Abstract
Stimulant use is an important driver of HIV/STI transmission among men who have sex with men (MSM). Evaluating factors associated with increased stimulant use is critical to inform HIV prevention programming efforts. This study seeks to use machine learning variable selection techniques to determine characteristics associated with increased stimulant use and whether these factors differ by HIV status. Data from a longitudinal cohort of predominantly Black/Latinx MSM in Los Angeles, CA was used. Every 6 months from 8/2014–12/2020, participants underwent STI testing and completed surveys evaluating the following: demographics, substance use, sexual risk behaviors, and last partnership characteristics. Least absolute shrinkage and selection operator (lasso) was used to select variables and create predictive models for an interval increase in self-reported stimulant use across study visits. Mixed-effects logistic regression was then used to describe associations between selected variables and the same outcome. Models were also stratified based on HIV status to evaluate differences in predictors associated with increased stimulant use. Among 2095 study visits from 467 MSM, increased stimulant use was reported at 20.9% (n = 438) visits. Increased stimulant use was positively associated with unstable housing (adjusted [a]OR 1.81; 95% CI 1.27–2.57), STI diagnosis (1.59; 1.14–2.21), transactional sex (2.30; 1.60–3.30), and last partner stimulant use (2.21; 1.62–3.00). Among MSM living with HIV, increased stimulant use was associated with binge drinking, vaping/cigarette use (aOR 1.99; 95% CI 1.36–2.92), and regular use of poppers (2.28; 1.38–3.76). Among HIV-negative MSM, increased stimulant use was associated with participating in group sex while intoxicated (aOR 1.81; 95% CI 1.04–3.18), transactional sex (2.53; 1.40–2.55), and last partner injection drug use (1.96; 1.02–3.74). Our findings demonstrate that lasso can be a useful tool for variable selection and creation of predictive models. These results indicate that risk behaviors associated with increased stimulant use may differ based on HIV status and suggest that co-substance use and partnership contexts should be considered in the development of HIV prevention/treatment interventions.
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The data that support the findings of this study are available from the others upon reasonable request and with permission from the UCLA Office of Human Research Participant Protection.
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mSTUDY is funded by the National Institute on Drug Abuse (U01 DA036267). CSB was supported by the National Institute of Mental Health (T32 MH080634) and the National Institute on Drug Abuse (K23 DA054004). WSC, SS, and PMG were supported by the Center for HIV Identification, Prevention, and Treatment Services (CHIPTS; P30 MH058107).
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Blair, C.S., Javanbakht, M., Comulada, W.S. et al. Comparing Factors Associated with Increased Stimulant Use in Relation to HIV Status Using a Machine Learning and Prediction Modeling Approach. Prev Sci 24, 1102–1114 (2023). https://doi.org/10.1007/s11121-023-01561-x
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DOI: https://doi.org/10.1007/s11121-023-01561-x