通过贝叶斯对压抑的临时COVID-19死亡人数的归算,为决策提供更好的数据
Szu-Yu Zoe Kao1, M Shane Tutwiler2, Donatus U Ekwueme1
1Division of Cancer Prevention and Control, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
PloS one
|August 3, 2023
概括
这项研究开发了一种贝叶斯方法,用于归因美国各县缺少的COVID-19死亡数据. 将被压制的COVID-19死亡人数归入,可以改善估计,并有助于识别死亡率差异.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 准确的COVID-19死亡率数据对于公共卫生至关重要.
- 暂时的死亡率数据往往有压抑的计数,限制细粒度分析.
- 需要及时和详细的数据来了解疾病的影响.
研究的目的:
- 开发一种方法,将被压制的COVID-19死亡人数归咎于美国临时死亡数据.
- 根据季度,县和年龄提供准确的细粒度数据.
- 为了改善COVID-19死亡率的估计.
主要方法:
- 采用贝叶斯的方法,对美国3,138个县的COVID-19死亡人数进行计算.
- 考虑了数据的复杂性:多层结构,零计数,倾斜分布和不同颗粒度.
- 将三个模型与不同的先前假设进行比较 (非信息化,弱信息化,年龄特定信息化).
主要成果:
- 原始数据低估了国家COVID-19死亡人数18.60%.
- 与国家报告相比,假设数据显示高估值在2.23%至3.57%之间.
- 根据原始死亡率和年龄标准化的死亡率,确定了受影响最严重的县的差异.
结论:
- 贝叶斯对被压制的县级,年龄特定的COVID-19死亡人数的归算提高了数据的准确性.
- 改进的估计有助于公共卫生官员识别COVID-19死亡率差异.
- 该方法支持更好地了解大流行病在不同的人口统计和地点的影响.
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