Hostname: page-component-89b8bd64d-n8gtw Total loading time: 0 Render date: 2026-05-11T13:53:44.545Z Has data issue: false hasContentIssue false
  • English
  • Français

The Importance of Year of Birth in Two-Dimensional Mortality Data

Published online by Cambridge University Press:  10 June 2011

S. J. Richards ,
J. G. Kirkby  and
I. D. Currie
S. J. Richards
Affiliation:
4 Caledonian Place, Edinburgh EH11 4AS, U.K. Email: stephen@richardsconsulting.co.uk Web: www.richardsconsulting.co.uk; Tel: +44(0)131 315 4470
Get access Rights & Permissions [Opens in a new window]

Abstract

Late-life mortality patterns are of crucial interest to actuaries assessing longevity risk. One important explanatory variable is year of birth. We present the results of various analyses demonstrating this, including a statistical model which lends weight to the importance of year-of-birth effects in both population and insured data. We further find that a model based on age and year of birth fits United Kingdom mortality data better than a model based on age and period, suggesting that cohort effects are more significant than period effects. The financial implications of these cohort effects are considerable for portfolios with long-term longevity exposure, such as annuities written by insurance companies and defined benefit pension schemes.

Keywords

Mortality ImprovementsCohortsEarly Life ConditionsMoving AveragesKernel SmoothingPenalised Splines

Information

Type
Sessional meetings: papers and abstracts of discussions
Information
British Actuarial Journal , Volume 12 , Issue 1 , 01 March 2006 , pp. 5 - 38
Copyright
Copyright © Institute and Faculty of Actuaries 2006

