Income inequality life expectancy and causespeci

Authors: Yannan Hu Frank J van Lenthe Johan P Mackenbach

Publish Date: 2015/07/16

Volume: 30, Issue: 8, Pages: 615-625

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Abstract

Whether income inequality is related to population health is still open to debate We aimed to critically assess the relationship between income inequality and mortality in 43 European countries using comparable data between 1987 and 2008 controlling for timeinvariant and timevariant countrylevel confounding factors Annual data on income inequality expressed as Gini index based on net household income were extracted from the Standardizing the World Income Inequality Database Data on life expectancy at birth and agestandardized mortality by cause of death were obtained from the Human Lifetable Database and the World Health Organization European Health for All Database Data on infant mortality were obtained from the United Nations World Population Prospects Database The relationships between income inequality and mortality indicators were studied using country fixed effects models adjusted for time trends and country characteristics Significant associations between income inequality and many mortality indicators were found in pooled crosssectional regressions indicating higher mortality in countries with larger income inequalities Once the country fixed effects were added all associations between income inequality and mortality indicators became insignificant except for mortality from external causes and homicide among men and cancers among women The significant results for homicide and cancers disappeared after further adjustment for indicators of democracy education transition to national independence armed conflicts and economic freedom Crosssectional associations between income inequality and mortality seem to reflect the confounding effects of other country characteristics In a European context national levels of income inequality do not have an independent effect on mortalityWhether income inequality harms population health is still open to debate Since Wilkinson 1 postulated the hypothesis that income inequality was not simply a summary of the balance of income between the rich and poor but is a health risk in its own right 2 a wide array of studies including multilevel studies within countries and crosscountry ecological studies examined the link between income distribution and population health 3 4 However no agreement has yet been reached because of discrepancies between the results of different studiesInternational comparative studies linking income inequality to mortality suffer from limited comparability of the income inequality measures between countries and over time 5 6 7 The Luxembourg Income Study LIS 8 regarded as the “gold standard” is the first choice for many studies 1 5 9 10 because of its high quality and comparability It covers however only a limited set of countryyear observations which may be the reason why many studies using this database performed a crosssectional analysis The Deininger and Squire database 1996 is often chosen as an alternative source 6 11 12 and provides more observations but at a substantial loss of comparability The World Income Inequality Database WIID covers the most comprehensive set of income inequality statistics It incorporates several data sources and enables researchers to maximize comparability by choosing data based on the criteria of comparability but potentially leads to a risk of not piecing together the information in a meaningful way 13 14 The more recently developed Standardizing the World Income Inequality Database SWIID maximizes comparability for the broadest available set of countryyear observations and as such is better suited than other income inequality datasets for crosscountry comparative research 14With some exceptions 9 12 crosssectional studies found significantly worse population health at higher levels of income inequality 1 15 16 17 However these associations sometimes diminished after adjustment for observed country characteristics 3 4 6 11 18 suggesting there is a substantial risk of confounding Fixed effect models which require longitudinal data are able to adjust for unobservable timeinvariant confounding variables by linking changes in income inequality to changes in health Studies using fixed effects models to study the effect of income inequality on population health often reported insignificant results 6 7 11 19 20 21 However these studies pooled men and women together 6 7 10 11 19 22 23 used relatively old data 6 11 19 restricted the outcome to infant mortality 21 or ignored some potential timevariant confounders 6 10 19 20 23 24 Only few studies investigated diseasespecific outcomes which would help to interpret findings on the basis of existing knowledge on determinants of population health and could point towards potential pathways through which income inequality may harm population health 7 11 19 Studies specifically assessing the association between income inequality and mortality in a European context which would be important for policy makers in Europe are also limited in number 25 26Using the SWIID data we therefore aimed to refine and extend previous studies by critically investigating the relationships between income inequality and a set of diseasespecific mortality indicators by gender in fixed effects models for 43 European countries over the period 1987–2008 21For income inequality we made use of a new dataset called Standardizing the World Income Inequality Database SWIID Using the Gini index as measure SWIID took version 20c of the WIID 27 as the startingpoint and standardized it based on the inequality observations from the LIS 8 Standardizing procedures were applied to account for differences in a population coverage eg whether data cover all or nearly all of a country’s population b income reference units eg household per capita household adult equivalent or household without adjustment of number of people and c the definition of income eg net income gross income expenditures or unidentified income Finally missing observations were imputed based on proximate years using a custom multipleimputation algorithm 14 In this study we extracted information on the Gini index based on net household income posttax posttransfer from SWIID version 40 covering 43 European countries with 879 countryyear observationsData on life expectancy at birth and agestandardized mortality by cause of death at all ages further referred to as “mortality indicators” were extracted from the Human Lifetable Database wwwlifetablede and the World Health Organization European Health for All Database http//dataeurowhoint/hfadb/ Data on infant mortality measured as infant deaths per 1000 live births were obtained from the United Nations World Population Prospects database http//esaunorg/wpp/ExcelData/mortalityhtm All mortality rates are logtransformed for normalization ICDcode numbers are reported in a previous paper 28

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