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适应不断变化的流行病的适应性指标:对区域级COVID-19风险指标的动态方法
Alyssa M Bilinski1, Joshua A Salomon2, Laura A Hatfield3
1Departments of Health Services, Policy and Practice & Biostatistics, Brown University, Providence, RI 02912.
概括
新的适应性COVID-19风险指标改善了严重疾病和死亡率的预测. 这种实时方法在不断变化的流行病期间提供了比静态指标更好的政策指导.
科学领域:
- 流行病学和公共卫生.
- 生物统计学和预测建模
- 卫生政策和决策科学 卫生政策和决策科学
背景情况:
- 现有的COVID-19风险指标,如CDC社区水平,在预测严重结果和适应新变种和免疫力转变方面表现出局限性.
- 目前的指标往往缺乏关于假阳性和假阴性预测之间的平衡的透明度,这影响了它们对有针对性的干预措施的有用性.
- 流行病的动态性质需要风险评估工具,这些工具可以随着流行病学景观和人口免疫力变化而演变.
研究的目的:
- 开发和评估一个框架,以评估风险指标的预测准确性,基于严重疾病和死亡率等与政策相关的结果.
- 引入一种用于实时更新风险值的新方法,以提高公共卫生风险指标的相关性和性能.
- 将适应性风险指标与静态指标的性能进行比较,以预测未来严重的COVID-19结果.
主要方法:
- 建立了一个框架来评估预测准确性,允许明确的优先级,尽量减少关于严重疾病和死亡率的虚假阴性与虚假阳性信号.
- 提出了一种实时适应方法来更新风险值,使"高风险"地区的动态指定成为可能.
- 适应性指标的表现与使用州和县级数据的静态指标进行了比较,以预测3周前的死亡率和重症监护室 (ICU) 使用率.
主要成果:
- 与静态指标相比,适应性方法显著改善了预测3周前死亡率和ICU使用率的预测,无论是在州级还是县级.
- 在适应性框架内,仅使用新入院患者作为指标,可与包含病例和住院病床使用的指标相比较.
- 该研究表明,由于COVID-19大流行中流行病学指标和政策相关结果之间的关系不断变化,适应性指标至关重要.
结论:
- 适应性风险指标为指导COVID-19大流行期间公共卫生政策的静态指标提供了一种优越的方法,特别是在预测严重结果时.
- 拟议的框架允许决策者根据对虚假阴性信号和虚假阳性信号的具体偏好量身定制风险评估.
- 实时更新风险值对于保持公共卫生监测工具在快速发展的健康危机中的相关性和准确性至关重要.
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