估计先前感染所提供的保护,以防止再感染:采用检测负的研究设计
Houssein H Ayoub1, Milan Tomy1,2,3, Hiam Chemaitelly2,3,4
1Mathematics Program, Department of Mathematics and Statistics, College of Arts and Sciences, Qatar University, Doha, Qatar.
American journal of epidemiology
|December 7, 2023
概括
测试负的设计准确地估计了对之前感染的保护,以及对SARS-CoV-2再感染的保护,即使是未经记录的感染. 这种方法对于跟踪免疫力下降和变种保护来说非常有效.
科学领域:
- 流行病学 流行病学
- 传染病建模 传染病建模
- 公共卫生 公共卫生
背景情况:
- 随着COVID-19的流行,需要对新型SARS-CoV-2变种的免疫力进行快速评估.
- 对公共卫生战略来说,估计先前感染在预防再感染方面的有效性 ($P{E}_S$) 是至关重要的.
- 传统的队列研究可能很慢,无法及时提供估计.
研究的目的:
- 建立测试-负,病例-对照研究设计的理论基础和实际适用性,以估计$P{E}_S$.
- 评估潜在偏差的影响,如未经记录的感染和疫苗接种,对$P{E}_S$估计.
- 应用和验证测试负面设计使用来自卡塔尔的真实世界数据.
主要方法:
- 使用数学建模来评估测试负面设计的有效性,以估计$P{E}_S$.
- 该研究分析了感染状况和疫苗接种错误分类对$P{E}_S$估计的影响.
- 测试负面设计应用于卡塔尔国家级COVID-19测试数据.
主要成果:
- 测试负的设计提供了最小的和可以忽略不计的差异,从真正的P{E}_S$值,随着流行病的进展.
- 错误对先前感染状况的分类导致低估了P{E}_S$,特别是当超过50%的人口被感染时.
- 卡塔尔对SARS-CoV-2Alpha和Beta变体的估计P{E}_S$分别为97.0%和85.5%,通过队列数据验证.
结论:
- 试验阴性,病例对照设计是一种可行的和强大的方法来估计对再感染的保护 ($P{E}_S$).
- 这种设计有效地估计了$P{E}_S$及其减弱,为人口免疫提供了及时的见解.
- 这种方法即使在大量未经记录的感染中也被证明是可靠的,并且根据队列研究的结果得到了验证.
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