Drug Prices After Patent Expirations in High-Income Countries and Implications for Cost-Effectiveness Analyses
Bibliographic record
Abstract
Importance: Understanding how patent expirations affect drug prices is crucial because price changes directly inform accurate cost-effectiveness assessments. This study investigates the association between patent expirations and drug prices in 8 high-income countries and evaluates how the changes affect cost-effectiveness assessments. Objective: To analyze how the expiration of drug patents is associated with drug price changes and to assess the implications of these price changes for cost-effectiveness evaluations. Design, Setting, and Participants: This cohort study performed an event study design using data from 8 high-income countries to assess the association between patent expiration and drug prices, and created a simulation model to understand the implications for cost-effectiveness analyses. The simulation cost-effectiveness model analyzed the implications of including or ignoring postpatent price dynamics. Exposure: Drug patent expiration. Main Outcomes and Measures: Change in drug prices and differences in incremental cost-effectiveness ratios when considering vs ignoring postpatent price dynamics. Results: The sample comprised 505 drugs undergoing patent expiration in Australia, Canada, France, Germany, Japan, Switzerland, UK, and US. Price decreases were statistically significant over the 8 years after patent expiration, with the fastest price declines observed in the US: 32% (95% CI, 24%-39%) in year 1 after patent expiration and 82% (95% CI, 71%-89%) in the 8 years after patent expiration. Estimates for other nations ranged from a decrease of 64% in Australia to 18% in Switzerland in the 8 years after expiration. The cost-effectiveness simulation model indicated that not accounting for generic entry into the market may produce biased incremental cost-effectiveness ratios of 40% to -40%, depending on the scenario. Conclusions and Relevance: The findings of this cohort study demonstrate that drug prices were reduced substantially after patent expirations in high-income countries. Therefore, incorporating information on patent status and pricing dynamics in cost-effectiveness assessment analyses is necessary for producing accurate economic evaluations of new drugs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.057 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".