Measuring Health Inequalities Research Articles

Measuring health inequalities: implications of choosing different socioeconomic indicators.

We aimed to verify the association between different socioeconomic indicators and self-rated health in a nationally representative sample of older adults. This cross-sectional study analyzed the baseline data from the Brazilian Longitudinal Study of Aging (ELSI-Brazil), a population-based cohort study of persons aged 50 years or older. Data was collected using a household and an individual questionnaire at participants' households. Self-rated health was assessed by a global self-rating item. Three socioeconomic indicators were assessed: individual income, per capita household income, and wealth index. Poisson regression models were performed to estimate the prevalence ratio (PR) and 95% confidence intervals (95%CI) of self-rated health and each socioeconomic indicator, adjusting for covariates. In total, 9,390 older adults answered the outcome question. Whilst for the individual income indicator only the richest quintile showed a statistically significant association with the outcome (PR: 0.90; 95%CI: 0.87; 0.93), for the per capita household income, the fourth (PR: 0.95; 95%CI: 0.91; 0.98) and the fifth quintiles (PR: 0.90; 95%CI: 0.86; 0.94) remained associated with the outcome. Regarding the wealth index, only the second quintile was not associated with the outcome, with lower prevalence of poor self-rated health as richer was the quintile, showing a social gradient. The wealth index seems to be a more adequate indicator, as it reflects resources accumulated over the life course.

Impact of workers’ Remittances on Households’ Standard of Living: A Case Study of District Kech Balochistan

