Income inequality, primary care, and health indicators

BACKGROUND: The significant association of income inequality with a variety of health indicators is receiving increasing attention. There has also been increasing evidence of a link between primary care and improved health status. We examined the joint relationship between income inequality, availability of primary care, and various health indicators to determine whether primary care has an impact on health indicators by modifying the adverse effect of income inequality. METHODS: Our ecologic study used the US states as the units of analysis. In analyzing the data, we looked at the associations among income inequality, primary care, specialty care, smoking, and health indicators, using Pearson's correlation coefficients for intercorrelations and the adjusted multiple regression procedure. To examine the effect of inequality and primary care on health outcome indicators, we conducted path analyses according to a causal model in which inequality affects health both directly and indirectly through its impact on primary care. RESULTS: Our study indicates that both primary care and income inequality exerted a strong and significant direct influence on life expectancy and total mortality (P <.01). Primary care also exerted a significant direct influence on stroke and postneonatal mortality (P <.01). Although levels of smoking are also influential, the effect of income inequality and primary care persists after controlling for smoking. Primary care serves as one pathway through which income inequality influences population-level mortality and at least some other health outcome indicators. CONCLUSIONS: It appears possible that a primary care orientation may, in part, overcome the severe adverse effects on health of income inequalities.     

Primary care, social inequalities, and all-cause, heart disease, and cancer mortality in US counties, 1990

OBJECTIVES: We tested the association between the availability of primary care and income inequality on several categories of mortality in US counties. METHODS: We used cross-sectional analysis of data from counties (n=3081) in 1990, including analysis of variance and multivariate ordinary least squares regression. Independent variables included primary care resources, income inequality, and sociodemographics. RESULTS: Counties with higher availability of primary care resources experienced between 2% and 3% lower mortality than counties with less primary care. Counties with high income inequality experienced between 11% and 13% higher mortality than counties with less inequality. CONCLUSIONS: Primary care resources may partially moderate the effects of income inequality on health outcomes at the county level.     

Private medical insurance and saving: evidence from the British Household Panel Survey

This paper uses the British Household Panel Survey for the years 1996-2000 to investigate the relationship between saving and private medical insurance in the UK. Because the National Health Service (NHS) gives comprehensive health coverage and is generally free at source, one would not expect private medical insurance to crowd-out saving. However, the NHS being characterised by long waiting lists and generally poor quality, many people prefer to use private health services. In such circumstances, those individuals who are not covered by private medical insurance, and who are therefore more exposed to facing unexpected out-of-pocket private health care expenditures or income losses while waiting for public treatment might save more for precautionary reasons than those who are covered. According to our findings, which are based on a wide range of econometric specifications, there is a positive association between insurance coverage and saving, suggesting that private medical insurance does not generally crowd-out private saving. However, we found some evidence of crowding-out in those areas where the quality of medical facilities is perceived as poor, and in rural areas, characterised by fewer NHS providers.     

Growing old before growing rich: inequality in health service utilization among the mid-aged and elderly in Gansu and Zhejiang Provinces, China

ABSTRACT: BACKGROUND: China's recent growth in income has been unequally distributed, resulting in an unusually rapid retreat from relative income equality, which has impacted negatively on health services access. There exists a significant gap between health care utilization in rural and urban areas and inequality in health care access due to differences in socioeconomic status is increasing. We investigate inequality in service utilization among the mid-aged and elderly, with a special attention of health insurance. METHODS: This paper measures the income-related inequality and horizontal inequity in inpatient and outpatient health care utilization among the mid-aged and elderly in two provinces of China. The data for this study come from the pilot survey of the China Health and Retirement Longitudinal Study in Gansu and Zhejiang. Concentration Index (CI) and its decomposition approach were deployed to reflect inequality degree and explore the source of these inequalities. RESULTS: There is a pro-rich inequality in the probability of receiving health service utilization in Gansu (CI outpatient = 0.067; CI inpatient = 0.011) and outpatient for Zhejiang (CI = 0.016), but a pro-poor inequality in inpatient utilization in Zhejiang (CI = -0.090). All the Horizontal Inequity Indices (HI) are positive. Income was the dominant factor in health care utilization for out-patient in Gansu (40.3 percent) and Zhejiang (55.5 percent). The non-need factors' contribution to inequity in Gansu and Zhejiang outpatient care had the same pattern across the two provinces, with the factors evenly split between pro-rich and pro-poor biases. The insurance schemes were strongly pro-rich, except New Cooperative Medical Scheme (NCMS) in Zhejiang. CONCLUSIONS: For the middle-aged and elderly, there is a strong pro-rich inequality of health care utilization in both provinces. Income was the most important factor in outpatient care in both provinces, but access to inpatient care was driven by a mix of income, need and non-need factors that significantly differed across and within the two provinces. These differences were the result of different levels of health care provision, different out-of-pocket expenses for health care and different access to and coverage of health insurance for rural and urban families. To address health care utilization inequality, China will need to reduce the unequal distribution of income and expand the coverage of its health insurance schemes.     

