1 Department of Epidemiology and Center for Social Epidemiology and Population Health, University of Michigan, USA.
2 Department of Social Medicine, University of Bristol, UK.
Correspondence: John Lynch, Center for Social Epidemiology and Population Health, University of Michigan, 1214 South University, Ann Arbor, MI 48104-2548, USA. E-mail: jwlynch{at}umich.edu
Keywords Income inequality, population health, race
Imagine that the research programme on income inequality and health is the ship SS Income Inequality. Think back to the launch ceremoniesenthusiastic passengers, a well-intentioned captain with a stout ship, on a journey full of promise. But then storms, arguments about the vessels sturdiness, leaks in the hull, attack by pirates, course alterations, and suggestions of sabotage by mutinous ex-crew membersyou get the idea. This metaphor is used light heartedly as way of capturing some of the to and fro within the research programme on income inequality and health and does not diminish anyones efforts to shed light on the important question of how income inequality might affect health.
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The current debate |
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In the US, the relationship between income inequality and mortality is a mask for the effects of race; whites die younger in cities and states where there is a larger fraction of the population that is black.1
Deaton and Lubotsky explicated the argument as follows:
This divergent behavior of black and white incomes means that the income difference between blacks and whites is larger in cities with larger black populations, which is what induces the relationship between overall income inequality and racial composition. Of course, this does not [emphasis in the original] mean that racial composition and income inequality are the same thing, nor that either is an equally valid marker for the same underlying health risk.2
Subramanian and Kawachi responded that:
In other words, it has been claimed that the effect of per cent black trumps the effects of state income inequality on health, and that the real culprit behind poor health achievement is racial heterogeneity, not income inequality per se.3
Deaton and colleagues had used individual and aggregate data for US states,4 and aggregate data for US states and cities2 to show there was no effect of income inequality on mortality risk after controlling for the proportion of African Americans living in the area. In their current paper, Subramanian and Kawachi use multilevel analysis of Census and Current Population Survey data to show that there is an effect of state-level income inequality on self-rated poor health, after adjustment for per cent black and an extensive array of other individual-level covariates including education, income, health insurance, and employment status. In other words, they find the opposite of Deaton and colleagues racial heterogeneity (as measured by per cent black) does not trump the effects of income inequality on self-ratings of poor health. They conclude that their results ... at least in the case of the US, may settle some of the current disputes.3
Subramanian and colleagues are no strangers to these skirmishes. They have previously engaged another pair of marauding economistsJennifer Mellor and Jeffrey Milyowho argued in several publications that there is no reliable effect of income inequality on health in the US either in time series analyses or after control for regional differences.57 In a recent interchange, Mellor and Milyo7 and Subramanian and colleagues8 reached opposite conclusions using the same data but employing different modelling strategies. Readers were left to adjudicate which one was correct. A case could be made that they both were, because they were asking somewhat different questions. In that case, Mellor and Milyos question was whether there was an effect of income inequality after controlling for unmeasured regional differences, so they used a fixed-effects model with regional dummy variables and arrived at a negative answer. Subramanian and colleagues question was not really the same. They asked: after accounting for the geographical clustering of individuals in states and regions, was there an effect of income inequality on self-rated health? Thus, they set out to try to explain, rather than adjust for, the between-region variation. They used a fixed- and random-effects model that accounted for regional and state clustering to arrive at the opposite answer.
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Settling the dispute? |
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Second, the studies are difficult to compare because they differ in regard to the outcome, dataset, and modelling strategya fact Subramanian and Kawachi acknowledge in their paper. An important issue here relates to the imprecise way the word health is usednot just in the literature related to income inequality but in the social determinants field in general. Deaton studied mortality and Subramanian and Kawachi studied self-rated health. The usual approach for justifying the use of self-rated health as a valid outcomefollowed here by Subramanian and Kawachiis to cite studies10,11 that show self-rated health is a strong predictor of mortality. Thus, the claim by Subramanian and Kawachi that their analysis settles the dispute relies on the assumption that mortality and self-rated poor health are reasonably interchangeable as outcomes.
