Shifting dollars, saving lives: what might happen to mortality rates, and socio-economic inequalities in mortality rates, if income was redistributed?

Personal or household income predicts mortality risk, with each additional dollar of income conferring a slightly smaller decrease in the mortality risk. Regardless of whether levels of income inequality in a society impact on mortality rates over and above this individual-level association (i.e., the 'income inequality hypothesis'), the current consensus is that narrowing income distributions will probably improve overall health status and reduce socio-economic inequalities in health. Our objective was to quantify this impact in a national population using 1.3 million 25-59-year-old respondents to the New Zealand 1996 census followed-up for mortality over 3 years. We modelled 10-40% shifts of everyone's income to the mean income (equivalent to 10-40% reductions in the Gini coefficient). The strength of the income-mortality association was modelled using rate ratios from Poisson regression of mortality on the logarithm of equivalised household income, adjusted for confounders of age, marital status, education, car access, and neighbourhood socio-economic deprivation. Overall mortality reduced by 4-13% following 10-40% shifts in everyone's income, respectively. Inequalities in mortality reduced by 12-38% following 10-40% shifts in everyone's income. Sensitivity analyses suggested that halving the strength of the income-mortality association (i.e., assuming our multivariable estimate still overestimated the causal income-mortality association) would result in 2-6% reductions in overall mortality and 6-19% reductions in inequalities in mortality in this New Zealand setting. Many commentators have noted the non-linear association of income with mortality predicts that narrowing the income distribution will both reduce overall mortality rates and reduce inequalities in mortality. Quantifying such reductions can only be done with considerable uncertainty. Nevertheless, we tentatively suggest that the gains in overall mortality will be modest (although still potentially worthwhile from a policy perspective) and the reductions in inequalities in mortality will be more substantial.     

Do social and economic reforms change socioeconomic inequalities in child mortality? A case study: New Zealand 1981–1999

BACKGROUND: Socioeconomic inequalities in child mortality are known to exist; however the trends in these inequalities have not been well examined. This study examines the trends in child mortality inequality between 1981 and 1999 against the background of the rapid and dramatic social and economic restructuring in New Zealand during this time period. METHODS: Record linkage studies of census and mortality records of all New Zealand children aged 0-14 years on census night 1981, 1986, 1991, 1996, each followed up for three years for mortality between ages 1-14 years. Socioeconomic position was measured using maternal education, household income, and highest occupational class in the household. Standardised mortality rates, rate ratios, and rates differences as well as regression based measures of inequality were calculated. RESULTS: Mortality in all socioeconomic groups fell between 1981 and 1999. Socioeconomic inequality in child mortality existed by all measures of socioeconomic position, however only trends by income suggested a change over time: the relative index of inequality increased from 1.5 in 1981-84 to 1.8 in 1996-99 (p trend 0.06), but absolute inequality remained stable (slope index of inequality 15/100 000 in 1981-84 and 14/100 000 in 1996-99. CONCLUSIONS: Dramatic changes in income in New Zealand possibly translated into increasing relative inequality in child mortality by income, but not by education or occupational class. The a priori hypothesis that socioeconomic inequalities in child mortality would have increased in New Zealand during a period of rapid structural reform and widening income inequalities was only partly supported.     

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.     

Unemployment and suicide. Evidence for a causal association?

OBJECTIVES: To determine the independent associations of labour force status and socioeconomic position with death by suicide. DESIGN: Cohort study assembled by anonymous and probabilistic record linkage of census and mortality records. PARTICIPANTS: 2.04 million respondents to the New Zealand 1991 census aged 18-64 years. MAIN OUTCOME MEASURE: Suicide in the three years after census night. RESULTS: The age adjusted odds ratios (95% confidence intervals) of death by suicide among 25 to 64 year olds who were unemployed compared with employed were 2.46 (1.10 to 5.49) for women and 2.63 (1.87 to 3.70) for men. Similarly increased odds ratios were observed for the non-active labour force compared with the employed. Strong age only adjusted associations of suicide death with the socioeconomic factors of education (men only), car access, and household income were observed. Compared with those who were married on census night, the non-married had odds ratios of suicide of 1.81 (1.22 to 2.69) for women and 2.08 (1.66 to 2.61) for men. In a multivariable model the association of socioeconomic factors with suicide reduced to the null. However, marital status and labour force status remained strong predictors of suicide death. Unemployment was also strongly associated with suicide death among 18-24 year old men. Sensitivity analyses suggested that confounding by mental illness might explain about half, but not all, of the association between unemployment and suicide. CONCLUSIONS: Being unemployed was associated with a twofold to threefold increased relative risk of death by suicide, compared with being employed. About half of this association might be attributable to confounding by mental illness.     

