Leonardo Zanini Ferreira

Background: Demand for family planning satisfied (DFPS) is one of the core indicators for monitoring reproductive health. However, it involves a series of questions that are not always available in national health surveys, hindering the international comparability and tracking progress in those settings. This study updates an alternative method for calculating DFPS based on contraceptive prevalence (CPR) when direct estimation is not feasible. Methods: Based on survey data from 1,099 subnational regions across 103 countries, we fitted least-squares regression models that predicts DFPS for both any contraceptive methods and modern methods. A fractional polynomial approach was employed to account for non-linear relationships. Model performance was assessed using a 5-fold cross-validation strategy, evaluating bias, mean absolute error and correlation. Results: The models, using CPR and the difference between total and modern CPR as predictors, were able to explain over 97% of the variability of DFPS by any and modern contraceptives. Bias and the magnitude of the errors were around 0.1 in the cross-validated sample. Validating the results for other inequality dimensions beyond subnational region yielded even better metrics. Conclusions: The predicted estimates proved to be a good approximation for DFPS in circumstances where direct estimation is not possible. This study confirms that modeling DFPS through contraceptive prevalence remains valid and extends its applicability to DFPS based on modern methods. Furthermore, the estimates were robust for varying inequality dimensions, allowing equity analyses to be performed when appropriate.

Background: The composite coverage index (CCI) provides an integrated perspective towards universal health coverage in the context of reproductive, maternal, newborn and child health. Given the sample design of most household surveys does not provide coverage estimates below the first administrative level, approaches for achieving more granular estimates are needed. We used a model-based geostatistical approach to estimate the CCI at multiple resolutions in Peru. Methods: We generated estimates for the eight indicators on which the CCI is based for the departments, provinces, and areas of 5 × 5 km of Peru using data from two national household surveys carried out in 2018 and 2019 plus geospatial covariates. Bayesian geostatistical models were fit using the INLA-SPDE approach. We assessed model fit using cross-validation at the survey cluster level and by comparing modelled and direct survey estimates at the department-level. Results: CCI coverage in the provinces along the coast was consistently higher than in the remainder of the country. Jungle areas in the north and east presented the lowest coverage levels and the largest gaps between and within provinces. The greatest inequalities were found, unsurprisingly, in the largest provinces where populations are scattered in jungle territory and are difficult to reach. Conclusions: Our study highlighted provinces with high levels of inequality in CCI coverage indicating areas, mostly low-populated jungle areas, where more attention is needed. We also uncovered other areas, such as the border with Bolivia, where coverage is lower than the coastal provinces and should receive increased efforts. More generally, our results make the case for high-resolution estimates to unveil geographic inequities otherwise hidden by the usual levels of survey representativeness.

Background: Geospatial approaches are increasingly used to produce fine spatial scale estimates of reproductive, maternal, newborn and child health (RMNCH) indicators in low- and middle-income countries (LMICs). This study aims to describe important methodological aspects and specificities of geospatial approaches applied to RMNCH coverage and impact outcomes and enable non-specialist readers to critically evaluate and interpret these studies. Methods: Two independent searches were carried out using Medline, Web of Science, Scopus, SCIELO and LILACS electronic databases. Studies based on survey data using geospatial approaches on RMNCH in LMICs were considered eligible. Studies whose outcomes were not measures of occurrence were excluded. Results: We identified 82 studies focused on over 30 different RMNCH outcomes. Bayesian hierarchical models were the predominant modeling approach found in 62 studies. 5 × 5 km estimates were the most common resolution and the main source of information was Demographic and Health Surveys. Model validation was under reported, with the out-of-sample method being reported in only 56% of the studies and 13% of the studies did not present a single validation metric. Uncertainty assessment and reporting lacked standardization, and more than a quarter of the studies failed to report any uncertainty measure. Conclusions: The field of geospatial estimation focused on RMNCH outcomes is clearly expanding. However, despite the adoption of a standardized conceptual modeling framework for generating finer spatial scale estimates, methodological aspects such as model validation and uncertainty demand further attention as they are both essential in assisting the reader to evaluate the estimates that are being presented.

