Explaining income-related inequalities in doctor utilisation in Europe

This paper presents new international comparative evidence on the factors driving inequalities in the use of GP and specialist services in 12 EU member states. The data are taken from the 1996 wave of the European Community Household Panel (ECHP). We examine two types of utilisation (the probability of a visit and the conditional number of positive visits) for two types of medical care: general practitioner and medical specialist visits using probit, truncated Negbin and generalised Negbin models. We find little or no evidence of income-related inequity in the probability of a GP visit in these countries. Conditional upon at least one visit, there is even evidence of a somewhat pro-poor distribution. By contrast, substantial pro-rich inequity emerges in virtually every country with respect to the probability of contacting a medical specialist. Despite their lower needs for such care, wealthier and higher educated individuals appear to be much more likely to see a specialist than the less well-off. This phenomenon is universal in Europe, but stronger in countries where either private insurance cover or private practice options are offered to purchase quicker and/or preferential access. Pro-rich inequity in subsequent visits adds to this access inequity but appears more related to regional disparities in utilisation than to other factors. Despite decades of universal and fairly comprehensive coverage in European countries, utilisation patterns suggest that rich and poor are not treated equally.     

The role of income differences in explaining social inequalities in self rated health in Sweden and Britain.

STUDY OBJECTIVE: To analyse to what extent differences in income, using two distinct measures-as distribution across quintiles and poverty-explain social inequalities in self rated health, for men and women, in Sweden and Britain. DESIGN: Series of cross sectional surveys, the Swedish Survey of Living Conditions (ULF) and the British General Household Survey (GHS), during the period 1992-95. PARTICIPANTS AND SETTING: Swedish and British men and women aged 25-64 years. Approximately 4000 Swedes and 12 500 Britons are interviewed each year in the cross sectional studies used. The sample contains 15 766 people in the Swedish dataset and 49 604 people in the British dataset. MAIN RESULTS: The magnitude of social inequalities in less than good self rated health was similar in Sweden and in Britain, but adjusting for income differences explained a greater part of these in Britain than in Sweden. In Britain the distribution across income quintiles explained 47% of the social inequalities in self rated health among women and 31% among men, while in Sweden it explained, for women 13% and for men 20%. Poverty explained 22% for British women and 8% for British men of the social inequalities in self rated health, while in Sweden poverty explained much less (men 2.5% and women 0%). CONCLUSIONS: The magnitude of social inequalities in self rated health was similar in Sweden and in Britain. However, the distribution of income across occupational social classes explains a larger part of these inequalities in Britain than in Sweden. One reason for this may be the differential exposure to low income and poverty in the two countries.     

Inequalities in self-reported physical health in the United States, 1993-1999

This study investigated inequalities in physically healthy days in the United States during 1993-1999, by socioeconomic and demographic group. The generalized entropy GE(2) and other indices were computed using data from the Behavioral Risk Factor Surveillance System survey, 1993-1999. The results indicate that GE(2) for the US population increased by 17% during 1993-1999. Low-to-middle income groups had the highest increases in inequalities during this time (51-66%), whereas the least educated, Asian/Pacific Islanders, American Indians/Alaska Natives, the oldest, the youngest, and the richest had the lowest (-14-10%). In 1999, inequalities ranged from 0.0153 (income>or=$50 000) to 0.112 (income<$10 000). Inequalities have increased during 1993-1999 and vary substantially across groups. The American Indians/Alaska Natives experienced the highest inequalities whereas Asians/Pacific-Islanders exhibited the lowest inequalities. More attention should be given to within-group inequalities.     

Robust rankings of socioeconomic health inequality using a categorical variable

When assessing socioeconomic health inequalities, researchers often draw upon measures of income inequality that were developed for ratio scale variables. As a result, the use of categorical data (such as self‐reported health status) produces rankings that may be arbitrary and contingent to the numerical scale adopted. In this paper, we develop a method that overcomes this issue by providing conditions for which these rankings are invariant to the numerical scale chosen by the researcher. In doing so, we draw on the insight provided by Allison and Foster (2004) and extend their method to the dimension of socioeconomic inequality by exploiting the properties of rank‐dependent indices such as Wagstaff (2002) achievement and extended concentration indices. We also provide an empirical illustration using the National Institute of Health Survey 2012.     

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.     

