Income-related Health Inequality Research Articles

The Measurement of Health and the Connection between Health Inequality and Income

We examine the extent to which self-reported health measures suffer from reporting bias and then characterize how this reporting bias affects the estimation of income-related health inequality as measured by the concentration index. We run a comprehensive set of tests of reporting bias using several self-reported health measures and several clinical measures of health from the National Health and Nutritional Examination Surveys. Our results confirm the existence of significant, positive, income-related reporting bias and also suggest that higher income individuals react more strongly to a change in objective, clinical health measures. We find that self-reported health measures significantly overstate the degree of income-related health inequality relative to clinical health measures. Parallel to and in support of the analysis described above, we propose the use of a multidimensional measure of clinical health in the context of measuring income-related health inequality.

Lifestyle and Income-related Inequality in Health in South Africa

BackgroundMany low- and middle-income countries are experiencing an epidemiological transition from communicable to non-communicable diseases. This has negative consequences for their human capital development, and imposes a growing economic burden on their societies. While the prevalence of such diseases varies with socioeconomic status, the inequalities can be exacerbated by adopted lifestyles of individuals. Evidence suggests that lifestyle factors may explain the income-related inequality in self-reported health. Self-reported health is a subjective evaluation of people’s general health status rather than an objective measure of lifestyle-related ill-health.MethodThe objective of this paper is to expand the literature by examining the contribution of smoking and alcohol consumption to health inequalities, incorporating more objective measures of health, that are directly associated with these lifestyle practices. We used the National Income Dynamic Study panel data for South Africa. The corrected concentration index is used to measure inequalities in health outcomes. We use a decomposition technique to identify the contribution of smoking and alcohol use to inequalities in health.ResultsWe find significant smoking-related and income-related inequalities in both self-reported and lifestyle-related ill-health. The results suggest that smoking and alcohol use contribute positively to income-related inequality in health. Smoking participation accounts for up to 7.35% of all measured inequality in health and 3.11% of the inequality in self-reported health. The estimates are generally higher for all measured inequality in health (up to 14.67%) when smoking duration is considered. Alcohol consumption accounts for 27.83% of all measured inequality in health and 3.63% of the inequality in self-reported health.ConclusionThis study provides evidence that inequalities in both self-reported and lifestyle-related ill-health are highly prevalent within smokers and the poor. These inequalities need to be explicitly addressed in future programme planning to reduce health inequalities in South Africa. We suggest that policies that can influence poor individuals to reduce tobacco consumption and harmful alcohol use will improve their health and reduce health inequalities.

Income-related inequalities in diseases and health conditions over the business cycle

How business cycles affect income-related distribution of diseases and health disorders is largely unknown. We examine how the prevalence of thirty diseases and health conditions is distributed across the income spectrum using survey data collected in Iceland in 2007, 2009 and 2012. Thus, we are able to take advantage of the unusually sharp changes in economic conditions in Iceland during the Great Recession initiated in 2008 and the partial recovery that had already taken place by 2012 to analyze how income-related health inequality changed across time periods that can be described as a boom, crisis and recovery. The concentration curve and the concentration index are calculated for each disease, both overall and by gender. In all cases, we find a considerable income-related health inequality favoring higher income individuals, with a slight increase over the study period. Between 2007 and 2009, our results indicate increased inequality for women but decreased inequality for men. Between 2009 and 2012 on the contrary, men’s inequality increases but women’s decreases. The overarching result is thus that the economic hardship of the crisis temporarily increased female income-related health inequality, but decreased that of men.

Dynamic Changes in Determinants of Inequalities in Health in Europe with Focus on Retired - With Particular Regard to Retired Danes

Earlier studies of health inequality across European countries have shown intriguing results, in particular with respect to retirement status as one of the determinants of health inequality. A priori, one would expect that inequality in health and income would be associated. Theory suggests that health deteriorates with age, in particular for low income groups. Moreover, as income declines after retirement, elderly people tend to rank lower in the relative income ranking. Consequently, retirement status, and in particular early retirement due to health problems, is expected to contribute to inequalities in income-related inequalities in health. The present paper contributes to previous knowledge by looking further into the contribution by retired Europeans to income-related inequalities in health and the development in this contribution over time. The study is based on data from the first and the fourth waves of the Survey of Health, Ageing and Retirement in Europe (SHARE), including individuals born in 1954 or earlier (wave 1) and 1960 or earlier (wave 2) from 10 European countries. Income-related inequality in health is measured using the concentration index. A decomposition of the index into its determinants allows a calculation of the contribution of each determinant’s separate contribution to inequality in health. The results presented here indicate that retirement status contributes substantially to income-related inequality in health across European countries, and that the variation can be explained by income differences as well as health differences, depending on the country considered. Furthermore, it is indicated that the contribution from retirement status falls for certain countries due to improved socioeconomic status as well as improved health of the retired.

