Early Treatment of Hepatitis C Virus Improves Health Outcomes and Yields Cost-Savings: A Modeling Study in Argentina
Bibliographic record
Abstract
OBJECTIVE: Most untreated hepatitis C virus (HCV) patients develop chronic infection and severe complications, including death. Direct-acting antivirals in early stages of liver fibrosis reduce complications and healthcare costs. However, therapy is often delayed, and patients in early stages have limited access to effective treatments. We assessed the clinical and economic effect of treating chronic HCV at early versus late stages of disease in Argentina. METHODS: A Markov model of the natural HCV history was used to forecast lifetime liver-related and economic outcomes from social security sector perspective. Healthcare use and transition probabilities were drawn from literature. Demographic characteristics of the patients and treatment attributes were based on data from registrational trials of glecaprevir/pibrentasvir. RESULTS: Lower rates of all hepatic complications and liver-related mortality were predicted when treatment was initiated in mild versus advanced disease. Sustained virologic response rates were similar among all stages. Higher quality-adjusted life years (QALYs) were predicted when treatment was initiated in mild (F0-F1) versus moderate (F2-F3) or advanced (F4) liver disease (11.5, 9.9, and 7.5 QALYs, respectively). Delaying treatment increased long-term total lifetime costs (F4: AR$ 1 437 816; F2-F3: AR$ 967 673; F0-F1: AR$ 954 018; 37.10 AR$=1 USD, Nov 2018 exchange rate) and provided fewer QALYs. CONCLUSIONS: Our study show early treatment was a dominant strategy compared with treatment in advanced stages of liver disease. These results may help health policy makers take actions to reduce health and economic burden of HCV in Argentina.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".