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Scott KM, Al-Hamzawi AO, Andrade LH, et al. Associations Between Subjective Social Status and DSM-IV Mental Disorders: Results From the World Mental Health Surveys. JAMA Psychiatry. 2014;71(12):1400–1408. doi:10.1001/jamapsychiatry.2014.1337
The inverse social gradient in mental disorders is a well-established research finding with important implications for causal models and policy. This research has used traditional objective social status (OSS) measures, such as educational level, income, and occupation. Recently, subjective social status (SSS) measurement has been advocated to capture the perception of relative social status, but to our knowledge, there have been no studies of associations between SSS and mental disorders.
To estimate associations of SSS with DSM-IV mental disorders in multiple countries and to investigate whether the associations persist after comprehensive adjustment of OSS.
Design, Setting, and Participants
Face-to-face cross-sectional household surveys of community-dwelling adults in 18 countries in Asia, South Pacific, the Americas, Europe, and the Middle East (N = 56 085). Subjective social status was assessed with a self-anchoring scale reflecting respondent evaluations of their place in the social hierarchies of their countries in terms of income, educational level, and occupation. Scores on the 1 to 10 SSS scale were categorized into 4 categories: low (scores 1-3), low-mid (scores 4-5), high-mid (scores 6-7), and high (scores 8-10). Objective social status was assessed with a wide range of fine-grained objective indicators of income, educational level, and occupation.
Main Outcomes and Measures
The Composite International Diagnostic Interview assessed the 12-month prevalence of 16 DSM-IV mood, anxiety, and impulse control disorders.
The weighted mean survey response rate was 75.2% (range, 55.1%-97.2%). Graded inverse associations were found between SSS and all 16 mental disorders. Gross odds ratios (lowest vs highest SSS categories) in the range of 1.8 to 9.0 were attenuated but remained significant for all 16 disorders (odds ratio, 1.4-4.9) after adjusting for OSS indicators. This pattern of inverse association between SSS and mental disorders was significant in 14 of 18 individual countries, and in low-, middle-, and high-income country groups but was significantly stronger in high- vs lower-income countries.
Conclusions and Relevance
Significant inverse associations between SSS and numerous DSM-IV mental disorders exist across a wide range of countries even after comprehensive adjustment for OSS. Although it is unclear whether these associations are the result of social selection, social causation, or both, these results document clearly that research relying exclusively on standard OSS measures underestimates the steepness of the social gradient in mental disorders.
Decades of research1-4 have established that socioeconomic status is inversely associated with many mental disorders. Most of this research has used traditional indicators of socioeconomic status, such as educational level, income, and occupation, referred to herein as measures of objective social status (OSS). However, a recent development in the research on the associations between socioeconomic status and health has been the evaluation of subjective social status (SSS). Most studies5-14 have found that SSS is associated with physical health and psychological distress even after controlling for OSS, a finding that has been explained by the idea that SSS captures subjective judgment of relative social position.7,14 Relative social position has become a topic of great interest based on striking findings from the income inequality and physical health literature, such as that African American men with a 4-fold higher income than Costa Rican men nonetheless have a 9-year shorter life expectancy.15 This shorter life expectancy has been attributed in part to the psychosocial effects of relative deprivation and status anxiety caused by the lower relative social position of African Americans.15,16 More recently, greater income inequality among wealthy countries has been associated with a higher prevalence of mental disorders.17
Although the use of SSS measures in mental health research has been advocated,18 prior studies have typically used measures of psychological distress, such as the 36-Item Short Form Health Survey19 or General Health Questionnaire,20 and, to our knowledge, research on SSS and individual mental disorders has not been carried out. Examining a range of mental disorders is important because much of the past research on social stratification and mental health has measured depression as the outcome, but concepts of relative deprivation and status insecurity imply a wide range of emotional responses including anger, frustration, hostility, and anxiety.18 The present study used data from 20 of the World Health Organization World Mental Health (WMH) surveys to examine associations of SSS with 16 DSM-IV disorders, with the aim of determining whether these associations persist after controlling for multiple fine-grained measures of OSS. Because prior research10,21 has suggested that SSS associations with health vary by culture we estimated SSS associations with mental disorders in individual countries. In addition, because the association between income inequality and mental disorders has only been found in wealthy countries,17 we examined associations in countries grouped by income level and tested whether associations vary across high-, middle-, and low-income countries.