State-level income inequality and family burden of U.S. families raising children with special health care needs

Growing evidence supports the hypothesis that income inequality within a nation influences health outcomes net of the effect of any given household's absolute income. We tested the hypothesis that state-level income inequality in the United States is associated with increased family burden for care and health-related expenditures for low-income families of children with special health care needs. We analyzed the 2005-06 wave of the National Survey of Children with Special Health Care Needs, a probability sample of approximately 750 children with special health care needs in each state and the District of Columbia in the US Our measure of state-level income inequality was the Gini coefficient. Dependent measures of family caregiving burden included whether the parent received help arranging or coordinating the child's care and whether the parent stopped working due to the child's health. Dependent measures of family financial burden included absolute burden (spending in past 12 months for child's health care needs) and relative burden (spending as a proportion of total family income). After controlling for a host of child, family, and state factors, including family income and measures of the severity of a child's impairments, state-level income inequality has a significant and independent association with family burden related to the health care of their children with special health care needs. Families of children with special health care needs living in states with greater levels of income inequality report higher rates of absolute and relative financial burden.     

Income inequality,area-level poverty,perceived aversion to inequality,and self-rated health in Japan

In this study we conduct a multilevel analysis to investigate the association between regional income inequality and self-rated health in Japan, based on two nationwide surveys. We confirm that there is a significant association between area-level income inequality and individual-level health assessment. We also find that health assessment tends to be more sensitive to income inequality among lower income individuals, and to degree of area-level poverty, than income inequality for the society as a whole. In addition, we examine how individuals are averse to inequality, based on the observed association between inequality and self-rated health.     

Neo-materialist theory and the temporal relationship between income inequality and longevity change

This study uses a neo-materialist perspective to develop theoretical predictions regarding temporal ties between income inequality and change in population health. The argument focuses on the relationship between income inequality and adoption of longevity-enhancing innovations. It asserts that longevity change should be influenced by preexisting levels of income inequality and that, consequently, income inequality can cause differential longevity improvement across jurisdictions even if inequality levels remain unchanged. State-level U.S. data from 1970 to 2000 are used to jointly model the effects of initial levels and change in income inequality on 10-year life expectancy change. Results confirm that states with higher levels of inequality experienced less subsequent improvement in life expectancy. Contrary to findings from prior research, analyses also reveal a strong negative association between change in inequality and change in longevity once initial levels of inequality and other state characteristics are controlled. Finally, direct tests of the relationship between income inequality and the adoption of innovations in quality of medical care indicate that the two are highly related and that differences in the average quality of care can account for the negative cross-sectional association between income inequality and life expectancy.     

Primary care, income inequality, and self-rated health in the United States: a mixed-level analysis.

Using the 1996 Community Tracking Study household survey, the authors examined whether income inequality and primary care, measured at the state level, predict individual morbidity as measured by self-rated health status, while adjusting for potentially confounding individual variables. Their results indicate that distributions of income and primary care within states are significantly associated with individuals' self-rated health; that there is a gradient effect of income inequality on self-rated health; and that individuals living in states with a higher ratio of primary care physician to population are more likely to report good health than those living in states with a lower such ratio. From a policy perspective, improvement in individuals' health is likely to require a multi-pronged approach that addresses individual socioeconomic determinants of health, social and economic policies that affect income distribution, and a strengthening of the primary care aspects of health services.     

