

12-month/ Age of onset,
median (IQR)b
lifetime,mean ± SE

Belgium

49.4 ± 2.5

14.1 ± 1.0

28.5 ± 1.9

5.0 ± 0.5

10.0 ± 1.0

35.2(2.8

29.4(20.9 to 41.3)

France65.0 ± 1.7

21.0 ± 1.1

32.3 ± 1.4

5.9 ± 0.6

9.0 ± 0.9

27.9(2.6

28.4(19.3 to 38.9)

Germany

43.1 ± 1.4

9.9 ± 0.6

23.0 ± 1.3

3.0 ± 0.3

6.9 ± 0.6

30.1 ± 2.1

27.6(18.6 to 39.6)

Israel45.1 ± 0.8

10.2 ± 0.5

22.6 ± 1.0

6.1 ± 0.4

13.5 ± 0.8

59.6 ± 2.3

25.5(18.1 to 38.5)

Italy

44.9 ± 1.7

9.9 ± 0.5

22.1 ± 1.0

3.0 ± 0.2

6.7 ± 0.5

30.2 ± 1.9

27.7(19.1 to 39.1)

Japan

29.9 ± 0.8

6.6 ± 0.5

22.2 ± 1.4

2.2 ± 0.4

7.4 ± 1.2

33.3 ± 4.2

30.1(20.8 to 45.3)

Netherlands

53.2 ± 1.6

17.9 ± 1.0

33.6 ± 1.8

4.9 ± 0.5

9.2 ± 1.0

27.3 ± 2.6

27.2(19.3 to 39.5)

New Zealand61.9 ± 0.6

17.8 ± 0.4

28.7 ± 0.6

6.6 ± 0.3

10.6 ± 0.5

37.0 ± 1.5

24.2(16.1 to 34.5)cSpain

37.7 ± 1.0

10.6 ± 0.5

28.2 ± 1.2

4.0 ± 0.3

10.6 ± 0.8

37.5 ± 1.9

30.0(19.7 to 44.3)

United States

62.0 ± 0.9

19.2 ± 0.5

30.9 ± 0.7

8.3 ± 0.3

13.3 ± 0.5

43.1 ± 1.2

22.7(15.1 to 34.6)

Total

52.3 ± 0.4

14.6 ± 0.2

28.1 ± 0.3

5.5 ± 0.1

10.6 ± 0.2

37.7 ± 0.7

25.7(17.3 to 37.2)

Sao Pâulo,Brazil66.0 ± 1.0

18.4 ± 0.8

27.9 ± 1.1

10.4 ± 0.6

15.8 ± 0.8

56.7 ± 1.5

24.3(17.2 to 35.8)

Colombia

58.6 ± 1.1

13.3 ± 0.6

22.6 ± 1.0

6.2 ± 0.4

10.6 ± 0.7

46.7 ± 2.6

23.5(15.6 to 33.6)

Pondicherry,India

25.0 ± 0.9

9.0 ± 0.5

35.9 ± 1.5

4.5 ± 0.4

18.0 ± 1.4

50.0 ± 3.0

31.9(24.5 to 42.7)

Lebanon57.7 ± 1.8

10.9 ± 0.9

18.9 ± 1.3

5.5 ± 0.7

9.5 ± 1.2

50.0 ± 3.7

23.8(17.5 to 32.8)

Mexico40.6 ± 1.1

8.0 ± 0.5

19.6 ± 1.2

4.0 ± 0.3

9.8 ± 0.8

50.0 ± 2.7

23.5(16.7 to 34.0)

Shenzen, China 54.6 ± 0.9

6.5 ± 0.4

12.0 ± 0.7

3.8 ± 0.3

6.9 ± 0.5

58.0 ± 2.6

18.8(14.9 to 23.4)

South Africa

56.1 ± 1.3

9.8 ± 0.7

17.4 ± 1.2

4.9 ± 0.4

8.6 ± 0.8

49.6 ± 2.7

22.3(15.8 to 33.8)

Ukraine82.4 ± 1.1

14.6 ± 0.7

17.7 ± 0.8

8.4 ± 0.6

10.2 ± 0.7

57.8 ± 2.2

27.8(18.7 to 39.6)

Total

54.1 ± 0.4

11.1 ± 0.2

19.8 ± 0.4

5.9 ± 0.2

10.5 ± 0.3

53.3 ± 0.9

24.0(17.0 to 34.8)

I. High-income

II. Low- to
middleincomeaAssessed in part I sample. Prevalence for the pooled samples (developed and developing) include respondents ages 18+. Prevalence for individual countries areassessed for the total sample in the country.
b
IQR, interquartile range.

countries (t = 7.5, P < 0.001) (Table 2). Among the highincome countries, the ratio ranged from ≤ 30% in France,
Germany, Italy and the Netherlands to > 40% in the USA
and Israel. Among the low- to middle-income countries,

the lowest ratios were in Colombia (46.7%) and South
Africa (49.6%), and the highest (57-58%) in Brazil, China
and Ukraine. Consistent with these results, the 30-day prevalence of MDE was somewhat lower in high-income

Bromet et al. BMC Medicine 2011, 9:90
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Page 7 of 16

Table 3 Comparisons of functional impairment (WHO-DAS Global Scores)a by recency of DSM-IV/CIDI major depressive
episodes in the 18 countries participating in the WMH surveys
Past 30 days