Migration of Workers is a common phenomenon in underdeveloped regions. Lack of jobs opportunities and poverty in home country compel workers to migrate to developed countries.  The objective of this research is to study the impact of remittances on standard of living of recipients’ households in district Kech (Balochistan), Pakistan. workers’ remittances are essential in developing standard of living.  For this purpose, survey-based data of 200 respondents is used to construct the standard of living index by Principal Component Analysis (PCA). The result supports the hypothesis that workers’ remittances have affected standard of living of the recipients’ families positively and significantly. Also providing training and make them learn skills which are necessary components for migrant workers to earn handsome salaries and incomes in overseas so that their finincial contribution enable the recipents’ family to live properious life. References Abbas, , Sabir, H. M., Shehzadi, A., & Abbas, Q. (2014). Impact of workers’ remittances on household welfare in district Jhang (a case study of Tehsil 18 Hazari). Journal of Finance and Economics, 2(4), 131-135. Ahmad, , Ahmad, A., & Hayat, M. F. (2013). Foreign remittances and economic growth in Pakistan: An empirical investigation. Journal of Basic and Applied Scientific Research, 3(7)813-819, 2013. Ajaero, C. K., Nzeadibe, C. T., Obisie-Nmehielle, N., & Ike, C. C. (2018). The linkages between international migration, remittances and household welfare in Nigeria. Migration and Development, 7(1), 40-54. Akhter, N., & Islam, M. (2019). The Impact of Migration and Migrant Remittances on Household Poverty in Bangladesh. Journal of Poverty Alleviation & International Development, 10(1), 43-59. Chiswick, B., & Hatton, T. J. (2003). International migration and the integration of labor markets. In Globalization in historical perspective. 65-120. University of Chicago Chowdhury, I. A. (2014). Impacts of remittance on the socio-economic condition of Bangladesh: An analysis. Innovative Issues and Approaches in Social Science, 7(3), 23-43. Cuong, N. V., & Linh, V. H. (2018). The impact of migration and remittances on household welfare: Evidence from Vietnam. Journal of International Migration and Integration, 19(4), 945-963. Deaton, A., & Paxson, C. (1998). Economies of scale, household size, and the demand for food. Journal of political economy, 106(5), 897-930. Dhungana, A. R., & Pandit, D. (2014). Socio-economic impact of remittance on households in Lekhnath Municipality, Kaski, Nepal. Economic Literature, 12(7), 39- 49. Eversole, R., & Johnson, M. (2014). Migrant remittances and household development: an anthropological analysis. Development Studies Research. An Open Access Journal, 1(1), 1-15. Government of Balochistan. (2018-19). Finance department of Balochistan. https:// balochistan.gov.pk Government of Pakistan. (2021-22). Economic survey of Pakistan. http://www.finance. gov.pk/survey_1213.html Grace, B. Y., Lee, D. J., Sirgy, M. J., & Bosnjak, M. (2019). Household Income, Satisfaction with Standard of Living, and Subjective Well-Being. The Moderating Role of Happiness Materialism. Journal of Happiness Studies, 1-22. Gunnsteinsson,, Labrique, A. B., West Jr, K. P., Christian, P., Mehra, S., Shamim, A. A., ... & Klemm, R. D. (2010). Constructing indices of rural living standards in Northwestern Bangladesh. Journal of health, population, and nutrition, 28(5), 509. Howe, L. D., Hargreaves, J. R., Ploubidis, G. B., De Stavola, B. L., & Huttly, S. R. (2011). Subjective measures of socio-economic position and the wealth index: a comparative analysis. Health policy and planning, 26(3), 223-232. Javaid, (2017). Impact of remittances on consumption and investment (Case Study of Tehsil Sargodha, Punjab, Pakistan). Journal of Finance and Economics, (5)4, 156-163. Khan, M. (2010). Socio-economic impact of overseas workers remittances on the recipient households in district Swabi, Pakistan (Doctoral dissertation, NWFP Agricultural University Peshawar, Pakistan).http://prr.hec.gov.pk/jspui/handle/ 123456789//290 (Online). Khan, , Rahim, T., Shah, M. K., Alam, A., Naeem, M., & Zeb, J. (2008). An assessment of the volume of workers remittances and their socio-economic impact in recipient regions of North-West Frontier Province, Pakistan (A Case Study of Dir District). Sarhad Journal of Agriculture (Pakistan). 24(3), 513-520. Niaz, , Mushtaq, A., Nisar, A., Musawar, S., Intikhab, A., & Muhammad, J. (2010). The impact of foreign remittances on the socio-economic conditions of households (a case study of rural areas in Dir Lower, NWFP). Sarhad Journal of Agriculture, 26(1), 141-145. Pakistan Bureau of Statistic, (2017) Government of Pakistan. Http://www.pbs.gov. pk/content/pakistan-statistical-year-book-2017. Peković, D. (2017). The effects of remittances on poverty alleviation in transition countries. Journal of International Studies, 10(4), 37-46. Tsaurai, K. (2018). The impact of remittances on poverty alleviation in selected emerging markets. Comparative Economic Research, 21(2), 51-68. Vyas, S., & Kumaranayake, L. (2006). Constructing socio-economic status indices: how to use principal components analysis. Health policy and planning, 21(6), 459-468. Wadood, N., & Hossain, A. (2017). Microeconomic impact of remittances on household welfare: Evidences from Bangladesh. Business and Economic Horizons (BEH), 13(1), 10-29. Wagstaff, A., & Watanabe, N. (2003). What difference does the choice of SES make in health inequality measurement? Health economics, 12(10), 885-890.

Bread and Social Justice: Measurement of Social Welfare and Inequality Using Anthropometrics

We address the question of the measurement of health achievement and inequality in the context of variables exhibiting an inverted‐U relation with health and well‐being. The chosen approach is to measure separately achievement and inequality in the health increasing range of the variable, from a lower survival bound a to an optimum value m, and in the health decreasing range from m to an upper survival bound b. Because in the health decreasing range, the equally distributed equivalent value associated with a distribution is decreasing in progressive transfers, the paper introduces appropriate relative and absolute achievement and inequality indices to be used for variables exhibiting a negative association with well‐being. We then discuss questions pertaining to consistent measurement across health attainments and shortfalls, as well as the ordering of distributions exhibiting an inverted‐U relation with well‐being. An illustration of the methodology is provided using a group of five Arab countries.