A Health \u2018Kuznets\u2019 Curve\u2019? Cross-Sectional and Longitudinal Evidence on Concentration Indices\u2019

The distribution of income related health inequalities appears to exhibit changing patterns when both developing countries and developed countries are examined. This paper tests for the existence of a health Kuznets’ curve; that is, an inverse U-shape pattern between economic developments (as measured by GDP per capita) and income-related health inequalities (as measured by concentration indices). We draw upon both cross sectional (the World Health Survey) and a long longitudinal (the European Community Household Panel survey) dataset. Our results suggest evidence of a health Kuznets’ curve on per capita income. We find a polynomial association where inequalities decline when GDP per capita reaches a magnitude ranging between $26,000 and $38,700. That is, income-related health inequalities rise with GDP per capita, but tail off once a threshold level of economic development has been attained.

Explaining mental health inequalities in Northern Sweden: a decomposition analysis

ABSTRACT Background: There has been a substantial increase of income inequalities in Sweden over the last 20 years, which also could be reflected in health inequalities, including mental health inequalities. Despite the growing body of literature focusing on health inequalities in Sweden, income-related inequalities in mental health have received little attention. Particularly scarce are research from Northern Sweden and examinations of the social determinants of health inequalities. Objectives: The present study seeks to provide evidence regarding inequalities in mental health in Northern Sweden. The specific aims were to (1) quantify the income-related inequality in mental health in Northern Sweden, and (2) determine the contribution of social determinants to the inequality. Methods: The study population comprised 25,646 participants of the 2014 Health on Equal Terms survey in the four northernmost counties of Sweden, aged 16 to 84 years old. Income-related inequalities in mental health were quantified by the concentration index and further decomposed by applying Wagstaff-type decomposition analysis. Results: The overall concentration index of mental health in Northern Sweden was −0.15 (95% CI: −0.17 to −0.13), indicating income inequalities in mental health disfavoring the less affluent population. The decomposition analysis results revealed that socio-economic conditions, including employment status (31%), income (22.6%), and cash margin (14%), made the largest contribution to the pro-rich inequalities in mental health. The second-largest contribution came from demographic factors, mainly age (11.3%) and gender (6%). Psychosocial factors were of smaller importance, with perceived discrimination (8%) and emotional support (3.4%) making moderate contributions to the health inequalities. Conclusions: The present study demonstrates substantial income-related mental health inequalities in Northern Sweden, and provides insights into their underpinnings. These findings suggest that addressing the root causes is essential for promoting mental health equity in this region.

Wie kommt die Gesellschaft unter die Haut? Eine Mediatoranalyse gesundheitlicher Ungleichheit mit den Daten des Sozioökonomischen Panels 2011

Background: The relationship between socioeconomic status and health is explained by the unequal distribution of material, psychosocial and behavioral factors. The aim of this study is to analyze the relative contribution of these factors for the association between income and different indicators of health and diseases. Method: Analyses were based on data from the "German Socioeconomic Panel (GSOEP)" in 2011. We estimated the relative contribution of material, psychosocial and behavioral factors in income-related inequalities in health by logistic mediator analyses focusing on following outcomes: self-rated health, diabetes, cardiac disease, sleep disorder, joint diseases, depression and chronical back trouble. Results: The material, psychosocial and behavioral factors mediate between 45.2 and 97.7% of the association between income and health, depending on the health outcome. The material factors contributed most to health inequalities, separately (28.6-59.8%) and together with the psychosocial factors (39.8-87.4%). Health behavior plays a prominent role in diabetes and contributed separately 32.5% to the inequalities. Material deprivation and economic worries were the most significant indicators contributing to inequalities in health. Conclusions: The results indicate the high relevance of material, psychosocial and behavioral factors for explaining income-related inequalities in health. The identified patterns across multiple health outcomes illustrate the relevance of psychosocial and material factors and the need for health policy to increase the awareness about these mechanisms in the medical education and practice as well as in prevention.

Socioeconomic gradient in health in Canada: Is the gap widening or narrowing?