Are manual workers at higher risk of death than non-manual employees when living in Swedish municipalities with higher income inequality?

OBJECTIVES: To test the hypothesis that manual workers are at higher risk of death than are non-manual employees when living in municipalities with higher income inequality. DESIGN: Hierarchical regression was used for the analysis were individuals were nested within municipalities according to the 1990 Swedish census. The outcome was all-cause mortality 1992-1998. The income measure at the individual level was disposable family income weighted against composition of family; the income inequality measure used at the municipality level was the Gini coefficient. PARTICIPANTS: The study population consisted of 1 578 186 people aged 40-64 years in the 1990 Swedish census, who were being reported as unskilled or skilled manual workers, lower-, intermediate-, or high-level non-manual employees. RESULTS: There was no significant association between income inequality at the municipality level and risk of death, but an expected gradient with unskilled manual workers having the highest risk and high-level non-manual employees having the lowest. However, in the interaction models the relative risk (RR) of death for high-level non-manual employees was decreasing with increasing income inequality (RR = 0.77; 95% CI, 0.63-0.93), whereas the corresponding risk for unskilled manual workers increased with increasing income inequality (RR = 1.24; 95% CI, 1.06-1.46). The RRs for skilled manual, low- and medium- level non-manual employees were not significant. Controlling for income at the individual level did not substantially alter these findings, neither did potential confounders at the municipality level. CONCLUSIONS: The findings suggest that there could be a differential impact from income inequality on risk of death, dependent on individuals' social position.     

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.     

Social context and depression after a disaster: the role of income inequality

STUDY OBJECTIVE: To examine the association between neighbourhood income inequality and depression, both overall and among those with different levels of income, in the post-disaster context. DESIGN: A representative cross sectional random digit dial telephone survey was conducted. SETTING: New York City (NYC) six months after September 11, 2001. PARTICIPANTS: 1570 respondents were interviewed, of whom 1355 provided residence information permitting their inclusion in this analysis. Past six month depression was assessed using a lay administered instrument consistent with DSM-IV criteria. Income inequality was measured with the Gini coefficient. MAIN RESULTS: The sample was demographically representative of NYC (56.2% female, 35.7% white, 6.3% Asian 24.2% African American, 29.7% Hispanic, and 4.2% other race or ethnicity) and the prevalence of past six month depression was 12.4%. In a final adjusted model, neighbourhood level income inequality was positively associated with depression but this association was not significant (beta = 7.58, p = 0.1). However, among those with low individual income (< 20,000 US dollars) there was a strong significant association between income inequality and depression (beta = 35.02, p<0.01), while there was no association among those with higher income. CONCLUSIONS: In the post-disaster context, neighbourhood level income inequality was associated with depression among persons with lower income; this group may be more socially or economically marginalized and dependent on local resources. Future research should examine potential mechanisms through which income inequality and other features of the social context may affect mental health in the post-disaster context.     

Inequality and mortality: long-run evidence from a panel of countries

We investigate whether changes in economic inequality affect mortality in rich countries. To answer this question we use a new source of data on income inequality: tax data on the share of pretax income going to the richest 10% of the population in Australia, Canada, France, Germany, Ireland, the Netherlands, New Zealand, Spain, Sweden, Switzerland, the UK, and the US between 1903 and 2003. Although this measure is not a good proxy for inequality within the bottom half of the income distribution, it is a good proxy for changes in the top half of the distribution and for the Gini coefficient. In the absence of country and year fixed effects, the income share of the top decile is negatively related to life expectancy and positively related to infant mortality. However, in our preferred fixed-effects specification these relationships are weak, statistically insignificant, and likely to change their sign. Nor do our data suggest that changes in the income share of the richest 10% affect homicide or suicide rates.     

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.     

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.     

Trends in the association between average income, poverty and income inequality and life expectancy in Spain

In this paper, we study the relation between life expectancy and both average income and measures of income inequality in 1980 and 1990, using the 17 Spanish regions as units of analysis. Average income was measured as average total income per household. The indicators of income inequality used were three measures of relative poverty-the percentage of households with total income less than 25%, 40% and 50% of the average total household income-the Gini index and the Atkinson indices with parameters alpha=1, 1.5 and 2. Pearson and partial correlation coefficients were used to evaluate the association between average income and measures of income inequality and life expectancy. None of the correlation coefficients for the association between life expectancy and average household income was significant for men. The association between life expectancy and average household income in women, adjusted for any of the measures of income inequality, was significant in 1980, although this association decreased or disappeared in 1990 after adjusting for measures of poverty. In both men and women, the partial correlation coefficients between life expectancy and the measures of relative income adjusted for average income were positive in 1980 and negative in 1990, although none of them was significant. The results with regard to women confirm the hypothesis that life expectancy in the developed countries has become more dissociated from average income level and more associated with income inequality. The absence of a relation in men in 1990 may be due to the large impact of premature mortality from AIDS in regions with the highest average total income per household and/or smallest income inequality.     