A review of the inclusion of equity stratifiers for the measurement of health inequalities within health and social care data collections in Ireland

BackgroundHealth equity differs from the concept of health inequality by taking into consideration the fairness of an inequality. Inequities may be culturally specific, based on social relations within a society. Measuring these inequities often requires grouping individuals. These groupings can be termed equity stratifiers. The most common groupings affected by health inequalities are summarised by the acronym PROGRESS (Place of residence, Race, Occupation, Gender, Religion, Education, Socioeconomic status, Social capital). The aim of this review was to examine the use of equity stratifiers in routinely collected health and social care data collections in Ireland.MethodsOne hundred and twenty data collections were identified from the Health Information and Quality Authority (HIQA) document, “Catalogue of national health and social care data collections: Version 3.0”. Managers of all the data collections included were contacted and a data dictionary was requested where one was not available via the HIQA website. Each of the data dictionaries available was reviewed to identify the equity stratifiers recorded.ResultsEighty-three of the 120 data collections were considered eligible to be included for review. Twenty-nine data dictionaries were made available. There was neither a data dictionary available nor a response to our query from data collection managers for twenty-three (27.7%) of the data collections eligible for inclusion. Data dictionaries were from national data collections, regional data collections and national surveys. All data dictionaries contained at least one of the PROGRESS equity stratifiers. National surveys included more equity stratifiers compared with national and regional data collections. Definitions used for recording social groups for the stratifiers examined lacked consistency.ConclusionsWhile there has been much discussion on tackling health inequalities in Ireland in recent years, health and social care data collections do not always record the social groupings that are most commonly affected. In order to address this, it is necessary to consider which equity stratifiers should be used for the Irish population and, subsequently, for agreed stratifiers to be incorporated into routine health data collection. These are lessons that can be shared internationally as other countries begin to address deficits in their use of equity stratifiers.

Improving health equity and ending the HIV epidemic in the USA: a distributional cost-effectiveness analysis in six cities.

In the USA, Black and Hispanic or Latinx individuals continue to be disproportionately affected by HIV. Applying a distributional cost-effectiveness framework, we estimated the cost-effectiveness and epidemiological impact of two combination implementation approaches to identify the approach that best meets the dual objectives of improving population health and reducing racial or ethnic health disparities. We adapted a dynamic, compartmental HIV transmission model to characterise HIV micro-epidemics in six US cities: Atlanta, Baltimore, Los Angeles, Miami, New York, and Seattle. We considered combinations of 16 evidence-based interventions to diagnose, treat, and prevent HIV transmission according to previously documented levels of scale-up. We then identified optimal combination strategies for each city, with the distribution of each intervention implemented according to existing service levels (proportional services approach) and the racial or ethnic distribution of new diagnoses (between Black, Hispanic or Latinx, and White or other ethnicity individuals; equity approach). We estimated total costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios of strategies implemented from 2020 to 2030 (health-care perspective; 20-year time horizon; 3% annual discount rate). We estimated three measures of health inequality (between-group variance, index of disparity, Theil index), incidence rate ratios, and rate differences for the selected strategies under each approach. In all cities, optimal combination strategies under the equity approach generated more QALYs than those with proportional services, ranging from a 3·1% increase (95% credible interval [CrI] 1·4-5·3) in New York to more than double (101·9% [75·4-134·6]) in Atlanta. Compared with proportional services, the equity approach delivered lower costs over 20 years in all cities except Los Angeles; cost reductions ranged from $22·9 million (95% CrI 5·3-55·7 million) in Seattle to $579·8 million (255·4-940·5 million) in Atlanta. The equity approach also reduced incidence disparities and health inequality measures in all cities except Los Angeles. Equity-focused HIV combination implementation strategies that reduce disparities for Black and Hispanic or Latinx individuals can significantly improve population health, reduce costs, and drive progress towards Ending the HIV Epidemic goals in the USA. National Institute on Drug Abuse.

The 2011 and 2016 iterations of the Ontario Marginalization Index: updates, consistency and a cross-sectional study of health outcome associations.

The Ontario Marginalization Index (ON-Marg) is an area-based measure used widely to measure health inequalities in Ontario. Recently, the index was updated for 2011 and 2016. The loss of the 2011 long-form census required the use of alternative data sources for the 2011 version. This paper describes the update of ON-Marg, assesses consistency in the indices across census years using Dissemination Areas, and examines associations between ON-Marg 2016 and four health and social outcomes to demonstrate its potential to measure health inequalities. ON-Marg was created using factor analysis. Differences in quintile assignment was compared over time to assess whether the use of taxfiler, immigration, property assessment, and health card address data in 2011 affected consistency in measurement of marginalization. Inequalities in rates of overall mortality, gonorrhea incidence, mental health emergency department visits, and alcohol retail locations across quintiles of ON-Marg 2016 were quantified using the Relative Index of Inequality. Depending on the dimension, between 81% and 96% of DAs showed limited or no changes in quintiles of marginalization between 2006, 2011 and 2016. Of the 45-64% of DAs that did not change quintile between 2006 and 2016, 1.8% to 8.8% of DAs in 2011 differed by two or more quintiles. Findings showed significant differences in rates of health and social outcomes across quintiles of ON-Marg 2016, with strength and directionality varying by dimension of ON-Marg. Alternative data sources did not substantially affect the consistency of the 2011 version of ON-Marg. The updated ON-Marg is a comprehensive tool that can be used to study health inequalities in Ontario.

