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A New Model of Ageing and Mortality

Published online by Cambridge University Press:  10 June 2011

Eugene M. G. Milne
Eugene M. G. Milne
Affiliation:
Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, UK. Tel: +44 (0)191 210 6400; E-mail: Eugene.Milne@newcastle.ac.uk
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Abstract

The Nested Binomial Model presented in this paper is a new approach to modelling mortality and survival in humans and other species that seeks to reconcile individual life course risk trajectories and those population mortality patterns that arise from inter-individual heterogeneity. In describing individual trajectories it partitions mortality risk into two main elements: ‘redundancy’ and ‘interactive risk'. Interactive risk is volatile, increasing or decreasing with time and circumstance, while redundancy is a quantity which declines in a linear and largely invariable fashion throughout life. Although a biological correlate for redundancy is not identified, this assumption allows strikingly realistic modelling of mortality and survival curves, late-life mortality deceleration, Strehler-Mildvan correlation, mortality plateaux and slowing of mortality. Simple assumptions with regard to heterogeneity of parameters within the model allow close approximation to the entire human mortality curve, and provide a rationale for observed and otherwise paradoxical population mortality phenomena. As such, it fulfils biodemographic criteria for a comprehensive theory of ageing. Future challenges are to reconcile its theoretical structure with empirical findings in the biology of ageing and to render it in a form that can become a usable actuarial tool.

Keywords

Mortality LawsSurvival ModelsGompertzMakehamBiological Models
Type
Sessional meetings: papers and abstracts of discussions
Information
British Actuarial Journal , Volume 15 , Issue S1 , 2009 , pp. 213 - 234
Copyright
Copyright © Institute and Faculty of Actuaries 2009

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References

REFERENCES

Beard, R.E. (1964). Some observations on stochastic processes with particular reference to mortality studies. International Congress of Actuaries, 3, 463–477.Google Scholar
Carnes, B.A. & Olshansky, S.J. (1997). A biologically motivated partitioning of mortality. Experimental Gerontology, 32, 615–631.CrossRefGoogle ScholarPubMed
Dolejs, J. (1997). The extension of Gompertz law's validity. Mechanisms of Ageing and Development, 99, 233–244.CrossRefGoogle ScholarPubMed
Finch, C.E. (1990). Longevity, senescence and the genome. University of Chicago Press, Chicago.Google Scholar
Gavrilov, L. & Gavrilova, N. (2006). Reliability theory of aging and longevity. In: Masoro, E.J. & Austad, S.N. (eds.). Handbook of the Biology of Aging, 6th ed. Academic Press, Oxford, 3–42.Google Scholar
Gompertz, B. (1825). On the nature of the function expressive of the law of human mortality and on a new mode of determining life contingencies. Philosophical Transactions of the Royal Society of London, A 115, 513–585.Google Scholar
Hayflick, L. (1984). When does aging begin? Research on Aging, 6, 99–103.CrossRefGoogle ScholarPubMed
Makeham, W.M. (1867). On the law of mortality. Journal of the Institute of Actuaries, 13, 325–358.Google Scholar
Masoro, E.J. (2006). Caloric restriction and aging: controversial issues. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 61, 14–19.CrossRefGoogle ScholarPubMed
Mildvan, A.S. & Strehler, B.L. (1960). A critique of theories of mortality. In: Strehler, B.L. (ed.). Biology of Aging, American Institute of Biological Sciences, Publication No. 6. American Institute of Biological Sciences, Washington.Google Scholar
Milne, E.M.G. (2006). When does human ageing begin? Mechanisms of Ageing and Development, 127, 290–297.CrossRefGoogle ScholarPubMed
Milne, E.M.G. (2008). The natural distribution of survival. Journal of Theoretical Biology, 255, 223–236.CrossRefGoogle ScholarPubMed
Milne, E.M.G. (2009). Dynamics of human mortality. Experimental Gerontology, 45, 180–187.CrossRefGoogle ScholarPubMed
Olshansky, S.J. & Carnes, B.A. (1997). Ever since Gompertz. Demography, 34, 1–15.CrossRefGoogle ScholarPubMed
Olshansky, S.J., Carnes, B.A. & Grahn, D. (1998). Confronting the boundaries of human longevity. American Scientist, 86, 52–61.CrossRefGoogle Scholar
Richards, S.J. (2010). A handbook of parametric survival models for actuarial use (in preparation).Google Scholar
Strehler, B.L. & Mildvan, A.S. (1960). General theory of mortality and aging. Science, 132, 14–21.CrossRefGoogle ScholarPubMed
Vanfleteren, J.R., De Vreese, A. & Braeckman, B.P. (1998). Two-parameter logistic and Weibull equations provide better fits to survival data from isogenic populations of Caenorhabditis elegans in axenic culture than does the Gompertz model. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 53, B393-B403; discussion B404-B408.CrossRefGoogle ScholarPubMed
Wachter, K.W. (2003). Hazard curves and life span prospects. In: Carey, J.R. & Tuljapurkar, S. (eds.). Population and Development Review, New York.Google Scholar
Wilmoth, J. & Horiuchi, S. (1999). Do the oldest old grow old more slowly?. In: Robine, J.-M., Forette, B., Franceschi, C. & Allard, M.. (eds.). The Paradoxes of Longevity. Springer-Verlag, Berlin Heidelberg, 35–59.CrossRefGoogle Scholar
Witten, M. (1988). A return to time, cells, systems, and aging: V. Further thoughts on Gompertzian survival dynamics — the geriatric years. Mechanisms of Ageing and Development, 46, 175–200.CrossRefGoogle ScholarPubMed