2023/11/21 by Wala Draidi Areed, Aiden Price, Areed, Wala Draidi +7
Decision Sciences · Economics, Econometrics and Finance · Social Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #Health disparities and outcomes #Spatial and Panel Data Analysis #demographic modeling and climate adaptation
paper · pdf · doi:10.48550/arxiv.2311.12349
openalex publication_date 2023/11/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the field of population health research, understanding the similarities between geographical areas and quantifying their shared effects on health outcomes is crucial. In this paper, we synthesise a number of existing methods to create a new approach that specifically addresses this goal. The approach is called a Bayesian spatial Dirichlet process clustered heterogeneous regression model. This non-parametric framework allows for inference on the number of clusters and the clustering configurations, while simultaneously estimating the parameters for each cluster. We demonstrate the efficacy of the proposed algorithm using simulated data and further apply it to analyse influential factors affecting children's health development domains in Queensland. The study provides valuable insights into the contributions of regional similarities in education and demographics to health outcomes, aiding targeted interventions and policy design.