An International Review of Health Technology Assessment Approaches to Prescription Drugs and Their Ethical Principles
Bibliographic record
Abstract
In many countries, health technology assessment (HTA) organizations determine the economic value of new drugs and make recommendations regarding appropriate pricing and coverage in national health systems. In the US, recent policy proposals aimed at reducing drug costs would link drug prices to six countries: Australia, Canada, France, Germany, Japan, and the UK. We reviewed these countries' methods of HTA and guidance on price and coverage recommendations, analyzing methods and guidance documents for differences in (1) the methodologies HTA organizations use to conduct their evaluations and (2) considerations they use when making recommendations. We found important differences in the methods, interpretations of HTA findings, and condition-specific carve-outs that HTA organizations use to conduct evaluations and make recommendations. These variations have ethical implications because they influence the recommendations of HTA organizations, which affect access to the drug through national insurance and price negotiations with manufacturers. The differences in HTA approaches result from the distinct political, social, and cultural contexts of each organization and its value judgments. New cost-containment policies in the US should consider the ethical implications of the HTA reviews that they are considering relying on to negotiate drug prices and what values should be included in US pricing policy.
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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 | 0.029 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".