Opioid agonist treatment improves progression through the HIV cascade of care among people living with HIV who use unregulated opioids
Bibliographic record
Abstract
OBJECTIVES: Opioid agonist treatment (OAT) has been shown to improve certain HIV-related treatment measures among people with HIV (PHIV) with opioid use disorder (OUD). However, there is limited data on the impacts of OAT along the whole HIV cascade of care. DESIGN AND METHODS: Using data from an ongoing cohort of PHIV who use drugs in Vancouver, Canada, we used cumulative link mixed-effects models to estimate the independent effect of OAT on achieving progressive steps in the HIV cascade among participants using unregulated opioids daily, after adjusting for confounders. RESULTS: Between 2005 and 2017, we recruited 639 PHIV regularly using opioids (median age 42 years, 59% male, 56% White), of whom 70% were on OAT at their baseline visit. Engagement in OAT showed a nonsignificant trend with higher linkage to HIV care (adjusted partial proportional odds ratio [APPO] = 1.75, 95% confidence interval [CI]: 0.83-3.69), and significantly higher cumulative odds of successfully achieving subsequent HIV cascade steps: on ART (APPO = 3.85, 95% CI: 2.33-6.37); adherent to ART (APPO = 3.15, 95% CI: 2.15-4.62); and HIV viral suppression (APPO = 2.18, 95% CI: 1.51-3.14). CONCLUSIONS: This study found a high level of OAT engagement among PHIV using unregulated opioids and that OAT engagement resulted in significantly increased progression through some of the higher steps of the HIV cascade. While these findings are encouraging, they highlight the need to reach populations off OAT to maximize the clinical and community-level benefits of ART.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".