Freeman et al. BMC Public Health (2016) 16:1098 DOI 10.1186/s12889-016-3638-0 RESEARCH ARTICLE Open Access The role of socio-economic status in depression: results from the COURAGE (aging survey in Europe) Aislinne Freeman1, Stefanos Tyrovolas2,3, Ai Koyanagi2,3, Somnath Chatterji3, Matilde Leonardi4, Jose Luis Ayuso-Mateos5,6, Beata Tobiasz-Adamczyk7, Seppo Koskinen8, Christine Rummel-Kluge1 and Josep Maria Haro2,5,9* Abstract Background: Low socio-economic status (SES) has been found to be associated with a higher prevalence of depression. However, studies that have investigated this association have been limited in their national scope, have analyzed different components of SES separately, and have not used standardized definitions or measurements across populations. The aim of the current study was to evaluate the association between SES and depression across three European countries that represent different regions across Europe, using standardized procedures and measurements and a composite score for SES. Method: Nationally-representative data on 10,800 individuals aged ≥18 from the Collaborative Research on Ageing in Europe (COURAGE) survey conducted in Finland, Poland and Spain were analyzed in this cross-sectional study. An adapted version of the Composite International Diagnostic Interview was used to identify the presence of depression, and SES was computed by using the combined scores of the total number of years educated (0–22) and the quintiles of the country-specific income level of the household (1–5). Multivariable logistic regression was used to assess the association between SES and depression. Results: Findings reveal a significant association between depression and SES across all countries (p ≤ 0.001). After adjusting for confounders, the odds of depression were significantly decreased for every unit increase in the SES index for Finland, Poland and Spain. Additionally, higher education significantly decreased the odds for depression in each country, but income did not. Conclusion: The SES index seems to predict depression symptomatology across European countries. Taking SES into account may be an important factor in the development of depression prevention strategies across Europe. Keywords: Depression, Socioeconomic status, Cross-national, Income, Education Background countries across Europe [3, 4], which may be a reflection Depression is a significant public health issue which of the role of contextual factors, such as economic, transcends communities and countries. It is the leading demographic and environmental factors on the develop- cause of disability worldwide, and the global burden of ment and prevalence of depression [5–9]. Beset by grow- depression is on the rise [1, 2]. The prevalence of de- ing national and international inequalities in income, pression varies considerably both within and between education and wealth, socioeconomic status (SES) has come into focus as a crucial determinant of depression * Correspondence: jmharo@pssjd.org 2 [10]. The role of SES in depression is an important theme, Parc Sanitari Sant Joan de Déu, Universitat de Barcelona, Fundació Sant Joan de Déu/CIBERSAM, Dr. Antoni Pujadas, 42, Sant Boi de Llobregat, 08830 and there is a large body of literature which illustrates the Barcelona, Spain negative association between SES and depression [11–13] 5 Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud where according to Lorant and colleagues, low SES- Mental, CIBERSAM, Monforte de Lemos 3-5, Pabellón 11, 28029 Madrid, Spain Full list of author information is available at the end of the article individuals have higher odds of being depressed [14]. © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Freeman et al. BMC Public Health (2016) 16:1098 Page 2 of 8 To date, many studies have evaluated the role of SES east and the south of Europe respectively, and taking on depression, using individual levels of stratification into consideration their populations, health and welfare related to income, education, occupation, social class, or characteristics (median age, life expectancy and sex wealth [10, 13–17]. However, despite this, there is a ratio) [20]. In Poland and Spain, a stratified multistage dearth of research that uses standardized measures or clustered design was used using strata according to definitions for SES, or that compare this association geographical administrative and catchment area sizes. between countries of different socio-economic and Municipalities and census units were systematically cultural contexts using such measures. The Collabora- selected with probabilities proportional to the popula- tive Research on Ageing in Europe (COURAGE), from tion size. Age strata were used to select households, and which our data was derived, is among the few large individuals were randomly selected from inhabitants in a population-based nationally-representative health studies certain age group within the household. In Finland, a that apply standardized designs and procedures across two-stage clustered sampling design was used and strata all survey populations. The countries included in the were created based on the largest towns and university COURAGE survey were deliberately chosen to represent hospital regions. Systematic sampling was