Najjuuko Claire, Brathwaite Rachel, Mutumba Massy, Childress Saltanat, Nannono Sylivia, Namatovu Phionah, Lu Chenyang, Ssewamala Fred M
International Center for Child Health and Development, Brown School, Washington University in St. Louis, St. Louis, MO, 63130, USA.
Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, 63130, USA.
AIDS Behav. 2025 Sep 18. doi: 10.1007/s10461-025-04840-6.
Substance use among youth is a significant public health issue, particularly in low resource settings in Sub-Saharan Africa (SSA), where it contributes to HIV transmission and poor engagement in HIV care. This study employs machine learning (ML) techniques to develop models for predicting problematic substance use (PSU) among youth living with HIV (YLHIV) in Uganda, aiming to identify important multilevel risk factors and compare predictive performance of ML algorithms. Utilizing a cross-sectional dataset of 200 YLHIV aged 18-24 in Uganda, we trained and evaluated six predictive models, through 10-fold cross validation. Model performance was assessed using area under receiver operating characteristic curve (AUROC), and precision recall curve (AUPRC). Subsequent feature importance analysis revealed key predictors of PSU. The random forest model achieved the best discriminative performance with an AUROC of 0.78 (0.01) and AUPRC of 0.75 (0.02). Key predictors of PSU spanned individual, interpersonal, and community dimensions including depression, sexual risk-taking behaviors, monthly income, adverse childhood experiences, family involvement in selling alcohol, friends enabling access to alcohol, exposure to community educational campaigns against alcohol, household size, and knowledge of alcohol effects on HIV treatment. Our findings highlight ML's potential in predicting PSU among YLHIV and provide insights to guide targeted interventions and support policy formulations mitigating PSU effects on HIV management.
青少年药物使用是一个重大的公共卫生问题，在撒哈拉以南非洲（SSA）的资源匮乏地区尤为突出，在这些地区，药物使用会导致艾滋病毒传播以及艾滋病毒治疗参与度低。本研究采用机器学习（ML）技术开发模型，以预测乌干达感染艾滋病毒的青少年（YLHIV）中的问题性药物使用（PSU），旨在识别重要的多层次风险因素并比较ML算法的预测性能。利用乌干达200名年龄在18至24岁之间的YLHIV的横断面数据集，我们通过10倍交叉验证训练并评估了六个预测模型。使用受试者操作特征曲线下面积（AUROC）和精确召回率曲线（AUPRC）评估模型性能。随后的特征重要性分析揭示了PSU的关键预测因素。随机森林模型实现了最佳判别性能，AUROC为0.78（0.01），AUPRC为0.75（0.02）。PSU的关键预测因素涵盖个人、人际和社区层面，包括抑郁、性冒险行为、月收入、童年不良经历、家庭参与酒精销售、朋友提供酒精获取途径、接触社区反对酒精的教育活动、家庭规模以及对酒精对艾滋病毒治疗影响的了解。我们的研究结果突出了ML在预测YLHIV中PSU方面的潜力，并为指导有针对性的干预措施和支持减轻PSU对艾滋病毒管理影响的政策制定提供了见解。
