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亚组分布对人类呼吸道合胞病毒季节性影响的全球系统分析。

Impact of Subgroup Distribution on Seasonality of Human Respiratory Syncytial Virus: A Global Systematic Analysis.

机构信息

Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China.

Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases, Johannesburg, South Africa.

出版信息

J Infect Dis. 2024 Mar 1;229(Supplement_1):S25-S33. doi: 10.1093/infdis/jiad192.

Abstract

BACKGROUND

Previous studies reported inconsistent findings regarding the association between respiratory syncytial virus (RSV) subgroup distribution and timing of RSV season. We aimed to further understand the association by conducting a global-level systematic analysis.

METHODS

We compiled published data on RSV seasonality through a systematic literature review, and unpublished data shared by international collaborators. Using annual cumulative proportion (ACP) of RSV-positive cases, we defined RSV season onset and offset as ACP reaching 10% and 90%, respectively. Linear regression models accounting for meteorological factors were constructed to analyze the association of proportion of RSV-A with the corresponding RSV season onset and offset.

RESULTS

We included 36 study sites from 20 countries, providing data for 179 study-years in 1995-2019. Globally, RSV subgroup distribution was not significantly associated with RSV season onset or offset globally, except for RSV season offset in the tropics in 1 model, possibly by chance. Models that included RSV subgroup distribution and meteorological factors explained only 2%-4% of the variations in timing of RSV season.

CONCLUSIONS

Year-on-year variations in RSV season onset and offset are not well explained by RSV subgroup distribution or meteorological factors. Factors including population susceptibility, mobility, and viral interference should be examined in future studies.

摘要

背景

先前的研究报告称,呼吸道合胞病毒(RSV)亚群分布与 RSV 季节时间之间的关联存在不一致的结果。我们旨在通过进行全球性的系统分析来进一步了解这种关联。

方法

我们通过系统文献回顾和国际合作者共享的未发表数据,汇编了 RSV 季节性的已发表数据。使用 RSV 阳性病例的年度累积比例(ACP),我们将 RSV 季节的开始和结束定义为 ACP 分别达到 10%和 90%。构建了考虑气象因素的线性回归模型,以分析 RSV-A 比例与相应 RSV 季节开始和结束的关联。

结果

我们纳入了来自 20 个国家的 36 个研究地点,提供了 1995 年至 2019 年期间 179 个研究年的数据。在全球范围内,除了在 1 个模型中,热带地区的 RSV 季节结束时间与 RSV 亚群分布有关,可能是偶然的,否则 RSV 亚群分布与 RSV 季节开始或结束时间之间没有显著关联。包括 RSV 亚群分布和气象因素的模型仅能解释 RSV 季节时间变化的 2%-4%。

结论

RSV 季节开始和结束的逐年变化不能很好地用 RSV 亚群分布或气象因素来解释。在未来的研究中,应检查包括人群易感性、流动性和病毒干扰在内的因素。

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