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Two-Dimensional Mortality Data: Patterns and Projections

Published online by Cambridge University Press:  10 June 2011

S. J. Richards ,
J. R. Ellam ,
J. Hubbard ,
J. L. C. Lu ,
S. J. Makin  and
K. A. Miller
S. J. Richards
Affiliation:
4 Caledonian Place, Edinburgh EH11 4AS, U.K. Tel: +44(0)131 315 4470; Email: stephen@richardsconsulting.co.uk; Web: www.richardsconsulting.co.uk
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Abstract

Patterns and trends in late-life mortality are of growing financial importance. The growth in pension liabilities, both public and private, are of crucial interest to governments, insurers and companies with defined benefit pension schemes. This paper explores the patterns in international mortality data, and draws important lessons for actuaries in the United Kingdom.

Keywords

Mortality ImprovementsCohort EffectsPeriod EffectsPenalised SplinesGLMsSurvival ModelsCause of Death
Type
Sessional meetings: papers and abstracts of discussions
Information
British Actuarial Journal , Volume 13 , Issue 3 , 01 September 2007 , pp. 479 - 536
Copyright
Copyright © Institute and Faculty of Actuaries 2007

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References

Akaike, H. (1987). Factor analysis and AIC. Psychometrica, 52, 317–333.CrossRefGoogle Scholar
Bernoulli, D. (1766). Essai d'une nouvelle analyse de la mortalite causee par la petite verole. Mem. Math. Phy. Acad. Roy. Sci., Paris.Google Scholar
De Boor, C. (2001). A practical guide to splines. Applied Mathematical Sciences, 27, SpringerVerlag, New York.Google Scholar
Bunker, J.P., Frazier, H.S. & Mosteller, F. (1994). Improving health: measuring effects of medical care. The Milbank Quarterly, 72(2), 225–257.CrossRefGoogle ScholarPubMed
Capewell, S., Morrison, C.E. & McMurray, J.J. (1999). Contribution of modern cardiovascular treatment and risk factor changes to the decline in coronary heart disease mortality in Scotland between 1975 and 1994. Heart, 81, 380–386.CrossRefGoogle Scholar
Capewell, S., Beaglehole, R., Sheddon, M. & McMurray, J. (2000). Explanation for the decline in coronary heart-disease mortality rates in Auckland, New Zealand, between 1982 and 1993. Circulation, 102, 1511–1516.CrossRefGoogle Scholar
CDC (Center for Disease Control) (2006). Tobacco use among adults in the United States, 2005. Morbidity and Mortality Weekly Report 2006, 55(42), 1145–1148.Google Scholar
CMIB (Mortality Sub-Committee) (2002). An interim basis for adjusting the 92 Series mortality projections for cohort effects. Working paper no. 1.Google Scholar
CMIB (Mortality Sub-Committee) (2004). Projecting future mortality: a discussion paper. Working paper no. 3.Google Scholar
CMIB (Mortality Committee) (2005a). Projecting future mortality: towards a proposal for a stochastic methodology. Working paper no. 15.Google Scholar
CMIB (Mortality Committee) (2005b). Stochastic projection methodologies: further progress and P-Spline model features, example results and implications. Working paper no. 20.Google Scholar
CMIB (Mortality Committee) (2006). The graduation of the CMI 1999-2002 mortality experience: final 00 series mortality tables - assured lives. Working paper no. 21.Google Scholar
CMIB (Mortality Committee) (2007). Stochastic projection methodologies: Lee-Carter model: features, example results and implications. Working paper no. 25.Google Scholar
Cox, D.R. (1972). Regression models and life tables. Journal of the Royal Statistical Society, Series B, 24, 187–220 (with discussion).Google Scholar
Craven, P. & Wahba, G. (1979). Smoothing noisy data with spline functions. Numer. Math., 31, 377–403.CrossRefGoogle Scholar
Currie, I.D., Durban, M. & Eilers, P.H.C. (2003). Using P-splines to extrapolate two-dimensional Poisson data. Proceedings of 18th International Workshop on Statistical Modelling, Leuven, Belgium, 97–102.Google Scholar
Currie, I.D., Durban, M. & Eilers, P.H.C. (2004a). Array regression: an approach to smoothing data on arrays. Unpublished paper.Google Scholar
Currie, I.D., Durban, M. & Eilers, P.H.C. (2004b). Smoothing and forecasting mortality rates. Statistical Modelling, 4, 279–298.CrossRefGoogle Scholar
Doll, R. & Hill, A.B. (1954). The mortality of doctors in relation to their smoking habits: a preliminary report. British MedicalJournal 1954, ii, 1451–1455.CrossRefGoogle Scholar
Doll, R., Peto, R., Boreham, J. & Sutherland, I. (2004). Mortality in relation to smoking: 50 years' observations on male British doctors. British Medical Journal 2004, 328, 1519.Google ScholarPubMed
Durban, M., Currie, I.D. & Eilers, P.H.C. (2002). Using P-splines to smooth two-dimensional Poisson data. Proceedings of 17th International Workshop on Statistical Modelling, Chania, Crete, 207–214.Google Scholar
Eilers, P.H.C. & Marx, B.D. (1996). Flexible smoothing with B-splines and penalties. Statist. Sci., 11, 89–121.CrossRefGoogle Scholar
ERA (2003). Enrolled actuaries report. 28(4), Winter.Google Scholar
