The health impact of delaying direct‐acting antiviral treatment for chronic hepatitis C: A decision‐analytic approach
Bibliographic record
Abstract
BACKGROUND & AIMS: Direct-acting antivirals (DAAs) are highly effective, but expensive treatments for chronic hepatitis C (CHC). To manage costs, drug plans worldwide have rationed access to DAAs in a variety of ways. This study quantifies the health impact of formulary restrictions and presents a clinical decision tool for informing treatment timing decisions. METHODS: A decision-analytic model was developed to quantify the health impact of delaying DAAs for subpopulations stratified by age, fibrosis level, viral genotype, and injection drug use over their lifetime. The health impact was quantified in terms of quality-adjusted life expectancy (quality-adjusted life years, or QALYs) and life expectancy (years). RESULTS: Deferring DAAs for patients with no or mild fibrosis (F0/F1) for 1-5 years is unlikely to result in life expectancy losses and leads only to marginal losses of 0.02-0.06 QALYs per year of delay. However, for 30-50-year-olds with advanced fibrosis (≥F3) delays as short as a year results in a considerable health loss (0.25-1.04 QALYs and 0.19-1.53 years). Reimbursement limits for those with substance use are associated with large health losses. People who actively inject drugs with advanced fibrosis (≥F3) may lose 0.18-1.05 QALYs and 0.13-1.16 years per year of delay, despite the risk of reinfection and competing mortality. Results are robust to parameter uncertainty and key assumptions. CONCLUSIONS: We present a clinical decision tool for informing treatment timing for various CHC subpopulations. In general, findings suggest that patients with at least moderate fibrosis should be treated promptly regardless of active drug use.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".