Prescription Drugs: Trends in Usual and Customary Prices for Commonly Used Drugs
Notice bibliographique
Résumé
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."
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,009 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».