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在一项多中心血清流行病学工作场所队列研究中,SARS-CoV-2 抗体至少能预防 6 个月的再次感染。

SARS-CoV-2 antibodies protect against reinfection for at least 6 months in a multicentre seroepidemiological workplace cohort.

机构信息

Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.

Barcelona Supercomputing Center (BSC), Barcelona, Spain.

出版信息

PLoS Biol. 2022 Feb 10;20(2):e3001531. doi: 10.1371/journal.pbio.3001531. eCollection 2022 Feb.

Abstract

Identifying the potential for SARS-CoV-2 reinfection is crucial for understanding possible long-term epidemic dynamics. We analysed longitudinal PCR and serological testing data from a prospective cohort of 4,411 United States employees in 4 states between April 2020 and February 2021. We conducted a multivariable logistic regression investigating the association between baseline serological status and subsequent PCR test result in order to calculate an odds ratio for reinfection. We estimated an odds ratio for reinfection ranging from 0.14 (95% CI: 0.019 to 0.63) to 0.28 (95% CI: 0.05 to 1.1), implying that the presence of SARS-CoV-2 antibodies at baseline is associated with around 72% to 86% reduced odds of a subsequent PCR positive test based on our point estimates. This suggests that primary infection with SARS-CoV-2 provides protection against reinfection in the majority of individuals, at least over a 6-month time period. We also highlight 2 major sources of bias and uncertainty to be considered when estimating the relative risk of reinfection, confounders and the choice of baseline time point, and show how to account for both in reinfection analysis.

摘要

确定 SARS-CoV-2 再感染的可能性对于了解可能的长期疫情动态至关重要。我们分析了 2020 年 4 月至 2021 年 2 月期间美国 4 个州的一个前瞻性队列的 4411 名员工的纵向 PCR 和血清学检测数据。我们进行了多变量逻辑回归分析,以调查基线血清学状态与随后的 PCR 检测结果之间的关联,从而计算再感染的优势比。我们估计再感染的优势比范围为 0.14(95%CI:0.019 至 0.63)至 0.28(95%CI:0.05 至 1.1),这意味着基线时存在 SARS-CoV-2 抗体与随后 PCR 阳性检测的可能性降低约 72%至 86%,这是基于我们的点估计。这表明,SARS-CoV-2 的初次感染在大多数人中至少在 6 个月的时间内提供了对再感染的保护。我们还强调了在估计再感染的相对风险时需要考虑的两个主要偏倚和不确定性来源,即混杂因素和基线时间点的选择,并展示了如何在再感染分析中考虑这两个因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7218/8865659/26dea499c695/pbio.3001531.g001.jpg

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