Critical assessment of uncertainty in economic evaluations on influenza vaccines for the elderly population in Spain - Raúl Ortiz-de-Lejarazu Leonardo; Javier Díez Domingo; Ángel Gil de Miguel; Federico Martinón Torres; Esther Redondo Margüello; Juan Luis López-Belmonte Claver; Paloma I. Palomo-Jiménez; J. Manel Farré Avellà; José María Abellán Perpiñán (BMC Infectious Diseases)
Abstract Background Influenza is a seasonal infection with a huge impact on morbidity and mortality in older adults, for whom vaccination is recommended. New influenza vaccines for this population have been introduced in Spain in the past 5 years, and a number of cost-effectiveness analyses (CEA) have been published to aid healthcare decision-making. The objective of this study was to assess possible sources of uncertainty in the CEAs of influenza vaccines for the older adults in Spain. Methods A systematic review was performed to identify Spanish CEAs published since 2016. Potential sources of structural, methodologic and parametric uncertainty in CEA results were systematically analysed using the TRansparent Uncertainty ASsessmenT (TRUST) Tool, quality assessment checklists, and the WHO guidance on economic evaluations of influenza vaccine strategies. The primary sources of efficacy/effectiveness were analysed in depth to ascertain whether they could support the conclusions of the respective CEAs. Results Seven CEAs were included. Overall, they were designed and performed in accordance with the applicable guidelines; however, some critical sources of uncertainty were detected, mainly: (1) the choice and use of efficacy/effectiveness data (real-world single season studies, meta-analyses including studies with high risk of bias and/or high heterogeneity with biased interpretation); (2) use of fewer than 5 seasons to estimate influenza burden; (3) generalized use of influenza-like illness data to estimate effectiveness and burden, among others. Conclusions Seemingly well-designed studies may conceal important sources of uncertainty that affect the results. This must be taken into account when interpreting results to support decision-making.