Health differences between European countries

This paper examines self-reported health among individuals in 21 European countries. The purpose is to analyze how both individual- and country-level characteristics influence health. The study is based on data from the European Social Survey (ESS) conducted in 2003 and employs hierarchical modelling (N=38,472). We present three main findings: (1) individual-level characteristics, such as age, education, economic satisfaction, social network, unemployment, and occupational status are related to the health of individuals, both for women and men; (2) we tested how societal features, such as public expenditure on health, socioeconomic development, lifestyle, and social capital (social trust) were related to subjective health. Among the country-level characteristics, socioeconomic development, measured as GDP per capita (logarithm), is the indicator that is most strongly associated with better health, after controlling for individual-level characteristics; (3) the eastern European countries stand out as the countries where individuals report the poorest health. In our models, the individual-level variables explain 60% of the variance between countries, whereas 40% is explained by the macro-level variables.     

Self-reported health: reliability and consequences for health inequality measurement

Self-reported health (SRH) is one of the most frequently employed measures for assessing income-related health inequalities between counties. A previous study has shown that 28% of respondents changed their assessment of their health status when asked a SRH question on two occasions in the same survey (first as part of self-completed questionnaire and then in a personal interview). This study re-examines this issue using another survey where SRH was again asked twice of respondents, but this time the personal interview was first and self-completion second. We find the same variation in responses, but the predominant direction is away from the 'extreme' categories 'Excellent' and 'Poor' which is the opposite direction to the previous study. We therefore conclude that the most likely explanation is a mode of administration effect that makes people less likely to choose the extreme categories in a self-completion questionnaire, but not a personal interview. However, this effect has a relatively minor impact on measures of inequality. This is due to a large proportion of the movement (i.e. movement to the middle) not being related to income and hence does not systematically impact on the cumulative distribution of health across this measure of socio-economic status.     

The contextual‐level effects of social trust on health in transitional countries: Instrumental variable analysis of 26 countries

We analyse the effect of contextual‐level social capital on health status in a sample of 26 transitional countries of Central and South Europe, Mongolia, and the former Soviet Union for 2006‐2010 (N = 51 911). Contextual‐level social capital is conceptualized as country‐level social trust, while health status is conceptualized as self‐rated health. We use ordinary least squares and instrumental variable regressions to address endogeneity and especially to rule out reverse causality. Both instrumental variable and ordinary least squares regressions suggest a strong positive effect of country‐level trust on health. This finding is consistent for the whole sample as well as separate regional estimations.     

收入相关健康不平等实证研究

本文应用自报健康资料测算了上海市4区（县）的健康集中指数，考察收入相关健康不平等。研究不仅从实证角度阐述了收入相关健康不平等的测算方法，而且研究结果表明在样本地区存在收入相关健康不平等，并提示改善低收入人群的经济状况特别是收入状况对改善健康的重要性。     

Socioeconomic inequalities in mental health in Australia: Explaining life shock exposure

BackgroundRecent research suggests that there exists a strong link between life shocks and mental health. However, research on the distributional aspects of these shocks on mental health status is limited. In the health inequality literature no Australian studies have examined this relationship.ObjectiveThis study examines the distributional impact of life shocks (negative life events and financial hardships) on mental health inequality among different socioeconomic groups in a longitudinal setting in Australia.MethodsThis study analysed the data of 13,496 individuals from the Household, Income and Labour Dynamics in Australia (HILDA) survey, waves 12–17 (2012–2017). Using concentration index and Blinder-Oaxaca approaches, the study decomposed socioeconomic inequalities in mental health and changes in inequalities in mental health over the study period. The study used frailty indices to capture the severity of life shocks experienced by an individual.ResultsThe results suggest that exposure to just one life shock will result in a greater risk of mental disorder in the most disadvantaged socioeconomic groups. The results also indicate that 24.7%–40.5% of pro-rich socioeconomic mental health inequality are due to life shocks. Financial hardship shocks contributes to 21.6%–35.4% of inequality compared with 2.3%–5.4% inequality generated by negative life event shocks across waves.ConclusionsLower SES groups experience more life shocks than higher SES groups and in turn generate higher socioeconomic mental health inequality. Policies aimed at reducing socioeconomic inequality in mental health should account for these shocks when designing interventions.     

