Assessment of FDA-Approved Drugs Not Recommended for Use or Reimbursement in Other Countries, 2017-2020
Bibliographic record
Abstract
Importance: Drug expenditures in the US are higher than in any other country and are projected to continue increasing, so US health systems may benefit from evaluating international regulatory and reimbursement decision-making of new drugs. Objective: To evaluate regulatory decisions and health technology assessments (HTAs) in Australia, Canada, and the UK regarding new drugs approved by the US Food and Drug Administration (FDA) in 2017 through 2020, as well as to estimate the US cost per patient per year for drugs receiving negative recommendations. Design and Setting: In this cross-sectional study, recommendations issued by agencies in Australia, Canada, and the UK were collected for new drugs approved by the FDA in 2017 through 2020. All data were current as of May 31, 2022. Exposures: Authorizations and HTAs in selected countries. Main Outcomes and Measures: All FDA-approved drugs were matched by active ingredient to decision summary reports published by drug regulators and HTA agencies in Australia, Canada, and the UK. Regulatory approval concordance and reasons for negative recommendations were assessed using descriptive statistics. For drugs not recommended by an international agency, the annual US drug cost per patient was estimated from FDA labeling and wholesale acquisition costs. Results: The FDA approved 206 new drugs in 2017 through 2020, of which 162 (78.6%) were granted marketing authorization by at least 1 other regulatory agency at a median (IQR) delay of 12.1 (17.7) months following US approval. Conversely, 5 FDA-approved drugs were refused marketing authorization by an international regulatory agency due to unfavorable benefit-to-risk assessments. An additional 42 FDA-approved drugs received negative reimbursement recommendations from HTA agencies in Australia, Canada, or the UK due to uncertainty of clinical benefits or unacceptably high prices. The median (IQR) US cost of the 47 drugs refused authorization or not recommended for reimbursement by an international agency was $115 281 ($166 690) per patient per year. Twenty drugs were for oncology indications, and 36 were approved by the FDA through expedited regulatory pathways or the Orphan Drug Act. Conclusions and Relevance: This cross-sectional study assessed reasons for which drugs recently approved by the FDA were refused marketing authorization or not recommended for public reimbursement in other countries. Drugs with limited international market presence may require close examination by US health care professionals and health systems.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".