Examining the Impacts of Measurement Error in Quantifying Health Disparities: A Case Study on Type-2 Diabetes and the Food Environment

Disparities in healthy eating relate to disparities in well-being, leading to disproportionate rates of diseases like type-2 diabetes in communities that face more challenges in accessing nutritious food. Quantifying these disparities is key to developing targeted interventions, and there are limitations with the currently available methods and data that we are working to resolve. Namely, available data on disease rates are usually aggregate, which smooths over details about the individuals and communities within them. Further, aggregate disease data often comprise small area estimates, which carry additional uncertainty. In this project, we investigate the relationship between patients' food environment and the risk of diabetes using individual-level data from electronic health records at a large academic medical center. Using various health disparities methods, we quantify whether patients with worse access to healthy food face a higher burden of prevalent type-2 diabetes. Still, we face measurement error in food access since it is collected using inaccurate distance calculations. Finally, we discuss the impact of using error-prone food environment measures to detect health disparities in these data.