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

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  • Wang, Jueyu
  • McDonald, Noreen

Abstract

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.

Suggested Citation

  • Wang, Jueyu & McDonald, Noreen, 2021. "Health Care Visits During the COVID-19 Pandemic: A Spatial and Temporal Analysis of Mobile Device Data," SocArXiv ghptz, Center for Open Science.
    • Handle: RePEc:osf:socarx:ghptz
    DOI: 10.31219/osf.io/ghptz
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    Cited by:

    1. Daniela Arias-Molinares & Juan Carlos García-Palomares & Gustavo Romanillos & Javier Gutiérrez, 2023. "Uncovering spatiotemporal micromobility patterns through the lens of space–time cubes and GIS tools," Journal of Geographical Systems, Springer, vol. 25(3), pages 403-427, July.
    2. Yang Song & Sungmin Lee & Amaryllis H Park & Chanam Lee, 2023. "COVID-19 impacts on non-work travel patterns: A place-based investigation using smartphone mobility data," Environment and Planning B, , vol. 50(3), pages 642-659, March.
    3. Elif Bozkaya & Levent Eriskin & Mumtaz Karatas, 2023. "Data analytics during pandemics: a transportation and location planning perspective," Annals of Operations Research, Springer, vol. 328(1), pages 193-244, September.
    4. Nie, Qifan & Qian, Xinwu & Guo, Shuocheng & Jones, Steven & Doustmohammadi, Mehrnaz & Anderson, Michael D., 2022. "Impact of COVID-19 on paratransit operators and riders: A case study of central Alabama," Transportation Research Part A: Policy and Practice, Elsevier, vol. 161(C), pages 48-67.
    5. Wang, Jueyu & Kaza, Nikhil & McDonald, Noreen C. & Khanal, Kshitiz, 2022. "Socio-economic disparities in activity-travel behavior adaptation during the COVID-19 pandemic in North Carolina," Transport Policy, Elsevier, vol. 125(C), pages 70-78.

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