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U.S. income inequality is increasing at a rate not seen in over a decade, which could further fuel health inequities between the rich and poor, according to a new report [...]

Background Gun violence has shortened the average life expectancy of Americans, and better knowledge about the root causes of gun violence is crucial to its prevention. While some empirical evidence exists regarding the impacts of social and economic factors on violence and firearm homicide rates, to the author's knowledge, there has yet to be a comprehensive and comparative lagged, multilevel investigation of major social determinants of health in relation to firearm homicides and mass shootings. Methods and findings This study used negative binomial regression models and geolocated gun homicide incident data from January 1, 2015, to December 31, 2015, to explore and compare the independent associations of key state-, county-, and neighborhood-level social determinants of health-social mobility, social capital, income inequality, racial and economic segregation, and social spending-with neighborhood firearm-related homicides and mass shootings in the United States, accounting for relevant state firearm laws and a variety of state, county, and neighborhood (census tract [CT]) characteristics. Latitude and longitude coordinates on firearm-related deaths were previously collected by the Gun Violence Archive, and then linked by the British newspaper The Guardian to CTs according to 2010 Census geographies. The study population consisted of all 74,134 CTs as defined for the 2010 Census in the 48 states of the contiguous US. The final sample spanned 70,579 CTs, containing an estimated 314,247,908 individuals, or 98% of the total US population in 2015. The analyses were based on 13,060 firearm-related deaths in 2015, with 11,244 non-mass shootings taking place in 8,673 CTs and 141 mass shootings occurring in 138 CTs. For area-level social determinants, lag periods of 3 to 17 years were examined based on existing theory, empirical evidence, and data availability. County-level institutional social capital (levels of trust in institutions), social mobility, income inequality, and public welfare spending exhibited robust relationships with CT-level gun homicide rates and the total numbers of combined non-mass and mass shooting homicide incidents and non-mass shooting homicide incidents alone. A 1-standard deviation (SD) increase in institutional social capital was linked to a 19% reduction in the homicide rate (incidence rate ratio [IRR] = 0.81, 95% CI 0.73-0.91, p < 0.001) and a 17% decrease in the number of firearm homicide incidents (IRR = 0.83, 95% CI 0.73-0.95, p = 0.01). Upward social mobility was related to a 25% reduction in the gun homicide rate (IRR = 0.75, 95% CI 0.66-0.86, p < 0.001) and a 24% decrease in the number of homicide incidents (IRR = 0.76, 95% CI 0.67-0.87, p < 0.001). Meanwhile, 1-SD increases in the neighborhood percentages of residents in poverty and males living alone were associated with 26%-27% and 12% higher homicide rates, respectively. Study limitations include possible residual confounding by factors at the individual/household level, and lack of disaggregation of gun homicide data by gender and race/ethnicity. Conclusions This study finds that the rich-poor gap, level of citizens' trust in institutions, economic opportunity, and public welfare spending are all related to firearm homicide rates in the US. Further establishing the causal nature of these associations and modifying these social determinants may help to address the growing gun violence epidemic and reverse recent life expectancy declines among Americans., Author(s): Daniel Kim * Introduction The US is grappling with a worsening epidemic of gun homicides. Between 2001 and 2013, guns took the lives of more Americans than the total [...]

