Association Between Various Metrics of the Rural Food Environment and Diabetes and Obesity.

INTRODUCTION: Associations between unhealthy food environments and increased risk of diabetes or obesity have been studied extensively in urban areas but less so in rural areas. To address this gap, we performed a geographically detailed study of the rural food environment, summarizing and assessing various measures and identifying their association with a higher rate of diabetes and obesity. METHODS: We surveyed 1,311 residents of Sullivan County, New York, collecting data on demographic characteristics and health status in January 2021. We assessed the food environment by cataloging restaurants and retail food stores and used least absolute shrinkage and selection operator (LASSO) regression to identify metrics strongly associated with diabetes and obesity (body mass index >30.0). We compared absolute proximity, density, relative proportions, and modified retail food environment index metrics. We applied fixed distance bands and nearest-neighbor approaches for proportions. RESULTS: Older age (LASSO = +0.276), nearest-neighbor proportion of fast food restaurants (LASSO = +0.155), and low income (LASSO = +0.150) were associated with higher diabetes rates. Nearest-neighbor proportion (LASSO = +0.269), density per square mile of groceries/supermarkets (LASSO = +0.247), and Hispanic ethnicity (LASSO = +0.181) were the strongest predictors of obesity. Nearest-neighbor metrics helped with the problem of frequent zeros: the least skewed measures were fast food restaurants with 20 nearest neighbors (P = .37 for skewness) and counter-service restaurants with 20 nearest neighbors (P = .81). CONCLUSION: Nearest-neighbor approaches were less skewed to other metrics based on fixed distance bands, proximity, and density. Food environment metrics must be fully described and validated, especially in rural environments, before they are applied in research on critical health outcomes.