Stillbirth outcome capture and classification in population-based surveys: EN-INDEPTH study

Area-level measures are often used to approximate socioeconomic status (SES) when individual-level data are not available. However, no national studies have examined the validity of these measures in approximating individual-level SES. Data came from ~ 3,471,000 participants in the Mortality Disparities in American Communities study, which links data from 2008 American Community Survey to National Death Index (through 2015). We calculated correlations, specificity, sensitivity, and odds ratios to summarize the concordance between individual-, census tract-, and county-level SES indicators (e.g., household income, college degree, unemployment). We estimated the association between each SES measure and mortality to illustrate the implications of misclassification for estimates of the SES-mortality association. Participants with high individual-level SES were more likely than other participants to live in high-SES areas. For example, individuals with high household incomes were more likely to live in census tracts (r = 0.232; odds ratio [OR] = 2.284) or counties (r = 0.157; OR = 1.325) whose median household income was above the US median. Across indicators, mortality was higher among low-SES groups (all p < .0001). Compared to county-level, census tract-level measures more closely approximated individual-level associations with mortality. Moderate agreement emerged among binary indicators of SES across individual, census tract, and county levels, with increased precision for census tract compared to county measures when approximating individual-level values. When area level measures were used as proxies for individual SES, the SES-mortality associations were systematically underestimated. Studies using area-level SES proxies should use caution when selecting, analyzing, and interpreting associations with health outcomes.     

The relationship between primary care, income inequality, and mortality in US States, 1980-1995

OBJECTIVES: This study tests the robustness of the relationships between primary care, income inequality, and population health by (1) assessing the relationship during 4 time periods-1980, 1985, 1990 and 1995; (2) examining the independent effect of components of the primary care physician supply; (3) using 2 different measures of income inequality (Robin Hood index and Gini coefficient); and (4) testing the robustness of the association by using 5-year time-lagged independent variables. DATA SOURCES/STUDY SETTING: Data are derived from the Compressed Mortality Files, the US Department of Commerce and the Census Bureau, the National Center for Health Statistics, the Centers for Disease Control and Prevention, and the American Medical Association Physician Master File. The unit of analysis was the 50 US states over a 15-year period. STUDY DESIGN: Ecological, cross-sectional design for 4 selected years (1980, 1985, 1990, 1995), and incorporating 5-year time-lagged independent variables. The main outcome measure is age-standardized, all-cause mortality per 100,000 population in all 50 US states in all 4 time periods. DATA COLLECTION/EXTRACTION METHODS: The study used secondary data from publicly available data sets. The CDC WONDER/PC software was used to obtain mortality data and directly standardize them for age to the 1980 US population. Data used to calculate the income inequality measure came from the US census population and housing summary tapes for the years 1980 to 1995. Counts of the number of households that fell into each income interval along with the total aggregate income and the median household income were obtained for each state. The Gini coefficient for each state was calculated using software developed for this purpose. RESULTS: In weighted multivariate regressions, both contemporaneous and time-lagged income inequality measures (Gini coefficient, Robin Hood Index) were significantly associated with all-cause mortality (P <.05 for both measures for all time periods). Contemporaneous and time-lagged primary care physician-to-population ratios were significantly associated with lower all-cause mortality (P <.05 for all 4 time periods), whereas specialty care measures were associated with higher mortality (P <.05 for all time periods, except 1990, where P <.1). Among primary care subspecialties, only family medicine was consistently associated with lower mortality (P <.01 for all time periods). CONCLUSIONS: Enhancing primary care, particularly family medicine, even in states with high levels of income inequality, could lead to lower all-cause mortality in those states.     