Objective To explore the presence and magnitude of--and change in--socioeconomic and health inequalities between and within Brazil, the Russian Federation, India, China and South Africa--the countries known as BRICS--between 1990 and 2010. Methods Comparable data on socioeconomic and health indicators, at both country and primary subnational levels, were obtained from publicly available sources. Health inequalities between and within countries were identified and summarized by using standard gap and gradient metrics. Findings Four of the BRICS countries showed increases in both income level and income inequality between 1990 and 2010. The exception was Brazil, where income inequality decreased over the same period. Between-country inequalities in level of education and access to sanitation remained mostly unchanged but the largest between-country difference in mean life expectancy increased, from 9 years in 1990 to 20 years in 2010. Throughout the study period, there was disproportionality in the burden of disease between BRICS. However, the national infant mortality rate fell substantially over the study period in all five countries. In Brazil and China, the magnitude of subnational income-related Inequalities in infant mortality, both absolute and relative, also decreased substantially. Conclusion Despite the economic prosperity and general improvements in health seen since 1990, profound inequalities in health persist both within and between BRICS. However, the substantial reductions observed--within Brazil and China--in the inequalities in income-related levels of infant mortality are encouraging. Objectif Etudier la presence et l'importance--et les variations--des inegalites sodoeconomiques et sanitaires entre et a l'interleur du Bresil, de la Federatlon de Russle, de linde, de la Chine et de l'Afrique du Sud les pays connus sous le nom de BRICS--entre 1990 et 2010. Methodes Des donnees comparables sur les indicateurs socioeconomiques et sanitaires, a la fols aux niveaux national et infranational primaire, ont ete tirees de sources accessibles au public. Les inegalites sanitaires entre et a l'interieur des pays ont ete identifiees et synthetisees a l'aide des ecarts types et des mesures de gradient. Resultats Quatre pays du groupe BRICS ont montre des augmentations a la fois du niveau des revenus et des inegalites des revenus entre 1990 et 2010. L'exception a ete le Bresil, oii l'inegalite des revenus a diminue sur la meme periode. Les inegalites entre les pays dans le niveau de l'education et l'acces aux installations sanitaires n'ont pratiquement pas change, mais la difference la plus marquee entre les pays dans l'esperance de vie moyenne a augmente, passant de 9 ans en 1990 a 20 ans en 2010. Sur toute la periode de l'etude, la charge de morbidite etait disproportionnee entre les pays du groupe BRICS. Cependant, le taux national de mortalite infantile a considerablement chute pendant la periode d'etude dans l'ensemble des 5 pays. Au Bresil et en Chine, l'importance des inegalites infranationales en matiere de mortalite infantile, a la fols absolue et relative, qul sont liees aux revenus, a egalement considerablement diminue. Conclusion Malgre la prosperite economique et les ameliorations generales en matiere de sante observees depuis 1990, de profondes inegalites en matiere de sante persistent a la fols entre et a l'interleur des pays du groupe BRICS. Cependant, les importantes diminutions observees dans les inegalites--a l'interieur du Bresil et de la Chine -en matiere de taux de mortalite infantile, qui sont liees aux revenus, sont encourageantes. Objetivo Explorarla presencia y magnitud, asi como los cambios en las desigualdades socioeconomicas y sanitarias entre y dentro de Brasil, la Federacion de Rusia, India, China y Sudafrica, los paises conocidos como BRICS, entre 1990 y 2010. Metodos Se analizaron fuentes accesibles al publico para obtener datos comparables sobre los indicadores socioeconomicos y de salud tanto a nivel nacional como subnacional primario. Se identificaron y resumieron las desigualdades sanitarias entre paises con ayuda de metricas de brecha estandar y de gradiente. Resultados Cuatro de los paises BRICS mostraron incrementos tanto a nivel de ingresos como de desigualdad de ingresos entre 1990 y 2010. La excepcion fue Brasil, donde la desigualdad de ingresos se redujo en el mismo periodo. Las desigualdades entre los paises a nivel educativo y de acceso a la sanidad permanecieron en su mayoria sin cambios, si bien la diferencia entre paises relativa a la esperanza media de vida mas grande aumento de 9 anos en 1990 a 20 anos en el ano 2010. A lo largo del periodo de estudio se observo una desproporcion en la carga de la enfermedad en los BRICS. Sin embargo, la tasa de mortalidad infantil nacional se redujo sustancialmente durante el periodo de estudio en los cinco paises. En Brasil y China, la magnitud de las desigualdades relacionadas con los ingresos a nivel subnacional en la mortalidad infantil, tanto en terminos absolutos como relativos, tambien se redujo sustancialmente. Conclusion A pesar de la prosperidad economica y la mejora general de la salud que se observa desde 1990, aun persisten desigualdades muy importantes en materia de salud tanto dentro, como entre los paises BRICS. Sin embargo, las reducciones sustanciales de las desigualdades a nivel de ingresos de la mortalidad infantil observadas en Brasil y China son alentadoras., Introduction One of the most pressing issues in the global agenda for health development is the pervasiveness of inequality. The World Economic Forum recently identified widening economic inequality as the [...]

