Identifying Clusters of Recent and Rapid HIV Transmission Through Analysis of Molecular Surveillance Data.
Identifying Clusters of Recent and Rapid HIV Transmission Through Analysis of Molecular Surveillance Data.
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DOI：
10.1097/qai.0000000000001856
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发表时间：
2018-12-15
期刊：
影响因子：
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通讯作者：
Hernandez AL
中科院分区：
文献类型：
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作者：
Oster AM;France AM;Panneer N;Bañez Ocfemia MC;Campbell E;Dasgupta S;Switzer WM;Wertheim JO;Hernandez AL
检测艾滋病毒近期和快速传播有助于优先考虑对传播风险最高的人群进行预防和早期治疗。 HIV基因序列数据可以识别传播集群，但以前的方法无法区分近期快速传播的集群。我们评估了一种分析方法来识别美国的此类集群。我们分析了向国家艾滋病毒监测系统报告的 156,553 个部分 HIV-1 聚合酶序列，并使用两个遗传距离阈值（0.5% 和 1.5%）和两个诊断时间段（所有年份和 2013-2015 年，即最近的诊断）推断传播集群。对于快速增长的簇（2015 年诊断数≥5），分子钟系统发育分析估计了簇内所有分歧事件的最近共同祖先的时间。使用这些系统发育来估计簇传播率。 1.5% 的距离阈值使用所有诊断识别出 103 个快速增长的簇，使用最近的诊断识别出 73 个簇；在 0.5% 的情况下，使用所有诊断确定了 15 个簇，使用最近的诊断确定了 13 个簇。分子钟分析估计，使用最近的诊断以 0.5% 确定的 13 个簇的多样化时间中位数为 4.7 年，而使用其他方法的时间为 6.5-13.2 年。 13 个感染率为 0.5% 的聚集性病例的传播率为 33/100 人年，而此前国家估计的传播率为 4/100 人年。我们的方法确定了集群的传播率是之前国家估计值的 8 倍。这种方法可以识别参与快速传播的群体，并帮助项目有效地指导和优先考虑有限的公共卫生资源。
Detecting recent and rapid spread of HIV can help prioritize prevention and early treatment for those at highest risk of transmission. HIV genetic sequence data can identify transmission clusters, but previous approaches have not distinguished clusters of recent, rapid transmission. We assessed an analytic approach to identify such clusters in the United States. We analyzed 156,553 partial HIV-1 polymerase sequences reported to the National HIV Surveillance System and inferred transmission clusters using two genetic distance thresholds (0.5% and 1.5%) and two time periods for diagnoses (all years and 2013–2015, i.e., recent diagnoses). For rapidly growing clusters (with ≥5 diagnoses during 2015), molecular clock phylogenetic analysis estimated the time to most recent common ancestor for all divergence events within the cluster. Cluster transmission rates were estimated using these phylogenies. A distance threshold of 1.5% identified 103 rapidly growing clusters using all diagnoses and 73 using recent diagnoses; at 0.5%, 15 clusters were identified using all diagnoses and 13 using recent diagnoses. Molecular clock analysis estimated that the 13 clusters identified at 0.5% using recent diagnoses had been diversifying for a median of 4.7 years, compared with 6.5–13.2 years using other approaches. The 13 clusters at 0.5% had a transmission rate of 33/100 person-years, compared with previous national estimates of 4/100 person-years. Our approach identified clusters with transmission rates 8 times those of previous national estimates. This method can identify groups involved in rapid transmission and help programs effectively direct and prioritize limited public health resources.