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

Published online by Cambridge University Press:  17 November 2014

Daniel H. Alai*
Affiliation:
School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, Kent CT2 7NF, UK
Séverine Arnold (-Gaille)
Affiliation:
Department of Actuarial Science, Faculty of Business and Economics (HEC Lausanne), University of Lausanne, 1015 Lausanne, Switzerland
Michael Sherris
Affiliation:
CEPAR, Risk and Actuarial Studies, UNSW Australia Business School, Sydney, NSW 2052, Australia
*
*Correspondence to: Daniel H. Alai, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, Kent CT2 7NF, UK. Tel: +44 (0) 1227 824753; Fax: +44 (0) 1227 827932; E-mail: d.h.alai@kent.ac.uk

Abstract

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.

Type
Papers
Copyright
© Institute and Faculty of Actuaries 2014 

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