The cost-effectiveness of hepatitis A vaccination in patients with chronic hepatitis C
Bibliographic record
Abstract
Infection with hepatitis A virus (HAV) occasionally leads to acute liver failure and has a higher fatality rate in patients with chronic hepatitis C virus (HCV). Vaccination of patients with HCV against HAV is effective and well tolerated. This study examines the cost-effectiveness of HAV vaccination in North American patients with chronic HCV. A decision analysis model was constructed to compare 3 HAV vaccination strategies in adult patients with chronic HCV over a period of 5 years: (1) vaccinate no patients (treat none); (2) vaccinate only susceptible (anti-HAV negative) patients (selective); or (3) vaccinate all patients without prior testing of immune status (universal). Probabilities and direct costs were estimated from hospital data and the literature. The cost per patient for the 3 vaccination strategies were: treat none, $2.00; selective, $56.00; and universal, $82.00. For every 1,000,000 patients with HCV vaccinated over a 5-year period, the selective strategy prevented 128 symptomatic cases of HAV, 3 liver transplantations, and 3 deaths owing directly to HAV compared with the treat none strategy. In addition, the selective strategy costs an additional $427,000 per patient with HAV prevented, and $23 million per HAV-related death averted, compared with the treat none strategy. The results were most sensitive to the incidence of HAV infection; vaccination increased costs if the annual rate of infection was less than 0.56% (baseline, 0.01%). Vaccination of North American patients with chronic HCV against HAV infection is not a cost-effective therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".