Nicole Bergen

Socioeconomic inequalities in risk factors for non communicable diseases in low-income and middle-income countries: results from the World Health Survey

BackgroundMonitoring inequalities in non communicable disease risk factor prevalence can help to inform and target effective interventions. The prevalence of current daily smoking, low fruit and vegetable consumption, physical inactivity, and heavy episodic alcohol drinking were quantified and compared across wealth and education levels in low- and middle-income country groups.MethodsThis study included self-reported data from 232,056 adult participants in 48 countries, derived from the 2002–2004 World Health Survey. Data were stratified by sex and low- or middle-income country status. The main outcome measurements were risk factor prevalence rates reported by wealth quintile and five levels of educational attainment. Socioeconomic inequalities were measured using the slope index of inequality, reflecting differences in prevalence rates, and the relative index of inequality, reflecting the prevalence ratio between the two extremes of wealth or education accounting for the entire distribution. Data were adjusted for confounding factors: sex, age, marital status, area of residence, and country of residence.ResultsSmoking and low fruit and vegetable consumption were significantly higher among lower socioeconomic groups. The highest wealth-related absolute inequality was seen in smoking among men of low- income country group (slope index of inequality 23.0 percentage points; 95% confidence interval 19.6, 26.4). The slope index of inequality for low fruit and vegetable consumption across the entire distribution of education was around 8 percentage points in both sexes and both country income groups. Physical inactivity was less prevalent in populations of low socioeconomic status, especially in low-income countries (relative index of inequality: (men) 0.46, 95% confidence interval 0.33, 0.64; (women) 0.52, 95% confidence interval 0.42, 0.65). Mixed patterns were found for heavy drinking.ConclusionsDisaggregated analysis of the prevalence of non-communicable disease risk factors demonstrated different patterns and varying degrees of socioeconomic inequalities across low- and middle-income settings. Interventions should aim to reach and achieve sustained benefits for high-risk populations.

Socioeconomic inequality in the prevalence of noncommunicable diseases in low- and middle-income countries: Results from the World Health Survey

BackgroundNoncommunicable diseases are an increasing health concern worldwide, but particularly in low- and middle-income countries. This study quantified and compared education- and wealth-based inequalities in the prevalence of five noncommunicable diseases (angina, arthritis, asthma, depression and diabetes) and comorbidity in low- and middle-income country groups.MethodsUsing 2002–04 World Health Survey data from 41 low- and middle-income countries, the prevalence estimates of angina, arthritis, asthma, depression, diabetes and comorbidity in adults aged 18 years or above are presented for wealth quintiles and five education levels, by sex and country income group. Symptom-based classification was used to determine angina, arthritis, asthma and depression rates, and diabetes diagnoses were self-reported. Socioeconomic inequalities according to wealth and education were measured absolutely, using the slope index of inequality, and relatively, using the relative index of inequality.ResultsWealth and education inequalities were more pronounced in the low-income country group than the middle-income country group. Both wealth and education were inversely associated with angina, arthritis, asthma, depression and comorbidity prevalence, with strongest inequalities reported for angina, asthma and comorbidity. Diabetes prevalence was positively associated with wealth and, to a lesser extent, education. Adjustments for confounding variables tended to decrease the magnitude of the inequality.ConclusionsNoncommunicable diseases are not necessarily diseases of the wealthy, and showed unequal distribution across socioeconomic groups in low- and middle-income country groups. Disaggregated research is warranted to assess the impact of individual noncommunicable diseases according to socioeconomic indicators.

Equity-oriented monitoring in the context of universal health coverage.

As part of the Universal Health Coverage Collection, Ahmad Reza Hosseinpoor and colleagues discuss methodological considerations for equity-oriented monitoring of universal health coverage, and propose recommendations for monitoring and target setting.

Socio-demographic determinants of caregiving in older adults of low- and middle-income countries

BACKGROUND caregivers make substantial contributions to health and social systems, but many low-resource settings lack reliable data about the determinants and experiences of older adults who are caregivers. OBJECTIVE we identified socio-demographic determinants of caregiving among older adults of low- and middle-income countries, and compared determinants of specific categories of caregiving tasks. SUBJECTS a total of 34,289 adults aged 60 or older from a pooled sample of 48 low- and middle-income countries. METHODS prevalence values for caregiving and categories of caregiving tasks were calculated according to socio-demographic variables, for the overall sample and for each study country. Multivariate analyses assessed associations between caregiving variables and socio-demographic determinants, adjusting for health score and country of residence. RESULTS overall, 15% of older adults provided care, with varying prevalence according to study country. The prevalence of caregiving was significantly higher in women, and among adults aged 60-69, the college educated, the wealthy, those living in a household of two people and urban residents. No prevalence differences were reported for the employment status or health score. The odds of caregiving were greater for women, younger age groups and higher education levels, controlling for confounders. The likelihood of participating in specific categories of caregiving differed by sex, age, marital status, education, employment status and household size, but was not associated with household economic status, area of residence or health score.

