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Abstract Addressing health inequity is a central component of the Sustainable Development Goals and a priority of the World Health Organization (WHO). WHO supports countries in strengthening their health information systems in order to better collect, analyze and report health inequality data. Improving information and research about health inequality is crucial to identify and address the inequalities that lead to poorer health outcomes. Building analytical capacities of individuals, particularly in low-resource areas, empowers them to build a stronger evidence-base, leading to more informed policy and programme decision-making. However, health inequality analysis requires a unique set of skills and knowledge. This paper describes three resources developed by WHO to support the analysis of inequality data by non-statistical users using Microsoft Excel, a widely used and accessible software programme. The resources include a practical eLearning course, which trains learners in the preparation and reporting of disaggregated data using Excel, an Excel workbook that takes users step-by-step through the calculation of 21 summary measures of health inequality, and a workbook that automatically calculates these measures with the user’s disaggregated dataset. The utility of the resources is demonstrated through an empirical example.

The year 2024 marks the 50th anniversary of the World Health Organization (WHO) Expanded Program on Immunization (EPI) [...]

Immunization through vaccines among children has contributed to improved childhood survival and health outcomes globally. However, vaccine coverage among children is unevenly distributed across settings and populations. The measurement of inequalities is essential for understanding gaps in vaccine coverage affecting certain sub-populations and monitoring progress towards achieving equity. Our study aimed to characterize the methods of reporting inequalities in childhood vaccine coverage, inclusive of the settings, data source types, analytical methods, and reporting modalities used to quantify and communicate inequality. We conducted a scoping review of publications in academic journals which included analyses of inequalities in vaccination among children. Literature searches were conducted in PubMed and Web of Science and included relevant articles published between 8 December 2013 and 7 December 2023. Overall, 242 publications were identified, including 204 assessing inequalities in a single country and 38 assessing inequalities across more than one country. We observed that analyses on inequalities in childhood vaccine coverage rely heavily on Demographic Health Survey (DHS) or Multiple Indicator Cluster Surveys (MICS) data (39.3%), and papers leveraging these data had increased in the last decade. Additionally, about half of the single-country studies were conducted in low- and middle-income countries. We found that few studies analyzed and reported inequalities using summary measures of health inequality and largely used the odds ratio resulting from logistic regression models for analyses. The most analyzed dimensions of inequality were economic status and maternal education, and the most common vaccine outcome indicator was full vaccination with the recommended vaccine schedule. However, the definition and construction of both dimensions of inequality and vaccine coverage measures varied across studies, and a variety of approaches were used to study inequalities in vaccine coverage across contexts. Overall, harmonizing methods for selecting and categorizing dimensions of inequalities as well as methods for analyzing and reporting inequalities can improve our ability to assess the magnitude and patterns of inequality in vaccine coverage and compare those inequalities across settings and time.

We assessed socioeconomic inequalities in social protection coverage among the public, men and women living with the human immunodeficiency virus (MLHIV, WLHIV), and adolescent girls and young women (AGYW). We used population-based data from Cameroon, Côte d'Ivoire, Ethiopia, Eswatini, Kenya, Lesotho, Malawi, Namibia, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe. We constructed concentration curves (CC) and computed concentration indices (CIX) for each country and population group. A CC represents the cumulative percentage of social protection coverage plotted on the y-axis against the cumulative proportion of the population-ranked by socioeconomic status from the poorest to the richest-on the x-axis. The CIX quantifies the concentration of social protection coverage among the poor or the rich. The sample size ranged from 10,197 in Eswatini to 29,577 in Tanzania. Social protection coverage among the public varied from 5.2% (95% Confidence Interval 4.5%-6.0%) in Ethiopia to 39.9% (37.0%-42.8%) in Eswatini. It ranged from 6.9% (5.7%-8.4%) MLHIV in Zambia to 45.0% (41.2-49.0) among WLHIV in Namibia. Among AGYW, it varied from 4.4% (3.6-5.3) in Ethiopia to 44.6% (40.8-48.5) in Eswatini. Socioeconomic inequalities in social protection coverage favored the poor in 11/13 countries surveyed. It favored the rich in Cameroon and was undefined in Côte d'Ivoire. The CIX in these 11 countries ranged from -0.080 (p = 0.002) among the public in Malawi to -0.372 (p< 0.001) among WLHIV in Zimbabwe. In 8 of these 11 countries, ≥15% of people from the poorest households reported receiving social protection. Only in countries with higher levels of social protection coverage did most people from the poorest households achieve high coverage. Social protection coverage was low and favored the poor. Pro-poor social protection is insufficient to reach the poor. Research is required to reach the poorest households with social protection in Africa.

Abstract As part of its commitment to advance health equity, the World Health Organization (WHO) has a developed area of work to promote and strengthen health inequality monitoring. This includes an emphasis on the collection, analysis and use of disaggregated health data, which are central to evidence-informed decision making. The aim of this paper is to review WHO's work on health inequality monitoring, namely the 2022–27 Inequality monitoring and analysis strategy and corresponding activities, resources and tools. The strategy has three goals pertaining to: strengthening capacity for health inequality monitoring; generating and disseminating the latest evidence on health inequality and supporting data disaggregation; and developing and refining health inequality monitoring methods, resources and best practices. In alignment with these goals, WHO has published reference materials focused on conceptual approaches to health inequality monitoring, which are applied in the global State of Inequality report series. The Health Inequality Monitoring eLearning channel on OpenWHO and capacity building workshops and webinars facilitate the uptake and application of inequality monitoring practices across diverse settings and stakeholders. A key tool available to support the analysis and reporting aspects of health inequality monitoring is the Health Equity Assessment Toolkit (HEAT) application, which allows users to explore data interactively. The Health Inequality Data Repository, a collection of the largest publicly available database of disaggregated data from around the globe, further enables inequality monitoring and analyses. This collection of resources is an important contribution to promote health inequality monitoring across diverse settings. The uptake of evidence from health inequality monitoring remains crucial to the advancement of equity as part of global health and development initiatives.

