National hepatitis B and C estimates for 2021: Measuring Canada’s progress towards eliminating viral hepatitis as a public health concern
Bibliographic record
Abstract
Background: Hepatitis B virus (HBV) and hepatitis C virus (HCV) infections are major causes of morbidity and mortality worldwide. Measuring the epidemiological burden of HCV and HBV in Canada is essential to measure progress towards global elimination targets and to ultimately eliminate viral hepatitis as a public health concern. Objective: This study aimed to provide the first national estimates of HBV prevalence and unawareness, and to update estimates of HCV incidence, prevalence, and unawareness in the general population and key populations in Canada for 2021. Progress towards elimination targets for 2025, namely incidence, awareness, mortality, and HBV vaccination, was also assessed. Methods: A combination workbook method and mathematical modelling was used to estimate the prevalence and unawareness of chronic hepatitis B (CHB), prevalence and incidence of anti-HCV antibodies, and the prevalence and unawareness of chronic hepatitis C (CHC). Results: The estimated prevalence of CHB was 0.68% (plausible range: 0.40%-0.97%) or 262,000 (152,000-371,000) people in the general population, of whom 42.5% (33.9%-51.0%) were unaware of their infection. Immigrants from countries where HBV is common had the highest prevalence at 4.2% (1.9%-5.6%). An estimated 8,212 new HCV infections occurred in 2021, and the estimated prevalence of CHC was 0.56% (0.15%-0.97%) or 214,000 (58,500-369,000) people, of whom 41.5% (34.3%-48.8%) were unaware of their infection. People who inject drugs had the highest prevalence and largest proportion who were unaware at 36.9% (12.6%-55.1%) and 49.9% (29.0%-70.2%), respectively. Conclusion: While the overall viral hepatitis burden is low in the general Canadian population, these estimates indicate that certain populations and communities remain disproportionately affected. Although Canada has met some of the 2025 targets, more work is needed. To this end, efforts to obtain and standardize provincial and national data will be required to measure progress towards all elimination targets.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".