BEYOND TRIAL AND ERROR: QUANTIFYING THE IMPACT OF MANAGED ENTRY AGREEMENTS FOR ATIDARSAGENE AUTOTEMCEL AS A REAL-WORLD CASE STUDY
Author(s)
Andrea Greco, BSc, MSc1, Jurriaan Gort, MSc1, Anna Grootendorst, BSc, MSc1, Geert Frederix, PhD2, Lotty Hooft, PhD1, Renske MT ten Ham, MSc, PharmD, PhD1.
1Julius Centre for Health Sciences and Primary Care, Department of Epidemiology & Health Economics, Utrecht, Netherlands, 2HAN University of Applied Sciences, Nijmegen, The Netherlands., Nijmegen, Netherlands.
1Julius Centre for Health Sciences and Primary Care, Department of Epidemiology & Health Economics, Utrecht, Netherlands, 2HAN University of Applied Sciences, Nijmegen, The Netherlands., Nijmegen, Netherlands.
OBJECTIVES: To retrospectively quantify the impact of managed entry agreements (MEAs) on incremental net health effect (iNHE) and budget impact, using atidarsagene autotemcel (AA) for metachromatic leukodystrophy (MLD) in the Netherlands as a real-world case study, and to assess whether these metrics can prospectively inform future policy decisions on whether and which MEAs should be implemented for high-cost therapies with uncertain value propositions.
METHODS: We recreated a cohort state-transition Markov model from a societal perspective. Model structure, assumptions, and inputs were retrieved from the Dutch Healthcare Institute (Zorginstituut Nederland; ZIN) 2023 assessment report, reflecting the original decision context. AA was compared with best supportive care (BSC) for the eligible Dutch MLD population. The base case was defined as an upfront payment at list price and compared with four MEA-scenarios:1)39% Discount, 2)Five-year Spread payments, 3)Gross-Motor-Function-based agreement with rebates, and 4)Hybrid agreement proposed by ZIN, combining cohort-specific discounts, five-year spread payments, and an outcome-based agreement. MEA-scenarios only modified payment amount, timing, or outcome-contingent rebates.
RESULTS: Assuming ZIN’s base case, AA generated 7.83 incremental QALYs per patient versus BSC weighted across three subpopulations. At a willingness-to-pay threshold of €80,000/QALY, the Incremental Cost-Effectiveness Ratio (ICER) was €378,785/QALY, with an iNHE of -29.26, indicating a net health loss. All MEA-scenarios improved iNHE compared with upfront-payment, though only scenario 4 generated positive cumulative population-level iNHE over time. Nevertheless, all MEA-scenarios reduced budget impact over a 20-year horizon, from €37.85 million under upfront payment to €26.64, €33.46, €33.76, and €16.82 million under Scenarios 1-4, respectively.
CONCLUSIONS: Quantifying iNHE alongside budget impact provides insight into intended and unintended consequences of MEAs, thereby prospectively informing whether an MEA is warranted and which designs merit further consideration. Future research should assess the feasibility of applying these metrics using latest clinical evidence for AA and value-of-information methods to align MEA design with evidence generation.
METHODS: We recreated a cohort state-transition Markov model from a societal perspective. Model structure, assumptions, and inputs were retrieved from the Dutch Healthcare Institute (Zorginstituut Nederland; ZIN) 2023 assessment report, reflecting the original decision context. AA was compared with best supportive care (BSC) for the eligible Dutch MLD population. The base case was defined as an upfront payment at list price and compared with four MEA-scenarios:1)39% Discount, 2)Five-year Spread payments, 3)Gross-Motor-Function-based agreement with rebates, and 4)Hybrid agreement proposed by ZIN, combining cohort-specific discounts, five-year spread payments, and an outcome-based agreement. MEA-scenarios only modified payment amount, timing, or outcome-contingent rebates.
RESULTS: Assuming ZIN’s base case, AA generated 7.83 incremental QALYs per patient versus BSC weighted across three subpopulations. At a willingness-to-pay threshold of €80,000/QALY, the Incremental Cost-Effectiveness Ratio (ICER) was €378,785/QALY, with an iNHE of -29.26, indicating a net health loss. All MEA-scenarios improved iNHE compared with upfront-payment, though only scenario 4 generated positive cumulative population-level iNHE over time. Nevertheless, all MEA-scenarios reduced budget impact over a 20-year horizon, from €37.85 million under upfront payment to €26.64, €33.46, €33.76, and €16.82 million under Scenarios 1-4, respectively.
CONCLUSIONS: Quantifying iNHE alongside budget impact provides insight into intended and unintended consequences of MEAs, thereby prospectively informing whether an MEA is warranted and which designs merit further consideration. Future research should assess the feasibility of applying these metrics using latest clinical evidence for AA and value-of-information methods to align MEA design with evidence generation.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
HPR4
Topic
Economic Evaluation, Health Policy & Regulatory, Methodological & Statistical Research
Topic Subcategory
Reimbursement & Access Policy, Risk-sharing Approaches
Disease
Genetic, Regenerative & Curative Therapies, Neurological Disorders, No Additional Disease & Conditions/Specialized Treatment Areas, Pediatrics, Rare & Orphan Diseases