This study used data from 20 surveys in 18 countries (Table 1). All respondents provided written informed consent, and procedures for protecting respondents were approved and monitored for compliance by the institutional review boards in each country.22 A stratified, multistage, clustered area probability sampling strategy was used to select adult respondents. Most of the surveys were based on nationally representative household (or population register) samples; surveys in Colombia, Mexico, and Shenzhen were based on nationally representative household samples in urbanized areas. The weighted mean response rate across all surveys included in this article was 75.2% (Table 1). The surveys listed in Table 1 are grouped by World Bank country income classification into categories of low to lower-middle income, upper-middle income, and high income. For ease of reference these are referred to as low-, middle-, and high-income country groups in the text, although in the tables they retain the full descriptive labels.
The central WMH staff trained bilingual supervisors in each country. The World Health Organization translation protocol was used to translate instruments and training materials. Some surveys were carried out in bilingual form and others were carried out exclusively in the country's official language. Translation, back-translation, and harmonization of the WMH interview used standardized procedures that are discussed elsewhere.22 In most countries, internal subsampling was used to reduce respondent burden and mean interview time by dividing the interview into 2 parts. All respondents completed part 1, which included the core diagnostic assessment of most mental disorders. All part 1 respondents who met lifetime criteria for any mental disorder and a probability sample of respondents without mental disorders were administered part 2 of the survey (at the same interview sitting), which assessed the remaining mental disorders and collected a range of other information. Part 2 respondents were weighted by the inverse of their probability of selection for part 2 of the interview to adjust for differential sampling, resulting in an unbiased sample. The analyses in this study are based on the part 2 subsample (n = 56 085).
Additional weights were used to adjust for differential probabilities of selection within households, to adjust for nonresponse, and to match the samples to population sociodemographic distributions. Measures taken to ensure data accuracy, cross-national consistency, and protection of the respondents are described in detail elsewhere.22,23
All surveys used the WMH survey version of the World Health Organization Composite International Diagnostic Interview, 3.0,23 a fully structured interview administered to assess lifetime history and 12-month prevalence of DSM-IV mental disorders. The disorders included in the present article were anxiety disorders (panic disorder, agoraphobia without panic, specific phobia, social phobia, posttraumatic stress disorder, generalized anxiety disorder, and obsessive-compulsive disorder), mood disorders (major depressive disorder/dysthymia as well as bipolar broad [I, II] and subthreshold), substance use disorders (alcohol abuse and dependence and drug abuse and dependence), and impulse control disorders (intermittent explosive disorder, bulimia nervosa, and binge-eating disorder).
The SSS was measured with the MacArthur subjective social status scale, which is the most widely used indicator of SSS and has good reliability and validity.5,8,14,24 Participants were given a drawing of a ladder with 10 rungs described as follows: “Think of this ladder as representing where people stand in [country of interview]. At the top of the ladder are the people who are the best off—those who have the most money, the most education, and the most respected jobs. At the bottom are the people who are the worst off—those who have the least money, least education, and the least respected jobs or no job. The higher up you are on the ladder, the closer you are to the people at the very top; the lower you are, the closer you are to the people at the very bottom. Please place a large X on the rung where you think you stand at this time in your life, relative to the other people in [country of interview]. What is the number to the right of the rung where you placed the X?”
Educational level was assessed by self-report of the number of years of schooling completed. Three education variables were created for each respondent. These were the number of years of education, country-relative education score (number of years of education divided by the weighted median education [in years] for the respondent’s country), and neighborhood-relative education score (number of years of education divided by the weighted median education [in years] for each neighborhood [primary sampling unit] in the respondent’s country).
