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Modelling Income Protection Claim Termination Rates by Cause of Sickness II: Mortality of UK Assured Lives

Published online by Cambridge University Press:  10 May 2011

H. R. Waters
Affiliation:
Department of Actuarial Mathematics and Statistics, and the Maxwell Institute for the Mathematical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, U.K. :, Email: [email protected]

Abstract

This is the second of three papers in which we present methods and results for the estimation and modelling of claim termination rates for Income Protection (IP) insurance, allowing for different causes of claim. In the first paper we discussed recoveries. In this and the third paper we develop models for the mortality of IP claimants.

We model this mortality as the sum of two components: a base, or background, mortality, which is a function of age and calendar year, but not of the specific cause of sickness or its current duration, and a cause-specific element which does depend on the current duration of the sickness. In this paper we discuss the modelling of the base mortality. In particular, we use data supplied by the Continuous Mortality Investigation relating to UK assured lives from 1975 (males) and 1983 (females) to 2003 to develop models of mortality which are functions of sex, age and calendar year. Such models are of interest in their own right, particularly at a time when expected future lifetimes are increasing.

The modelling of the cause-specific component of the mortality model is discussed in Paper III.

Type
Papers
Copyright
Copyright © Institute and Faculty of Actuaries 2009

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References

References for Papers I, II and III

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716723.CrossRefGoogle Scholar
CMIR2 (1976). Continuous Mortality Investigation Reports: Number 2. The Institute of Actuaries and the Faculty of Actuaries.Google Scholar
CMIR6 (1983). Continuous Mortality Investigation Reports: Number 6. The Institute of Actuaries and the Faculty of Actuaries.Google Scholar
CMIR8 (1986). Continuous Mortality Investigation Reports: Number 8. The Institute of Actuaries and the Faculty of Actuaries.Google Scholar
CMIR12 (1991). Continuous Mortality Investigation Reports: Number 12. The Institute of Actuaries and the Faculty of Actuaries.Google Scholar
CMIR15 (1996). Continuous Mortality Investigation Reports: Number 15. The Institute of Actuaries and the Faculty of Actuaries.Google Scholar
CMIR17 (1999). Continuous Mortality Investigation Reports: Number 17. The Institute of Actuaries and the Faculty of Actuaries.Google Scholar
CMIR18 (2000). Continuous Mortality Investigation Reports: Number 18. The Institute of Actuaries and the Faculty of Actuaries.Google Scholar
CMIR22 (2005). Continuous Mortality Investigation Reports: Number 22. The Institute of Actuaries and the Faculty of Actuaries.Google Scholar
CMIWP5 (2004). Continuous Mortality Investigation Working Paper: Number 5. The Institute of Actuaries and the Faculty of Actuaries.Google Scholar
CMIWP23 (2006). Continuous Mortality Investigation Working Paper: Number 23. The Institute of Actuaries and the Faculty of Actuaries.Google Scholar
Cordeiro, I.M.F. (1998). A stochastic model for the analysis of permanent health insurance claims by cause of disability. Ph.D. Thesis, Heriot-Watt University, Edinburgh.Google Scholar
Cordeiro, I.M.F. (2002). A multiple state model for the analysis of permanent health insurance claims by cause of disability. Insurance: Mathematics and Economics, 30, 167186.Google Scholar
Cox, D.R. (1972). Regression Models and Life Tables (with Discussion). Journal of the Royal Statistical Society. Series B (Methodological), 34(2), 187220.Google Scholar
Devlin, T.F. & Weeks, B.J. (1986). Spline functions for logistic regression modeling. Proc. 11th Annual SAS Users Group Intnl Conf. Cary NC: SAS Institute, Inc., 646651.Google Scholar
Dickman, P.W., Sloggett, A., Hills, M. & Hakulinen, T. (2004). Regression models for relative survival. Statistics in Medicine, 23, 5164.CrossRefGoogle ScholarPubMed
Forfar, D.O., McCutcheon, J.J. & Wilkie, A.D. (1988). On graduation by mathematical formula. Journal of The Institute of Actuaries, 115(1), 1149.CrossRefGoogle Scholar
ICD8 (1967). Manual of the International Statistical Classification of Diseases, Injuries and Causes of Death, 8th Edition. World Health Organisation.Google Scholar
Kluwer (2001). Income protection insurance 2001. Croner Publications and Kluwer Publishing, United Kingdom.Google Scholar
Lee, R.D. & Carter, L. (1992). Modeling and forecasting the time series of U.S. mortality. Journal of the American Statistical Association, 87, 659671.Google Scholar
Ling, S.Y. (2008). Supporting document for Ph.D thesis at http://www.ma.hw.ac.uk/~singyee/.Google Scholar
Ling, S.Y. (2009). Models for income protection insurance incorporating cause of sickness. Ph.D. Thesis, Heriot-Watt University, Edinburgh.Google Scholar
Ling, S.Y., Waters, H.R. & Wilkie, A.D. (2009a). Modelling income protection claim termination rates by cause of sickness I: recoveries. Annals of Actuarial Science, 4, 199239.CrossRefGoogle Scholar
Ling, S.Y., Waters, H.R. & Wilkie, A.D. (2009b). Modelling income protection claim termination rates by cause of sickness II: mortality of UK assured lives. Annals of Actuarial Science, 4, 241259.CrossRefGoogle Scholar
Ling, S.Y., Waters, H.R. & Wilkie, A.D. (2009c). Modelling income protection claim termination rates by cause of sickness III: mortality. Annals of Actuarial Science, 4, 261286.CrossRefGoogle Scholar
McCullagh, P. & Nelder, J. A. (1989). Generalized linear models. Chapman and Hall, United Kingdom.CrossRefGoogle Scholar
Pitt, D.G.W. (2007). Modelling the claim duration of income protection insurance policyholders using parametric mixture models. Annals of Actuarial Science, 2(1), 124.CrossRefGoogle Scholar
Renshaw, A.E. & Haberman, S. (1995). On the graduation associated with a multiple state model in permanent health insurance. Insurance: Mathematics and Economics, 17(1), 117.Google Scholar
Renshaw, A.E. & Haberman, S. (2000). Modelling the recent time trends in UK permanent health insurance recovery, mortality and claim inception transition intensities. Insurance: Mathematics and Economics, 27(3), 365396.Google Scholar
Sanders, A.J. & Silby, N.F. (1988). Actuarial aspects of PHI in the UK. Journal of the Institute of Actuaries Students' Society, 31, 157.Google Scholar
Therneau, T. & Grambsch, P. (1994). Proportional hazards tests and diagnostics based on weighted residuals. Biometrika, 81(3), 515526.Google Scholar
Willetts, R.C., Gallop, A.P., Leandro, P.A., Lu, J.L.C., Macdonald, A.S., Miller, K.A., Richards, S.J., Robjohns, N., Ryan, J.P. & Waters, H.R. (2004). Longevity in the 21st century. British Actuarial Journal, 10, 685832.CrossRefGoogle Scholar