Measuring Health Inequalities Research Articles

Health inequality of rural-to-urban migrant workers in eastern China and its decomposition: a comparative cross-sectional study.

As a specific group with high health inequality, it is crucial to improve the health status and health inequalities of rural-to-urban migrant workers. This study aimed to evaluate the health inequality of migrant and urban workers in China and decompose it. A cross-sectional study was carried out, using a standardized questionnaire to obtain basic information, self-rated health to evaluate health status, concentration index to measure health inequalities, and WDW decomposition to analyze the causes of health inequalities. The concentration index of health for migrants was 0.021 and 0.009 for urban workers. The main factors contributing to health inequality among rural-to-urban migrant workers included income, exercise, and age. In contrast, the main factors of health inequality among urban workers included income, the number of chronic diseases, social support, and education. There were health inequalities in both rural-to-urban migrant and urban workers. The government and relevant authorities should formulate timely policies and take targeted measures to reduce income disparities among workers, thereby improving health inequality.

Temperature exposure and health inequality

This paper analyzes the relationship among temperature exposure, health inequality, and adaptive behavior in China, a country with diverse climate zones ranging from the cold, arid northwest to the warm, humid south. Utilizing follow-up survey data spanning from 2000 to 2015, this paper constructs a measure of health inequality at the community level. Our findings reveal that extremely cold or hot days increase the health inequality at the community level. Specifically, with each incremental rise in the day’s extreme temperature, the Gini coefficient of community health rises by 0.0032, which constitutes approximately 0.4% of the community average. Furthermore, vulnerable communities, which are characterized by low average income, education levels, per capita electricity consumption, and urbanization, are highly susceptible to the effects of extreme temperatures on health equality. From the perspective of adaptive behavior, residents in high-income communities exhibit elevated electricity consumption and engage in frequent preventive health check-ups during extreme temperature exposure. This phenomenon may exacerbate social polarization because well-resourced individuals and communities are better equipped to withstand the challenges posed by climate change and extreme weather events compared with impoverished ones. Addressing this disparity requires local managers to implement comprehensive policies and interventions that can enhance the adaptability of low-income communities, improve public health facilities, and promote social equity.

Systematic comparison of household income, consumption, and assets to measure health inequalities in low- and middle-income countries

There has been no systematic comparison of how the three most common measures to quantify household SES—income, consumption, and asset indices—could impact the magnitude of health inequalities. Microdata from 22 Living Standards Measurement Study surveys were compiled and concentration indices, relative indices of inequality, and slope indices of inequality were calculated for underweight, stunting, and child deaths using income, consumption, asset indices, and hybrid predicted income. Meta-analyses of survey year subgroups (pre-1995, 1995–2004, and post-2004), outcomes (child deaths, stunting, and underweight), and World Bank country-income status (low, low-middle, and upper-middle) were then conducted. Asset indices and the related hybrid income proxy result in the largest magnitudes of health inequalities for all 12 overall outcomes, as well as most country-income and survey year subgroupings. There is no clear trend of health inequality magnitudes changing over time, but magnitudes of health inequality may increase as country-income levels increase. There is no significant difference between relative and absolute inequality measures, but the hybrid predicted income measure behaves more similarly to asset indices than the household income it is supposed to model. Health inequality magnitudes may be affected by the choice of household SES measure and should be studied in further detail.

Aversion to health inequality — Pure, income-related and income-caused

We design a novel experiment to identify aversion to pure (univariate) health inequality separately from aversion to income-related and income-caused health inequality. Participants allocate resources to determine health of individuals. Identification comes from random variation in resource productivity and information on income and its causal effect. We gather data (26,286 observations) from a sample of UK adults (n = 337) and estimate pooled and participant-specific social preferences while accounting for noise. The median person has strong aversion to pure health inequality, challenging the health maximisation objective of economic evaluation. Aversion to health inequality is even stronger when it is related to income. However, the median person prioritises health of poorer individuals less than is assumed in the standard measure of income-related health inequality. On average, aversion to that inequality does not become stronger when low income is known to cause ill-health. There is substantial heterogeneity in all three types of inequality aversion.