Past 12 monthsb> 12 months ago

No lifetime MDE

Mean ± SEn

Mean ± SEn

Mean ± SEn

Mean ± SE

n

SDc

F

Belgium

11.3 ± 3.3+42

7.5 ± 2.1+71

3.5 ± 0.5254

3.2 ± 0.7

676

8.4

3.2*

France13.6 ± 2.6+38

6.6 ± 1.1 +134

4.0 ± 0.5476

3.2 ± 0.4

788

8.4

7.9*

Germany

15.3 ± 5.4+36

4.1 ± 1.073

2.4 ± 0.4263

2.7 ± 0.3

951

7.9

2.8

Israel21.5 ± 2.4+82

9.7 ± 1.0+

208

6.8 ± 0.8+211

5.0 ± 0.2

4358

12.0

24.7*

I. High-income

Italy

15.4 ± 2.5+58

6.3 ± 2.0+71

3.0 ± 0.4+323

2.1 ± 0.2

1327

6.8

13.5*

Japan

2.9 ± 2.611

2.9 ± 0.8+49

1.5 ± 0.2125

1.0 ± 0.2

882

4.4

2.3

Netherlands

17.9 ± 2.4+42

5.7 ± 1.193

5.1 ± 0.4341

4.1 ± 0.5

618

8.8

12.5*

New Zealand11.7 ± 1.0+292

5.0 ± 0.5+606

3.2 ± 0.2+1473

2.5 ± 0.1

5064

7.6

32.9*

Spain

15.7 ± 2.2+

109

6.1 ± 1.0+138

4.3 ± 0.6+425

2.1 ± 0.2

1449

7.6

24.9*

United States

15.8 ± 1.2+297

7.6 ± 0.6+496

4.3 ± 0.2+1002

3.0 ± 0.2

3896

9.0

54.9*

Total

15.3 ± 0.7+1005

6.8 ± 0.3+1942

3.9 ± 0.1+4903

3.0 ± 0.1

20,0968.9

149.4*

Sao Pâulo, Brazil

12.9 ± 1.6+260

6.8 ± 0.6+280

4.1 ± 0.5+413

1.8 ± 0.2

1989

7.8

44.0*

Colombia

5.1 ± 1.4+82

3.3 ± 0.7+

194

1.8 ± 0.3+316

0.9 ± 0.1

1789

4.2

11.1*

Pondicherry, India

2.3 ± 0.5+71

3.2 ± 0.8+83

1.5 ± 0.4153

1.0 ± 0.1

1066

4.3

3.9*

Lebanon

9.3 ± 1.5+72

3.7 ± 0.8+71

3.4 ± 0.7+162

1.5 ± 0.2

726

5.8

17.3*

Mexico8.0 ± 1.8+117

3.4 ± 0.7+142

1.4 ± 0.3+250

0.6 ± 0.1

1853

4.3

8.5*

Shenzen, China

3.3 ± 0.5+101

1.7 ± 0.4+144

0.6 ± 0.1138

0.5 ± 0.0

2093

2.4

16.9*

Ukraine14.8 ± 1.2+

229

10.91.1+

159

6.8 ± 1.1+

238

3.7 ± 0.3

1093

9.7

46.3*

Total

10.1 ± 0.7+932

5.2 ± 0.3+1073

3.1 ± 0.2+1670

1.3 ± 0.1

10,6086.1

112.6*

dII. Low- to middle-income*Significantly different at the .05 level based on a 3 degree of freedom test
+Significantly different from respondents with no lifetime MDE at the +.05 level, 2-sided testaWHO-DAS: World Health Organization-Disability Assessment Scheduleb
Excludes respondents with MDE in the past 30 daysc
SD: Standard deviation of the impairment score in the total sampledData for South Africa are not available.

(mean ± SE 1.8% ± 0.1%) than in low- to middle-income(2.6 ± 0.1%) countries.
The last column of Table 2 shows that the median
retrospectively reported age of onset (AOO) was similar
for high-income and low- to middle-income countries(25.7 vs. 24.0, respectively) and that the interquartile
ranges were largely overlapping. The 95% confidence
intervals indicate that across all countries, the risk period for onset of depression ranges from the mid-lateadolescence to the early 40s. In high-income countries,
the earliest median AOO estimates occurred in the USA(22.7) and New Zealand (24.2), whereas the latest werein Spain (30.0) and Japan (30.1). In low- to middleincome countries, the earliest median AOO estimates
were in China (18.8) and South Africa (22.3), and thelatest in Ukraine (27.8) and India (31.9).
Impairment

As expected, MDE was associated with substantial
impairment in the WMH data. Moreover, the degree of

impairment increased progressively with recency of
MDE. (Table 3) This was true in both high and low- to
middle-income countries, apart from Japan, in which
the impairment level was exceptionally low. We notethat the non-MDE comparison group, which has thelowest level of impairment, comprised not only healthy
respondents but also respondents with other DSM-IV
diagnoses (Table 3).
For respondents with current MDE, the mean level of
impairment was between approximately five (highincome) and eight (low- to middle-income) times ashigh as for respondents without MDE, with differencesin mean scores of 12.3 (15.3 minus 3.0) in high-income
countries and 8.8 (10.1 minus 1.3) in low- to middleincome countries. To put these differences into perspective, the mean differences in all high-income countries
combined (15.3-3.0 = 12.3) and in all low- to middleincome countries combined (10.1-1.3 = 8.9) were both
equal to 1.4 standard deviations on the impairment
scale in those countries. Effect sizes such as these areBromet et al. BMC Medicine 2011, 9:90
http://www.biomedcentral.com/1741-7015/9/90

large [25]. The biggest differences (greater than sevenfold) are in Italy, Spain, Brazil and Mexico and the smallest (less than fivefold) in Belgium, Israel, India andUkraine. Respondents with MDE in the past year (butnot currently) reported impairment scores between
approximately twofold (high-income) and fourfold (lowto middle-income) that of the non-MDE group,
although this difference was not significant in Germanyor the Netherlands. The largest mean differences(greater than threefold) were in low- to middle-income
countries (Mexico, Brazil, Colombia, India and China),
and the smallest (approximately twofold) in four highincome countries (France, Belgium, Israel, New Zealand). In seven countries, (five high-income and two lowto middle-income), there was no significant difference inimpairment between respondents with MDE prior to thepast year and the non-MDE subsample. In five countries(Spain, Brazil, Colombia, Lebanon and Mexico), theMDE positive group had an approximately twofold
higher level of impairment than the non-MDE group.
The association between prevalence and impairment

