Modelling cause-of-death mortality and the impact of cause-elimination
| Autor: | Daniel H. Alai, Séverine Arnold, Michael Sherris |
|---|---|
| Přispěvatelé: | Arnold (-Gaille) S. |
| Rok vydání: | 2014 |
| Předmět: |
Statistics and Probability
Economics and Econometrics Actuarial science Mortality rate Probabilistic logic World health Force of mortality Cause-of-death mortality Multinomial logistic regression Cause-elimination Life expectancy 22 Mortality forecasts Public pension Econometrics Economics Multinomial distribution Multinomial model Statistics Probability and Uncertainty QA Mortality trends Cause of death |
| Zdroj: | Annals of Actuarial Science, vol. 9, no. 1, pp. 167-186 |
| ISSN: | 1748-5002 1748-4995 |
| DOI: | 10.1017/s174849951400027x |
| Popis: | Changes in underlying mortality rates significantly impact insurance business as well as private and public pension systems. Individual mortality studies have data limitations; aggregate mortality studies omit many relevant details. The study of causal mortality represents the middle ground, where population data is used while cause-of-death information is retained. We use internationally classified cause-of-death categories and data obtained from the World Health Organization. We model causal mortality simultaneously in a multinomial logistic framework. Consequently, inherent dependence amongst the competing causes is accounted for. This framework allows us to investigate the effects of improvements in, or the elimination of, cause-specific mortality in a sound probabilistic way. This is of particular interest for scenario-based forecasting purposes. We show the multinomial model is more conservative than a force-of-mortality approach. Finally, we quantify the impact of cause-elimination on aggregate mortality using residual life expectancy and apply our model to a French case study. |
| Databáze: | OpenAIRE |
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