Abstract
BackgroundInequality in healthcare across population groups in low-income countries is a growing topic of interest in global health. The Lives Saved Tool (LiST), which uses health intervention coverage to model maternal, neonatal, and child health outcomes such as mortality rates, can be used to analyze the impact of within-country inequality.MethodsData from nationally representative household surveys (98 surveys conducted between 1998 and 2014), disaggregated by wealth quintile, were used to create a LiST analysis that models the impact of scaling up health intervention coverage for the entire country from the national average to the rate of the top wealth quintile (richest 20% of the population). Interventions for which household survey data are available were used as proxies for other interventions that are not measured in surveys, based on co-delivery of intervention packages.ResultsFor the 98 countries included in the analysis, 24–32% of child deaths (including 34–47% of neonatal deaths and 16–19% of post-neonatal deaths) could be prevented by scaling up national coverage of key health interventions to the level of the top wealth quintile. On average, the interventions with most unequal coverage rates across wealth quintiles were those related to childbirth in health facilities and to water and sanitation infrastructure; the most equally distributed were those delivered through community-based mass campaigns, such as vaccines, vitamin A supplementation, and bednet distribution.ConclusionsLiST is a powerful tool for exploring the policy and programmatic implications of within-country inequality in low-income, high-mortality-burden countries. An “Equity Tool” app has been developed within the software to make this type of analysis easily accessible to users.
Highlights
Inequality in healthcare across population groups in low-income countries is a growing topic of interest in global health
The analysis presented here used nationally representative household survey data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) for 98 countries [8, 9]
The impact of reducing inequality was even greater for neonatal deaths: There were 2,416,983 neonatal deaths in the baseline scenario and 1,274,459 neonatal deaths in the scale-up scenario in 2017, meaning that 47% of neonatal deaths were prevented
Summary
Inequality in healthcare across population groups in low-income countries is a growing topic of interest in global health. The Lives Saved Tool (LiST), which uses health intervention coverage to model maternal, neonatal, and child health outcomes such as mortality rates, can be used to analyze the impact of within-country inequality. Health outcomes, is often very different for the richest and poorest people in a single country. This disparity can be measured in many different ways, perhaps the simplest of which is the difference in intervention coverage across income groups [2, 3]. An important policy and program planning tool for examining the link between intervention coverage and health outcomes is the Lives Saved Tool (LiST) modeling software [4]. It has been used by groups such as Save the
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