Immunization of pregnant women against tetanus is a key strategy for reducing tetanus morbidity and mortality while also achieving the goal of maternal and neonatal tetanus elimination. Despite substantial progress in improving newborn protection from tetanus at birth through maternal immunization, umbilical cord practices and sterilized and safe deliveries, inequitable gaps in protection remain. Notably, an infant’s tetanus protection at birth is comprised of immunization received by the mother during and before the pregnancy (e.g., through childhood vaccination, booster doses, mass vaccination campaigns, or during prior pregnancies). In this work, we examine wealth-related inequalities in maternal tetanus toxoid containing vaccination coverage before pregnancy, during pregnancy, and at birth for 72 low- and middle-income countries with a recent Demographic and Health Survey or Multiple Indicator Cluster Survey (between 2013 and 2022). We summarize coverage levels and absolute and relative inequalities at each time point; compare the relative contributions of inequalities before and during pregnancy to inequalities at birth; and examine associations between inequalities and coverage levels. We present the findings for countries individually and on aggregate, by World Bank country income grouping, as well as by maternal and neonatal tetanus elimination status, finding that most of the inequality in tetanus immunization coverage at birth is introduced during pregnancy. Inequalities in coverage during pregnancy are most pronounced in low- and lower-middle-income countries, and even more so in countries which have not achieved maternal and neonatal tetanus elimination. These findings suggest that pregnancy is a key time of opportunity for equity-oriented interventions to improve maternal tetanus immunization coverage.

Abstract Background The Sustainable Development Goals have helped to focus attention on the importance of reducing inequality and ‘leaving no one behind’. Monitoring health inequalities is essential for providing evidence to inform policies, programmes and practices that can close existing gaps and achieve health equity. The Health Equity Assessment Toolkit (HEAT and HEAT Plus) software was developed by the World Health Organization to facilitate the assessment of within-country health inequalities. Results HEAT contains a built-in database of disaggregated health data, while HEAT Plus allows users to upload and analyze inequalities using their own datasets. Version 4.0 of the software incorporated enhancements to the toolkit’s capacity for equity assessments. This includes a multilingual interface, interactive and downloadable visualizations, flexibility to analyze inequalities using any dataset of disaggregated data, and the built-in calculation of 19 summary measures of inequality. This paper outlines the improved features and functionalities of the HEAT and HEAT Plus software since their original release, highlighted through an example of how the toolkit can be used to assess inequalities in the COVID-19 pandemic era. Conclusions The features of the HEAT and HEAT Plus software make it a valuable tool for analyzing and reporting inequalities related to the COVID-19 pandemic, as well as its indirect impacts on inequalities in other health and non-health areas, providing evidence to inform equity-oriented interventions and strategies.

Abstract Background The coronavirus pandemic has exposed existing social inequalities in relation to disease preventive behaviors, risk of exposure, testing and healthcare access, and consequences as a result of illness and containment measures across different population groups. However, due to a lack of data, to date there has been limited evidence of the extent of such within-country inequalities globally. Methods We examined education-related inequalities in four COVID-19 prevention and testing indicators within 90 countries, using data from the University of Maryland Social Data Science Center Global COVID-19 Trends and Impact Survey, in partnership with Facebook, over the period 1 June 2021 to 31 December 2021. The overall level of education-related inequalities, as well as how they differ across country income groups and how they have changed over time were analyzed using the Slope Index of Inequality (SII) and the Relative Index of Inequality (RII). We also assessed whether these education-related inequalities were associated with government policies and responses. Results Education-related inequalities in beliefs, mask wearing, social distancing and testing varied across the study countries. Mask wearing and beliefs in the effectiveness of social distancing and mask wearing were overall more common among people with a higher level of education. Even after controlling for other sociodemographic and health-related factors, social distancing practice was higher among the most educated in low/lower middle income countries, but was higher overall among the least educated in high income countries. Overall there were low education-related inequalities in COVID-19 testing, though there was variation across countries. Conclusions The study highlights important within-country education-related differences in COVID-19 beliefs, preventive behaviors and testing, as well as differing trends across country income groups. This has implications for considering and targeting specific population groups when designing public health interventions and messaging during the COVID-19 pandemic and future health emergencies.

Abstract Background Health inequality monitoring can generate important evidence to inform and motivate changes to policy, programmes and practices. However, the potential of health inequality monitoring practices to quantify inequalities between population subgroups and track progress on the advancement of health equity is under-realized. Capacity strengthening on health inequality monitoring can play an important role in enhancing political will for the generation and use of disaggregated data and for wider adoption of this practice to inform health decision-making. There is a lack of widely available and accessible training materials related to health inequality monitoring that may be used by a range of stakeholders. Objective In this paper, we describe the design, development and implementation of the Health Inequality Monitoring channel on the OpenWHO eLearning platform. We discuss the anticipated impact and potential opportunities for these eLearning courses to contribute to strengthened health inequality monitoring practices. Results The Health Inequality Monitoring channel on the OpenWHO platform is a self-directed learning environment, designed to meet the immediate learning needs of users. The channel contains three series of courses: health inequality monitoring foundations courses; topic-specific health inequality monitoring courses; and health inequality monitoring skill building courses. Courses are primarily targeted to monitoring and evaluation officers, data analysts, academics and researchers, public health professionals, medical and public health students, and others with a general interest in health data and inequality monitoring. Conclusions WHO eLearning courses on health inequality monitoring are addressing the need for capacity strengthening in the collection, analysis and reporting of inequality data. They introduce learners to the foundational concepts, best practices, tools and skills required to conduct health inequality monitoring. The courses on the Health Inequality Monitoring channel demonstrate how technical information can be simplified and presented to broad audiences in a manner that is highly accessible to learners. The Health Inequality Monitoring channel on OpenWHO is an innovative and necessary addition to existing tools and resources to support the advancement of health equity.

