Author/Authors :
Mohammadshahi ، Marita National Institute for Health Research - Tehran University of Medical Sciences (TUMS) , Gharib-Naseri ، Zahra Department of Pharmacoeconomics and Pharmaceutical Management - School of Pharmacy - Shahid Beheshti University of Medical Sciences , Mobinizadeh ، Mohammadreza National Institute for Health Research - Tehran University of Medical Sciences (TUMS) , Fakoorfard ، Zeinab National Institute for Health Research - Tehran University of Medical Sciences (TUMS) , Hosseinzadeh Lotfi ، Farhad Department of Mathematics - Islamic Azad University, Science and Research Branch , Sharafi ، Hamid Department of Mathematics - Islamic Azad University, Science and Research Branch , Abooee ، Parisa National Institute for Health Research - Tehran University of Medical Sciences (TUMS) , Akbari Sari ، Ali Department of Health Economics and Management - School of Public Health - Tehran University of Medical Sciences , Olyaeemanesh ، Alireza National Institute for Health Research - Tehran University of Medical Sciences (TUMS)
Abstract :
Background: Rare diseases, characterized by low prevalence and high complexity, pose significant challenges to health systems due to the uncertainty surrounding the best diagnostic methods and availability of effective treatments. Objectives: This study aimed to introduce new methods for prioritizing orphan drugs, with a pilot application to hemophilia, spinal muscular atrophy (SMA), cystic fibrosis (CF), and multiple sclerosis (MS). Methods: This quantitative research, conducted at Iran’s National Institute for Health Research from 2021 to 2023, employs multi-criteria decision-making models (MCDA) to evaluate the efficacy of health care technologies for rare and hard to cure diseases. A preliminary model was developed based on a pilot selection of seven medications, for which comprehensive data was extracted. The clinical efficacy was assessed using quality adjusted life years (QALY) as a metric. The model design was grounded on three critical factors: The annual cost of intervention per individual, the annual number of eligible patients for intervention, and the Proportion of expenses covered by the governmental budget. Additionally, the model incorporated various constraints and a regulatory coefficient, denoted as “w,” to enhance its robustness. Results: By running the model, the coverage of selected medications through model optimization, revealing the following percentages: Alemtuzumab (30%), ocrelizumab (30%), emicizumab (6%), dornase alfa (29%), tobramycin (2%), and spinraza (0.02%). Additionally, the corresponding monetary coverage in Iranian Rials is reported as follows: Alemtuzumab (27,144,000,000 IRR), ocrelizumab (109,645,200,000 IRR), emicizumab (17,360,490,000 IRR), dornase alfa (43,350,930,000 IRR), tobramycin (1,268,505,000 IRR), and spinraza (350,000,000 IRR). Conclusions: This model has tried to solve the shortcomings of the existing models regarding the prioritization of orphan drugs by combining different factors to improve access to essential treatments for rare diseases, although it can be generally asserted that no unique model can answer all policymakers’ questions regarding budget allocation of rare diseases and orphan products.