Income was assessed by asking respondents to estimate their total family household income from all sources in the past 12 months, before tax or any other deductions were applied, with show cards providing multiple income brackets in the currency of their country from which they could select the appropriate response. Respondents were also asked about personal income, but household income was used in the present analysis. Four income variables were created for each respondent: income percentile, income adjusted for household size, country-relative income score, and neighborhood-relative income score. These 2 latter scores were created in a manner analogous to the educational level scores.
Occupational type was based on the respondent’s information about occupation at the time of the interview and classified into one of 28 occupation types or as not working at the time of the interview. Occupational status was categorized as working (weighted percentage, 59.2%), student (4.9%), homemaker (12.6%), retired (11.5%), and other (11.9%).
Scores on the 1 to 10 SSS scale were grouped into 4 categories: low (scores 1-3), low-mid (scores 4 and 5), high-mid (scores 6 and 7), and high (scores 8-10). The high group was the reference group in all of the regression models. Participants who did not answer the question (for all countries combined, 3.6% [range across all countries, 0.3%-10.1%]), as well as those with missing data or outlying scores greater than 10 (0.6% [range, 0%-2.8%]) were excluded from the analyses. Country-specific logistic regression models estimated the associations of SSS with the aggregated indicator of any 12-month mental disorder controlling for current age, age squared, sex, and country. Models then estimated associations of SSS with any mental disorder in pooled country income groups, additionally adjusting for all of the OSS variables (income percentile score, income adjusted for household size, years of education, occupational type, occupational status, neighborhood-relative income score, country-relative income score, neighborhood-relative years of education, and country-relative years of education, plus squared versions of the income and education variables). We tested whether there were differences in strength of associations between SSS and mental disorders across low-, middle-, and high-income country groups by including cross-product terms for the interaction of SSS with dummy variables representing high-income countries and middle-income countries (low-income countries used as the reference) without and then with adjustment for OSS.
In all countries combined, logistic regression models estimated the associations of SSS with specific 12-month mental disorders, controlling for current age, age squared, sex, and country and then for all the OSS indicators. Sex moderation of associations was investigated, but the associations did not vary materially for men and women. Significant age moderation of associations was found whereby associations were strongest for the 2 middle-aged groups. However, because the inverse SSS to mental disorder gradient was evident for all age groups, we report results for all ages combined, controlling for current age and including age squared in the models to capture some of the nonlinearities in the relationship between SSS, age, and mental disorders.
Because the WMH data are both clustered and weighted, the design-based Taylor series linearization method25 was implemented. The SUDAAN, version 11, software system (RTI International) was used to estimate SEs and evaluate the statistical significance of coefficients.
The distributions of the original 10-point scale (Table 2) and 4 derived SSS categories (Table 3) are reported by country income groupings. The scores were approximately normally distributed, with all scores on the 10-point scale observed in each country income group. However, both the 10-point scale and 4-category distributions differed significantly across country income groups (χ2 = 25.1, P < .001; and χ2 = 57.8, P < .001, respectively). The nature of the differences is clearest in the 4-category distribution, where it can be seen that the proportions scoring low (in the 1-3 range) are larger in the lower-middle–income countries (18.3%) and upper-middle–income countries (17.6%) than in the high-income countries (10.3%).
Table 4 reports the associations of the 3 lower categories of SSS (relative to the highest category) with the aggregated indicator of any 12-month mental disorder in individual countries, in low-, middle-, and high-income country groups and among all countries combined. For all countries combined, there was a graded inverse association of SSS with any mental disorder, with ORs for low, low-mid, and high-mid SSS categories of 2.5, 1.7, and 1.3, respectively. This inverse gradient was evident in all countries except Japan and Nigeria and was significant in 14 of 18 countries and in 15 of 20 individual surveys.
The associations between SSS and any mental disorder for each pooled set of countries grouped by income level were stronger for the high-income countries (ORs for low, low-mid, and high-mid SSS categories, 3.1, 1.9, and 1.3, respectively) compared with upper-middle–income countries (ORs, 2.0, 1.5, and 1.3, respectively) and low-income countries (ORs, 2.0, 1.5, and 1.2, respectively). This country income group difference was statistically significant (χ26 = 22.2; P = .001). This interaction effect remained significant after inclusion of OSS in the models (χ26 = 16.1; P = .01); the results from models adjusted for OSS are also presented in Table 4.