GAD (Government Actuary's Department) (2001). National population projections: review of methodology for projecting mortality. NSQR Series No. 8.Google Scholar
Gavrilov, LA. & Gavrilova, N.S. (1991). Biology of life span: a quantitative approach. Harwood Academic Publisher.Google Scholar
Gompertz, B. (1825). The nature of the function expressive of the law of human mortality. Philosophical Transactions of the Royal Society.Google Scholar
Goss, S.C. (2005). Testimony before the Subcommittee on Social Security of the House Committee on Ways and Means, 24 May 2005. Statement of Stephen C. Goss, Chief Actuary, Social Security Administration.Google Scholar
Hayflick, L. (2000). The future of ageing. Nature, 408(6809), 267–269.CrossRefGoogle ScholarPubMed
Human Mortality Database (2007). HMD. University of California, Berkeley (U.S.A.), and Max Planck Institute for Demographic Research (Germany). URL www.mortality.orgGoogle Scholar
Hunink, M.G.M., Goldman, L. & Tosteson, A.N.A. (1997). The recent decline in mortality from coronary heart disease, 1980-1990. Journal of the American Medical Association, 277(7), 535–541.CrossRefGoogle ScholarPubMed
Kirkby, J.G. & Currie, I.D. (2006). Modelling mortality data on the Lexis diagram. Proceedings of 21st International Workshop on Statistical Modelling, Galway, 279–285.Google Scholar
Kuulasmaa, K., Tunstall-Pedoe, H. & Dobson, A. (2000). Estimation of contribution of changes in classic risk factors to trends in coronary-event rates across the WHO MONICA Project populations. The Lancet, 355, 675–687.CrossRefGoogle ScholarPubMed
Lickint, F. (1929). Tabak und Tabakrauch als atiologischer Factor des Carcinoms. Zeitschrift fur Krebsforschung 1929, 30, 349–365.CrossRefGoogle Scholar
McCullagh, P. & Nelder, JA. (1989). Generalized linear models. 2nd ed. Monographs on Statistics and Applied Probability 37, Chapman and Hall, London.Google Scholar
Nolte, E. & McKee, M. (2004). Does health care save lives? Avoidable mortality revisited. The Nuffield Trust.Google Scholar
OECD (2005). OECD health data 2005: how does the United States compare? OECD.Google Scholar
ONS (2006). Office for National Statistics website. URL www.statistics.gov.ukGoogle Scholar
Philips, L. (1990). Hanging on the metaphone. Computer Language, 1990, 7(12), 39–43.Google Scholar
Proctor, R.N. (2001). Commentary: Schairer and Schoeniger's forgotten tobacco epidemiology and the Nazi quest for racial purity. International Journal of Epidemiology 2001, 30, 31–34.CrossRefGoogle Scholar
R Development Core Team (2004). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL www.r-project.orgGoogle Scholar
Renshaw, A.E. (1991). Actuarial graduation practice and generalised linear and non-linear models. Journal of the Institute of Actuaries, 118, 295–312.CrossRefGoogle Scholar
Richards, S.J. & Jones, G.L. (2004). Financial aspects of longevity risk. Paper presented to the Staple Inn Actuarial Society.Google Scholar
Richards, S.J., Kirkby, J.G. & Currie, I.D. (2006). The importance of year of birth in two-dimensional mortality data. British Actuarial Journal, 12, 5–61.CrossRefGoogle Scholar
Rodu, B. (2004). Swedish tobacco use: smoking, smokeless, and history. American Council on Science and Health.Google Scholar
Rodu, B. & Cole, P. (2004). The burden of mortality from smoking: comparing Sweden with other countries in the European Union. European Journal of Epidemiology, 19, 129–131.CrossRefGoogle ScholarPubMed
Schairer, E. & Schoniger, E. (1943). Lungenkrebs und Tabakverbrauch. Zeitschrift fur Krebsforschung, 1943, 54, 261–269.Google Scholar
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464.CrossRefGoogle Scholar
Shojania, K.G. & Burton, E.C. (2004). The persistent value of the autopsy. American Family Physician.Google Scholar
Shojania, K.G., Burton, E.C., McDonald, K.M. & Goldman, L. (2002). The autopsy as an outcome and performance measure. Evidence Report/Technology Assessment. Number 58. Agency for Healthcare Research and Quality Publication No. 03-E002.Google Scholar
Tunstall-Pedoe, H., Vanuzzo, D. & Hobbs, M. (2000). Estimation of contribution of changes in coronary care to improving survival, event rates and coronary heart-disease mortality across the WHO MONICA Project populations. The Lancet, 355, 688–700.CrossRefGoogle ScholarPubMed
Unal, B., Critchley, J.A. & Capewell, S. (2004). Explaining the decline in coronary heart-disease mortality in England and Wales between 1981 and 2000. Circulation, 109, 1101–1107.CrossRefGoogle ScholarPubMed
Unal, B., Critchley, J.A. & Capewell, S. (2005). Life-years gained from modern cardiological treatments and population risk-factor changes in England and Wales, 1981-2000. American Journal of Public Health, 95(1), 103–107.CrossRefGoogle ScholarPubMed
Willets, 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, 695–898.CrossRefGoogle Scholar
Willets, R.C. (2004). The cohort effect: insights and explanations. British Actuarial Journal, 10, 833–877.CrossRefGoogle Scholar
WHO (2007). World Health Organisation website. URL www.who.intGoogle Scholar