Measuring Health Inequalities Research Articles

A three-stage approach to measuring health inequalities and inequities.

IntroductionMeasurement of health inequities is fundamental to all health equity initiatives. It is complex because it requires considerations of ethics, methods, and policy. Drawing upon the recent developments in related specialized fields, in this paper we incorporate alternative definitions of health inequity explicitly and transparently in its measurement. We propose a three-stage approach to measuring health inequities that assembles univariate health inequality, univariate health inequity, and bivariate health inequities in a systematic and comparative manner.MethodsWe illustrate the application of the three-stage approach using the Joint Canada/United States Survey of Health, measuring health by the Health Utilities Index (HUI). Univariate health inequality is the distribution of the observed HUI across individuals. Univariate health inequity is the distribution of unfair HUI – components of HUI associated with ethically unacceptable factors – across individuals. To estimate the unfair HUI, we apply two popular definitions of inequity: “equal opportunity for health” (health outcomes due to factors beyond individual control are unfair), and “policy amenability” (health outcomes due to factors amenable to policy interventions are unfair). We quantify univariate health inequality and inequity using the Gini coefficient. We assess bivariate inequities using a regression-based decomposition method.ResultsOur analysis reveals that, empirically, different definitions of health inequity do not yield statistically significant differences in the estimated amount of univariate inequity. This derives from the relatively small explanatory power common in regression models describing variations in health. As is typical, our model explains about 20% of the variation in the observed HUI. With regard to bivariate inequities, income and health care show strong associations with the unfair HUI.ConclusionsThe measurement of health inequities is an excitingly multidisciplinary endeavour. Its development requires interdisciplinary integration of advances from relevant disciplines. The proposed three-stage approach is one such effort and stimulates cross-disciplinary dialogues, specifically, about conceptual and empirical significance of definitions of health inequities.Electronic supplementary materialThe online version of this article (doi:10.1186/s12939-014-0098-y) contains supplementary material, which is available to authorized users.

Trends in income-related health inequalities in self-assessed health in Korea, 1998–2011

This study assesses income-related health inequalities in self-assessed health (SAH) and its trend from 1998 to 2011 in Korea that covers important time periods of financial crisis and post-crisis. Data came from the Korean National Health and Nutrition Examination Survey from 1998 to 2011. A population-representative sample aged 46 years and older was analysed. SAH was used as an indicator of health status, with household equivalence income as a proxy for socio-economic position. Age-adjusted prevalence rates of SAH were analysed to estimate both absolute and relative measures of health inequalities and the trend over time by the relative index of inequality (RII) and the slope index of inequality (SII). Results indicated that the highest level of health inequalities was found among men aged 46–59 years, especially in 2001 and 2005. For men, there was no clear, consistent pattern of increase or decrease in the trend over time. On the other hand, increasing trends in the RII and SII were found for women, except for women aged 46–59 years who reported a decreasing trend in the SII. Trends in health inequalities over time were influenced by economic crisis, demonstrating the need for macro-level economic policies as well as health policies addressing health gaps.

An Information Theory Based Framework for the Measurement of Population Health

This paper proposes a new framework for the measurement of population health and the ranking of the health of different geographies. Since population health is a latent variable, studies which measure and rank the health of different geographies must aggregate observable health attributes into one summary measure. We show that the methods used in nearly all the literature to date implicitly assume that all attributes are infinitely substitutable. Our method, based on the measurement of multidimensional welfare and inequality, minimizes the entropic distance between the summary measure of population health and the distribution of the underlying attributes. This summary function coincides with the constant elasticity of substitution and Cobb-Douglas production functions and naturally allows different assumptions regarding attribute substitutability or complementarity. To compare methodologies, we examine a well-known ranking of the population health of U.S. states, America’s Health Rankings. We find that states’ rankings are somewhat sensitive to changes in the weight given to each attribute, but very sensitive to changes in aggregation methodology. Our results have broad implications for well-known health rankings such as the 2000 World Health Report, as well as the measurement of health inequality.