In all countries combined, a graded, inverse pattern of association was found with all mental disorders unadjusted for OSS (Table 5). Odds ratios for the lowest SSS category relative to the highest ranged from 1.8 for intermittent explosive disorder and obsessive-compulsive disorder to 9.0 for drug dependence, with the ORs for most disorders falling between 2.0 and 4.0.
Adjustment for OSS attenuated associations to a variable degree across disorders but most strongly for the substance use disorders and some of the anxiety disorders. Despite this attenuation, SSS remained significantly associated with all disorders, with most ORs remaining greater than 2.0. Odds ratios for the lowest SSS category relative to the highest ranged from 1.4 for alcohol abuse to 4.9 for drug dependence. Of individual disorders, SSS was most strongly associated with drug dependence, but when considering associations between SSS and disorder groups, these were smallest in magnitude for any substance use disorder (OR, 1.6 for the lowest SSS category relative to the highest) and largest for any mood disorder (OR, 2.7 for the lowest SSS category relative to the highest).
In this general population sample from 18 countries, graded inverse associations were found between SSS and all mental disorders, where SSS was measured as a subjective perception of position in the country-specific hierarchy in terms of income, educational level, and occupation. This pattern of association between SSS and mental disorders was evident in 18 of 20 surveys, significant in 15 of 20 surveys, and was significantly stronger in high- than in lower-income countries. Subjective social status remained associated with all mental disorders after adjustment for a large set of fine-grained OSS indicators.
Limitations of the study include the likelihood that sample selection biases (whereby respondents with the most severe mental disorders and the lowest socioeconomic status are less likely to be included in the sample) may have restricted the range of measures and so attenuated the strength of associations. A further limitation is that our measures of OSS were restricted to educational level, income, and occupation; inclusion of other measures of OSS, such as wealth (assets), may have reduced the independent effects of SSS. Finally, the cross-sectional design of the study prevents clarification of the temporal nature of the associations so that social causation and selection effects cannot be disentangled.
Within the context of these limitations, to our knowledge, this study provides the first investigation of the relationship between SSS and diagnostic measures of a wide range of mental disorders. Most prior research on SSS has been limited to measures of psychological distress,5,7,14 and the present study shows that low SSS is associated with higher risk of all 16 mental disorders investigated. Moreover, we found that associations between SSS and mental disorders persisted after more comprehensive adjustment for OSS than was achieved in most prior studies. The major explanation advanced for why there are independent associations of SSS with health outcomes after controlling for OSS is that SSS measures subjective perception of relative social position.14 Perception of lower relative social position has been hypothesized to increase the risk of mental illness through a sense of relative deprivation and status insecurity, with associated feelings of shame, distrust, frustration, and anxiety.7,14,17,26,27 Our findings of inverse associations between SSS and all mental disorders appear to offer considerable support for this hypothesis, although as noted above we cannot determine the relative contribution of social causation vs social selection processes.
The stronger association between SSS and mental disorders in the high- relative to lower-income countries is interesting in light of a recent research17 finding that greater income inequality was associated with a higher prevalence of mental disorders in a group of high-income countries. In the present study, the stronger association between SSS and mental disorders in the high-income countries persisted even after adjustment for objective differences in absolute and relative household income, so this finding cannot be attributed to higher levels of income inequality in high- relative to lower-income countries. Indeed, income inequality was highest in some of the middle-income countries included in this study. Considering other explanations for our finding of a steeper SSS–mental disorder gradient in high-income countries, one contributing factor could be that advertising and media are more influential in high-income countries and that this has the effect of making social inequalities more visible and encouraging social comparisons17; this effect in turn could heighten status competition and status insecurity17,27 leading to stronger associations between SSS and mental disorders in high-income countries. Another possibility is that lower SSS may be more detrimental to mental health in high-income countries owing to values that are more common in wealthy countries, where success is evaluated in terms of individual achievement and prestige.28,29 In this regard it is interesting that Japan, considered a collectivist culture with a strong ethos of social relativism,29 was the only high-income country in this study with no clear SSS–mental disorder gradient.30 However, most countries, including many generally considered collectivist, exhibited associations between SSS and mental disorders.