Abstract Background Monitoring health inequalities is an important task for health research and policy, to uncover who is being left behind – and where – and to inform effective and equitable policies and programmes to tackle existing inequities. The choice of which measure to use to monitor and analyse health inequalities is thereby not trivial. This article explores a new measure of socioeconomic deprivation status (SDS) to monitor health inequalities. Methods The SDS measure was constructed using the Alkire-Foster method. It includes eight indicators across two equally weighted dimensions (education and living standards) and specifies a four-level gradient of socioeconomic deprivation at the household-level. We conducted four exercises to examine the value-added of the proposed SDS measure, using Demographic and Health Surveys data. First, we examined the discriminatory power of the new measure when applied to outcomes in four select reproductive, maternal, neonatal, and child health (RMNCH) indicators across six countries: skilled birth attendance, stunting, U5MR, and DTP3 immunisation. Then, we analysed the behaviour and association of the new SDS measure vis-à-vis the DHS Wealth Index, including chi-squared test and Pearson correlation coefficient. Third, we analysed the robustness of the SDS measure results to changes in its structure, using pairwise comparisons and Kendal Tau-b rank correlation. Finally, we illustrated some of the advantageous properties of the new measure, disaggregation and decomposition, on Haitian data. Results 1) Higher levels of socioeconomic deprivation are generally consistently associated with lower levels of achievements in the RMNCH indicators across countries. 2) 87% of all pairwise rank comparisons across a range of SDS measure structures were robust. 3) SDS and DHS Wealth Index are associated, but with considerable cross-country variation, highlighting their complementarity. 4) Haitian households in rural areas experienced, on average, more severe socioeconomic deprivation as well as lower levels of RMNCH achievement than urban households. Conclusions The proposed SDS measure adds analytical possibilities to the health inequality monitoring literature, in line with ethically and conceptually well-founded notions of absolute, multidimensional disadvantage. In addition, it allows for breakdown by its dimensions and components, which may facilitate nuanced analyses of health inequality, its correlates, and determinants.

Objectives Despite significant progress in childhood vaccination coverage globally, substantial inequality remains. Remote rural populations are recognised as a priority group for immunisation service equity. We aimed to link facility and individual data to examine the relationship between distance to services and immunisation coverage empirically, specifically using a rural population.Design and setting Retrospective cross-sectional analysis of facility data from the 2013–2014 Malawi Service Provision Assessment and individual data from the 2015–2016 Malawi Demographic and Health Survey, linking children to facilities within a 5 km radius. We examined associations between proximity to health facilities and vaccination receipt via bivariate comparisons and logistic regression models.Participants 2740 children aged 12–23 months living in rural areas.Outcome measures Immunisation coverage for the six vaccines included in the Malawi Expanded Programme on Immunization schedule for children under 1 year at time of study, as well as two composite vaccination indicators (receipt of basic vaccines and receipt of all recommended vaccines), zero-dose pentavalent coverage, and pentavalent dropout.Findings 72% (706/977) of facilities offered childhood vaccination services. Among children in rural areas, 61% were proximal to (within 5 km of) a vaccine-providing facility. Proximity to a vaccine-providing health facility was associated with increased likelihood of having received the rotavirus vaccine (93% vs 88%, p=0.004) and measles vaccine (93% vs 89%, p=0.01) in bivariate tests. In adjusted comparisons, how close a child was to a health facility remained meaningfully associated with how likely they were to have received rotavirus vaccine (adjusted OR (AOR) 1.63, 95% CI 1.13 to 2.33) and measles vaccine (AOR 1.62, 95% CI 1.11 to 2.37).Conclusion Proximity to health facilities was significantly associated with likelihood of receipt for some, but not all, vaccines. Our findings reiterate the vulnerability of children residing far from static vaccination services; efforts that specifically target remote rural populations living far from health facilities are warranted to ensure equitable vaccination coverage.

Immunization, hailed as one of the most successful public health interventions in the world, has contributed to major advancements in health as well as social and economic development [...]

Substantial progress in maternal and neonatal tetanus elimination has been made in the past 40 years, with dramatic reductions in neonatal tetanus incidence and mortality. However, twelve countries have still not achieved maternal and neonatal tetanus elimination, and many countries that have achieved elimination do not meet key sustainability thresholds to ensure long-lasting elimination. As maternal and neonatal tetanus is a vaccine-preventable disease (with coverage of the infant conferred by maternal immunization during and prior to pregnancy), maternal tetanus immunization coverage is a key metric for monitoring progress towards, equity in, and sustainability of tetanus elimination. In this study, we examine inequalities in tetanus protection at birth, a measure of maternal immunization coverage, across 76 countries and four dimensions of inequality via disaggregated data and summary measures of inequality. We find that substantial inequalities in coverage exist for wealth (with lower coverage among poorer wealth quintiles), maternal age (with lower coverage among younger mothers), maternal education (with lower coverage among less educated mothers), and place of residence (with lower coverage in rural areas). Inequalities existed for all dimensions across low- and lower-middle-income countries, and across maternal education and place of residence across upper-middle-income countries. Though global coverage changed little over the time period 2001–2020, this obscured substantial heterogeneity across countries. Notably, several countries had substantial increases in coverage accompanied by decreases in inequality, highlighting the need for equity considerations in maternal and neonatal tetanus elimination and sustainability efforts.

Since December 2020, COVID-19 vaccines have become increasingly available to populations around the globe. A growing body of research has characterised inequalities in COVID-19 vaccination coverage. This scoping review aims to locate, select and assess research articles that report on within-country inequalities in COVID-19 vaccination coverage, and to provide a preliminary overview of inequality trends for selected dimensions of inequality. We applied a systematic search strategy across electronic databases with no language or date restrictions. Our inclusion criteria specified research articles or reports that analysed inequality in COVID-19 vaccination coverage according to one or more socioeconomic, demographic or geographic dimension of inequality. We developed a data extraction template to compile findings. The scoping review was carried out using the PRISMA-ScR checklist. A total of 167 articles met our inclusion criteria, of which half (n = 83) were conducted in the United States. Articles focused on vaccine initiation, full vaccination and/or receipt of booster. Diverse dimensions of inequality were explored, most frequently relating to age (n = 127 articles), race/ethnicity (n = 117 articles) and sex/gender (n = 103 articles). Preliminary assessments of inequality trends showed higher coverage among older population groups, with mixed findings for sex/gender. Global research efforts should be expanded across settings to understand patterns of inequality and strengthen equity in vaccine policies, planning and implementation.

