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Identifying and classifying poor and rich groups in cities depends on several factors. Using data from available nationally representative surveys from 38 sub-Saharan African countries, we aimed to identify, through different poverty classifications, the best classification in urban and large city contexts. Additionally, we characterized the poor and rich groups in terms of living standards and schooling. We relied on absolute and relative measures in the identification process. For absolute ones, we selected people living below the poverty line, socioeconomic deprivation status and the UN-Habitat slum definition. We used different cut-off points for relative measures based on wealth distribution: 30%, 40%, 50%, and 60%. We analyzed all these measures according to the absence of electricity, improved drinking water and sanitation facilities, the proportion of children out-of-school, and any household member aged 10 or more with less than 6 years of education. We used the sample size, the gap between the poorest and richest groups, and the observed agreement between absolute and relative measures to identify the best measure. The best classification was based on 40% of the wealth since it has good discriminatory power between groups and median observed agreement higher than 60% in all selected cities. Using this measure, the median prevalence of absence of improved sanitation facilities was 82% among the poorer, and this indicator presented the highest inequalities. Educational indicators presented the lower prevalence and inequalities. Luanda, Ouagadougou, and N'Djaména were considered the worst performers, while Lagos, Douala, and Nairobi were the best performers. The higher the human development index, the lower the observed inequalities. When analyzing cities using nationally representative surveys, we recommend using the relative measure of 40% of wealth to characterize the poorest group. This classification presented large gaps in the selected outcomes and good agreement with absolute measures.

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Rapid urbanization is likely to be associated with suboptimal access to essential health services. This is especially true in cities from sub-Saharan Africa (SSA), where urbanization is outpacing improvements in infrastructure. We assessed the current situation in regard to several markers of maternal, newborn, and child health, including indicators of coverage of health interventions (demand for family planning satisfied with modern methods, at least four antenatal care visits (ANC4+), institutional birth, and three doses of DPT vaccine[diphtheria, pertussis and tetanus]) and health status (stunting in children under 5 years, neonatal and under-5 mortality rates) among the poor and non-poor in the most populous cities from 38 SSA countries. We analyzed 136 population-based surveys (year range 2000-2019), contrasting the poorest 40% of households (referred to as poor) with the richest 60% (non-poor). Coverage in the most recent survey was higher for the city non-poor compared to the poor for all interventions in virtually all cities, with the largest median gap observed for ANC4+ (13.5 percentage points higher for the non-poor). Stunting, neonatal, and under-5 mortality rates were higher among the poor (7.6 percentage points, 21.2 and 10.3 deaths per 1000 live births, respectively). The gaps in coverage between the two groups were reducing, except for ANC4, with similar median average annual rate of change in both groups. Similar rates of change were also observed for stunting and the mortality indicators. Continuation of these positive trends is needed to eliminate inequalities in essential health services and child survival in SSA cities.

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BACKGROUND: Within-country inequalities in birth registration coverage (BRC) have been documented according to wealth, place of residence and other household characteristics. We investigated whether sex of the head of household was associated with BRC. METHODS: Using data from nationally-representative surveys (Demographic and Health Survey or Multiple Indicator Cluster Survey) from 93 low and middle-income countries (LMICs) carried out in 2010 or later, we developed a typology including three main types of households: male-headed (MHH) and female-led with or without an adult male resident. Using Poisson regression, we compared BRC for children aged less than 12 months living the three types of households within each country, and then pooled results for all countries. Analyses were also adjusted for household wealth quintiles, maternal education and urban-rural residence. RESULTS: BRC ranged from 2.2% Ethiopia to 100% in Thailand (median 79%) while the proportion of MHH ranged from 52.1% in Ukraine to 98.3% in Afghanistan (median 72.9%). In most countries the proportion of poor families was highest in FHH (no male) and lowest in FHH (any male), with MHH occupying an intermediate position. Of the 93 countries, in the adjusted analyses, FHH (no male) had significantly higher BRC than MHH in 13 countries, while in eight countries the opposite trend was observed. The pooled analyses showed t BRC ratios of 1.01 (95% CI: 1.00; 1.01) for FHH (any male) relative to MHH, and also 1.01 (95% CI: 1.00; 1.01) for FHH (no male) relative to MHH. These analyses also showed a high degree of heterogeneity among countries. CONCLUSION: Sex of the head of household was not consistently associated with BRC in the pooled analyses but noteworthy differences in different directions were found in specific countries. Formal and informal benefits to FHH (no male), as well as women's ability to allocate household resources to their children in FHH, may explain why this vulnerable group has managed to offset a potential disadvantage to their children.

