Background Obesity contributes substantially to chronic disease multimorbidity in the U.S., yet no composite metric exists to quantify the cumulative burden of obesity-related chronic diseases at the community level. We aimed to develop and validate the obesity-related chronic disease index (ORCDi) and examine its spatial distribution and associations with social vulnerability domains. Methods We used Centers for Disease Control and Prevention (CDC) PLACES 2024 estimates for nine obesity-related conditions across 83,522 U.S. census-tracts. Principal component analysis was applied to derive the ORCDi, and chronic disease properties were assessed using Cronbach's α, Guttman's λ6, item–total correlations, Bartlett's test, and Kaiser-Meyer-Olkin (KMO) adequacy. Convergent validity was evaluated using regression models against social vulnerability indices and rural-urban commuting area (RUCA). Spatial dependence and clustering or hotspots were assessed using Global Moran's I and Local Indicators of Spatial Association (LISA). Findings The ORCDi demonstrated internal consistency (Cronbach's α = 0.94; Guttman's λ6 = 0.97) and a coherent factor structure (KMO = 0.81; Bartlett's χ 2 = 1068,915; p < 0.0001). The first principal component accounted for 70.52% of the total variance and exhibited significant factor loadings for chronic obstructive pulmonary disease (0.94), stroke (0.94), high blood pressure (0.93), coronary heart disease (0.91), and diabetes (0.89), supporting a shared multimorbidity dimension underlying the index. Convergent validity was supported by moderate associations with socioeconomic vulnerability (β = 15.02; R 2 = 0.19), age and disability (β = 15.74; R 2 = 0.21), and rurality (β = 1.42; R 2 = 0.14). Spatial analysis demonstrated notable geographic clustering (Moran's I=0.71; p < 0.0001), with high–high ORCDi concentrations in the Deep South, Mississippi Delta, Appalachia, and parts of Texas and Oklahoma. Interpretation The ORCDi is a composite measure of obesity-related chronic disease burden at the community level. The index demonstrated significant internal consistency, substantial geographic variation, and meaningful associations with socioeconomic disadvantage and rural health disparities. Future studies should evaluate its performance in independent datasets and assess its utility for public health surveillance and resource allocation. Funding The study was not supported by external funding.
Ahmmad et al. (Wed,) studied this question.