The contribution of smoking and obesity to income-related inequalities in health in England

Reducing avoidable inequalities in health is a priority in many health care systems, including the NHS in Great Britain. Evidence suggests that lifestyle factors may play a role in explaining socioeconomic inequalities in health. In this paper we measure the contribution of smoking and obesity to income-related inequality in health. We use the corrected concentration index to measure inequality across time and areas of England, and decomposition methods to quantify directly the contribution of smoking and obesity to income-related inequality. Instrumental variables regression is used to test the endogeneity of smoking and obesity. We use data from nine rounds of the Health Survey for England (1998–2006). The results show that there are significant income-related health inequalities in England, that the extent of the inequality varies by area, and that in some areas it has increased over time. Nationally, smoking and obesity make a significant but modest contribution to income-related inequality in health (2.3% and 1.2%, respectively). Despite the reduction in smoking prevalence, the contribution of smoking has slightly increased over time, due to its increasing concentration among the poor and its negative effect on health. While the prevalence of obesity is increasing, it is more equally distributed across society. The prevalence of these problems varies between areas, and so does the contribution they make to income-related inequalities in health.     

Explaining variations in breast cancer screening across European countries

Objective

In this study I aim to explore the statistical causes of country differences in mammography screening among women aged 50–69 years in 13 European countries. I focus on the relative importance of individual (e.g. age, education, etc.) and institutional (e.g. public screening programmes) factors in explaining these differences.

Data and methods

I use individual level data from the first three waves (2004–2006–2009) of the SHARE as well as regional and country level data on institutional factors. The analytical approach is based on multilevel statistical models, which allow me to analyse the contribution of individual and institutional factors in explaining the variation in breast cancer screening across European countries.

Results

I find that the standard deviation in screening rates across countries increases slightly from 19.5 to 20.8 per cent after controlling for individual factors. Observed individual factors such as age, education, health status, etc., do not significantly contribute to the explanation of cross-country differences. In contrast, after controlling for observed institutional factors such as the availability of an organised screening programme, the standard deviation drops from 20.86 to 12.92 per cent. These factors can statistically explain about 40 per cent of the between-country differences in screening rates. Moreover, I found that these institutional factors seem to prevent a woman from considering a mammogram “not necessary”.

Conclusion

This analysis provides important insights about patient’s attitudes and understanding of benefits of breast cancer prevention and highlights the importance of the availability of an organised screening programme for screening differences across European countries.     

Health inequalities according to educational level in different welfare regimes: a comparison of 23 European countries

The object of this study was to determine whether the magnitude of educational health inequalities varies between European countries with different welfare regimes. The data source is based on the first and second wave of the European Social Survey. The first health indicator describes people's mental and physical health in general, while the second reports cases of any limiting longstanding illness. Educational inequalities in health were measured as the difference in health between people with an average number of years of education and people whose educational years lay one standard deviation below the national average. Moreover, South European welfare regimes had the largest health inequalities, while countries with Bismarckian welfare regimes tended to demonstrate the smallest. Although the other welfare regimes ranked relatively close to each other, the Scandinavian welfare regimes were placed less favourably than the Anglo-Saxon and East European. Thus, this study shows an evident patterning of magnitudes of health inequalities according to features of European welfare regimes. Although the greater distribution of welfare benefits within the Scandinavian countries are likely to have a protective effect for disadvantaged cities in these countries, other factors such as relative deprivation and class-patterned health behaviours might be acting to widen health inequalities.     

Job insecurity and health: A study of 16 European countries

Although the number of insecure jobs has increased considerably over the recent decades, relatively little is known about the health consequences of job insecurity, their international pattern, and factors that may modify them. In this paper, we investigated the association between job insecurity and self-rated health, and whether the relationship differs by country or individual-level characteristics. Cross-sectional data from 3 population-based studies on job insecurity, self-rated health, demographic, socioeconomic, work-related and behavioural factors and lifetime chronic diseases in 23,245 working subjects aged 45–70 years from 16 European countries were analysed using logistic regression and meta-analysis. In fully adjusted models, job insecurity was significantly associated with an increased risk of poor health in the Czech Republic, Denmark, Germany, Greece, Hungary, Israel, the Netherlands, Poland and Russia, with odds ratios ranging between 1.3 and 2.0. Similar, but not significant, associations were observed in Austria, France, Italy, Spain and Switzerland. We found no effect of job insecurity in Belgium and Sweden. In the pooled data, the odds ratio of poor health by job insecurity was 1.39. The association between job insecurity and health did not differ significantly by age, sex, education, and marital status. Persons with insecure jobs were at an increased risk of poor health in most of the countries included in the analysis. Given these results and trends towards increasing frequency of insecure jobs, attention needs to be paid to the public health consequences of job insecurity.     

