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.     

Income inequality and adult nutritional status: anthropometric evidence from a pre-industrial society in the Bolivian Amazon

Evidence has been accumulated about the adverse effects of income inequality on individual health in industrial nations, but we know less about its effect in small-scale, pre-industrial rural societies. Income inequality should have modest effects on individual health. First, norms of sharing and reciprocity should reduce the adverse effects of income inequality on individual health. Second, with sharing and reciprocity, personal income will spill over to the rest of the community, attenuating the protective role of individual income on individual health found in industrial nations. We test these ideas with data from Tsimane' Amerindians, a foraging and farming society in the Bolivian Amazon. Subjects included 479 household heads (13+ years of age) from 58 villages. Dependent variables included anthropometric indices of short-run nutritional status (body-mass index (BMI), and age- and sex-standardized z-scores of mid-arm muscle area and skinfolds). Proxies for income included area deforested per person the previous year and earnings per person in the last 2 weeks. Village income inequality was measured with the Gini coefficient. Income inequality did not correlate with anthropometric indices, most likely because of negative indirect effects from the omission of social-capital variables, which would lower the estimated impact of income inequality on health. The link between BMI and income and between skinfolds and income resembled a U and an inverted U; income did not correlate with mid-arm muscle area. The use of an experimental research design might allow for better estimates of how income inequality affects social capital and individual health.     

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.     

Income inequality and health in Ontario: a multilevel analysis

OBJECTIVE: To examine the association of income inequality at the public health unit level with individual health status in Ontario. METHODS: Cross-sectional multilevel study carried out among subjects aged 25 years or older residing in 42 public health units in Ontario. Individual-level data drawn from 30,939 respondents in 1996-97 Ontario Health Survey. Median area income and income inequality (Gini coefficient) calculated from 1996 census. Self-rated health status (SRH) and Health Utilities Index (HUI-3) scores were used as main outcomes. RESULTS: Controlling for individual-level factors including income, respondents living in public health units in the highest tercile of income inequality had odds ratios of 1.20 (95% CI 1.04 - 1.38) for fair/poor self-rated health, and 1.11 (95% CI 1.01 - 1.22) for HUI score below the median, compared with people living in public health units in the lowest tercile. Controlling further for median area income had little effect on the association. CONCLUSION: Income inequality was significantly associated with individual self-reported health status at public health unit level in Ontario, independent of individual income.     

Income incongruity, relative household income, and preterm birth in the Black Women's Health Study

Relative income may be a better predictor of health outcomes than absolute income. We examined two measures of relative income—income incongruity and relative household income—in relation to preterm birth in a study of U.S. Black women. Income incongruity is a measure that compares the median household income of an individual's residential area with that of others who have the same level of marital status and education, but who may live in different areas. Relative household income is a measure that compares an individual's household income with the median household income of her residential area. We used data collected biennially (1997–2003) from participants in the Black Women's Health Study: 6257 singleton births were included in the income incongruity analyses and 5182 in the relative household income analyses; 15% of the births were preterm. After adjusting for confounders, we found no overall association of income incongruity or relative household income with preterm birth. For relative household income, but not for income incongruity, there was suggestive evidence that neighborhood composition modified the association with preterm birth: higher relative household income was associated with higher risk of preterm birth in neighborhoods with a high percentage of Black residents, and higher relative household income was associated with lower risk in neighborhoods with a low percentage of Black residents.     

