OBJECTIVES: Universal recommendations for products can be challenging due to a lack of standardization among economic models. We compared the way data is utilized between two published economic evaluations for the use of nirsevimab in RSV and the resulting impact on their conclusions

METHODS: A dynamic transmission and static decision model were compared to assess the impact of nirsevimab on the prevention of RSV-medically attended lower respiratory tract infections for all infants 0-11 months in age experiencing their first RSV season in the UK. For an accurate comparison both models were closely aligned with respect to the population of interest, strategy of immunization and risk of health events. Both models examined the potential reduction in hospitalisations and general practitioner (GP) visits by comparing two scenarios: universal administration of nirsevimab following a seasonal strategy with National Immunisation Programme (NIP)-integrated catch-up versus no coverage.

RESULTS: The static model estimated 16,255 hospitalizations (66% reduction) and 102,633 GP visits (64% reduction) could be prevented compared to 5,852 hospitalizations (28% reduction) and 5,108 prevented GP visits (29% reduction) estimated by the dynamic model. Model structure and prophylaxis efficacy were the primary contributors to the discrepancy between model results. The most important difference was the implementation of protection, with nirsevimab maintaining protection for the full 150 days in the static model (as per the licence), while in the dynamic model efficacy was estimated to drop to 50%, 37% at day 103, 150 resulting in a much faster loss of protection. With similar assumptions for protection used in the static model, prevented hospitalizations and GP visits reduced to 9,578 (39% reduction) and 57,718 (36% reduction), respectively.

CONCLUSIONS: Differences around key model parameters can have a significant impact on the economically justifiable price of a product. Consultation with manufacturers on key treatment parameters is vital to ensure the validity of the analyses.