Objectives. I examined the association between income inequality and population health and tested whether this association was mediated by interpersonal trust or public expenditures on health. Methods. Individual data on trust were collected from 48641 adults in 33 countries. These data were linked to country data on income inequality, public health expenditures, healthy life expectancy, and adult mortality. Regression analyses tested for statistical mediation of the association between income inequality and population health outcomes by country differences in trust and health expenditures. Results. Income inequality correlated with country differences in trust (r= -0.51), health expenditures (r=-0.45), life expectancy (r=-0.74), and mortality (r=0.55). Trust correlated with life expectancy (r=0.48) and mortality (r=-0.47) and partly mediated their relations to income inequality. Health expenditures did not correlate with life expectancy and mortality, and health expenditures did not mediate links between inequality and health. Conclusions. Income inequality might contribute to short life expectancy and adult mortality in part because of societal differences in trust. Societies with low levels of trust may lack the capacity to create the kind of social supports and connections that promote health and successful aging. (Am J Public Health. 2010;100:2311-2315. doi: 10.2105/AJPH.2009.189134)

This article examines social stratification in individual health trajectories for multiple cohorts in the context of China's dramatically changing macro-social environment. Using data from the China Health and Nutrition Survey, we find significant socioeconomic status (SES) differences in the mean level of health and that these SES differentials generally diverge over the life course. We also find strong cohort variations in SES disparities in the mean levels of health and health trajectories. The effect of education on health slightly decreases across successive cohorts. By contrast, the income gap in health trajectories diverges for earlier cohorts but converges for most recent cohorts. Both effects are more pronounced in rural areas. Given that these cohort effects are opposite those reported in recent U.S. studies, we discuss China's unique social, economic, and political settings. We highlight the association between SES and health behaviors, China's stage of epidemiologic transition, and the changing power of the state government and its implications for health care. Keywords socioeconomic status, health disparity, social change, cohort, life course DOI: 10.1177/0003122409359165

The relationship between adult heights and income distribution in India is explored. Income inequality will have an effect on the dispersion of height so that height inequality might serve as an indicator of income in the absence of this data.

Objectives. We investigated whether the processes underlying the association between income inequality and population health are related to those responsible for the socioeconomic gradient in health and whether health disparities are smaller when income differences are narrower. Methods. We used multilevel models in a regression analysis of 10 age- and cause-specific US county mortality rates on county median household incomes and on state income inequality. We assessed whether mortality rates more closely related to county income were also more closely related to state income inequality. We also compared mortality gradients in more- and less-equal states. Results. Mortality rates more strongly associated with county income were more strongly associated with state income inequality: across all mortality rates, r=-0.81; P=.004. The effect of state income inequality on the socioeconomic gradient in health varied by cause of death, but greater equality usually benefited both wealthier and poorer counties. Conclusions. Although mortality rates with steep socioeconomic gradients were more sensitive to income distribution than were rates with flatter gradients, narrower income differences benefit people in both wealthy and poor areas and may, paradoxically, do little to reduce health disparities. (Am J Public Health. 2008;98:699-704. doi: 10.2105/AJPH.2007.109637)

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. Keywords: Norway; Health inequalities; Income inequality; Mortality; Multi-level analysis

