13. Quantifying the impact of measurement error on health disparities models

Healthy eating is an important part of living a healthy life, and unequal access to healthy foods can perpetuate health disparities. When not everyone has the same level of access to healthy foods, it can result in disproportionately high rates of disease in communities with fewer healthy food options. Previous studies exploring this topic have used county- or census tract-level data on both disease and food access, which captures a broad range of diverse communities. However, counties and census tracts cannot provide specific details about the individuals and communities within them. In this project, we investigate the relationship between the distance from patients' homes to the nearest healthy foods store (proximity) and the prevalence of diabetes. How proximity to healthy foods is measured poses an additional challenge, as distance measures are either computationally simple and inaccurate (straight-line distances), or computationally complex and accurate (map-based distances). To approach these questions, we extract patient information (including diabetes diagnoses) from the electronic health record (EHR), geocode patients' home addresses, and calculate both straight-line and map-based proximity to healthy foods. Using rate ratios, relative indices of inequality, and concentration curves, we quantify whether patients with farther proximities to healthy foods (indicating worse access) face a higher burden of prevalent diabetes. Finally, we discuss the impact of using inaccurate access measures to quantify health disparities.

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Women in Statistics and Data Science 2024