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Modelling Cause-of-Death Mortality and the Impact of Cause-Elimination

SSRN Electronic Journal
2013
  • 4
    Citations
  • 2,594
    Usage
  • 1
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    4
    • Citation Indexes
      4
  • Usage
    2,594
    • Abstract Views
      2,342
    • Downloads
      252
  • Captures
    1
  • Ratings
    • Download Rank
      243,278

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.

Bibliographic Details

Daniel H. Alai; Severine Arnold (-Gaille); Michael Sherris

Elsevier BV

Cause-of-Death Mortality; Multinomial Logistic Regression; Cause-Elimination; Life Expectancy; Mortality Forecasts

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