Trends in Socioeconomic and Racial/Ethnic Inequalities in Self-Assessed Health, Disability, and Mental Health in California: Findings from CHIS 2001–2011

Despite growing interest in health inequalities across socioeconomic and racial/ethnic groups, research remains scarce on the subject for California. The purpose of this study is to assess magnitude and trends in socioeconomic and race/ethnicity inequalities in self-assessed health, disability, and psychological distress. This study used the population-representative sample aged 45 and older (n = 181,964) in 2001–2011 California Health Interview Survey (CHIS). Based on poverty as socioeconomic position and race/ethnicity as nominal social group, health inequalities and the trend over time were examined by estimating population-weighted measures on both absolute and relative terms. The most economically disadvantaged Californians showed the highest rates of poor self-assessed health, disability, and serious psychological distress for the entire study period. Socioeconomic health inequalities have widened in poor self-assessed health over time for women, while the inequalities appeared to narrow for disability and psychological distress for both men and women. Race/ethnicity inequalities in disability widened considerably for men and women, while those of psychological distress declined from 2005 to 2011. Socioeconomic and race/ethnicity health inequalities are present and persistent in California. Trends in health inequalities appear to reflect potential impact of recent financial crisis and legislative changes in California. Consistent measurement and monitoring of health inequalities are needed to develop socioeconomic and health policies that address health inequalities in California.

Measuring Health Inequality with Realization of Conditional Potential Life Years (RCPLY)

Health inequalities that are avoidable and unfair are considered more relevant for policy intervention. Recent work has improved on using life years (LY) to measure longevity inequality by developing a health indicator—the Realization of Potential Life Years (RePLY)—to adjust for unavoidable mortality risks. This approach, however, estimates unavoidable mortality risks by using the globally lowest mortality risks for each age–sex group of any country, and thus, benchmarking countries at different levels of development against the same unavoidable mortality risks without considering their heterogeneity. The current paper proposes to attempt to control for a country’s national resources in estimating their (conditional) avoidable mortality risks. This allows the construction of a new health indicator—Realization of Conditional Potential Life Years (RCPLY). This paper presents and contrasts the empirical results for LY, RePLY and RCPLY based on life tables for 136 countries from the year 2009.

Handbook on Health Inequality Monitoring

Handbook on Health Inequality Monitoring

Evolución de las desigualdades sociales en la mortalidad general de la ciudad de Cádiz (1992–2007)

ObjectiveTo analyze trends in socioeconomic inequality in mortality in the city of Cadiz (Spain) from 1992 to 2007. MethodsAn ecological study was performed of trends over 3 cross-sections, with the census tract as the unit of analysis. Deaths were grouped into three periods: 1992-1996, 1997-2001 and 2002-2007 and were then classified according to a deprivation index of the census tract. We calculated adjusted rates by the direct method and three measures of health inequality. ResultsOf 18,586 deaths, 96.7% was geocoded to a census tract. The population-attributable risk decreased in men and women, respectively, from 15.4% and 12.2% in 1992-1996 to 9.3% and 5.6% in 2002-2007. The other measures, slope index and the relative index also showed a decline in inequality but only among women. ConclusionsDespite a decreasing trend, social inequalities are a substantial component in the distribution of overall mortality in the city of Cadiz.

From a conservative to a liberal welfare state: Decomposing changes in income-related health inequalities in Germany, 1994–2011

Individual socio-economic status and the respective socio-economic and political contexts are both important determinants of health. Welfare regimes may be linked with health and health inequalities through two potential pathways: first, they may influence the associations between socio-economic status and health. Second, they may influence the income-related distributions of socio-economic determinants of health within a society. Using the Socio-Economic Panel (SOEP) for the years 1994–2011, we analyze how income-related health inequalities evolved in the context of the transformation from a conservative to a liberal welfare system in Germany. We use the concentration index to measure health inequalities, and the annual concentration indices are decomposed to reveal how the contributions of the explanatory variables age, sex, income, education, and occupation changed over time. The changes in the contributions are further decomposed to distinguish whether changes in health inequalities stem from redistributions of the explanatory variables, from changes in their associations with health, or from changes in their means. Income-related health inequalities to the disadvantage of the economically deprived roughly doubled over time, which can largely be explained by changes in the contributions of individual characteristics representing weaker labor market positions, particularly income and unemployment. The social and labor market reforms coincide with the observed changes in the distributions of these characteristics and, to a lesser extent, with changes of their associations with health.

Reference value sensitivity of measures of unfair health inequality.

Most politicians and ethical observers are not interested in pure health inequalities, as they want to distinguish between different causes of health differences. Measures of "unfair" inequality - direct unfairness and the fairness gap, but also the popular standardized concentration index - therefore neutralize the effects of what are considered to be "legitimate" causes of inequality. This neutralization is performed by putting a subset of the explanatory variables at reference values, e.g. their means. We analyze how the inequality ranking of different policies depends on the specific choice of reference values. We show with mortality data from the Netherlands that the problem is empirically relevant and we suggest a statistical method for fixing the reference values.

