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At least 19 recordsLinked to original sources
PubMed2001

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

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

Data Collection↗
PubMed2003

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

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

Health Policy↗
PubMed1984

Supplemental Security Income for the aged, blind, and disabled; income, resources and exclusions deeming of income and resources: Medicaid program; financial requirements for categorically needy--HHS. Final rules.

The Social Security Administration (SSA) is adopting as final, interim regulations on deeming of income and resources, which was published on Friday, June 4, 1982. These rules address the problem of certain individuals who require costly medical care and who under the usual Supplemental Security Income (SSI) deeming rules are ineligible for SSI and Medicaid as long as they live with their families. These rules also address the problems of individuals who remain institutionalized because returning home for less costly medical treatment would result in loss of SSI and Medicaid eligibility. The rules provide that the Secretary, in appropriate circumstances, will not apply the usual SSI rules for deeming the income and resources of certain family members to a noninstitutionalized individual. This is a temporary policy that will deal with this concern while States, if they choose, develop appropriate programs of home and community-based services under their Medicaid programs.

Eligibility Determination↗
PubMedDate not supplied

Prognostic relevance of census-derived individual respondent incomes versus household incomes.

BACKGROUND: Census-based measures of income derived from median income of a geographic area are often used in health research. Many national census surveys gather information on both the respondent's individual income and the income for the entire household, giving researchers a choice of census income measures. We compared the extent to which individual respondent income and household income (both obtained from census data) are associated with outcomes in a cohort of patients with cardiac disease. METHODS: We used data from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH), where postal codes were linked to the Postal Code Conversion File (PCCF) to determine each patient's census Dissemination Areas (DA). DA-derived median household income and median individual income were obtained from the 2001 Canadian Census and survival outcomes were then directly determined for income groupings defined by quintile. Two-year survival adjusted for age and sex was described with a proportional hazards analysis. RESULTS: There were 9,397 patients undergoing cardiac catheterization between January 1, 2001 and March 31, 2002, with complete DA-level median income measures. Household income quintiles yielded a wider spread of survival across quintiles (range of 2-year estimated survival, 91.8% to 95.9% for household income versus 92.8% to 95.6% for respondent income), as well as a more progressive decline in survival as income decreased. This progressive decline was not seen for the respondent income measure. CONCLUSIONS: The greater spread and progressive decline of survival for household income relative to respondent income leads us to conclude that household income is the better socio-economic determinant of health in our data and for the outcome measure we studied.

Aged↗
PubMed2003

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.

Female↗
PubMed2005

Whose health is affected by income inequality? A multilevel interaction analysis of contemporaneous and lagged effects of state income inequality on individual self-rated health in the United States.

The empirical relationship between income inequality and health has been much debated and discussed. Recent reviews suggest that the current evidence is mixed, with the relationship between state income inequality and health in the United States (US) being perhaps the most robust. In this paper, we examine the multilevel interactions between state income inequality, individual poor self-rated health, and a range of individual demographic and socioeconomic markers in the US. We use the pooled data from the 1995 and 1997 Current Population Surveys, and the data on state income inequality (represented using Gini coefficient) from the 1990, 1980, and 1970 US Censuses. Utilizing a cross-sectional multilevel design of 201,221 adults nested within 50 US states we calibrated two-level binomial hierarchical mixed models (with states specified as a random effect). Our analyses suggest that for a 0.05 change in the state income inequality, the odds ratio (OR) of reporting poor health was 1.30 (95% CI: 1.17-1.45) in a conditional model that included individual age, sex, race, marital status, education, income, and health insurance coverage as well as state median income. With few exceptions, we did not find strong statistical support for differential effects of state income inequality across different population groups. For instance, the relationship between state income inequality and poor health was steeper for whites compared to blacks (OR=1.34; 95% CI: 1.20-1.48) and for individuals with incomes greater than $75,000 compared to less affluent individuals (OR=1.65; 95% CI: 1.26-2.15). Our findings, however, primarily suggests an overall (as opposed to differential) contextual effect of state income inequality on individual self-rated poor health. To the extent that contemporaneous state income inequality differentially affects population sub-groups, our analyses suggest that the adverse impact of inequality is somewhat stronger for the relatively advantaged socioeconomic groups. This pattern was found to be consistent regardless of whether we consider contemporaneous or lagged effects of state income inequality on health. At the same time, the contemporaneous main effect of state income inequality remained statistically significant even when conditioned for past levels of income inequality and median income of states.