As noted in the introduction, a previous cross-national
WHO study carried out in primary-care waiting-roomsamples found that depressed respondents in countries
in which the prevalence of depression was estimated tobe highest reported the lowest average levels of impairment associated with their depression, whereas the highest impairment was reported by depressed respondents
in countries in which the prevalence of depression was
estimated to be lowest [15]. We investigated this issue
in the WMH data by creating a small data file in which
each survey was treated as a separate observation, andthe variables were the measures of prevalence (reported
in Table 2) and a measure of impairment associatedwith MDE (based on the results reported in Table 3).
However, the impairment scores differed from those in
Table 3 in that they represented the difference in mean
impairment scores of respondents with 12-month MDE
compared with those with no lifetime history of MDE inthe survey. This difference was taken to represent theeffect of recent MDE as assessed by the CIDI on impairment in the survey.
Unlike the earlier primary-care study, we found thatthe association between prevalence and impairment was
positive. (Table 4 Table 5) This was true not only in the
total sample of all countries (r = 0.48) but also when we
looked separately at high-income (r = 0.34) and low- to
middle-income (r = 0.80) countries. In addition, when
we deconstructed these associations into correlations of
impairment with the two components of prevalence (thepercentage of respondents endorsing an MDE stem
question and the conditional prevalence estimate of
MDE of screen-positives), we found that the first

Page 8 of 16

correlation was considerably stronger than the second inboth the total sample of countries (r = 0.45, 0.11) and inlow- to middle-income countries (r = 0.76, 0.04),
whereas the first correlation was stronger than the second in high-income countries (r = 0.17, 0.45).
Sociodemographic factors

Tables 4 and 5 show the bivariate associations of thesociodemographic characteristics with 12-month MDE(tables showing the country-specific distributions of thedemographic variables and 12-month prevalences of
MDE are available upon request). Consistent with previous epidemiologic studies, women were on average
twice as likely as men to be classified as having MDE.
This difference was significant in 15 of the eighteencountries, and even in the three exceptions (Belgium,
Germany and China), women had higher rates thanmen. In the developed countries, the significant odds
ratios ranged from 1.6 in Israel to 2.7 in Spain, and in
the developing countries, they ranged from 1.9 in India
and Colombia to 2.6 in Brazil. The association between
gender and MDE did not differ significantly between
high-income and low- to middle-income countries (c21
= 2.3, P = 0.13).
The associations between age group and MDE varied
considerably between countries. In two high-income andfive low- to middle-income countries, there were no significant associations. In six high-income countries andin Brazil, respondents in the youngest age group (18 to34) were 3 to 5.5 times as likely to have MDE as thosein the oldest age group (65+), but in India and Ukraine,
young age was associated with low risk. The 35 to 49
year age group was also at increased risk for MDE, especially in New Zealand (OR = 4.4), the USA (OR = 3.9)
and Brazil (OR = 3.3); in Ukraine, however, they had asignificantly lower risk than those in the oldest age
group. Mid-life (ages 50 to 64), encompasses a period oftransition from work to retirement in many countries.
Compared with respondents age 65+, participants inthis group had an increased risk of MDE in eight highincome countries and Brazil, with ORs ranging from 1.6(Spain) to 3.1 (USA). Overall, the association between
age and MDE was significantly stronger in high-income
than low- to middle-income countries (c23 = 67.1, p <.001).
Marital status was a consistently significant correlateof MDE. Being separated was associated with increased
risk of MDE in twelve countries, with odds ratios varying from < 4.0 in five countries to > 8.0 in India (OR =8.2), Japan (OR = 10.8) and Lebanon (OR = 19.3). Being
divorced was associated with MDE in seven of the ten
developed and four of the eight developing countries,
with unusually high ORs in Japan (OR = 5.1), China(OR = 6.2) and Ukraine (OR = 4.2). Being widowed was

Parameter

Total, OR(95% CI)

Belgium, OR(95% CI)France, OR(95% CI)

Germany, OR(95% CI)

Israel, OR(95% CI)

Italy, OR(95% CI)

Japan, OR(95% CI)

Netherlands, OR(95% CI)New Zealand, OR(95% CI)

Spain, OR(95% CI)

USA, OR(95% CI)

Women

1.8
(1.6 to 2.0)*

1.6(0.9 to 2.8)

1.7
(1.2 to 2.5)*

1.7(1.0 to 3.0)

1.6
(1.2 to 2.1)*

2.5(1.6 to 3.8)*

2.3(1.4 to 4.0)*

2.3(1.5 to 3.5)*

1.7(1.4 to 2.1)*

2.7(1.9 to 3.8)*

1.7(1.4 to 2.1)*

Men

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

18-34

2.7
(2.3 to 3.1)*

2.6(0.9 to 7.7)

3.5
(1.7 to 7.4)*

3.8(1.6 to 9.2)*

1.1(0.7 to 1.6)

0.8
(0.5 to 1.5)

4.8
(2.3 to 10.0)*

2.6(1.2 to 5.7)*

5.5(3.9 to 7.8)*

1.0(0.6 to 1.7)

4.3
(3.1 to 6.0)*

35-49

2.2
(1.9 to 2.6)*

2.2(1.0 to 4.8)*

2.5(1.2 to 5.3)*

2.3(1.0 to 5.5)

1.0
(0.6 to 1.4)

0.7
(0.4 to 1.3)

2.7
(1.3 to 5.6)*

2.5(1.2 to 5.4)*

4.4(3.2 to 6.2)*

1.1(0.7 to 1.6)

3.9
(2.7 to 5.5)*

50-64

2.0
(1.7 to 2.3)*

2.5(1.1 to 5.6)*

2.3(1.1 to 4.7)*

2.4(1.0 to 5.5)*

1.0(0.7 to 1.6)

1.2
(0.7 to 2.1)

2.4
(1.2 to 4.8)*

1.9(0.9 to 3.8)*

2.9(2.0 to 4.1)*

1.6(1.1 to 2.3)*

3.1(2.1 to 4.5)*

65+

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

Separated

3.6(2.9 to 4.6)*

7.3(1.8 to 29.7)*

6.2(1.8 to 21.3)*-

2.6(1.0 to 6.8)

2.8
(1.1 to 7.5)*

10.8(2.1 to 55.6)*-

3.4(2.4 to 4.8)*

3.2(1.3 to 7.7)*

4.0(2.7 to 6.0)*

Divorced

2.1(1.8 to 2.5)*

1.9(0.7 to 5.3)

1.1
(0.5 to 2.5)