conducted so different cultural and economic statuses in Europe. that the sample size in each stratum was proportional to As yet, there has been no “gold standard” put forward the base population. The differences in socio-economic for measuring SES, and as such, due to variation in meas- gradients between Finland, Poland, and Spain provide urement techniques, the transferability and comparability opportunities to compare the effects of social security of existing findings are limited. Traditionally, the measure- mechanisms and aging outcomes. The individual ment of SES can encompass several different indicators, response rates were 53.4, 66.5 and 69.9 %, in Finland, which often results in gradients of varying slopes [18]. Poland and Spain respectively. Sampling weights were Moreover, the numerous interchangeable terms used to generated to account for the complex study design in describe SES create complexity in interpreting findings. each country. Post-stratification corrections were made Using standardized measurements and definitions for SES to the weights to adjust for the population distribution allows for comparison, particularly between different obtained from the national census from each country. countries, and ensures that the same component is being Information was gathered through household inter- measured. This is important particularly for good research views, where interviews were conducted face-to-face by practice, and for clinical application. Computer-Assisted Personal Interviewing (CAPI). All the In addition to this, the majority of the studies investi- interviewers participated in a training course for the ad- gating this association are not representative of an entire ministration of the survey. Quality control procedures population or country, nor have previous studies focused were implemented during fieldwork [17]. The instruments collectively on the current countries in question. More- were translated from English into Finnish, Polish, and over, in epidemiological studies investigating the rela- Spanish following the World Health Organization (WHO) tionship between depression and measures of SES, translation guidelines for assessment instruments, which standardized measurements or definitions for SES and included a forward translation, a targeted back-translation, depression were lacking. review by a bilingual expert group and a detailed transla- Given the increasing burden of depression globally [6], tion report [21]. These three countries followed this same the deepening challenge of income inequality [19], as systematic methodology and utilized the same standard- well as the lack of global evidence on the association ized questionnaire to collect information on health and between depression and SES among cross-country popu- well-being among non-institutionalized adult populations. lations using systematic measures, the aim of the present Further details of the survey can be found elsewhere [22]. study was to evaluate and compare the association be- tween SES (as a composite score), education and income Depression with depression in three model European countries. An adapted version of the Composite International Diagnostic Interview (CIDI 3.0) was used to assess the Methods presence of depression in the previous 12 months [23]. Design Depression was confirmed if a certain number of symp- The COURAGE was a cross-sectional, general popula- toms of depression were endorsed by the respondent, as tion survey of non-institutionalized adults (aged calculated by an algorithm based on the DSM-IV for ≥18 years), conducted in Finland, Poland and Spain Major Depressive Disorder [24]. between 2011 and 2012 (n = 10,800 individuals: Finland 1976; Poland 4071; and Spain 4753). These countries Socio-demographic and lifestyle characteristics were selected to give a broad representation across Participants were asked to provide various socio- different European regions, representing the north, the demographic information (e.g. age and sex). Marital Freeman et al. BMC Public Health (2016) 16:1098 Page 3 of 8 status was categorized as single/never married; married/ conducted to account for the heterogeneity between co-habiting; or divorced/widowed. Country-wise quin- countries. The baseline characteristics were compared tiles of household income before tax were calculated between depressed and non-depressed. Continuous vari- asking the specific question ¨ which categorization best ables (SES index) was presented as mean ± SD, and represents the total personal earnings income of all fam- categorical variables as percentages. Chi square tests ily members (including yourself ) in the past 12 months, were used to test the association between depressed and before taxes? (Count only wages and other stipends from non-depressed for each variable. Logistic regression their employment, not pensions, investments, or other analyses with multiple variables were conducted to income)¨. Next the household income variable was cor- assess the association between education, income or SES rected with the use of household income by social secur- index (independent variable) and depression (dependent ity retirement benefits, by any income from government variable) for the individual countries. All models were assistance programs