Association between attendance at religious services and self-reported health in 22 European countries

There are consistent reports of protective associations between attendance at religious services and better self-rated health but existing data rarely consider the social or individual context of religious behaviour. This paper investigates whether attendance at religious services is associated with better self-rated health in diverse countries across Europe. It also explores whether the association varies with either individual-level (gender, educational, social contact) or country-level characteristics (overall level of religious practice, corruption, GDP). Cross-sectional data from round 2 of the European Social Survey were used and 18,328 men and 21,373 women from 22 European countries were included in multilevel analyses, with country as higher level.     

Thank goodness for stickiness: Unravelling the evolution of income-related health inequalities before and after the Great Recession in Europe

The Great Recession in Europe sparked concerns that the crisis would lead to increased income related health inequalities (IRHI). Did this come to pass, and what role, if any, did government transfers play in the evolution of these inequalities? Motivated by these questions, this paper seeks to (i) study the evolution of IRHI during the crisis, and (ii) decompose these evolutions to examine the separate roles of government versus market transfers. Using panel data for 7 EU countries from 2004 to 2013, we find no evidence that IRHI persistently rose after 2008, even in countries most affected by the crisis. Our decomposition reveals that, while the health of the poorest did indeed worsen during the crisis, IRHI were prevented from increasing by the relative stickiness of old age pension benefits compared to the market incomes of younger groups. Austerity measures weakened the IRHI reducing effect of government transfers.     

Societal characteristics and health in the former communist countries of Central and Eastern Europe and the former Soviet Union: a multilevel analysis

OBJECTIVES: To examine whether, in former communist countries that have undergone profound social and economic transformation, health status is associated with income inequality and other societal characteristics, and whether this represents something more than the association of health status with individual socioeconomic circumstances. DESIGN: Multilevel analysis of cross-sectional data. SETTING: 13 Countries from Central and Eastern Europe and the former Soviet Union. PARTICIPANTS: Population samples aged 18+ years (a total of 15 331 respondents). MEAN OUTCOME MEASURES: Poor self-rated health. RESULTS: There were marked differences among participating countries in rates of poor health (a greater than twofold difference between the countries with the highest and lowest rates of poor health), gross domestic product per capita adjusted for purchasing power parity (a greater than threefold difference), the Gini coefficient of income inequality (twofold difference), corruption index (twofold difference) and homicide rates (20-fold difference). Ecologically, the age- and sex-standardised prevalence of poor self-rated health correlated strongly with life expectancy at age 15 (r = -0.73). In multilevel analyses, societal (country-level) measures of income inequality were not associated with poor health. Corruption and gross domestic product per capita were associated with poor health after controlling for individuals' socioeconomic circumstances (education, household income, marital status and ownership of household items); the odds ratios were 1.15 (95% confidence interval 1.03 to 1.29) per 1 unit (on a 10-point scale) increase in the corruption index and 0.79 (95% confidence interval 0.68 to 0.93) per $5000 increase in gross domestic product per capita. The effects of gross domestic product and corruption were virtually identical in people whose household income was below and above the median. CONCLUSION: Societal measures of prosperity and corruption, but not income inequalities, were associated with health independently of individual-level socioeconomic characteristics. The finding that these effects were similar in persons with lower and higher income suggests that these factors do not operate exclusively through poverty.     

Longitudinal methods to investigate the role of health determinants in the dynamics of income-related health inequality

The usual starting point for understanding changes in income-related health inequality (IRHI) over time has been regression-based decomposition procedures for the health concentration index. However the reliance on repeated cross-sectional analysis for this purpose prevents both the appropriate specification of the health function as a dynamic model and the identification of important determinants of the transition processes underlying IRHI changes such as those relating to mortality. This paper overcomes these limitations by developing alternative longitudinal procedures to analyse the role of health determinants in driving changes in IRHI through both morbidity changes and mortality, with our dynamic modelling framework also serving to identify their contribution to long-run or structural IRHI. The approach is illustrated by an empirical analysis of the causes of the increase in IRHI in Great Britain between 1999 and 2004.     

Inequality aversion in income,health, and income-related health

Based on a survey of a sample of the general public, we estimate inequality aversion across income, health, and bivariate income-health. Inequality aversion is domain specific: mean inequality aversion is greater for income than for health, but the underlying distributions of aversion attitudes differ, with a highly bi-modal distribution of inequality-aversion values for health in which nearly half the participants display very low aversion and nearly half display very high aversion. Aversion to income-related health inequality is greater than that to income or health alone. Consistent with previous literature, we find only weak associations between aversion attitudes and individual characteristics. The magnitude of the estimates implies potentially large gains in welfare from reducing inequality in these domains.