A Closer Look at Rural-Urban Health Disparities: Associations Between Obesity and Rurality Vary by Geospatial and Sociodemographic Factors

Background: Obesity affects over one-third of older adults in the United States. Both aging and obesity contribute to an increased risk for chronic disease, early mortality, and additional health care utilization. Obesity rates are higher in rural areas than in urban areas, although findings are mixed. The objectives of this study are to assess potential nonlinearity in the association between rurality and obesity, and to evaluate the potential for socioeconomic status and geographic area to moderate the associations between rurality and obesity. Methods: Using a representative sample of adults aged 65 and above from the Behavioral Risk Factor Surveillance System, obesity (BMI ≥ 30 kg/m2) was modeled against the primary exposure of rural-urban status, as measured by the Index of Relative Rurality. Binary logistic regression models were used to estimate the odds of obesity by rurality both as a continuous variable and by decile of rurality. Models were then stratified by per-capita income and state to assess potential moderation by these factors. Results: The prevalence of obesity in older adults was highest in intermediate rurality areas (OR in rurality decile #5 1.134, 95% CI: 1.086-1.184) and lowest in the most rural and most urban areas. Obesity was highest in low- and middle-income areas, regardless of rural-urban status. In high-income areas, obesity among older adults was highest in areas of intermediate rurality and lowest in the most rural areas (OR 0.726, 95% CI: 0.606-0.870) and more urban areas, showing a J-shaped association. There were substantial differences in the associations between rurality and obesity in older adults among states. Conclusion: Associations between rurality and obesity varied by degree of rurality, socioeconomic status, and geography. Therefore, traditional “one-size-fits-all” approaches to reducing rural-urban health disparities in older adults may be more effective if tailored to the area-specific rural-urban gradients in health