and other sources (i.e., pensions, in- adjusted for age, sex, and marital status. The results vestments, child support or alimony). The 1st quintile illustrating education and income included in the and the 5th quintile represented the lowest and the models individually have been presented in order to highest level of wealth respectively. Education was based enhance our knowledge of those aspects of SES that are on the total number of years of education received, with critical for depression, and also to facilitate comparisons 0 being the minimum and 22 the maximum. with existing studies. The sample weighting and the complex study design were taken into account in all Socio-economic status analyses to generate nationally-representative estimates. The indicators education and income have been chosen Results from regression models are presented as odds as components of the SES index as they have strong the- ratios and 95 % Confidence Intervals (CIs). All reported oretical associations with depression [18, 25–30]. The p-values were based on two-sided test, where the level of use of years educated and income level as components statistical significance was set at p < 0.05. SPSS software, of the SES index is also supported as they are more version 19 was used for all statistical calculations (SPSS applicable to modern society, are based mainly on Inc., Chicago IL, USA). numerical, self-report data, and are easy to obtain. The omission of occupation based measures can be justified Results because it is not applicable to people who are currently The analytical sample size was 10,800 (Finland, 1976; unemployed (students, jobless individuals, retired Poland, 4071; Spain, 4753). The mean age ± SD in Finland people, stay-at-home mothers, etc.), many occupational was 50 ± 0.43, 46 ± 0.42 in Poland, and 48 ± 0.33 in Spain. based measures are outdated [31] and also because oc- The prevalence of depression was 4 % in Finland and cupation may have different meanings for different birth Poland, and 9 % in Spain (p ≤ 0.001), where females had a cohorts and in different geographical settings (which significantly higher prevalence of depression than males in may make international comparisons problematic) [18]. each country (≤0.001). In terms of age groups, Finland Based on existing literature [32–36], a composite score had a higher proportion of the younger age group who for SES, using the determinants of education and were categorized as depressed, and Spain had a higher income, was generated. Education and income included proportion of the older age group who were categorized in the same model may lead to biased estimates due to as depressed (p ≤ 0.001). collinearity, therefore, the composite score was gener- Table 1 presents the association between sociodemo- ated in order to counteract this. The composite score of graphic characteristics by depression status and by coun- SES was computed by using the total number of years try. Table 1 also illustrates significant differences between educated (0–22) and the quintiles of the country-specific depressed and non-depressed people for age, marital income level of the household (1–5). Education level status, education, and income. Most noteworthy for the and household income level in multi-adjusted models current study, findings reveal a significant association could also be independently added. However, this between depressed and non-depressed people for SES analysis would raise co-linearity issues, which influences across all countries. the robustness of the model’s estimates. In order to pro- Logistic regression analyses were conducted to assess the vide an accurate estimate of respondents’ SES, these two association between indices of SES (education, Model 1; variables were multiplied to create combined scores and income, Model 2) and the index for SES (Model 3), ranging from 0 to 110. with depression as the outcome (see Table 2). After adjust- ing for the various confounders (sex, age, and marital Statistical analysis status) the logistic regression models illustrated that for all Data was available on 10,800 participants (Finland, 18 %; countries (Finland, Poland and Spain), the odds of depres- Poland, 38 %; Spain 44 %). Country-wise analyses were sion were significantly decreased for every unit increase in Freeman et al. BMC Public Health (2016) 16:1098 Page 4 of 8 Table 1 Demographic characteristics of the COURAGE study sample, stratified by depression status, and by country Finland (%) Poland (%) Spain (%) Depressed Non-Depressed p Depressed Non-Depressed p Depressed Non-Depressed p (n = 80) (n = 1854) (n = 214) (n = 3726) (n = 510) (n = 4073) Sex Male 21.1 49.2 ≤0.001 36.2 48.0 0.054 31.0 51.1 ≤ 0.001 Age Group 0.21 0.04 ≤0.0001 18–39 41.8 32.1 26.7 42.0 21.8 38.2 40–64 42.1 45.2 50.0 40.9 50.1 41.9 65+ 16.1 22.7 23.3 17.1 28.1 19.9 Marital Status ≤ 0.001 0.004 ≤ 0.001 Single 31 25 23 17 21 28 Married/Cohabiting 38 60 49 65 45 58 Divorced/Widowed 31 15 28 13 34 14 No. Yrs Education mean ± SD 12.09 ± 3.8 12.2 ± 4.1 0.819 11.05 ± 3.9 11.75 ± 3.7 0.009 8.27 ± 5.7 10.9 ± 5.6 ≤ 0.001 Income Quintiles ≤ 0.001 0.004 ≤ 0.001 1 (Poorest) 25.2 22.6 38.2 22.9 12.4 23.5 2 (Poorer) 31.3 15.2 18.1 13.7 32.5 14.7 3 (Middle) 19.3 18.1 11.1 13.8 24.2 19.6 4 (Rich) 14.9 27.9 10.8 22.3 18.2 