Oral Abstracts From Adherence 2023 Conference.

IF 2.2 Q3 INFECTIOUS DISEASES

Journal of the International Association of Providers of AIDS Care Pub Date : 2023-01-01 DOI:10.1177/23259582231196717

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Abstract

Background: To achieve ending the HIV Epidemic goals, key populations, including sexual minority men, need to adhere to evidence-based biomedical interventions including antiretroviral therapy as treatment (ART) or preexposure prophylaxis (PrEP). The present study integrates traditional and machine learning methods to evaluate whether a common set of factors can predict adherence to ART for both treatment and prevention. Method: Participants included 365 sexual minority men taking antiretroviral therapy as treatment or PrEP in South Florida. Survey respondents provided information on adherence to treat-ment or PrEP and demographic, psychosocial, and behavioral factors potentially associated with adherence. Data were analyzed using machine learning algorithms that are simple to interpret such as Classi ﬁ cation and Regression Tree and LASSO regression variable selection, techniques that require specialized extra steps to look inside “ black box models ” like Multivariate Adaptive Regression Spline (MARS) and Random Forest models, and traditional stepwise logistic regression to identify factors associated with adherence. Results: Taking ART for HIV treatment or PrEP was not an important predictor for adherence in any of the models. Rather, the models suggested that a common set of predictors can be used to predict adherence to ART for both treatment and PrEP. Race/ethnicity was identi ﬁ ed by all models as an important predictor of adherence. Additionally, depressive symptoms, anxiety symptoms, and substance use were identi-ﬁ ed as an important adherence predictor by at least three (of ﬁ ve) models. Determinants less commonly identi ﬁ ed as important for adherence were alcohol use (CART and LASSO only), sexual orientation (CART only), self-esteem, and condomless sex (Random Forest and LASSO only). Conclusion:

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