Bayesian analysis of population health data

TL;DR

This paper demonstrates the application of Bayesian hierarchical models to analyze large-scale population health data, incorporating spatial, demographic, and socioeconomic factors to better understand disease incidence and timing.

Contribution

It introduces Bayesian hierarchical models for population health analysis, applying them to a large dataset with spatial, demographic, and socioeconomic variables.

Findings

Bayesian models effectively capture spatial and individual variation.

Spatial effects significantly influence stroke risk and timing.

Models provide insights for public health decision-making.

Abstract

The analysis of population-wide datasets can provide insight on the health status of large populations so that public health officials can make data-driven decisions. The analysis of such datasets often requires highly parameterized models with different types of fixed and randoms effects to account for risk factors, spatial and temporal variations, multilevel effects and other sources on uncertainty. To illustrate the potential of Bayesian hierarchical models, a dataset of about 500 000 inhabitants released by the Polish National Health Fund containing information about ischemic stroke incidence for a 2-year period is analyzed using different types of models. Spatial logistic regression and survival models are considered for analyzing the individual probabilities of stroke and the times to the occurrence of an ischemic stroke event. Demographic and socioeconomic variables as well as drug prescription information are available at an individual level. Spatial variation is considered by means of region-level random effects.