23.3 5 (Richest) 9.2 16.2 21.9 27.2 12.7 18.9 SES Index (0–110) 31.9 ± 22.0 38.7 ± 25.5 ≤ 0.02 27.4 ± 23.6 34.9 ± 24.2 ≤ 0.001 23.3 ± 21.5 32.6 ± 26.2 ≤ 0.001 the SES index (OR 0.99, 95 % CI 0.98–0.99; OR 0.98, 95 % 0.01), Poland (rho = 0.31, p < 0.01) and Spain (rho = 0.17, CI = 0.975–0.99, OR 0.99, 95 % CI 0.984–0.99, respectively). p < 0.01). Income was found to be a significant predictor of depres- sion in Finland and Poland, but not in Spain. Discussion Concerning the correlation of SES components by Our findings illustrate that for each country, higher country analysis, a positive correlation was observed education and a higher SES index score act as protective between education and income in Finland (rho = 0.45, p < factors against depression. A higher income was Table 2 Results from the multivariable logistic regression analysis on the association between indices of socio-economic status on depression by country Model 1 Model 2 Model 3 OR 95 % CI OR 95 % CI OR 95 % CI Depression Finland Yrs of Education 0.94 0.89–0.985 Income (Quintile) 0.84 0.714–0.987 SES Index (0–110) 0.991 0.98–0.997 Depression Poland Yrs of Education 0.934 0.88–0.983 Income (Quintile) 0.84 0.71–0.98 SES Index (0–110) 0.986 0.975–0.99 Depression Spain Yrs of Education 0.913 0.887–0.939 Income (Quintile) 1.0 0.914–1.09 SES Index (0–110) 0.989 0.984–0.995 Model 1 includes years educated, and was adjusted for sex, age, and marital status. Model 2 includes income quintiles, and was also adjusted for sex, age and marital status. Model 3 includes SES index, and was also adjusted for sex, age and marital status Freeman et al. BMC Public Health (2016) 16:1098 Page 5 of 8 associated with lower odds of having depression in research by having demonstrated that a composite score Finland and Poland, but not in Spain. To the best of our of SES based on two components of SES (education and knowledge, this is the first multi-national European income) can in fact predict depression symptomatology study that has evaluated the association between SES as reliably across three countries. a concrete index and depression, using nationally- An explanation for why higher income quintiles were representative data from three countries using standard- not associated with depression in Spain can be postu- ized instruments. This allowed for a comparison be- lated when considering the context of the countries at tween the different settings (Spain, Finland and Poland), the time of data collection. At the time of the data which has not been done previously. collection, the prevalence of depression in Spain (9 %) The findings from the current study, which illustrates was found to be more than double the rate of depression the impact that a higher SES has on preventing depres- compared to both Finland (4 %) and Poland (4 %). This sion, reinforces findings from previous studies. Results discrepancy in rates of depression can be attributed to a from a meta-analysis on socioeconomic inequalities in number of factors. Firstly, at the time of data collection, depression conducted by Lorant and colleagues [29] Spain was embroiled in a financial crisis, where un- revealed conclusively that low SES individuals had employment was at a record high, exceeding 20 % [42]. higher odds of being depressed. This study also found For this same period, the unemployment rate in Poland that for each additional year of education, the odds of and Finland were both under 10 % [43, 44]. In light of being depressed decreased by 3 % and a 1 % increase in the fact that there were no methodological differences the income ranking led to a 0.74 decrease in the log between the countries in the study design, instruments odds of being depressed [29]. Additionally, the correl- or definitions used, the discrepancy in the prevalence of ation between income and education which was found depression may also be attributed to cultural differences. across all countries in the current study support previ- Such cultural variances may involve the willingness to ous studies of European regions which found that high report, the differences in the amount of stigma attached to levels of educational attainment were found to be signifi- depression between the countries (which would influence cantly and robustly associated with higher income [37]. the reliability of the self-report measure due to self- The findings which outline that education has a role presentation biases), and different environmental stressors in the prevalence of depression supports previous find- (e.g. urbanization) [45–47]. Further research on under- ings from an epidemiological study of major depressive standing the cross-cultural differences in depression is disorder (MDD), which illustrated an inverse association needed in order to examine how these factors interact with between the prevalence of MDD and level of education each other and influence the prevalence rates. [38]. Additionally, a large prospective study demon- By reason of the culture explanation, this may also be strated that lower education was associated with an a valid explanation as to why a lower income was not increased risk of depression at follow-up [20]. In the found to be associated with depression in Spain. Perhaps European context, a Norwegian cross-sectional study