Low socioeconomic status (SES) is generally associated with high psychiatric morbidity, more disability, and poorer access to health care. Among psychiatric disorders, depression exhibits a more controversial association with SES. The authors carried out a meta-analysis to evaluate the magnitude, shape, and modifiers of such an association. The search found 51 prevalence studies, five incidence studies, and four persistence studies meeting the criteria. A random effects model was applied to the odds ratio of the lowest SES group compared with the highest, and meta-regression was used to assess the dose-response relation and the influence of covariates. Results indicated that low-SES individuals had higher odds of being depressed (odds ratio = 1.81, p < 0.001), but the odds of a new episode (odds ratio = 1.24, p = 0.004) were lower than the odds of persisting depression (odds ratio = 2.06, p < 0.001). A dose-response relation was observed for education and income. Socioeconomic inequality in depression is heterogeneous and varies according to the way psychiatric disorder is measured, to the definition and measurement of SES, and to contextual features such as region and time. Nonetheless, the authors found compelling evidence for socioeconomic inequality in depression. Strategies for tackling inequality in depression are needed, especially in relation to the course of the disorder.

/pdf/socioeconomic-inequalities-in-depression-a-meta-analysis-3r302tn1jr.pdf

Socioeconomic Inequalities in Depression: A Meta-Analysis

This book shows how to implement a variety of analytic tools that allow health equity - along different dimensions and in different spheres - to be quantified. Questions that the techniques can help provide answers for include the following: Have gaps in health outcomes between the poor and the better-off grown in specific countries or in the developing world as a whole? Are they larger in one country than in another? Are health sector subsidies more equally distributed in some countries than in others? Is health care utilization equitably distributed in the sense that people in equal need receive similar amounts of health care irrespective of their income? Are health care payments more progressive in one health care financing system than in another? What are catastrophic payments? How can they be measured? How far do health care payments impoverish households? This volume has a simple aim: to provide researchers and analysts with a step-by-step practical guide to the measurement of a variety of aspects of health equity. Each chapter includes worked examples and computer code. The authors hope that these guides, and the easy-to-implement computer routines contained in them, will stimulate yet more analysis in the field of health equity, especially in developing countries. They hope this, in turn, will lead to more comprehensive monitoring of trends in health equity, a better understanding of the causes of these inequities, more extensive evaluation of the impacts of development programs on health equity, and more effective policies and programs to reduce inequities in the health sector.

/pdf/analyzing-health-equity-using-household-survey-data-a-guide-3npie5e3u0.pdf

Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and their Implementation

Analyzing health equity using household survey data : a guide to techniques and their implementation

https://pure.uva.nl/ws/files/3441498/3261_30170y.pdf

Socioeconomic inequalities in morbidity and mortality in western Europe

There is a large and persistent association between education and health. In this paper, we review what is known about this link. We first document the facts about the relationship between education and health. The education ‘gradient’ is found for both health behaviors and health status, though the former does not fully explain the latter. The effect of education increases with increasing years of education, with no evidence of a sheepskin effect. Nor are there differences between blacks and whites, or men and women. Gradients in behavior are biggest at young ages, and decline after age 50 or 60. We then consider differing reasons why education might be related to health. The obvious economic explanations – education is related to income or occupational choice – explain only a part of the education effect. We suggest that increasing levels of education lead to different thinking and decision-making patterns. The monetary value of the return to education in terms of health is perhaps half of the return to education on earnings, so policies that impact educational attainment could have a large effect on population health. David M. Cutler Department of Economics Harvard University 1875 Cambridge Street Cambridge, MA 02138 dcutler@harvard.edu and NBER Adriana Lleras-Muney Department of Economics and Woodrow Wilson School Princeton University 320 Wallace Hall Princeton, NJ 08544 alleras@princeton.edu and NBER

/pdf/education-and-health-evaluating-theories-and-evidence-3w2ygrjbo2.pdf

Education and Health: Evaluating Theories and Evidence

https://repub.eur.nl/pub/11021/IncomeRelatedInequalities_1997.pdf

Income-related inequalities in health: some international comparisons.

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Income-related inequalities in health: some international comparisons.