The measurement of health inequalities: does status matter?

Approaches to measuring health inequalities are often problematic because they use methods that are inappropriate for categorical data. In this paper we focus on “pure” or univariate health inequality (rather than income-related or bivariate health inequality) and use a concept of individual status that allows a consistent treatment of such data. We take alternative versions of the status concept and apply methods for treating categorical data to examine self-assessed health inequality for the countries included in the World Health Survey. We also use regression analysis on the apparent determinants of these health inequality estimates. We show that the status concept that is used will affect health-inequality rankings across countries and the way health inequality is related to countries’ median health, income, demographics and governance.

Parameterizing standard measures of income and health inequality using choice experiments.

When measuring inequality using conventional inequality measures, ethical assumptions about distributional preferences are often implicitly made. In this paper, we ask whether the ethical assumptions underlying the concentration index for income-related health inequality and the Gini index for income inequality are supported in a representative sample of the Swedish population using an internet-based survey. We find that the median subject has preferences regarding income-related health inequality that are in line with the ethical assumptions implied by the concentration index, but put higher weight on the poor than what is implied by the Gini index of income inequality. We find that women and individuals with a poorer health status put higher weight on the poor than men and healthier individuals. Ethically flexible inequality measures, such as the s-Gini index and the extended concentration index, imply that researchers have to choose from a toolbox of infinitely many inequality indices. The results of this paper are indicative of which indices (i.e. which parameter values) reflect the views of the population regarding how inequality should be defined.

Inequality of opportunities in health and death: an investigation from birth to middle age in Great Britain.

ObjectiveWe assess the existence of unfair inequalities in health and death using the normative framework of inequality of opportunities, from birth to middle age in Great Britain.MethodsWe use data from the 1958 National Child Development Study, which provides a unique opportunity to observe individual health from birth to the age of 54, including the occurrence of mortality. We measure health status combining self-assessed health and mortality. We compare and statistically test the differences between the cumulative distribution functions of health status at each age according to one childhood circumstance beyond people’s control: the father’s occupation.ResultsAt all ages, individuals born to a ‘professional’, ‘senior manager or technician’ father report a better health status and have a lower mortality rate than individuals born to ‘skilled’, ‘partly skilled’ or ‘unskilled’ manual workers and individuals without a father at birth. The gap in the probability to report good health between individuals born into high social backgrounds compared with low, increases from 12 percentage points at age 23 to 26 at age 54. Health gaps are even more marked in health states at the bottom of the health distribution when mortality is combined with self-assessed health.ConclusionsThere is increasing inequality of opportunities in health over the lifespan in Great Britain. The tag of social background intensifies as individuals get older. Finally, there is added analytical value to combining mortality with self-assessed health when measuring health inequalities.

Desigualdad social en México en el uso de servicios de tamizaje en adultos: un análisis de las encuestas nacionales de salud 2006 y 2012

To measure health inequality in the use of screen-ing services in adults from 20 to 59 years of age from the 2006 and 2012 national health and nutrition surveys. dults (detection of diabetes, hypertension, breast cancer, cervical cancer and prostate cancer), the Kuznets index, the slope inequality index and the health concentration index were estimated. Considering as social indicators schooling, ethnicity, unemployment, socioeconomic level and type of health protection. The coverage of the five tests increased, but the inequality observed only decreased in the interventions in women; and in the case of the detection of prostate cancer it was increased. While it is important to monitor the performance of curative services, the challenge remains to ensure effective and equitable access to early diagnosis services.

A decomposition analysis of inequalities in Low birth weight in Sri Lanka: findings from the Demographic and Health survey- 2016.