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.

Introduction Wealth-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. Methods 83 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. Results The 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. Conclusion There 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.

Background: With the adoption of the Sustainable Development Goals (SDGs), there is a renewed commitment of tackling the varied challenges of undernutrition, particularly stunting (SDG 2.2). Health equity is also a priority in the SDG agenda and there is an urgent need for disaggregated analyses to identify disadvantaged subgroups. We compared time trends in socioeconomic inequalities obtained through stratification by wealth quintiles and deciles for stunting prevalence. Methods: We used 37 representative Demographic and Health Surveys and Multiple Indicator Cluster surveys from nine Latin American and Caribbean (LAC) countries conducted between 1996 and 2016. Stunting in children under-5 years was assessed according to the 2006 WHO Child Growth Standards and stratified by wealth quintiles and deciles. Within-country socioeconomic inequalities were measured through concentration index (CIX) and slope index of inequality (SII). We used variance-weighted least squares regression to estimate annual changes. Results: Eight out of nine countries showed a statistical evidence of reduction in stunting prevalence over time. Differences between extreme deciles were larger than between quintiles in most of countries and at every point in time. However, when using summary measures of inequality, there were no differences in the estimates of SII with the use of deciles and quintiles. In absolute terms, there was a reduction in socioeconomic inequalities in Peru, Honduras, Dominican Republic, Belize, Suriname and Colombia. In relative terms, there was an increase in socioeconomic inequalities in Peru, Bolivia, Haiti, Honduras and Guatemala. Conclusions: LAC countries have made substantial progress in terms of reducing stunting,. Nevertheless, renewed actions are needed to improve equity. Particularly in those countries were absolute and relative inequalities did not change over time such Bolivia and Guatemala. Finer breakdowns in wealth distribution are expected to elucidate more differences between subgroups; however, this approach is relevant to cast light on those subgroups that are still lagging behind within populations and inform equity-oriented health programs and practices.

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.

Birth registration, and the possession of a birth certificate as proof of registration, has long been recognized as a fundamental human right. Data from a functioning civil registration and vital statistics (CRVS) system allows governments to benefit from accurate and universal data on birth and death rates. However, access to birth certificates remains challenging and unequal in many low and middle-income countries. This paper examines wealth, urban/rural and gender inequalities in birth certificate coverage. We analyzed nationally representative household surveys from 94 countries between 2000 and 2014 using Demographic Health Surveys and Multiple Indicator Cluster Surveys. Birth certificate coverage among children under five was examined at the national and regional level. Absolute measures of inequality were used to measure inequalities in birth certificate coverage by wealth quintile, urban/rural residence and sex of the child. Over four million children were included in the analysis. Birth certificate coverage was over 90% in 29 countries and below 50% in 36 countries, indicating that more than half the children under five surveyed in these countries did not have a birth certificate. Eastern & Southern Africa had the lowest average birth certificate coverage (26.9%) with important variability among countries. Significant wealth inequalities in birth certificate coverage were observed in 74 countries and in most UNICEF regions, and urban/rural inequalities were present in 60 countries. Differences in birth certificate coverage between girls and boys tended to be small. We show that wealth and urban/rural inequalities in birth certificate coverage persist in most low and middle income countries, including countries where national birth certificate coverage is between 60 and 80%. Weak CRVS systems, particularly in South Asia and Africa lead rural and poor children to be systematically excluded from the benefits tied to a birth certificate, and prevent these children from being counted in national health data. Greater funding and attention is needed to strengthen CRVS systems and equity analyses should inform such efforts, especially as data needs for the Sustainable Development Goals expand. Monitoring disaggregated data on birth certificate coverage is essential to reducing inequalities in who is counted and registered. Strengthening CRVS systems can enable a child’s right to identity, improve health data and promote equity.