Macro-to-Micro Links in the Relation between Income Inequality and Mortality

A growing literature points to links between income inequality and mortality. Any examination of the link should distinguish, both theoretically and empirically, between shifts in inequality that result from changes in the bottom and top of the income distribution. When state-level data from the U.S. censuses of 1980 and 1990 were used to measure differences in mortality, the results indicated that inequality measures reflecting depth of poverty show stronger correlations with mortality than do inequality measures reflecting heights of affluence. In addition, longitudinal data from the Panel Study of Income Dynamics were used to relate state-level inequality measures to individual-level data on mortality. This comparison revealed significant associations between degree of income inequality in state of residence and individual risk of death only for nonelderly individuals with middle-class incomes in 1990.     

Income distribution and mortality: implications from a comparison of individual-level analysis and multilevel analysis with Swedish data

BACKGROUND: This follow-up study analyses whether there is an association between income distribution in Swedish municipalities and risk of death from all causes in the total Swedish population aged 40-64 years and compares the results obtained with analyses performed on individual-level analysis and multilevel analysis. METHODS: Individual-level data on social and economic circumstances were obtained from various official records and were linked to the national cause-of-death register. Analyses were made with two methods, an individual-level regression and a multilevel regression. The study population comprised all people 40-64 years of age in the 1990 Swedish census, altogether 2.57 million people in 284 municipalities. RESULTS: The main results showed that in the individual-level regression the income distribution showed a positive and significant association (risk ratio = 1.29; 95% CI = 1.24-1.34) with higher mortality for those living in municipalities with higher income inequality. This association was not found in the multilevel regression analysis (RR = 1.03; 95%CI = 0.94-1.13). CONCLUSION: There seems to be no association between income distribution and mortality in Sweden when considering the possibility of clustering in municipalities. Further studies on the relationship between income inequality and health should aim at elucidate processes within area-level units.     

A multilevel analysis of income inequality and cardiovascular disease risk factors

Recent research has suggested that inequality in the distribution of income is associated with increased mortality, even after accounting for average income levels. Using data from the Behavioral Risk Factor Surveillance System (BRFSS), we investigated whether inequality in the distribution of income within US states is related to the prevalence of four cardiovascular disease risk factors (body mass index (BMI), history of hypertension, sedentarism, and smoking). Multilevel models (including both state-level and individual-level variables) were used to examine associations of state inequality with risk factor levels before and after adjustment for individual-level income. For three of the four risk factors investigated (BMI, hypertension, and sedentarism), state inequality was associated with increased risk factor levels, particularly at low income levels (annual household incomes <$25,000), with associations persisting after adjustment for individual-level income. Inequality was also positively associated with smoking, but associations were either stronger or only present at higher income levels. Associations of inequality with the outcomes were statistically significant in women but not in men. Although not conclusive, findings for three of the four risk factors are suggestive of a contextual effect of income inequality, particularly among persons with lower incomes.     

Income inequality and limitations in activities of daily living: a multilevel analysis of the 2003 American Community Survey

OBJECTIVES: This study examined whether state income inequality was associated with an individual's limitations in activities of daily living (ADL) when controlling for the individual's demographic and socio-economic characteristics. STUDY DESIGN AND METHODS: The study was based on secondary analyses of data collected in the 2003 American Community Survey (ACS). The ACS is a national survey of Americans with a 96.7% response rate. The sample used for this research included 645,835 participants aged 25 years and older. A multilevel model with a non-linear logit link function was used. RESULTS: A 0.05 increase in the Gini coefficient (a measure of state level income inequality) was associated with an increase of 11% in the odds of ADL limitations [odds ratio (OR) 1.11, 95% confidence intervals 1.01-1.22] even after controlling for the individual's demographic and socio-economic characteristics. These elevated odds are comparable with those associated with women in comparison with men (OR 1.12). A separate analysis indicated that individuals in the three least equitable states had consistently higher probabilities of ADL limitations across the whole economic spectrum when compared with individuals in the three most equitable states. CONCLUSIONS: State-level income inequality and individual income levels were significant independent predictors of ADL limitations. The impact of any future changes in state-level income inequality or shifts in individual income levels in the USA could be used to further investigate if this relationship is causal.     