Birth weight is a crucial indicator of mothers and infants nutritional status. It determines a newborn's likelihood of survival, their growth and their psychological development. This study examines the socio-economic inequalities of low birth weight in Sri Lanka using the first island-wide Demographic and Health Survey (DHS) conducted in 2016. Nearly 17% of babies are reported as low birth weight (LBW) and the prevalence has stagnated for nearly two decades in Sri Lanka. LBW is indicative of inequalities in particular population subgroups. There is a lack of research on LBW inequalities and the contribution of different socio-economic determinants to these inequalities in Sri Lanka. A stepwise multivariate linear regression, health inequality measures and decomposition method are used to examine inequalities in LBW. Maternal body mass index (BMI), height, antenatal visits, birth interval, wealth and ethnicity are significantly associated with mean birth weight. Findings reveal that inequalities exist, where LBW is concentrated among the poorest households. The decomposition results highlight maternal BMI, education and ethnicity as major contributing factors for such inequalities. These findings suggest prioritising the nutritional needs of mothers and relevant interventions to address inequalities in birth weight to reduce the stagnated LBW in Sri Lanka.

Abstract P468: Geographic and Socioeconomic Inequalities in Poor Cardiovascular Health in Canada

Background: Poor cardiovascular health (CVH), characterized by clinical risk and unhealthy lifestyle habits, is a leading cause of death and disease worldwide. Despite advances in healthcare and policy, there remains an inequitable burden of poor CVH among Canadian sub-populations based on socioeconomic status and geographic region. Using recently published data and a national toolkit, this study aimed to quantify inequalities in poor CVH across the Canadian population. Methods: We conducted a cross-sectional study on Canadian adults, ≥20 years, from the nationally representative Canadian Community Health Survey 2017. Using the American Heart Association’s CVH Index, CVH was defined for each individual as a summed score of 7 components, where 1 point was awarded for achieving ideal health in each component. A total score of 0-2 points indicated poor overall CVH. The Canadian Institute for Health Information (CIHI) Measuring Health Inequalities Toolkit, a standardized methodological approach to analyzing health inequalities in population-based data using pre-defined stratifications and second-level interactions, was used to quantify inequalities in poor CVH based on Toolkit-defined stratifications in sex, income, urban/rural status, and region of residence. The regional distribution of CVH was mapped using ArcGIS software. Results: Approximately 7% of Canadians had poor CVH, representing 2 million Canadians. Poor dietary habits were noted in 99.0% of the population, with poor body mass index and poor physical activity noted in 58.1% and 42.3%, respectively. The eastern provinces of Newfoundland and Labrador and New Brunswick had the greatest proportion of health regions with poor CVH. An examination of the largest CVH inequalities across provinces revealed that females in the lowest income tercile residing in Prince Edward Island were 8-times more likely to experience poor CVH than females in the highest income tercile (RR 8.43, 95%CI 7.09-10.04). Additionally, females in New Brunswick residing in rural regions were almost 3-times more likely to experience poor CVH than females residing in urban regions (RR 3.07, 95%CI 1.07-4.87). In most provinces, income and urban/rural inequalities among males were observed but were of smaller magnitude than the inequalities among females. Conclusion: The greatest inequalities in poor CVH were experienced by females in the lowest income groups. Urban/rural inequalities in CVH were complex and varied by geographic region. The CIHI toolkit is a robust and systematic approach to understanding health inequalities that will enable comparison between studies and health/disease states, and thus facilitate comparative policy evaluation and population health priority setting.

Socioeconomic gradients of adverse birth outcomes and related maternal factors in rural and urban Alberta, Canada: a concentration index approach

ObjectiveUsing a summary measure of health inequalities, this study evaluated the distribution of adverse birth outcomes (ABO) and related maternal risk factors across area-level socioeconomic status (SES) gradients in urban...

