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性网络在研究细菌性阴道病和性传播感染如何增加随后再次感染的风险中的作用。

The role of sexual networks in studies of how BV and STIs increase the risk of subsequent reinfection.

机构信息

Institute of Tropical Medicine,Nationale straat 155, Antwerp,Belgium.

The Ohio State University College of Public Health,Columbus, Ohio,USA.

出版信息

Epidemiol Infect. 2018 Nov;146(15):2003-2009. doi: 10.1017/S0950268818002157. Epub 2018 Sep 5.

Abstract

Prior studies have demonstrated that both bacterial vaginosis (BV) and sexually transmitted infections (STIs) are strong independent risk factors for subsequent STI. In observational studies of this biological enhancement (BE) hypothesis, it is important to adjust for the risk of STI exposure so that the independent effect of BE can be assessed. We sought to model if two markers of local sexual network (partner concurrency and cumulative number of STIs) represented residual confounding in the models of risk for subsequent infection in a study that screened 3620 women for STIs every 3 months for a year. Mixed-effects logistic regression was used to calculate the odds ratios for an incident diagnosis of Chlamydia trachomatis, Neisseria gonorrhoeae, Trichomonas vaginalis and BV following a diagnosis of any of these four at the prior visit, controlling for the cumulative number of STIs and partner concurrency variables. We found that partner concurrency and cumulative number of STIs were each associated with incident infection, and in general, controlling for these variables reduced the strength of the association between prior and incident infections. We conclude that the frequently found association between prior and incident STIs is associated with both BE and sexual network structure.

摘要

先前的研究表明细菌性阴道病(BV)和性传播感染(STI)都是随后发生 STI 的强有力的独立危险因素。在对这种生物学增强(BE)假说的观察性研究中,重要的是要调整 STI 暴露的风险,以便可以评估 BE 的独立影响。我们试图在一项研究中建立模型,如果局部性网络(伴侣同时感染和累积性 STI 数量)的两个标志物代表风险模型中的残余混杂因素,该研究每 3 个月对 3620 名女性进行 STI 筛查,为期一年。混合效应逻辑回归用于计算在先前就诊时诊断出任何一种衣原体、淋病奈瑟菌、阴道毛滴虫和 BV 后,后续感染的诊断为衣原体、淋病奈瑟菌、阴道毛滴虫和 BV 的比值比,控制累积性 STI 和伴侣同时感染变量。我们发现伴侣同时感染和累积性 STI 数量均与感染事件相关,并且通常,控制这些变量会降低先前和感染事件之间的关联强度。我们得出结论,先前和感染事件之间经常发现的关联与 BE 和性网络结构都有关。

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