Dynamic Mortality Modeling: Incorporating Predictions of Future General Population Mortality Into Cost-Effectiveness Analysis

Abstract

ObjectivesHealth economic models commonly apply observed general population mortality rates to simulate future deaths in a cohort. This is potentially problematic, because mortality statistics are records of the past, not predictions for the future. We propose a new dynamic general population mortality modeling approach, which enables analysts to implement predictions of future changes in mortality rates. The potential implications of moving from a conventional static approach to a dynamic approach are illustrated using a case study. MethodsThe model utilized in National Institute for Health and Care Excellence appraisal TA559, axicabtagene ciloleucel axi for diffuse large B-cell lymphoma, was replicated. National mortality projections were taken from the UK Office for National Statistics. Mortality rates by age and sex were updated each modeled year with the first modeled year using 2022 rates, the second modeled year 2023 and so on. A total of 4 different assumptions were made around age distribution: fixed mean age, lognormal, normal, and gamma. The dynamic model outcomes were compared with those from a conventional static approach. ResultsIncluding dynamic calculations increased the undiscounted life-years attributed to general population mortality by 2.4 to 3.3 years. This led to an increase in discounted incremental life-years within the case study of 0.38 to 0.45 years (8.1%-8.9%), and a commensurate impact on the economically justifiable price of £14 456 to £17 097. ConclusionsThe application of a dynamic approach is technically simple and has the potential to meaningfully affect estimates of cost-effectiveness analysis. Therefore, we call on health economists and health technology assessment bodies to move toward use of dynamic mortality modeling in future.