Department of City and Regional Planning, University of North Carolina at Chapel Hill, New East Building, CB3140, Chapel Hill, NC, 27599, United States.
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.
Health Place. 2021 Nov;72:102679. doi: 10.1016/j.healthplace.2021.102679. Epub 2021 Sep 28.
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.
由于 COVID-19 导致的交通中断，加剧了医疗服务提供和获取的困难，这可能导致医疗服务使用的变化。本研究使用 SafeGraph 的移动设备数据，探讨了 2020 年期间医疗保健兴趣点（POI）访问的时间模式，并研究了这些模式如何与北卡罗来纳州人口普查街区组层面的社会人口和空间特征相关。具体来说，我们使用 K-medoid 时间序列聚类方法，确定了三种不同类型的医疗设施访问的时间模式。此外，通过估计多项逻辑回归模型，我们发现，老年人、少数族裔、低收入人群和无交通工具人群比例较高的人口普查街区组，在大流行期间面临医疗服务获取减少的风险最高，并且在大流行之前的医疗服务获取水平较低。研究结果表明，在大流行期间进行面对面医疗访问的能力分配不均，这突显了为特定社会人口群体量身定制政策策略的重要性，以确保公平获得和提供医疗保健。
