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<p>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.</p>

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Identifying Predictors of Problematic Substance Use Among Youth Living with HIV in Uganda: A Machine Learning Approach

  • 期刊论文

  • 发表日期：

    2025年9月18日

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  • Claire Najjuuko,
  • Rachel Brathwaite,
  • Massy Mutumba,
  • Saltanat Childress,
  • Sylivia Nannono,
  • Phionah Namatovu,
  • Chenyang Lu,
  • Fred M. Ssewamala

摘要

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.