Primary care,self-rated health,and reductions in social disparities in health

OBJECTIVE: To examine the extent to which good primary-care experience attenuates the adverse association of income inequality with self-reported health. DATA SOURCES: Data for the study were drawn from the Robert Wood Johnson Foundation sponsored 1996-1997 Community Tracking Study (CTS) Household Survey and state indicators of income inequality and primary care. STUDY DESIGN: Cross-sectional, mixed-level analysis on individuals with a primary-care physician as their usual source of care. The analyses were weighted to represent the civilian noninstitutionalized population of the continental United States. DATA COLLECTION/EXTRACTION METHODS: Principal component factor analysis was used to explore the stricture of the primary-care indicators and examine their construct validity. Income inequality for the state in which the community is located was measured by the Gini coefficient, calculated using income distribution data from the 1996 current population survey. Stratified analyses compared proportion of individuals reporting had health and feeling depressed with those with good and bad primary-care experiences for each of the four income-inequality strata. A set of logistic regressions were performed to examine the relation between primary-care experience, income inequality, and self-rated health. PRINCIPAL FINDINGS: Good primary-care experience, in particular enhanced accessibility and continuity, was associated with better self-reported health both generally and mentally. Good primary-care experience was able to reduce the adverse association of income inequality with general health although not with mental health, and was especially beneficial in areas with highest income inequality. Socioeconomic status attenuated, but did not eliminate, the effect of primary-care experience on health. In conclusion, good primary-care experience is associated not only with improved self-rated overall and mental health but also with reductions in disparities between more- and less-disadvantaged communities in ratings of overall health.     

Relationship between premature mortality and socioeconomic factors in black and white populations of US metropolitan areas

OBJECTIVE: examined the association of mortality with selected socioeconomic indicators of inequality and segregation among blacks and whites younger than age 65 in 267 US metropolitan areas. The primary aim of the analysis was to operationalize the concept of institutional racism in public health. METHODS: Socioeconomic indicators were drawn from Census and vital statistics data for 1989-1991 and included median household income; two measures of income inequality; percentage of the population that was black; and a measure of residential segregation. RESULTS: Age-adjusted premature mortality was 81% higher in blacks than in whites, and median household income was 40% lower. Income inequality, as measured by the Gini coefficient, was greater within the black population (0.45) than within the white population (0.40; p < 0.001). To confirm that the proxy socioeconomic variables were relevant markers of population health status, regression analysis was performed initially on data for the total population. These variables were all independently and significantly related to premature mortality (p < or = 0.01; R(2) = 0.74). Income inequality for the total population was significantly correlated with premature mortality (r = 0.33). Black (r = 0.26) and white (r = 0.20) population-specific correlations between income inequality and premature mortality, while still significant, were smaller. Residential segregation was significantly related to premature mortality and income inequality for blacks (r = 0.38 for both); among whites, however, segregation was modestly correlated with premature mortality (r = 0.19) and uncorrelated with income inequality. Regional analyses demonstrated that the association of segregation with premature mortality was much more pronounced in the South and in areas with larger black populations. CONCLUSION: Social factors such as income inequality and segregation strongly influence premature mortality in the US. Ecologic studies of the relationships among social factors and population health can measure attributes of the social context that may be relevant for population health, providing the basis for imputing macro-level relationships.     

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.     

An investigation of the longitudinal relationship between neighbourhood income inequality and individual self-rated health in England

There are mixed findings on whether neighbourhood income inequality leads to better self-rated health (SRH) or not. This study considers two hypotheses: individuals living in more unequal neighbourhoods have better SRH and the level of neighbourhood income inequality and its impact on SRH is moderated by household and neighbourhood level income related variables. Data from Waves 8–10 of the UK Household Longitudinal Study for respondents living in England at wave 8 were used. Neighbourhood income inequality was measured using Gini coefficients of household income from the Pay As You Earn and benefits systems for Lower Super Output Areas. Longitudinal ordinal multilevel models predicted self-rated health in 2016–18, 2017–19 and 2019-20 by income inequality and its interaction with household income, neighbourhood median income and neighbourhood deprivation, conditional on individual educational attainment, age, sex, ethnic group, years lived in current residence, region of residence and study wave. There were 24,889 respondents analysed over three waves. SRH was worse for those living in more income equal neighbourhoods. There was no indication that neighbourhood inequality was moderated by household income, neighbourhood median income or neighbourhood deprivation. These findings are in line with the balance of existing evidence and support policy interventions that aim to create mixed communities for the purpose of improving population health.     