on that for Spanish people, income is not the main protect- adults found that low education levels ware significantly ive factor against depression, and the likes of good social associated with depression [39]. This association was networks [48], sunlight [49] and diet [50] may all be consistent across all the countries in the current study. important protective factors against depression in Spain. In terms of income, our findings demonstrated that The interpretation of the current evidence is complex, higher income quintiles were associated with depression, however, and the findings that a lower SES is associated however only in Finland and Poland. Other European with depression can be interpreted differently in each of studies also found that personal economy was strongly the settings, as the effect of SES on depression may not and independently associated with depression [40, 41]. be due to the same reason in all settings. For instance, Additionally, our results support the use of the SES living conditions, lifestyle choices, health and welfare index as a composite score for SES, using both years in characteristics and culture may contribute to the vari- education and income level [32–36]. These two indica- ability in the prevalence of depression. For example, tors are supported as components for the SES index as Spain has limited mental health coverage compared to they have a strong theoretical association with depres- most other European countries, and therefore may sion, and the data was easily obtained and applicable to account for the higher rates of depression compared to our study sample [25–29]. The multiple-indicator Poland and Finland. One explanation for the finding that approach to establish SES has the advantage of providing lower income was not associated with depression in more information and greater flexibility [33–36]. The Spain may be that because 80 % of depression patients current findings contribute to existing research in in Spain live with their families. This is a larger percent- depression, illustrating that depression and low SES are age than any other EU country [51], and may account inextricably linked, but it also builds upon previous for why household income alone is not associated with Freeman et al. BMC Public Health (2016) 16:1098 Page 6 of 8 depression in Spain. In Finland, studies have found that has been historically and theoretically regarded as crucial depression is associated with retirement, which may be components of SES. due to the fact that retirees are economically inactive [52] – this supports previous studies which found that Conclusions household income is a significant determinant of depres- The SES index (composed of education and income) ap- sive symptoms [41, 53]. Additionally, Finland has one of pears to predict depression symptomatology across Euro- the strongest income gradients in health compared to pean countries. In all countries, years of education but not other Scandinavian countries [54], and as such, it seems income level was related to depression. In light of the intuitive why in the current study, income was found to dearth of cross-national research looking at the role of SES be associated with depression in Finland. Regarding on depression in Europe, these findings have been enlight- interpreting the findings of the Polish data, similar cir- ening; however, longitudinal studies are needed to provide cumstances can account for the finding that income is further transparency regarding the direction of causality in inversely associated with depression. The level of health the relationship between SES and depression. In terms of care funding in Poland represents one of the factors implications for policy, a concrete and valid index of SES is which contributes to the emergence and persistence of required in order to inform policy and research initiatives. inequalities in health, including that of depression [55], The findings also support the notion that resources should and this is reflected in the data which illustrates that in- be allocated to developing strategies to enhance economic come is negatively associated with depression in Poland. growth and educational programmes in low SES areas in order to have positive benefits that will protect against the Strengths and limitations development and persistence of depression. The strength and novelty of the current study is that it is Abbreviations the first paper that presents the relationship between SES CAPI: Computer-assisted personal interviewing; CI: Confidence interval; and depression among three European countries by apply- CIDI: Composite international diagnostic interview; COURAGE: Collaborative ing standardized designs and procedures across all survey Research on Ageing in Europe; DSM-IV: Diagnostic Statistical Manual IV; MDD: Major depressive disorder; SES: Socio-economic status; WHO: World populations. This common methodology allows to us to Health Organization investigate the role or SES in countries with different cul- tural and economic statuses in Europe, maximizes cross- Acknowledgments AF received a Marie Curie fellowship from the European Commission in national transferability and comparability of the findings, order to conduct the research. The research leading to these