Prescription Drugs: Trends in Usual and Customary Prices for Commonly Used Drugs
Bibliographic record
Abstract
Correspondence issued by the Government Accountability Office with an abstract that begins "Prescription drug spending in 2009 totaled approximately $250 billion, of which $78 billion--or about 31 percent--was spent by the federal government. Prescription drug spending by the federal government, patients, and third-party payers, including employers, is driven by many factors, including the prices paid for drugs. In 2007 we reported on trends in retail prices--known as usual and customary (U&C) prices--for prescription drugs. We found that the average U&C price for the commonly used brand-name prescription drugs we reviewed increased about 6 percent per year from January 2000 through January 2007. Some media reports have suggested that prescription drug prices may have increased more during the debate leading up to passage of the Patient Protection and Affordable Care Act (PPACA) in March 2010 compared to other recent years. We were requested to examine recent trends in drug prices for brand-name and generic pharmaceuticals. In this report, we (1) examine U&C price trends for commonly used prescription drugs from 2006 through the first quarter of 2010, the latest available data at the time of our analysis, and compare these trends to those of other medical consumer goods and services, and (2) examine price trends using drug prices other than U&C. Congress also asked us to provide information on the extent to which prices for individual brand-name drugs changed over the course of this analysis period. In order to determine U&C price trends from 2006 through the first quarter of 2010, we selected four baskets of drugs that were commonly used by consumers during our analysis period. To select our baskets, we used prescription drug utilization data from the Blue Cross Blue Shield Federal Employee Program (BCBS FEP), a large, nationwide insurance plan that covers nearly 5 million individuals. We selected the first basket of drugs based on drug name in order to examine overall price trends of both brand-name and generic drugs. We used BCBS FEP utilization data to identify 100 commonly used drugs, and we considered the brand-name and generic versions to be distinct drugs with distinct levels of utilization. We selected the second and third baskets of drugs to examine trends for brand-name and for generic drugs separately. The second and third baskets of drugs were subsets of the first basket and contained the 55 brand-name and the 45 generic drugs, respectively, from the first basket of 100 drugs. We selected the fourth basket of drugs in order to account for the growing national shift in consumer utilization from brand-name to generic versions of drugs. We used BCBS FEP utilization data to again select 100 commonly used drugs--this time based on the active ingredient rather than drug name. In selecting this fourth basket of drugs based on active ingredient, we considered the brand-name and generic versions of drugs with the same active ingredient to be the same drug. The degree of overlap between the contents of the fourth basket and the first basket was high: at least 95 percent of the utilization in one basket was also in the other."
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".