Modeling time-varying spatial accessibility to healthcare: A system dynamic approach

Abstract

Spatial accessibility to healthcare is essential for policymakers to identify health disparities and develop targeted interventions. Current modeling approaches poorly capture temporal dynamics of contributing factors, and few have represented dynamic interactions among these factors. Further, validating current models is hindered by data limitations and methodological challenges. To fill these gaps, I propose a new modeling approach that produces time-varying and empirically validated measures of healthcare accessibility. Specifically, I develop a system dynamic model to represent interplay between population demand and healthcare supply over time. The model simultaneously estimated people's potential accessibility to healthcare and their utilization over time and space, then validated these estimates with actual hospitalization data. To illustrate the model's practical use, a working prototype was constructed to estimate and validate healthcare accessibility by day and by ZIP code in Florida, USA, during a disease outbreak in 2022-23. The results indicate that system dynamic modeling offers a robust framework for monitoring healthcare accessibility fluctuations across regions and time periods, thereby guiding the development of timely and targeted interventions to reduce disparities.