The role of gender inequality in childhood immunization is an emerging area of focus for global efforts to improve immunization coverage and equity. Recent studies have examined the relationship between gender inequality and childhood immunization at national as well as individual levels; we hypothesize that the demonstrated relationship between greater gender equality and higher immunization coverage will also be evident when examining subnational-level data. We thus conducted an ecological analysis examining the association between the Subnational Gender Development Index (SGDI) and two measures of immunization—zero-dose diphtheria-tetanus-pertussis (DTP) prevalence and 3-dose DTP coverage. Using data from 2010–2019 across 702 subnational regions within 57 countries, we assessed these relationships using fractional logistic regression models, as well as a series of analyses to account for the nested geographies of subnational regions within countries. Subnational regions were dichotomized to higher gender inequality (top quintile of SGDI) and lower gender inequality (lower four quintiles of SGDI). In adjusted models, we find that subnational regions with higher gender inequality (favoring men) are expected to have 5.8 percentage points greater zero-dose prevalence than regions with lower inequality [16.4% (95% confidence interval (CI) 14.5–18.4%) in higher-inequality regions versus 10.6% (95% CI 9.5–11.7%) in lower-inequality regions], and 8.2 percentage points lower DTP3 immunization coverage [71.0% (95% CI 68.3–73.7%) in higher-inequality regions versus 79.2% (95% CI 77.7–80.7%) in lower-inequality regions]. In models accounting for country-level clustering of gender inequality, the magnitude and strength of associations are reduced somewhat, but remain statistically significant in the hypothesized direction. In conjunction with published work demonstrating meaningful associations between greater gender equality and better childhood immunization outcomes in individual- and country-level analyses, these findings lend further strength to calls for efforts towards greater gender equality to improve childhood immunization and child health outcomes broadly.

Gender-related barriers to immunization are key targets to improve immunization coverage and equity. We used individual-level demographic and health survey data from 52 low- and middle-income countries to examine the relationship between women’s social independence (measured by the Survey-based Women’s emPowERment (SWPER) Global Index) and childhood immunization. The primary outcome was receipt of three doses of the diphtheria-tetanus-pertussis vaccine (DTP3) among children aged 12–35 months; we secondarily examined failure to receive any doses of DTP-containing vaccines. We summarized immunization coverage indicators by social independence tertile and estimated crude and adjusted summary measures of absolute and relative inequality. We conducted all analyses at the country level using individual data; median results across the 52 examined countries are also presented. In crude comparisons, median DTP3 coverage was 12.3 (95% CI 7.9; 16.3) percentage points higher among children of women with the highest social independence compared with children of women with the lowest. Thirty countries (58%) had a difference in coverage between those with the highest and lowest social independence of at least 10 percentage points. In adjusted models, the median coverage was 7.4 (95% CI 5.0; 9.1) percentage points higher among children of women with the highest social independence. Most countries (41, 79%) had statistically significant relative inequality in DTP3 coverage by social independence. The findings suggest that greater social independence for women was associated with better childhood immunization outcomes, adding evidence in support of gender-transformative strategies to reduce childhood immunization inequities.

This study explores the association between childhood immunization and gender inequality at the national level. Data for the study include annual country-level estimates of immunization among children aged 12–23 months, indicators of gender inequality, and associated factors for up to 165 countries from 2010–2019. The study examined the association between gender inequality, as measured by the gender development index and the gender inequality index, and two key outcomes: prevalence of children who received no doses of the DTP vaccine (zero-dose children) and children who received the third dose of the DTP vaccine (DTP3 coverage). Unadjusted and adjusted fractional logit regression models were used to identify the association between immunization and gender inequality. Gender inequality, as measured by the Gender Development Index, was positively and significantly associated with the proportion of zero-dose children (high inequality AOR = 1.61, 95% CI: 1.13–2.30). Consistently, full DTP3 immunization was negatively and significantly associated with gender inequality (high inequality AOR = 0.63, 95% CI: 0.46–0.86). These associations were robust to the use of an alternative gender inequality measure (the Gender Inequality Index) and were consistent across a range of model specifications controlling for demographic, economic, education, and health-related factors. Gender inequality at the national level is predictive of childhood immunization coverage, highlighting that addressing gender barriers is imperative to achieve universal coverage in immunization and to ensure that no child is left behind in routine vaccination.

As a key step in advancing the sustainable development goals, the World Health Organisation (WHO) has placed emphasis on building capacity for measuring and monitoring health inequalities. A number of resources have been developed, including the Health Equity Assessment Toolkit (HEAT), a software application that facilitates the assessment of within-country health inequalities. Following user demand, an Upload Database Edition of HEAT, HEAT Plus, was developed. Launched in July 2017, HEAT Plus allows users to upload their own databases and assess inequalities at the global, national or subnational level for a range of (health) indicators and dimensions of inequality. The software is open-source, operates on Windows and Macintosh platforms and is readily available for download from the WHO website. The flexibility of HEAT Plus makes it a suitable tool for both global and national inequality assessments. Further developments will include interactive graphs, maps and translation into different languages.

National health inequality monitoring needs considerably more investment to realize equity-oriented health improvements in countries, including advancement towards the Sustainable Development Goals. Following an overview of national health inequality monitoring and the associated resource requirements, we highlight challenges that countries may encounter when setting up, expanding or strengthening national health inequality monitoring systems, and discuss opportunities and key initiatives that aim to address these challenges. We provide specific proposals on what is needed to ensure that national health inequality monitoring systems are harnessed to guide the reduction of health inequalities.

