Identification and validation of a multi-assay algorithm for cross-sectional HIV incidence estimation in populations with subtype C infection
Identification and validation of a multi-assay algorithm for cross-sectional HIV incidence estimation in populations with subtype C infection
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DOI：
10.1002/jia2.25082
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发表时间：
2018-02-28
影响因子：
6
通讯作者：
Eshleman, Susan H.
中科院分区：
文献类型：
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作者：
Laeyendecker, Oliver;Konikoff, Jacob;Eshleman, Susan H.
介绍横断面方法可以用来估计艾滋病毒的发病率监测和预防研究。我们评估了检测和多检测算法（MAAs）的发病率估计在亚型C settings.Methods我们分析了样本从个人与亚型C感染已知的感染持续时间（2442样本278成人;血清转换后0.1至9.9年）。MAA包括以下1-4项测定：限制性抗原亲合力测定（LAg-亲合力）、BioRad-Avidity测定、CD 4细胞计数和病毒载量（VL）。我们用不同的检测方法和检测截止值评估了23，400个MAA。我们确定了具有最大平均窗口期的MAA，其中阴影的95%置信区间（CI）上限为
Introduction Cross-sectional methods can be used to estimate HIV incidence for surveillance and prevention studies. We evaluated assays and multi-assay algorithms (MAAs) for incidence estimation in subtype C settings.Methods We analysed samples from individuals with subtype C infection with known duration of infection (2442 samples from 278 adults; 0.1 to 9.9 years after seroconversion). MAAs included 1-4 of the following assays: Limiting Antigen Avidity assay (LAg-Avidity), BioRad-Avidity assay, CD4 cell count and viral load (VL). We evaluated 23,400 MAAs with different assays and assay cutoffs. We identified the MAA with the largest mean window period, where the upper 95% confidence interval (CI) of the shadow was