Does income inequality modify the association between air pollution and health?

BACKGROUND: It has been hypothesized that socioeconomic status may act as an effect modifier of the association between air pollution and health. In this study, we investigated whether income inequality may modify the association between fine particulate pollution and self-reported health. METHODS: We combined several different sources of data. Demographic and socio-economic data, at the individual level, were drawn from the 2001 US Behavioral Risk Factor Surveillance System (BRFSS). County-level particulate pollution data for the year 2001 were provided by the US Environmental Protection Agency. State-level income inequality was measured by the Gini index using US census data from the year 2000. We used a hierarchical logistic regression to model the association between general self-reported health and fine particulate pollution accounting for income inequality as an effect modifier and controlling for the usual confounders. RESULTS: We found that when income inequality is low (10th percentile of the Gini distribution), the odds of reporting fair or poor health for a 10microg/m3 increase in particulate pollution is 1.34 (95% confidence interval 1.21-1.48). The analogous odds ratio for higher income inequality (60th percentile of the Gini distribution) is 1.11 (95% confidence interval 1.06-1.16). CONCLUSIONS: Income inequality was found to be an effect modifier of the association between general self-reported health and particulate pollution. However, these findings challenged our hypothesis that people living in higher income inequality areas are more vulnerable to the impact of air pollution. We discuss the factors driving these results.     

Has the relation between income inequality and life expectancy disappeared? Evidence from Italy and top industrialised countries

OBJECTIVE: To investigate the relation between income inequality and life expectancy in Italy and across wealthy nations. DESIGN AND SETTING: Measure correlation between income inequality and life expectancy at birth within Italy and across the top 21 wealthy countries. Pearson correlation coefficients were calculated to study these relations. Multivariate linear regression was used to measure the association between income inequality and life expectancy at birth adjusting for per capita income, education, and/or per capita gross domestic product. DATA SOURCES: Data on the Gini coefficient (income inequality), life expectancy at birth, per capita income, and educational attainment for Italy came from the surveys on Italian household on income and wealth 1995-2000 and the National Institute of Statistics information system. Data for industrialised nations were taken from the United Nations Development Program's human development indicators database 2003. RESULTS: In Italy, income inequality (beta = -0.433; p<0.001) and educational attainment (beta = 0.306; p<0.001) were independently associated with life expectancy, but per capita income was not (beta = 0.121; p>0.05). In cross national analyses, income inequality had a strong negative correlation with life expectancy at birth (r = -0.864; p<0.001). CONCLUSIONS: In Italy, a country where health care and education are universally available, and with a strong social safety net, income inequality had an independent and more powerful effect on life expectancy at birth than did per capita income and educational attainment. Italy had a moderately high degree of income inequality and an average life expectancy compared with other wealthy countries. The cross national analyses showed that the relation between income inequality and population health has not disappeared.     

Does inequality in self-assessed health predict inequality in survival by income? Evidence from Swedish data

This paper empirically addresses two questions using a large, individual-level Swedish data set which links mortality data to health survey data. The first question is whether there is an effect of an individual's self-assessed health (SAH) on his subsequent survival probability and if this effect differs by socioeconomic factors. Our results indicate that the effect of SAH on mortality risk declines with age-probably because of adjustment towards 'milder' overall health evaluations at higher ages-but does not seem to differ by indicators of socioeconomic status (SES) like income or education. This finding suggests that there is no systematic adjustment of SAH by SES and therefore that any measured income-related inequality in SAH is unlikely to be biased by reporting error. The second question is: how much of the income-related inequality in mortality can be explained by income-related inequality in SAH? Using a decomposition method, we find that inequality in SAH accounts for only about 10% of mortality inequality if interactions are not allowed for, but its contribution is increased to about 28% if account is taken of the reporting tendencies by age. In other words, omitting the interaction between age and SAH leads to a substantial underestimation of the partial contribution of SAH inequality by income. These results suggest that the often observed inequalities in SAH by income do have predictive power for the-less often observed-inequalities in survival by income.     

Does government provision of healthcare explain the relationship between income inequality and low birthweight?

This paper estimates the relationship between state and county income inequality and low birthweight (LBW) in the U.S. It examines whether more unequal societies are also less healthy because such societies have lower investment in population health. The model includes an extensive list of community and individual controls and community fixed-effects. Results show that unequal states in fact have greater social investments, and absent these investments children born in such states would be more likely to be LBW. Using alternate measures of inequality reveals that income inequality in the upper tail of the income distribution is not related to LBW; but inequality in the lower tail of the income distribution is associated with increased LBW where the supply of healthcare mitigates the effect of income inequality. Consistent with prior findings, county income inequality is not significantly related to LBW.     

Advancing the income inequality – health hypothesis

The notion that income inequality exerts an influence on health status (over and above the long-known effects of individual or household income) has received a great deal of attention in social epidemiology, medical sociology and economics in the past 20 years. And although a clear consensus on the hypothesis is yet to emerge, a new wave of empirical studies has strengthened the case for seeing income inequality as a social determinant of health. This article examines the current trajectory of the income inequality – health literature, and explores two issues that will be critical to its development in the coming years: (1) the need to re-examine the epistemological grounding of this research area, with a corresponding shift towards blurring the division between positivism and critical realism, and (2) the value of re-considering the geo-political ‘frame’ of studies in this field, with a move towards a truly global analysis of the health effects of income inequality.     

