Cost-Effectiveness of Treating Hepatitis C with Sofosbuvir/Ledipasvir in Germany
Bibliographic record
Abstract
BACKGROUND: Infections with the hepatitis C virus (HCV) are a global public health problem. Long-term consequences are the development of liver cirrhosis and hepatocellular carcinoma. Newly introduced direct acting antivirals, especially interferon-free regimens, have improved rates of sustained viral response above 90% in most patient groups and allow treating patients who were ineligible for treatment in the past. These new regimens have replaced former treatment and are recommended by current guidelines. However, there is an ongoing discussion on high pharmaceutical prices. Our aim was to assess the long-term cost-effectiveness of treating hepatitis C genotype 1 patients with sofosbuvir/ledipasvir (SOF/LDV) treatment in Germany. MATERIAL AND METHODS: We used a Markov cohort model to simulate disease progression and assess cost-effectiveness. The model calculates lifetime costs and outcomes (quality-adjusted life years, QALYs) of SOF/LDV and other strategies. Patients were stratified by treatment status (treatment-naive and treatment-experienced) and absence/presence of cirrhosis. Different treatment strategies were compared to prior standard of care. Sensitivity analyses were performed to evaluate model robustness. RESULTS: Base-case analyses results show that in treatment-naive non-cirrhotic patients treatment with SOF/LDV dominates the prior standard of care (is more effective and less costly). In cirrhotic patients an incremental cost-effectiveness ratio (ICER) of 3,383 €/QALY was estimated. In treatment-experienced patients ICERs were 26,426 €/QALY and 1,397 €/QALY for treatment-naive and treatment-experienced patients, respectively. Robustness of results was confirmed in sensitivity analyses. CONCLUSIONS: Our analysis shows that treatment with SOF/LDV is cost-effective compared to prior standard of care in all patient groups considering international costs per QALY thresholds.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.001 | 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".