Cost-effectiveness of screening for hepatitis C in Canada
Bibliographic record
Abstract
BACKGROUND: The seroprevalence of hepatitis C virus (HCV) infection among Canadians is estimated at 0.3% to 0.9%. Of those with chronic HCV infection, 10% to 20% will experience advanced liver disease by 30 years of infection. Targeted screening seems a plausible strategy. We aimed to estimate the health and economic effects of various screening and treatment strategies for chronic HCV infection in Canada. METHODS: We used a state-transition model to examine the cost-effectiveness of 4 screening strategies: no screening; screen and treat with pegylated interferon plus ribavarin; screen and treat with pegylated interferon and ribavarin-based direct-acting antiviral agents; and screen and treat with interferon-free direct-acting antivirals. We considered Canadian residents in 2 age groups: 25-64 and 45-64 years of age. We obtained model data from the literature. We predicted deaths related to chronic HCV infection, costs, quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios. RESULTS: We found that screening and treating would prevent at least 9 HCV-related deaths per 10,000 persons screened over the lifetime of the cohort. Screening was associated with QALY increases of 0.0032 to 0.0095 and cost increases of $124 to $338 per person, which translated to an incremental cost-effectiveness ratio of $34,359 to $44,034 per QALY gained, relative to no screening, depending on age group screened and antiviral therapy received. INTERPRETATION: A selective one-time HCV screening program for people 25-64 or 45-64 years of age in Canada would likely be cost-effective. Identification of silent cases of chronic HCV infection and the offer of treatment when appropriate could extend the lives of Canadians at reasonable cost.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".