Alcohol and drug use severity are independently associated with antiretroviral adherence in the current treatment era
Bibliographic record
Abstract
Substance use in people with HIV (PWH) negatively impacts antiretroviral therapy (ART) adherence. However, less is known about this in the current treatment era and the impact of specific substances or severity of substance use. We examined the associations of alcohol, marijuana, and illicit drug use (methamphetamine/crystal, cocaine/crack, illicit opioids/heroin) and their severity of use with adherence using multivariable linear regression in adult PWH in care between 2016 and 2020 at 8 sites across the US. PWH completed assessments of alcohol use severity (AUDIT-C), drug use severity (modified ASSIST), and ART adherence (visual analogue scale). Among 9400 PWH, 16% reported current hazardous alcohol use, 31% current marijuana use, and 15% current use of ≥1 illicit drugs. In multivariable analysis, current methamphetamine/crystal use, particularly common among men who had sex with men, was associated with 10.1% lower mean ART adherence (p < 0.001) and 2.6% lower adherence per 5-point higher severity of use (ASSIST score) (p < 0.001). Current and more severe use of alcohol, marijuana, and other illicit drugs were also associated with lower adherence in a dose-dependent manner. In the current HIV treatment era, individualized substance use treatment, especially for methamphetamine/crystal, and ART adherence should be prioritized.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".