Health care visits during the Covid-19 pandemic
A spatial and temporal analysis of mobile device data
Bibliographic Data
| ID | 12545939 |
|---|---|
| Authors | Jueyu Wang (0000-0003-1568-0195, University of North Carolina at Chapel Hill, corresponding author), Noreen Mcdonald (0000-0002-4854-7035, University of North Carolina at Chapel Hill), Abigail L Cochran (0000-0002-7866-4865, University of North Carolina at Chapel Hill), Lindsay Oluyede (0000-0002-1039-8409, University of North Carolina at Chapel Hill), Mary Wolfe (0000-0001-6043-1433, University of North Carolina at Chapel Hill), Lauren Prunkl (0000-0001-8136-4874, University of North Carolina at Chapel Hill) |
| Year | 2021 |
| Volume | 72 |
| Pages | 102679-102679 |
| Publication date | 2021-09-28 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Health & Place (JOURNAL) |
| Journal identifiers | ISSN: 1353-8292 • E-ISSN: 1873-2054 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.healthplace.2021.102679 |
| PMID | 34628150 |
| OpenAlex | W3203306083 |
| Language | EN |
| Citations received | 16 |
| References cited | 27 |
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
Business · Census · Coronavirus disease 2019 (COVID-19 · Disease · Economic growth · Economics · Environmental health · Geography · Health care · Multinomial logistic regression · Pandemic · Population · Sociology · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Demography · Human Mobility and Location-Based Analysis · Medicine
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| Unique citing works | 16 |
|---|---|
| Citations per year | 4 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 15 |