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Record 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 on OpenAlexaboutno aff
Karen Facey, Jaime Espín, E. Kent, Angèl Link, Elena Nicod, Aisling O’Leary, Entela Xoxi, Inneke van de Vijver, Tatyana Benisheva, Andrius Vagoras, Sheela Upadhyaya

Bibliographic record

VenuePharmacoEconomics · 2021
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsnot available
FundersHorizon 2020Università BocconiAustralian Government
KeywordsPopulationPopulation healthReimbursementMedicineHealth economicsEuropean unionHealth careBusinessPublic healthEconomic growthEnvironmental healthNursingEconomics

Abstract

fetched live from 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.105
metaresearch head score (Gemma)0.197
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.555

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1050.197
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.003
Scholarly communication0.0050.005
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.037
GPT teacher head0.375
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations47
Published2021
Admission routes1
Has abstractyes

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