3.1
(1.4 to 7.1)*

2.2(1.5 to 3.4)*

0.6(0.1 to 5.0)

5.1
(2.1 to 12.6)*

2.7(1.5 to 4.9)*

2.8(2.0 to 3.8)*

3.3(1.2 to 8.9)*

1.7(1.3 to 2.3)*

Widowed1.4(1.2 to 1.7)*

1.4(0.6 to 3.4)

1.5
(0.7 to 3.2)

2.3
(1.2 to 4.5)*

2.1(1.4 to 3.3)*

1.5(0.9 to 2.7)

0.9
(0.4 to 2.1)

0.8
(0.3 to 2.2)

1.3
(0.9 to 1.8)

1.4
(0.9 to 2.2)

1.2
(0.8 to 1.9)

Nevermarried

1.8(1.6 to 2.0)*

1.3(0.6 to 2.9)

2.0
(1.2 to 3.5)*

2.6(1.6 to 4.2)*

1.4(1.0 to 1.9)*

1.5(1.0 to 2.2)

3.1
(1.6 to 5.7)*

1.8(1.0 to 3.4)

2.3
(1.8 to 3.0)*

0.9(0.6 to 1.4)

1.8
(1.5 to 2.1)*

Currently
married

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.8
(1.6 to 2.0)*
With others 1.9(1.7 to 2.2)*

1.3(0.6 to 2.7)
1.5
(0.8 to 2.8)

1.4
(0.9 to 2.3)
2.2
(1.4 to 3.7)*

2.5(1.6 to 3.9)*
2.9(1.6 to 5.1)*

2.1(1.5 to 2.9)*
1.6(1.2 to 2.1)*

1.7(1.1 to 2.8)*
1.4(0.9 to 2.1)

2.9
(1.2 to 6.8)*
3.0(1.6 to 5.6)*

1.6(1.1 to 2.4)*

1.8(1.4 to 2.3)*

1.0(0.7 to 1.6)

1.7
(1.4 to 2.2)*

1.9(0.8 to 4.3)

2.5
(2.0 to 3.1)*

1.2(0.8 to 1.7)

1.8
(1.5 to 2.1)*

With
spouse

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

Low

1.7
(1.5 to 2.0)*

1.3(0.7 to 2.6)

2.4
(1.2 to 4.6)*

2.7(1.3 to 5.6)*

1.1(0.7 to 1.7)

1.3
(0.6 to 2.9)

0.5
(0.2 to 1.4)

1.1
(0.6 to 1.9)

2.2
(1.6 to 3.0)*

1.0(0.5 to 2.0)

2.1
(1.5 to 2.8)*

Low
average

1.3(1.1 to 1.5)*

1.1(0.5 to 2.4)

1.4
(0.7 to 2.6)

1.6
(0.7 to 3.5)

0.9
(0.6 to 1.3)

1.2
(0.6 to 2.3)

0.5
(0.2 to 1.2)

0.8
(0.4 to 1.8)

1.5
(1.2 to 2.0)*

HighaverageHigh

1.1(0.9 to 1.2)
1.0

0.9
(0.4 to 2.0)
1.0

1.3
(0.7 to 2.3)
1.0

1.6
(0.9 to 3.1)
1.0

0.8
(0.6 to 1.1)
1.0

0.9
(0.5 to 1.5)
1.0

0.5
(0.2 to 1.3)
1.0

0.8
(0.5 to 1.6)
1.0

1.3
(1.0 to 1.7)
1.0

1.1
(0.7 to 1.8)
1.0
(0.6 to 1.5)

1.4
(1.0 to 1.8)*
1.1(0.8 to 1.5)

1.0

1.0

1.0
(0.9 to 1.2)

1.4
(0.8 to 2.5)-

1.0
(0.2 to 4.3)

1.5
(1.0 to 2.2)*

1.5(0.9 to 2.5)

0.2
(0.1 to 0.6)*

1.1(0.6 to 1.9)

0.9
(0.7 to 1.2)

1.2
(0.7 to 1.9)

1.4
(1.1 to 1.8)*

GenderAgeBromet et al. BMC Medicine 2011, 9:90
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Table 4 Associations of demographic characteristics with 12-month DSM-IV/CIDI major depressive episode in high-income countries (bivariate analyses)aMaritalstatusb

Living arrangements
Alone

IncomeLow

Page 9 of 16

Education

Bromet et al. BMC Medicine 2011, 9:90
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Table 4 Associations of demographic characteristics with 12-month DSM-IV/CIDI major depressive episode in high-income countries (bivariate analyses)a(Continued)
Low
average

1.1(1.0 to 1.3)

0.9
(0.4 to 2.2)-

1.6
(0.4 to 5.8)

1.3
(0.9 to 1.8)

0.8
(0.4 to 1.4)

0.7
(0.4 to 1.4)

1.3
(0.7 to 2.6)

1.1
(0.8 to 1.4)

0.8
(0.5 to 1.3)

1.2
(0.9 to 1.5)

Highaverage

1.1(0.9 to 1.3)

1.8
(0.8 to 3.8)-

1.1
(0.2 to 5.2)

1.0
(0.7 to 1.6)

0.9
(0.5 to 1.5)

0.7
(0.3 to 1.6)

1.5
(0.7 to 3.2)

0.9
(0.7 to 1.1)

1.0
(0.7 to 1.6)

1.4
(1.0 to 1.8)*

High

1.0

1.0

-

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

*Significant at the 0.05 level, two-sided test.
a
All models were bivariate models with the sociodemographic factors as predictors and 12-month MDE as the response variable. The models for total(first column) control for countries. The models for income were estimated in part II samples, whereas all other models were estimated in part I samples.
b
In some countries, people were categorized as separated/widowed/divorced because they were known to have married previously but not any longer, but the specific category was unknown. These cases were
dropped from the model using marriage as the predictor. Specifically, there was one such case in Japan, two cases in the USA, one case in New Zealand, and ninety-one in the ESEMeD countries(France, Germany, Italy, Netherlands, Spain, Belgium).

