Global Estimation of Neonatal Mortality using a Bayesian Hierarchical Splines Regression Model
Monica Alexander, Leontine Alkema

TL;DR
This paper introduces a Bayesian splines regression model to estimate neonatal mortality rates globally, accounting for trends in overall child mortality and country-specific variations, with results adopted by the UN.
Contribution
The paper presents a novel Bayesian hierarchical splines regression approach for estimating neonatal mortality, incorporating U5MR relationships and country-specific trends.
Findings
Above U5MR of 34, neonatal deaths are 54% of under-five deaths.
A 1% increase in U5MR decreases NMR/U5MR ratio by 0.6%.
Model adopted by UN for global neonatal mortality estimation.
Abstract
In recent years, much of the focus in monitoring child mortality has been on assessing changes in the under-five mortality rate (U5MR). However, as the U5MR decreases, the share of neonatal deaths (within the first month) tends to increase, warranting increased efforts in monitoring this indicator in addition to the U5MR. A Bayesian splines regression model is presented for estimating neonatal mortality rates (NMR) for all countries. In the model, the relationship between NMR and U5MR is assessed and used to inform estimates, and spline regression models are used to capture country-specific trends. As such, the resulting NMR estimates incorporate trends in overall child mortality while also capturing data-driven trends. The model is fitted to 195 countries using the database from the United Nations Interagency Group for Child Mortality Estimation, producing estimates from 1990, or…
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