Promoting health equity: WHO health inequality monitoring at global and national levels

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.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.

Monitoring inequality: an emerging priority for health post-2015

The Millennium Development Goals focused on poverty and development and reducing inequalities between countries.1 Progress was monitored through national averages without adequate attention to within-country inequality. The post-2015 sustainable development goals (SDG) stress “leaving no one behind” – with goal 10 specifically calling for the reduction of inequality, within and among countries.2 Monitoring of inequalities within countries focuses on indicators and dimensions of inequality that are particularly relevant to each country. Drawing upon the outputs of within-country inequality monitoring, policies can be tailored to be maximally effective in reducing inequalities.3 At the same time, having comparable disaggregated data across countries is important to track within-country inequality at a regional or global level. One of the SDG targets specifically addresses the importance of disaggregated data, calling on countries to increase “…the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts.”2 Such disaggregated data are vital to identify where and why inequalities exist and ensure that policies, programmes and practices are successful in reaching the most vulnerable. Many countries have made major progress in monitoring health inequalities through household surveys such as Demographic and Health Surveys. However, currently, many basic indicators of health and well-being are not consistently available in a form that permits disaggregation by sociodemographic factors and subnational regions. For instance, relatively few studies have provided the global situation of within-country inequality in risk factors of noncommunicable diseases by socioeconomic status.4,5 Household health surveys now need to be extended to include more emphasis on noncommunicable diseases and injuries. Investments should be made across different data sources, including birth and death registration (with cause of death), health facility and community information systems and administrative data on health infrastructure, workforce and financing. Countries should develop technical capacity to conduct health inequality analysis and establish reporting practices that effectively communicate clear messages facilitating action. Global standards are now required to enable better international comparisons of within-country health inequality. A coordinated effort to support standardized data collection, analysis and reporting in all countries will strengthen global and regional monitoring of inequality in health. The World Health Organization (WHO) Health Equity Monitor contains comparable disaggregated data from 94 countries on the topic of reproductive, maternal, newborn and child health.6 Using these data, a flagship report was developed to showcase best practices in reporting inequalities in low- and middle-income countries.7 Expanding such activities to cover other health topics and more countries would enable wider adoption of health inequality monitoring. This would serve to increase the accountability of health systems, and also other sectors whose actions have an impact on population health. Goal 3 of the SDGs is “to ensure healthy lives and promote well-being for all at all ages.”2 Thus, all health-related SDG targets necessitate a pro-equity approach that promotes accelerated progress among the disadvantaged to close within-country gaps. The endorsement of universal health coverage as an SDG target demonstrates a commitment to equity, aiming to ensure that everyone who needs health services is able to get them without undue financial hardship. The progressive realization of universal health coverage seeks accelerated gains by disadvantaged populations, thereby narrowing coverage gaps and improving the health of the broader population.8–10 In a similar spirit, equity should be a focus of other health-related sustainable development targets. A multitude of factors underlie successful health inequality monitoring systems: political will, financial resources, popular support, advocacy and technical expertise. Strengthening health inequality monitoring requires the engagement of diverse partners, including ministries of health, national statistical offices and other relevant sectors of governments, United Nations agencies, funding agencies, academic institutions, civil society organizations and the private sector. Building a multidisciplinary network of experts in the area of health inequality monitoring with diverse strengths and perspectives will foster the development of expertise to tackle health inequity. The post-2015 era presents an opportunity for WHO and its partners to strengthen health inequality monitoring across all health topics at global, national and subnational levels. An equity-based approach to improving population health will move the world closer to the ideal of healthy lives and well-being for all.

Monitoring subnational regional inequalities in health: measurement approaches and challenges.

