The role of prevention strategies in achieving HCV elimination in Canada: what are the remaining challenges?
Bibliographic record
Abstract
Background: The worldwide economic, health, and social consequences of drug use disorders are devastating. Injection drug use is now a major factor contributing to hepatitis C virus (HCV) transmission globally, and it is an important public health concern. Methods: This article presents a narrative review of scientific evidence on public health strategies for HCV prevention among people who inject drugs (PWID) in Canada. Results: A combination of public health strategies including timely HCV detection and harm reduction (mostly needle and syringe programmes and opioid substitution therapy) have helped to reduce HCV transmission among PWID. The rising prevalence of pharmaceutical opioid and methamphetamine use and associated HCV risk in several Canadian settings has prompted further innovation in harm reduction, including supervised injection facilities and low-threshold opioid substitution therapies. Further significant decreases in HCV incidence and prevalence, and in corresponding disease burden, can only be accomplished by reducing transmission among high-risk persons and enhancing access to HCV treatment for those at the greatest risk of disease progression or viral transmission. Highly effective and tolerable direct-acting antiviral therapies have transformed the landscape for HCV-infected patients and are a valuable addition to the prevention toolkit. Curing HCV-infected persons, and thus eliminating new infections, is now a real possibility. Conclusions: Prevention strategies have not yet ended HCV transmission, and sharing of injecting equipment among PWID continues to challenge the World Health Organization goal of eliminating HCV as a global public health threat by 2030. Future needs for research, intervention implementation, and uptake in Canada are discussed.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".