Page 10 of 16

Total, OR (95% Brazil, OR (95% Colombia, OR
CI)
CI)(95% CI)

India, OR (95% Lebanon, OR
CI)(95% CI)

Mexico, OR (95% China, OR (95% South Africa, OR
CI)
CI)(95% CI)

Ukraine, OR(95% CI)

Women

2.1 (1.8 to 2.3)*

2.6 (1.9 to 3.5)*

1.9 (1.4 to 2.7)*

1.9 (1.3 to 2.7)*

2.1 (1.3 to 3.4)*

2.1 (1.5 to 2.9)*

1.2 (0.8 to 1.7)

2.2 (1.5 to 3.2)*

2.5 (2.0 to 3.0)*

Men

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

18-34

0.9 (0.8 to 1.1)

3.0 (1.6 to 5.7)*

4.9 (0.9 to 28.3)

0.4 (0.2 to 1.0)*

1.7 (0.8 to 3.7)

0.4 (0.2 to 1.2)

2.8 (0.8 to 9.4)

1.3 (0.6 to 2.6)

0.4 (0.3 to 0.6)*

35-49

1.0 (0.8 to 1.2)

3.3 (1.7 to 6.5)*

3.9 (0.7 to 23.1)

1.2 (0.6 to 2.5)

2.2 (1.0 to 5.2)

0.5 (0.2 to 1.3)

1.6 (0.5 to 5.3)

1.6 (0.8 to 3.2)

0.5 (0.3 to 0.7)*

50-64

1.0 (0.8 to 1.3)

2.5 (1.4 to 4.5)*

3.4 (0.6 to 20.1)

1.5 (0.7 to 3.2)

1.9 (0.9 to 4.1)

0.5 (0.2 to 1.5)

0.8 (0.2 to 3.3)

1.7 (0.8 to 3.6)

0.7 (0.5 to 1.1)

65+
Maritalstatusb
Separated

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.7 (1.3 to 2.2)*

1.6 (1.1 to 2.3)*

0.9 (0.6 to 1.6)

8.2 (2.2 to 30.6)* 19.3 (5.0 to 74.4)*

1.9 (1.0 to 3.6)*-

2.7 (0.7 to 9.6)

6.6 (1.1 to 38.0)*

Divorced

3.0 (2.4 to 3.9)*

3.0 (1.9 to 4.9)*

1.2 (0.3 to 4.3)-

1.2 (0.4 to 3.8)

6.2 (2.2 to 17.3)*

2.1 (1.3 to 3.5)*

4.2 (2.9 to 6.2)*

Widowed

GenderAge

0.8 (0.2 to 4.2)

2.7 (2.2 to 3.2)*

1.1 (0.7 to 1.9)

1.6 (0.9 to 2.9)

2.2 (1.5 to 3.2)*

1.4 (0.6 to 3.6)

2.7 (1.5 to 5.0)*

4.1 (0.8 to 20.7)

2.3 (1.3 to 4.0)*

8.0 (5.3 to 12.0)*

Never married 1.0 (0.9 to 1.1)

1.0 (0.7 to 1.3)

1.3 (0.9 to 1.8)

0.3 (0.1 to 0.6)*

1.3 (0.8 to 2.0)

0.8 (0.5 to 1.3)

1.4 (1.0 to 1.9)

0.7 (0.5 to 1.0)*

0.8 (0.5 to 1.3)

Currently
married

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

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Table 5 Associations of demographic characteristics with 12-month DSM-IV/CIDI major depressive episode in low- to middle-income countries: bivariate
analyses)a

Living arrangements
Alone

1.6 (1.4 to 1.9)*

1.3 (0.8 to 2.0)

0.6 (0.3 to 1.2)

1.2 (0.6 to 2.5)

1.3 (0.7 to 2.5)

1.0 (0.5 to 1.9)

1.6 (1.1 to 2.3)*

1.2 (0.8 to 1.9)

2.5 (1.9 to 3.3)*

With others

1.2 (1.1 to 1.3)*

1.2 (1.0 to 1.5)

1.3 (0.9 to 1.8)

0.6 (0.4 to 0.9)*

1.4 (0.8 to 2.2)

1.1 (0.8 to 1.6)

0.9 (0.4 to 2.1)

0.9 (0.6 to 1.2)

1.5 (1.1 to 2.0)*

With spouse
Income

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

Low

1.1 (0.9 to 1.3)

1.0 (0.7 to 1.5)

1.4 (0.8 to 2.3)

1.7 (0.9 to 3.2)

1.0 (0.5 to 2.3)

2.1 (1.4 to 3.2)*

0.7 (0.4 to 1.2)

0.9 (0.6 to 1.4)

0.7 (0.4 to 1.3)

Low average

1.1 (0.9 to 1.3)

1.1 (0.8 to 1.6)

1.5 (0.9 to 2.5)

2.1 (1.2 to 3.8)*

1.1 (0.5 to 2.5)

1.5 (1.0 to 2.3)

0.6 (0.3 to 1.0)

0.9 (0.5 to 1.4)

0.8 (0.4 to 1.4)

High average

1.0 (0.8 to 1.2)

1.2 (0.8 to 1.8)

0.8 (0.5 to 1.3)

2.0 (1.2 to 3.5)*

1.3 (0.6 to 2.7)

1.4 (0.8 to 2.2)

0.5 (0.3 to 0.8)*

0.5 (0.3 to 1.2)

0.9 (0.5 to 1.7)

High

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.1 (1.0 to 1.4)

0.7 (0.5 to 1.1)

0.9 (0.6 to 1.4)

14.1 (3.4 to 58.9) 1.2 (0.7 to 2.1)*

2.1 (1.3 to 3.2)*

0.2 (0.1 to 0.6)*

0.5 (0.2 to 1.5)

2.3 (1.4 to 3.8)*

Low average

1.1 (0.9 to 1.3)

1.1 (0.8 to 1.6)

1.2 (0.7 to 1.9)

1.0 (0.1 to 12.5)

1.2 (0.6 to 2.4)

1.6 (1.0 to 2.5)

0.5 (0.3 to 0.7)*

2.1 (1.1 to 4.1)*

1.3 (0.9 to 1.8)

High averageHigh

0.9 (0.8 to 1.1)
1.0

0.9 (0.7 to 1.2)
1.0

0.9 (0.5 to 1.5)
1.0

3.9 (0.6 to 27.0)
1.0

1.3 (0.7 to 2.4)
1.0

1.2 (0.7 to 1.9)
1.0

0.8 (0.5 to 1.2)
1.0

1.0 (0.5 to 1.9)
1.0

1.0 (0.6 to 1.5)
1.0

Education
Low

Page 11 of 16*Significant at the 0.05 level, two-sided t-testaAll models were bivariate models with the sociodemographic factors as predictors and 12-month MDE as the response variable. The models for total (first column) control for countries. The models for income were
estimated in part II samples, whereas all other models were estimated in part I samples.
b
In some countries, people were categorized as separated/widowed/divorced because they were known to have married previously but not anymore, but the specific category was unknown. These cases were
dropped from the model using marriage as the predictor. There were 48 cases in India, 35 in Brazil, 664 in Ukraine and 1 case in South Africa.