Child mortality inequalities and socioeconomic determinants of health in Iran, 2016-2018.

Measuring health inequalities is essential to inform policy making and for monitoring implementation to reduce avoidable and unfair differences in health status. We conducted a geospatial analysis of child mortality in Iran using death records from the Ministry of Health and Medical Education from 2016 to2018 stratified by sex, age, province, and district, and household expenditure and income survey data from the Statistical Center of Iran collected in 2017. We applied multilevel mixed-effect models and detected significant inequality in child mortality and the impact of socioeconomic factors, especially household income. We advocate for using mortality rate for young children (< 5 years old) as an indicator for assessing the impact of interventions to reduce inequalities among various socioeconomic groups. We also recommend to design and implement comprehensive and longitudinal data collection systems for accurate, regular, and specific monitoring of health inequalities.

Reproducing differential racialisation: social triage in migrant mental health services in England

Abstract In England, coverage for treatment of post-traumatic stress disorder (PTSD), a health condition that disproportionately affects forced migrants, is universal, in principle provided free of charge to all. Yet, multiple informal access barriers typically arise and informal systems of social triage may emerge. Using intersectional analysis, this study asks what political and moral rationalities inform social triage in the NHS. It is particularly attentive to how and why a supposedly universal service reproduces differential racialisation, in which multiple and co-existing populations are stratified and ranked as more or less belonging to a nation. Fieldwork was conducted in two waves, in 2015-2016 and in 2019-2021. It included six months of participant observation in an NGO; 21 semi-structured interviews with health professionals across 16 different NHS and NGO service providers, purposively sampled until saturation was reached; six interviews with mental health commissioners and national policymakers; and analysis of grey literature. Transcripts and fieldnotes were analysed inductively to identify themes using NvivoR1. Results were validated by interviewees. Despite being covered, undocumented migrants and asylum seekers were systematically excluded from NHS PTSD services, through social triage. People with refugee status were prioritised. Mental health care providers consciously and subconsciously reproduced differential racialisation, which generated health inequality. This was facilitated by: austerity; omitting immigration status in measurement of health inequality; securitisation of mental health services and minoritised Muslim populations; and bifurcated activism in which the forced migrant sector was disconnected from established struggles to combat racism in mental health care. Policies extending coverage to undocumented migrants and asylum seekers must be accompanied by extra monitoring, as well as financial, political and social support to service providers. Key messages • Health professionals informally, but systematically, reproduce differential racialisation in the rationing of health care through practices such as obfuscation and silencing critique. • Informal systems of social triage erode universal health coverage. This is exacerbated in contexts of austerity, where health professionals use their discretion to ration limited resources.

Does exposure to democracy decrease health inequality?

Abstract Exposure to (a liberal) democracy can have an impact on both the political attention and visibility of the needs of marginalized populations, as well as the design of health policies that can influence the distribution of population health. This paper investigates the effect of exposure to democracy, that is, the number of years spent in a democracy as measured by democracy indexes, on various measures of inequality in self-reported health across European countries. We use an instrumental variable strategy to leverage the potential endogeneity of a country’s exposure to democracy, drawing on both bivariate (socioeconomic) and univariate health inequality measures. Our estimates provide evidence that an additional year in a democracy reduces both bivariate (income-related) health inequality and overall (univariate) health inequality. Our preferred specification suggests a two-point rank reduction in inequality with an additional year under a democracy. The effect is mainly driven by a reduction of “health poverty” alongside other effects.

