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

Cheríe S Blair1, Marjan Javanbakht2, W Scott Comulada3,4

  • 1Department of Medicine, Division of Infectious Diseases, David Geffen School of Medicine at UCLA, 10833 Le Conte Avenue, CHS 52-215, Los Angeles, CA, 90095, USA. cherieblair@mednet.ucla.edu.

Prevention Science : the Official Journal of the Society for Prevention Research
|June 16, 2023
PubMed
Summary
This summary is machine-generated.

Stimulant use increases HIV/STI risks for men who have sex with men (MSM). Factors like unstable housing and transactional sex are linked to increased stimulant use, differing by HIV status.

Keywords:
HIVMen who have sex with menStimulantsSubstance use

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Area of Science:

  • Public Health
  • Epidemiology
  • Machine Learning in Health

Background:

  • Stimulant use is a significant factor in HIV and sexually transmitted infection (STI) transmission among men who have sex with men (MSM).
  • Understanding the characteristics associated with increased stimulant use is crucial for developing effective HIV prevention strategies for this population.

Purpose of the Study:

  • To employ machine learning techniques for identifying factors associated with increased stimulant use among MSM.
  • To investigate whether these influencing factors vary based on HIV status (positive vs. negative).

Main Methods:

  • Utilized data from a longitudinal cohort of predominantly Black/Latinx MSM in Los Angeles, CA, spanning from August 2014 to December 2020.
  • Employed the Least Absolute Shrinkage and Selection Operator (LASSO) for variable selection and predictive model creation for increased stimulant use.
  • Used mixed-effects logistic regression, stratified by HIV status, to analyze associations between selected variables and increased stimulant use.

Main Results:

  • Increased stimulant use was observed in 20.9% of study visits.
  • Overall, increased stimulant use was associated with unstable housing, STI diagnosis, transactional sex, and partner's stimulant use.
  • Among MSM living with HIV, increased stimulant use correlated with binge drinking, vaping/cigarette use, and popper use. Among HIV-negative MSM, associations included group sex while intoxicated, transactional sex, and partner's injection drug use.

Conclusions:

  • Machine learning, specifically LASSO, proved effective for variable selection and predictive modeling in this context.
  • The study highlights that risk behaviors linked to increased stimulant use differ significantly between HIV-positive and HIV-negative MSM.
  • Findings underscore the need to consider co-substance use and partnership dynamics in designing targeted HIV prevention and treatment interventions for MSM.