Simoes Eduardo J, Schmaltz Chester L, Jackson-Thompson Jeannette
University of Missouri School of Medicine, Department of Health Management and Informatics, CE707 CS&E Bldg., DC006.00 Columbia, MO 65212, USA.
MU Institute for Data Science and Informatics, USA.
Prev Med Rep. 2021 Dec;24:101624. doi: 10.1016/j.pmedr.2021.101624. Epub 2021 Oct 25.
By 21 October 2020, the coronavirus disease (COVID-19) epidemic in the United States (US) had infected 8.3 million people, resulting in 61,364 laboratory-confirmed hospitalizations and 222,157 deaths. Currently, policymakers are trying to better understand this epidemic, especially the human-to-human transmissibility of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in relation to social, populational, air travel related and environmental exposure factors. Our study used 50 US states' public health surveillance datasets (January 1-April 1, 2020) to measure associations of confirmed COVID-19 cases, hospitalizations and deaths with these variables. Using the resulting associations and multivariate regression (Negative Binomial and Poisson), predicted cases, hospitalizations and deaths were generated for each US state early in the epidemic. Factors associated with a significantly increased risk of COVID-19 disease, hospitalization and death included: population density, enplanement, Black race and increased sun exposure; in addition, COVID-19 disease and hospitalization were also associated with morning humidity. Although predictions of the number of cases, hospitalizations and deaths due to COVID-19 were not accurate for every state, those states with a combination of large number of enplanements, high population density, high proportion of Black residents, high humidity or low sun exposure may expect more rapid than expected growth in the number of COVID-19 events early in the epidemic.
截至2020年10月21日,美国的冠状病毒病(COVID-19)疫情已感染830万人,导致61364例实验室确诊的住院病例和222157例死亡。目前,政策制定者正试图更好地了解这一疫情,尤其是新型严重急性呼吸综合征冠状病毒2(SARS-CoV-2)在人与人之间的传播能力与社会、人口、航空旅行相关及环境暴露因素之间的关系。我们的研究使用了美国50个州的公共卫生监测数据集(2020年1月1日至4月1日)来衡量确诊的COVID-19病例、住院病例和死亡病例与这些变量之间的关联。利用所得的关联以及多变量回归(负二项式和泊松回归),在疫情早期为美国每个州生成了预测的病例数、住院病例数和死亡病例数。与COVID-19疾病、住院和死亡风险显著增加相关的因素包括:人口密度、登机人数、黑人种族以及日照增加;此外,COVID-19疾病和住院还与早晨湿度有关。尽管对每个州因COVID-19导致的病例数、住院病例数和死亡病例数的预测并不准确,但那些登机人数多、人口密度高、黑人居民比例高、湿度高或日照少的州,可能预计在疫情早期COVID-19事件数量的增长速度会比预期更快。