Assessing the Impact of the 340B Drug Pricing Program: A Scoping Review of the Empirical, Peer‐Reviewed Literature
Notice bibliographique
Résumé
Policy Points The 340B Drug Pricing Program accounts for roughly 1 out of every 100 dollars spent in the $4.3 trillion US health care industry. Decisions affecting the program will have wide-ranging consequences throughout the US safety net. Our scoping review provides a roadmap of the questions being asked about the 340B program and an initial synthesis of the answers. The highest-quality evidence indicates that nonprofit, disproportionate share hospitals may be using the 340B program in margin-motivated ways, with inconsistent evidence for increased safety net engagement; however, this finding is not consistent across other hospital types and public health clinics, which face different incentive structures and reporting requirements. CONTEXT: Despite remarkable growth and relevance of the 340B Drug Pricing Program to current health care practice and policy debate, academic literature examining 340B has lagged. The objectives of this scoping review were to summarize i) common research questions published about 340B, ii) what is empirically known about 340B and its implications, and iii) remaining knowledge gaps, all organized in a way that is informative to practitioners, researchers, and decision makers. METHODS: We conducted a scoping review of the peer-reviewed, empirical 340B literature (database inception to March 2023). We categorized studies by suitability of their design for internal validity, type of covered entity studied, and motivation-by-scope category. FINDINGS: The final yield included 44 peer-reviewed, empirical studies published between 2003 and 2023. We identified 15 frequently asked research questions in the literature, across 6 categories of inquiry-motivation (margin or mission) and scope (external, covered entity, and care delivery interface). Literature with greatest internal validity leaned toward evidence of margin-motivated behavior at the external environment and covered entity levels, with inconsistent findings supporting mission-motivated behavior at these levels; this was particularly the case among participating disproportionate share hospitals (DSHs). However, included case studies were unanimous in demonstrating positive effects of the 340B program for carrying out a provider's safety net mission. CONCLUSIONS: In our scoping review of the 340B program, the highest-quality evidence indicates nonprofit, DSHs may be using the 340B program in margin-motivated ways, with inconsistent evidence for increased safety net engagement; however, this finding is not consistent across other hospital types and public health clinics, which face different incentive structures and reporting requirements. Future studies should examine heterogeneity by covered entity types (i.e., hospitals vs. public health clinics), characteristics, and time period of 340B enrollment. Our findings provide additional context to current health policy discussion regarding the 340B program.
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 | 0,104 | 0,325 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,054 | 0,047 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,009 | 0,008 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,005 | 0,002 |
| 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 ».