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 Census 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 facilities. Furthermore, by estimating multinomial logit models, we find that Census Block Groups with higher percentages of elderly persons, minorities, low-income individuals, and people without vehicle access are areas most at-risk for decreased health care access during the pandemic and exhibit lower health care access prior to 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 equitable health care access and delivery. •Use time-series clustering to identify temporal patterns of medical visits during the pandemic•Neighborhoods with different social and spatial demographics exhibit disparate patterns.•Medical visits dropped during the lockdown and did not return the pre-pandemic levels.•Less advantaged neighborhoods had lower visits all the time and a slower recovery.