results and serves as the first time an accurate comparison can be received funding from the People Programme (Marie Curie Actions) of the made in a European context. Another major strength of European Union’s Seventh Framework Programme FP7/2007–2013 under REA grant agreement n° 316795. Stefano Tyrovola’s work was supported by this paper is the large sample size that was available, which the Foundation for Education and European Culture (IPEP), the Sara Borrell was drawn from representative samples of non-clinical postdoctoral programme (reference no. CD15/00019 from the Instituto de populations. Moreover, regarding the nature of the study, Salud Carlos III (ISCIII - Spain) and the Fondos Europeo de Desarrollo Regional (FEDER). Ai Koyanagi’s work was supported by the Miguel Servet relatively few studies have examined SES cross-nationally, contract financed by the CP13/00150 project, integrated into the National not to mention focused on specific European countries R + D + I and funded by the ISCIII - General Branch Evaluation and that represent different cultural and economic statuses. Promotion of Health Research - and the European Regional Development Fund (ERDF). The research leading to these results has received funding Additionally, our research proposes a composite score for from the European Community’s Seventh Framework Programme (FP7/ SES based on income and education, which is also novel 2007-2013) under grant agreement number 223071 (COURAGE in Europe), for the outcome of depression across European countries. from the Instituto de Salud Carlos III-FIS research grants number PS09/ 00295 and PS09/01845, and from the Spanish Ministry of Science and Some methodological limitations should be taken in to Innovation ACI-Promociona (ACI2009-1010). The study was also supported consideration when interpreting the findings. Firstly, the by the Centro de Investigación Biomédica en Red de Salud Mental cross-sectional nature of the study renders it difficult to (CIBERSAM), Instituto de Salud Carlos III. The funders had no role in study design, data collection and analysis, interpretation of data, decision to draw any clear conclusions regarding the direction of publish, or preparation of the manuscript. the SES - depression relationship, and thus limits the applicability to determine whether any of the SES indices Funding The funding bodies had no role in the study design; the collection, analysis predicts depression over time. Another challenge of the or interpretation of data; in the writing of the report; or in the decision to current study is that there is no formal consensus submit the article for publication. regarding the definition or measurement for SES, there- fore limiting comparison to past papers. Additionally, in- Availability of data and materials Permission to use data from the COURAGE study was obtained from the country formation on social security, labor market attachment coordinators of this study in Finland, Poland, and Spain. Due to legal restrictions, and ageing outcomes were beyond the scope of the data from the COURAGE study is currently not publically available. current study. Finally, the variables ‘occupation’ and ‘pres- Authors’ contributions tige’ were omitted from the generated SES index, which ST conceived the study idea. AK, ST and AF analysed and interpreted the data, some may argue is a major shortcoming, as occupation and AF wrote the main body of the text. ST and AK contributed to the drafting of Freeman et al. BMC Public Health (2016) 16:1098 Page 7 of 8 the manuscript, interpreted the data, and commented for intellectual content. AK, 12. Muntaner C, Eaton WW, Miech R, O’Campo P. Socioeconomic position and ST, ML, CRK commented on the manuscript for intellectual content. JLAM, ML, major mental disorders. Epidemiol Rev. 2004;26:53–62. BTA, SC, SK, JMH organized and undertook the survey. All authors read and 13. Jo S-J, Yim HW, Bang MH, Lee MO, Jun T-Y, Choi J-S, et al. The association approved the final manuscript. between economic status and depressive symptoms: an individual and community level approach. Psychiatry Investigation. 2011;8(3):194–200. Competing interests 14. Lorant V, Croux C, Weich S, Deliege D, Mackenbach J, Ansseau M. The authors declare that they have no competing interests. Depression and socio-economic risk factors: 7-year longitudinal population study. Brit J Psychiat. 2007;190:293–8. 15. Patal V, Araya R, de Lima M, Ludermir A, Todd C. Women, poverty and Consent for publication common mental disorders in four restructuring societies. Soc Sci Med. 1999; Not applicable. 49:1461–71. 16. Li Z, Page A, Martin G, Taylor R. Attributable risk of psychiatric and Ethics approval and consent to participate socio-economic factors for suicide from individual-level, population-based Ethical approval for the COURAGE survey was obtained from the WHO studies: a systematic review. Soc Sci Med. 2011;72(4):608–16. Ethical Review Committee and local ethics research review boards (Helsinki 17. Ustun T, Chatterji S, Mechbal A, Murray C, WHS Collaborating Groups. and Uusimaa Hospital District, Finland; Jagiellonian University Medical Quality assurance in surveys: standards, guidelines and procedures. In: College, Krakow, Poland; Parc Sanitari Sant Joan de Déu, Barcelona, Spain). Household sample surveys in developing and transition countries. New York. 2005. Author details 18. Galobardes B, Shaw M, Lawlow DA, Lynch JW, Davy Smith G. Indicators of 1 socioeconomic position (part 1). J Epidemiol Commun H. 2006;60:7–12. Klinik und Poliklinik für Psychiatrie und Psychotherapie der Universität Leipzig, Leipzig, Germany. 