The Impact of Sociodemographic and Socioeconomic Factors on the Burden of Cataract in Small Island Developing States (SIDS) in the Caribbean from 1990 to 2016

ABSTRACTPurpose: To study the impact of sociodemographic and socioeconomic factors on the cataract burden in Caribbean small island developing states (SIDS) using disability-adjusted life-years (DALYs).Methods: National and regional age and sex specific cataract DALY numbers and rates from 1990 to 2016 for Caribbean SIDS, were extracted from the Global Burden of Disease Study 2016. The human development index (HDI), healthcare access and quality (HAQ) index, and the World Bank’s classification of economies were used as socioeconomic status indicators. The Gini coefficient, Atkinson, Theil and concentration indices were used to measure health inequality. Paired Wilcoxon signed rank test, Pearson correlation, and linear regression analyses were performed to evaluate the sociodemographic and socioeconomic factors associated with differences in cataract burden.Results: Men had higher age-standardized DALY rates than women (P < .001) with median rates of 90.72 (Interquartile range [IQR], 87.8–94.2) and 83.94(IQR, 80.9–86.5), respectively. The burden of cataract increased with age. Upper-middle income countries had higher age-standardized DALY rates than high income countries (P < .001), with median rates of 90.1 (IQR, 86.8–93.4) and 79.8 (IQR, 77.5–81.8), respectively. Age-standardized DALY rates were inversely correlated with HDI (r = – 0.61, β = – 51.56 [P < .05]) and HAQ (r = – 0.68, β = – 0.46 [P < .01). Between-country inequality was negligible, and the burden of cataract is greater among the poor.Conclusion: Global trends of socioeconomic factors were confirmed. Significantly, men had higher age-standardized DALY rates than women. This is an area for further research.

Inequality of health stock and the relation to national wealth

BackgroundThe decline in global and between-country health inequality is a major challenge to overcome. However, few studies have systematically investigated the relationship between inequality of health stock and national wealth. From an economic perspective, health can be viewed as a durable capital stock that produces an output of healthy time. Therefore, in this paper, we focused on health capital to investigate the relationship between inequalities of national health and national wealth.MethodsBased on health stock data from 1990 to 2015 for 140 countries, we estimated Gini coefficients of health stock to investigate associations with a well-known economic flow indicator, Gross Domestic Product (GDP), stock-based national wealth indicator, Inclusive Wealth Index (IWI), and firm-level net income.ResultsThe estimated Gini coefficient of global health stock shows that health stock has experienced a global decline. The Gini coefficient for low-income countries (LICs) showed the fastest decline in health stock, dropping from 0.69 to 0.66 in 25 years. Next, rapid population growth and the rise in the youth share of the working-age population in LICs were most likely contributing factors to the decline in inequality. Most countries that experienced positive health stock growth also indicated a strong positive relationship with GDP and IWI. However, some countries showed a negative relationship with natural capital, which is a part of IWI. In addition, firm-level net income showed no obvious associations with health stock, GDP and IWI.ConclusionsWe argue that a negative relationship between health stock and natural capital is a sign of unstable development because sustainable development involves maintaining not only GDP but also IWI, as it is a collective set of assets or wealth comprising human, produced and natural capital. Moreover, in our analysis of firm-level income data, we also discuss that income will be influenced by other factors, such as innovations, human resources, organization culture and strategy. Therefore, the paper concludes that health stock is a vital component in measuring health inequality and health-related Sustainable Development Goals (SDGs). Thus, IWI is more comprehensive in measuring national wealth and can complement GDP in measuring progress toward sustainable development.

Comparison of the difference between income quintiles with the slope index of inequality

Abstract Background The difference between income quintiles in health is relatively well accepted by the general public as a measure of health inequality. However, the slope index of inequality (SII) in health reflects the patterns of all social groups, including the middle 60%, and it could therefore be considered more academically desirable. If these two measures are closely correlated, the widespread use of the difference between income quintiles in health would be better supported. This study was conducted to compare differences between income quintiles in life expectancy (LE) and healthy life expectancy (HLE) with the SII. Methods Data on LE and HLE by income quintile from all 252 subnational districts of Korea were obtained using the National Health Information Database of the National Health Insurance Service and the Korea Community Health Survey. The SII was estimated with linear regression analysis, and Pearson correlation coefficients between differences between income quintiles and the SII were computed. Results The correlation coefficients between differences between income quintiles and the SII were generally high: 0.97 (95% CI: 0.96-0.98) for LE in men and women combined and 0.96 (95% CI: 0.94-0.97) for HLE in men and women combined. In most districts, the SII was greater than the difference between income quintiles. Conclusions Differences between income quintiles were closely correlated with the SII. The widespread use of differences between income quintiles in health as a measure of health inequality may be preferable for communicating results of health inequality measurements to the public. Key messages Differences in life expectancy and health life expectancy between income quintiles were closely correlated with the slope index of inequality in life expectancy and healthy life expectancy. The widespread use of differences between income quintiles in health as a measure of health inequality may be preferable for communicating results of health inequality measurements to the public.

