Progress toward hepatitis C virus elimination among people living with HIV–hepatitis C virus coinfection in Canada
Bibliographic record
Abstract
Background: Direct-acting antivirals (DAAs) for hepatitis C virus (HCV) were approved in Canada in 2013. By 2018, treatment restrictions had been lifted in all provinces, making DAAs universally accessible. Here, we aim to assess progress toward HCV elimination in the DAA era (2014-2022) within the Canadian Co-infection Cohort. Methods: The CCC is a multisite open cohort of people living with HIV-HCV coinfection with cohort visits scheduled biannually. We calculated annual cure rates overall by province and key population: gay bisexual and other men who have sex with men, people who inject drugs, and Indigenous peoples. Results: Among the 952 participants eligible for DAAs, 65% (n = 623) were cured and 3% (n = 26) achieved late clearance without treatment by the end of the study period. Of the 35% (n = 303) who were not cured, 46% (n = 138) were lost to follow-up, 31% (n = 95) died, and 23% (n = 70) had insufficient data to determine their cure status. Annual cure rates increased from 2014 to 2019 and then decreased across the cohort and in all key populations. From 2015 to 2018, gay bisexual and other men who have sex with men had the highest cure rates, but by 2019 people who inject drugs and Indigenous peoples caught up. Annual cure rates dropped in all provinces with the onset of the COVID-19 pandemic (after 2019). Conclusions: Universal accessibility of DAAs was an important step toward HCV elimination among people living with HIV-HCV coinfection, but this is not sufficient alone. Efforts are needed to re-engage those who remain uncured and reduce mortality in people living with HIV-HCV coinfection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".