An Analysis of Trends of Generic Drugs and Biologic Prices in the U.S.
Bibliographic record
Abstract
Introduction: Drug prices are a significant problem for the US healthcare system. Generic and biosimilar market competition reduces drug and biologic prices, results in substantial savings, and improves patient access and outcomes. As the costs of healthcare and prescription drugs continue to rise, the discussion about drug prices has taken a prominent place at the forefront of national discourse. This study assessed impacting trends in prices of generic, biosimilar, and reference products in the US. We also evaluated the effect of the Affordable Care Act (ACA) of 2010 on biologic prices.\nMethods: We extracted a list of the new generic drugs approved by the FDA from January 2013 to December 2018 from the FDA website and the acquisition cost from the Centers for Medicare and Medicaid Services (CMS) National Average Drug Acquisition Cost (NADAC) database. We extracted a list of biosimilar products approved by the FDA as of September 2021 from the FDA website and Average Sales Price (ASP) from the 1st quarter of 2005 to the 3rd quarter of 2021 from the CMS website database. We segmented the data using inflection point detection and conducted piecewise linear regression to find differences in the trends for the segments defined by the inflection points. We used R Project for Statistical Computing version 3.6.1 for the analysis.\nResults:The median generic prices at market entry represented 65.7% of the brand prices and 26.0% three years after generic entry. The median reference drug prices increased by 1.0% per month in the pre-generic period, remained constant in the generic entry period (3 months before to 6 months after generic entry), and increased by 0.4% per month in the post-generic period. Biosimilar prices represented 75% of the reference biologic prices at market entry. The median prices of reference biologics declined after biosimilar competition, while the prices of biologics without competition increased (94.5 vs. 163.6, p\nConclusions: Market competition significantly reduced drug and biologic prices and affected the price trends of reference products. The prices of drug and biologic reference products decreased after biosimilar market entry. Prices of biologics significantly increased after the implementation of the ACA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".