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TL;DR
It found that a common set of predictors, including race/ethnicity, depressive symptoms, anxiety, and substance use, was important for adherence regardless of whether the medication was for treatment or prevention.
Journal of the International Association of Providers of AIDS Care
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It found that a common set of predictors, including race/ethnicity, depressive symptoms, anxiety, and substance use, was important for adherence regardless of whether the medication was for treatment or prevention.
The study integrated traditional stepwise logistic regression with machine learning algorithms (CART, LASSO, MARS, Random Forest) to identify factors associated with adherence. Instead, a common set of predictors was identified, with race/ethnicity being significant in all models, and depressive symptoms, anxiety symptoms, and substance use significant in at least three of five models. Other factors like alcohol use, sexual orientation, self-esteem, and condomless sex were less consistently identified. The study's implication is that a unified set of psychosocial and behavioral factors may guide adherence interventions for sexual minority men using ART for either purpose.