Modelling cause-of-death mortality and the impact of cause-elimination

The analysis of causal mortality provides rich insight into changes in mortality trends that are hidden in population-level data. Therefore, we develop and apply a multinomial logistic framework to model causal mortality. We use internationally classified cause-of-death categories and data obtained...

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Bibliographic Details
Published in:Annals of actuarial science 2015-03, Vol.9 (1), p.167-186
Main Authors: Alai, Daniel H., Arnold (-Gaille), Séverine, Sherris, Michael
Format: Article
Language:English
Subjects:
Actuarial science
Causality
Investigations
Life expectancy
Life insurance
Mortality
Predation
Risk factors
Studies
Survival analysis
Trends
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Summary:The analysis of causal mortality provides rich insight into changes in mortality trends that are hidden in population-level data. Therefore, we develop and apply a multinomial logistic framework to model causal mortality. We use internationally classified cause-of-death categories and data obtained from the World Health Organization. Inherent dependence amongst the competing causes is accounted for in the framework, which also allows us to investigate the effects of improvements in, or the elimination of, cause-specific mortality. This has applications to scenario-based forecasting often used to assess the impact of changes in mortality. The multinomial model is shown to be more conservative than commonly used approaches based on the force of mortality. We use the model to demonstrate the impact of cause-elimination on aggregate mortality using residual life expectancy and apply the model to a French case study.
ISSN:1748-4995
1748-5002
DOI:10.1017/S174849951400027X