This study found inverse graded associations between SSS and 16 DSM-IV mental disorders that remained strong after adjustment for a large set of detailed OSS indicators. This pattern of association was evident in almost all countries but was significantly stronger in high- than in lower-income countries. Although interpretation of the associations between SSS and mental disorders is far from clear cut, the strength and consistency of these associations suggests that further research is warranted and should use prospective designs that can help distinguish between social causation and selection processes. The study findings indicate that research into the social gradient in mental health that relies on the standard OSS measures of income, educational levels, and occupation will underestimate the steepness of the gradient.
Submitted for Publication: March 11, 2014; final revision received May 7, 2014; accepted June 6, 2014.
Corresponding Author: Kate M. Scott, PhD, Department of Psychological Medicine, University of Otago, PO Box 913, Dunedin, New Zealand (firstname.lastname@example.org).
Published Online: October 29, 2014. doi:10.1001/jamapsychiatry.2014.1337.
Author Contributions: Dr Scott had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Scott, Kessler.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Scott.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Scott, Lim.
Obtained funding: Scott, Andrade, Borges, Caldas-de-Almeida, Gureje, Karam, Kawakami, Levinson, Navarro-Mateu, Posada-Villa, Torres, Williams.
Administrative, technical, or material support: Gureje, Hu, Karam, Lee, Posada-Villa, Torres.
Study supervision: Scott.
Conflict of Interest Disclosures: Dr Karam has received unrestricted support for his research from AstraZeneca, Eli Lilly and Company, GalaxoSmithKline, Hikma Pharmaceuticals, Janssen Cilag, Lundbeck, Novartis, and Servier. Dr Kessler has been a consultant for Analysis Group, GlaxoSmithKline Inc, Kaiser Permanente, Merck & Co, Inc, Ortho-McNeil Janssen Scientific Affairs, Pfizer Inc, Sanofi-Aventis Groupe, Shire US Inc, SRA International, Inc, Takeda Global Research & Development, Transcept Pharmaceuticals Inc, Wellness and Prevention, Inc, and Wyeth-Ayerst; has served on advisory boards for Eli Lilly and Company, Mindsite, and Wyeth-Ayerst; and has had research support for his epidemiologic studies from Analysis Group Inc, Bristol-Myers Squibb, Eli Lilly and Company, EPI-Q, Ortho-McNeil Janssen Scientific Affairs, Pfizer Inc, Sanofi-Aventis Groupe, and Shire US, Inc. He owns stock in Datastat, Inc. No other disclosures were reported.
Funding/Support: The World Health Organization (WHO) World Mental Health (WMH) Survey Initiative is supported by the US National Institute of Mental Health (NIMH) (grant R01 MH070884), the John D. and Catherine T. MacArthur Foundation, the Pfizer Foundation, the US Public Health Service (grants R13-MH066849, R01-MH069864, and R01 DA016558), the Fogarty International Center (grant FIRCA R03-TW006481), the Pan American Health Organization, Eli Lilly and Company, Ortho-McNeil Pharmaceutical, GlaxoSmithKline, and Bristol-Myers Squibb. The São Paulo Megacity Mental Health Survey (Brazil) is supported by the State of São Paulo Research Foundation Thematic Project grant 03/00204-3. The Bulgarian Epidemiological Study of Common Mental Disorders (2014 EPIBUL) is supported by the Ministry of Health and the National Center for Public Health Protection. The Colombian National Study of Mental Health is supported by the Ministry of Social Protection. The Mental Health Study Medellín–Colombia was carried out and supported jointly by the Center for Excellence on Research in Mental Health (CES University) and the Secretary of Health of Medellín. Implementation of the Iraq Mental Health Survey and data entry were carried out by the staff of the Iraqi Ministry of Health and Ministry of Planning with direct support from the Iraq Mental Health Survey team with funding from both the Japanese and European Funds through United Nations Development Group Iraq Trust Fund. The Israel National Health Survey is funded by the Ministry of Health with support from the Israel National Institute for Health Policy and Health