Bridging Hepatitis C Care Gaps: A Modeling Approach for Achieving the WHO’s Targets in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) has set hepatitis C (HCV) elimination targets for 2030. Understanding existing gaps in the "HCV care-cascade" is essential for meeting these targets. We aimed to identify the level of service scale-up needed along the "HCV care-cascade" to achieve the WHO's HCV elimination targets in Ontario, Canada. METHODS: By employing a decision analytic model, we projected the quality-adjusted life years (QALYs) and healthcare costs for individuals with HCV in Ontario. We increased RNA testing and treatment rates to 98%, followed by increasing antibody testing uptake until we achieved the WHO's mortality target (i.e., a 65% reduction in liver-related mortality by 2030 vs. 2015). RESULTS: Without scaling up by 2030, the expected QALYs and costs per person were 9.156 and CAD 48,996, respectively. Improved RNA testing and treatment rates reduced liver-related deaths to 3.3/100,000, a 57% reduction from 2015. Further doubling the antibody testing rates can achieve the WHO's mortality target in 2035, but not in 2030. Compared to the status quo, such program would be cost-effective considering a 50,000 CAD/QALY gained threshold if annual implementation costs stayed under 2.3 M CAD/100,000 people. CONCLUSIONS: Doubling the antibody testing rates, along with increased RNA testing and treatment rates, showed promise in meeting the WHO's goals by 2035.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 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".