Department of Health and Social Behavior, Harvard School of Public Health, 677 Huntington Avenue, KRESGE BLDG 7th Floor, Boston, MA 021156096, USA. E-mail: svsubram{at}hsph.harvard.edu
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Abstract |
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Methods Using multilevel statistical models, we examined the association between state income inequality and poor self-rated health. The analysis was based on the pooled 1995 and 1997 Current Population Surveys, comprising 201 221 adults nested within 50 US states.
Results Controlling for the individual effects of age, sex, race, marital status, education, income, health insurance coverage, and employment status, we found a significant effect of state income inequality on poor self-rated health. For every 0.05-increase in the Gini coefficient, the odds ratio (OR) of reporting poor health increased by 1.39 (95% CI: 1.26, 1.51). Additionally controlling for the proportion of the state population who are black did not explain away the effect of income inequality (OR = 1.30; 95% CI: 1.15, 1.45). While being black at the individual level was associated with poorer self-rated health, no significant relationship was found between poor self-rated health and the proportion of black residents in a state.
Conclusion Our finding demonstrates that neither race, at the individual level, nor racial composition, as measured at the state level, explain away the previously reported association between income inequality and poorer health status in the US.
Accepted 22 May 2003
While the ecological association between life expectancy and income inequality at the cross-national level was introduced more than 25 years ago,1,2 empirical investigations of this issue began in earnest following the 1992 paper by Wilkinson3 that re-introduced the topic to public health. Since then the issue has continued to arouse controversy. Growing evidence to support this claim from the US48 has been countered by contradictory evidence elsewhere.913
Some recent editorials and commentaries have concluded that the support for the income inequality hypothesis is dissipating14 or that we have possibly arrived at the end of the story.15 The basis for such conclusions seems to be grounded on the following. First, the empirical evidence from countries other than the US, comprised mainly of OECD countries, has failed to confirm an association between income inequality and worse health status. We have argued elsewhere8 that negative tests of the income inequality hypothesis were mostly conducted in countries that are more egalitarian than the US, such as Sweden,12 Japan,11 Canada,9 Denmark,10 and New Zealand.13 In countries that are more unequal than the US, such as Chile, we have found an association between income inequality and worse health.16,17 Secondly, the ecological association between income inequality and health has been challenged on the basis of residual confounding by individual income18 or educational attainment.19 However, in US data at least, appropriate multilevel models that controlled for individual income and educational attainment have ruled out potential confounding by these variables as a plausible explanation for the association between state income inequality and worse health.48
Most recently, it has been argued that the relationship between income inequality and poor health is an artefact of race.20,21 It is well established that black Americans have worse health status compared with white Americans, due to the effects of persistent racism and more limited economic opportunities.22 It has also not escaped notice that the US states with higher levels of income inequality also tend to have higher proportions of black residents.23 These states tend to be clustered in the American Southeast, and no doubt reflect the enduring legacies of slavery, segregation, and continuing disparities in opportunities for black residents in those states. Importantly, we, as well as others, have established that the association between state income inequality and poor health is not confounded by individual race.48 In other words, controlling for individual race does not remove the effect of state income inequality on morbidity and mortality. Higher income inequality is associated with worse health for both the white and black residents of a state.
However, it has been argued recently that researchers have neglected to additionally control for the racial composition of the state, i.e. the proportion of a states residents who are black. It has been contended that controlling for per cent black at the state level removes the effect of income inequality on health. This is a different criticism from the one concerning potential confounding by individual race, for it contends that racial composition as a contextual variable confounds the association between state income inequality and health. Once the fraction black is included in the regression, the effect of income inequality on health disappears. As Deaton and Lubotsky21 have stated:
The obvious interpretation of these results is that the effect of inequality on health is spurious, reflecting a failure to control for race, or something that is correlated with racethough not income inequality.
Deaton and Lubotsky21 go on to report that mortality among whites is higher in states where a larger fraction of the population is black. They suggest that:
Some of the discussions of why inequality affects healthlack of social cohesion, lack of trust, the heterogeneity of tastes that reduce the ability to provide public goodsmight provide starting points for a discussion of why white mortality is higher when whites live in states that are more racially mixed.
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.