Background: Inequalities in health represent a major problem in many countries, including Indonesia. Addressing health inequality is a central component of the Sustainable Development Goals and a priority of the World Health Organization (WHO). WHO provides technical support for health inequality monitoring among its member states. Following a capacity-building workshop in the WHO South-East Asia Region in 2014, Indonesia expressed interest in incorporating health-inequality monitoring into its national health information system. Objectives: This article details the capacity-building process for national health inequality monitoring in Indonesia, discusses successes and challenges, and how this process may be adapted and implemented in other countries/settings. Methods: We outline key capacity-building activities undertaken between April 2016 and December 2017 in Indonesia and present the four key outcomes of this process. Results: The capacity-building process entailed a series of workshops, meetings, activities, and processes undertaken between April 2016 and December 2017. At each stage, a range of stakeholders with access to the relevant data and capacity for data analysis, interpretation and reporting was engaged with, under the stewardship of state agencies. Key steps to strengthening health inequality monitoring included capacity building in (1) identification of the health topics/areas of interest, (2) mapping data sources and identifying gaps, (3) conducting equity analyses using raw datasets, and (4) interpreting and reporting inequality results. As a result, Indonesia developed its first national report on the state of health inequality. A number of peer-reviewed manuscripts on various aspects of health inequality in Indonesia have also been developed. Conclusions: The capacity-building process undertaken in Indonesia is designed to be adaptable to other contexts. Capacity building for health inequality monitoring among countries is a critical step for strengthening equity-oriented national health information systems and eventually tackling health inequities.

Background: Achieving the Sustainable Development Goal of ‘ensuring healthy lives and promoting well-being for all at all ages’ necessitates regular monitoring of inequality in the availability of health-related infrastructure and access to services, and in health risks and outcomes. Objectives: To quantify subnational regional inequality in Indonesia using a composite index of public health infrastructure, services, behavioural risk factors and health outcomes: the Public Health Development Index (PHDI). Methods: PHDI is a composite index of 30 public health indicators from across the life course and along the continuum of care. An overall index and seven topic-specific sub-indices were calculated using data from the 2013 Indonesian Basic Health Survey (RISKESDAS) and the 2011 – Village Potential Survey (PODES). These indices were analysed at the national, province and district levels. Within-province inequality was calculated using the Weighted Index of Disparity (IDISW). Results: National average PHDI overall index was 54.0 (out of a possible 100); scores differed between provinces, ranging from 43.9 in Papua to 65.0 in Bali. Provinces in western regions of Indonesia tended to have higher overall PHDI scores compared to eastern regions. Large variations in province averages were observed for the non-communicable diseases sub-index, environmental health sub-index and infectious diseases sub-index. Provinces with a similar number of districts and with similar overall scores on the PHDI index showed different levels of relative within-province inequality. Greater within-province relative inequalities were seen in the environmental health and health services provisions sub-indices as compared to other indices. Conclusions: Achieving the goal of ensuring healthy lives and promoting well-being for all at all ages in Indonesia necessitates having a more focused understanding of district-level inequalities across a wide range of public health infrastructure, service, risk factor and health outcomes indicators, which can enable geographical comparison while also revealing areas for intervention to address health inequalities.

The task of health inequality monitoring is not possible without the availability of appropriate and high-quality data at various levels. Data source mapping – a process by which data sources are systematically enlisted, their properties detailed and each source appraised for the purposes of monitoring – is an essential initial step for health inequality monitoring. We outline a simple process along with a template for data source mapping and its application in Indonesia, concluding with the lessons learned from this process, in terms of both challenges as well as the opportunities and advantages arising from the use of equity-related data from the Indonesian health information system.

Despite advances in scaling up new vaccines in low- and middle-income countries, the global number of unvaccinated children has remained high over the past decade. We used 2000–2019 household survey data from 154 surveys representing 89 low- and middle-income countries to assess within-country, economic-related inequality in the prevalence of one-year-old children with zero doses of diphtheria–tetanus–pertussis (DTP) vaccine. Zero-dose DTP prevalence data were disaggregated by household wealth quintile. Difference, ratio, slope index of inequality, concentration index, and excess change measures were calculated to assess the latest situation and change over time, by country income grouping for 17 countries with high zero-dose DTP numbers and prevalence. Across 89 countries, the median prevalence of zero-dose DTP was 7.6%. Within-country inequalities mostly favored the richest quintile, with 19 of 89 countries reporting a rich–poor gap of ≥20.0 percentage points. Low-income countries had higher inequality than lower–middle-income countries and upper–middle-income countries (difference between the median prevalence in the poorest and richest quintiles: 14.4, 8.9, and 2.7 percentage points, respectively). Zero-dose DTP prevalence among the poorest households of low-income countries declined between 2000 and 2009 and between 2010 and 2019, yet economic-related inequality remained high in many countries. Widespread economic-related inequalities in zero-dose DTP prevalence are particularly pronounced in low-income countries and have remained high over the previous decade.

BACKGROUND:Monitoring universal health coverage in reproductive, maternal and child health requires appropriate indicators for assessing coverage and equity. In 2008, the composite coverage index (CCI)-a weighted average of eight indicators reflecting family planning, antenatal and delivery care, immunizations and management of childhood illnesses-was proposed. In 2017, the CCI formula was revised to update the family planning and diarrhea management indicators. We explored the implications of adding new indicators to the CCI. METHODS:We analysed nationally representative surveys to investigate how addition of early breastfeeding initiation (EIBF), tetanus toxoid during pregnancy and post-natal care for babies affected CCI levels and the magnitude of wealth-related inequalities. We used Pearson's correlation coefficient to compare different formulations, and the slope index of inequalities [SII] and concentration index [CIX] to assess absolute and relative inequalities, respectively. RESULTS:47 national surveys since 2010 had data on the eight variables needed for the original and revised formulations, and on EIBF, tetanus vaccine and postnatal care, related to newborn care. The original CCI showed the highest average value (65.5%), which fell to 56.9% when all 11 indicators were included. Correlation coefficients between pairs of all formulations ranged from 0.93 to 0.99. When analysed separately, 10 indicators showed higher coverage with increasing wealth; the exception was EIBF (SII = -2.1; CIX = -0.5). Inequalities decreased when other indicators were added, especially EIBF-the SII fell from 24.8 pp. to 19.2 pp.; CIX from 7.6 to 6.1. The number of countries with data from two or more surveys since 2010 was 30 for the original and revised formulations and 15 when all the 11 indicators were included. CONCLUSIONS:Given the growing importance of newborn mortality, it would be desirable to include relevant coverage indicators in the CCI, but this would lead a reduction in data availability, and an underestimation of coverage inequalities. We propose that the 2017 version of the revised CCI should continue to be used.

