Department of Geography and Planning, Appalachian State University, P.O. Box 32066, Boone, NC 28608, United States of America.
Department of Nutrition and Healthcare Management, Appalachian State University, Boone, NC, United States of America.
Sci Total Environ. 2021 Jan 15;752:141946. doi: 10.1016/j.scitotenv.2020.141946. Epub 2020 Aug 25.
Deaths from the COVID-19 pandemic have disproportionately affected older adults and residents in nursing homes. Although emerging research has identified place-based risk factors for the general population, little research has been conducted for nursing home populations. This GIS-based spatial modeling study aimed to determine the association between nursing home-level metrics and county-level, place-based variables with COVID-19 confirmed cases in nursing homes across the United States. A cross-sectional research design linked data from Centers for Medicare & Medicaid Services, American Community Survey, the 2010 Census, and COVID-19 cases among the general population and nursing homes. Spatial cluster analysis identified specific regions with statistically higher COVID-19 cases and deaths among residents. Multivariate analysis identified risk factors at the nursing home level including, total count of fines, total staffing levels, and LPN staffing levels. County-level or place-based factors like per-capita income, average household size, population density, and minority composition were significant predictors of COVID-19 cases in the nursing home. These results provide a framework for examining further COVID-19 cases in nursing homes and highlight the need to include other community-level variables when considering risk of COVID-19 transmission and outbreaks in nursing homes.
标题:美国养老院中 COVID-19 确诊病例的地理信息系统空间建模研究
摘要:
COVID-19 大流行造成的死亡人数 disproportionately 对老年人和养老院居民产生了影响。尽管新兴研究已经确定了一般人群的基于地点的风险因素,但针对养老院人群的研究甚少。
本基于 GIS 的空间建模研究旨在确定养老院级别指标与县级别、基于地点的变量与美国养老院中 COVID-19 确诊病例之间的关联。
采用横截面研究设计,将医疗保险和医疗补助服务中心、美国社区调查、2010 年人口普查以及普通人群和养老院中 COVID-19 病例的数据进行了关联。采用空间聚类分析识别出居民 COVID-19 病例和死亡人数统计学上较高的特定区域。采用多变量分析确定了养老院层面的风险因素,包括罚款总数、总人员配备水平和注册护士人员配备水平。人均收入、平均家庭规模、人口密度和少数族裔构成等县一级或基于地点的因素是养老院中 COVID-19 病例的重要预测因素。
在养老院中,特定的县和位置与 COVID-19 确诊病例和死亡人数显著相关。
这些结果为进一步研究养老院中的 COVID-19 病例提供了框架,并强调在考虑养老院中 COVID-19 传播和疫情爆发的风险时,需要纳入其他社区层面的变量。