Estimating chronic hepatitis C prevalence in British Columbia and Ontario, Canada, using population‐based cohort studies
Bibliographic record
Abstract
Patients identified as having chronic hepatitis C (CHC) infection can be effectively and rapidly treated using direct-acting antiviral agents. However, there remains a substantial burden of subclinical undetected infection. This study estimates the prevalence and undiagnosed proportion of CHC in British Columbia (BC) and Ontario, Canada, using a model-based approach, informed by provincial population-level health administrative data. A two-step approach was used: Step 1) Two population-based retrospective analyses of administrative health data for a cohort of British Columbians and a cohort of Ontarians with CHC were conducted to generate population-level statistics of CHC-related health events; Step 2) using a validated natural history model of hepatitis C virus (HCV) infection, the historical prevalence of CHC was back-calculated from the data collected in Step 1. Our retrospective study found that, in BC and Ontario, the number of newly diagnosed CHC cases is declining yearly while the complications of the disease are increasing yearly. BC had a 2014 CHC prevalence of 1.04% (95% CI: 0.84%-1.44%), with 33.3% (95% CI: 25.5%-42.0%) of CHC cases undiagnosed. Ontario had a 2014 CHC prevalence of 0.91% (95% CI: 0.83%-1.02%) with 36.0% (95% CI: 31.2%-38.9%) of CHC cases undiagnosed. Our study offers robust estimates based on the integration of a validated natural history model with population-level health administrative data on HCV-related events, which can provide vital evidence for policymakers to develop appropriate policies to achieve elimination targets. Our approach can also be applied to produce robust region-specific estimates in other countries.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".