Household crowding associated with childhood otitis media hospitalisations in New Zealand

Objective : To examine the association between hospitalisations for otitis media and area‐level measures of household crowding among children in New Zealand. Methods : Counts of hospital admissions for otitis media by census area unit were offset against population data from the 2006 national census. Area‐level household crowding, exposure to tobacco smoke in the home, equivalised income and individual‐level characteristics age and sex were adjusted for. To examine effect modification by ethnicity, three separate poisson models were examined for the total, Māori and non‐Māori populations. Results : Household crowding was significantly associated with hospital admissions for otitis media after adjustment in all three models. Neighbourhoods with the highest compared to the lowest proportion of crowded homes exhibited incidence rate ratios of 1.25 (95%CI 1.12–1.37) in the total population, 1.59 (95%CI 1.21–2.04) in the Māori restricted model and 1.17 (95%CI 1.06–1.32) in the non‐Māori restricted model. Conclusions : Otitis media hospitalisations are associated with area‐level measures of household crowding and other risk factors in this ecological study. The largest increase in otitis media incidence relative to neighbourhood rates of household crowding was exhibited among Māori cases of otitis media. Implications : This study adds weight to the growing body of literature linking infectious disease risk to overcrowding in the home.     

Income inequality and self rated health in Britain

STUDY OBJECTIVE: Several studies have reported an association between income inequality and increased mortality, but few have used net income data, controlled for individual income, or evaluated sensitivity to the choice of inequality measure. The study tested the hypotheses that people in regions of Britain with the greatest income inequality would report worse health than those in other regions, after adjusting for individual socioeconomic circumstances. DESIGN: Cross sectional survey. SETTING: England, Wales, and Scotland. PARTICIPANTS: 8366 people living in private households. MAIN RESULTS: Regional income inequality, measured using the Gini index, was associated with worse self rated health, especially among those with the lowest incomes (adjusted OR 1.55, 95% CI 1.24 to 1.92) (p<0.001). This association was not robust to the choice of income inequality measure, being maximal for the Gini coefficient and weakest when using indices that are more sensitive to income differences among those at the top or bottom of the income distribution. CONCLUSIONS: The study found limited evidence of an association between income inequality and worse self rated health in Britain, which was greatest among those with the lowest individual income levels. As regions with the highest income inequality were also the most urban, these findings may be attributable to characteristics of cities rather than income inequality. The variation in this association with the choice of income inequality measure also highlights the difficulty of studying income distributions using summary measures of income inequality.     

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.     

The Effects of Race,Socioeconomic Status,and Household Structure on Injury Mortality in Children and Young Adults

Objectives: Injuries are the leading killer of young persons in the United States, yet significant gaps in our understanding of this cause of death remain. By examining the independent influences of race, education, income, household structure, and residential location on injury mortality in young persons, this study addresses these gaps. Method: Using data from the National Longitudinal Mortality Study, survival analysis is used to examine the injury mortality risk faced by 0 to 17 year olds over a nine-year follow-up period. Separate models are estimated for homicide, suicide, unintentional injury deaths, and all injury deaths. Results: Household head's education has an independent effect on youth homicide and unintentional injury mortality risk. By contrast, family income and household structure do not have independent effects on any of the injury outcomes. Finally, much of the excess homicide risk faced by young African-Americans is explained by underlying racial differentials in socioeco-nomic status, household structure, and residential location. Conclusions: By finding an independent effect of household head's education on youth mortality risk from homicide and unintentional injuries, this study adds to the large body of evidence linking socioeconomic differentials to inequality in life chances.     

Are Mortality Differences and Trends by Education Any Better or Worse in New Zealand? A Comparison Study with Norway,Denmark and Finland, 1980–1990s

During the 1980s and early 1990s New Zealand experienced major social and economic change, decreasing all-cause mortality rates for the majority ethnic group, and high (but falling) cardiovascular disease (CVD) mortality rates. This paper explores whether inequalities in mortality by education were greater, and increased more, in New Zealand than in Nordic countries (Denmark, Finland, Norway), and determines the contribution of CVD to these differences and trends. Methods: We used mortality rates for 30–59 year olds by education, and slope (SII) and relative (RII) indices of inequality, calculated from comparable linked census mortality data. Results: Mortality inequalities in New Zealand were at the high end of the Nordic range when standardised by age only, but were mid-range when also standardised by ethnicity. Over time, relative inequalities in all-cause mortality increased similarly in all countries. In New Zealand a large increase in inequality for cardiovascular disease (CVD) mortality was the major contributor. In contrast both CVD and other causes of death were important drivers of increasing inequalities in Nordic countries. Absolute inequalities in all-cause mortality were stable over time among males across all countries, and increased modestly among females. The contribution of CVD to absolute inequality was stable or decreasing over time in all countries. Conclusion: Overall, inequalities in mortality in New Zealand did not widen more rapidly than in northern European countries. However, rapid social and economic change may have affected trends in CVD mortality among low educated men and women, and especially the ethnic minority groups.     

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 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.