Spatial bayesian modeling of diabetes mellitus (DM) risk in the United States.

ABSTRACT:

Background

Spatial targeting of disease burden has long been instrumental to achieving optimal health resource allocation in the United States (US). Building on a previous study, the current research examined the spatial variation and hotspots of Diabetes Mellitus (DM) risk across the US.

Methods

The outcome variable was the number of DM cases per county acquired from the CDC-Behavioral Risk Factor Surveillance System (BRFSS). Air pollution data (PM_2.5 & NO₂) were acquired from the Socioeconomic Data and Application Centre (SEDAC), obesity cases from the BRFSS, and maximum temperature data from WorldClim. The percentage of alcoholics and smokers was obtained from the County Health Rankings & Roadmaps Association. We model the spatial pattern of DM risks

SUBMITTER: Asori M 

PROVIDER: S-EPMC12754990 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature