Quantifying the insurance value for rare diseases: Duchenne muscular dystrophy.

OBJECTIVES: To quantify the magnitude of an ISPOR novel value element, insurance value, as applied to new treatments for a rare, severe disease with pediatric onset: Duchenne muscular dystrophy (DMD). STUDY DESIGN: Prospective survey of individuals planning to have children in the future. METHODS: A survey was administered to US adults (aged ≥ 21 years) planning to have a child in the future to elicit willingness to pay (WTP) for insurance coverage for a new hypothetical DMD treatment that improved mortality and morbidity relative to the current standard of care. To identify an indifference point between status quo insurance and insurance with additional cost that would cover the treatment if respondents had a child with DMD, a multiple random staircase design was used. Insurance value-the value individuals receive from a reduction in future health risks-was calculated as the difference between respondent's WTP and what a risk-neutral individual would pay. The risk-neutral value was the product of the (1) probability of having a child with DMD (decision weighted), (2) quality-adjusted life-years (QALYs) gained from the new treatment, and (3) WTP per QALY. RESULTS: Among 207 respondents, 80.2% (n = 166) were aged 25 to 44 years, and 59.9% (n = 124) were women. WTP for insurance coverage of the hypothetical treatment was $973 annually, whereas the decision-weighted risk-neutral value was $452 per year. Thus, insurance value constituted 53.5% ($520) of value for new DMD treatments. CONCLUSIONS: Individuals planning to have children in the future are willing to pay more for insurance coverage of novel DMD treatments than is assumed under risk-neutral, QALY-based frameworks.

Paving the way for future gene therapies: A case study of scientific spillover from delandistrogene moxeparvovec.

Gene therapies have potential to improve outcomes of severe diseases after only a single administration. Novel therapies are continually being developed using knowledge gained from prior successes, a concept known as scientific spillover. Gene therapy advancement requires extensive development at each stage: preclinical work to create and evaluate vehicles for delivery of the therapy, design of clinical development programs, and establishment of a large-scale manufacturing process. Pioneering gene therapies are generating spillover as investigators confront myriad issues specific to this treatment modality. These include frameworks for construct engineering, dose evaluation, patient selection, outcome assessment, and safety monitoring. Consequently, the benefits of these therapies extend beyond offering knowledge for treating any one disease to establishing new platforms and paradigms that will accelerate advancement of future gene therapies. This impact is even more profound in rare diseases, where developing therapies in isolation may not be possible. This review describes some instances of scientific spillover in healthcare, and specifically gene therapy, using delandistrogene moxeparvovec (SRP-9001), a gene therapy recently approved by the US Food and Drug Administration for the treatment of ambulatory pediatric patients aged 4-5 years with Duchenne muscular dystrophy with a confirmed mutation in the DMD gene, as a case study.

Assessing the value of delandistrogene moxeparvovec (SRP-9001) gene therapy in patients with Duchenne muscular dystrophy in the United States.

Background: Delandistrogene moxeparvovec (SRP-9001) is an investigational gene therapy that may delay progression of Duchenne muscular dystrophy (DMD), a severe, rare neuromuscular disease caused by DMD gene mutations. Early cost-effectiveness analyses are important to help contextualize the value of gene therapies for reimbursement decision making. Objective: To determine the potential value of delandistrogene moxeparvovec using a cost-effectiveness analysis. Study design: A simulation calculated lifetime costs and equal value of life years gained (evLYG). Inputs included extrapolated clinical trial results and published utilities/costs. As a market price for delandistrogene moxeparvovec has not been established, threshold analyses established maximum treatment costs as they align with value, including varying willingness-to-pay up to $500,000, accounting for severity/rarity. Setting: USA, healthcare system perspective Patients: Boys with DMD Intervention: Delandistrogene moxeparvovec plus standard of care (SoC; corticosteroids) versus SoC alone Main outcome measure: Maximum treatment costs at a given willingness-to-pay threshold Results: Delandistrogene moxeparvovec added 10.30 discounted (26.40 undiscounted) evLYs. The maximum treatment cost was approximately $5 M, assuming $500,000/evLYG. Varying the benefit discount rate to account for the single administration increased the estimated value to #$5M, assuming $500,000/evLYG. Conclusion: In this early economic model, delandistrogene moxeparvovec increases evLYs versus SoC and begins to inform its potential value from a healthcare perspective.

Assessing the impact of single or short-term administration on a therapy's cost-effectiveness: a hypothetical disease-agnostic model.

AIMS: Assessing the value of single or short-term therapies (SSTs) within traditional cost-effectiveness analyses (CEAs) has been a topic of discussion as the number of SSTs increases, particularly regarding the effect of discounting on valuation. To quantify the impact of discounting in economic evaluations, a CEA of a hypothetical SST and equivalent chronic therapy was conducted using standard methods. MATERIALS AND METHODS: A lifetime Markov model was developed for a hypothetical chronic, progressive disease that could be treated with an SST, chronic therapy, or no novel treatment, termed standard of care (SoC). Incremental cost-effectiveness ratios (ICERs) with quality-adjusted life years (QALYs) comparing SST vs. SoC and an equivalent chronic therapy vs. SoC were assessed from a payer perspective. Both treatments had equal benefits and undiscounted lifetime costs; 3% discounting was applied to costs/benefits in the base case, and the impact of discounting was assessed. RESULTS: In the base case example, both the SST and equivalent chronic therapy vs. SoC had ICERs of $86,000/QALY without discounting. With 3% discounting, the ICER for the SST increased by 116% ($186,000/QALY) while the ICER for the chronic therapy increased by 10% ($95,000/QALY) despite equal clinical benefit. In scenario analyses, the ICER of the SST was consistently higher than the equivalent chronic therapy across a range of assumptions/inputs. Varying the cost/benefit discount rates had a greater impact on the SST. Differences in the ICERs between the therapies increased with increasing life expectancy/time horizon. LIMITATIONS: The simple model structure may not be reflective of acute or more complex diseases. Also, the scenario of perfect equivalency in efficacy and lifetime costs is hypothetical. CONCLUSIONS: This quantitative assessment showed the extent to which SST CEAs are highly sensitive to discounting, resulting in worse value assessments for SSTs than equivalent chronic therapies.

Commercial health plans use of patient subgroup restrictions: An analysis of orphan and US Food and Drug Administration-expedited programs.

BACKGROUND: Health plans apply utilization management criteria to guide their enrollees' access to prescription drugs. Patient subgroup restrictions (ie, clinical prerequisites for drug coverage) are a form of utilization management that have not been thoroughly investigated. OBJECTIVE: To examine the frequency with which large US commercial health plans impose patient subgroup restrictions beyond the US Food and Drug Administration (FDA) label in their coverage policies for orphan drugs and for drugs included in 1 or more FDA-expedited programs. To determine how consistently these patient subgroup restrictions align with eligibility criteria specified in each drug's pivotal clinical trial(s). METHODS: The Tufts Medical Center Specialty Drug Evidence and Coverage (SPEC) database was used, which includes coverage policies issued by 17 large US commercial health plans. SPEC contained 3,786 orphan drug policies and 4,027 FDA-expedited drug policies (current as of December 2020). SPEC data on plans' patient subgroup restrictions were assessed for the first objective. Each patient subgroup restriction was benchmarked against the corresponding eligibility criteria for a drug's pivotal clinical trial(s) for the second objective. To do so, the "Clinical Studies" section of the drug's FDA label was reviewed or, if necessary, the published manuscript describing the drug's pivotal trial(s). Patient subgroup restrictions were categorized as follows: (1) "consistent," the restriction and trial eligibility criterion are equivalent; (2) "same measure, more stringent," the restriction and trial eligibility criteria depend on the same measure, but the plan coverage is more restrictive; (3) "same measure, less stringent," the restriction and trial eligibility criteria depend on the same measure, but the plan coverage is less restrictive; and (4) "not consistent," the restriction and trial eligibility criteria depend on different measures. RESULTS: Health plans imposed patient subgroup restrictions in 20.2% of orphan drug policies (frequency varied by health plan, 11.7%-36.6%), and in 21.8% of FDA-expedited drug policies (frequency varied by health plan, 11.1%-47.9%). Of the 936 patient subgroup restrictions in orphan drug policies, 60.3% were categorized as consistent; 7.3% as same measure, more stringent; 12.0% as same measure, less stringent; and 20.5% as not consistent. Of the 1,070 patient subgroup restrictions in FDA-expedited drug policies, 57.5% were categorized as consistent; 6.7% as same measure, more stringent; 16.0% as same measure, less stringent; and 19.8% as not consistent. CONCLUSIONS: Patient subgroup restrictions for orphan drugs and FDA-expedited programs varied substantially across health plans, potentially resulting in inconsistent access to a given therapy across the approved patient population. Patient subgroup restrictions tend to be consistent with eligibility criteria specified in pivotal clinical trials. DISCLOSURES: This study was funded by Sarepta Therapeutics, Inc. Alexa C Klimchak and Lauren E Sedita are employees of Sarepta Therapeutics, Inc., and may own stock/options in the company.

Time-Dependent Cardiovascular Treatment Benefit Model for Lipid-Lowering Therapies.

Background With the availability of new lipid-lowering therapy options, there is a need to compare the expected clinical benefit of different treatment strategies in different patient populations and over various time frames. We aimed to develop a time-dependent model from published randomized controlled trials summarizing the relationship between low-density lipoprotein cholesterol lowering and cardiovascular risk reduction and to apply the model to investigate the effect of treatment scenarios over time. Methods and Results A cardiovascular treatment benefit model was specified with parameters as time since treatment initiation, magnitude of low-density lipoprotein cholesterol reduction, and additional patient characteristics. The model was estimated from randomized controlled trial data from 22 trials for statins and nonstatins. In 15 trials, the new time-dependent model had better predictions than cholesterol treatment trialists' estimations for a composite of coronary heart disease death, nonfatal myocardial infarction, and ischemic stroke. In explored scenarios, absolute risk reduction ≥2% with intensive treatment with high-intensity statin, ezetimibe, and high-dose proprotein convertase subtilisin/kexin type 9 inhibitor compared with high- or moderate-intensity statin alone were achieved in higher-risk populations with 2 to 5 years of treatment, and lower-risk populations with 9 to 11 years of treatment. Conclusions The time-dependent model accurately predicted treatment benefit seen from randomized controlled trials with a given lipid-lowering therapy by incorporating patient profile, timing, duration, and treatment type. The model can facilitate decision making and scenario analyses with a given lipid-lowering therapy strategy in various patient populations and time frames by providing an improved assessment of treatment benefit over time.