Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of cause-of-death mortality and actuarial implications

  • Kwon, Hyuk-Sung (Department of Statistics and Actuarial Science, Soongsil University) ;
  • Nguyen, Vu Hai (Department of Statistics and Actuarial Science, Soongsil University)
  • Received : 2019.07.02
  • Accepted : 2019.09.27
  • Published : 2019.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Mortality study is an essential component of actuarial risk management for life insurance policies, annuities, and pension plans. Life expectancy has drastically increased over the last several decades; consequently, longevity risk associated with annuity products and pension systems has emerged as a crucial issue. Among the various aspects of mortality study, a consideration of the cause-of-death mortality can provide a more comprehensive understanding of the nature of mortality/longevity risk. In this case study, the cause-of-mortality data in Korea and the US were analyzed along with a multinomial logistic regression model that was constructed to quantify the impact of mortality reduction in a specific cause on actuarial values. The results of analyses imply that mortality improvement due to a specific cause should be carefully monitored and reflected in mortality/longevity risk management. It was also confirmed that multinomial logistic regression model is a useful tool for analyzing cause-of-death mortality for actuarial applications.

Keywords

References

  1. Alai DH, Arnold S, and Sherris M (2014). Modelling cause-of-death mortality and the impact of cause-elimination, Annals of Actuarial Science, 9, 167-186. https://doi.org/10.1017/S174849951400027X
  2. Arias E, Heron M, and Tejada-Vera B (2013). United States life tables eliminating certain causes of death, 1999-2001, National Vital Statistics Reports, 61, 1-218.
  3. Arnold S and Sherris M (2013). Forecasting mortality trends allowing for cause-of-death mortality dependence, North American Actuarial Journal, 17, 273-282. https://doi.org/10.1080/10920277.2013.838141
  4. Arnold S and Sherris M (2015). Causes-of-death mortality: what do we know on their dependence?, North American Actuarial Journal, 19, 116-128. https://doi.org/10.1080/10920277.2015.1011279
  5. Boumezoued A, Labit Hardy H, El Karoui N, and Arnold S (2018). Causes-of-death mortality: What can be learned from population dynamics?, Insurance: Mathematics and Economics, 78, 301-315. https://doi.org/10.1016/j.insmatheco.2017.09.015
  6. De Jong P and Heller GZ (2008). Generalized Linear Models for Insurance Data, Cambridge University Press, New York.
  7. Desesquelles AF, Salvatore MA, Pappagallo M, Frova L, Pace M, Mesle F, and Egidi V (2012). Analysing multiple causes of death: which methods for which data? An application to the cancerrelated mortality in France and Italy, European Journal of Population, 28, 467-498. https://doi.org/10.1007/s10680-012-9272-3
  8. Dickson DCM, Hardy MR, and Waters HR (2013). Actuarial Mathematics for Life Contingent Risks (2nd ed), Cambridge University Press, New York.
  9. Dimitrova D, Haberman S, and Kalshev V (2013). Dependent competing risks: cause elimination and its impact on survival, Insurance: Mathematics and Economics, 53, 464-477. https://doi.org/10.1016/j.insmatheco.2013.07.008
  10. European Insurance and Occupational Pension Authority (2014). Technical Specification for the Preparatory Phase (Part I), 203-204. Available from: https://eiopa.europa.eu
  11. Foreman KJ, Li G, Best N, and Ezzati M (2016). Small area forecasts of cause-specific mortality: application of a Bayesian hierarchical model to US vital registration data, Journal of the Royal Statistical Society (Applied Statistics Series C), 66, 121-139. https://doi.org/10.1111/rssc.12157
  12. Frees E (2010). Regression Modeling with Actuarial and Financial Applications, Cambridge University Press, New York.
  13. Goodman RA, Manton GG, Nolan TF, Bregman DJ, and Hinman AR (1982). Mortality data analysis using a multiple-cause approach, Journal of the American Medical Association, 247, 793-796. https://doi.org/10.1001/jama.1982.03320310041026
  14. Heron M and Anderson RN (2016). Changes in the leading cause of death: recent patterns in heart disease and cancer mortality, NCHS Data Brief, 254 (August 2016).
  15. Hong L and Yang L (2019). Modeling cause-of-death mortality using hierarchical Archimedean copula, Scandinavian Actuarial Journal, 2019, 247-272. https://doi.org/10.1080/03461238.2018.1546224
  16. Israel RA, Rosenberg HM, and Curtin LR (1986). Analytical potential for multiple cause-of-death data, American Journal of Epidemiology, 124, 161-179. https://doi.org/10.1093/oxfordjournals.aje.a114375
  17. Tolley HD, and Poss SS (1976). Life table techniques for multiple-cause mortality, Demography, 13, 541-564. https://doi.org/10.2307/2060509
  18. McNown R and Rogers A (1992). Forecasting cause-specific mortality using time series methods, International Journal of Forecasting, 8, 413-432. https://doi.org/10.1016/0169-2070(92)90056-F
  19. Moreno-Betancur M, Sadaoui H, Piffaretti C, and Rey G (2017). Survival analysis with multiple causes of death extending the competing risks model, Epidemiology, 28, 12-19. https://doi.org/10.1097/EDE.0000000000000531
  20. Poston DL and Min H (2008). The multinomial regression modeling of the cause-of-death mortality of the oldest old in the U.S., Journal of Modern Applied Statistical Methods, 7, 597-606. https://doi.org/10.22237/jmasm/1225513380
  21. Richards SJ (2009). Selected issues in modelling mortality by cause and in small populations, British Actuarial Journal, 15, 267-283. https://doi.org/10.1017/S1357321700005602
  22. Wilmoth JR (1995). Are mortality projections always more pessimistic when disaggregated by cause of death?, Mathematical Population Studies, 5, 293-319. https://doi.org/10.1080/08898489509525409
  23. Yun JW and Son M (2016). Forecasting cause-specific mortality in Korea up to year 2032, Journal of Korean Medical Science, 31, 1181-1189. https://doi.org/10.3346/jkms.2016.31.8.1181