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less consistently and more modestly associated with
MDE with the exception of Ukraine, where widows
were eight times as likely as married men and women
to have MDE. In the high-income countries, there was asignificantly increased OR of MDE among the nevermarried. However, India and South Africa were the only
two low- to middle-income samples with significant
ORs, and in these countries never being married was
associated with low risk. Overall, the association
between marital status and MDE differed significantly
between high and low- to middle-income countries (c23
= 124.4, P < 0.001), due to stronger associations ofbeing separated and never married with depression in
high-income countries, and stronger associations ofbeing divorced and widowed with depression in low- to
middle-income countries. In contrast to marital status,
living arrangements per se were more modestly associated with MDE. This association was significant ineight of the high-income countries and in Ukraine andChina, with the overall difference in the association
between high and low- to middle-income countries significant (c 2 2 = 39.0, P < 0.001) due to a higher OR
between being unmarried but living with others in highthan in low- to middle-income countries.
The poorest respondents in France, Germany, New
Zealand and the USA had an approximately twofold
increased odds of MDE compared with those in thehighest income group. In the low- to middle-income
countries, in comparison, income was not significantly
related to MDE. This stronger association between
income and MDE in higher-income countries was significant overall (c23 = 19.3, P < 0.001). Similarly, among
the non-Asian countries, low education was significantly
associated with MDE only in Israel, the USA, Mexico
and Ukraine. The findings for the Asian countries weremore complex. In India, respondents with the lowest
education were 14 times as likely to have MDE as thosewith the highest education. In Japan and China, thereverse pattern was found, with the least educated having the lowest risk of MDE. The association between
education and MDE overall did not differ significantly
between high and low- to middle-income countries (c23
= 6.2, P = 0.10).

Discussion
Consistent with previous cross-national reports, theWMH MDE prevalence estimates varied considerably
between countries, with the highest prevalence estimatesfound in some of the wealthiest countries in the world.
However, contrary to our initial expectation, we foundno evidence that this wide cross-national variation wasdue as much to cross-national differences in endorsing
diagnostic stem questions as to conditional prevalenceof MDE among respondents who endorsed a diagnostic

Page 12 of 16

stem. The ratio of the highest to lowest screen-positive
rates across countries (3.3) was very similar to the ratioof the highest to lowest conditional prevalence rates
among screen-positives (3.0). As expected, we also
found that MDE was associated with substantial impairment. However, contrary to our initial expectation, we
did not find that cross-national differences in prevalence
estimates were inversely related to differences in averagelevel of impairment associated with depression; indeed,
the opposite pattern was found.
Taken together, these results argue against the suggestion that the wide cross-national variation in depression
prevalence estimates in the WMH surveys and previous
epidemiologic studies is due to the threshold for defining clinically significant depression in standard diagnostic interviews differing across countries. If that were thecase, we would expect that the cases of depression
detected in countries with the lowest estimated prevalence of depression would be the most severe cases,
resulting in high impairment rates among these cases,
whereas the opposite would be true in countries withthe highest estimated prevalence of depression. Furthermore, we would expect that reports of core depressive
symptoms would be more similar across countries thanestimates of disorder prevalence. Neither of these expectations was borne out in the WMH data. A question
can be raised by our results regarding why the associations between prevalence and impairment were so different from those reported in the earlier WHO study[15]; however, it is important to bear in mind that thisearlier study was based on primary-care samples, forwhich selection bias regarding seeking help on the basisof either distress or impairment might induce a morenegative association between these two variables thanexists in the population. The WMH surveys, by contrast,
are based on general population samples, for which theselection bias issues that occur in treatment samples do
not arise.
Although these results add indirect support to a substantive interpretation of the cross-national differencesin MDE found here, they shed no light on why thesedifferences exist. Differences in stress exposure, in reactivity to stress, and in endogenous depression unrelated
to environmental provoking factors are all possibilities.
On one level, it seems counterintuitive that people inhigh-income countries should experience more stressthan those in low- to middle-income countries. However, it has been suggested that depression is to someextent an illness of affluence [26]. A related argument isthat income inequality, which is for the most part
greater in high than low- to middle-income countries,
promotes a wide variety of chronic conditions that
includes depression [27]. Further analyses of the WMHdata might be able to shed some light on theseBromet et al. BMC Medicine 2011, 9:90
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perspectives; however such an analysis was beyond thescope of the current report, which focused on the evaluation of a more methodological interpretation of theobserved cross-national differences in depression prevalence estimates.
In considering a substantive interpretation of our findings, it is noteworthy that although lifetime prevalence
estimates were found to be significantly higher in highthan low- to middle-income countries overall, no significant difference in 12-month prevalence was found. The
ratio of 12-month to lifetime prevalence estimates,
furthermore, was significantly higher in low- to middleincome than in high-income countries. It might be thatthese results reflect genuinely lower lifetime prevalencebut higher persistence of depression in low- to middleincome than high-income countries, but another plausible and more parsimonious explanation is that error inrecall of previous lifetime episodes is higher in low- to
middle-income than high-income countries. Longitudinal data collection would be required to document sucha difference rigorously [28,29]. Although such data do
not exist in all WMH series, it is important to recognize
this possibility of cross-national variation in recall error
before launching an extensive investigation of substantive explanations. It might be that a fruitful focus of
subsequent WMH analysis would be on the youngest
respondents, where lifetime recall error might be least
pronounced. Alternatively, it might be that the investigation of cross-national differences in lifetime prevalence
should be abandoned in favor of a focus on recent prevalence in recognition of the plausibility of significant
cross-national variation in recall error of lifetime
prevalence.
Another implication of the methodological limitationof the WMH surveys being all cross-sectional is that it
made it impossible to determine the temporal directionof the associations examined between depression andthe sociodemographic variables. This means that eventhough variables such as education and marital status
were considered predictors of depression, they might
actually have been consequences or involved in reciprocal causal relationships with depression. However,within the context of that limitation, the sociodemographic patterns reported here are broadly consistent
with those found in previous community epidemiologic
surveys of depression [2,5,7,9,13], adding to confidence
in the generalizability of the WMH finding.
The results reported here have several other limitations, relating more generally to the WMH findings[30]. Some of the most important of these issues involve
sampling. The response rates varied widely. Although
the response rates did not appear to be related todepression prevalence, it is possible that in some settings, particularly those where treatment is unavailable,