Researchers have found a positive association between income inequality and poor individual health. To explain the link, researchers have hypothesized that income inequality erodes community social capital, which unleashes negative emotions, stress, and stress behaviors that hurt health. Few studies have tested the hypothesized path. Here we estimate the association between (a) village income inequality and social capital, and (b) three distinct negative emotions (anger, fear, sadness) and one stress behavior (alcohol consumption). We use four quarters of panel data (2002-2003) from 655 adults in 13 villages of a foraging-farming society in the Bolivian Amazon (Tsimane'). We found that: (1) village income inequality was associated with more negative emotions but with less alcohol consumption, (2) social capital always bore a negative association with outcomes, and (3) results held up after introducing many changes to the main model. We conclude that village income inequality probably affects negative emotions and stress behaviors through other paths besides social capital because we conditioned for social capital. One such path is an innate dislike of inequality, which might have pre-human origins. Our prior research with the Tsimane' suggests that village income inequality bore an insignificant association with individual health. Therefore, village income inequality probably affects negative emotions and stress behaviors before undermining health. Keywords: Bolivia; Tsimane'; Health; Income inequality; Emotions; Social capital

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.socscimed.2005.08.059 Byline: Tony Blakely, Nick Wilson Keywords: Income redistribution; Income; Mortality; Modelling; Inequality; New Zealand Abstract: 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. Author Affiliation: Wellington School of Medicine, Otago University, Wellington, New Zealand

Several scholars have put forward the view that the estimates by Rodgers [(1979). Income and inequality as determinants of mortality: An international cross-section analysis. Population Studies, 33 (2), 343-351], Flegg [(1982). Inequality of income, illiteracy and medical care as determinants of infant mortality in underdeveloped countries. Population Studies, 36 (3), 441-458] and Waldmann [(1992). Income distribution and infant mortality. Quarterly Journal of Economics, 107 (4), 1283-1302] showing a negative cross-country association between income inequality and population health, cannot be replicated from recent data. In view of the importance of this matter, the present study further examines the issue from the most recent, and probably more accurate, data for the largest cross-country sample used in this line of research. The main conclusion is that the negative cross-country association between income inequality and good health, reported by Rodgers, Flegg, and Waldmann, is replicated very well. The different findings indicated by some scholars may have been due to their samples or the models being unusual. Therefore, the recent skepticism about the existence of such a negative association needs to be reconsidered. Several additional points are also noted. First, income inequality shows significance even after an index of ethnic heterogeneity is included. Second, ethnic heterogeneity itself has a negative association with population health. Third, income inequality retains significance in the presence of a measure of social capital. Fourth, however, the association between the measure of social capital and population health appears weak. Fifth, a simple analysis does not support the view that the positive association between income inequality and infant mortality in less developed countries (LDCs) may just be a reflection of the role of poverty. Finally, there is some support for the proposition that while income may be relatively more important for health in LDCs, the role of income inequality may be stronger in developed economies. Keywords: Income inequality; Population health; Cross-country data; Poverty

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.

A number of studies have found that mortality rates are positively correlated with income inequality across the cities and states of the US. We argue that this correlation is confounded by the effects of racial composition. Across states and Metropolitan Statistical Areas (MSAs), the fraction of the population that is black is positively correlated with average white incomes, and negatively correlated with average black incomes. Between-group income inequality is therefore higher where the fraction black is higher, as is income inequality in general. Conditional on the fraction black, neither city nor state mortality rates are correlated with income inequality. Mortality rates are higher where the fraction black is higher, not only because of the mechanical effect of higher black mortality rates and lower black incomes, but because white mortality rates are higher in places where the fraction black is higher. This result is present within census regions, and for all age groups and both sexes (except for boys aged 1-9). It is robust to conditioning on income, education, and (in the MSA results) on state fixed effects. Although it remains unclear why white mortality is related to racial composition, the mechanism working through trust that is often proposed to explain the effects of inequality on health is also consistent with the evidence on racial composition and mortality. Keywords: Mortality, Inequality; Race, Cities; States; Income, USA

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. Keywords: Inequality; Income; Life expectancy; Spain

Sociological studies on cross-national male and female suicide rates have found numerous factor related to variation in suicide rates. The present study focuses on how perceived differences in income inequality impact suicide rates in 12 different countries, unlike most prior studies on income inequality and suicide that use measures of actual income inequality. Using G. Fasso's (1999) index on perceived income inequality, the author found that perceived income inequality is related more strongly and more consistently to male suicide rates than female suicide rates. Relative to other variables used in this analysis, perceived income inequality also accounts for most of the difference in the gender gap of male versus female suicide rates.