新农合对改善医疗服务利用不平等的影响——基于2004年和2006年的调查数据

考察了新农合对与收入相关的医疗服务利用不平等的影响,以及新农合对2004和2006年之间医疗服务利用不平等改善的贡献。为此,将医疗服务利用不平等分解为四个部分,收入、与医疗需要相关的变量、其他变量和残差项。农村地区以＂是否就诊＂度量的不平等程度较小,但以＂是否去较高层级医疗机构就诊＂度量的不平等程度则明显有利于富人,这一不平等程度到2006年有所改善。新农合在2004年有利于富人的医疗服务利用,但这一作用在2006年有所下降。在2004—2006年,新农合的覆盖面迅速扩大,新农合对医疗服务利用不平等的改善有所贡献,尤其对女性医疗服务利用不平等的改善更为明显。但新农合对于在较高层级机构就诊的不平等改善贡献不明显,主要的贡献来自于收入效应。     

Some mechanisms linking economic inequality and infant mortality

We use data from the 1985, 1987 and 1991 United States Vital Statistics Linked Infant Birth and Death Records to assess the relationship between state-level economic inequality and an infant's probability of death. We find that economic inequality is associated with higher neonatal mortality even after we control mother's age and race and state characteristics that are likely to be associated with both inequality and infant death. Inequality is not associated with post-neonatal mortality. We assess three mechanisms that could link income inequality and infant deaths: non-linearity in the relationship between parental income and infant death, economic segregation, and state health care spending. Our evidence suggests that non-linearity in the relationship between family income and infant health accounts for little of the relationship between inequality and infant death. However inequality is associated with greater economic segregation, which in turn is associated with a higher probability of infant death. This effect is partially offset by the fact that inequality is also associated with state spending on health care, which is in turn associated with lower death rates. The increase in economic segregation increased infant deaths more than the increase in health care spending reduces them, so the net effect of economic inequality is to increase infant deaths especially in the first month after birth.     

Primary care, social inequalities and all-cause, heart disease and cancer mortality in US counties: a comparison between urban and non-urban areas

OBJECTIVE: The objective of this study was to test whether the association between primary care and income inequality on all-cause, heart disease and cancer mortality at county level differs in urban (Metropolitan Statistical Area-MSA) compared with non-urban (non-MSA) areas. STUDY DESIGN: The study consisted of a cross-sectional analysis of county-level data stratified by MSA and non-MSA areas in 1990. Dependent variables included age and sex-standardized (per 100,000) all-cause, heart disease and cancer mortality. Independent variables included primary care resources, income inequality, education levels, unemployment, racial/ethnic composition and income levels. METHODS: One-way analysis of variance and multivariate ordinary least squares regression were employed for each health outcome. RESULTS: Among non-MSA counties, those in the highest income inequality category experienced 11% higher all-cause mortality, 9% higher heart disease mortality, and 9% higher cancer mortality than counties in the lowest income inequality quartile, while controlling for other health determinants. Non-MSA counties with higher primary care experienced 2% lower all-cause mortality, 4% lower heart disease mortality, and 3% lower cancer mortality than non-MSA counties with lower primary care. MSA counties with median levels of income inequality experienced approximately 6% higher all-cause mortality, 7% higher heart disease mortality, and 7% higher cancer mortality than counties in the lowest income inequality quartile. MSA counties with low primary care (less than 75th percentile) had significantly lower levels of all-cause, heart disease and cancer mortality than those counties with high primary care. CONCLUSIONS: In non-MSA counties, increasing primary physician supply could be one way to address the health needs of rural populations. In MSA counties, the association between primary care and health outcomes appears to be more complex and is likely to require intervention that focuses on multiple fronts.     

The effect of primary care physician supply and income inequality on mortality among blacks and whites in US metropolitan areas

OBJECTIVES: This study assessed whether income inequality and primary care physician supply have a different effect on mortality among Blacks compared with Whites. METHODS: We conducted a multivariate ecologic analysis of 1990 data from 273 US metropolitan areas. RESULTS: Both income inequality and primary care physician supply were significantly associated with White mortality (P < .01). After the inclusion of the socioeconomic status covariates, the effect of income inequality on Black mortality remained significant (P < .01), but the effect of primary care physician supply was no longer significant (P > .10), particularly in areas with high income inequality. CONCLUSIONS: Improvement in population health requires addressing socioeconomic determinants of health, including income inequality and primary care availability and access.     