Page 13 of 16

the most depressed people were unable to participate.
Some surveys only included metropolitan areas, whereas
others involved national samples. This too may haveaffected estimates of cross-national variation in prevalence. In addition, the surveys did not include institutionalized patients, people in jails and prisons, people inthe military, people who were too intoxicated to beinterviewed, or people with severe cognitive or physicaldisabilities. The samples also reflected survivor bias,
which could be of considerable importance for understanding differences between high-income and low- to
middle-income countries, given the gap in life expectancy of 10 to 15 years between people in developed
and developing countries [31]. Thus, the rates reported
here provide conservative estimates of MDE prevalence.
A final noteworthy sample bias is that South Africa was
the only African country included in this report [32]even though the WMH survey was also conducted in
Nigeria [33]. Nigeria was excluded because of the extremely low prevalence of MDE (3.1% lifetime; 1.1% 12month) and other disorders. These low prevalence estimates raise questions about the willingness of respondents in the Nigerian survey to disclose symptoms tostrangers or lay interviewers, and the appropriateness of
the CIDI structure for that setting [34]. They also
reduced our statistical power to examine the associations of depression considered in the Nigerian data. A
similar experience may have occurred in another African
population-based survey using the CIDI that was notpart of the WMH series. That survey, carried out in
Addis Ababa, also found low rates of affective disorders[35]. Given the high level of exposure to trauma inextremely poor countries such as these [36], research isurgently needed to determine the best approaches tostudy the prevalence of mental disorders in thesesettings.
The measure of MDE also had inherent limitations.
The structure of the CIDI, including the choice of stem
questions in the screening section, may have led tounderestimates of depression in some settings. As noted
above in the section on measurement, the interview
translation, back-translation and harmonization process
in the WMH surveys included customization withincountries of the terms used to describe the core symptoms of depression (that is, sadness, depression, loss ofinterest) based on clinical experiences of local collaborators and the results of pilot studies [19]. However, noattempt was made to develop distinct cut-off points in
the CIDI diagnostic algorithms for different countries or
to go beyond the DSM-IV criteria to develop distinct
criteria for different countries that might have increased
our ability to detect depression or depression-equivalents. It is noteworthy that in the countries for which
we carried out blinded clinical reappraisal interviews

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with subsamples of WMH respondents, we found noevidence for systematic bias in the diagnostic thresholdfor depression [18], but clinical reappraisal interviews
were not carried out in all WMH countries, and it isconceivable that such studies would have found systematic differences in the ability of the CIDI to detect
clinical depression across countries.
Despite these limitations, the WMH data provide useful new information about the epidemiology of MDE. We
found wide variation not only in the prevalence of MDE
but also in the proportion of people who endorsed diagnostic stem questions for MDE, a pattern that has seldom
been examined in previous epidemiologic studies [37].
We found cross-national consistency, by contrast, in the
impairment associated with MDE. This association has toour knowledge never been considered previously incross-national community epidemiologic surveys. Our
results confirm the public-health importance of major
depression as a commonly occurring and seriously
impairing condition with a generally early AOO and persistent course in a wide range of countries. In addition,
we replicated previous findings on the sociodemographic
correlates of MDE. We also documented an intriguing
opposite-sign pattern of differences between high andlow- to middle-income countries in estimates of lifetime
prevalence and persistence of MDE, which might be due
to differences in recall error. Future research on crossnational differences in depression needs to take this pattern into consideration, and to develop a workable strategy to deal with the possibility of differential recall error
as a plausible contributor to cross-national differences inprevalence estimates.
Acknowledgements
This report is carried out in conjunction with the World Health Organization
World Mental Health (WMH) Survey Initiative. We thank the WMH staff forassistance 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, thePfizer Foundation, the USA Public Health Service (R13-MH066849, R01MH069864 and R01 DA016558), the Fogarty International Center (FIRCA R03TW006481), the Pan American Health Organization, the Eli Lilly & Company
Foundation, Ortho-McNeil Pharmaceutical, Inc., GlaxoSmithKline, SanofiAventis and Bristol-Myers Squibb. A complete list of WMH publications canbe found at http://www.hcp.med.harvard.edu/wmh/.
The Brazil Megacity Mental Health Survey is supported by the State of Brazil
Research Foundation (FAPESP) Thematic Project Grant 03/00204-3. The
Chinese World Mental Health Survey Initiative is supported by the Pfizer
Foundation. The Colombian National Study of Mental Health (NSMH) is
supported by the Ministry of Social Protection. The ESEMeD project isfunded by the European Commission (Contracts QLG5-1999-01042; SANCO
2004123), the Piedmont Region (Italy), Fondo de Investigación Sanitaria,
Instituto de Salud Carlos III, Spain (FIS 00/0028), Ministerio de Ciencia yTecnología, Spain (SAF 2000-158-CE), Departament de Salut, Generalitat deCatalunya, Spain, Instituto de Salud Carlos III (CIBER CB06/02/0046, RETICS
RD06/0011 REM-TAP) and other local agencies, and by an unrestrictededucational grant from GlaxoSmithKline. The Epidemiological Study on
Mental Disorders in India was funded jointly by Government of India andWHO. The Israel National Health Survey is funded by the Ministry of Healthwith support from the Israel National Institute for Health Policy and HealthPage 14 of 16

