Examining the lag time between state-level income inequality and individual disabilities: a multilevel analysis

State-level income inequality has been found to have an effect on individual health outcomes, even when controlled for important individual-level variables such as income, education, age, and gender. The effect of income inequality on health may not be immediate and may, in fact, have a substantial lag time between exposure to inequality and eventual health outcome. We used the 2006 American Community Survey to examine the association of state-level income inequality and 2 types of physical disabilities. We used 6 different lag times, ranging between 0 and 25 years, on the total sample and on those who resided in their state of birth. Income inequality in 1986 had the strongest correlation with 2006 disability levels. Odds ratios were consistently 10% higher for those born in the same state compared with the total population.     

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.     

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,household income,and health status in Canada: a prospective cohort study

OBJECTIVES: This study sought to determine whether income inequality, household income, and their interaction are associated with health status. METHODS: Income inequality and area income measures were linked to data on household income and individual characteristics from the 1994 Canadian National Population Health Survey and to data on self-reported health status from the 1994, 1996, and 1998 survey waves. RESULTS: Income inequality was not associated with health status. Low household income was consistently associated with poor health. The combination of low household income and residence in a metropolitan area with less income inequality was associated with poorer health status than was residence in an area with more income inequality. CONCLUSIONS: Household income, but not income inequality, appears to explain some of the differences in health status among Canadians.     

Social capital, income inequality, and mortality.

OBJECTIVES: Recent studies have demonstrated that income inequality is related to mortality rates. It was hypothesized, in this study, that income inequality is related to reduction in social cohesion and that disinvestment in social capital is in turn associated with increased mortality. METHODS: In this cross-sectional ecologic study based on data from 39 states, social capital was measured by weighted responses to two items from the General Social Survey: per capita density of membership in voluntary groups in each state and level of social trust, as gauged by the proportion of residents in each state who believed that people could be trusted. Age-standardized total and cause-specific mortality rates in 1990 were obtained for each state. RESULTS: Income inequality was strongly correlated with both per capita group membership (r = -.46) and lack of social trust (r = .76). In turn, both social trust and group membership were associated with total mortality, as well as rates of death from coronary heart disease, malignant neoplasms, and infant mortality. CONCLUSIONS: These data support the notion that income inequality leads to increased mortality via disinvestment in social capital.     

The impact of income inequality on individual and societal health: absolute income, relative income and statistical artefacts

The relative income hypothesis, that relative income has a direct effect on individual health, has become an important part of the literature on health inequalities. This paper presents a four-quadrant diagram, which shows the effect of income, relative income and aggregation bias on individual and societal health. The model predicts that increased income inequality reduces average health regardless of whether relative income affects individual health. If relative income does have a direct effect then societal health will decrease further.     

Modelling health,income and income inequality: the impact of income inequality on health and health inequality

A framework is developed to analyse the impact of the distribution of income on individual health and health inequality, with individual health modelled as a function of income and the distribution of income. It is demonstrated that the impact of income inequality can generate non-concave health production functions resulting in a non-concave health production possibility frontier. In this context, the impact of different health policies are considered and it is argued that if the distribution of income affects individual health, any policy aimed at equalising health, which does not account for income inequality, will lead to unequal distributions of health. This is an important development given current UK government attention to reducing health inequality.     

A multilevel analysis of the effects of neighbourhood income inequality on individual self-rated health in Hong Kong

We examined the effect on self-rated health of neighbourhood-level income inequality in Hong Kong, which has a high and growing Gini coefficient. Data were derived from two population household surveys in 2002 and 2005 of 25,623 and 24,610 non-institutional residents aged 15 or over. We estimated neighbourhood-level Gini coefficients in each of 287 Government Planning Department Tertiary Planning Units. We used multilevel regression analysis to assess the association of neighbourhood income inequality with individual self-perceived health status. After adjustment for both individual- and household-level predictors, there was no association between neighbourhood income inequality, median household income or household-level income and self-rated health. We tested for but did not find any statistical interaction between these three income-related exposures. These findings suggest that neighbourhood income inequality is not an important predictor of individual health status in Hong Kong.     

Peripherality, income inequality, and life expectancy: revisiting the income inequality hypothesis

BACKGROUND: Recent criticisms of the income inequality and health hypothesis have stressed the lack of consistent significant evidence for the stronger effects of income inequality among rich countries. Despite such criticisms, little attention has been devoted to the income-based criteria underlying the stratification of countries into rich/poor groups and whether trade patterns and world-system role provide an alternative means of stratifying groups. METHODS: To compare income-based and trade-based criteria, 107 countries were grouped into four typologies: (I) high/low income, (II) OECD membership/non-membership, (III) core/non-core, and (IV) non-periphery/periphery. Each typology was tested separately for significant differences in the effects of income inequality between groups. Separate group comparison tests and regression analyses were conducted for each typology using Rodgers (1979) specification of income, income inequality, and life expectancy. Interaction terms were introduced into Rodgers specification to test whether group classification moderated the effects of income inequality on health. RESULTS: Results show that the effects of income inequality are stronger in the periphery than non-periphery (IV) (-0.76 vs -0.23; P < 0.05). An incremental F-test confirmed significant differences in the coefficient subsets between the two groups (F(2,101) = 6.31; P < 0.01). CONCLUSIONS: Cross-national analyses of income inequality and population health have assumed (i) income differences between countries best capture global stratification and (ii) the negative effects of income inequality are stronger in high-income countries. However, present findings emphasize (i) the importance of measuring global stratification according to trading patterns and (ii) the strong, negative effects of income inequality on life expectancy among peripheral populations.     