Adult↗
PubMed2004

Income, income inequality, and cardiovascular disease mortality: relations among county populations of the United States, 1985 to 1994.

OBJECTIVES: Despite the major contribution of cardiovascular disease (CVD) to total mortality, and reports demonstrating strong relations between income and CVD, the joint relations of population-level income and income inequality with CVD mortality are not well described. This study was undertaken to describe relations among population-level income, income equality, and mortality due to cardiovascular disease, coronary heart disease, and stroke. METHODS: County income distributions were determined from 1990 census data, and CVD mortality rates were obtained from the Compressed Mortality File. Relations among income, income inequality, and CVD mortality were examined in stratified and Poisson regression analyses. RESULTS: County income was inversely related and income inequality was directly related to CVD, coronary heart disease, and stroke mortality. Relations were strongest for stroke. Relations of stroke mortality with income inequality were strongest in low-income populations. CONCLUSIONS: The CVD mortality experiences of county populations are related to both income and income distribution in a complex, disease-dependent manner. The authors' findings are especially relevant to the Southeast, a region of high income inequality, low income, and high stroke mortality.

Adult↗
PubMed2006

For whom is income inequality most harmful? A multi-level analysis of income inequality and mortality in Norway.

This study investigates the degree to which contextual income inequality in economic regions in Norway affected mortality during the 1990s, above the effects of mean regional income and individual-level confounders. A further objective is to explore whether income inequality effects on mortality differed between socioeconomic groups. Data were constructed by linkages of administrative registers encompassing all Norwegian inhabitants. The outcome variable was all-cause mortality during 6 years (i.e., died 1994-1999 or alive end of 1999). Men and women aged 25-66 in 1993 were analysed. Regions' mean income and income inequality (in terms of gini coefficients) were calculated from consumption-units-adjusted family disposable income. Individual-level variables included sex, age, marital status, individual income, education, and being a recipient of health-related welfare benefits. Multilevel logistic regression models were fitted for 2,197,231 individuals nested within 88 regions. After adjusting for regional mean income and individual-level variables, the odds ratio (OR) for mortality 1994-1999 was 1.028 (95% CI 1.023-1.033) on the gini variable multiplied by 100. Analyses of cross-level interactions indicated some, albeit modest, income inequality effects on mortality in the upper income and educational categories. Among those with low individual income, low education, and among recipients of health-related welfare benefits, mortality effects of higher regional income inequality were significantly stronger than among those more advantageously placed in the social structure. The results of this study differ from previous studies which have suggested that contextual income inequality has a minor impact on population health in egalitarian countries. The results indicate that in Norway, neither a comparatively egalitarian income distribution nor generous and comprehensive welfare institutions hindered the emergence of regional-level income inequality effects on mortality, and these effects were particularly marked among socioeconomically disadvantaged groups. Explanations for the results are discussed.

Adult↗
PubMed2006

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

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

Commerce↗
PubMed2005

The effect of income question design in health surveys on family income, poverty and eligibility estimates.

OBJECTIVE: To compare systematic differences between an "omnibus" income measure that asks for total family income amounts with a central survey item and an aggregated income measure that sums specific amounts of income obtained from multiple income sources from everyone within a family. DATA SOURCE: The 2001 Current Population Survey-Demographic Supplement (CPS-DS). Data were collected from 78,000 households from February through April 2001. STUDY DESIGN: First, we compare the omnibus family income to the aggregated family income amounts for each family. Second, we use the various aggregated family income sources to predict whether there is a mismatch between the omnibus and aggregated family income amounts. Finally, we assign a new aggregated amount of income that is restricted to be within the range of the omnibus amount to observe differences in poverty rates. DATA COLLECTION: Data were extracted from University of Michigan's ICPSR website. PRINCIPAL FINDINGS: There is a great deal of variation between the omnibus family income measure and the aggregated family income measure, with the omnibus amount generally being lower than the aggregated. As a result, the percent of people estimated to be in poverty is higher using the omnibus income item. CONCLUSIONS: Health surveys generally rely on an omnibus income measure and analysts should be aware that the income estimates derived from it are limited with respect to poverty determination, and the related concept of eligibility estimation. Analysts of health surveys should also consider matching respondents or multiple imputation to improve the usability of the data.

Adolescent↗
PubMed2001

Ecological analysis of teen birth rates: association with community income and income inequality.

