A global comparison of hepatitis B & C drug pricing
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Drug pricing is a major driver of healthcare spending in the United States (US) and the cost of medications in the US is up to three times higher than other countries. This cross-sectional study aims to investigate the current price differences between hepatitis B (HBV) and hepatitis C (HCV) antiviral therapies in the US as compared to peer high-income countries. MATERIALS AND METHODS: Publicly available drug formularies for Canada, UK, Japan, France, Germany, Italy, and Australia were used to collect 2024 prices for seven HBV medications (lamivudine, adefovir, tenofovir disoproxil fumarate (TDF), tenofovir alafenamide fumarate, entecavir, peginterferon alfa-2a, emtricitabine/TDF) and seven HCV medications (sofosbuvir/velpatasvir, sofosbuvir/ledipasvir, sofosbuvir, ribavirin, elbasvir/grazoprevir, glecaprevir/pibrentasvir, sofosbuvir/velpatasvir/voxilaprevir). US prices were obtained from UpToDate®'s listed representative average wholesale price and Medicare Part D 2022 drug prices. RESULTS: US prices for HBV originator medications were on average 4.71x (range 1.99-6.17x) the prices in the peer countries. US generic HBV drug prices for TDF, entecavir, and emtricitabine/TDF were on average 45% cheaper or 0.55x less than the average generic prices in peer countries (range 0.48-0.66x). US originator prices for HCV medications were on average 1.83x the prices in peer countries (range 0.63-2.66x). CONCLUSIONS: HBV and HCV originator medications cost significantly more in the US compared to seven other major industrial countries. However, the introduction of HBV generic medications has lowered the cost of treatment for patients in the US. Future adoption of international reference pricing may help bridge remaining pricing disparities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".