IntroductionWealth-related inequalities in reproductive, maternal, neonatal and child health have been widely studied by dividing the population into quintiles. We present a comprehensive analysis of wealth inequalities for the composite coverage index (CCI) using national health surveys carried out since 2010, using wealth deciles and absolute income estimates as stratification variables, and show how these new approaches expand on traditional equity analyses.Methods83 low-income and middle-income countries were studied. The CCI is a combined measure of coverage with eight key reproductive, maternal, newborn and child health interventions. It was disaggregated by wealth deciles for visual inspection of inequalities, and the slope index of inequality (SII) was estimated. The correlation between coverage in the extreme deciles and SII was assessed. Finally, we used multilevel models to examine how the CCI varies according to the estimated absolute income for each wealth quintile in the surveys.ResultsThe analyses of coverage by wealth deciles and by absolute income show that inequality is mostly driven by coverage among the poor, which is much more variable than coverage among the rich across countries. Regardless of national coverage, in 61 of the countries, the wealthiest decile achieved 70% or higher CCI coverage. Well-performing countries were particularly effective in achieving high coverage among the poor. In contrast, underperforming countries failed to reach the poorest, despite reaching the better-off.ConclusionThere are huge inequalities between the richest and the poorest women and children in most countries. These inequalities are strongly driven by low coverage among the poorest given the wealthiest groups achieve high coverage irrespective of where they live, overcoming any barriers that are an impediment to others. Countries that ‘punched above their weight’ in coverage, given their level of absolute wealth, were those that best managed to reach their poorest women and children.

Subnational inequalities have received limited attention in the monitoring of progress towards national and global health targets during the past two decades. Yet, such data are often a critical basis for health planning and monitoring in countries, in support of efforts to reach all with essential interventions. Household surveys provide a rich basis for interventions coverage indicators on reproductive, maternal, newborn and child health (RMNCH) at the country first administrative level (regions or provinces). In this paper, we show the large subnational inequalities that exist in RMNCH coverage within 39 countries in sub-Saharan Africa, using a composite coverage index which has been used extensively by Countdown to 2030 for Women’s, Children’s and Adolescent’s Health. The analyses show the wide range of subnational inequality patterns such as low overall national coverage with very large top inequality involving the capital city, intermediate national coverage with bottom inequality in disadvantaged regions, and high coverage in all regions with little inequality. Even though nearly half of the 34 countries with surveys around 2004 and again around 2015 appear to have been successful in reducing subnational inequalities in RMNCH coverage, the general picture shows persistence of large inequalities between subnational units within many countries. Poor governance and conflict settings were identified as potential contributing factors. Major efforts to reduce within-country inequalities are required to reach all women and children with essential interventions.

Abstract Background It is widely recognised that the pursuit of sustainable development cannot be accomplished without addressing inequality, or observed differences between subgroups of a population. Monitoring health inequalities allows for the identification of health topics where major group differences exist, dimensions of inequality that must be prioritised to effect improvements in multiple health domains, and also population subgroups that are multiply disadvantaged. While availability of data to monitor health inequalities is gradually improving, there is a commensurate need to increase, within countries, the technical capacity for analysis of these data and interpretation of results for decision-making. Prior efforts to build capacity have yielded demand for a toolkit with the computational ability to display disaggregated data and summary measures of inequality in an interactive and customisable fashion that would facilitate interpretation and reporting of health inequality in a given country. Methods To answer this demand, the Health Equity Assessment Toolkit (HEAT), was developed between 2014 and 2016. The software, which contains the World Health Organization’s Health Equity Monitor database, allows the assessment of inequalities within a country using over 30 reproductive, maternal, newborn and child health indicators and five dimensions of inequality (economic status, education, place of residence, subnational region and child’s sex, where applicable). Results/Conclusion HEAT was beta-tested in 2015 as part of ongoing capacity building workshops on health inequality monitoring. This is the first and only application of its kind; further developments are proposed to introduce an upload data feature, translate it into different languages and increase interactivity of the software. This article will present the main features and functionalities of HEAT and discuss its relevance and use for health inequality monitoring.

Abstract Background While surveys in high-income countries show that women generally have poorer self-reported health than men, much less is known about gender differences in other regions of the world. Such data can be used to examine the determinants of sex differences. Methods We analysed data on respondents 18 years and over from the World Health Surveys 2002–04 in 59 countries, which included multiple measures of self-reported health, eight domains of functioning and presumptive diagnoses of chronic conditions. The age-standardized female excess fraction was computed for all indicators and analysed for five regional groups of countries. Multivariate regression models were used to examine the association between country gaps in self-reported health between the sexes with societal and other background characteristics. Results Women reported significantly poorer health than men on all self-reported health indicators. The excess fraction was 15 % for the health score based on the eight domains, 28 % for “poor” or “very poor” self-rated health on the single question, and 26 % for “severe” or “extreme” on a single question on limitations. The excess female reporting of poorer health occurred at all ages, but was smaller at ages 60 and over. The female excess was observed in all regions, and was smallest in the European high-income countries. Women more frequently reported problems in specific health domains, with the excess fraction ranging from 25 % for vision to 35 % for mobility, pain and sleep, and with considerable variation between regions. Angina, arthritis and depression had female excess fractions of 33, 32 and 42 % respectively. Higher female prevalence of the presumptive diagnoses was observed in all regional country groups. The main factors affecting the size of the gender gap in self-reported health were the female-male gaps in the prevalence of chronic conditions, especially arthritis and depression and gender characteristics of the society. Conclusions Large female-male differences in self-reported health and functioning, equivalent to a decade of growing older, consistently occurred in all regions of the world, irrespective of differences in mortality levels or societal factors. The multi-country study suggests that a mix of biological factors and societal gender inequalities are major contributing factors to gender gap in self-reported measures of health.