OBJECTIVES: To examine whether per capita income and income inequality are independently associated with teen birth rate in populous U.S. counties. METHODS: This study used 1990 U.S. Census data and National Center for Health Statistics birth data. Income inequality was measured with the 90:10 ratio, a ratio of percent of cumulative income held by the richest and poorest population deciles. Linear regression and analysis of variance were used to assess associations between county-level average income, income inequality, and teen birth rates among counties with population greater than 100,000. RESULTS: Among teens aged 15-17, income inequality and per capita income were independently associated with birth rate; the mean birth rate was 54 per 1,000 in counties with low income and high income inequality, and 19 per 1,000 in counties with high income and low inequality. Among older teens (aged 18-19) only per capita income was significantly associated with birth rate. CONCLUSIONS: Although teen childbearing is the result of individual behaviors, these findings suggest that community-level factors such as income and income inequality may contribute significantly to differences in teen birth rates.

Adolescent↗
PubMed2006

Mortality of HIV-1-infected patients in the first year of antiretroviral therapy: comparison between low-income and high-income countries.

BACKGROUND: Highly active antiretroviral therapy (HAART) is being scaled up in developing countries. We compared baseline characteristics and outcomes during the first year of HAART between HIV-1-infected patients in low-income and high-income settings. METHODS: 18 HAART programmes in Africa, Asia, and South America (low-income settings) and 12 HIV cohort studies from Europe and North America (high-income settings) provided data for 4810 and 22,217, respectively, treatment-naïve adult patients starting HAART. All patients from high-income settings and 2725 (57%) patients from low-income settings were actively followed-up and included in survival analyses. FINDINGS: Compared with high-income countries, patients starting HAART in low-income settings had lower CD4 cell counts (median 108 cells per muL vs 234 cells per muL), were more likely to be female (51%vs 25%), and more likely to start treatment with a non-nucleoside reverse transcriptase inhibitor (NNRTI) (70%vs 23%). At 6 months, the median number of CD4 cells gained (106 cells per muL vs 103 cells per muL) and the percentage of patients reaching HIV-1 RNA levels lower than 500 copies/mL (76%vs 77%) were similar. Mortality was higher in low-income settings (124 deaths during 2236 person-years of follow-up) than in high-income settings (414 deaths during 20,532 person-years). The adjusted hazard ratio (HR) of mortality comparing low-income with high-income settings fell from 4.3 (95% CI 1.6-11.8) during the first month to 1.5 (0.7-3.0) during months 7-12. The provision of treatment free of charge in low-income settings was associated with lower mortality (adjusted HR 0.23; 95% CI 0.08-0.61). INTERPRETATION: Patients starting HAART in resource-poor settings have increased mortality rates in the first months on therapy, compared with those in developed countries. Timely diagnosis and assessment of treatment eligibility, coupled with free provision of HAART, might reduce this excess mortality.

Adolescent↗
PubMed2000

State income inequality, household income, and maternal mental and physical health: cross sectional national survey.

OBJECTIVE: To examine the association of state income inequality and individual household income with the mental and physical health of women with young children. DESIGN: Cross sectional study. Individual level data (outcomes, income, and other sociodemographic covariates) from a 1991 follow up survey of a birth cohort established in 1988. State level income inequality calculated from the income distribution of each state from 1990 US census. SETTING: United States, 1991. PARTICIPANTS: Nationally representative stratified random sample of 8060 women who gave birth in 1988 and were successfully contacted (89%) in 1991. MAIN OUTCOME MEASURES: Depressive symptoms (Center for Epidemiologic Studies depression score >15) and self rated health RESULTS: 19% of women reported depressive symptoms, and 7.5% reported fair or poor health. Compared with women in the highest fifth of distribution of household income, women in the lowest fifth were more likely to report depressive symptoms (33% v 9%, P<0.001) and fair or poor health (15% v 2%, P<0. 001). Compared with low income women in states with low income inequality, low income women in states with high income inequality had a higher risk of depressive symptoms (odds ratio 1.6, 95% confidence interval 1.0 to 2.6) and fair or poor health (1.8, 0.9 to 3.5). CONCLUSIONS: High income inequality confers an increased risk of poor mental and physical health, particularly among the poorest women. Both income inequality and household income are important for health in this population.

Adolescent↗
PubMed2005

Income as mediator of the effect of occupation on the risk of myocardial infarction: does the income measurement matter?

