Developing HIV risk prediction tools in four African settings
Developing HIV risk prediction tools in four African settings
复制标题
DOI：
10.1111/tmi.13916
复制
发表时间：
2023-07-27
影响因子：
3.3
通讯作者：
Hansen，Christian Holm
中科院分区：
文献类型：
--
作者：
Kansiime，Sheila;Hansen，Christian Holm
艾滋病毒风险预测工具是结束艾滋病毒大流行的努力的一个关键组成部分。我们的目的是创建和验证工具，用于识别个人在非洲setting.MethodsWe流行和事件的HIV的最高风险，以确定风险因素的HIV流行和发病率在一个多国HIV疫苗试验准备队列研究中的HIV高风险的个人，并使用确定的因素来创建和验证工具，预测HIV的风险。我们还评估了性能的VOICE风险评分在预测艾滋病毒的发病率之间的妇女在coherent.ResultsThe流行的艾滋病毒预测工具创建了良好的预测能力[曲线下面积（AUC）= 0.70，95%CI 0.66-0.74]。它包括以下参与者变量：年龄，性别，娱乐性药物使用，无保护的男性对男性肛交，有其他伴侣的性伴侣，交易性行为和有一个长途卡车司机/矿工的伴侣。不可能建立一个有效的艾滋病毒发病率预测工具。VOICE风险评分高（≥7）的参与者HIV发病率略高，但该工具在我们的研究中表现不佳（AUC = 0.58，95%CI 0.51-0.64：Harrell一致性指数= 0.59）。现有的事件HIV预测工具可能需要修改，以包括特定背景的预测因子，如日历周期，参与者职业，研究中心，然后在与开发它们的环境不同的环境中采用。
ObjectiveHIV risk prediction tools are a critical component of efforts to end the HIV pandemic. We aimed to create and validate tools for identifying individuals at highest risk of prevalent and incident HIV in an African setting.MethodsWe used Logistic regression and Poisson regression to determine risk factors for HIV prevalence and incidence in a multi‐country HIV vaccine trial preparedness cohort study among individuals at high risk of HIV, and used the identified factors to create and validate tools that predict HIV risk. We also assessed the performance of the VOICE risk score in predicting HIV incidence among women in the cohort.ResultsThe prevalent HIV prediction tool created had good predictive ability [area under the curve (AUC) = 0.70, 95% CI 0.66–0.74]. It included the following participant variables: age, sex, recreational drug use, unprotected male‐to‐male anal sex, a sexual partner who had other partners, transactional sex and having a partner who was a long‐distance truck driver/miner. It was not possible to create a valid HIV incidence prediction tool. Participants with high VOICE risk scores (≥7) had slightly higher HIV incidence but this tool performed poorly within our study (AUC = 0.58, 95% CI 0.51–0.64: Harrell's concordance index = 0.59).ConclusionWe created a prevalent HIV prediction tool that could be used to increase efficiency in diagnosis of HIV and linkage to care in sub‐Saharan Africa. Existing incident HIV prediction tools may need modification to include context‐specific predictors such as calendar period, participant occupation, study site, before adoption in settings different from those in which they were developed.