Role of Geographic Risk Factors in COVID-19 Epidemiology: Longitudinal Geospatial Analysis.
Role of Geographic Risk Factors in COVID-19 Epidemiology: Longitudinal Geospatial Analysis.
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DOI：
10.1016/j.mayocpiqo.2021.06.011
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Patten C
中科院分区：
文献类型：
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作者：
Juhn YJ;Wheeler P;Wi CI;Bublitz J;Ryu E;Ristagno EH;Patten C
对中西部地区2019冠状病毒病（COVID-19）的地理时空趋势进行分析，以确定和表征COVID-19的热点地区。我们进行了一项基于人群的纵向监测，评估了2020年3月11日至2020年10月31日期间明尼苏达州奥姆斯特德县检测确诊COVID-19病例流行的半月地理空间趋势。由于MN奥姆斯特德县的城市地区占人口的84%，占所有COVID-19病例的86%，因此我们在研究期间通过半英里带宽的核密度分析确定了MN奥姆斯特德县城市地区（罗切斯特和其他小城市）的COVID-19热点。截至2020年10月31日，共有37141人（30%）至少接受过一次检测，其中2433人（7%）检测呈阳性。不同种族的检测率相似：29%（黑人），30%（西班牙裔），25%（亚洲人）和31%（白人）。十个市区热点共有220个地址，共590宗个案（每个地址2.68宗）；而热点以外地区共有1292个地址，共1843宗个案（每个地址1.43宗）。总体而言，居住在热点地区的人口占12%，占所有COVID-19病例的24%。热点集中在有低收入公寓和移动房屋社区的社区。生活在热点地区的人往往是少数民族，社会经济背景较低。地理和居住风险因素可能在很大程度上解释了COVID-19的总体负担及其相关的种族/民族和社会经济差异。结果可以在地理空间上指导针对COVID-19风险人群的社区外展工作（例如检测/追踪和疫苗推广）。
To perform a geospatial and temporal trend analysis for coronavirus disease 2019 (COVID-19) in a Midwest community to identify and characterize hot spots for COVID-19. We conducted a population-based longitudinal surveillance assessing the semimonthly geospatial trends of the prevalence of test confirmed COVID-19 cases in Olmsted County, Minnesota, from March 11, 2020, through October 31, 2020. As urban areas accounted for 84% of the population and 86% of all COVID-19 cases in Olmsted County, MN, we determined hot spots for COVID-19 in urban areas (Rochester and other small cities) of Olmsted County, MN, during the study period by using kernel density analysis with a half-mile bandwidth. As of October 31, 2020, a total of 37,141 individuals (30%) were tested at least once, of whom 2433 (7%) tested positive. Testing rates among race groups were similar: 29% (black), 30% (Hispanic), 25% (Asian), and 31% (white). Ten urban hot spots accounted for 590 cases at 220 addresses (2.68 cases per address) as compared with 1843 cases at 1292 addresses in areas outside hot spots (1.43 cases per address). Overall, 12% of the population residing in hot spots accounted for 24% of all COVID-19 cases. Hot spots were concentrated in neighborhoods with low-income apartments and mobile home communities. People living in hot spots tended to be minorities and from a lower socioeconomic background. Geographic and residential risk factors might considerably account for the overall burden of COVID-19 and its associated racial/ethnic and socioeconomic disparities. Results could geospatially guide community outreach efforts (eg, testing/tracing and vaccine rollout) for populations at risk for COVID-19.