Objectives
Health economic models commonly apply observed general population mortality rates to simulate future deaths in a cohort. This is potentially problematic, as mortality statistics are records of the past, not predictions of the future. We propose a new dynamic general population mortality modelling approach which enables ...
Objectives
Health economic models commonly apply observed general population mortality rates to simulate future deaths in a cohort. This is potentially problematic, as mortality statistics are records of the past, not predictions of the future. We propose a new dynamic general population mortality modelling approach which enables analysts to implement predictions of future changes in mortality rates. The potential implications moving from a conventional static approach to a dynamic approach is illustrated using a case-study.
Methods
The model utilised in NICE appraisal TA559, axi-cel 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 modelled year with the first modelled year using 2022 rates, the second modelled year 2023 and so on. Four different assumptions were made around age distribution: fixed mean age; lognormal, normal and gamma. The dynamic model outcomes were compared to those from a conventional static approach.
Results
Including dynamic calculations increased the undiscounted life years attributed to general population mortality by 2.4–3.3 years. This led to an increase in discounted incremental life years within the case study of 0.38–0.45 years (8.1–8.9%), and a commensurate impact on the economically justifiable price of £14,456–£17,097.
Conclusions
The application of a dynamic approach is technically simple and has the potential to meaningfully impact estimates of cost-effectiveness analysis. As a result, we call on health economists and HTA bodies to move towards use of dynamic mortality modelling in future.