Measuring socioeconomic health inequalities in presence of multiple categorical information

While many of the measurement approaches in health inequality measurement assume the existence of a ratio-scale variable, most of the health information available in population surveys is given in the form of categorical variables. Therefore, the well-known inequality indices may not always be readily applicable to measure health inequality as it may result in the arbitrariness of the health concentration index's value. In this paper, we address this problem by changing the dimension in which the categorical information is used. We therefore exploit the multi-dimensionality of this information, define a new ratio-scale health status variable and develop positional stochastic dominance conditions that can be implemented in a context of categorical variables. We also propose a parametric class of population health and socioeconomic health inequality indices. Finally we provide a twofold empirical illustration using the Joint Canada/United States Surveys of Health 2004 and the National Health Interview Survey 2010.

Use of different subjective health indicators to assess health inequalities in an urban immigrant population in north-western Italy: a cross-sectional study

BackgroundDespite the steady growth of the immigrant population in Italy, data on the health status of immigrants are scarce. Our main goals were to measure Health-Related Quality of Life (HRQoL), Self-Rated Health (SRH) and morbidity among immigrants in Genoa. We aimed to assess the relative contribution of some social, structural and behavioral determinants to “within-group” health disparities.MethodsWe enrolled 502 subjects by means of snowball sampling. The SF-12 questionnaire, integrated with socio-demographic and health-related items, was used. Multivariate logistic and Poisson regression models were applied in order to identify characteristics associated with poor SRH, lower SF-12 scores and prevalence of self-reported morbidities.ResultsSubjects showed relatively moderate levels of HRQoL (median physical and mental scores of 51.6 and 47.3, respectively) and about 15% of them rated their health as fair or poor. Lower scores in the physical dimension of HRQoL were associated with the presence of morbidities and immigration for work and religious reasons, while those who had migrated for religious and family reasons displayed a lower probability of lower scores in the mental dimension of HRQoL. Poor SRH was associated with female gender, overweight/obesity and presence of morbidities. Moreover, compared with immigrants from countries with a low human development index, immigrants from highly developed societies showed significantly lower odds of reporting poor SRH. About one-third of respondents reported at least one medical condition, while the prevalence of multi-morbidity was 10%. Females, over 45-year-olds, overweight and long-term immigrants had a higher prevalence of medical conditions.ConclusionsOur study confirms the presence of health inequalities within a heterogeneous immigrant population. HRQoL, SRH and morbidity are valid, relatively rapid and cheap tools for measuring health inequalities, though they do so in different ways. These indicators should be used with caution and, if possible, simultaneously, as they could help to identify and to monitor more vulnerable subjects among immigrants.

Decomposing health achievement and socioeconomic health inequalities in presence of multiple categorical information

Although most of the measurement approaches in health inequality measurement assume the existence of a ratio-scale variable, most of the information on health available in population surveys is given in the form of categorical variables. This may result in the arbitrariness of the health concentration index's value. Makdissi and Yazbeck (2012) adopt a counting approach to overcome this problem. In this paper, we use this counting approach and decompose socioeconomic health inequality and achievement by categories of health problems and by region in the United States using the National Health Interview Survey 2010.

Using Inequality Measures to Incorporate Environmental Justice into Regulatory Analyses

Formally evaluating how specific policy measures influence environmental justice is challenging, especially in the context of regulatory analyses in which quantitative comparisons are the norm. However, there is a large literature on developing and applying quantitative measures of health inequality in other settings, and these measures may be applicable to environmental regulatory analyses. In this paper, we provide information to assist policy decision makers in determining the viability of using measures of health inequality in the context of environmental regulatory analyses. We conclude that quantification of the distribution of inequalities in health outcomes across social groups of concern, considering both within-group and between-group comparisons, would be consistent with both the structure of regulatory analysis and the core definition of environmental justice. Appropriate application of inequality indicators requires thorough characterization of the baseline distribution of exposures and risks, leveraging data generally available within regulatory analyses. Multiple inequality indicators may be applicable to regulatory analyses, and the choice among indicators should be based on explicit value judgments regarding the dimensions of environmental justice of greatest interest.

