Examining the impacts of measurement error in quantifying health disparities: a case study on type-2 diabetes and the food environment

Abstract 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. They can be driven by individual- and neighborhood-level factors, like a person’s distance from home to the nearest grocery store or the socioeconomic status of their community, respectively. 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 or more food-insecure households in their neighborhood face a higher burden of prevalent type-2 diabetes. Still, we have measurement error in the food environment variables (access and food insecurity) since they are collected using inaccurate distance calculations and survey data. Finally, we discuss the impact of using error-prone food environment measures to detect health disparities in these data.