U.S. state-level social capital and health-related quality of life: multilevel evidence of main, mediating, and modifying effects

PURPOSE: The aim of this study was to investigate the relation between state-level social capital and adult health-related quality of life (HRQOL) in the United States. METHODS: Using data from the 2001 Behavioral Risk Factor Surveillance System survey and other surveys and administrative sources, we conducted a two-level, multivariable analysis of 173,236 adults in 48 U.S. states to estimate the associations of state-level social capital (along two scales) with individual-level self-rated general health and the numbers of recent days of poor physical health, poor mental health, and activity limitation. RESULTS: For each social capital scale, living in a state intermediate or high (vs. low) in social capital was each associated with 10% to 11% lower odds of fair/poor health. Higher state-level social capital also predicted fewer recent days of poor physical and mental health and activity limitation. Differential returns of social capital to HRQOL according to state-level mean income and individual-level age and race/ethnicity were observed. Furthermore, evidence was found compatible with mediation by social capital of income inequality effects on HRQOL. CONCLUSIONS: This study yields new evidence consistent with protective effects of state-level social capital on individual HRQOL. Promoting social capital may provide a means of improving the health-related quality of life of Americans.     

Income Inequality and Mortality in US Counties: Does Minority Racial Concentration Matter?

This study examined (1) the relationship between income inequality and mortality among all counties in the contiguous United States to ascertain whether the relationships found for states and metropolitan areas extend to smaller geographic units and (2) the influence of minority racial concentration on the inequality-mortality linkage. METHODS: This county-level ecologic analysis used data from the Compressed Mortality Files and the US Census. Weighted least squares regression models of age-, sex-, and race-adjusted county mortality rates were estimated to examine the additive and interactive effects of income inequality and minority racial concentration. RESULTS: Higher income inequality at the county level was significantly associated with higher total mortality. Higher minority racial concentration also was significantly related to higher mortality and interacted with income inequality. CONCLUSIONS: The relationship between income inequality and mortality is robust for counties in the United States. Minority concentration interacts with income inequality, resulting in higher mortality in counties with low inequality and a high percentage of Blacks than in counties with high inequality and a high percentage of Blacks.     

Income inequality and mortality: a multilevel prospective study of 521 248 individuals in 50 US states

BACKGROUND: Some of the most consistent evidence in favour of an association between income inequality and health has been among US states. However, in multilevel studies of mortality, only two out of five studies have reported a positive relationship with income inequality after adjustment for the compositional characteristics of the state's inhabitants. In this study, we attempt to clarify these mixed results by analysing the relationship within age-sex groups and by applying a previously unused analytical method to a database that contains more deaths than any multilevel study to date. METHODS: The US National Longitudinal Mortality Study (NLMS) was used to model the relationship between income inequality in US states and mortality using both a novel and previously used methodologies that fall into the general framework of multilevel regression. We adjust age-sex specific models for nine socioeconomic and demographic variables at the individual level and percentage black and region at the state level. RESULTS: The preponderance of evidence from this study suggests that 1990 state-level income inequality is associated with a 40% differential in state level mortality rates (95% CI = 26-56%) for men 25-64 years and a 14% (95% CI = 3-27%) differential for women 25-64 years after adjustment for compositional factors. No such relationship was found for men or women over 65. CONCLUSIONS: The relationship between income inequality and mortality is only robust to adjustment for compositional factors in men and women under 65. This explains why income inequality is not a major driver of mortality trends in the United States because most deaths occur at ages 65 and over. This analysis does suggest, however, the certain causes of death that occur primarily in the population under 65 may be associated with income inequality. Comparison of analytical techniques also suggests coefficients for income inequality in previous multilevel mortality studies may be biased, but further research is needed to provide a definitive answer.     

