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Background: Despite important progress, the burden of under-5 mortality remains unacceptably high, with an estimated 5.3 million deaths in 2018. Lack of access to health care is a major risk factor for under-5 mortality, and distance to health care facilities has been shown to be associated with less access to care in multiple contexts, but few such studies have used a counterfactual approach to produce causal estimates.Methods: We combined retrospective reports on 18,714 births between 1980 and 1998 from the 2000 Malawi Demographic and Health Survey with a 1998 health facility census that includes the date of construction for each facility, including 335 maternity or maternity/dispensary facilities built in rural areas between 1980 and 1998. We estimated associations between distance to nearest health facility and (i) under-5 mortality, using Cox proportional hazards models, and (ii) maternal health care utilization (antenatal visits prior to delivery, place of delivery, receiving skilled assistance during delivery, and receiving a check-up following delivery), using linear probability models. We also estimated the causal effect of reducing the distance to nearest facility on those outcomes, using a two-way fixed effects approach.Findings: We found that greater distance was associated with higher mortality (hazard ratio 1.007 for one additional kilometer [95%CI 1.001 to 1.014]) and lower health care utilization (for one additional kilometer: 1.2 percentage point (pp) increase in homebirth [95%CI 0.8 to 1.5]; 0.8 pp. decrease in at least three antenatal visits [95% CI - 1.4 to - 0.2]; 1.2 pp. decrease in skilled assistance during delivery [95%CI - 1.6 to - 0.8]). However, we found no effects of a decrease in distance to the nearest health facility on the hazard of death before age 5 years, nor on antenatal visits prior to delivery, place of delivery, or receiving skilled assistance during delivery. We also found that reductions in distance decrease the probability that a woman receives a check-up following delivery (2.4 pp. decrease for a 1 km decrease [95%CI 0.004 to 0.044]).Conclusion: Reducing under-5 mortality and increasing utilization of care in rural Malawi and similar settings may require more than the construction of new health infrastructure. Importantly, the effects estimated here likely depend on the quality of health care, the availability of transportation, the demand for health services, and the underlying causes of mortality, among other factors. [ABSTRACT FROM AUTHOR]

Background: In 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., Methods: We 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., Results: We 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., Conclusions: Indirect 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.