Inequalities in Health: Definitions, Concepts and Measurements—An Application in the Regional Health Authority in Italy

Policymakers, researchers, and public health practitioners have long sought not only to improve overall population health but also to reduce or eliminate differences in health based on geography, race/ethnicity, socioeconomic status, and other social factors. Italian healthcare authorities and health policy makers are called to help to both solve existing inequities in accessing healthcare and remove barriers to healthcare. In this context, COVID-19 has highlighted the aspect of inequalities. Our paper proposes an overview of different methods of measuring health inequalities and their applications, both in regional and national contexts, in absolute and relative scales. The first involves an application on accessibility to an obstetrics clinic in a district of Marche region, connected to the educational level of the female population. The second, calculates the indices on inequalities in the infant mortality rate in the Italian regions in relation to income. Finally, the three indices are calculated considering the rate of self-perceived health in relation with the GDP per capita. To achieve this goal, we use the Kuznets index, the slope index of inequality and the concentration index as measures of social inequalities. The measurement of health inequities is an excitingly multidisciplinary endeavor. Its development requires interdisciplinary integration of advances from relevant disciplines. The proposed approach is one such effort and stimulates cross-disciplinary dialogues, specifically, about conceptual and empirical significance of definitions of health inequities.Graphical

How important is it to avoid indices of deprivation that include health variables in analyses of health inequalities?

ObjectivesThis study aimed to quantify the difference in mortality inequalities using the Scottish Index of Multiple Deprivation (SIMD) and the Income and Employment Index (IEI; a subindex of SIMD, which excludes health) as ranking measures in Scotland. Study designThis ecological study was a cross-sectional analysis of routine administrative data. MethodsData from the 2020 SIMD and the subindex using data from only the Income and Employment domains, the IEI, were obtained. The correlation between data zones, percentage of data zones that changed deprivation tenth and differences in the Slope Index of Inequality (SII) and Relative Index of Inequality (RII) for Standardised Mortality Ratios (SMRs) across tenths were compared when data zones were ranked by SIMD and IEI. ResultsThere was a close correlation between data zones ranked by SIMD and IEI (R2 = 0.96). When data zones were ranked by IEI, 18.7% of data zones moved to a lower deprivation tenth, and 20.8% of data zones moved to a higher deprivation tenth, compared with SIMD. However, only a negligible number of data zones moved two or more tenths. The SMRs across deprivation tenths were very similar between the SIMD and IEI, as were the summary health inequality measures of SII (87.3 compared with 85.7) and RII (0.88 and 0.86). ConclusionAlthough there is a logical problem in using deprivation indices that include health outcomes to rank areas to calculate the scale of health inequalities, the impact of using an alternative subindex containing only data from the income and employment domains is minimal. For population-wide analyses of health inequalities in Scotland, the SIMD does not introduce a substantial bias in the health inequalities summary measures despite substantial movement of small areas between ranked population tenths. Although not examined here, this is likely to be relevant to other similar indices across the United Kingdom.

Inequalities in Violent Death across Income Levels among Young Males and Females in Countries of the Americas

Violent deaths (i.e., those due to road traffic injury, homicide, and suicide) are among the most important causes of premature and preventable mortality in young people. This study aimed at exploring inequalities in violent death across income levels between males and females aged 10 to 24 years from the Americas in 2015, the SDG baseline year. In a cross-sectional ecological study design, eleven standard summary measures of health inequality were calculated separately for males and females and for each cause of violent death, using age-adjusted mortality rates and average income per capita for 17 countries, which accounted for 87.9% of the target population. Premature mortality due to road traffic injury and homicide showed a pro-poor inequality pattern, whereas premature mortality due to suicide showed a pro-rich inequality pattern. These inequalities were statistically significant (p < 0.001), particularly concentrated among young males, and dominated by homicide. The ample array of summary measures of health inequality tended to generate convergent results. Significant inequalities in violent death among young people seems to be in place across countries of the Americas, and they seem to be socially determined by both income and gender. These findings shed light on the epidemiology of violent death in young people and can inform priorities for regional public health action. However, further investigation is needed to confirm inequality patterns and to explore underlying mechanisms, age- and sex-specific vulnerabilities, and gender-based drivers of such inequalities.