UNDERSTANDING THE VERTICAL EQUITY JUDGEMENTS UNDERPINNING HEALTH INEQUALITY MEASURES

The choice of income-related health inequality measures in comparative studies is often determined by custom and analytical concerns, without much explicit consideration of the vertical equity judgements underlying alternative measures. This note employs an inequality map to illustrate how these judgements determine the ranking of populations by health inequality. In particular, it is shown that relative indices of inequality in health attainments and shortfalls embody distinct vertical equity judgments, where each may represent ethically defensible positions in specific contexts. Further research is needed to explore people's preferences over distributions of income and health.

Impact of Selective Evidence Presentation on Judgments of Health Inequality Trends: An Experimental Study

Reducing health inequalities is a key objective for many governments and public health organizations. Whether inequalities are measured on the absolute (difference) or relative (ratio) scale can have a significant impact on judgments about whether health inequalities are increasing or decreasing, but both of these measures are not often presented in empirical studies. In this study we investigated the impact of selective presentation of health inequality measures on judgments of health inequality trends among 40 university undergraduates. We randomized participants to see either a difference or ratio measure of health inequality alongside raw mortality rates in 5 different scenarios. At baseline there were no differences between treatment groups in assessments of inequality trends, but selective exposure to the same raw data augmented with ratio versus difference inequality graphs altered participants’ assessments of inequality change. When absolute inequality decreased and relative inequality increased, exposure to ratio measures increased the probability of concluding that inequality had increased from 32.5% to 70%, but exposure to difference measures did not (35% vs. 25%). Selective exposure to ratio versus difference inequality graphs thus increased the difference between groups in concluding that inequality had increased from 2.5% (95% CI −9.5% to 14.5%) to 45% (95% CI 29.4 to 60.6). A similar pattern was evident for other scenarios where absolute and relative inequality trends gave conflicting results. In cases where measures of absolute and relative inequality both increased or both decreased, we did not find any evidence that assignment to ratio vs. difference graphs had an impact on assessments of inequality change. Selective reporting of measures of health inequality has the potential to create biased judgments of progress in ameliorating health inequalities.

Exploring variations in childhood stunting in Nigeria using league table, control chart and spatial analysis

BackgroundStunting, linear growth retardation is the best measure of child health inequalities as it captures multiple dimensions of children’s health, development and environment where they live. The developmental priorities and socially acceptable health norms and practices in various regions and states within Nigeria remains disaggregated and with this, comes the challenge of being able to ascertain which of the regions and states identifies with either high or low childhood stunting to further investigate the risk factors and make recommendations for action oriented policy decisions.MethodsWe used data from the birth histories included in the 2008 Nigeria Demographic and Health Survey (DHS) to estimate childhood stunting. Stunting was defined as height for age below minus two standard deviations from the median height for age of the standard World Health Organization reference population. We plotted control charts of the proportion of childhood stunting for the 37 states (including federal capital, Abuja) in Nigeria. The Local Indicators of Spatial Association (LISA) were used as a measure of the overall clustering and is assessed by a test of a null hypothesis.ResultsChildhood stunting is high in Nigeria with an average of about 39%. The percentage of children with stunting ranged from 11.5% in Anambra state to as high as 60% in Kebbi State. Ranking of states with respect to childhood stunting is as follows: Anambra and Lagos states had the least numbers with 11.5% and 16.8% respectively while Yobe, Zamfara, Katsina, Plateau and Kebbi had the highest (with more than 50% of their under-fives having stunted growth).ConclusionsChildhood stunting is high in Nigeria and varied significantly across the states. The northern states have a higher proportion than the southern states. There is an urgent need for studies to explore factors that may be responsible for these special cause variations in childhood stunting in Nigeria.

Using a Survey to Estimate Health Expectancy and Quality-Adjusted Life Expectancy to Assess Inequalities in Health and Quality of Life

BackgroundThere has been a policy debate in the United Kingdom about moving beyond traditional measures of life expectancy and economic output to developing more meaningful ways of measuring national well-being. ObjectiveTo test whether quality adjusted life expectancy (QALE) was a useful indicator of health inequalities. MethodsEuroQol five-dimensional questionnaire data from a well-being survey was combined with actuarial life expectancy (LE) data to estimate healthy LE (HLE), that is, years of life lived in good health, and QALE, that is, quality-adjusted life-years (QALYs) lived for Wirral, a borough in the north west of England. ResultsThe gap between Wirral and the most deprived areas was 4.45 years for LE, 5.34 for QALE, and 7.55 for HLE. The gap in QALE was 20% greater than the gap in LE, while the gap in HLE was 70% greater. ConclusionsThe fact that the QALE gap value lies between the HLE value and the LE value suggests that QALE is a more sensitive indicator than HLE, as in this study QALE is derived from 243 possible EuroQol five-dimensional questionnaire profiles whereas HLE is based only on whether or not an individual rates his or her health as good, a binary variable. This study discusses how QALE could be a useful indicator for measuring health inequalities in future, especially as cost utility and QALYs are seen as the gold standard used by the National Institute for Health and Clinical Excellence in the United Kingdom to measure outcomes for health interventions in England, and discusses how a monetary valuation of QALYs could be used to put a societal cost on health inequalities.