BackgroundNotwithstanding a general improvement in health status, the socioeconomic gradient in health remains a public health challenge worldwide. ObjectiveUsing longitudinal data from the National Population Health Survey (NPHS, n=17,276), we examined trends in socioeconomic gradients in two health indicators, viz. the Health Utility Index (HUI) and the Frailty Index (FI), among Canadian adults (25 years and older) between 1998/9–2010/11. MethodsThe relative and slope indices of inequality (RII and SII, respectively) were employed to summarize income- and education-based inequality in the FI and the HUI in Canada as whole, and in five regions: the Atlantic provinces, Quebec, Ontario, the Prairies and British Columbia. ResultsWe found that education- and income-related inequalities in health were present in all five regions of Canada. The estimated RIIs and SIIs suggested that education-related inequalities in the FI and the HUI increased among women. The results also revealed that relative and absolute income-related inequalities in the HUI increased in Canada, especially among women. Both absolute and relative inequalities indicated that income-related inequalities in the HUI increased in Quebec and in the Prairies over time. ConclusionPersistent and growing socioeconomic inequalities in health in Canada over the past one and half decades should warrant more attention. The mechanisms underlying socioeconomic-related inequalities in Canada are less clear. Therefore, further studies are required to identify effective polices to reduce the socioeconomic gradient in health in Canada.

Association between Natural Resources for Outdoor Activities and Physical Inactivity: Results from the Contiguous United States.

Protected areas including national/state parks and recreational waters are excellent natural resources that promote physical activity and interaction with Nature, which can relieve stress and reduce disease risk. Despite their importance, however, their contribution to human health has not been properly quantified. This paper seeks to evaluate quantitatively how national/state parks and recreational waters are associated with human health and well-being, taking into account of the spatial dependence of environmental variables for the contiguous U.S., at the county level. First, we describe available natural resources for outdoor activities (ANROA), using national databases that include features from the Protected Areas Database, NAVSTREETS, and ATTAINSGEO 305(b) Waters. We then use spatial regression techniques to explore the association of ANROA and socioeconomic status factors on physical inactivity rates. Finally, we use variance analysis to analyze ANROA’s influence on income-related health inequality. We found a significantly negative association between ANROA and the rate of physical inactivity: ANROA and the spatial effect explained 69%, nationwide, of the variation in physical inactivity. Physical inactivity rate showed a strong spatial dependence—influenced not only by its own in-county ANROA, but also by that of its neighbors ANROA. Furthermore, community groups at the same income level and with the highest ANROA, always had the lowest physical inactivity rate. This finding may help to guide future land use planning and community development that will benefit human health and well-being.

The Income-Health Relationship 'Beyond the Mean': New Evidence from Biomarkers.

The relationship between income and health is one of the most explored topics in health economics but less is known about this relationship at different points of the health distribution. Analysis based solely on the mean may miss important information in other parts of the distribution. This is especially relevant when clinical concern is focused on the tail of the distribution and when evaluating the income gradient at different points of the distribution and decomposing income-related inequalities in health is of interest. We use the unconditional quantile regression approach to analyse the income gradient across the entire distribution of objectively measured blood-based biomarkers. We apply an Oaxaca-Blinder decomposition at various quantiles of the biomarker distributions to analyse gender differentials in biomarkers and to measure the contribution of income (and other covariates) to these differentials. Using data from the Health Survey for England, we find a non-linear relationship between income and health and a strong gradient with respect to income at the highest quantiles of the biomarker distributions. We find that there is heterogeneity in the association of health to income across genders, which accounts for a substantial percentage of the gender differentials in observed health. Copyright © 2016 John Wiley & Sons, Ltd.

Income-related health inequality of migrant workers in China and its decomposition: An analysis based on the 2012 China Labor-force Dynamics Survey data

BackgroundAlthough migrant workers are a vulnerable group in China, they demonstrably contribute to the country's economic growth and prosperity. This study aimed to describe and assess the inequality of migrant worker health in China and its association with socioeconomic determinants. MethodsThe data utilized in this study were obtained from the 2012 China Labor-force Dynamics Survey conducted in 29 Chinese provinces. This study converted the self-rated health of these migrant workers into a general cardinal ill-health score. Determinants associated with migrant worker health included but were not limited to age, marital status, income, and education, among other factors. Concentration index, concentration curve, and decomposition of the concentration index were employed to measure socioeconomic inequality in migrant workers' health. ResultsProrich inequality was found in the health of migrant workers. The concentration index was −0.0866, as a score indicator of ill health. Decomposition of the concentration index revealed that the factors most contributing to the observed inequality were income, followed by gender, age, marital status, and smoking history. ConclusionIt is generally known that there is an unequal socioeconomic distribution of migrant worker health in China. In order to reduce the health inequality, the government should make a substantial effort to strengthen policy implementation in improving the income distribution for vulnerable groups. After this investigation, it is apparent that the findings we have made warrant further investigation.

