Alcohol consumption, substance use, and depression in relation to HIV Pre-Exposure Prophylaxis (PrEP) nonadherence among gay, bisexual, and other men-who-have-sex-with-men
Bibliographic record
Abstract
Abstract Background Although HIV pre-exposure prophylaxis (PrEP) substantially diminishes the likelihood of HIV acquisition, poor adherence can decrease the HIV-protective benefits of PrEP. The present investigation sought to identify the extent to which alcohol consumption, substance use, and depression were linked to PrEP nonadherence among gay, bisexual, and other men-who-have-sex-with-men (gbMSM). Methods gbMSM (age ≥ 18, prescribed PrEP for ≥3 months) were recruited from two clinics in Toronto, Canada for an e-survey assessing demographics; PrEP nonadherence (4-day PrEP-focused ACTG assessment); hazardous and harmful alcohol use (AUDIT scores of 8–15 and 16+, respectively); moderate/high risk substance use (NIDA M-ASSIST scores > 4); depression (CESD-10 scores ≥10); and other PrEP-relevant factors. The primary outcome, PrEP nonadherence, entailed missing one or more PrEP doses over the past 4 days. A linear-by-linear test of association assessed whether increasing severity of alcohol use (i.e., based on AUDIT categories) was linked to a greater occurrence of PrEP nonadherence. Univariate logistic regression was employed to determine factors associated with PrEP nonadherence, and factors demonstrating univariate associations at the p < .10 significance level were included in a multivariate logistic regression model. Additive and interactive effects involving key significant factors were assessed through logistic regression to evaluate potential syndemic-focused associations. Results A total of 141 gbMSM (Mean age = 37.9, white = 63.1%) completed the e-survey. Hazardous/harmful drinking (31.9%), moderate/high risk substance use (43.3%), and depression (23.7%) were common; and one in five participants (19.9%) reported PrEP nonadherence. Increasing alcohol use level was significantly associated with a greater likelihood of nonadherence (i.e., 15.6, 25.0, and 44.4% of low-risk, hazardous, and harmful drinkers reported nonadherence, respectively (χ2(1) = 4.79, p = .029)). Multivariate logistic regression demonstrated that harmful alcohol use (AOR = 6.72, 95%CI = 1.49–30.33, p = .013) and moderate/high risk cocaine use (AOR = 3.11, 95%CI = 1.01–9.59, p = .049) independently predicted nonadherence. Furthermore, an additive association emerged, wherein the likelihood of PrEP nonadherence was highest among those who were hazardous/harmful drinkers and moderate/high risk cocaine users (OR = 2.25, 95%CI = 1.19–4.25, p = .013). Depression was not associated with nonadherence. Conclusions Findings highlight the need to integrate alcohol- and substance-focused initiatives into PrEP care for gbMSM. Such initiatives, in turn, may help improve PrEP adherence and reduce the potential for HIV acquisition among this group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".