A Data Driven Approach for Prioritizing COVID-19 Vaccinations in the Midwestern United States.

Online journal of public health informatics Pub Date : 2021-03-12 eCollection Date: 2021-01-01 DOI:10.5210/ojphi.v13i1.11621
Greg Arling, Matthew Blaser, Michael D Cailas, John R Canar, Brian Cooper, Joel Flax-Hatch, Peter J Geraci, Kristin M Osiecki, Apostolis Sambanis
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引用次数: 6

Abstract

Considering the potential for widespread adoption of social vulnerability indices (SVI) to prioritize COVID-19 vaccinations, there is a need to carefully assess them, particularly for correspondence with outcomes (such as loss of life) in the context of the COVID-19 pandemic. The University of Illinois at Chicago School of Public Health Public Health GIS team developed a methodology for assessing and deriving vulnerability indices based on the premise that these indices are, in the final analysis, classifiers. Application of this methodology to several Midwestern states with a commonly used SVI indicates that by using only the SVI rankings there is a risk of assigning a high priority to locations with the lowest mortality rates and low priority to locations with the highest mortality rates. Based on the findings, we propose using a two-dimensional approach to rationalize the distribution of vaccinations. This approach has the potential to account for areas with high vulnerability characteristics as well as to incorporate the areas that were hard hit by the pandemic.

在美国中西部优先接种COVID-19疫苗的数据驱动方法
考虑到广泛采用社会脆弱性指数(SVI)来优先考虑COVID-19疫苗接种的可能性,有必要仔细评估它们,特别是在COVID-19大流行背景下与结果(如生命损失)的对应关系。伊利诺伊大学芝加哥公共卫生学院公共卫生地理信息系统小组开发了一种评估和得出脆弱性指数的方法,其前提是这些指数归根结底是分类器。将这一方法应用于具有常用SVI的中西部几个州表明,如果只使用SVI排名,就有可能给死亡率最低的地点分配高优先级,而给死亡率最高的地点分配低优先级。基于这些发现,我们建议使用二维方法来合理化疫苗接种的分配。这一方法有可能考虑到具有高度脆弱性特征的地区,并纳入受这一流行病严重打击的地区。
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