Molecular epidemiology (ME) is one tool used to end the HIV epidemic in the United States. We combined clinical and behavioral data with HIV sequence data to identify any overlap in clusters generated from different sequence datasets; to characterize HIV transmission clusters; and to identify correlates of clustering among people living with HIV (PLWH) in Washington, District of Columbia (DC). First, Sanger sequences from DC Cohort participants, a longitudinal HIV study, were combined with next-generation sequences (NGS) from participants in a ME substudy to identify clusters. Next, demographic and self-reported behavioral data from ME substudy participants were used to identify risks of secondary transmission. Finally, we combined NGS from ME substudy participants with Sanger sequences in the DC Molecular HIV Surveillance database to identify clusters. Cluster analyses used HIV-Transmission Cluster Engine to identify linked pairs of sequences (defined as distance ≤1.5%). Twenty-eight clusters of ≥3 sequences (size range: 3–12) representing 108 (3%) participants were identified. None of the five largest clusters (size range: 5–12) included newly diagnosed PLWH. Thirty-four percent of ME substudy participants (n = 213) reported condomless sex during their last sexual encounter and 14% reported a Syphilis diagnosis in the past year. Seven transmission clusters (size range: 2–19) were identified in the final analysis, each containing at least one ME substudy participant. Substudy participants in clusters from the third analysis were present in clusters from the first analysis. Combining HIV sequence, clinical and behavioral data provided insights into HIV transmission that may not be identified using traditional epidemiological methods alone. Specifically, the sexual risk behaviors and STI diagnoses reported in the substudy survey may not have been disclosed during Partner Services activities and the survey data complemented clinical data to fully characterize transmission clusters. These findings can be used to enhance local efforts to interrupt transmission and avert new infections.

中文翻译：

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使用 HIV 传播集群引擎在华盛顿特区的一群 HIV 感染者中描述 HIV 风险行为和集群的特征


分子流行病学 (ME) 是用于结束美国艾滋病毒流行的一种工具。我们将临床和行为数据与 HIV 序列数据相结合，以识别从不同序列数据集生成的簇中的任何重叠；描述艾滋病毒传播集群的特征；并确定华盛顿哥伦比亚特区 (DC) 艾滋病毒感染者 (PLWH) 聚集的相关性。首先，将 DC 队列参与者的桑格序列（一项纵向 HIV 研究）与 ME 子研究参与者的下一代序列 (NGS) 相结合，以识别簇。接下来，ME 子研究参与者的人口统计和自我报告的行为数据被用来识别二次传播的风险。最后，我们将 ME 亚研究参与者的 NGS 与 DC 分子 HIV 监测数据库中的 Sanger 序列相结合，以识别簇。聚类分析使用 HIV-Transmission Cluster Engine 来识别链接的序列对（定义为距离≤1.5%）。确定了代表 108 (3%) 名参与者的 28 个≥3 个序列的簇（大小范围：3-12）。五个最大的感染群（规模范围：5-12）中没有一个包括新诊断的感染者。 34% 的 ME 子研究参与者 ( n = 213) 报告在他们最近一次性行为中发生了无安全套性行为，14% 的人报告在过去一年中诊断出梅毒。最终分析确定了七个传播簇（大小范围：2-19），每个簇至少包含一名 ME 子研究参与者。第三次分析中分组的子研究参与者也存在于第一次分析中的分组中。结合艾滋病毒序列、临床和行为数据，可以深入了解艾滋病毒的传播情况，而仅使用传统的流行病学方法可能无法识别这一点。 具体而言，子研究调查中报告的性危险行为和性传播感染诊断可能未在合作伙伴服务活动期间披露，调查数据补充了临床数据，以充分描述传播集群的特征。这些发现可用于加强当地阻断传播和避免新感染的努力。