Evaluating Within-county Disparities in Health Outcomes using Synthetic Data

Music City Center 

Investigating trends in health outcomes at fine geographic levels is crucial for identifying and addressing geographic disparities, but the data necessary for those analyses is often not publicly available due to the potential risk of disclosure of sensitive information of the underlying data subjects. Recent work at the intersection of spatial statistics and data privacy has aimed to develop methods suitable for the production of spatially referenced synthetic data with provable privacy guarantees that can preserve the disparities present in the original data. In this study, we use the differentially private Poisson-gamma mechanism to produce a synthetic dataset comprised of annual tract-level heart disease related death counts stratified by age, race, and sex for the state of Minnesota. We then shift our focus on Minnesota's most populous county and compare an analysis of spatiotemporal trends in heart disease death rates for Hennepin County from 2010-2019 produced by the synthetic data to those based on the true data.