Abstract
Healthy foods are essential for a healthy life, but accessing healthy food can be more challenging for some people than others. This disparity in food access may lead to disparities in well-being, potentially with disproportionate rates of diseases in communities that face more challenges in accessing healthy food (i.e., low-access communities). Identifying low-access, high-risk communities for targeted interventions is a public health priority, but current methods to quantify food access rely on distance measures that are either computationally simple (like the length of the shortest straight-line route) or accurate (like the length of the shortest map-based driving route), but not both. We propose a multiple imputation approach to combine these distance measures, allowing researchers to harness the computational ease of one with the accuracy of the other. The approach incorporates straight-line distances for all neighborhoods and map-based distances for just a subset, offering comparable estimates to the "gold standard" model using map-based distances for all neighborhoods and improved efficiency over the "complete case" model using map-based distances for just the subset. Through the adoption of a measurement error framework, information from the straight-line distances can be leveraged to compute informative placeholders (i.e., impute) for any neighborhoods without map-based distances. Using simulations and data for the Piedmont Triad region of North Carolina, we quantify and compare the associations between various health outcomes (diabetes and obesity) and neighborhood-level proximity to health foods. The imputation procedure also makes it possible to predict the full landscape of food access in an area without requiring map-based measurements for all neighborhoods.
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Sarah Lotspeich is an assistant professor in Statistical Sciences at Wake Forest University, with a secondary appointment in Biostatistics and Data Science at the Wake Forest University School of Medicine. Sarah earned her PhD in biostatistics from Vanderbilt University in 2021, and completed a postdoctoral fellowship at the University of North Carolina at Chapel Hill in 2022. Her research tackles challenges in analyzing error-prone observational data, focusing on international HIV cohorts, electronic health records, and health disparities. She also develops methods for statistical modeling with censored covariates, applicable to Huntington's disease. Sarah has published in peer-reviewed statistical, clinical, and epidemiological journals, and she is the 2023 recipient of the David P. Byar Early Career Award from the ASA Biometrics Section. She enthusiastically mentors student research as a co-leader of the Spatial and Environmental Statistics in Health (SESH) Lab at Wake Forest and the Missing and INcomplete Data (MIND) Lab at the University of North Carolina at Chapel Hill. She co-organizes Florence Nightingale Day at Wake Forest annually, engaging local students in statistics and data science, and holds elected positions in the Caucus for Women in Statistics and Data Science and other professional organizations. When she's not writing code, you can find Sarah cross-stitching, adventuring new places, or rewatching The Mindy Project.