Abstract
BackgroundIn populations that lack vital registration systems, under-5 mortality (U5M) is commonly estimated using survey-based approaches, including indirect methods. One assumption of indirect methods is that a mother’s survival and her children’s survival are not correlated, but in populations affected by HIV/AIDS this assumption is violated, and thus indirect estimates are biased. Our goal was to estimate the magnitude of the bias, and to create a predictive model to correct it.MethodsWe used an individual-level, discrete time-step simulation model to measure how the bias in indirect estimates of U5M changes under various fertility rates, mortality rates, HIV/AIDS rates, and levels of antiretroviral therapy. We simulated 4480 populations in total and measured the amount of bias in U5M due to HIV/AIDS. We also developed a generalized linear model via penalized maximum likelihood to correct this bias.ResultsWe found that indirect methods can underestimate U5M by 0–41% in populations with HIV prevalence of 0–40%. Applying our model to 2010 survey data from Malawi and Tanzania, we show that indirect methods would underestimate U5M by up to 7.7% in those countries at that time. Our best fitting model to correct bias in U5M had a root median square error of 0.0012.ConclusionsIndirect estimates of U5M can be significantly biased in populations affected by HIV/AIDS. Our predictive model allows scholars and practitioners to correct that bias using commonly measured population characteristics. Policies and programs based on indirect estimates of U5M in populations with generalized HIV epidemics may need to be reevaluated after accounting for estimation bias.
Highlights
In populations that lack vital registration systems, under-5 mortality (U5M) is commonly estimated using survey-based approaches, including indirect methods
Empirical studies demonstrate that the survival of a mother and that of her children are highly correlated in populations affected by Human immunodeficiency virus (HIV)/Acquired immune deficiency syndrome (AIDS) [14]
Based on the results of the simulations, we developed a parsimonious predictive model of bias as a function of a subset of these variables, and we used the predictive model to adjust estimates based on empirical data from Malawi and Tanzania
Summary
In populations that lack vital registration systems, under-5 mortality (U5M) is commonly estimated using survey-based approaches, including indirect methods. 1990 U5M levels by 2015, and SDG3 calls for a reduction of U5M to at least 25 per 1000 live births by 2030. Most child deaths occur in countries that lack or have incomplete vital registration systems. In such populations, survey- and census-based methods for mortality rate estimation are commonly used. Survey-based methods include direct and indirect estimation. The former requires the collection of a full birth history, that is, date of birth and age at death, if appropriate, for every live birth a woman has had.
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