National Hepatitis C estimates: Incidence, prevalence, undiagnosed proportion and treatment, Canada, 2019
Bibliographic record
Abstract
Background: Estimates of the number of hepatitis C virus (HCV) infections are important for monitoring efforts aimed at preventing disease transmission, especially following the introduction of a highly effective treatment. This report provides updated estimates of HCV incidence, prevalence, undiagnosed proportion and treatment in Canada. Methods: A combination of back calculation modelling and a modified version of the workbook method were used to estimate the incidence and prevalence of anti-HCV positive persons, the prevalence of chronic HCV infection and the undiagnosed proportion. The number of people treated for chronic HCV was estimated using administrative pharmaceutical data. Results: An estimated 9,470 new infections occurred in 2019, corresponding to an incidence rate of 25 per 100,000 population, a 7.7% decrease since 2015. The estimated prevalence of anti-HCV antibodies in the Canadian population was 1.03% (plausible range: 0.83%-1.38%), and the estimated prevalence of chronic HCV was 0.54% (plausible range: 0.40%-0.79%). The overall proportion of anti-HCV positive persons who were undiagnosed was estimated at 24% of all infections, with individuals born between 1945 and 1975 being the priority population the most likely to be undiagnosed. An estimated 74,500 people with chronic HCV have been treated since the introduction of direct-acting antivirals in 2014. Conclusion: Estimates of HCV incidence and prevalence are key metrics to guide interventions and resource allocation. While our estimates show that HCV incidence has decreased in Canada in recent years and treatment of chronic HCV has continued to increase, ongoing efforts are required to reduce the burden of HCV in Canada.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".