A0492
Title: Connecting healthy food proximity and disease: Straight-line vs. map-based distances
Authors: Sarah Lotspeich - Wake Forest University (United States) [presenting]
Abstract: Healthy foods are essential for a healthy life, but accessing healthy food can be more challenging for some people. 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. Identifying low-access, high-risk communities for targeted interventions is a public health priority. Current methods to quantify food access rely on distance measures that are either computationally simple (like the shortest straight-line route) or accurate (like the shortest map-based driving route), but not both. A multiple imputation approach is proposed to combine these distance measures, harnessing 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 a measurement error framework, straight-line distances are leveraged to impute for neighborhoods without map-based distances. Using simulations and data for North Carolina, U.S.A., the associations of diabetes and obesity are quantified with neighborhood-level proximity to healthy foods. Imputation also makes it possible to predict an area's full food access landscape from incomplete data.