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Akaike, H. (1987). Factor analysis and AIC. Psychometrica, 52, 317–333.CrossRefGoogle Scholar
Ames, B.N. (1998). Micro-nutrients prevent cancer and delay ageing. Toxicology Letters, 102–103, 5–18.CrossRefGoogle Scholar
Bengtsson, T. & Lindstrom, M. (2000). Childhood misery and disease in later life. Population Studies (Camb.), 54(3), 263–277.CrossRefGoogle ScholarPubMed
de Boor, C. (2001). A practical guide to splines (revised edition). Applied Mathematical Sciences, 27.Google Scholar
CMIB (Continuous Mortality Investigation Bureau) (1999). Report number 17. Institute and Faculty of Actuaries.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) (2005). Projecting future mortality: towards a proposal for a stochastic methodology. Working paper No. 15.Google Scholar
Craven, P. & Wahba, G. (1979). Smoothing noisy data with spline functions. Numerische Mathematik, 31, 377–390.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
Doblhammer, G. & Vaupel, J.W. (2001). Lifespan depends on month of birth. Proceedings of the National Academy of Sciences of the United States of America, 98, 5, 2934–2939.CrossRefGoogle ScholarPubMed
Doll, R. & Hill, A.B. (1954). The mortality of doctors in relation to their smoking habits: a preliminary report. British Medical Journal, 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-.CrossRefGoogle 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. Statistical Science, 11, 89–121.CrossRefGoogle Scholar
Finch, C.E. & Crimmins, E.M. (2004). Inflammatory exposure and historical changes in human life-spans. Science, 305, 1736.CrossRefGoogle ScholarPubMed
Forey, B.A., Lee, P.N. & Fry, J.S. (1993). Updating U.K. estimates of age, sex and period specific cumulative constant tar cigarette consumption per adult. Thorax, 53, 875–878.CrossRefGoogle Scholar
GAD (Government Actuary's Department) (2003). Interim life tables. www.gad.gov.ukGoogle Scholar
Gavrilov, L.A., Gavrilova, N.S., Semenova, V.G., Evdokushkina, G.N., Krut'ko, V.N., Gavrilova, A.L., Evdokushkina, N.N. & Lapshin, E.V. (1997). Maternal age and lifespan of offspring. Doklady Akademii Nauk, 354, 4.Google ScholarPubMed
Gavrilov, L.A. & Gavrilova, N.S. (1999). Season of birth and human longevity. Journal of Anti-Aging Medicine, 2(4), 365–366.CrossRefGoogle Scholar
Gavrilov, L.A. & Gavrilova, N.S. (2000). Human longevity and parental age at conception. In (Robine, et al., eds.) Sex and longevity: sexuality, gender, reproduction, parenthood. Springer-Verlag, Berlin, Heidelberg.Google Scholar
Gavrilov, L.A. & Gavrilova, N.S. (2001). The reliability theory of aging and longevity. Journal of Theoretical Biology, 213, 527–545.CrossRefGoogle ScholarPubMed
Gavrilov, L.A. & Gavrilova, N.S. (2003). Early-life factors modulating lifespan, In (Rattan, S.I.S., ed.) Modulating aging and longevity. Kluwer Academic Publishers, Dordrecht, The Netherlands.Google Scholar
Gavrilov, L.A. & Gavrilova, N.S. (2004). Early-life programming of aging and longevity: the idea of high initial damage load (the HIDL hypothesis). Annals of the New York Academy of Sciences, 1019, 496–501.CrossRefGoogle ScholarPubMed
Gavrilova, N.S., Gavrilov, L.A., Evdokushkina, G.N. & Semyonova, V.G. (2002). Early The Importance of Year of Birth in Two-Dimensional Mortality Data 35 life conditions and later sex differences in adult lifespan. Paper presented at 2002 annual meeting of Population Association of America, May 9-11 2002, Atlanta.Google Scholar
Gluckman, P.D. & Hanson, M.A. (2004). Living with the past: evolution, development, and patterns of disease. Science, 17 September 2004, 305.CrossRefGoogle Scholar
Izumoto, Y., Inoue, S. & Yasuda, N. (1999). Schizophrenia and the influenza epidemics of 1957 in Japan. Biological Psychiatry, 46, 1, 119–124.CrossRefGoogle Scholar
Kermack, W.O., McKendrick, A.G. & McKinley, P.L. (1934). Death rates in Britain and Sweden: some regularities and their significance. Lancet, 698–703. (Reprinted in International Journal of Epidemiology, 2001, 30, 678-683).CrossRefGoogle Scholar
Lee, P.N., Fry, J.S. & Forey, B.A. (1990). Trends in lung cancer, chronic obstructive lung disease, and emphysema death rates for England and Wales 1941-85 and their relation to trends in cigarette smoking. Thorax, 45, 657–665.CrossRefGoogle ScholarPubMed
Liestol, K. (1981). A note on the influence of factors early in development on later reproductive function. Annals of Human Biology, 8(6), 559–565.CrossRefGoogle ScholarPubMed
Madjid, M., Aboshady, I., Awan, I., Litovsky, S. & Ward Casscells, S. (2004). Influenza and cardiovascular disease: is there a causal relationship? Texas Heart Institute Journal, 31.Google ScholarPubMed
McCarron, P., Okasha, M., McEwan, J. & Smith, G.D. (2002). Height in young adulthood and risk of death from cardio-respiratory disease. American Journal of Epidemiology, 155(8), 683–687.CrossRefGoogle Scholar
McCullagh, P. & Nelder, J.A. (1989). Generalized linear models, 2nd ed.Chapman and Hall, London.CrossRefGoogle Scholar
Nadaraya, E.A. (1964). On estimating regression. Theory of Probability and Applications, 9.CrossRefGoogle Scholar
Peltonen, M. & Asplund, K. (1996). Age-period-cohort effects on stroke mortality in Sweden from 1969-1993 and forecasts up to the year 2003. Stroke, 27(11), 1981–1985.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
Reinert-Azambuja, M.I. (2004). Spanish flu and early 20th-century expansion of a coronary heart disease-prone subpopulation. Texas Heart Institute Journal, 31.Google 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
Rosebloom, T.J., van der Meulen, J.H., Osmond, C., Barker, D.J., Ravelli, A.C. & Bleker, O.P. (2001). Adult survival after prenatal exposure to the Dutch famine of 1944-45. Paediatric Perinatal Epidemiology, 15(3), 220–225.CrossRefGoogle Scholar
Schwartz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 462–464.Google Scholar
Selten, J.P., Brown, A.S., Moons, K.G., Slaets, J.P., Susser, E.S. & Kahn, R.S. (1999). Prenatal exposure to influenza as a risk factor for adult schizophrenia. Schizophrenia Research, 38(2-3), 85–91.CrossRefGoogle Scholar
Stanner, S.A., Bulmer, K., Andres, C., Lantseva, O.E., Borodina, V., Poteen, V.V. & Yudkin, J.S. (1997). Does malnutrition in utero determine diabetes and coronary heart disease in adulthood? Results from the Leningrad siege study. British Medical Journal, 315, 1342–1348.CrossRefGoogle ScholarPubMed
Todd, G.F., Lee, P.N. & Wilson, M.J. (1976). Cohort analysis of cigarette smoking and of mortality from four associated diseases. Occasional paper 3, Tobacco Research Council, London.Google Scholar
Vijayakumar, M., Fall, C.H., Osmond, C. & Barker, D.J. (1995). Birth weight, weight at one year, and left ventricular mass in adult life. British Heart Journal, 73(4), 363–367.CrossRefGoogle ScholarPubMed
Wand, M.P. & Jones, C.M. (1995). Kernel smoothing, Monographs on Statistics and Applied Probability, Chapman and Hall.CrossRefGoogle Scholar
Watson, G.S. (1964). Smooth regression analysis. Sankhya, Series A.Google Scholar
Willets, R.C. (1999). Mortality in the next millennium. Paper presented to the Staple Inn Actuarial Society.Google Scholar
Willets, R.C. (2004). The cohort effect: insights and explanations. British Actuarial Journal, 10, 833–877.CrossRefGoogle Scholar