In this paper, we set out to test the claim that the racial composition of a state (defined as the proportion of the states population who are black, and hereafter truncated to proportion black) confounds the association between state income inequality and individual poor self-rated health in the US using an explicitly multilevel analytical strategy.24 While our study and that of Deaton and Lubotsky21 are not strictly comparable in terms of the outcome studied, choice of predictors considered, or the analytical/modelling strategy adopted, we believe our findings can nevertheless inform the general discussion on the relationship between income inequality, race, and health.
While the technical criteria for confounding are themselves the subject of on-going debate, Rothman and Greenland provide reasonably normative criteria for defining confounding.25 In the context of the analyses presented here, self-rated health and the state income inequality are the outcome and exposure of interest, respectively, while proportion black is the potential confounder. For proportion black to be a confounding variable three conditions must be met. First, proportion black must be an independent risk factor for individual self-rated poor health; second, proportion black must be associated with the exposure of interest, i.e. state income inequality; and third, proportion black must not be causally affected by state income inequality. While the last criterion is likely to be true in this setting (in that the income distribution does not cause racial composition at the state level), the first two criteria are empirically testable.
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Methods |
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Outcome variable
We used the question related to self-rated health, available on the CPS, as our outcome variable. Self-rated health was determined by an individuals response to the question, Would you say your health in general is excellent, very good, good, fair, or poor? Following previous analyses,6,7 we collapsed the five categories to form a dichotomous outcome of self-rated health: 0 for excellent, very good, and good; and 1 for fair or poor. Over 27 studies in the US and elsewhere have established that self-reported health is highly predictive of subsequent mortality, independent of other medical, behavioural, and/or psychosocial factors.28 Approximately 15% of the sample population reported being in fair/poor health (Table 1).
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Statistical and methodological framework
The statistical modelling framework in this paper anticipates that individual poor self-rated health is clustered within the spatial context of the state to which they belong. Given our primary interest in the state-level variables associated with the Gini coefficient (and proportion black), the clustering of outcomes is not a nuisance that needs to be minimized, adjusted, or corrected. Rather, the idea is to explain the state-level clustering of poor self-rated health. This spatial clustering in the outcome was modelled by explicitly partitioning the individual and state-based sources of variation. Failure to differentiate the level-contingent nature of different exposures may lead to under- or over-estimation of the regression coefficients as well as the standard errors. Multilevel statistical techniques provide a technically robust framework to analyse the dependent nature of the outcome variable.29 The principles underlying multilevel modelling procedures have been extensively discussed elsewhere.24
At the risk of simplifying, the multilevel techniques allow estimation of: (1) the average relationship between poor self-rated health and individual exposures across all states (fixed parameters); (2) the variation between states that cannot be accounted for by individual factors (random-parameters); and (3) the effect of state-level predictors on poor self-rated health (fixed parameters) and the extent to which they explain between-state variation (random parameters). The multilevel modelling of 201 221 individuals (at level-1) nested within 50 states (at level-2) was achieved through the multilevel binomial non-linear logit link model using Predictive/Penalized Quasi-likelihood Procedure (PQL) second approximation procedures.30 Models were calibrated using the Restricted Maximum Likelihood procedure as implemented within MLwiN software version 1.631 that utilizes the Restrictive Iterative Generalized Least Squares algorithm.29 Estimates from the different calibrated models are presented in Table 2. Essentially, the aim of the models was to ascertain the extent to which the fixed state-level effects contribute to the improved prediction of self-rated poor health, rather than exploring the differential partitioning of variation in self-rated poor health. Our modelling strategy, meanwhile, is geared towards testing specifically the extent to which an unadjusted coefficient of state income inequality gets attenuated by including the different individual- and state-level covariates that have been identified as potential confounders to the relationship between state income inequality and health.
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Results |
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Model 2 in Table 2 conditions the relationship between poor self-rated health and Gini coefficient on a wide range of potential individual covariates. We found a statistically significant effect of state Gini coefficient (OR for 0.05-increase in Gini = 1.39; 95% CI: 1.23, 1.58) independent of age, sex, race, marital status, educational attainment, household equivalized income, access to health insurance, and employment status. In others words, these individual variables do not explain the independent and unique multilevel relationship between state income-inequality and poor self-rated health.
Model 3 in Table 2 considers the potential confounding effect of a states racial composition but omits the individual race-effects on poor self-rated health. This simulates the logic underlying the model presented by Deaton and Lubotsky,21 although our multilevel model is not strictly comparable to the ecological model reported by Deaton and Lubotsky.21 The results for the Gini coefficient in Model 3 (Table 2
) suggest that the statistically significant and independent effect of state income-inequality (OR for 0.05-increase in Gini = 1.29; 95% CI: 1.11, 1.49) remains even after accounting for the states racial composition, proportion black, contrary to what was reported by Deaton and Lubotsky.21 At the same time, we also found a marginally significant effect for proportion black (OR for 0.05-increase in proportion black = 1.05; 95% CI: 1.02, 1.08).
In the final model (Model 4, Table 2) we tested whether the effect of Gini remains significant after accounting for both individual race and proportion black at the state level. We again found the effect of Gini coefficient to be statistically significant (OR for 0.05-increase in Gini = 1.31; 95% CI: 1.13, 1.50), independent of racial composition (both at the individual and the state level). The marginally significant effect of proportion black in Model 3 became statistically non-significant in this model, after taking account of individual race (OR for 0.05-increase in proportion black = 1.03; 95% CI: 0.99, 1.06). In other words, we found no independent effect of per cent black at the state level on individual health after controlling for individual race.
Although not reported here, we also examined two multilevel interaction effects on poor self-rated health: (1) between individual race categories and state proportion black; and (2) between individual race categories and state Gini. Neither of these effects was statistically significant.
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Discussion |
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We also argue that the apparent effect of proportion black on self-rated poor health itself seems to be artefact of failing to account for the association between race and self-rated poor health at the individual level. As shown in Table 3, conditional on state income inequality, the OR for proportion black while not considering individual race is 1.05 (95% CI: 1.02, 1.08) and accounting the racial differences in health at the individual level, barely changes the point estimates, while rendering the 95% CI statistically insignificant (OR = 1.03, 95% CI: 0.99, 1.06).
What lessons can we learn from this exercise? First, our exercise highlights the need to be cautious while exploring the effects of variables that have ambiguous meaning. Variables such as proportion black in a state may be capturing a host of conditions that may or may not be pertinent for an evaluation of the potentially causal association between state income inequality and health. Deaton and Lubotsky21 acknowledge that they had no a priori reasoning for including this variable in their models, when they state that, it remains unclear why mortality is related to racial composition. The task of developing clear rationale and justification is critical when considering variables measured at the contextual level.
Furthermore, Deaton and Lubotsky21 make no distinction whether proportion black was being used in a unique way that is different from controlling for confounding by individual race. If the concern is for the latter, then, within a multilevel statistical framework, we need not go beyond Model 2 that we presented, as any clustering of health outcomes is conditional on individual race-based clustering. On the other hand, if proportion black is being conceptualized as a pure contextual variable (as a proxy for some sort of racial miasma effect that is independent of individual race composition) then it calls for further conceptual justification. As it turns out, there is no racial miasma effect at the state level. Meanwhile, racial heterogeneity (homogeneity) as reflected through the spatial aspects of US demography (at different levels of geographical aggregation) is an important area of public health research that requires some attention and may be critical to develop a multilevel understanding of the relationship between state income inequality and health.32
Second, researchers must recognize the empirical limits to testing for the presence or absence of state income inequality on health. For instance, even though we considered alternate specifications for proportion black (in order to accommodate a more convincing consideration of the confounding bias) there may be serious issues of power that one should not overlook. Since there is no means to increase the sample size of US states, researchers need to consider the limits to quantitatively conduct extensive tests of ecological confounding and future research may need to consider testing the relationship in other settings.8,16
Third, while we have shown elsewhere that controlling for US census divisions (considered as a regional confounder) does not explain away the association between state income inequality and self-rated health,8 future research may wish to consider investigating the substantive differences in the state income inequalityhealth relationship across regions or other sub national levels. At the same time, though, the challenge would be in conceptualizing and specifying what could constitute the regional level.
Finally, given the substantial differences in state policies on direct and indirect aspects of income distribution, we believe that the causal process of income inequality is perhaps most closely related to the state level in the US. However, it is important to consider other levels (such as the census tracts or counties or metropolitan areas) as factors such as residential and racial segregation at these geographical levels may be influenced by the income inequality at the state level. Thus, besides the issue of what level matters for income inequality, the critical issue is to explore other contextual pathways (typically at lower levels of aggregation) that may mediate the relationship between state income inequality and individual health.
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Conclusion |
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KEY MESSAGES
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Acknowledgments |
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References |
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