How within-country inequalities and co-coverage may affect LiST estimates of lives saved by scaling up interventions

Cesar G Victora,Aluisio J D Barros,Tanya Malpica-Llanos,Neff Walker

doi:10.1186/1471-2458-13-s3-s24

Cesar G Victora, Aluisio J D Barros

Open Access

https://doi.org/10.1186/1471-2458-13-s3-s24

Copy DOI

Abstract

Lives-saved estimates calculated by LiST include the implicit assumptions that there are no inequalities among different socioeconomic groups, and also that the likelihood of a mother or child receiving a given intervention is independent from the probability of receiving any other interventions. It is reasonable to assume that, as a consequence of these assumptions, LiST estimates may exaggerate the numbers of lives saved in a population, by ignoring the fact that coverage is likely to be lower and mortality higher among the poor than the rich, and also by failing to take into account that coverage with different interventions may be clustered at individual mothers and children – a phenomenon described as co-coverage. We used data from 127 DHS surveys to estimate how much these two assumptions may bias estimates produced by LiST, and conclude that under real-life conditions bias occurred in both directions, with LiST results either over or underestimating the more complex estimates. With few exceptions, bias tended to be small (less than 10% in either direction).

Highlights

The Lives Saved Tool, or LiST is being increasingly used to estimate the impact of changes in intervention coverage on the mortality of children under five years of age [1,2,3]
In this article we explore how two separate issues may affect the validity of national LiST estimates
Co-coverage per se displaced a substantial proportion of the population to the group receiving both interventions, when overall SBA coverage was high. Even under these rather extreme assumptions, bias was consistently less than 10.2%. To our knowledge, this is the first examination of how LiST estimates may be affected by socioeconomic inequalities in coverage and mortality, and by the fact that interventions tend to be clustered in the same children

Summary

Introduction

The Lives Saved Tool, or LiST (http://www.jhsph.edu/ departments/international-health/IIP/list/) is being increasingly used to estimate the impact of changes in intervention coverage on the mortality of children under five years of age [1,2,3]. LiST is a computer-based tool that estimates the impact of scaling up interventions on maternal, neonatal and child mortality. The main purpose of this tool is to promote evidence-based program planning for maternal, neonatal, and child health [4]. The model works by establishing a baseline description of the country or region in terms of demographic information, nutritional status, causes of deaths and levels of risk factors and exposure variables (levels of intrauterine growth restriction, breastfeeding patterns and rates by age, percent of the population exposed to falciparum), and current coverage of over 60 interventions. The model allows users to create various scenarios where they can scale up different intervention packages and estimate the

Methods

Results

Conclusion