Diabetes prevalence in the state of Alabama: identifying the risk factors

Diabetes mellitus, a chronic metabolic disorder characterized by elevated blood glucose, remains a pressing public health challenge in the United States. This study aims to identify spatial clusters of diabetes and examine associated factors at a granular scale using the state of Alabama. Data on diabetes prevalence, socioeconomic, environmental and behavioural risk factors were extracted at the census tract level from the CDC PLACES Project. Moran's I and Getis-Ord Gi* were first used to assess the spatial autocorrelation and spatial clusters of diabetes, respectively. Due to the existence of spatial autocorrelation (Moran's I = 0.275, p<0.001) of diabetes prevalence, three additional spatial statistical techniques, including the Spatial Lag Model (SLM), the Spatial Error Model (SEM) and Geographically Weighted Regression (GWR), were used to examine its associated factors while detecting the local spatial variations. Several significant clusters of high diabetes prevalence were found in most counties in the middle, known as the Black Belt. The GWR model (R2 = 0.921 & AICc = 2414.0) outperformed SLM and SEM and was therefore used to explore the strong spatial heterogeneity in the associated risk factors. Statistically significant predictors identified were smoking, drinking, obesity, poverty, and age 65+. These localized findings enable governments to develop interventions targeting risk factors to address diabetes prevalence in the state of Alabama.