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Health care visits during the COVID-19 pandemic: A spatial and temporal analysis of mobile device data.
Wang, Jueyu; McDonald, Noreen; Cochran, Abigail L; Oluyede, Lindsay; Wolfe, Mary; Prunkl, Lauren.
  • Wang J; Department of City and Regional Planning, University of North Carolina at Chapel Hill, New East Building, CB3140, Chapel Hill, NC, 27599, United States. Electronic address: Oliva.wang@unc.edu.
  • McDonald N; Department of City and Regional Planning, University of North Carolina at Chapel Hill, New East Building, CB3140, Chapel Hill, NC, 27599, United States. Electronic address: noreen@unc.edu.
  • Cochran AL; Department of City and Regional Planning, University of North Carolina at Chapel Hill, New East Building, CB3140, Chapel Hill, NC, 27599, United States. Electronic address: acochran@unc.edu.
  • Oluyede L; Department of City and Regional Planning, University of North Carolina at Chapel Hill, New East Building, CB3140, Chapel Hill, NC, 27599, United States. Electronic address: oluyede@live.unc.edu.
  • Wolfe M; Center for Health Equity Research, University of North Carolina at Chapel Hill, 323 MacNider Hall, 333 South Columbia Street, Chapel Hill, NC, 27599-7240, United States. Electronic address: mary_wolfe@med.unc.edu.
  • Prunkl L; Department of City and Regional Planning, University of North Carolina at Chapel Hill, New East Building, CB3140, Chapel Hill, NC, 27599, United States. Electronic address: lauren.prunkl@unc.edu.
Health Place ; 72: 102679, 2021 11.
Article in English | MEDLINE | ID: covidwho-1440041
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 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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Telemedicine / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Limits: Aged / Humans Language: English Journal: Health Place Journal subject: Epidemiology / Public Health Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Telemedicine / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Limits: Aged / Humans Language: English Journal: Health Place Journal subject: Epidemiology / Public Health Year: 2021 Document Type: Article