Inequalities and Policy Gaps in Maternal Health: Evidence from Empowered Action Group States in India

Background: Health inequality in maternal health is one of the serious challenges currently faced by public health experts. Maternal mortality in Empowered Action Group (EAG) states is highest and so are the health inequalities prevalent. Therefore, we examine to understand maternal health inequality and the risk factors with respect to the EAG states in India. Methods: A cross-sectional data from NFHS-4 (2015-16) for EAG states of India was used. Bi-variate, multivariate logistic regression, and probabilities were carried out to investigate the factors. Also, concentration indices were used to measure health inequality. Findings: Our finding showed four outcomes of maternal health-antenatal care of at least 4 visits (ANC 4+), institutional delivery, contraption-use, and unmet-need. Our finding revealed the average predicted probability of women currently married using ANC 4+ in rural area is 5 percent higher than that of urban areas for EAG states. Lower contraception use, institutional delivery, and unmet need are observed among urban women. The results also indicate that better maternal health is heavily concentrated among the richer households, while negative concentration index of unmet need clearly reflects the greater demand for higher unmet need among the poor households in the EAG states. Conclusions: Challenges of inequalities still persist at large in maternal health, but to achieve better health these inequalities must be reduced. Since inequality mainly affects poor households due to lower level of income. Therefore, specific measures must be taken from a demand-side perspective in order to enhance their income and reduce the disparities in the EAG states of India.

Desigualdades en la cobertura y en la calidad de la atención prenatal en Perú, 2009-2019

RESUMENObjetivo.Describir la cobertura en la atención prenatal de calidad y la evolución de sus desigualdades en embarazadas peruanas en el período 2009-2019.Métodos.Análisis transversal con datos de la Encuesta Demográfica y de Salud Familiar de los años 2009, 2014 y 2019 sobre los cuidados prenatales; se consideró el número de visitas durante el embarazo y su calidad. Se calcularon medidas absolutas y relativas de desigualdad en salud de grupos estratificados.Resultados.La cobertura del número de visitas prenatales aumentó de 77,22% en el 2009 a 87,52% en el 2019. Asimismo, entre las mujeres de áreas rurales y urbanas, la brecha relativa por área de residencia disminuyó de 15% (2009) a 3% (2019), mientras que el porcentaje de embarazadas sin acceso a visitas de calidad decreció de 45,16% (2009) a 29,35% (2019). En el acceso a la calidad de controles, la desigualdad absoluta por quintiles de riqueza se redujo a casi la mitad, de 55,96% a 25,95%. Sin embargo, en este indicador, para el 2019 la diferencia relativa aún favorece a las universitarias 37% más respecto a las embarazadas sin escolaridad.Conclusiones.En Perú se han ido cerrando las brechas de las desigualdades de acceso a las visitas prenatales. Sin embargo, el conjunto de los datos nacionales oculta desigualdades entre poblaciones en la atención de calidad. La desigualdad en el acceso a la calidad de atención debería ser utilizada como indicador que permita el monitoreo de la cobertura de visitas prenatales.

Summary Measures of Health Inequality: A Review of Existing Measures and Their Application.

Measuring and monitoring health inequalities is key to achieving health equity. While disaggregated data are commonly used to assess differences in health between different population subgroups, summary measures of health inequality also play a vital role in monitoring health inequalities. Building on disaggregated data, they quantify the level of inequality in a single number and are useful to compare inequality over time and across different health indicators, programmes and settings. We provide a comprehensive overview of existing summary measures of health inequality, including their definition, calculation, interpretation and application. The use of these measures is illustrated based on an example from the WHO’s Health Equity Monitor database using the WHO’s Health Equity Assessment Toolkit (HEAT) software. We discuss the strengths and limitations of different measures and provide guidance for selecting suitable summary measures for analysing health inequalities and communicating results. Summary measures of health inequality should form an integral part of health inequality monitoring to inform equity-oriented policies and programmes.

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.&nbsp; 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.&nbsp; 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. 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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.