南非青少年女孩和年轻妇女中 HIV 的预测风险评分：确定需要 HIV 暴露前预防的人群。

A prediction risk score for HIV among adolescent girls and young women in South Africa: identifying those in need of HIV pre-exposure prophylaxis.

机构信息

Faculty of Medicine and Family Health, Division of Epidemiology and Biostatistics, Stellenbosch University, Cape Town, South Africa.

Health Systems Research Unit, South African Medical Research Council, Cape Town, South Africa.

出版信息

HIV Res Clin Pract. 2023 Jun 3;24(1):2221377.

PMID:37292011

Abstract

BACKGROUND

In sub-Saharan Africa (SSA), adolescent girls and young women (AGYW) have the highest risk of acquiring HIV. This has led to several studies aimed at identifying risk factors for HIV in AGYM. However, a combination of the purported risk variables in a multivariate risk model could be more useful in determining HIV risk in AGYW than one at a time. The purpose of this study was to develop and validate an HIV risk prediction model for AGYW.

METHODS

We analyzed HIV-related HERStory survey data on 4,399 AGYW from South Africa. We identified 16 purported risk variables from the data set. The HIV acquisition risk scores were computed by combining coefficients of a multivariate logistic regression model of HIV positivity. The performance of the final model at discriminating between HIV positive and HIV negative was assessed using the area under the receiver-operating characteristic curve (AUROC). The optimal cut-point of the prediction model was determined using the Youden index. We also used other measures of discriminative abilities such as predictive values, sensitivity, and specificity.

RESULTS

The estimated HIV prevalence was 12.4% (11.7% - 14.0) %. The score of the derived risk prediction model had a mean and standard deviation of 2.36 and 0.64 respectively and ranged from 0.37 to 4.59. The prediction model's sensitivity was 16. 7% and a specificity of 98.5%. The model's positive predictive value was 68.2% and a negative predictive value of 85.8%. The prediction model's optimal cut-point was 2.43 with sensitivity of 71% and specificity of 60%. Our model performed well at predicting HIV positivity with training AUC of 0.78 and a testing AUC of 0.76.

CONCLUSION

A combination of the identified risk factors provided good discrimination and calibration at predicting HIV positivity in AGYW. This model could provide a simple and low-cost strategy for screening AGYW in primary healthcare clinics and community-based settings. In this way, health service providers could easily identify and link AGYW to HIV PrEP services.

摘要

背景

在撒哈拉以南非洲（SSA），青少年女孩和年轻妇女（AGYW）感染 HIV 的风险最高。这导致了几项旨在确定 AGYM 中 HIV 风险因素的研究。然而，将多个假定的风险变量组合在一个多变量风险模型中，可能比一次一个变量更有助于确定 AGYW 的 HIV 风险。本研究的目的是为 AGYW 开发和验证一种 HIV 风险预测模型。

方法

我们分析了来自南非的 4399 名 AGYW 的 HIV 相关 HERStory 调查数据。我们从数据集中确定了 16 个假定的风险变量。通过组合 HIV 阳性多元逻辑回归模型的系数来计算 HIV 获得风险评分。使用接受者操作特征曲线（AUROC）下的面积来评估最终模型在区分 HIV 阳性和 HIV 阴性方面的性能。使用约登指数确定预测模型的最佳切点。我们还使用了其他区分能力的衡量标准，如预测值、敏感性和特异性。

结果

估计的 HIV 流行率为 12.4%（11.7%-14.0%）。推导的风险预测模型的得分平均值和标准差分别为 2.36 和 0.64，范围为 0.37 至 4.59。预测模型的敏感性为 16.7%，特异性为 98.5%。该模型的阳性预测值为 68.2%，阴性预测值为 85.8%。预测模型的最佳切点为 2.43，敏感性为 71%，特异性为 60%。我们的模型在预测 HIV 阳性方面表现良好，训练 AUC 为 0.78，测试 AUC 为 0.76。