Correlates of unequal access to preventive care in China: a multilevel analysis of national data from the 2011 China Health and Nutrition Survey.

BackgroundPreventive care has an essential role in reducing income-related health inequalities. Despite a general consensus of the need of shifting focus from disease treatment to wellness and prevention, little is known about inequalities in access to preventive care in China. Our study aimed to explore the inequalities in preventive care usage and factors that were associated with such inequalities among Chinese adults.MethodsMultilevel logistic regression analyses were performed using national data from the 2011 Chinese Health and Nutrition Survey. The study sample comprised 13,483 adults who were covered by Basic Social Medical Insurance (BSMI). We analyzed individual socioeconomic status (marital status, education attainment, annual household income per capita, and medical insurance) and contextual factors for their influence on preventive care usage (region of residence and type of community) after controlling for health needs (age, sex, and health condition).ResultsOut of the participants, 6.9 % received preventive care services over the past four weeks and 3.9 % went for a general physical examination prior to the survey. We noted regional disparities in the overall use of preventive care and specific use of general physical examination, with residents from central and northeastern regions less likely to use preventive care including general physical examination than in the more affluent eastern region. Lower levels of education and income were associated with reduced use of preventive care. Subscriptions to less generous social medical insurance programs such as Urban Resident-based Medical Insurance Scheme or New Rural Cooperative Medical Scheme were associated with decreased specific use of general physical examinations, but not overall use of preventive care.ConclusionsInequalities in preventive care usage were evident in China, and were associated with health needs and socioeconomic characteristics. Current health insurance arrangements may fail to reduce inequalities relating to preventive care. A fair and more coherent policy across all BSMI schemes is needed.

A general method for decomposing the causes of socioeconomic inequality in health

We introduce a general decomposition method applicable to all forms of bivariate rank dependent indices of socioeconomic inequality in health, including the concentration index. The technique is based on recentered influence function regression and requires only the application of OLS to a transformed variable with similar interpretation. Our method requires few identifying assumptions to yield valid estimates in most common empirical applications, unlike current methods favoured in the literature. Using the Swedish Twin Registry and a within twin pair fixed effects identification strategy, our new method finds no evidence of a causal effect of education on income-related health inequality.

Trends in Income-Related Health Inequalities in Canada.

Trends in Income-Related Health Inequalities in Canada.

Assessing Income-Related Health Inequality and Horizontal Inequity in China

The study aims to investigate both income-related health inequality and horizontal equity in urban and rural China. The 4th and 5th National Health Services Survey, and extended samples in Shaanxi Province surveyed in 2008 and 2013, were analysed. Health outcome was measured using the EQ-5D-3L utility, scored by the Chinese-specific tariff. The concentration index was calculated to measure the degree of income-related health inequality and was further decomposed to study the strength of different contributing factors to explain health inequality. The horizontal inequity was further measured based on the decomposition results. The final study sample consists of 15,505 respondents in 2008 and 48,808 respondents in 2013. Descriptive analysis shows that compared to 2008, respondents in both urban and rural China reported worse HRQoL in 2013. There was a pro-rich inequality of HRQoL in both urban and rural China. Controlling for demographic factors, the pro-rich inequity of HRQoL remains. Economic and educational statuses are found to be two key factors explaining the pro-rich inequity. The establishment of basic medical insurance has shown a mixed effect on reducing health inequality. Strategies to reduce the inequality of residents’ economic and educational status, through further implementing the poverty reduction policies, should be prioritised by the local government.

Health inequalities regarding territorial differences in Hungary by discussing life expectancy

Since the middle of the 1990s, Hungary has seen substantial increases in life expectancy. Despite this improvement, many health outcomes remain poor, placing Hungary among the countries in the European Union with worse health status. Based on the general state of health of the population, Hungary belongs with the middle-ground countries of the world. Majority of the health indicators are worse than the average of OECD’s values, and this is especially true regarding the mortality rate of the middle-aged male population. The main objective of the study is to investigate health inequalities with regional differences in Hungary. It is still worth explaining how health inequalities and inequities have changed in terms of space and time after the Hungarian economic and political transition. The territorial range of the study includes the national and regional levels (NUTS3) with the micro-regional level (LAU1). The statistical analysis is based on the use of life expectancy in addition to some mortality indicators. Data for 1990–2014 were examined to define health effects of the Hungarian transition as well as the consequences of the latest economic crisis. Improvements in health along with growth of regional inequalities were found in Hungary since the second half of the 1990s. Larger relative inequalities were observed between Western and Eastern Hungary based on its higher and lower income. Gender differences are also significant in life expectancy. Poor health among the unemployed people was detected, which is a socio-economic effect of the latest economic crisis. In Hungary, income-related health inequalities persist; however, their degree has changed in space and time over the last 25 years. For a comprehensive description of health in Hungary, assessment of the poor health of lower income social groups and the regional level of health inequalities is needed.

