Model-based projection of health and economic effects of screening for hepatitis C in Canada
Bibliographic record
Abstract
BACKGROUND: Because most hepatitis C virus (HCV) infections are asymptomatic and often unrecognized, screening for hepatitis C has been proposed as a plausible public health strategy. We examined the health and economic consequences of a selective one-time hepatitis C screening program for specific populations in the context of current treatment patterns. METHODS: We used a state-transition model to evaluate 2 general strategies: no screening, and screen and treat with direct-acting antiviral agents. We examined these strategies for 4 different target populations (scenarios): 1) asymptomatic people not at high risk for HCV infection, 2) immigrant populations with high prevalence, 3) a birth cohort of people aged 25-64 years and 4) a birth cohort of people aged 45-64 years of age. We obtained model data from the published literature and expert opinions. We used a payer perspective, a lifetime time horizon and a 5% discount rate. RESULTS: Screening would prevent 49.7%, 57.4%, 64.1% and 49.6% of HCV-related deaths over the lifetime of the cohort for scenarios 1, 2, 3 and 4, respectively. Screening would produce incremental-cost-effectiveness ratios between $31 468/quality-adjusted life-year and $50 490/quality-adjusted life-year. Probabilistic sensitivity analyses indicated that the chance that screening would be cost-effective at $50 000 willingness-to-pay threshold was 39.5%, 63.2%, 58.4% and 58.1% for scenarios 1, 2, 3 and 4, respectively. INTERPRETATION: Our analyses suggest that a one-time hepatitis C screening and treatment program in Canada is likely to be cost-effective for scenarios 2, 3 and 4. The screening programs we have evaluated would identify asymptomatic people with chronic HCV infection and would enable medical treatment to be offered if needed before the development of advanced liver disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".