利用全州监测数据预测 HIV 传播簇的增长。
Prediction of HIV Transmission Cluster Growth With Statewide Surveillance Data.
机构信息
Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC.
Communicable Disease Branch, Division of Public Health, North Carolina Department of Health and Human Services, Raleigh, NC.
出版信息
J Acquir Immune Defic Syndr. 2019 Feb 1;80(2):152-159. doi: 10.1097/QAI.0000000000001905.
BACKGROUND
Prediction of HIV transmission cluster growth may help guide public health action. We developed a predictive model for cluster growth in North Carolina (NC) using routine HIV surveillance data.
METHODS
We identified putative transmission clusters with ≥2 members through pairwise genetic distances ≤1.5% from HIV-1 pol sequences sampled November 2010-December 2017 in NC. Clusters established by a baseline of January 2015 with any sequences sampled within 2 years before baseline were assessed for growth (new diagnoses) over 18 months. We developed a predictive model for cluster growth incorporating demographic, clinical, temporal, and contact tracing characteristics of baseline cluster members. We internally and temporally externally validated the final model in the periods January 2015-June 2016 and July 2016-December 2017.
RESULTS
Cluster growth was predicted by larger baseline cluster size, shorter time between diagnosis and HIV care entry, younger age, shorter time since the most recent HIV diagnosis, higher proportion with no named contacts, and higher proportion with HIV viremia. The model showed areas under the receiver-operating characteristic curves of 0.82 and 0.83 in the internal and temporal external validation samples.
CONCLUSIONS
The predictive model developed and validated here is a novel means of identifying HIV transmission clusters that may benefit from targeted HIV control resources.
背景
预测 HIV 传播簇的增长情况可能有助于指导公共卫生行动。我们利用北卡罗来纳州（NC）的常规 HIV 监测数据开发了一种用于预测簇增长的预测模型。
方法
我们通过 HIV-1 pol 序列的成对遗传距离≤1.5%，从 2010 年 11 月至 2017 年 12 月在 NC 采集的样本中确定了具有≥2 名成员的可能传播簇。在基线为 2015 年 1 月的基础上建立的簇，只要在基线前 2 年内采集了任何序列，就会在 18 个月内评估其增长情况（新诊断）。我们开发了一个预测模型，纳入了基线簇成员的人口统计学、临床、时间和接触者追踪特征，以预测簇的增长。我们在 2015 年 1 月至 2016 年 6 月和 2016 年 7 月至 2017 年 12 月的内部和时间外部验证了最终模型。
结果
簇的增长由较大的基线簇大小、诊断与 HIV 护理入院之间的时间较短、年龄较小、最近一次 HIV 诊断后的时间较短、无命名接触者的比例较高、HIV 病毒载量较高来预测。该模型在内部和时间外部验证样本中的受试者工作特征曲线下面积分别为 0.82 和 0.83。
结论
在这里开发和验证的预测模型是一种识别可能受益于有针对性的 HIV 控制资源的 HIV 传播簇的新方法。
Zotero 插件