Services Research and the National Insurance Institute of Israel. The WMH Japan survey is supported by the Grant for Research on Psychiatric and Neurological Diseases and Mental Health (grants H13-SHOGAI-023, H14-TOKUBETSU-026, and H16-KOKORO-013) from the Japan Ministry of Health, Labour and Welfare. The Lebanese National Mental Health Survey is supported by the Lebanese Ministry of Public Health, the WHO (Lebanon), National Institute of Health/Fogarty International Center (grant R03 TW006481-01), Sheikh Hamdan Bin Rashid Al Maktoum Award for Medical Sciences, anonymous private donations to the Institute for Development, Research, Advocacy and Applied Care, Lebanon, and unrestricted grants from AstraZeneca, Eli Lilly and Company, GlaxoSmithKline, Hikma Pharmaceuticals, Janssen Cilag, Lundbeck, Novartis, and Servier. The Mexican National Comorbidity Survey is supported by the National Institute of Psychiatry Ramon de la Fuente (INPRFMDIES 4280) and by the National Council on Science and Technology (grant CONACyTG30544-H), with supplemental support from the PanAmerican Health Organization. New Zealand Te Rau Hinengaro: The New Zealand Mental Health Survey is supported by the New Zealand Ministry of Health, Alcohol Advisory Council, and the Health Research Council. The Nigerian Survey of Mental Health and Well-being is supported by the WHO (Geneva), the WHO (Nigeria), and the Federal Ministry of Health, Abuja, Nigeria. The Northern Ireland Study of Mental Health is funded by the Health & Social Care Research & Development Division of the Public Health Agency. The Chinese WMH Survey Initiative is supported by the Pfizer Foundation. The Shenzhen Mental Health Survey is supported by the Shenzhen Bureau of Health and the Shenzhen Bureau of Science, Technology, and Information. The Peruvian WMH Study was funded by the National Institute of Health of the Ministry of Health of Peru. The Polish project Epidemiology of Mental Health and Access to Care was carried out by the Institute of Psychiatry and Neurology in Warsaw in consortium with the Department of Psychiatry at the Medical University in Wroclaw and the National Institute of Public Health–National Institute of Hygiene in Warsaw and in partnership with Psykiatrist Institut Vinderen, Universitet, Oslo. The project was funded by the Norwegian Financial Mechanism and the European Economic Area Mechanism as well as the Polish Ministry of Health. The Portuguese Mental Health Study was carried out by the Department of Mental Health, Faculty of Medical Sciences, Nova University of Lisbon, with collaboration of the Portuguese Catholic University, and was funded by Champalimaud Foundation, Gulbenkian Foundation, Foundation for Science and Technology, and the Ministry of Health. The South Africa Stress and Health Study is supported by the US NIMH (grant R01-MH059575) and National Institute of Drug Abuse with supplemental funding from the South African Department of Health and the University of Michigan. The Psychiatric Enquiry to General Population in Southeast Spain–Murcia project has been financed by the Regional Health Authorities of Murcia (Servicio Murciano de Salud and Consejería de Sanidad y Política Social) and Fundación para la Formación e Investigación Sanitarias of Murcia. The Ukraine Comorbid Mental Disorders During Periods of Social Disruption study is funded by the US NIMH (grant RO1-MH61905). The US National Comorbidity Survey Replication is supported by the NIMH (grant U01-MH60220) with supplemental support from the National Institute of Drug Abuse, the Substance Abuse and Mental Health Services Administration, the Robert Wood Johnson Foundation (grant 044708), and the John W. Alden Trust. Work on this article was funded by a grant from the Health Research Council of New Zealand (Dr Scott).
Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Additional Contributions: We thank the staff of the WMH Data Collection and Data Analysis Coordination Centers for assistance with instrumentation, fieldwork, and consultation on data analysis. A complete list of all within-country and cross-national WMH publications can be found at http://www.hcp.med.harvard.edu/wmh/.
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