Approaches and Alternatives to the Wealth Index to Measure Socioeconomic Status Using Survey Data: A Critical Interpretive Synthesis

Monitoring progress towards the Sustainable Development Goals by 2030 requires the global community to disaggregate targets along socio-economic lines, but little has been published critically analyzing the appropriateness of wealth indices to measure socioeconomic status in low- and middle-income countries. This critical interpretive synthesis analyzes the appropriateness of wealth indices for measuring social health inequalities and provides an overview of alternative methods to calculate wealth indices using data captured in standardized household surveys. Our aggregation of all published associations of wealth indices indicates a mean Spearman’s rho of 0.42 and 0.55 with income and consumption, respectively. Context-specific factors such as country development level may affect the concordance of health and educational outcomes with wealth indices and urban–rural disparities can be more pronounced using wealth indices compared to income or consumption. Synthesis of potential future uses of wealth indices suggests that it is possible to quantify wealth inequality using household assets, that the index can be used to study SES across national boundaries, and that technological innovations may soon change how asset wealth is measured. Finally, a review of alternative approaches to constructing household asset indices suggests lack of evidence of superiority for count measures, item response theory, and Mokken scale analysis, but points to evidence-based advantages for multiple correspondence analysis, polychoric PCA and predicted income. In sum, wealth indices are an equally valid, but distinct measure of household SES from income and consumption measures, and more research is needed into their potential applications for international health inequality measurement.

Evaluating health performance and inequalities in the Italian region of Marche

A worldwide selection of models that are used to measure health service performance were appraised. The internationally recognised Health System Performance Assessment tool was chosen for testing in a local health authority in the Marche, Italy. Local, regional, national and international comparisons were also used. A complementary means of measuring health inequality involving the Concentration Index enabled a holistic evaluation of the local health environment. While the approach addressed a comprehensive range of issues, limitations with data availability were found to present genuine constraints that require future action. Nevertheless, valuable lessons were learned for policy makers, with the relationship between socioeconomic inequalities and systematic variations in health indicators highlighted. The Health System Performance Assessment tool presents the opportunity for strategic alignment in performance measurement.

Measuring Health Inequality in US: A Composite Index Approach

In this paper, we use the standardized mortality rates for 21 mutual exclusive causes of death to propose a composite index of US county-level health performances in 1980–2014 interval. We aggregate mortality rates by the stochastic multi-criteria acceptability analysis (SMAA), in order to avoid any a priori judgement on the importance given to a specific cause of death. The total observed inequality among counties is then decomposed to estimate the variability between and within states by means of the Theil index on SMAA outcomes. On average, there has been a decrease in the Composite Index of mortality from 1980 to 2014, but while the majority of counties had an increase in health conditions, some counties have shown a decrease in health performances in the same interval. This may be the reason of a persistent increase of total inequality among counties, with inequality within states constantly higher than inequality between states, both responsible of the growing inequality levels of health performances in the period analysed.

On the measurement of health and its effect on the measurement of health inequality

We examine the extent to which self-reported health measures suffer from income-related reporting heterogeneity and then characterize how this reporting heterogeneity affects the estimation of income-related health inequality. We run a comprehensive set of tests of reporting heterogeneity using several self-reported health measures and several clinical measures of health from the National Health and Nutritional Examination Surveys. We propose the use of a multidimensional measure using clinical indicators of health in the context of measuring income-related health inequality, and we examine the extent of income-related health inequality, as measured by the concentration index, using both self-reported measures of health and the multidimensional clinical measure. Our results confirm the existence of significant, positive, income-related reporting heterogeneity and also suggest that higher income individuals react more strongly to a change in clinical health measures. Using self-assessed health suggests that income-related health inequality is about three times larger than when using more objective, self-reported health measures and ten times larger than when using the multidimensional clinical measure of health.