Social inequalities and the common mental disorders - A systematic review of the evidence

Department of Psychiatry, University of Leicester, Leicester, UK.
Social Psychiatry and Psychiatric Epidemiology (Impact Factor: 2.54). 06/2003; 38(5):229-37. DOI: 10.1007/s00127-003-0627-2
Source: PubMed


Of two large-scale government-commissioned studies of common mental disorders in the UK, one found occupational social class to be the strongest marker of risk while the other showed no clear relationship. This study reviews the published evidence on the links between conventional markers of social position and the common mental disorders in developed countries.
Inclusion criteria covered general population based studies with broad social class variation; samples of 3,000 or more adults of working age; identification of mental illness by validated instruments; social position identified by explicit standard markers; fieldwork undertaken since 1980; published output on key areas of interest. Incompatible study methods and concepts made statistical pooling of results invalid.
Of nine studies, eight provide evidence of an association between one or more markers of less privileged social position and higher prevalence of common mental disorders. For some individual indicators in particular studies, no clear trend was evident, but no study showed a contrary trend for any indicator. The more consistent associations were with unemployment, less education and low income or material standard of living. Occupational social class was the least consistent marker.
Common mental disorders are significantly more frequent in socially disadvantaged populations. More precise indicators of education, employment and material circumstances are better markers of increased rates than occupational social class.

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    • "Impoverished adults are at high risk for experiencing mood, anxiety, and substance abuse disorders (Fryers, Melzer, and Jenkins, 2003; Lorant et al., 2007; Sareen et al., 2011). Moreover, low-income minority women carry an undue burden of mental illness, as they are less likely than white women to seek or receive adequate mental health care (Young et al., 2001). "

    Health and education in early childhood: Predictors, interventions, and policies, 1st edited by A. J. Reynolds, A.J. Rolnick, J.A. Temple, 08/2015: chapter 9: pages 234-256; Cambridge University Press., ISBN: ISBN-13: 9781107038349
    • "These results corroborate the results found in Gispert et al. (2006) study with data from the Catalonia Health Survey. There are many studies showing the association between unemployment and mental health problems at the individual level (Alonso & Lépine, 2007; Artazcoz et al., 2004; Fryers et al., 2003; Haro et al., 2006), however, there is scarce literature that explores this impact at a contextual level. These results are particularly important considering the context of Spain, which historically presents high rates of unemployment (Legido-Quigley et al., 2013). "
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    ABSTRACT: The aim of this study was to analyze inequalities in the prevalence of poor mental health and their association with socioeconomic variables and with the care network in the Autonomous Communities in Spain. A cross-sectional multilevel study was performed, which analyzed individual data from the National Health Survey in Spain (ENS), in 2006 ( n = 29,476 people over the age of 16). The prevalence of poor mental health was the dependent variable, measured by the General Health Questionnaire (GHQ-12 > = 3). Individual and contextual socioeconomic variables, along with mental health services in the Autonomous Communities, were included as independent variables. Models of multilevel logistic regression were used, and odds ratios (OR) were obtained, with confidence intervals (CI) of 95%. The results showed that there are inequalities in the prevalence of poor mental health in Spain, associated to contextual variables, such as unemployment rate (men OR 1.04 CI 1.01–1.07; women OR 1.02 CI 1.00–1.05). On the other hand, it was observed that inequalities in the mental health care resources in the Autonomous Communities also have an impact on poor mental health.
    05/2015; 18:E27. DOI:10.1017/sjp.2015.28
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    • "In a meta-analysis that comprises more than 50 cross-national epidemiological studies, Lorant et al. (2003) find that income level is negatively correlated with the risk of depression. Similar findings are reported in a related survey by Fryers et al. (2003). Weich et al. (2001) use cross-sectional survey data for Britain to show that individuals with the lowest incomes score worst in the mental-health section of the General Health Questionnaire (GHQ). "
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    ABSTRACT: We show that the effect of income deprivation upon mental health is determined by an individual's non-cognitive skills. Income deprivation is operationalized as the Yitzhaki index, i.e., as a function of the sum of income differences between an individual and others in her reference group who are more affluent. Non-cognitive skills are extracted from a Locus of Control questionnaire and the Big Five Inventory, a self-report measurement of an individual in regard to five aspects of personality: conscientiousness, neuroticism, extraversion, agreeableness and open-mindedness. The results, based on the 2002-2010 waves of the German Socio-Economic Panel dataset (SOEP), show that deprivation exerts a significant negative effect on mental health. However, neurotic individuals are more deprivation-sensitive than are others. Compared to the mean effect, a one standard deviation rise in neuroticism is associated with a deprivation effect that is 36.6% and 51.9% larger among men and women, respectively. Although to a lesser extent, extraverted men and conscientious women are also found to be more deprivation-sensitive than are others, the corresponding figures being 31.1% and 45.9%, respectively. These findings suggest that personality differences should be taken into account in the design of policies, practices and initiatives aimed at alleviating the well-being costs of income deprivation.
    Economics & Human Biology 04/2015; 17:16-28. DOI:10.1016/j.ehb.2014.11.004 · 1.90 Impact Factor
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