Can we afford not to screen and treat hepatitis C virus infection in Canada?
Bibliographic record
Abstract
Background: Screening for hepatitis C virus (HCV) followed by direct-acting antiviral (DAA) treatment in individuals born between 1945 and 1964 has been shown to be both effective and cost-effective, but the question of affordability remains unresolved. We looked at long-term cost and health outcomes of HCV screening for Ontario up to 2030. Methods: We used a validated state-transition model to analyze the budget and health impact of HCV screening followed by DAA treatment in individuals born between 1945 and 1964 versus current practice. We used a payer's perspective, discounting costs at an annual rate of 1.5%. Costs, liver-related deaths, and hepatocellular carcinoma (HCC) and decompensated cirrhosis (DC) cases detected were measured over a 14-year period. Results: By 2030, the cost of implementing a HCV screening program for individuals born between 1945 and 1964 will add an additional $845 million to the Ontario health care budget. Sensitivity analyses showed that DAA costs had the largest effect on the budget, and decreasing DAA costs to $16,000 will lead to a significantly lower budget impact of $331 million. Regarding population health, a screen-and-treat strategy will prevent 1,199 cases of HCC, 1,565 cases of DC, and 1,665 liver-related deaths by 2030. Conclusions: Contrasting the budget impact of this HCV screening strategy with other recommended health services and technologies, we conclude that HCV screening should be considered affordable. If Canada is committed to meeting the targets set out by the World Health Organization, then provinces cannot afford to not expand current screening programs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.005 | 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".