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Cost-effectiveness analysis of anti–IL-5 therapies of severe eosinophilic asthma in Spain
Aim
To analyse the cost-effectiveness of MEP with standard of care (SoC) versus other anti-IL-5 therapies approved for the treatment of severe eosinophilic asthma (SEA) patients, within the Spanish National Health System (NHS) perspective.
Methods
A Markov model with a 4-week cycle length was used to compare MEP with BEN and RES as therapies added to SoC in the management of SEA, in terms of cost per QALY gained and incremental cost-effectiveness ratio (ICER). Costs (€2019) were obtained from public sources, while utilities and transition probabilities were retrieved from literature, e.g. network meta-analysis. Continuation criteria for biological treatment and reduction of oral corticosteroids (OCS) was set at 50% minimum reduction of exacerbation rate. Adverse events related to chronic OCS use included diabetes, osteoporosis, cataracts, acute myocardial infarct, and peptic ulcer. The analysis was performed over a 5-year time horizon from the National Healthcare System (NHCS) perspective, with a yearly discount rate of 3% applied to both costs and QALYs. Probabilistic sensitivity analysis and univariate deterministic sensitivity analysis were performed to address uncertainty around the cost-effectiveness results.
Results
On top of SoC, the model indicates that MEP is dominant (lower cost, higher benefit) compared to BEN and RES: For BEN and RES, respectively, treatment with MEP had a point estimate of 0.076 and 0.075 additional QALYs, and savings of €3,173.47 and €7,772.95 per patient. The findings were robust to variation as estimated using sensitivity analysis.
Conclusions
MEP is a cost-effective treatment in comparison with BEN and RES added to SoC for patients with SEA in the Spanish setting.
Authors
F J González-Barcala, X Muñoz-Gall, E Mariscal, A García, S Yang, G van de Wetering, J L Izquierdo-Alonso
Journal
Journal of Medical Economics
Therapeutic Area
Pulmonology and respiratory
Center of Excellence
Health Economic Modeling & Meta-analysis
Year
2021
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