Semiparametric Bayesian Density Estimation with Disparate Data Sources: A Meta-Analysis of Global Childhood Undernutrition

TL;DR

This paper introduces a Bayesian finite mixture model that combines individual and summary data to estimate childhood undernutrition distributions across 141 countries over time, addressing data incompleteness and reporting differences.

Contribution

It develops a hierarchical Bayesian model that integrates diverse data sources for dynamic, country-specific estimates of undernutrition distributions, including tail behaviors.

Findings

Effective estimation of childhood undernutrition distributions across countries.

Model captures nonlinear temporal changes and regional variations.

Addresses data gaps and reporting inconsistencies in global health data.

Abstract

Undernutrition, resulting in restricted growth, and quantified here using height-for-age z-scores, is an important contributor to childhood morbidity and mortality. Since all levels of mild, moderate and severe undernutrition are of clinical and public health importance, it is of interest to estimate the shape of the z-scores' distributions. We present a finite normal mixture model that uses data on 4.3 million children to make annual country-specific estimates of these distributions for under-5-year-old children in the world's 141 low- and middle-income countries between 1985 and 2011. We incorporate both individual-level data when available, as well as aggregated summary statistics from studies whose individual-level data could not be obtained. We place a hierarchical Bayesian probit stick-breaking model on the mixture weights. The model allows for nonlinear changes in time, and it borrows strength in time, in covariates, and within and across regional country clusters to make estimates where data are uncertain, sparse, or missing. This work addresses three important problems that often arise in the fields of public health surveillance and global health monitoring. First, data are always incomplete. Second, different data sources commonly use different reporting metrics. Last, distributions, and especially their tails, are often of substantive interest.