Modelling Cause-of-Death Mortality and the Impact of Cause-Elimination
SSRN Electronic Journal
2013
- 4Citations
- 2,594Usage
- 1Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
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.
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