Twelve measures of material well-being in the U.S. States were subjected to factor analysis. Four factors emerged: Security, Mastery, Harmony and Autonomy. Five self-reported measures of stress, mental health and general health are regressed upon the four quality of life measures. Only Harmony proved to be significantly associated with the stress-health factors. The elements of Harmony are shown to have a reasonable causative linkage to the stress-health factors. For another demonstration, money income of a representative sample of the U.S. population is shown to be associated with self-reported happiness, but with variation, enabling a classification of the population as to its Well-Being, Dissonance, Adaptation or Deprivation in the income-happiness relationship. Material well-being, thus, is shown to be linked to non-material, self-reported stress and health conditions.

In recent work on international comparisons of income-related inequalities in health, the concentration index has been used as a measure of health inequality. A drawback of this measure is that it is sensitive to whether it is estimated with respect to health or morbidity. An alternative would be to use the generalized concentration index that is based on absolute rather than relative health differences. In this methodological paper, we explore the importance of the choice of health inequality measure by comparing the income-related inequality in health status and morbidity between Sweden and Australia. This involves estimating a concentration index and a generalized concentration index for the eight-scale health profile of the Short Form 36 (SF-36) health survey. We then transform the scores for each scale into a measure of morbidity and show that whether the concentration index is estimated with respect to health or morbidity has an impact on the results. The ranking between the two countries is reversed for two of the eight dimensions of SF-36 and within both countries the ranking across the eight SF-36 scales is also affected. However, this change in ranking does not occur when the generalized concentration index is compared and we conclude with the implications of these results for reporting comparisons of income-related health inequality in different populations. Keywords: Health inequality; International comparisons; SF-36; Sweden; Australia

A new literature has recently emerged which suggests that among the developed economics, at least in terms of health status, the distribution of income may be more important than its absolute level. In this literature, the effect of income inequality, in particular, relative inequality on health status is tested by examining the relationship between aggregate mortality and a single measure of inequality (such as the Atkinson Index). In this paper we look at whether a single measure of income inequality, even augmented by a measure of representative income can at the aggregate level, distinguish between the effects of relative as opposed to absolute income. An alternative approach that uses disaggregated income to distinguish between the effects of changes in relative and absolute income levels is applied to data from the 1990 US Census and mortality figures from the National Centre for Health Statistics. Our results indicate that the rate of mortality is sensitive to absolute, but not relative poverty and therefore suggest that to improve the health of the poor the focus must be on raising their absolute standard of living. The results also indicate that government supported programs may have important health enhancing effects and may therefore represent a key policy tool to improve the health of those at the bottom of the income distribution. Keywords: Income inequality; Relative poverty; Absolute poverty; Inequality indices; Mortality; United States

This article describes an empirical exploration of relationships among aspects of thirty health districts in Saskatchewan, Canada. These aspects include social capital, income inequality, wealth, governance by regional health authorities and population health, the primary dependent variable. The social capital index incorporated associational and civic participation, average and median household incomes served as proxies for wealth, the degree of skew in the distribution of household incomes assessed income inequality while the model for effective governance by District Health Boards (DHBs) focused on reflection of health needs, policy making and implementation, fiscal responsibility and the integration and co-ordination of services. I found no evidence of a relationship between social capital in health districts and the performance of DHBs. Among the determinants of health, wealth appeared unrelated to age-standardised mortality rates while income inequality was positively and social capital was negatively related to mortality. Income inequality was not as strongly related to age-standardised mortality after controlling for social capital, and vice versa, suggesting the two may be co-mingled somehow when it comes to population health, although they were not significantly related to one another. Of the predictors of social capital the distribution of age in districts appeared to be the most salient; of the predictors of age-standardised mortality rates the gender composition of a district was most salient. Keywords: Social capital; Income inequality; Regional health governance; Canada