An exploratory multilevel analysis of income,income inequality and self-rated health of the elderly in China

In the last three decades, China has experienced rapid economic development and growing economic inequality, such that economic disparities between rural and urban areas, as well as coastal and interior areas have deepened. Since the late 1990s China has also experienced an ageing population which has attracted attention to the wellbeing of the rapidly growing number of elderly. This research aims to characterise province differences in health and to explore the effects of individual income and economic disparity in the form of income inequality on health outcomes of the elderly. The study is based on the Chinese Longitudinal Healthy Longevity Survey data collected in 2008 for 23 provinces. Multilevel logistic models are employed to investigate the relationship between income, income inequality and self-rated health for the elderly using both individual and province-level variables. Results are presented as relative odds ratios, and for province differentials as Median Odds Ratios. The analysis is deliberately exploratory so as to find evidence of income effects if they exist and particular attention is placed on how province-level inequality (contemporaneous and lagged) may moderate individual relationships. The results show that the health of the elderly is not only affected by individual income (the odds of poor health are 3 times greater for the elderly with the lowest income compared to those at the upper quartile) but also by a small main effect for province-level income inequality (odds ratio of 1.019). There are significant cross-level interactions such that where inequality is high there are greater differences between those with and without formal education, and between men and women with the latter experiencing poorer health.     

Air pollution, social deprivation, and mortality: a multilevel cohort study

BACKGROUND: It is becoming increasingly evident that exposure to air pollution and its adverse effects are not equitably distributed. Our goal was to investigate the role of social deprivation in explaining the effect of neighborhood differences in level of air pollution fine particulates (PM2.5) on mortality when the indicators of social deprivation are measured at both individual level and at neighborhood level. METHODS: All inhabitants registered in Oslo, Norway on 1 January 1992 in the age group 50-74 years (n = 105,359) constitute the study base. We used an air dispersion model (AirQUIS) to estimate levels of exposure in the period 1992-1995 in all 470 administrative neighborhoods. These data were linked to Census, educational, and death registries. Deaths were recorded in the period 1992-1998. MAIN RESULTS: PM2.5 was associated with most neighborhood-level indicators of deprivation, as was most clearly seen for type of dwelling and ownership of dwelling. The effect of PM2.5 on mortality was to some extent explained by these indicators independently of the corresponding individual-level indicators. CONCLUSIONS: Findings from this study suggest that socially deprived neighborhoods have higher exposure to air pollution. Deprivation at both the individual and neighborhood level is associated with air pollution, accounting for some of the excess mortality associated with air pollution in these neighborhoods.     

Sources of income,wealth and the length of life: An individual level study of mortality

The relationship between income and mortality is explored by examining mortality in each income decile and income source (from earnings, government or capital). Swedish individual level income data was analysed for approximately 6.5 million adults. The quality of our data is unprecedented for this type of study, in terms of size and completeness of population coverage and death registration. The results suggest that inequalities in mortality are marked even in Sweden, one of the affluent countries where the effects of health inequalities are assumed to be lowest worldwide. The only income source that was associated with beneficial outcomes for all population groups was earnings. Welfare payments, often associated with illness, are associated with higher mortality, particularly for men. Capital income (our ‘wealth’ indicator) generally reduces the risk of mortality but increases the risk for some younger groups.     

The associations between US state and local social spending,income inequality,and individual all-cause and cause-specific mortality: The National Longitudinal Mortality Study

ObjectiveTo investigate government state and local spending on public goods and income inequality as predictors of the risks of dying.MethodsData on 431,637 adults aged 30–74 and 375,354 adults aged 20–44 in the 48 contiguous US states were used from the National Longitudinal Mortality Study to estimate the impacts of state and local spending and income inequality on individual risks of all-cause and cause-specific mortality for leading causes of death in younger and middle-aged adults and older adults. To reduce bias, models incorporated state fixed effects and instrumental variables.ResultsEach additional $250 per capita per year spent on welfare predicted a 3-percentage point (− 0.031, 95% CI: − 0.059, − 0.0027) lower probability of dying from any cause. Each additional $250 per capita spent on welfare and education predicted 1.6-percentage point (− 0.016, 95% CI: − 0.031, − 0.0011) and 0.8-percentage point (− 0.008, 95% CI: − 0.0156, − 0.00024) lower probabilities of dying from coronary heart disease (CHD), respectively. No associations were found for colon cancer or chronic obstructive pulmonary disease; for diabetes, external injury, and suicide, estimates were inverse but modest in magnitude. A 0.1 higher Gini coefficient (higher income inequality) predicted 1-percentage point (0.010, 95% CI: 0.0026, 0.0180) and 0.2-percentage point (0.002, 95% CI: 0.001, 0.002) higher probabilities of dying from CHD and suicide, respectively.ConclusionsEmpirical linkages were identified between state-level spending on welfare and education and lower individual risks of dying, particularly from CHD and all causes combined. State-level income inequality predicted higher risks of dying from CHD and suicide.