2Parc Sanitari Sant Joan de Déu, Universitat de 19. OECD. Divided we stand: why income inequality keeps rising. Paris: OECD Barcelona, Fundació Sant Joan de Déu/CIBERSAM, Dr. Antoni Pujadas, 42, Publishing; 2011. Sant Boi de Llobregat, 08830 Barcelona, Spain. 3Department of Health 20. Eikemo TA, Huisman M, Bambra C, Kunst AE. Health inequalities according Statistics and Information Systems, World Health Organization, Geneva, to educational level in different welfare regimes: a comparison of 23 Switzerland. 4Department of Neurology, Public Health and Disability, Italian European countries. Sociol Health Illn. 2008;30:565–82. National Neurological Institute “Carlo Besta” Foundation IRCCS (Istituto di 21. World Health Organization. World Health Organization translation guidelines. ricovero e curaa carattere scientifico), Milan, Italy. 5Instituto de Salud Carlos III, 2013. http://www.who.int/substance_abuse/research_tools/translation/en/. Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Monforte 22. Perales J, Martin S, Ayuso-Mateos JL, et al. Factors associated with active de Lemos 3-5, Pabellón 11, 28029 Madrid, Spain. 6Department of Psychiatry, aging in Finland, Poland, and Spain. Int Psychogeriatr. 2014;15:1–13. Universidad Autónoma de Madrid, Instituto de Investigación Sanitaria Princesa (IP), 23. Haro JM, Arbabzadeh-Bouchez S, Brugha TS, de Girolamo G, Guyer ME, Jin Hospital Universitario la Princesa, Madrid, Spain. 7Epidemiology and Preventive R, Lepine JP, Mazzi F, Reneses B, Vilagut G, Sampson NA, Kessler RC. Medicine, Department of Medical Sociology, Jagiellonian University Medical Concordance of the Composite International Diagnostic Interview Version 3. College, Krakow, Poland. 8National Institute for Health and Welfare, Helsinki, 0 (CIDI 3.0) with standardized clinical assessments in the WHO World Finland. 9Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Mental Health surveys. Int J Methods Psychiatr Res. 2006;15(4):167–80. Dr. Antoni Pujadas, 42, 08830 Sant Boi de Llobregat, Barcelona, Spain. 24. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 4th ed. Washington, DC: Author; 1994. Received: 10 January 2016 Accepted: 4 September 2016 25. Kaplan GA, Roberts R, Camacho TC, Coyne JC. Psychosocial predictors of depression. Prospective evidence from the human population laboratory studies. Am J Epidemiol. 1987;125(2):206–20. References 26. Sargeant K, Bruce ML, Florio LP. Factors associated with 1-year outcome of 1. World Health Organisation. Factsheet on Depression. World Health major depression in the community. Arch Gen Psychiat. 1990;47:519–26. Organisation. 2012. http://www.who.int/mediacentre/factsheets/fs369/en/. 27. Miech RA, Shanahan MJ. Socioeconomic status and depression over the life Accessed 15 Aug 2015 course. J H Soc Behav. 2000;41(2):162–76. 2. Lépine JP, Briley M. The increasing burden of depression. Neuropsychiatr Dis 28. Mirowsky J, Ross CE. Education, personal control, lifestyle and health–a Treat. 2011;7 Suppl 1:3–7. human capital hypothesis. Res Aging. 1998;20:415–49. 3. Wittchen HU, Jacobi F. Size and burden of mental disorders in Europe—a 29. Lorant V, Deliege D, Eaton W, Robert A, Philippot P, Ansseau M. critical review and appraisal of 27 studies. Eur Neuropsychopharm. Socioeconomic inequalities in depression: a meta-analysis. Am J Epidemiol. 2005;15(4):357–76. 2003;157(2):98–112. 4. Kessler RC, Bromet EJ. The epidemiology of depression across cultures. 30. Virtanen M, Kivimaki M, Elovainio M, Linna A, Pentti J, Vahtera J. Annu Rev Public Health. 2013;34:119–38. Neighbourhood socioeconomic status, health and working conditions of 5. Ayuso-Mateos JL, Vazquez-Barquero JL, Dowrick C, Lehtinen V, Dalgard OS, school teachers. J Epidemiol Commun H. 2007;61(4):326–30. Casey P, Wilkinson C, Lasa L, Page H, Dunn G, Wilkinson G, ODIN Group. 31. Liberatos P, Link BG, Kelsey JL. The measurement of social class in Depressive disorders in Europe: prevalence figures from the ODIN study. Brit epidemiology. Epidemiol Rev. 1988;10:87–121. J Psychiatr. 2001;179:308–16. 32. Katsarou A, Tyrovolas S, Psaltopoulou T, Zeimbekis A, Tsakountakis N, 6. World Health Organization. The global burden of disease: 2004 Update. Bountziouka V, Gotsis E, Metallinos G, Polychronopoulos E, Lionis C, Geneva: The World Health Organization; 2008. Panagiotakos D. Socio-economic status, place of residence and dietary 7. Kovess-Masféty V, Alonso J, de Graaf R, Demyttenaere K. A European habits among the elderly: the Mediterranean islands study. Public Health approach to rural–urban differences in mental health: the ESEMeD 2000 Nutr. 2010;13(10):1614–21. comparative study. Can J Psychiatry. 2005;50(14):926–36. 33. Haug MR. Measurement in social stratification. Annu Rev Sociol. 1977;3:51–77. 8. Kessler RC, Aguilar-Gxiola S, Alonso J, Chatterji S, Lee S, Ormel J, Üstün TB, 34. Stevens G, Cho JH. Socioeconomic indexes and the new 1980 census Wang PS. The global burden of mental disorders: an update from the WHO occupational classification scheme. Soc Sci Res. 1985;14:142–68. World Mental Health (WMH) surveys. Epidemiol Psichiatr Soc. 2009;18(1):23–33. 