Relationship between household socio-economic status and under-five mortality in Rufiji DSS, Tanzania

Background: Disparities in health outcomes between the poor and the better off are increasingly attracting attention from researchers and policy makers. However, policies aimed at reducing inequity need to be based on evidence of their nature, magnitude, and determinants.Objectives: The study aims to investigate the relationship between household socio-economic status (SES) and under-five mortality, and to measure health inequality by comparing poorest/least poor quintile mortality rate ratio and the use of a mortality concentration index. It also aims to describe the risk factors associated with under-five mortality at Rufiji Demographic Surveillance Site (RDSS), Tanzania.Methods: This analytical cross sectional study included 11,189 children under-five residing in 7,298 households in RDSS in 2005. Principal component analysis was used to construct household SES. Kaplan–Meier survival incidence estimates were used for mortality rates. Health inequality was measured by calculating and comparing mortality rates between the poorest and least poor wealth quintile. We also computed a mortality concentration index. Risk factors of child mortality were assessed using Poisson regression taking into account potential confounders.Results: Under-five mortality was 26.9 per 1,000 person-years [95% confidence interval (CI) (23.7–30.4)]. The poorest were 2.4 times more likely to die compared to the least poor. Our mortality concentration index [−0.16; 95% CI (−0.24, −0.08)] indicated considerable health inequality. Least poor households had a 52% reduced mortality risk [incidence rate ratio (IRR) = 0.48; 95% CI 0.30–0.80]. Furthermore, children with mothers who had attained secondary education had a 70% reduced risk of dying compared to mothers with no education [IRR = 0.30; 95% CI (0.22–0.88)].Conclusion: Household socio-economic inequality and maternal education were associated with under-five mortality in the RDSS. Targeted interventions to address these factors may contribute towards accelerating the reduction of child mortality in rural Tanzania.Appendices available online under Reading Tools.

Examining the Link between Equity-Centered Health Policies, Autonomy and Decision-Making Process in Low and Middle Income Countries

Objective: The purpose of this article is to examine the main underlying social values that define health policy decisions. We focus on LMICs, many of which are at an early stage of implementation of these evidence-based policy processes. This review aimed at analyzing the ethical and procedural principles that underlie these social values, their potential conflict and the challenges of implementing a decision-making process according to these values in LMIC. Methods: Broad scoping search of international literature (December 2012) in PubMed, Cinahl, ISI Web of knowledge, Cochrane Library, and ProQuest. Search terms were “social values”, “healthcare/ health” and “low and middle income countries” (terms adapted according to each database). There was no pre-defined limit of year or language. We excluded “grey literature” documents only. From a total of 252 hits, we finally selected and fully read 51 of them. Review Article British Journal of Medicine & Medical Research, 3(4): 1517-1529, 2013 1518 Results: The translation of health policy decisions from high-income countries to LMICs is complex, as they have differences that might affect the expected outcomes. Decisions should primarily aim at improving population health. The measurement of socioeconomic status and social inequalities in health, which is also context-specific, it is another primary objective in decisions about healthcare policies. Autonomy and equity might conflict in scenarios of limited budget. However, individual autonomy should be limited when affects other s individuals autonomy and social welfare. Evidence and transparency in the decision process is highly valued, and expenditure in information for decision-making should be promoted since it increases population health. Conclusion: High-quality research evidence is paramount to implement health policies consistent with social values and the resources needed to produce relevant evidence can be considered a good use of public resources. A framework for decision-making should be anchored at least in three first order social values, improvement of population health, improving equity (in access to healthcare and health) and transparency. .

Verification on the structure of the holistic health of the elderly: Causal mechanisms and effect measurement of health inequalities by large-scale follow-up study

Verification on the structure of the holistic health of the elderly: Causal mechanisms and effect measurement of health inequalities by large-scale follow-up study