Metropolitan area income inequality and self-rated health--a multi-level study

We examined the association of income inequality measured at the metropolitan area (MA) and county levels with individual self-rated health. Individual-level data were drawn from 259,762 respondents to the March Current Population Survey in 1996 and 1998. Income inequality and average income were calculated from 1990 census data, the former using Gini coefficients. Multi-level logistic regression models were used. Controlling for sex, age, race, and individual-level household income, respondents living in high, medium-high, and medium-low income inequality MAs had odds ratios of fair/poor self-rated health of 1.20 (95% confidence interval 1.04-1.38), 1.07 (0.95-1.21), and 1.02 (0.91-1.15), respectively, compared to people living in the MAs with the lowest income inequality. However, we found only a small association of MA-level income inequality with fair/poor health when controlling further for average MA household income: odds ratios were 1.10 (0.95-1.28), 1.01 (0.89-1.14), and 1.00 (0.89-1.12), respectively. Likewise, we found only a small association of county-level income inequality with self-rated health although only 40.7% of the sample had an identified county on CPS data. Regarding the association of state-level income inequality with fair/poor health, we found the association to be considerably stronger among non-metropolitan (i.e. rural) compared to metropolitan residents.     

State-level income inequality and family burden of U.S. families raising children with special health care needs

Growing evidence supports the hypothesis that income inequality within a nation influences health outcomes net of the effect of any given household's absolute income. We tested the hypothesis that state-level income inequality in the United States is associated with increased family burden for care and health-related expenditures for low-income families of children with special health care needs. We analyzed the 2005-06 wave of the National Survey of Children with Special Health Care Needs, a probability sample of approximately 750 children with special health care needs in each state and the District of Columbia in the US Our measure of state-level income inequality was the Gini coefficient. Dependent measures of family caregiving burden included whether the parent received help arranging or coordinating the child's care and whether the parent stopped working due to the child's health. Dependent measures of family financial burden included absolute burden (spending in past 12 months for child's health care needs) and relative burden (spending as a proportion of total family income). After controlling for a host of child, family, and state factors, including family income and measures of the severity of a child's impairments, state-level income inequality has a significant and independent association with family burden related to the health care of their children with special health care needs. Families of children with special health care needs living in states with greater levels of income inequality report higher rates of absolute and relative financial burden.     

The effects of US state income inequality and alcohol policies on symptoms of depression and alcohol dependence

Mental health is likely to be influenced by contextual variables that emerge only at the level of the group. We studied the effect of two such group-level variables, within-state income inequality and alcohol tax policy, on symptoms of current depression and alcohol dependence in a US national sample, controlling for state-level and individual characteristics. A cross-sectional US national probability sample provided the individual-level data. State income data were obtained from the 1990 US census. The Gini coefficient (raw and adjusted) indicated income inequality. Outcome measures included current symptoms of depression and alcohol dependence. Controlling for individual-level variables and state median income, the odds of depressive symptoms was not positively associated with state income inequality. Controlling for individual-level variables, state median income and alcohol distribution method, a weak negative association between Gini and alcohol dependence was observed in women, but this association disappeared after additional adjustment for beer tax. No association was observed in men. Higher state beer tax was significantly associated with lower prevalence of alcohol dependence symptoms for both men and women. The results suggest that state income inequality does not increase the experience of alcohol dependence or depression symptoms. However, evidence was found for a protective effect of increased beer taxation against alcohol dependence symptoms, suggesting the need to further consider the impact of alcohol policies on alcohol use disorders.     

Does the state you live in make a difference? Multilevel analysis of self-rated health in the US

This paper investigates the different sources of variation between US states in self-rated health using multilevel statistical procedures. The different sources that are considered are based on individual- and state-level factors. Data for the analysis comes from the 1993-94 Behavioral Risk Factor Surveillance System and the 1986-90 General Social Surveys. Results show that individual-level factors (such as low income, being black, smoking) are strongly associated with self-rated poor health. Significant variation, however, remain between states after allowing for individual characteristics. Crucially, between-state variation in self-rated health is different for different income groups. State-level contextual effects are found for per-capita median-income and 'social capital'. While not strong, there seems to be a differential impact of state income-inequality on high-income groups, such that the affluent report better health from living in high inequality states. The paper substantiates the need to connect individual health to their macro socioeconomic context. Importantly, it is argued that without adopting an explicitly multilevel approach, the debate on linkages between individual health and income-inequality/social capital cannot be adequately addressed.     

