Disparities in uptake of direct‐acting antiviral therapy for hepatitis C among people who inject drugs in a Canadian setting
Bibliographic record
Abstract
BACKGROUND & AIMS: Despite the high burden of hepatitis C virus (HCV) infection among people who inject drugs (PWID), uptake of interferon-based therapies has been extremely low. Increasing availability of direct-acting antiviral (DAA)-based therapies offers the possibility of rapid treatment expansion with the goal of controlling the HCV epidemic. We evaluated DAA-based treatment uptake among HCV-positive PWID in Vancouver after introduction of DAAs in the government drug formulary. METHODS: Using data from three cohorts of PWID in Vancouver, Canada, we investigated factors associated with DAA-therapies uptake among participants with HCV between April 2015 and November 2017. RESULTS: Of the 915 HCV-positive PWID, 611 (66.8%) were recent PWID and 369 (40.3%) had HIV coinfection. During the study period, 146 (16.0%) initiated DAA-therapies, a rate of 6.0 per 100 person-year, with higher initiation rates among non-recent PWID and an increasing trend over time. In multivariable analysis, HIV coinfection (Adjusted Odds Ratio [AOR] = 2.29, 95% Confidence Interval [CI]: 1.55-3.40), white race (AOR = 1.56, 95% CI: 1.05-2.35), and engagement in HCV care (AOR = 1.94, 95% CI: 1.31-2.90) were positively associated with DAA-therapies uptake, while high-risk drinking (AOR = 0.47, 95% CI: 0.23-0.88) and daily crack use were negatively associated (AOR = 0.41, 95% CI: 0.17-0.85). Among recent PWID, engagement in opioid agonist therapy emerged as an independent correlate of DAA uptake. CONCLUSIONS: Despite increases in HCV treatment uptake among PWID after the introduction of DAAs in our setting, disparities in access remain. Social-structural and behavioural barriers to HCV care should be addressed for the success of any HCV elimination strategy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".