CP-157 Analysis of the expenditure on the treatment of hepatitis C virus in 2015
Bibliographic record
Abstract
Background With the advent of new treatments for hepatitis C, we have achieved high cure rates, although this entails a significant increase in drug spending. Purpose To describe and analyse spending on HCV treatment in 2015. Material and methods Data were collected prospectively from January 2015 to October 2015. The data collected were: number of patients, age, gender, total expenditure (TE), average expenditure per patient (AEPP) and percentage of expenditure per drug. The sources used were the software for prescription and dispensation SAVAC and Excel database. Results 75 patients (74.7% male) with a median age of 55 years were included. Regarding genotype, genotype 1 was the predominant one (84.4% of patients); genotypes 3 and 4 were 7.8% each. TE was 3 040 032€ and AEPP was 40 534€. The number of patients treated with each drug and the percentage of expenditure per drug were, respectively: 65 patients (73,4% TE) with Sovaldi (monotherapy or in combination with others drugs) or with Harvoni, 28 patients (11.65% TE) with simeprevir, 10 patients (9.22% TE) with Viekirax/Exviera, 6 patients (3.95% TE) with daclatasvir, 6 patients (<1% TE) with Pegasys and 34 patients (<1% TE) with ribavirin. The expenditure per genotype was distributed as follow: 2 564 978.63€ (84% TE) in genotype 1, 234 709.37€ (7.7% TE) in genotype 3 and 240 344€ (7.9%TE) in genotype 4. The cost per patient per genotpe was: 40 713.94€/patient in genotype 1, 39 118.22€/patient in genotype 3 and 38 390.6€/patient in genotype 4. Conclusion Solvadi and Harvoni accounted for more than 70% of total spending in this year. It is confirmed that the highest percentage of expenditure still went to genotype 1, although new treatments for HCV are indicated for most genotypes. Finally, note that even though there were more patients treated with Sovaldi than with Harvoni, the total cost attributable to each drug was similar. References and/or Acknowledgements Moshyk A, Martel MJ, Tahami Monfared AA, et al. Cost-effectiveness of daclatasvir plus sofosbuvir-based regimen for treatment of hepatitis C virus genotype 3 infection in Canada. J. Med Econ 2015; 1–12 McEwan P, Ward T, Webster S, et al. Estimating the cost-effectiveness of daclatasvir plus asunaprevir in difficult to treat Japanese patients chronically infected with hepatitis C genotype 1b No conflict of interest.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".