The Longitudinal Effects of Non-injection Substance Use on Sustained HIV Viral Load Undetectability Among MSM and Heterosexual Men in Brazil and Thailand: The Role of ART Adherence and Depressive Symptoms (HPTN 063).
Kiyomi TsuyukiSteven J ShoptawYusuf RansomeGordon ChauCarlos E Rodriguez-DiazRuth K FriedmanKriengkrai SrithanaviboonchaiSue LiMatthew J MimiagaKenneth H MayerSteven A SafrenPublished in: AIDS and behavior (2019)
The effect of non-injection substance use on HIV viral load (VL) is understudied in international settings. Data are from HPTN063, a longitudinal observational study of HIV-infected individuals in Brazil, Thailand, and Zambia, with focus on men with VL data (Brazil = 146; Thailand = 159). Generalized linear mixed models (GLMM) assessed whether non-injection substance use (stimulants, cannabis, alcohol, polysubstance) was associated with VL undetectability. ART adherence and depressive symptoms were examined as mediators of the association. In Thailand, substance use was not significantly associated with VL undetectability or ART adherence, but alcohol misuse among MSM was associated with increased odds of depression (AOR = 2.75; 95% CI 1.20, 6.32, p = 0.02). In Brazil, alcohol misuse by MSM was associated with decreased odds of undetectable VL (AOR = 0.34; 95% CI 0.13, 0.92, p = 0.03). Polysubstance use by heterosexual men in Brazil was associated with decreased odds of ART adherence (AOR = 0.25; 95% CI 0.08, 0.78, p = 0.02). VL suppression appears attainable among non-injection substance users. Substance use interventions among HIV-positive men should address depression, adherence, and VL undetectability.
Keyphrases
- antiretroviral therapy
- hiv infected
- hiv positive
- men who have sex with men
- depressive symptoms
- hiv testing
- human immunodeficiency virus
- hiv aids
- ultrasound guided
- sleep quality
- middle aged
- social support
- south africa
- glycemic control
- electronic health record
- big data
- physical activity
- alcohol consumption
- metabolic syndrome
- skeletal muscle
- machine learning
- data analysis
- hepatitis c virus
- weight loss