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Enregistrement W3180826043 · doi:10.1007/s40273-021-01050-5

Implementing Outcomes-Based Managed Entry Agreements for Rare Disease Treatments: Nusinersen and Tisagenlecleucel

2021· article· en· W3180826043 sur OpenAlex

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Notice bibliographique

RevuePharmacoEconomics · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueNeurogenetic and Muscular Disorders Research
Établissements canadiensnon disponible
Organismes subventionnairesHorizon 2020Università BocconiAustralian Government
Mots-clésPopulationPopulation healthReimbursementMedicineHealth economicsEuropean unionHealth careBusinessPublic healthEconomic growthEnvironmental healthNursingEconomics

Résumé

récupéré en direct d'OpenAlex

BACKGROUND AND OBJECTIVE: Enthusiasm for the use of outcomes-based managed entry agreements (OBMEAs) to manage uncertainties apparent at the time of appraisal/pricing and reimbursement of new medicines has waned over the past decade, as challenges in establishment, implementation and re-appraisal have been identified. With the recent advent of innovative treatments for rare diseases that have uncertainties in the clinical evidence base, but which could meet a high unmet need, there has been renewed interest in the potential of OBMEAs. The objective of this research was to review the implementation of OBMEAs for two case studies across countries in the European Union, Australia and Canada, to identify good practices that could inform development of tools to support implementation of OBMEAs. METHODS: To investigate how OBMEAs are being implemented with rare disease treatments, we collected information from health technology assessment/payer experts in countries that had implemented OBMEAs for either nusinersen in spinal muscular atrophy or tisagenlecleucel in two cancer indications. Operational characteristics of the OBMEAs that were publicly available were documented. Then, the experts discussed issues in implementing these OBMEAs and specific approaches taken to overcome challenges. RESULTS: The OBMEAs identified were based on individual outcomes to ensure appropriate use, manage continuation of treatment and in two cases linked to payment schedules, or they were population based, coverage with evidence development. For nusinersen, population-based OBMEAs are documented in Belgium, England and the Netherlands and individual-based schemes in Bulgaria, Ireland, Italy and Lithuania. For tisagenlecleucel, there were population-based schemes in Australia, Belgium, England and France and individual-based schemes in Italy and Spain. Comparison of the OBMEA constructs showed some clear published frameworks and clarity of the uncertainties to be addressed that were similar across countries. Agreements were generally made between the marketing authorisation holder and the payer with involvement of expert physicians. Only England and the Netherlands involved patients. Italy used its long-established, national, web-based, treatment-specific data collection system linked to reimbursement and Spain has just developed such a national treatment registry system. Other countries relied on a variety of data collection systems (including clinical registries) and administrative data. Durations of agreements varied for these treatments as did processes for interim reporting. The processes to ensure data quality, completeness and sufficiency for re-analysis after coverage with evidence development were not always clear, neither were analysis plans. CONCLUSIONS: These case studies have shown that important information about the constructs of OBMEAs for rare disease treatments are publicly available, and for some jurisdictions, interim reports of progress. Outcomes-based managed entry agreements can play an important role not only in reimbursement, but also in treatment optimisation. However, they are complex to implement and should be the exception and not the rule. More recent OBMEAs have developed document covenants among stakeholders or electronic systems to provide assurances about data sufficiency. For coverage with evidence development, there is an opportunity for greater collaboration among jurisdictions to share processes, develop common data collection agreements, and share interim and final reports. The establishment of an international public portal to host such reports would be particularly valuable for rare disease treatments.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,450
Score d'incertitude au seuil0,680

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,037
Tête enseignante GPT0,375
Écart entre enseignants0,338 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle