1 Department of Health Sciences, Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, 1110 Shimokato, Chuo-Shi, Yamanashi, 409-3898 Japan, 2 Department of Society, Human Development, and Health, Harvard School of Public Health, 677 Huntington Avenue, SPH 3, Floor 7, Boston, Massachusetts 02115, USA, 3 Center for Community-Based Research, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA, 4 Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts 02115, USA

Abstract

Abstract
Introduction
Methods
Results
Discussion
References

Objective To provide quantitative evaluations on the association between income inequality and health.

Design Random effects meta-analyses, calculating the overall relative risk for subsequent mortality among prospective cohort studies and the overall odds ratio for poor self rated health among cross sectional studies.

Data sources PubMed, the ISI Web of Science, and the National Bureau for Economic Research database.

Review methods Peer reviewed papers with multilevel data.

Results The meta-analysis included 59 509 857 subjects in nine cohort studies and 1 280 211 subjects in 19 cross sectional studies. The overall cohort relative risk and cross sectional odds ratio (95% confidence intervals) per 0.05 unit increase in Gini coefficient, a measure of income inequality, was 1.08 (1.06 to 1.10) and 1.04 (1.02 to 1.06), respectively. Meta-regressions showed stronger associations between income inequality and the health outcomes among studies with higher Gini (0.3), conducted with data after 1990, with longer duration of follow-up (>7 years), and incorporating time lags between income inequality and outcomes. By contrast, analyses accounting for unmeasured regional characteristics showed a weaker association between income inequality and health.

Conclusions The results suggest a modest adverse effect of income inequality on health, although the population impact might be larger if the association is truly causal. The results also support the threshold effect hypothesis, which posits the existence of a threshold of income inequality beyond which adverse impacts on health begin to emerge. The findings need to be interpreted with caution given the heterogeneity between studies, as well as the attenuation of the risk estimates in analyses that attempted to control for the unmeasured characteristics of areas with high levels of income inequality.

Introduction

Abstract
Introduction
Methods
Results
Discussion
References

Empirical studies have attempted to link income inequality with poor health, but recent systematic reviews have failed to reach a consensus because of mixed findings. The stakes in the debate are high because many developed countries have experienced a surge in income inequality during the era of globalisation, and if economic inequality is truly damaging to health, then even a "modest" association can amount to a considerable population burden. More than three quarters of the countries belonging to the Organisation for Economic Cooperation and Development (OECD) have in fact experienced a growing gap between rich and poor during the past two decades.1

Income inequality could damage health through two pathways. Firstly, a highly unequal society implies that a substantial segment of the population is impoverished, and poverty is bad for health. Secondly, and more contentiously, income inequality is thought to affect the health of not just the poor, but the better off in society as well. The so called spillover (or contextual) effects of inequality have in turn been attributed to the psychosocial stress resulting from invidious social comparisons,2 3 as well as the erosion of social cohesion.4 The public health importance and burden of income inequality are obviously broader under the second scenario.4 5 6 7 8

We sought to provide quantitative evaluations of the income inequality hypothesis by conducting a meta-analysis of prospective cohort studies and cross sectional studies on the association of income inequality with mortality and self rated health. We also quantitatively evaluated the potential factors explaining the differences between studies—for example, the "threshold effect" hypothesis posits the existence of a threshold of income inequality beyond which adverse impacts on health begin to emerge.4

Methods

Abstract
Introduction
Methods
Results
Discussion
References

Study selection

We followed published guidelines for meta-analyses of observational studies.9 Use of multilevel data (that is, simultaneous consideration of individual income as well as the distribution of income across area units within which individuals reside) is essential for testing the contextual effect of income inequality. As Subramanian and Kawachi have argued,4 only multilevel data can properly distinguish the contextual health effects of income inequality from the effect of individual income.10

In our meta-analysis we included cohort studies on the association between income inequality and mortality or cross sectional studies on the association between income inequality and self reported health. To be included studies had to use multilevel data—at least two levels including one or more region variable(s); address sample clustering caused by multilevel data structure; adjust for age, sex, and individual socioeconomic status; and be peer reviewed. We selected mortality and self rated health as health outcomes because these were the most commonly used validated indicators of health.11 In most cases self rated health was measured on a Likert scale with questions on respondents’ perceived health—for example, "Would you say that in general your health is: excellent, very good, good, fair, or poor?"w21 We also included in our sensitivity analysis two cohort analyses that did not address sample clustering.w11 w12

A researcher trained in online article searches (NK) searched papers written in any language published between January 1995 and July 2008, using PubMed, ISI Web of Science (Thomson Reuters), and the National Bureau of Economic Research database using the following keywords: "inequalit(y/ies)", "income", "Gini", "mortality", "death", and "health". The terms "dental", "human right(s)", and "screening" were used to exclude clearly irrelevant articles. We restricted the search period because a previous study found no multilevel study investigating the income inequality hypothesis published before 1996.4 We also reviewed all papers cited by the most recent systematic review by Wilkinson and Pickett,7 which covered all articles reviewed by other systematic reviews.4 6 12 We also reviewed expert suggestions.

Data extraction
Two investigators (NK and GS) independently extracted information on study design, data sources, country of data origin, sample size, number of cases, age, sex, estimations, response rate, follow-up rate, follow-up duration, measure of income inequality, outcome, outcome specifications (binary or ordinal/number of self rated health items), area unit over which income inequality was evaluated, adjustment variables, statistical modelling strategies, and methods for addressing data clustering. We resolved discrepancies between the data abstracted by the two investigators. If necessary, we contacted authors to obtain missing information on exact sample sizes,w3 signs of estimations,w7 distributions of income inequality measures,w30 and response rates.w14 If a cross sectional study pooled data from multiple years, we selected the models adjusted for years for which year adjusted models were available as we needed to have the estimate averaged throughout the period observed. When a paper reported multiple models with different income inequality measures, we selected the analyses using Gini coefficient, the most commonly used measure of income inequality (see box).