Comparing Factors Associated with Increased Stimulant Use in Relation to HIV Status Using a Machine Learning and Prediction Modeling Approach

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 predict ors 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 st imulant 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, inc...
Source: Prevention Science - Category: Psychiatry & Psychology Source Type: research