Services Research and the National Insurance Institute of Israel. The World
Mental Health Japan (WMHJ) Survey is supported by the Grant for Researchon Psychiatric and Neurological Diseases and Mental Health (H13-Shogai023, H14-Tokubetsu-026, H16-Kokoro-013) from the Japan Ministry of Health,Labour and Welfare. The Lebanese National Mental Health Survey(LEBANON) is supported by the Lebanese Ministry of Public Health, the WHO(Lebanon), Fogarty International, anonymous private donations to IDRAAC,
Lebanon, and unrestricted grants from Janssen Cilag, Eli Lilly,
GlaxoSmithKline, Roche and Novartis. The Mexican National Comorbidity
Survey (M-NCS) is supported by The National Institute of Psychiatry Ramonde la Fuente (INPRFMDIES 4280) and by the National Council on Scienceand Technology (CONACyT-G30544-H), with supplemental support from the
PanAmerican Health Organization (PAHO). Te Rau Hinengaro: The New
Zealand Mental Health Survey (NZMHS) is supported by the New Zealand
Ministry of Health, Alcohol Advisory Council and the Health ResearchCouncil. The South Africa Stress and Health Study (SASH) is supported by
the USA 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. The Ukraine
Comorbid Mental Disorders during Periods of Social Disruption (CMDPSD)
study is funded by the USA National Institute of Mental Health (RO1MH61905). The USA 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), the Substance Abuse and Mental Health Services Administration(SAMHSA), the Robert Wood Johnson Foundation (RWJF; Grant 044708), andthe John W. Alden Trusts.
The funding organizations had no role in the study design; in the collection,
analysis and interpretation of data; in the writing of the manuscript; and in
the decision to submit the manuscript for publication.
Author details
Department of Psychiatry, State University of New York at Stony Brook,
Putnam Hall - South Campus, Stony Brook, NY 11794-8790, NY, USA. 2Sectionof Psychiatric Epidemiology - LIM 23, Institute of Psychiatry, University of São
Paulo Medical School, Sãu Paulo, Brazil. 3Department of Health Care Policy,
Harvard Medical School, Boston, MA. 4Health Services Research Unit, IMIM
(Hospital del Mar Research Institute), Barcelona, Spain and CIBER en
Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain. 5IRCCS Centro S.
Giovanni di Dio Fatebenefratelli, Brescia, Italy. 6Netherlands Institute of
Mental Health and Addiction, Utrecht, The Netherlands. 7Department of
Psychiatry, University Hospital Gasthuisberg, Leuven, Belgium. 8Shenzhen
Institute of Mental Health & Shenzhen Kangning Hospital, Shenzhen, China.
9
Department of Clinical Psychology, Hiroshima International University,
Higashi-Hiroshima, Japan. 10Department of Psychiatry and ClinicalPsychology, Saint George Hospital University Medical Center, Balamand
University Medical School and the Institute for Development, Research,
Advocacy and Applied Care (IDRAAC), Beirut, Lebanon. 11Directorate Generalof Health Services, New Delhi, India. 12Ukrainian Psychiatric Association, Kiev,
Ukraine. 13Hôpital Lariboisière Fernand Widal, Assistance Publique Hôpitauxde Paris INSERM U 705, CNRS UMR 7157 University Paris Diderot and Paris
Descartes Paris, France. 14Research & Planning, Mental Health Services
Ministry of Health, Jerusalem, Israel. 15Institute of Social Medicine,
Occupational Health and Public Health University of Leipzig, Leipzig,
Germany. 16National Institute of Psychiatry, Mexico City, Mexico. 17The
University of Tasmania Statewide and Clinical Director Dept of Health andHuman Services New Town, Tasmania, Australia. 18Instituto Colombiano delSistema Nervioso, Bogota D.C., Colombia. 19Section of Psychiatric
Epidemiology, Institute of Psychiatry, School of Medicine, University of São
Paulo, São Paulo, Brazil. 20Department of Society, Human Development and
Health, Harvard School of Public Health, Boston, MA, USA.
1

Authors’ contributions
All authors were involved in data collection in their individual countries andparticipation in the design of the study for this report. EB, NAS, IH and RCK
carried out the data analyses. All authors made critical revisions andapproved the final manuscript.
Competing interests
RCK has been a consultant for AstraZeneca, Analysis Group, Bristol-Myers
Squibb, Cerner-Galt Associates, Eli Lilly & Company, GlaxoSmithKline Inc.,
HealthCore Inc., Health Dialog, Integrated Benefits Institute, John Snow Inc.,

Bromet et al. BMC Medicine 2011, 9:90
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Kaiser Permanente, Matria Inc., Mensante, Merck & Co, Inc., Ortho-McNeil
Janssen Scientific Affairs, Pfizer Inc., Primary Care Network, Research Triangle
Institute, Sanofi-Aventis Groupe, Shire USA Inc., SRA International, Inc., Takeda
Global Research & Development, Transcept Pharmaceuticals Inc. and WyethAyerst; has served on advisory boards for Appliance Computing II, Eli Lilly &Company, Mindsite, Ortho-McNeil Janssen Scientific Affairs, Plus One HealthManagement and Wyeth-Ayerst; and has had research support for his
epidemiological studies from Analysis Group Inc., Bristol-Myers Squibb, Eli
Lilly & Company, EPI-Q, GlaxoSmithKline, Johnson & Johnson
Pharmaceuticals, Ortho-McNeil Janssen Scientific Affairs., Pfizer Inc., SanofiAventis Groupe and Shire USA, Inc. The remaining authors report nocompeting interests.
Received: 9 June 2011 Accepted: 26 July 2011 Published: 26 July 2011
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Pre-publication history
The pre-publication history for this paper can be accessed here:
http://www.biomedcentral.com/1741-7015/9/90/prepub
doi:10.1186/1741-7015-9-90
Cite this article as: Bromet et al.: Cross-national epidemiology of DSM-IV
major depressive episode. BMC Medicine 2011 9:90.

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