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开发和验证一种临床预测工具,用于未接收更新的COVID-19疫苗

Katia J Bruxvoort¹, Lina S Sy², Richard Contreras²

¹Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35233, United States; Department of Research & Evaluation, Kaiser Permanente Southern California, 100 S. Los Robles Ave, 5th Floor, Pasadena, CA 91101, United States.

|April 11, 2025

概括

保持对SARS-CoV-2变种的保护需要更新的COVID-19疫苗. 一个经过验证的预测模型确定了与更新的疫苗未接收相关的因素,有助于针对性干预.

科学领域:

公共卫生公共卫生
流行病学流行病学
疫苗学疫苗学疫苗学

背景情况:

更新的COVID-19疫苗对于防止演变的SARS-CoV-2变种的保护至关重要.
识别那些不接受更新疫苗的风险人群对于公共卫生战略至关重要.

研究的目的:

开发和验证一个预测模型,用于未收到更新的 (2023-2024) COVID-19 疫苗.
通过使用电子健康记录数据,识别更新的COVID-19疫苗未接收的关键预测因素.

主要方法:

一个预测模型被开发和验证使用3,287,287个成年人的数据在凯泽永久南加州.
评估了15个电子健康记录变量,以评估它们与更新的COVID-19疫苗未接种的关联.
队列被分为开发和验证样本,以评估模型性能,使用比如Brier分数和C-统计等指标.

主要成果:

20.2%的成年人口接种了2023-2024年更新的COVID-19疫苗.
15变量预测模型表现中等,验证和简化2和6变量模型的结果类似.
之前未接种双价COVID-19疫苗和流感疫苗是最新疫苗未接种的最强预测因素.

结论:

一个简单的基于EHR的预测模型可以有效地识别那些不太可能接受更新的COVID-19疫苗的人.
这些发现可以为优化干预策略提供信息,以改善最新的COVID-19疫苗接种率.
利用易于获取的医疗保健数据可以加强针对疫苗可访问性的有针对性的公共卫生工作.

关键词:

这是COVID-19疫苗.预测模型的预测模型.吸收时间吸收时间

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