35. Stravraky KM, Kincade JE, Steward MA, Donner AP. The effect of socioeconomic 9. Pikhartova J, Chandola T, Kubinova R, Bobak M, Nicholson A, Pikhart H. factors on the early prognosis of cancer. J Chronic Dis. 1987;40:237–44. Neighbourhood socioeconomic indicators and depressive symptoms in the 36. Stockwell EG. A critical examination of the relationship between Czech Republic: a population based study. Int J Publ Health. 2009;54:283–93. socioeconomic status and mortality. Am J Pub H. 1963;53:956–64. 10. Krieger N, Williams DR, Moss NE. Measuring social class in US public health 37. Rodríguez-Pose A, Tselios V. Education and income inequality in the regions research: concepts, methodologies, and guidelines. Annu Rev Publ Health. of the European union SERC. London: Department of Geography & 1997;18:341–78. Environment, London School of Economics; 2008. 11. Andrade L, Caraveo-Anduaga JJ, Berglund P, et al. Crossnational 38. Gan Z, Li Y, Xie D, Shao C, Yang F, Shen Y, et al. The impact of educational comparisons of the prevalences and correlates of mental disorders. Bull status on the clinical features of major depressive disorder among Chinese World Health Organ. 2000;78:413–26. women. J Affect Disord. 2012;136(3):988–92. Freeman et al. BMC Public Health (2016) 16:1098 Page 8 of 8 39. Bjelland I, Krokstad S, Mykletun A, Dahl A, Tell GS, Tambs K. Does a higher education level protect against anxiety and depression? The HUNT study. Soc Sci Med. 2008;66(6):1334–45. 40. Molarius A, Berglund K, Eriksson C, Eriksson HG, Lindén-Boström M, Nordström E, Persson C, Sahlqvist L, Starrin B, Ydreborg B. Mental health symptoms in relation to socio-economic conditions and lifestyle factors–a population-based study in Sweden. BMC Public Health. 2009;9:302. 41. Lahelma E, Laaksone M, Marikainen P, Rahkonena O, Sarlio-Lähteenkorva S. Multiple measures of socioeconomic circumstances and common mental disorders. Soc Sci Med. 2006;63:1383–99. 42. Eurostat. EuroStat Database. Unemployment rate in Spain. European Central Bank. Brussels: European Commission. http://sdw.ecb.europa.eu/quickview. do?SERIES_KEY=132.STS.M.ES.S.UNEH.RTT000.4.000. Accessed 23 Oct 2015 43. Eurostat. EuroStat Database. Unemployment rate in Poland. European Central Bank. Brussels: European Commission. http://sdw.ecb.europa.eu/quickview. do?SERIES_KEY=132.STS.M.PL.S.UNEH.RTT000.4.000. Accessed 23 Oct 2015 44. Eurostat. EuroStat Database. Unemployment rate in Finland. European Central Bank. Brussels: European Commission. http://sdw.ecb.europa.eu/ quickview.do?SERIES_KEY=132.STS.M.FI.S.UNEH.RTT000.4.000 45. McKenzie K, Murray A, Booth T. Do urban environments increase the risk of anxiety, depression and psychosis? An epidemiological study. J Affect Disorders. 2013;150(3):1019–24. 46. Georg Hsu LK, Wan YM, Chang H, Summergrad P, Tsang BY, Chen H. Stigma of depression is more severe in Chinese Americans than Caucasian Americans. Psychiatry. 2008;71(3):210–8. 47. Berger JL, Addis ME, Reilly ED, Green JD. Effects of gender, diagnostic labels, and causal theories on willingness to report symptoms of depression. J Soc Clin Psych. 2012;31(5):439–57. 48. Tuesca-Molina R, Fierro Herrera N, Molinares Sosa A, Oviedo Martínez F, Polo Arjona Y, Polo Cueto J, Sierra Manrique I. Socializing groups as protective factor against depression in elderly people. Barranquilla, Colombia. 2003;77(5):595–604. 49. Kent ST, McClure LA, Crosson WL, Arnett DK, Waley VG, Sathiakumar N. Effect of sunlight exposure on cognitive function among depressed and non-depressed participants: a REGARDS cross-sectional study. Environ Health. 2009;8:34. 50. Skarupski KA, Tangney CC, Li H, Evans DA, Morris MC. Mediterranean diet and depressive symptoms among older adults over time. J Nutr Health Aging. 2013;17(5):441–5. 51. Gomez-Beneyto M, Asensio A, Berenguer MJ. Desinstitucionalización de enfermos mentales crónicos sin recursoscomunitarios. In: Espinosa J, editor. Cronicicidad en Psiquiatria. Madrid: Mayoría editorial; 1996. 52. Wiggins RD, Schofield P, Sacker A, Head J, Bartley M. Social position and minor psychiatric morbidity over time in the British household panel survey 1991–1998. J Epidemiol Commun H. 2004;58:779–87. 53. Meertens V, Scheepers P, Tax B. Depressive symptoms in the Netherlands 1975–1996: a theoretical framework and an empirical analysis of socio- demographic characteristics, gender differences and changes over time. Sociol Health Illn. 2003;25:208–31. 54. Huijts T, Eikemo TA, Skalicka V. Income-related health inequalities in the Nordic countries: examining the role of education, occupational class, and age. Soc Sci Med. 2010;71:1964–72. 55. World Health Organization. Social inequalities in health in Poland. Geneva: WHO; 2012. Submit your next manuscript to BioMed Central and we will help you at every step: • We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal • We provide round the clock customer support • Convenient online submission • Thorough peer review • Inclusion in PubMed and all major indexing services • Maximum visibility for your research Submit your manuscript at www.biomedcentral.com/submit