The Impact of Current Opioid Agonist Therapy on Hepatitis C Virus Treatment Initiation Among People Who Use Drugs From the Direct-acting Antiviral (DAA) Era: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Evidence that opioid agonist therapy (OAT) is associated with increased odds of hepatitis C virus (HCV) treatment initiation among people who use drugs (PWUD) is emerging. The objective of this study was to determine the association between current OAT and HCV treatment initiation among PWUD in a population-level linked administrative dataset. METHODS: The British Columbia Hepatitis Testers Cohort was used for this study, which includes all people tested for or diagnosed with HCV in British Columbia, linked to medical visits, hospitalizations, laboratory, prescription drug, and mortality data from 1992 until 2019. PWUD with injecting drug use or opioid use disorder and chronic HCV infection were identified for inclusion in this study. HCV treatment initiation was the main outcome, and subdistribution proportional hazards modeling was used to assess the relationship with current OAT. RESULTS: In total, 13 803 PWUD with chronic HCV were included in this study. Among those currently on OAT at the end of the study period, 47% (2704/5770) had started HCV treatment, whereas 22% (1778/8033) of those not currently on OAT had started HCV treatment. Among PWUD with chronic HCV infection, current OAT was associated with higher likelihood of HCV treatment initiation in time to event analysis (adjusted hazard ratio 1.84 [95% confidence interval {CI}, 1.50, 2.26]). CONCLUSIONS: Current OAT was associated with a higher likelihood of HCV treatment initiation. However, many PWUD with HCV currently receiving OAT have yet to receive HCV treatment. Enhanced integration between substance use care and HCV treatment is needed to improve the overall health of PWUD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".