Assessing the relationship between dental appearance and the potential for discrimination in Ontario, Canada

Poor oral health is influenced by a variety of individual and structural factors. It disproportionately impacts socially marginalized people, and has implications for how one is perceived by others. This study assesses the degree to which residents of Canada’s most populated province, Ontario, recognize income-related oral health inequalities and the degree to which Ontarians blame the poor for these differences in health, thus providing an indirect assessment of the potential for prejudicial treatment of the poor for having bad teeth. Data were used from a provincially representative survey conducted in Ontario, Canada in 2010 (n=2006). The survey asked participants questions about fifteen specific conditions (e.g. dental decay, heart disease, cancer) for which inequalities have been described in Ontario, and whether participants agreed or disagreed with various statements asserting blame for differences in health between social groups. Binary logistic regression was used to determine whether assertions of blame for differences in health are related to perceptions of oral health conditions. Oral health conditions are more commonly perceived as a problem of the poor when compared to other diseases and conditions. Among those who recognize that oral conditions more commonly affect the poor, particular socioeconomic and demographic characteristics predict the blaming of the poor for these differences in health, including sex, age, education, income, and political voting intention. Social and economic gradients exist in the recognition of, and blame for, oral health conditions among the poor, suggesting a potential for discrimination amongst socially marginalized groups relative to dental appearance. Expanding and improving programs that are targeted at improving the oral and dental health of the poor may create a context that mitigates discrimination.

Implications of Public Policy Change Models for Addressing Income-Related Health Inequalities

There are differing interpretations among health researchers about how income inequality leads to health inequalities, but there is consensus that income inequality is cause for concern. Alarms have also been sounded by public policy researchers and institutes, which see income inequality as having a corrosive effect on society by hindering economic growth, dampening political participation, and eroding quality of life. This article examines how various public policy models explain and promote policy change to reduce income inequality. The focus of this article is on income inequality, but its raison d’être is the assumption that income inequality is an important driver of the health inequalities that both diminish and prematurely end life.

The role of income inequality and social policies on income-related health inequalities in Europe.

IntroductionThe aim of the paper is to examine the role of income inequality and redistribution for income-related health inequalities in Europe. This paper contributes in two ways to the literature on macro determinants of socio-economic inequalities in health. First, it widens the distinctive focus of the research field on welfare state regimes to quantifiable measures such as social policy indicators. Second, looking at income differences completes studies on socio-economic health inequalities, which often analyse health inequalities based on educational differences.MethodsUsing data from the European Values Study (2008/2009), 42 European countries are available for analysis. Country characteristics are derived from SWIID, Eurostat, and ILO and include indicators for income inequality, social policies, and economic performance. The data is analysed by using a two-step hierarchical estimation approach: At the first step—the individual level—the effect of household income on self-assessed health is extracted and introduced as an indicator measuring income-related health inequalities at the second step, the country-level.ResultsIndividual-level analyses reveal that income-related health inequalities exist all across Europe. Results from country-level analyses show that higher income inequality is significantly positively related to higher health inequalities while social policies do not show significant relations. Nevertheless, the results show the expected negative association between social policies and health inequalities. Economic performance also has a reducing influence on health inequalities. In all models, income inequality was the dominating explanatory effect for health inequalities.ConclusionsThe analyses indicate that income inequality has more impact on health inequalities than social policies. On the contrary, social policies seemed to matter to all individuals regardless of socio-economic position since it is significantly positively linked to overall population health. Even though social policies are not significantly related to health inequalities, the power of public redistribution to impact health inequalities should not be downplayed. Social policies as a way of public redistribution are a possible instrument to reduce income inequalities which would in turn lead to a reduction in health inequalities.

Income-Related Inequalities in Oral Health Over Time among Adults from Australia, Brazil and USA.

Income-Related Inequalities in Oral Health Over Time among Adults from Australia, Brazil and USA.