Mortality and morbidity are both important population health indicators, but there are several issues that may raise difficulties in considering them as equivalent, especially in aetiological studies. First, social exposures associated with self-rated health may not be associated with mortality.12 For instance, while self-esteem was strongly associated with self-rated health, it did not predict mortality among a cohort of Finnish men.13 In The Netherlands, Mackenbach and colleagues showed how a range of psychosocial factors which were related to self-assessed poor health were not associated with mortality.14 Even with disease-specific measures it has been shown that morbidity and mortality do not measure the same thing. For example, in one study, self-reported cardiovascular disease morbidity was related to daily stress, whereas cardiovascular disease mortality was not.15 This suggests that a common tendency to report aspects of peoples lives as negative influences both the reporting of stress and the reporting of morbidity. Second, over the long term, mortality and morbidity transitions demonstrate countervailing trends, with declining mortality accompanied by increasing self-reported morbidity.16,17 This suggests that mortality and self-reported morbidity have somewhat different long-term determinants at the population level. The importance of determinants of trends has been demonstrated in regard to disentangling ischaemic and haemorrhagic stroke in relation to coronary heart disease trends. Lawlor and colleagues18 show how outcomes often considered closely related (stroke and heart disease) because they share common risk factors, have sub-components that show dramatically different long-term trends, and this implicates different determinants. Third, even contemporary short-term trends in self-rated poor health and mortality in the US do not follow the same pattern. Figure 1 shows the association between mortality change and change in self-rated health for US states from 1993 to 1998. If anything the weak association suggests that states that experienced larger increases in self-rated poor health had larger declines in mortality. Fourth, social inequalities in mortality and morbidity can follow different trends.19 For instance, in Korea educational inequality in mortality remained constant during the 1990s, while inequality in self-rated poor health increased.20
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Race/ethnic composition, income inequality, and mortality |
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While not wishing to pre-empt publication in another journal, we can report that Backlund and colleagues have conducted the largest multilevel analysis of mortality to date.27 It uses the restricted files of the NLMS and employs innovative statistical methodology to increase power. This study sheds light on two issues of relevance. First, they do find an effect of income inequality on mortality, after adjustment for an extensive set of individual-level covariates, including income and race. However, this effect is only observed among those aged 2564, with effects being considerably weaker among women. There is no income inequality effect among those older than 65. Second, the income inequality effect on working-age men is not explained by racial composition, although it does remove the weaker income inequality effect for women aged 2564. It is important to note though, that race/ethnic composition of the state is associated with increased mortality risk after adjustment for individual covariates and income inequality, in all age-sex groups, although more weakly for older men. So, there is a little something for everyone here. There is an independent effect of income inequality especially among younger men (supporting Lochner et al.). This effect is not completely removed by adjustment for race/ethnic composition (supporting Subramanian and Kawachi), but nevertheless, the race/ethnic composition of the state remains important to understanding mortality risk in all race/ethnic groups (supporting Deaton and Lubotsky).
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How to understand race/ethnic composition and population health a life-course approach |
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So, the obvious question is whether levels of illiteracy in the 1930s help explain the effects of current income inequality and race/ethnic composition on US state mortality in 1990. They do not. There is no association between 1930 illiteracy and 1990 mortality after adjustment for current income inequality (though there is a residual effect for 1930 black illiteracy). However, we should keep in mind two things. First, illiteracy may not be the best historical indicator; even by 1930 it characterizes the educational experience of only about 10% of the population. Second, all-cause mortality may not be a specific enough outcome. Nevertheless, the enduring correlation of illiteracy with current per cent black, education, and income inequality suggests that longer-term processes affect current determinants of population health. Future research should explore a wider range of indicators of historical conditions. Additionally, being able to observe links between historical population conditions and subsequent mortality is complicated by inter- and intra-national migration that changes the composition of the population and distribution of population sub-groups over time. There is no doubt that this is the case for the US from the latter half of the 19th century into the early decades of the 20th century.45
Despite these negative results for illiteracy, we think there is value in examining historical population life-course processes. First, they have been shown to be useful in international comparisons46 where migration is less of an issue. Second, our earlier cross-national study47 does suggest an effect of income inequality on infant mortality that may be real and important, and this may point to the potential importance of life-course processes, in that the 99.5% babies who survive in high income inequality places may carry residual effects that for the other 0.5% of babies lead to death, and these residual effects may influence long-term population health. Finally, at the individual level there is increasing evidence for the importance of life-course processes.34 These individual-level life-course processes acting across successive birth cohorts are the bedrock of future patterns of population health.
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Charting a new course |
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Subramanian and colleagues argue that we should search for new evidence, particularly in parts of the world that are even more unequal than the United States.8 However, before setting sail on this new course we should recognize that this amounts to shifting the goalposts. Such a strategy may well be informative, but the original income inequality hypothesis was intended to explain between and within country health differences among wealthy nations, where gross domestic product was not as influential for population health.5054 So this is more than simply a course correctionin practice it turns the original logic on its head. As the US is the most unequal of the wealthy nations, the countries with more inequality than the US will necessarily be less wealthy than industrialized western nations. Thus, this new strategy effectively means that health effects of income inequality are apparently now to be understood among less, rather than more, wealthy countries. To illustrate the implications, we took data from the Luxembourg Income Study55the most reliable international source on income inequality. This database does not cover all countries, but Table 3 shows that of the 29 countries included, there are only two with higher levels of income inequality than the USRussia and Mexicoand 26 with lower income inequality. According to their GDP per capita ranking, this list of 26 countries includes 16 of the worlds richest 20 nations.
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So, we are a bit confused as to where Subramanian and colleagues really stand. On one hand, in apparent defence of the SS Income Inequality, they repel attacks by pirates and back-hand sceptical mutineers. On the other hand, they give the impression that it is time to down-size operations in the US and move onto less wealthy, more unequal countries where income inequality is more likely to be expressed in population health. They may well be correct that investigations of this new version of the income inequality hypothesis will be informative. Indeed, we will be surprised if evidence cannot be found that income inequality is important in determining some health outcomes in some contexts. Nevertheless, there remain difficult issues for the research on income inequality and health. Two of the most salient will be availability of appropriate data and adequately incorporating appropriate time lags in regard to specific outcomes.48,59,60 Neither of these will be solved by looking at more unequal, less wealthy countries. Figure 2 shows male and female mortality and income inequality in Korea (19962000). As we have already shown in the US61 and the UK,50 income inequality and mortality trends move in opposite directionseven in less wealthy countries such as Koreawhich has comparable levels of income inequality to the US. This remains one of the most important challenges for understanding how income inequality may affect aspects of population health in rich or poor countries.
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Acknowledgments |
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References |
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