The Causal Effect of Opioid Agonist Treatment on Adherence to Direct-Acting Antiviral Treatment for Hepatitis C Virus
Bibliographic record
Abstract
Abstract Background Opioid agonist treatment (OAT) supports adherence in medication regimens for other concurrent conditions. However, sparse evidence is available on its effect on promoting retention to direct-acting antivirals (DAAs) for people with opioid use disorder (PWOUD) with concurrent hepatitis C virus (HCV). Our objective was to determine the causal impact of OAT exposure on DAA adherence among HCV-positive PWOUD. Methods We executed a retrospective study using linked population-level data for British Columbia, Canada (January 1996–September 2018). We estimated the effect of OAT on DAA adherence using generalized estimating equations (GEEs) and marginal structural modeling (MSM) for time-varying confounding. The primary outcome was 85% DAA adherence (minimum 6 of 7 days). Results We included 2820 HCV-positive PWOUD who initiated a DAA regimen (32.6% female, 83.9% previously accessing OAT), with 2410 (95% among uncensored episodes) completing the regimen. The GEE-adjusted odds ratio of DAA adherence after OAT exposure was 1.05 (0.89–1.23), whereas the MSM-adjusted odds ratio was 0.97 (0.78–1.22). Conclusions In a setting with universal healthcare and widespread access to OAT and DAA treatment, DAA regimen completion rates were high regardless of OAT, and engagement in OAT did not increase DAA adherence. Nonengagement in OAT should not preclude DAA treatment for PWOUD.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".