Bayesian Estimation of Population-Level Trends in Measures of Health Status

TL;DR

This paper introduces a Bayesian model that combines diverse health data sources to estimate global, regional, and country-specific health trends over time, accounting for data gaps and uncertainties.

Contribution

The novel Bayesian approach systematically integrates fragmentary health data, modeling nonlinear trends and borrowing strength across regions and age groups for improved estimates.

Findings

Decreasing blood pressure in high-income regions.

Increasing blood pressure in many lower-income regions.

Quantified uncertainty in health trend estimates.

Abstract

Improving health worldwide will require rigorous quantification of population-level trends in health status. However, global-level surveys are not available, forcing researchers to rely on fragmentary country-specific data of varying quality. We present a Bayesian model that systematically combines disparate data to make country-, region- and global-level estimates of time trends in important health indicators. The model allows for time and age nonlinearity, and it borrows strength in time, age, covariates, and within and across regional country clusters to make estimates where data are sparse. The Bayesian approach allows us to account for uncertainty from the various aspects of missingness as well as sampling and parameter uncertainty. MCMC sampling allows for inference in a high-dimensional, constrained parameter space, while providing posterior draws that allow straightforward inference on the wide variety of functionals of interest. Here we use blood pressure as an example health metric. High blood pressure is the leading risk factor for cardiovascular disease, the leading cause of death worldwide. The results highlight a risk transition, with decreasing blood pressure in high-income regions and increasing levels in many lower-income regions.