Income inequality and health. Coastal communities in British Columbia,Canada

OBJECTIVE: An imbalance in the distribution of economic resources, i.e., income inequality, is a characteristic of a community that may influence the aggregate health of the population. In North America, income inequality seems to be strongly related to mortality rates among American communities such as states and metropolitan areas but largely irrelevant for health at similar levels of geopolitical aggregation in Canada. This article summarizes relevant international and North American evidence and then explores relationships between income inequality and mortality rates among coastal communities in the province of British Columbia, Canada. METHODS: Cross-sectional analysis was conducted among twenty-four coastal communities in British Columbia, utilizing four measures based on the 1996 Census to measure income inequality and crude, age-standardized and age- and gender-specific mortality rates averaged over the five-year period 1994-98 to measure health. RESULTS: The three valid measures of income inequality were positively and significantly related to the crude mortality rate but were not significantly related to the age-standardized mortality rate. Two of the inequality measures were related to mortality rates for males aged 0-44 and for males aged 45-64 before but not after controlling for mean household income. DISCUSSION: Health researchers have yet to report a meaningful relationship between income inequality and population health within Canada. At the risk of committing the ecological fallacy, these findings provisionally support a psycho-social interpretation of the individual-level relationship between income and health wherein members of these communities compare themselves to an encompassing community, e.g., all Canadians.     

Socioeconomic inequality in voting participation and self-rated health

OBJECTIVES: This study tested the hypothesis that disparities in political participation across socioeconomic status affect health. Specifically, the association of voting inequality at the state level with individual self-rated health was examined. METHODS: A multilevel study of 279,066 respondents to the Current Population Survey (CPS) was conducted. State-level inequality in voting turnout by socioeconomic status (family income and educational attainment) was derived from November CPS data for 1990, 1992, 1994, and 1996. RESULTS: Individuals living in the states with the highest voting inequality had an odds ratio of fair/poor self-rated health of 1.43 (95% confidence interval [CI] = 1.22, 1.68) compared with individuals living in the states with the lowest voting inequality. This odds ratio decreased to 1.34 (95% CI = 1.14, 1.56) when state income inequality was added and to 1.27 (95% CI = 1.10, 1.45) when state median income was included. The deleterious effect of low individual household income on self-rated health was most pronounced among states with the greatest voting and income inequality. CONCLUSIONS: Socioeconomic inequality in political participation (as measured by voter turnout) is associated with poor self-rated health, independently of both income inequality and state median household income.     

County-Level Income Inequality and Depression among Older Americans

Objectives. To examine (1) whether county-level income inequality is associated with depression among Americans aged 70 and older, taking into consideration county-level mean household income and individual-level socioeconomic status (SES), demographic characteristics, and physical health, and (2) whether income inequality effects are stronger among people with lower SES and physical health.
Data Sources. The individual-level data from the first wave of the Assets and Health Dynamics among the Oldest Old survey (1993–1994) were linked with the county-level income inequality and mean household income data from the 1990 Census.
Study Design. Multilevel analysis was conducted to examine the association between income inequality (the Gini coefficient) and depression.
Principal Findings. Income inequality was significantly associated with depression among older Americans. Those living in counties with higher income inequality were more depressed, independent of their demographic characteristics, SES, and physical health. The association was stronger among those with more illnesses.
Conclusions. While previous empirical research on income inequality and physical health is equivocal, evidence for income inequality effects on mental health seems to be strong.     

Is Exposure to Income Inequality a Public Health Concern? Lagged Effects of Income Inequality on Individual and Population Health

