Economically Efficient Hepatitis C Virus Treatment Prioritization Improves Health Outcomes
Bibliographic record
Abstract
BACKGROUND: The total cost of treating the 3 million Americans chronically infected with hepatitis C virus (HCV) represents a substantial affordability challenge requiring treatment prioritization. This study compares the health and economic outcomes of alternative treatment prioritization schedules. METHODS: We developed a multiyear HCV treatment budget allocation model to evaluate the tradeoffs of 7 prioritization strategies. We used optimization to identify the priority schedule that maximizes population net monetary benefit (NMB). We compared prioritization schedules in terms of the number of individuals treated, the number of individuals who progress to end-stage liver disease (ESLD), and population total quality-adjusted life years (QALYs). We applied the model to the population of treatment-naive patients with a total annual HCV treatment budget of US$8.6 billion. RESULTS: First-come, first-served (FCFS) treats the fewest people with advanced fibrosis, prevents the fewest cases of ESLD, and gains the fewest QALYs. A schedule developed from optimizing population NMB prioritizes treatment in the first year to patients with moderate to severe fibrosis who are younger than 65 years, followed by older individuals with moderate to severe fibrosis. While this strategy yields the greatest population QALYs, prioritization by disease severity alone prevents more cases of ESLD. Sensitivity analysis indicated that the differences between prioritization schedules are greater when the budget is smaller. A 10% annual treatment price reduction enabled treatment 1 year sooner to several patient subgroups, specifically older patients and those with less severe liver fibrosis. CONCLUSION: In the absence of a sufficient budget to treat all patients, explicit prioritization targeting younger people with more severe disease first provides the greatest health benefits. We provide our spreadsheet model so that decision makers can compare health tradeoffs of different budget levels and various prioritization strategies with inputs tailored to their population.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".