Identifying drugs with the greatest increases and decreases in spending per beneficiary using Medicare Part D: A cross-sectional study
Bibliographic record
Abstract
IMPORTANCE: In the US, there are no effective regulations controlling how much the price of a medication can increase. A patchwork of studies examining the reasons for soaring prices has focused on medications that have received considerable media attention, like insulin, epinephrine, and colchicine. OBJECTIVE: To identify the 50 medications with the greatest increase in average spending per beneficiary and the 50 medications with the greatest decrease in average spending per beneficiary, and to identify the factors associated with spending increases. DESIGN, PARTICIPANTS: This cross-sectional study used publicly available data from the Medicare Part D Prescription Drug Program from 2014 to 2020. We included drugs dispensed to > 1000 beneficiaries in each study year and excluded those primarily administered intravenously. MAIN MEASURES: Percentage change in average spending per beneficiary from 2014 to 2020 was calculated for each drug. For each drug, we extracted the number of beneficiaries, the number of manufacturers, and the drug-specific total annual spending reported in the Medicare Part D data set. An online database search was conducted to identify the primary clinical indication, the availability of any generic versions, and the date of FDA approval for each drug. RESULTS: The 50 medications with the greatest increase in spending per beneficiary had a median increase of 362.4% (interquartile range [IQR]: 286.6%-563.0%), with a cumulative spending of almost $5 billion in 2020 alone. Most drugs with the greatest increases in spending per beneficiary had generic versions available (68%) and were approved by the FDA over 10 years ago (66%). Medications with the greatest increase in spending per beneficiary had a median of 1 manufacturer (IQR: 1-2), while medications with the greatest decrease in spending per beneficiary had a median of 9.5 manufacturers (IQR: 5-14). CONCLUSIONS: This study identified rapidly increasing costs of medications under Medicare Part D. Our findings demonstrate that off-patent medications can skyrocket in price, especially when there are few manufacturers of a given medication.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".