Health Care Visits During the COVID-19 Pandemic: A Spatial and Temporal Analysis of Mobile Device Data

Transportation disruptions caused by COVID-19 have exacerbated difficulties in health care delivery and access, which may lead to changes in health care use. This study uses mobile device data from SafeGraph to explore the temporal patterns of visits to health care points of interest (POIs) during 2020 and examines how these patterns are associated with socio-demographic and spatial characteristics at the block group level in North Carolina. Specifically, using the k-medoid time-series clustering method, we identify three distinct types of temporal patterns of visits to health care POI. Furthermore, by estimating the multinomial logit models, we find that socio-demographic and spatial characteristics are strongly correlated with both the intensity and trend of medical visits during the pandemic. The results suggest that the ability to conduct in-person medical visits during the pandemic has been unequally distributed, which highlights the importance of tailoring policy strategies for specific socio-demographic groups to ensure health care delivery and access in a timely, equitable, and safe manner.