Health care visits during the Covid-19 pandemic
A spatial and temporal analysis of mobile device data
Datos Bibliográficos
| ID | 12545939 |
|---|---|
| Autores | Jueyu Wang (0000-0003-1568-0195, University of North Carolina at Chapel Hill, autor de correspondencia), 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) |
| Año | 2021 |
| Volumen | 72 |
| Páginas | 102679-102679 |
| Fecha de publicación | 2021-09-28 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Health & Place (JOURNAL) |
| Identificadores de la revista | ISSN: 1353-8292 • E-ISSN: 1873-2054 |
| Editorial | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.healthplace.2021.102679 |
| PMID | 34628150 |
| OpenAlex | W3203306083 |
| Idioma | EN |
| Citas recibidas | 16 |
| Referencias citadas | 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
Regime-based spatiotemporal organization of ride-hailing mobility to hierarchical medical institutions
Investigating the Spatiotemporal Relationship between the Built Environment and Covid-19 Transmission
Migration Responses to the Covid-19 Pandemic
Uncovering Representation Bias in Large‐scale Cellular Phone‐based Data
Covid-19 impacts on non-work travel patterns
Unraveling the impact of Covid‐19 on Beijing’s subway system using a causal machine learning analysis
Integrating the Who, What, and Where of U.S. Retail Center Geographies
Spatio-temporal patterns of health service delivery and access to maternal, child, and outpatient healthcare in Volta region, Ghana
Association between immigrant concentration and mental health service utilization in the United States over time
Variability in Opioid-Related Drug Overdoses, Social Distancing, and Area-Level Deprivation during the Covid-19 Pandemic
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Understanding older adults’ travel behaviour and mobility needs during the Covid-19 pandemic through the lens of the hierarchy of travel needs
Durable Change in U.S. Urban Mobility Networks, 2019–2022
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Data clustering
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The association between socioeconomic status and mobility reductions in the early stage of England's Covid-19 epidemic
Quality of life in the urban environment and primary health services for the elderly during the Covid-19 pandemic
Transportation Barriers to Health Care in the United States
| Obras citantes distintas | 16 |
|---|---|
| Citas por año | 4 |
| Intervalo de citas | 2022 - 2026 (5) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 15 |