The Impact of Patent Expiry on Drug Prices: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the impact of patent expiry on drug prices by means of a systematic literature review. METHODS: A systematic literature search was performed in PubMed to identify all published literature on the impact of patent expiration on drug prices. Additional literature was identified using a less distinct syntax in Google Scholar and EconLit. Data extraction followed a standardized assessment form containing the domains study type, study aim, reported outcomes, number of drugs and drug classes assessed, and originators or generics assessed. RESULTS: The 16 identified studies that assessed impact of patent expiry on drug prices showed that price developments after patent expiration varied between countries. The included studies assessed price developments for the USA, Canada, Australia, the UK, the Netherlands, Germany and France, Spain, Italy, Norway, Sweden and Denmark. The number of drugs included within different studies ranged between 1 and 219. The identified studies indicated that drug prices decreased significantly after patent expiry with drug price ratios ranging from 6.6 to 66% 1-5 years after patent expiry. CONCLUSION: Drug prices decrease significantly after patent expiry. The extent of this price reduction varied greatly between products and countries. For this reason, country-specific analyses on price developments after patent expiry should be used when these are considered in decision making. Future research should be dedicated to gathering more country-specific data to reduce the uncertainty with regard to price developments.
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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 | 0.053 | 0.219 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".