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. Keywords: Income inequality; Socio-economic factors; Confounding factors; Multi-level studies; Self-rated health; Health status

Objectives: This study uses the counties of Texas to empirically test the predictions of Wilkinson's theory on the role of income and inequality in explaining health differentials in populations. Wilkinson predicts (1) that health is affected more by income inequality than average income in areas with large population, and (2) that health is affected more by average income than income inequality in areas with small population. We investigate how large the population of a unit must be for income inequality within the unit to affect mortality. Methods: Measures of income inequality were computed from the 1990 U.S. census data and mortality was computed from Vital Statistics data. Poisson regressions estimated the age-adjusted relative risk of the top quintile relative to the bottom quintile for equality and for income among selections of Texas counties based on population size. County ethnic composition, educational level, and health care access were controlled for. Results: Among counties with populations greater than 150,000, the risk of death was lower in counties with more equal income distribution than in counties with less equal income distribution. Among counties with population less than 150,000, median income affected relative risk in counties with less than 30 percent Hispanics, but not in those with more than 30 percent Hispanics. Conclusions: This study provides some support for the predictions of Wilkinson's theory.

The 1990s were a prosperous decade in the United States. The Economic Report of the President noted that during the 1990s, the economic performance of the United States was both […]

Objective: To measure the distribution of self-reported health by income in order to compare the level of health inequality in Australia with other industrialized countries. Method: Using data from the two National Health Surveys undertaken in 1989-90 and 1995, concentration indexes were calculated to quantify the distribution of self-reported health by equivalent income. The concentration index for Australia was compared with those reported for nine industrialized countries in Europe and North America. Results: The estimated income-related concentration indexes were -0.1172 in 1989-90 and -0.1094 in 1995. Conclusion: The level of health inequality is not significantly different from the US or the UK, but significantly greater than seven other European nations. Implications: Australia has significant income related health inequalities and the distribution of health appears to be more unequal than in many other industrialized nations. There is a need to further investigate and quantify those features of the anglophone societies that set them apart from some other industrialized nations.

Objectives. This study examined associations between income inequality and mortality in 282 US metropolitan areas. Methods. Income inequality measures were calculated from the 1990 US Census. Mortality was calculated from National Center for Health Statistics data and modeled with weighted linear regressions of the log age-adjusted rate. Results. Excess mortality between metropolitan areas with high and low income inequality ranged from 64.7 to 95.8 deaths per 100 000 depending on the inequality measure. In age-specific analyses, income inequality was most evident for infant mortality and for mortality between ages 15 and 64. Conclusions. Higher income inequality is associated with increased mortality at all per capita income levels. Areas with high income inequality and low average income had excess mortality of 139.8 deaths per 100 000 compared with areas with low inequality and high income. The magnitude of this mortality difference is comparable to the combined loss of life from lung cancer, diabetes, motor vehicle crashes, human immunodeficiency virus (HIV) infection, suicide, and homicide in 1995. Given the mortality burden associated with income inequality, public and private sector initiatives to reduce economic inequalities should be a high priority. (Am J Public Health. 1998; 88:1074-1080)

A number of studies have suggested that inequalities in the distribution of income may be an important cause of variations in the average level of population health among rich industrial nations. However, what is missing from the debate so far is any systematic review of evidence about the relationship between different measures of income distribution and indicators of population health. This paper aims to bridge that gap. First, it summarizes the recent English language literature on this topic and illustrates the methodological problems that weaken the inferences that can be derived from it. Secondly, it presents new empirical estimates of the relationship between different measures of income distribution, infant mortality and life expectancy based on the most authoritative data published to date. In contrast to most earlier studies, we find very little support for the view that income inequality is associated with variations in average levels of national health in rich industrial countries. Some possible explanations for these differences are outlined. Key words - inequality, income distribution, life expectancy, infant mortality, OECD countries