Objective. To examine the health consequences of exposure to income inequality.
Data Sources. Secondary analysis employing data from several publicly available sources. Measures of individual health status and other individual characteristics are obtained from the March Current Population Survey (CPS). State-level income inequality is measured by the Gini coefficient based on family income, as reported by the U.S. Census Bureau and Al-Samarrie and Miller (1967) . State-level mortality rates are from the Vital Statistics of the United States ; other state-level characteristics are from U.S. census data as reported in the Statistical Abstract of the United States .
Study Design. We examine the effects of state-level income inequality lagged from 5 to 29 years on individual health by estimating probit models of poor/fair health status for samples of adults aged 25–74 in the 1995 through 1999 March CPS. We control for several individual characteristics, including educational attainment and household income, as well as regional fixed effects. We use multivariate regression to estimate the effects of income inequality lagged 10 and 20 years on state-level mortality rates for 1990, 1980, 1970, and 1960.
Principal Findings. Lagged income inequality is not significantly associated with individual health status after controlling for regional fixed effects. Lagged income inequality is not associated with all cause mortality, but associated with reduced mortality from cardiovascular disease and malignant neoplasms, after controlling for state fixed-effects.
Conclusions. In contrast to previous studies that fail to control for regional variations in health outcomes, we find little support for the contention that exposure to income inequality is detrimental to either individual or population health.     

Income inequality and the double burden of under- and overnutrition in India

OBJECTIVES: Developing countries are increasingly characterised by the simultaneous occurrence of under- and overnutrition. This study examined the association between contextual income inequality and the double burden of under- and overnutrition in India. DESIGN: A population-based multilevel study of 77,220 ever married women, aged 15-49 years, from 26 Indian states, derived from the 1998-99 Indian National Family Health Survey data. The World Health Organization recommended categories of body mass index constituted the outcome, and the exposure was contextual measure of state income inequality based on the Gini coefficient of per capita consumption expenditure. Covariates included a range of individual demographic, socioeconomic, behavioural and morbidity measures and state-level economic development. RESULTS: In adjusted models, for each standard deviation increase in income inequality, the odds ratio for being underweight increased by 19% (p = 0.02) and the odds ratio for being obese increased by 21% (p<0.0001). Income inequality had a similar effect on the risk of being overweight as it did on the risk of obesity (p = 0.01), and state income inequality increased the risk of being pre-overweight by 9% (p = 0.01). While average levels of state economic development were strongly associated with degrees of overnutrition, no association was found with the risk of being underweight. CONCLUSIONS: Rapidly developing economies, besides experiencing paradoxical health patterns, are typically characterised by increased levels of income inequality. This study suggests that the twin burden of undernutrition and overnutrition in India is more likely to occur in high-inequality states. Focusing on economic equity via redistribution policies may have a substantial impact in reducing the prevalence of both undernutrition and overnutrition.     

Primary care, race, and mortality in US states

This study used US state-level data from 1985 to 1995 to examine the relationship of primary care resources and income inequality with all-cause mortality within the entire population, and in black and white populations. The study is a pooled ecological design with repeated measures using 11 years of state-level data (n=549). Analyses controlled for socioeconomic and demographic characteristics. Contemporaneous and time-lagged covariates were modeled, and all analyses were stratified by race/ethnicity. In all models, primary care was associated with lower mortality. An increase of one primary care doctor per 10,000 population was associated with a reduction of 14.4 deaths per 100,000. The magnitude of primary care coefficients was higher for black mortality than for white mortality. Income inequality was not associated with mortality after controlling for state-level sociodemographic covariates. The study provides evidence that primary care resources are associated with population health and could aid in reducing socioeconomic disparities in health.     

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.     

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.     

Associations between income inequality and mortality among US states: the importance of time period and source of income data

OBJECTIVES: We used census data to examine associations between income inequality and mortality among US states for each decade from 1949 to 1999 and tax return income data to estimate associations for 1989. METHODS: Cross-sectional correlation analyses were used to assess income inequality-mortality relationships. RESULTS: Census income analyses revealed little association between income inequality and mortality for 1949, 1959, or 1969. An association emerged for 1979 and strengthened for 1989 but weakened for 1999. When income inequality was based on tax return data, associations were weaker for both 1989 and 1999. CONCLUSIONS: The strong association between income inequality and mortality observed among US states for 1989 was not observed for other periods from 1949 through 1999. In addition, when tax return rather than census data were used, the association was weaker for 1989 and 1999. The potential for distal social determinants of population health (e.g., income inequality) to affect mortality is contingent on how such determinants influence levels of proximal risk factors and the time lags between exposure to those risk factors and effects on specific health outcomes.