What explains the association between neighborhood-level income inequality and the risk of fatal overdose in New York City?

Accidental drug overdose is a substantial cause of mortality for drug users. Using a multilevel case-control study we previously have shown that neighborhood-level income inequality may be an important determinant of overdose death independent of individual-level factors. Here we hypothesized that the level of environmental disorder, the level of police activity, and the quality of the built environment in a neighborhood mediate this association. Data from the New York City (NYC) Mayor's Management Report, the NYC Police Department, and the NYC Housing and Vacancy Survey were used to define constructs for the level of environmental disorder, the level of police activity and the quality of the built environment, respectively. In multivariable models the odds of death due to drug overdose in neighborhoods in the top decile of income inequality compared to the most equitable neighborhoods decreased from 1.63 to 1.12 when adjusting for the three potential mediators. Path analyses show that the association between income inequality and the rate of drug overdose mortality was primarily explained by an indirect effect through the level of environmental disorder and the quality of the built environment in a neighborhood. Implications of these findings for the reduction of drug overdose mortality associated with the distribution of income are discussed.     

Whose health is affected by income inequality? A multilevel interaction analysis of contemporaneous and lagged effects of state income inequality on individual self-rated health in the United States

The empirical relationship between income inequality and health has been much debated and discussed. Recent reviews suggest that the current evidence is mixed, with the relationship between state income inequality and health in the United States (US) being perhaps the most robust. In this paper, we examine the multilevel interactions between state income inequality, individual poor self-rated health, and a range of individual demographic and socioeconomic markers in the US. We use the pooled data from the 1995 and 1997 Current Population Surveys, and the data on state income inequality (represented using Gini coefficient) from the 1990, 1980, and 1970 US Censuses. Utilizing a cross-sectional multilevel design of 201,221 adults nested within 50 US states we calibrated two-level binomial hierarchical mixed models (with states specified as a random effect). Our analyses suggest that for a 0.05 change in the state income inequality, the odds ratio (OR) of reporting poor health was 1.30 (95% CI: 1.17-1.45) in a conditional model that included individual age, sex, race, marital status, education, income, and health insurance coverage as well as state median income. With few exceptions, we did not find strong statistical support for differential effects of state income inequality across different population groups. For instance, the relationship between state income inequality and poor health was steeper for whites compared to blacks (OR=1.34; 95% CI: 1.20-1.48) and for individuals with incomes greater than $75,000 compared to less affluent individuals (OR=1.65; 95% CI: 1.26-2.15). Our findings, however, primarily suggests an overall (as opposed to differential) contextual effect of state income inequality on individual self-rated poor health. To the extent that contemporaneous state income inequality differentially affects population sub-groups, our analyses suggest that the adverse impact of inequality is somewhat stronger for the relatively advantaged socioeconomic groups. This pattern was found to be consistent regardless of whether we consider contemporaneous or lagged effects of state income inequality on health. At the same time, the contemporaneous main effect of state income inequality remained statistically significant even when conditioned for past levels of income inequality and median income of states.     

What is the relationship between the minimally important difference and health state utility values? The case of the SF-6D

Background  

The SF-6D is a new single summary preference-based measure of health derived from the SF-36. Empirical work is required to determine what is the smallest change in SF-6D scores that can be regarded as important and meaningful for health professionals, patients and other stakeholders.     

Do social comparisons explain the association between income inequality and health?: Relative deprivation and perceived health among male and female Japanese individuals

Relative deprivation has been hypothesized as one of the pathways accounting for the link between income inequality and health. We tested this hypothesis in a large national sample of men and women in Japan. Our survey included a probability sample of 22,871 men and 24,243 women aged 25-64, from whom information was gathered on demographic variables, household income, occupation or employment status, and self-rated health. Our measure of relative deprivation was the Yitzhaki Index, which calculates the deprivation suffered by each individual as a function of the aggregate income shortfall for each person relative to everyone else with higher incomes in that person's reference group. We modeled several alternative reference groups, including others with the same occupation, others of the same age group, and others living in the same geographic area (prefecture), as well as combinations of these. Generalized estimating equations demonstrated that higher relative deprivation was associated with worse self-rated health. Even after controlling for absolute income as well as other sociodemographic factors, the odds ratio and its 95% confidence intervals (CI) for poor health ranged from 1.09 (95% CI: 1.02-1.16) to 1.18 (95% CI: 1.11-1.26) for men and from 1.10 (95% CI: 1.04-1.16) to 1.16 (95% CI: 1.09-1.23) for women per 1 million increase in the Yitzhaki Index. As such, relative income deprivation is associated with poor self-rated health independently of absolute income, and relative deprivation may be a mechanism underlying the link between income inequality and population health.     