BackgroundMonitoring inequalities based on subnational regions is a useful practice to unmask geographical differences in health, and deploy targeted, equity-oriented interventions. Our objective is to describe, compare and contrast current methods of measuring subnational regional inequality. We apply a selection of summary measures to empirical data from four low- or middle-income countries to highlight the characteristics and overall performance of the different measures.MethodsWe use data from Demographic and Health Surveys conducted in Bangladesh, Egypt, Ghana and Zimbabwe to calculate subnational regional inequality estimates for reproductive, maternal, newborn, and child health services generated from 11 summary measures: pairwise measures included high to low absolute difference, high to low relative difference, and high to low ratio; complex measures included population attributable risk, weighted variance, absolute weighted mean difference from overall mean, index of dissimilarity, Theil index, population attributable risk percentage, coefficient of variation, and relative weighted mean difference from overall mean. Four of these summary measures (high to low absolute difference, high to low ratio, absolute weighted mean difference from overall mean, and relative weighted mean difference from overall mean) were selected to compare their performance in measuring trend over time in inequality for one health indicator.ResultsOverall, the 11 different measures were more remarkable for their similarities than for their differences. Pairwise measures tended to support the same conclusions as complex summary measures–that is, by identifying same best and worst coverage indicators in each country and indicating similar time trends. Complex measures may be useful to illustrate more nuanced results in countries with a great number of subnational regions.ConclusionsWhen pairwise and complex measures lead to the same conclusions about the state of subnational regional inequality, pairwise measures may be sufficient for reporting inequality. In cases where complex measures are required, mean difference from mean measures can be easily communicated to non-technical audiences.

State of inequality in diphtheria-tetanus-pertussis immunisation coverage in low-income and middle-income countries: a multicountry study of household health surveys

Summary Background Immunisation programmes have made substantial contributions to lowering the burden of disease in children, but there is a growing need to ensure that programmes are equity-oriented. We aimed to provide a detailed update about the state of between-country inequality and within-country economic-related inequality in the delivery of three doses of the combined diphtheria, tetanus toxoid, and pertussis-containing vaccine (DTP3), with a special focus on inequalities in high-priority countries. Methods We used data from the latest available Demographic and Health Surveys and Multiple Indicator Cluster Surveys done in 51 low-income and middle-income countries. Data for DTP3 coverage were disaggregated by wealth quintile, and inequality was calculated as difference and ratio measures based on coverage in richest (quintile 5) and poorest (quintile 1) household wealth quintiles. Excess change was calculated for 21 countries with data available at two timepoints spanning a 10 year period. Further analyses were done for six high-priority countries—ie, those with low national immunisation coverage and/or high absolute numbers of unvaccinated children. Significance was determined using 95% CIs. Findings National DTP3 immunisation coverage across the 51 study countries ranged from 32% in Central African Republic to 98% in Jordan. Within countries, the gap in DTP3 immunisation coverage suggested pro-rich inequality, with a difference of 20 percentage points or more between quintiles 1 and 5 for 20 of 51 countries. In Nigeria, Pakistan, Laos, Cameroon, and Central African Republic, the difference between quintiles 1 and 5 exceeded 40 percentage points. In 15 of 21 study countries, an increase over time in national coverage of DTP3 immunisation was realised alongside faster improvements in the poorest quintile than the richest. For example, in Burkina Faso, Cambodia, Gabon, Mali, and Nepal, the absolute increase in coverage was at least 2·0 percentage points per year, with faster improvement in the poorest quintile. Substantial economic-related inequality in DTP3 immunisation coverage was reported in five high-priority study countries (DR Congo, Ethiopia, Indonesia, Nigeria, and Pakistan), but not Uganda. Interpretation Overall, within-country inequalities in DTP3 immunisation persist, but seem to have narrowed over the past 10 years. Monitoring economic-related inequalities in immunisation coverage is warranted to reveal where gaps exist and inform appropriate approaches to reach disadvantaged populations. Funding None.

Data Resource Profile: WHO Health Equity Monitor (HEM)

The Health Equity Monitor (HEM) is one component theme of the Global Health Observatory, the main statistics repository of the World Health Organization (WHO). Launched in 2013, HEM is a collaboration between: the WHO Department of Information, Evidence and Research (Geneva, Switzerland); the WHO Gender, Equity and Human Rights Team (Geneva, Switzerland); and the International Center for Equity in Health (ICEH) based in the Federal University of Pelotas (Pelotas, Brazil). HEM was created as a resource to promote and enable global and national health inequality monitoring, particularly within lowand middle-income countries, where data availability may be limiting. The practice of health inequality monitoring requires health data that are disaggregated by population subgroups (i.e. by dimensions of inequality); to this end, HEM contains high-quality, disaggregated health data that are comparable across countries and over time. Currently, reproductive, maternal, newborn and child health (RMNCH) is the featured topic of HEM, which contains indicators categorized under the following subthemes: reproductive health interventions; maternal health interventions; newborn and child health interventions; RMNCH interventions (composite index); and health outcomes. Data are disaggregated by dimensions of inequality including education, economic status, place of residence, subnational region and child’s sex (where applicable). The two main components of HEM are the data repository and the theme page. The HEM data repository contains re-analysed (secondary) data taken from large-scale, nationally representative household health surveys: Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). The primary data were collected at the household level from women aged 15–49 years. The HEM data repository contains data from nearly 250 DHS and MICS conducted in 94 countries during 1993–2013 (Table 1); almost three-quarters of these countries had surveys available from at least two time points. The data repository covers 34 RMNCH indicators, which are grouped by specified themes. The tables of the repository can be filtered according to indicator, dimension of inequality, country, year and data source. The HEM theme page supports the interpretation and reporting of the data from the repository. It contains a range of resources such as:

Area-Based Units of Analysis for Strengthening Health Inequality Monitoring

Inequalities in health persist worldwide and one of the starting points for remedial action is collecting data that reveal patterns of inequality. Current discussions about the best ways of monitoring health inequalities emphasize disaggregating data by variables such as socioeconomic status, geographical area or sex. The sustainable development goals (SDGs) adopted in 2015 include a call for countries to increase the availability of disaggregated data as part of the aim to strengthen data monitoring and accountability (SDG target 17.18).1 Yet countries have varying capacities for monitoring health inequality. This is due in part to data-related issues such as weaknesses in the health information systems, especially in many lowand middle-income countries; lack of availability or poor quality of health data; and a limited ability to disaggregate data across all health topics within countries.2 Overcoming these challenges in the long term requires substantial investments in the health information infrastructure.3,4 In the short-term, countries need innovative approaches to best harness the potential of their existing data to improve monitoring efforts. Current approaches to health inequality monitoring tend to focus on data collected through household health surveys. These provide two streams of data – about health indicators and about the dimensions of inequality – at the individual or household level. This makes such surveys the main source of data for within-country monitoring of health inequality especially in lowand middleincome countries. However, household health surveys have certain limitations. In many lowand middle-income countries they tend to cover only a narrow set of topics, such as reproductive, maternal, newborn and child health. Other health topics, such as infectious diseases or road traffic injuries, are rarely the focus of household surveys. Household health surveys and their consequent reporting tend to be done outside the regular activities of the health information system, and are resource intensive. Furthermore, data from household surveys may not be representative of small subpopulations of interest, and so cannot be used for certain purposes, such as assessing cross-district inequality, due to too small sample size at that administrative level. By increasing the use of area-based units of analysis, including greater integration of data from other reliable data sources – including vital registration systems, censuses and administrative data – the possibilities for health inequality monitoring may be strengthened and expanded across health topics. In this article we make the case for stratifying data at the level of subnational geographical regions, such as provinces, states or districts. The wider use of an area-based unit of analysis as a complementary way to analyse data at the individual or household level has certain practical advantages that are relevant to lowand middle-income countries as well as high-income countries. First, this approach opens up new possibilities concerning the data that can be used for within-country monitoring, in terms of both health data and data about dimensions of inequality. In some cases, individual or household data on both health and inequality dimensions may be unavailable in one data source; if these data were available from different data sources (e.g. those that collect data at the level of subnational regions), alternative ways of capturing area-level estimates may provide an insight into the extent of inequality. For instance, whereas data about economic status, race, ethnicity, migratory status or disability may not always be collected alongside health data at an individual or household level, they may be available by region. Subnational regions are often aligned with administrative districts, which facilitates the use of administrative-level data. For example, the distribution of health system inputs and outputs (e.g. service delivery) can be compared to health determinants (e.g. district-level poverty, education or employment). Second, since interventions to reduce inequities are likely to be implemented at the local administrative level, regional monitoring of health inequalities may be a useful tool for benchmarking, with implications for resource allocation, planning and evaluation. This is particularly true when a country’s health system administration is decentralized because substantial differences may exist across geographical areas.5 Third, area-based measures may provide a more intuitive understanding of health inequalities and may help to identify possible points for intervention. Geographically defined subpopulations are by nature easy to identify and locate, and health interventions may thus be effectively targeted to disadvantaged regions.6 For example, measuring health inequality on the basis of household wealth using asset-based indices may pose limitations in terms of identifying and reaching disadvantaged subpopulations, as the poorest segment of the population may be located throughout different regions of a country.7,8 Alongside these advantages, some caution is needed when adopting an area-based unit of analysis. There is the risk of committing a so-called ecological fallacy (i.e. making assumptions about individuals based on population-level patterns, or in this case, erroneously drawing conclusions about the health of individuals using area-based data). For instance, if richer districts were found to have a higher prevalence of road traffic injuries it could not be assumed that road traffic injuries are more prevalent among richer individuals. Also, ethical Area-based units of analysis for strengthening health inequality monitoring Ahmad Reza Hosseinpoor & Nicole Bergen