AIM: To investigate whether the effect of occupational grade on the risk of myocardial infarction (MI) is mediated by income with different aspects of income taken into account. METHODS: Data were used from three prospective population studies conducted in Copenhagen. A total of 16 665 employees, 43% women, aged 20-75 years, with an initial examination between 1974 and 1992 were followed up until 1999 for incident (hospital admission or fatal) MI. Register based information on job categories and income was used. RESULTS: During follow up, 855 subjects were diagnosed with a MI: 708 men and 147 women (in total 47% fatal). The hazards by household and individual income showed a graded effect with a hazard ratio (HR) of 1.43 (95% CI 1.12 to 1.83) for the lowest household income group compared with the highest, whereas equivalent income showed an inverse "J shape" effect with a HR of 1.55 (95% CI 1.25 to 1.82) for the third income group compared with the highest. HR for unskilled workers as compared with executive managers was reduced from 1.55 (95% CI 1.24 to 1.93) to 1.42 (95% CI 1.12 to 1.81) after adjustment for household income. CONCLUSIONS: Occupation and income are not mutually exclusive, but at least partly explained by or mediated through the other on the risk of MI. The mediating effect of income is independent of the choice of an income indicator. Income is not a big contributor to inequality in MI; probably because of the rather even income distribution in Denmark.

Adult↗
PubMed2003

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

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

Canada↗
PubMed2006

Median dermatology base incomes in senior academia and practice are comparable, but a significant income gap exists at junior levels.

BACKGROUND: The perception that dermatologists in practice have substantially higher incomes than in academics is often cited as the primary reason people choose to practice outside academic institutions. OBJECTIVE: We sought to compare the incomes of dermatologists in academics versus various practice settings. METHODS: Data from various surveys of dermatologists from 2002 to 2004 were adjusted for annual inflation to the year 2004 and compared. Benefits and bonuses were not included. The income level of clinical instructors, who are 7.0% of all academic dermatology faculty, were not available for inclusion. RESULTS: Median dermatology faculty income (combined average of assistant, associate, and professor levels) was 192,267 dollars, 12.0% less than the median practice income of 215,303 dollars. There was substantial variation across regions, institutions, and types of nonacademic practice. Median starting incomes for dermatology residency graduates were comparable in practice across multiple data sources (182,116 dollars-200,000 dollars) and private universities (189,336 dollars); however, both were significantly higher than median starting incomes in public universities (83,349 dollars). LIMITATIONS: This study relied on self-reported data. Although all attempts were made to use comparable information, variances in how data were collected and classified may exist. CONCLUSIONS: Initial income for those entering practice is equivalent to those entering academia in private universities; however, incomes for both of these groups are 2- to 3-fold higher than those entering academia in public universities. This discrepancy may discourage some recent trainees, some of whom have high debt and high expenses, from entering the field of academic dermatology. Because incomes in academia increase predictably with increasing rank, overall self-reported incomes for established dermatologists in practice and senior academia are comparable.

Academic Medical Centers↗
PubMed2002

Individual income, income distribution, and self rated health in Japan: cross sectional analysis of nationally representative sample.

OBJECTIVE: To assess the effects on self rated health of individual income and income distribution in Japan. DESIGN: Cross sectional analysis. Data collected on household income, self rated health, and other sociodemographic characteristics at the individual level from comprehensive survey of the living conditions of people on health and welfare in a nationally representative sample from each prefecture. SETTING: Prefectures in Japan. PARTICIPANTS: 80 899 people aged >15 years with full records in survey. MAIN OUTCOME MEASURES: Dichotomous variable for self rated health of each respondent (0 if excellent, very good or good; 1 if fair or poor). RESULTS: Inequality in income at the prefecture level measured by the Gini coefficient was comparable with that in other industrialised countries. Unadjusted odds ratios show a 14% increased risk (odds ratio 1.14, 95% confidence interval 1.02 to 1.27) in reporting poor or fair health for individuals living in prefectures with higher inequality in income. After adjustment, individual income was more strongly associated with self rated health than income inequality. Additional inclusion of regional effects showed that median income at the prefecture level was inversely related to self rated health. CONCLUSIONS: Individual income, probably relative to the median prefecture income, has a stronger association with self rated health than income inequality at the prefecture level.

Adolescent↗
PubMed2001

The effects of income inequality and income level on mortality vary by population size in Texas counties.

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.

Censuses↗