Income inequality and self-rated health in US metropolitan areas: A multi-level analysis

Income inequality has been found to affect health in a number of international and cross-national studies. Using data from a telephone survey of adults in the United States, this study analyzed the effect of metropolitan level income inequality on self-rated health. It combined individual data from the 2000 Behavioral Risk Factor Surveillance System with metropolitan level income data from the 2000 Census. After controlling for smoking, age, education, Black race, Hispanic ethnicity, sex, household income, and metropolitan area per capita income, this study found that for each 1 point rise in the GINI index (on a hundred point scale) the risk of reporting Fair or Poor self-rated health increased by 4.0% (95% confidence interval 1.6–6.5%). Given that self-rated health is a good predictor of morbidity and mortality, this suggests that metropolitan area income inequality is affecting the health of US adults.     

Income inequality, social capital and self-rated health and dental status in older Japanese

The erosion of social capital in more unequal societies is one mechanism for the association between income inequality and health. However, there are relatively few multi-level studies on the relation between income inequality, social capital and health outcomes. Existing studies have not used different types of health outcomes, such as dental status, a life-course measure of dental disease reflecting physical function in older adults, and self-rated health, which reflects current health status. The objective of this study was to assess whether individual and community social capital attenuated the associations between income inequality and two disparate health outcomes, self-rated health and dental status in Japan. Self-administered questionnaires were mailed to subjects in an ongoing Japanese prospective cohort study, the Aichi Gerontological Evaluation Study Project in 2003. Responses in Aichi, Japan, obtained from 5715 subjects and 3451 were included in the final analysis. The Gini coefficient was used as a measure of income inequality. Trust and volunteering were used as cognitive and structural individual-level social capital measures. Rates of subjects reporting mistrust and non-volunteering in each local district were used as cognitive and structural community-level social capital variables respectively. The covariates were sex, age, marital status, education, individual- and community-level equivalent income and smoking status. Dichotomized responses of self-rated health and number of remaining teeth were used as outcomes in multi-level logistic regression models. Income inequality was significantly associated with poor dental status and marginally significantly associated with poor self-rated health. Community-level structural social capital attenuated the covariate-adjusted odds ratio of income inequality for self-rated health by 16% whereas the association between income inequality and dental status was not substantially changed by any social capital variables. Social capital partially accounted for the association between income inequality and self-rated health but did not affect the strong association of income inequality and dental status.     

Income inequality and mortality in Italy

BACKGROUND: The relationship between income inequality and health remains controversial in terms of whether or not it exists and, if so, its extent and the mechanisms involved. This study examines the relationship between income inequality, as indicated by the Gini coefficient, and mortality in Italy. METHODS: Cross-sectional ecological study on the 57,138,489 inhabitants living in the 95 provinces existing in Italy in 1994. Multivariate weighted regression analysis of total and age-specific mortality, income inequality, gender, and interaction between income inequality and median income or geographical area. RESULTS: A positive association between income inequality and total mortality was observed for both genders in provinces with a low per capita income and in Southern and Central Italy. The effect was present for infants and for persons over 24 years of age; it was marked for the elderly, particularly women. A negative association with mortality was observed for males living in the North-west. Interactions between income inequality and median income, and between income inequality and geographical area were found. CONCLUSION: In Italy, the relationship between income inequality and health is mixed and not universal, in so far as a positive association was observed only in provinces with lower absolute income. Elderly persons living in Southern Italy represent the population subgroup most vulnerable to unequal income distribution. Income inequality can, in part, explain the historically higher mortality among women in Southern Italy compared to women in the North. These results indicate that income inequality affects the health of population subgroups differentially.     

Income inequality and depressive symptoms in South Africa: A longitudinal analysis of the National Income Dynamics Study

Research suggests that income inequality may detrimentally affect mental health. We examined the relationship between district-level income inequality and depressive symptoms among individuals in South Africa—one of the most unequal countries in the world—using longitudinal data from Wave 1 (2008) and Wave 3 (2012) of the National Income Dynamics Study. Depressive symptoms were measured using the Center for Epidemiological Studies of Depression Short Form while district Gini coefficients were estimated from census and survey sources. Age, African population group, being single, being female, and having lower household income were independently associated with higher depressive symptoms. However, in longitudinal, fixed-effects regression models controlling for several factors, district-level Gini coefficients were not significantly associated with depressive symptoms scores. Our results do not support the hypothesis of a causal link between income inequality and depressive symptoms in the short-run. Possible explanations include the high underlying levels of inequality in all districts, or potential lags in the effect of inequality on depression.     

