Global estimation of child mortality using a Bayesian B-spline Bias-reduction model

TL;DR

This paper introduces a Bayesian B-spline bias-reduction model to estimate global under-five mortality rates, effectively handling data limitations and biases in countries lacking reliable vital registration systems.

Contribution

The paper presents a novel Bayesian penalized B-spline regression model with bias correction and improved extrapolation techniques for estimating child mortality worldwide.

Findings

Model captures U5MR trends flexibly over time.

Provides credible intervals reflecting data biases.

Performs well in out-of-sample validation.

Abstract

Estimates of the under-five mortality rate (U5MR) are used to track progress in reducing child mortality and to evaluate countries' performance related to Millennium Development Goal 4. However, for the great majority of developing countries without well-functioning vital registration systems, estimating the U5MR is challenging due to limited data availability and data quality issues. We describe a Bayesian penalized B-spline regression model for assessing levels and trends in the U5MR for all countries in the world, whereby biases in data series are estimated through the inclusion of a multilevel model to improve upon the limitations of current methods. B-spline smoothing parameters are also estimated through a multilevel model. Improved spline extrapolations are obtained through logarithmic pooling of the posterior predictive distribution of country-specific changes in spline coefficients with observed changes on the global level. The proposed model is able to flexibly capture changes in U5MR over time, gives point estimates and credible intervals reflecting potential biases in data series and performs reasonably well in out-of-sample validation exercises. It has been accepted by the United Nations Inter-agency Group for Child Mortality Estimation to generate estimates for all member countries.