Income, income inequality and health: what can we learn from aggregate data?

It has been suggested that, especially in countries with high per capita income, there is an independent effect of income distribution on the health of individuals. One source of evidence in support of this relative income hypothesis is the analysis of aggregate cross-section data on population health, per capita income and income inequality. We examine the empirical robustness of cross-section analyses by using a new data set to replicate and extend the methodology in a frequently cited paper. The estimated relationship between income inequality and population health is not significant in any of our estimated models. We also argue there are serious conceptual difficulties in using aggregate cross-sections as a means of testing hypotheses about the effect of income, and its distribution, on the health of individuals.     

Income and health: what is the nature of the relationship?

The aim of this article is to examine the relationship between income and morbidity, both before and after controlling for other socio-economic variables. We use data from the Health and Lifestyle Survey (first wave), a national sample survey of adults, aged 18 upwards, in England, Wales and Scotland, conducted in 1984-1985. In total, 9003 interviews were achieved. We examine the shape of the relationship between household equivalised income and height, waist-hip ratio, respiratory function (FEV1), malaise, limiting longterm illness. These indices of morbidity, both self-reported and measured, are approximately linearly related to the logarithm of income, in all except very high and low incomes (this means that increasing income is associated with better health, but that there are diminishing returns at higher levels of income). A doubling of income is associated with a similar effect on health, regardless of the point at which this occurs, providing this is within the central portion (10-90%) of the income distribution. The effect of income on the health measures is comparable to that of the other socio-economic variables in combination. The shape of the relationship found between income and health is compatible with worse health in countries with greater income inequality, without the need to postulate any direct effect of income inequality itself.     

Exploring relative deprivation: is social comparison a mechanism in the relation between income and health?

During the last decade there has been a growing interest in the relation between income and health. The discussion has mostly focused on the individual's relative standing in the income distribution with the implicit understanding that the absolute level of income is not as relevant when the individual's basic needs are fulfilled. This study hypothesises relative deprivation to be a mechanism in the relation between income and health in Sweden: being relatively deprived in comparison to a reference group causes a stressful situation, which might affect self-rated health. Reference groups were formed by combining indicators of social class, age and living region, resulting in 40 reference groups. Within each of these groups a mean income level was calculated and individuals with an income below 70% of the mean income level in the reference group were considered as being relatively deprived. The results showed that more women than men were relatively deprived, but the effect of relative deprivation on self-rated health was more pronounced among men than among women. In order to estimate the importance of the effect of relative income versus the effect of absolute income, some analyses on the effect of relative deprivation on self-rated health were also carried out within different absolute income levels. When restricting the analysis to the lowest 40% of the income span the effect of relative deprivation almost disappeared. Relative deprivation may have a significant relation to health among men. However, for the 40% with the lowest income in the population the effect of relative deprivation on health is considerably reduced, possibly due to the more prominent relation between low absolute income and poor health.     

What is the health care product?

Because of the current competitive environment, health care providers (hospitals, HMOs, physicians, and others) are constantly searching for better products and better means for delivering them. The health care product is often loosely defined as a service. The authors develop a more precise definition of the health care product, product line, and product mix. A bundle-of-elements concept is presented for the health care product. These conceptualizations help to address how health care providers can segment their market and position, promote, and price their products. Though the authors focus on hospitals, the concepts and procedures developed are applicable to other health care organizations.     

For whom is income inequality most harmful? A multi-level analysis of income inequality and mortality in Norway

This study investigates the degree to which contextual income inequality in economic regions in Norway affected mortality during the 1990s, above the effects of mean regional income and individual-level confounders. A further objective is to explore whether income inequality effects on mortality differed between socioeconomic groups. Data were constructed by linkages of administrative registers encompassing all Norwegian inhabitants. The outcome variable was all-cause mortality during 6 years (i.e., died 1994-1999 or alive end of 1999). Men and women aged 25-66 in 1993 were analysed. Regions' mean income and income inequality (in terms of gini coefficients) were calculated from consumption-units-adjusted family disposable income. Individual-level variables included sex, age, marital status, individual income, education, and being a recipient of health-related welfare benefits. Multilevel logistic regression models were fitted for 2,197,231 individuals nested within 88 regions. After adjusting for regional mean income and individual-level variables, the odds ratio (OR) for mortality 1994-1999 was 1.028 (95% CI 1.023-1.033) on the gini variable multiplied by 100. Analyses of cross-level interactions indicated some, albeit modest, income inequality effects on mortality in the upper income and educational categories. Among those with low individual income, low education, and among recipients of health-related welfare benefits, mortality effects of higher regional income inequality were significantly stronger than among those more advantageously placed in the social structure. The results of this study differ from previous studies which have suggested that contextual income inequality has a minor impact on population health in egalitarian countries. The results indicate that in Norway, neither a comparatively egalitarian income distribution nor generous and comprehensive welfare institutions hindered the emergence of regional-level income inequality effects on mortality, and these effects were particularly marked among socioeconomically disadvantaged groups. Explanations for the results are discussed.