Income inequality and weight status in US metropolitan areas

Prior empirical studies have demonstrated an association between income inequality and general health endpoints such as mortality and self-rated health, and findings have been taken as support for the hypothesis that inequality is detrimental to individual health. Unhealthy weight statuses may function as an intermediary link between inequality and more general heath endpoints. Using individual-level data from the 1996-98 Behavioral Risk Factor Surveillance System, we examine the relationship between individual weight status and income inequality in US metropolitan areas. Income inequality is calculated with data from the 1990 US Census 5% Public Use Microsample. In analyses stratified by race-sex groups, we do not find a positive association between income inequality and weight outcomes such as body mass index, the odds of being overweight, and the odds of being obese. Among white women, however, we do find a statistically significant inverse association between inequality and each of these weight outcomes, despite adjustments for individual-level covariates, metropolitan-level covariates, and census region. We also find that greater inequality is associated with higher odds for trying to lose weight among white women, even adjusting for current weight status. Although our findings are suggestive of a contextual effect of metropolitan area income inequality, we do not find an increased risk for unhealthy weight outcomes, adding to recent debates surrounding this topic.     

Societal characteristics and health in the former communist countries of Central and Eastern Europe and the former Soviet Union: a multilevel analysis

OBJECTIVES: To examine whether, in former communist countries that have undergone profound social and economic transformation, health status is associated with income inequality and other societal characteristics, and whether this represents something more than the association of health status with individual socioeconomic circumstances. DESIGN: Multilevel analysis of cross-sectional data. SETTING: 13 Countries from Central and Eastern Europe and the former Soviet Union. PARTICIPANTS: Population samples aged 18+ years (a total of 15 331 respondents). MEAN OUTCOME MEASURES: Poor self-rated health. RESULTS: There were marked differences among participating countries in rates of poor health (a greater than twofold difference between the countries with the highest and lowest rates of poor health), gross domestic product per capita adjusted for purchasing power parity (a greater than threefold difference), the Gini coefficient of income inequality (twofold difference), corruption index (twofold difference) and homicide rates (20-fold difference). Ecologically, the age- and sex-standardised prevalence of poor self-rated health correlated strongly with life expectancy at age 15 (r = -0.73). In multilevel analyses, societal (country-level) measures of income inequality were not associated with poor health. Corruption and gross domestic product per capita were associated with poor health after controlling for individuals' socioeconomic circumstances (education, household income, marital status and ownership of household items); the odds ratios were 1.15 (95% confidence interval 1.03 to 1.29) per 1 unit (on a 10-point scale) increase in the corruption index and 0.79 (95% confidence interval 0.68 to 0.93) per $5000 increase in gross domestic product per capita. The effects of gross domestic product and corruption were virtually identical in people whose household income was below and above the median. CONCLUSION: Societal measures of prosperity and corruption, but not income inequalities, were associated with health independently of individual-level socioeconomic characteristics. The finding that these effects were similar in persons with lower and higher income suggests that these factors do not operate exclusively through poverty.     

Income inequality and health: multilevel analysis of Chilean communities

STUDY OBJECTIVE: The evidence supporting the effect of income inequality on health has been largely observed in societies far more egalitarian than the US. This study examines the cross sectional multilevel associations between income inequality and self rated poor health in Chile; a society more unequal than the US. DESIGN: A multilevel statistical framework of 98 344 people nested within 61 978 households nested within 285 communities nested within 13 regions. SETTING: The 2000 National Socioeconomic Characterization Survey (CASEN) data from Chile. PARTICIPANTS: Adults aged 18 and above. The outcome was a dichotomised self rated health (0 if very good, good or average; 1 if poor, or very poor). Individual level exposures included age, sex, ethnicity, marital status, education, employment status, type of health insurance, and household level exposures include income and residential setting (urban/rural). Community level exposures included the Gini coefficient and median income. Main results: Controlling for individual/household predictors, a significant gradient was observed between income and poor self rated health, with very poor most likely to report poor health (OR: 2.94) followed by poor (OR: 2.77), low (OR: 2.06), middle (OR: 1.73), high (OR: 1.38) as compared with the very high income earners. Controlling for household and community effects of income, a significant effect of community income inequality was observed (OR:1.22). CONCLUSIONS: Household income does not explain any of the between community differences; neither does it account for the effect of community income inequality on self rated health, with more unequal communities associated with a greater probability of reporting poor health.     

