Identifying community food environment profiles and their associations with adult obesity in the United States: A cluster-based multilevel analysis

OBJECTIVES: To identify county-level food environment typologies across the United States and examine their association with adult obesity prevalence using cluster analysis and multilevel modeling. STUDY DESIGN: Cross-sectional ecological study. METHODS: A k-means clustering approach was applied to 3143 U S. counties using 11 standardized indicators representing food access, food retail availability, and socioeconomic conditions. Adult obesity prevalence was analyzed using multilevel linear regression models with counties nested within states, adjusting for demographic and economic covariates. RESULTS: Four food environment profiles were identified: (1) High-Density, Mixed-Quality Food Environment, (2) Food Desert: Low Access, High Poverty, (3) Food Oasis: Balanced Access, High Income, and (4) Mixed Environment, Underserved. Predicted adult obesity prevalence was significantly higher in Clusters - High-Density, Mixed-Quality Food Environment and Food Desert: Low Access, High Poverty (approximately 36.2%) and lowest in Food Oasis: Balanced Access, High Income (34.5%) after adjustment for covariates (p < 0.001). Food environment profiles also differed substantially by region, racial and ethnic composition, and poverty levels. CONCLUSIONS: Distinct food environment typologies were associated with differences in adult obesity prevalence across U.S. counties. These findings highlight the importance of considering multidimensional structural and socioeconomic factors when addressing geographic disparities in obesity and support the need for place-based public health interventions.