Combining straight-line and map-based distances to investigate the connection between proximity to healthy foods and disease

TL;DR

This paper introduces a novel multiple imputation method that combines simple straight-line and accurate map-based distances to better identify communities at risk due to poor access to healthy foods, improving public health targeting.

Contribution

It develops a new approach that merges distance measures to efficiently and accurately assess food access disparities, enhancing analysis of health outcomes related to food proximity.

Findings

The method provides estimates comparable to full map-based models with greater efficiency.

Simulations and real data demonstrate improved accuracy in identifying high-risk communities.

The approach enables comprehensive food access mapping without extensive data collection.

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 two health outcomes (diabetes and obesity) and neighborhood-level access to healthy 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.