Income inequality and psychological distress at neighbourhood and municipality level: An analysis in the Netherlands

This study examines the associations between income inequality at neighbourhood and municipality level and psychological distress in a country with a relatively low income inequality, the Netherlands. Multilevel linear regression analyses were used to investigate associations between income inequality and mean income at the neighbourhood (n = 7803) and municipality (n = 406) level and psychological distress (scale range 10–50), in a country-wide sample of 343,327 individuals, adjusted for gender, age, ethnicity, marital status, education and household income. No significant association was found between neighbourhood income inequality and psychological distress after adjustment for individual and neighbourhood level confounding. However, a higher neighbourhood income inequality in neighbourhoods with the middle to highest mean neighbourhood incomes was associated with more psychological distress. Individuals living in municipalities with the highest income inequality reported 2.5% higher psychological distress compared to those living in municipalities with the lowest income inequality. Income inequality seems to matter more for mental health at the municipality than neighbourhood level.     

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.     

Squeezing Blood From a Stone: How Income Inequality Affects the Health of the American Workforce

Income inequality is very topical—in both political and economic circles—but although income and socioeconomic status are known determinants of health status, income inequality has garnered scant attention with respect to the health of US workers. By several measures, income inequality in the United States has risen since 1960. In addition to pressures from an increasingly competitive labor market, with cash wages losing out to benefits, workers face pressures from changes in work organization.We explored these factors and the mounting evidence of income inequality as a contributing factor to poorer health for the workforce.Although political differences may divide the policy approaches undertaken, addressing income inequality is likely to improve the overall social and health conditions for those affected.Income inequality in the United States is now a common theme in national policy debates, and both major parties are seemingly embracing the need to address it, although their messaging and the degree of importance they assign to the issue vary significantly.1,2 Although income itself and the broader construct of socioeconomic status are known key determinants of health status, income inequality has garnered scant attention with respect to health in general and with respect to the health of US workers specifically.Because income inequality is inexorably linked to employment, a more complete picture of the effects of inequality on health emerges when analyzed through the lens of the working population. Moreover, differences in income are associated with differences in occupations and work environments, potentially exacerbating the overall effect of income inequality on workers’ health.We considered trends in US workforce composition, income inequality, and work organization; how income inequality alone and together with income status affects health; and exemplary issues facing the large and growing health care workforce.     

Exploring the generalisability of the association between income inequality and self-assessed health

A growing between- and within-country literature suggests that the association between income inequality and health reflects individual- or area-level characteristics with which income inequality is associated, rather than the effects of income inequality per se. These studies also suggest that the association between income inequality and health is country-specific. Unresolved methodological issues include the geographical level at which to model the effects of income inequality, and the appropriate statistical methods to use. This study compares the results of single-level and multi-level logistic regression models estimating the association between income inequality and self-assessed health in local authorities in Scotland. The results suggest that there is a significant positive association between income inequality and health across local authorities in Scotland, even after adjusting for individual-level socio-economic status. They also suggest that there is significant local authority-level variation in self-assessed health, but this is small compared to the variation at the individual level. Income and other measures of individuals' socio-economic status are more strongly associated with self-assessed health than income inequality. This study provides further evidence that the income inequality:health association is place-specific. It also suggests that methodological choices regarding the ways of estimating the association between self-assessed health, individual-level socio-economic status and area-level income inequality may not make a substantive difference to the results when contextual effects are small. Further work is required to test the sensitivity of these conclusions to alternative levels of geographical aggregation.