TY - JOUR
T1 - How within-country inequalities and co-coverage may affect LiST estimates of lives saved by scaling up interventions
AU - Victora, Cesar G.
AU - Barros, Aluisio J.D.
AU - Malpica-Llanos, Tanya
AU - Walker, Neff
N1 - Funding Information:
The publication costs for this supplement were funded by a grant from the Bill & Melinda Gates Foundation to the US Fund for UNICEF (grant 43386 to “Promote evidence-based decision making in designing maternal, neonatal, and child health interventions in low-and middle-income countries”). The Supplement Editor is the principal investigator and lead in the development of the Lives Saved Tool (LiST), supported by grant 43386. He declares that he has no competing interests. This article has been published as part of BMC Public Health Volume 13 Supplement 3, 2013: The Lives Saved Tool in 2013: new capabilities and applications. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcpublichealth/supplements/13/S3.
Funding Information:
This work was supported in part by a grant from the Bill & Melinda Gates Foundation to support the work of the Child Health Epidemiology Reference Group.
PY - 2013
Y1 - 2013
N2 - 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).
AB - 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).
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U2 - 10.1186/1471-2458-13-S3-S24
DO - 10.1186/1471-2458-13-S3-S24
M3 - Review article
C2 - 24564259
AN - SCOPUS:84884307218
SN - 1471-2458
VL - 13
JO - BMC public health
JF - BMC public health
IS - SUPPL.3
M1 - S24
ER -