Income Inequality,HIV Stigma,and Preventing HIV Disease Progression in Rural Communities

Antiretroviral therapies (ART) suppress HIV replication, thereby preventing HIV disease progression and potentially preventing HIV transmission. However, there remain significant health disparities among people living with HIV, particularly for women living in impoverished rural areas. A significant contributing factor to HIV-related disparities is a stigma. And yet, the relative contributions of stigma, gender, socio-economics, and geography in relation to health outcomes are understudied. We examined the associations of internalized stigma and enacted stigma with community-level income inequality and HIV viral suppression—the hallmark of successful ART—among 124 men and 74 women receiving care from a publicly funded HIV clinic serving rural areas with high-HIV prevalence in the southeastern US. Participants provided informed consent, completed computerized interviews, and provided access to their medical records. Gini index was collected at the census tract level to estimate community-level income inequality. Individual-level and multilevel models controlled for point distance that patients lived from the clinic and quality of life, and included participant gender as a moderator. We found that for women, income inequality, internalized stigma, and enacted stigma were significantly associated with HIV suppression. For men, there were no significant associations between viral suppression and model variables. The null findings for men are consistent with gender-based health disparities and suggest the need for gender-tailored prevention interventions to improve the health of people living with HIV in rural areas. Results confirm and help to explain previous research on the impact of HIV stigma and income inequality among people living with HIV in rural settings.

     

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

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

Teen births, income inequality, and social capital: developing an understanding of the causal pathway

Many studies have demonstrated a relationship between income inequality and poor health, but how does income inequality impact health? One possible explanation is that greater income inequality undermines social capital (social cohesion, civic engagement, and mutual trust in a community). We conducted path analyses of the relationship between income inequality, poverty, and teen birth rate, testing for the mediating effect of social capital in 39 US states. Birth rate was affected by both poverty and income inequality, though income inequality appeared to affect teen birth rate primarily through its impact on social capital.     

Inequality,public health,and COVID-19: an analysis of the Spanish case by municipalities

The main objective of this work is to analyze whether inequality in income distribution has an effect on COVID-19 incidence and mortality rates during the first wave of the pandemic, and how the public health system mitigates these effects. To this end, the case of 819 Spanish municipalities is used, and a linear cross-sectional model is estimated. The results obtained allow us to conclude that a higher level of income inequality generates a higher rate of infections but not deaths, highlighting the importance of the Spanish National Health Service, which does not distinguish by income level. Likewise, early detection of infection measured by the number of primary care centers per 100,000 inhabitants, access to health care for the treatment of the most severe cases, unemployment as a proxy for job insecurity, climatic conditions, and population density are also important factors that determine how COVID-19 affects the population.

     

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

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

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

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

Evaluating the redistributive impact of public health expenditure using an insurance value approach

This article analyses the redistributive impact of public health expenditure in Spain using an insurance value approach to compute individual and household’s value of health services non-cash benefit. We model the intensity of use of different health care services using a count data framework on a nationally representative health care survey and then predict probabilities on the 2006 Spanish EU-SILC sample. This allows us to extend disposable income with the expected monetary value of public health services and to compare it with strictly cash income. Since non-cash income due to public health services is associated with health needs, we use needs-adjusted equivalence scales to perform distributional analysis and poverty/inequality comparisons. The results show that public health expenditure in Spain acts progressively on income distribution, and that health in-kind benefits, once considered as part of disposable income, can be extremely effective in reducing poverty and inequality.     

Decomposing the causes of inequalities in health care use: a micro-simulations approach

We propose an innovative method for the decomposition of factors associated with inequalities in the use of health care. We analyze individual data and make use of micro-simulations to evaluate the effect of heterogeneity of individual behaviors on inequality in access to care. Our study employs methods that, unlike earlier work, permits evaluation of heterogeneity of individual behaviors. We provide an application of this method by decomposing inequality of health care use in France in 1998. We show that half of the inequity in access to care is due to the heterogeneity of behaviors relative to the rank of individuals in the income distribution. This approach reconciles Oaxaca-like decompositions of inequality, focused on outcome gaps, with analyses involving decompositions of inequality by factors, focused on inequity indices.