Fusillo Tara
John F Kennedy High School Bellmore, NY United States.
JMIRx Med. 2020 Dec 2;1(1):e22470. doi: 10.2196/22470. eCollection 2020 Jan-Dec.
Pandemics including COVID-19 have disproportionately affected socioeconomically vulnerable populations.
Our objective was to create a repeatable modeling process to identify regional population centers with pandemic vulnerability.
Using readily available COVID-19 and socioeconomic variable data sets, we used stepwise linear regression techniques to build predictive models during the early days of the COVID-19 pandemic. The models were validated later in the pandemic timeline using actual COVID-19 mortality rates in high population density states. The mean sample size was 43 and ranged from 8 (Connecticut) to 82 (Michigan).
The New York, New Jersey, Connecticut, Massachusetts, Louisiana, Michigan, and Pennsylvania models provided the strongest predictions of top counties in densely populated states with a high likelihood of disproportionate COVID-19 mortality rates. For all of these models, values were less than .05.
The models have been shared with the Department of Health Commissioners of each of these states with strong model predictions as input into a much needed "pandemic playbook" for local health care agencies in allocating medical testing and treatment resources. We have also confirmed the utility of our models with pharmaceutical companies for use in decisions pertaining to vaccine trial and distribution locations.
包括新冠疫情在内的大流行对社会经济弱势群体的影响尤为严重。
我们的目标是创建一个可重复的建模过程，以识别具有大流行脆弱性的区域人口中心。
利用现有的新冠疫情和社会经济变量数据集，我们在新冠疫情早期使用逐步线性回归技术构建预测模型。这些模型在疫情后期使用高人口密度州的实际新冠死亡率进行了验证。平均样本量为43，范围从8（康涅狄格州）到82（密歇根州）。
纽约、新泽西、康涅狄格、马萨诸塞、路易斯安那、密歇根和宾夕法尼亚州的模型对人口密集州中新冠死亡率极有可能不成比例的顶级县提供了最强的预测。对于所有这些模型，p值均小于0.05。
这些模型已与这些州的卫生专员分享，这些模型的强大预测结果被用作急需的“大流行行动手册”的输入，供当地医疗机构分配医疗检测和治疗资源。我们还向制药公司证实了我们模型在疫苗试验和分发地点决策方面的实用性。