Background: Achieving universal health coverage, including quality essential service coverage and financial protection for all, is target 3.8 of the Sustainable Development Goals (SDG). As a result, an index of essential health service coverage indicators was selected by the UN as SDG indicator 3.8.1. We have developed an index for measuring SDG 3.8.1, describe methods for compiling the index, and report baseline results for 2015. Methods: 16 tracer indicators were selected for the index, which included four from within each of the categories of reproductive, maternal, newborn, and child health; infectious disease; non-communicable diseases; and service capacity and access. Indicator data for 183 countries were taken from UN agency estimates or databases, supplemented with submissions from national focal points during a WHO country consultation. The index was computed using geometric means, and a subset of tracer indicators were used to summarise inequalities. Findings: On average, countries had primary data since 2010 for 72% of the final set of indicators. The median national value for the service coverage index was 65 out of 100 (range 22–86). The index was highly correlated with other summary measures of health, and after controlling for gross national income and mean years of adult education, was associated with 21 additional years of life expectancy over the observed range of country values. Across 52 countries with sufficient data, coverage was 1% to 66% lower among the poorest quintile as compared with the national population. Sensitivity analyses suggested ranks implied by the index are fairly stable across alternative calculation methods. Interpretation: Service coverage within universal health coverage can be measured with an index of tracer indicators. Our universal health coverage service coverage index is simple to compute by use of available country data and can be refined to incorporate relevant indicators as they become available through SDG monitoring. Funding: Ministry of Health, Japan, and the Rockefeller Foundation.

Background: Health equity is a priority in the post-2015 sustainable development agenda and other major health initiatives. The World Health Organization (WHO) has a history of promoting actions to achieve equity in health, including efforts to encourage the practice of health inequality monitoring. Health inequality monitoring systems use disaggregated data to identify disadvantaged subgroups within populations and inform equity-oriented health policies, programs, and practices. Objective: This paper provides an overview of a number of recent and current WHO initiatives related to health inequality monitoring at the global and/or national level. Design: We outline the scope, content, and intended uses/application of the following: Health Equity Monitor database and theme page; State of inequality: reproductive, maternal, newborn, and child health report; Handbook on health inequality monitoring: with a focus on low- and middle-income countries; Health inequality monitoring eLearning module; Monitoring health inequality: an essential step for achieving health equity advocacy booklet and accompanying video series; and capacity building workshops conducted in WHO Member States and Regions. Conclusions: The paper concludes by considering how the work of the WHO can be expanded upon to promote the establishment of sustainable and robust inequality monitoring systems across a variety of health topics among Member States and at the global level.

OBJECTIVE: To assess international shortfall inequality in life expectancy at birth among women and men and the influence of geography and country income group. METHODS: The authors used estimates of life expectancy at birth, by sex, for 12 five-year periods between 1950-1955 and 2005-2010 and estimates of population for the midpoints of each period from the World population prospects, 2008 revision. Shortfall inequality was defined as the weighted average of the deviations of each country's average life expectancy by sex from the highest attained life expectancy by sex for each period. FINDINGS: International shortfall inequalities in life expectancy among men and among women decreased between 1950 and 1975 but stagnated thereafter. International shortfall inequality in life expectancy has been higher in women than in men, ranging from 1.9 to 2.9 years. Women in low-income countries have the biggest shortfall, currently at around 26.7 years. CONCLUSION: International shortfall inequality is higher among women than men primarily because women in low-income and lower-middle-income country groups show larger differences in life expectancy than men. Further investigation is needed to determine the pathways causing these inequalities.

OBJECTIVE: The objectives of this study were to report on socioeconomic inequality in childhood malnutrition in the developing world, to provide evidence for an association between socioeconomic inequality and the average level of malnutrition, and to draw attention to different patterns of socioeconomic inequality in malnutrition. METHODS: Both stunting and wasting were measured using new WHO child growth standards. Socioeconomic status was estimated by principal component analysis using a set of household assets and living conditions. Socioeconomic inequality was measured using an alternative concentration index that avoids problems with dependence on the mean level of malnutrition. FINDINGS: In almost all countries investigated, stunting and wasting disproportionately affected the poor. However, socioeconomic inequality in wasting was limited and was not significant in about one third of countries. After correcting for the concentration index's dependence on mean malnutrition, there was no clear association between average stunting and socioeconomic inequality. The latter showed different patterns, which were termed mass deprivation, queuing and exclusion. Although average levels of malnutrition were higher with the new WHO reference standards, estimates of socioeconomic inequality were largely unaffected by changing the growth standards. CONCLUSION: Socioeconomic inequality in childhood malnutrition existed throughout the developing world, and was not related to the average malnutrition rate. Failure to tackle this inequality is a cause of social injustice. Moreover, reducing the overall rate of malnutrition does not necessarily lead to a reduction in inequality. Policies should, therefore, take into account the distribution of childhood malnutrition across all socioeconomic groups.

OBJECTIVE: To measure the socioeconomic inequality in infant mortality in Iran*. METHODS: We analysed data from the provincially representative Demographic and Health Survey, which was done in Iran in 2000. We used a dichotomous hierarchical ordered probit model to develop an indicator of socioeconomic status of households. We assessed the inequality in infant mortality by using the odds ratio of infant mortality between the lowest and highest socioeconomic quintiles at both the provincial and national levels, and the concentration index, an inequality measure based on the entire socioeconomic distribution. RESULTS: We found a decreasing trend in the infant mortality rate in relation to socioeconomic quintiles. The poorest to richest odds ratio was 2.34 (95% CI = 1.78-3.09). The concentration index of infant mortality in Iran was -0.1789 (95% CI = -0.2193--0.1386). Furthermore, the inequality of infant mortality between the lowest and highest quintiles was significant and favoured the better-off in most of the provinces. However, this inequality varied between provinces. CONCLUSION: Socioeconomic inequality in infant mortality favours the better-off in the country as a whole and in most of its provinces, but the degree of this inequality varies between the provinces. As well as its national average, it is important to consider the provincial distribution of this indicator of population health.