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Studies of inequalities in child health have given limited attention to household structure and headship. The few existing reports on child outcomes in male and female-headed households have produced inconsistent results. The aim of our analyses was to provide a global view of the influence of sex of the household head on child health in cross-sectional surveys from up to 95 LMICs. Studied outcomes were full immunization coverage in children aged 12-23 months and stunting prevalence in under-five children. We analyzed the most recent nationally-representative surveys for each country (since 2010) with available data. After initial exploratory analyses, we focused on three types of households: a) male-headed household (MHH) comprised 73.1% of all households in the pooled analyses; b) female Headed Household (FHH) with at least one adult male represented 9.8% of households; and c) FHH without an adult male accounted for 15.0% of households. Our analyses also included the following covariates: wealth index, education of the child's mother and urban/rural residence. Meta-analytic approaches were used to calculate pooled effects across the countries with MHH as the reference category. Regarding full immunization, the pooled prevalence ratio for FHH (any male) was 0.99 (0.97; 1.01) and that for FHH (no male) was 0.99 (0.97; 1.02). For stunting prevalence, the pooled prevalence ratio for FHH (any male) was 1.00 (0.98; 1.02) and for FHH (no male) was 1.00 (0.98; 1.02). Adjustment for covariates did not lead to any noteworthy change in the results. No particular patterns were found among different world regions. A few countries presented significant inequalities with different directions of association, indicating the diversity of FHH and how complex the meaning and measurement of household headship may be. Further research is warranted to understand context, examine mediating factors, and exploring alternative definitions of household headship in countries with some association.

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OBJECTIVE: To investigate whether sub-Saharan African countries have succeeded in reducing wealth-related inequalities in the coverage of reproductive, maternal, newborn and child health interventions. METHODS: We analysed survey data from 36 countries, grouped into Central, East, Southern and West Africa subregions, in which at least two surveys had been conducted since 1995. We calculated the composite coverage index, a function of essential maternal and child health intervention parameters. We adopted the wealth index, divided into quintiles from poorest to wealthiest, to investigate wealth-related inequalities in coverage. We quantified trends with time by calculating average annual change in index using a least-squares weighted regression. We calculated population attributable risk to measure the contribution of wealth to the coverage index. FINDINGS: We noted large differences between the four regions, with a median composite coverage index ranging from 50.8% for West Africa to 75.3% for Southern Africa. Wealth-related inequalities were prevalent in all subregions, and were highest for West Africa and lowest for Southern Africa. Absolute income was not a predictor of coverage, as we observed a higher coverage in Southern (around 70%) compared with Central and West (around 40%) subregions for the same income. Wealth-related inequalities in coverage were reduced by the greatest amount in Southern Africa, and we found no evidence of inequality reduction in Central Africa. CONCLUSION: Our data show that most countries in sub-Saharan Africa have succeeded in reducing wealth-related inequalities in the coverage of essential health services, even in the presence of conflict, economic hardship or political instability.

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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.

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The Sustainable Development Goal (SDG) 17.18 recommends efforts to increase the availability of data disaggregated by income, gender, age, race, ethnicity, migratory status, disability and geographic location in developing countries. Surveys will continue to be the leading data source for disaggregated data for most dimensions of inequality. We discuss potential advances in the disaggregation of data from national surveys, with a focus on the coverage of reproductive, maternal, newborn and child health indicators (RMNCH). Even though the Millennium Development Goals were focused on national-level progress, monitoring initiatives such as Countdown to 2015 reported on progress in RMNCH coverage according to wealth quintiles, sex of the child, women's education and age, urban/rural residence and subnational geographic regions. We describe how the granularity of equity analyses may be increased by including additional stratification variables such as wealth deciles, estimated absolute income, ethnicity, migratory status and disability. We also provide examples of analyses of intersectionality between wealth and urban/rural residence (also known as double stratification), sex of the child and age of the woman. Based on these examples, we describe the advantages and limitations of stratified analyses of survey data, including sample size issues and lack of information on the necessary variables in some surveys. We conclude by recommending that, whenever possible, stratified analyses should go beyond the traditional breakdowns by wealth quintiles, sex and residence, to also incorporate the wider dimensions of inequality. Greater granularity of equity analyses will contribute to identify subgroups of women and children who are being left behind and monitor the impact of efforts to reduce inequalities in order to achieve the health SDGs.

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OBJECTIVE: To estimate HIV incidence in the general population in countries where there have been two recent household-based HIV prevalence surveys (the Dominican Republic, Mali, Niger, Tanzania, and Zambia). METHODS: We applied a validated method to estimate HIV incidence using HIV prevalence measurement in two surveys. RESULTS: We estimate incidence among men and women aged 15-44 years to be: 0.5/1000 person-years at risk in the Dominican Republic 2002-2007, 1.1/1000 in Mali 2001-2006, 0.6/1000 in Niger 2002-2006, 3.4/1000 in Tanzania 2004-2008, and 11.2/1000 in Zambia 2002-2007. The groups most at risk in these epidemics are typically 15-24-year-old women and 25-39-year-old men. Incidence appears to have declined in recent years in all countries, but only significantly among men in the Dominican Republic and Tanzania and women in Zambia. CONCLUSION: Using prevalence measurements to estimate incidence reveals the current level and age distribution of new infections and the trajectory of the HIV epidemic. This information is more useful than prevalence data alone and should be used to help determine priorities for interventions.

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