Medical chart-reported alcohol consumption, substance use, and mental health issues in association with HIV pre-exposure prophylaxis (PrEP) nonadherence among gay, bisexual, and other men-who-have-sex-with-men
Bibliographic record
Abstract
BACKGROUND: Although some evidence suggests that alcohol, substance use, and mental health issues diminish adherence to HIV Pre-Exposure Prophylaxis (PrEP) among gay, bisexual, and other men-who-have-sex-with-men (gbMSM), findings are somewhat inconsistent and have primarily derived from studies involving non-random samples. Medical chart extraction can provide unique insight by in part surmounting sampling-related limitations, as data for entire PrEP clinic populations can be examined. Our investigation entailed comprehensive chart extraction to assess the extent to which chart-reported alcohol, substance use, and mental health issues were associated with chart-reported PrEP nonadherence. METHODS: Data from medical charts of gbMSM at two PrEP clinics in Toronto, Canada were extracted for a retrospective 12-month period (02/2018-01/2019). Charts were reviewed for all patients who were 1) ≥ 18 years old; 2) gbMSM; 3) prescribed PrEP ≥ 3 months, and 4) not in a PrEP-related drug trial. Information regarding PrEP, alcohol, substance use, mental health, and sexual behavior was extracted. PrEP adherence was classified in terms of (1) any reported nonadherence, and (2) 'suboptimal adherence,' reflecting nonadherence patterns indicative of insufficient pharmacological protection from HIV. Multivariate logistic regression was employed to identify factors associated with adherence outcomes. RESULTS: Data were extracted from 4,292 clinic visits among 501 eligible patients (age: M = 39.1; duration on PrEP: M = 17.4 months; daily PrEP regimen = 93.8%). Hazardous/harmful drinking, club drug use, and mental health issues were reported among 8.8%, 22.2%, and 26.1% of patients, respectively. Any nonadherence and suboptimal adherence were reported among 37.5% and 12.4% of patients, respectively. Factors significantly associated with any nonadherence included age < 25 (AOR = 3.08, 95%CI = 1.54-6.15, p < .001), club drug use (AOR = 2.71, 95%CI = 1.65-4.47, p < .001), and condomless sex (AOR = 1.83, 95%CI = 1.19-2.83, p = .006). For suboptimal adherence, significant factors included age < 25 (AOR = 4.83, 95%CI = 2.28-10.22, p < .001), non-daily PrEP regimens (AOR = 2.94, 95%CI = 1.19-7.22, p = .019), missing PrEP appointments (AOR = 1.97, 95%CI = 1.09-3.55, p = .025), and club drug use (AOR = 1.97, 95%CI = 1.01-3.68, p = .033). Neither alcohol nor mental health issues were associated with nonadherence outcomes. CONCLUSIONS: Chart-indicated suboptimal adherence was present among a small subgroup of PrEP-prescribed gbMSM. Adherence-related interventions should target gbMSM who use club drugs, are younger, experience challenges attending PrEP care, and are prescribed non-daily regimens. Offering long-acting injectable PrEP when available and feasible may also improve PrEP's HIV-preventive impact among this population.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".