Public spending on orphan medicines: a review of the literature
Notice bibliographique
Résumé
BACKGROUND AND OBJECTIVE: Little is known about how much public payers spend on orphan medicines. This study aimed at identifying information on orphan medicine expenditure incurred by public payers that was published in literature globally and at possibly synthesising their shares as portion of the total pharmaceutical expenditure. METHODS: A literature review was undertaken using Medline, the Orphanet Journal of Rare Diseases and Google Scholar. Titles and abstracts were screened, and full texts of potentially qualifying studies were reviewed for inclusion. Included articles were analysed, and bibliometric parameters as well as public expenditure data on orphan medicines were retrieved. RESULTS: Six hundred forty three articles excluding duplicates were identified. After screening of the abstracts and a review of the full texts, 13 articles qualified for in-depth analysis.The 13 selected articles on orphan pharmaceutical expenditure were published between 2010 and 2018. Survey periods varied between 1 year and 12 years. One publication included 22 countries but the majority of the studies were related to a single country. Expenditure data was available in five of the 13 articles, and eight articles used 'expenditure proxies' such as sales data. Spending data had been sourced from public institutions (4 studies), private providers (5 studies) and a combination of both (3 studies, no information on data source in 1 study). In all included studies, secondary data were analysed. Reported expenditure shares for orphan medicines in relation to total pharmaceutical spend was frequently below 3%. Countries with higher shares included the USA, Canada, the Netherlands and Bulgaria-the latter reporting spending on orphan medicines as high as 9%. CONCLUSIONS: A low number of studies that informed about pharmaceutical spending on orphan medicines was published, thereof only a few explicitly analysed expenditure data of public payers. A conclusive synthesis of public spending on orphan medicines is a challenge given to the diversity in methodologies to measure expenditure. There is a need for further research to survey primary data of public spending for orphan medicines, based on a sound methodology to measure these data and to compare them internationally.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,028 | 0,131 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».