Monitoring inequalities in health is fundamental to the equitable and progressive realization of universal health coverage (UHC). A successful approach to global inequality monitoring must be intuitive enough for widespread adoption, yet maintain technical credibility. This article discusses methodological considerations for equity-oriented monitoring of UHC, and proposes recommendations for monitoring and target setting. Inequality is multidimensional, such that the extent of inequality may vary considerably across different dimensions such as economic status, education, sex, and urban/rural residence. Hence, global monitoring should include complementary dimensions of inequality (such as economic status and urban/rural residence) as well as sex. For a given dimension of inequality, subgroups for monitoring must be formulated taking into consideration applicability of the criteria across countries and subgroup heterogeneity. For economic-related inequality, we recommend forming subgroups as quintiles, and for urban/rural inequality we recommend a binary categorization. Inequality spans populations, thus appropriate approaches to monitoring should be based on comparisons between two subgroups (gap approach) or across multiple subgroups (whole spectrum approach). When measuring inequality absolute and relative measures should be reported together, along with disaggregated data; inequality should be reported alongside the national average. We recommend targets based on proportional reductions in absolute inequality across populations. Building capacity for health inequality monitoring is timely, relevant, and important. The development of high-quality health information systems, including data collection, analysis, interpretation, and reporting practices that are linked to review and evaluation cycles across health systems, will enable effective global and national health inequality monitoring. These actions will support equity-oriented progressive realization of UHC.

BackgroundWomen and men share similar health challenges yet women report poorer health. The study investigates the social determinants of self-reported health in women and men, and male-female differences in health.MethodsData on 103154 men and 125728 women were analysed from 57 countries in the World Health Survey 2002-2004. Item Response Theory was used to construct a composite measure of health. Associations between health and determinants were assessed using multivariate linear regression. Blinder-Oaxaca decomposition partitioned the inequality in health between women and men into an "explained" component that arises because men and women differ in social and economic characteristics, and an "unexplained" component due to the differential effects of these characteristics. Decomposition was repeated for 18 countries in the World Health Organization (WHO) African region and 19 countries in the WHO European region.ResultsWomen's health was significantly lower than men's. Health was associated with education, household economic status, employment, and marital status after controlling for age. In the pooled analysis decomposition showed that 30% of the inequality was "explained", of which almost 75% came from employment, education, marital status. The differential effects of being in paid employment increased the inequality. When countries in Africa and Europe were compared, the "explained" component (31% and 39% respectively) was largely attributed to the social determinants in the African countries and to women's longevity in the European countries. Being in paid employment had a greater positive effect on the health of males in both regions.ConclusionsWays in which age and the social determinants contribute to the poorer health status of women compared with men varies between groups of countries. This study highlights the need for action to address social structures, institutional discrimination and harmful gender norms and roles that differently influence health with ageing.

OBJECTIVES: To assess the magnitude and pattern of socioeconomic inequality in current smoking in low and middle income countries. METHODS: We used data from the World Health Survey [WHS] in 48 low-income and middle-income countries to estimate the crude prevalence of current smoking according to household wealth quintile. A Poisson regression model with a robust variance was used to generate the Relative Index of Inequality [RII] according to wealth within each of the countries studied. RESULTS: In males, smoking was disproportionately prevalent in the poor in the majority of countries. In numerous countries the poorest men were over 2.5 times more likely to smoke than the richest men. Socioeconomic inequality in women was more varied showing patterns of both pro-rich and pro-poor inequality. In 20 countries pro-rich relative socioeconomic inequality was statistically significant: the poorest women had a higher prevalence of smoking compared to the richest women. Conversely, in 9 countries women in the richest population groups had a statistically significant greater risk of smoking compared to the poorest groups. CONCLUSION: Both the pattern and magnitude of relative inequality may vary greatly between countries. Prevention measures should address the specific pattern of smoking inequality observed within a population.

INTRODUCTION: Tobacco smoking is a leading cause of premature death and disability, and over 80% of the world's smokers live in low- or middle-income countries. The objective of this study is to assess demographic and socioeconomic determinants of current smoking in low- and middle-income countries. METHODS: We used data, from the World Health Survey in 48 low-income and middle-income countries, to explore the impact of demographic and socioeconomic factors on the current smoking status of respondents. The data from these surveys provided information on 213,807 respondents aged 18 years or above that were divided into 4 pooled datasets according to their sex and country income group. The overall proportion of current smokers, as well as the proportion by each relevant demographic and socioeconomic determinant, was calculated within each of the pooled datasets, and multivariable logistic regression was used to assess the association between current smoking and these determinants. RESULTS: The odds of smoking were not equal in all demographic or socioeconomic groups. Some factors were fairly stable across the four datasets studied: for example, individuals were more likely to smoke if they had little or no education, regardless of if they were male or female, or lived in a low or a middle income country. Nevertheless, other factors, notably age and wealth, showed a differential effect on smoking by sex or country income level. While women in the low-income country group were twice as likely to smoke if they were in the lowest wealth quintile compared with the highest, the association was absent in the middle-income country group. CONCLUSION: Information on how smoking is distributed among low- or middle-income countries will allow policy makers to tailor future policies, and target the most vulnerable populations.

OBJECTIVE: To measure within-country wealth-related inequality in the health service coverage gap of maternal and child health indicators in sub-Saharan Africa and quantify its contribution to the national health service coverage gap. METHODS: Coverage data for child and maternal health services in 28 sub-Saharan African countries were obtained from the 2000-2008 Demographic Health Survey. For each country, the national coverage gap was determined for an overall health service coverage index and select individual health service indicators. The data were then additively broken down into the coverage gap in the wealthiest quintile (i.e. the proportion of the quintile lacking a required health service) and the population attributable risk (an absolute measure of within-country wealth-related inequality). FINDINGS: In 26 countries, within-country wealth-related inequality accounted for more than one quarter of the national overall coverage gap. Reducing such inequality could lower this gap by 16% to 56%, depending on the country. Regarding select individual health service indicators, wealth-related inequality was more common in services such as skilled birth attendance and antenatal care, and less so in family planning, measles immunization, receipt of a third dose of vaccine against diphtheria, pertussis and tetanus and treatment of acute respiratory infections in children under 5 years of age. CONCLUSION: The contribution of wealth-related inequality to the child and maternal health service coverage gap differs by country and type of health service, warranting case-specific interventions. Targeted policies are most appropriate where high within-country wealth-related inequality exists, and whole-population approaches, where the health-service coverage gap is high in all quintiles.