Advertisement

Introduction

  • Guillaume Wunsch
Chapter
Part of the European Studies of Population book series (ESPO, volume 11)

Abstract

“To be or not to be?” mankind has probably raised this question since the first men and women confronted life and death on earth. Will Saturn bring me old age? Though the answer depends upon the gods, the evil spirits, or disease, according to the times, Man has nevertheless sought his future in the leaves of the tea-cup, the palm of one’s hand, the crystal ball, or ... the life table. The history of the life table has been briefly sketched by D. Smith and N. Keyfitz (1977). Though the origins of the “mortality tabl” (as the French say) date back to the classic studies of Graunt, Halley, and Euler, a third century A.D. table of annuities, attributed to Ulpian, bears witness to the interest of the Romans for life annuities and therefore for compiling life experiences. Indeed, life tables are a subject of interest not only for demographers but also for actuaries and epidemiologists. The study of the extinction of a group of “lives” forms an important domain of insurance theory, and the construction of the life table is described in all actuarial books dealing with life insurance; for a recent example, see F.E. De Vylder (1997). Even if nowadays non-life insurance problems dominate actuarial theory, life contingencies still form the backbone of the insurance business. Life tables are also considered in epidemiology; see e.g. the textbook by J. Estève et al. (1993). Epidemiologists are however more interested in measuring the incidence and prevalence of diseases, and determining possible risk factors of morbidity and mortality, than in evaluating the mortality of the general population. As a corollary, epidemiology draws its data more from special surveys and registers, such as longitudinal heart studies or cancer registries, than from general population statistics such as vital registration and census.

Keywords

Life Table Cohort Effect Vital Registration Compete Risk Model Insurance Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Andersen, B. (1990). Methodological Errors in Medical Research. Blackwell, London.Google Scholar
  2. Anderson, S., A. Auquier, W.W. Hauck, D. Oakes, W. Vandaele, H.I. Weisberg (1980). Statistical Methods for Comparative Studies. Wiley, New York.Google Scholar
  3. Blossfeld, H.P., G. Rohwer (1995). Techniques of Event History Modeling. New Approaches to Causal Analysis. Lawrence Erlbaum, Mahwah.Google Scholar
  4. Collett, D. (1994). Modelling Survival Data in Medical Research. Chapman & Hall, London. De Vylder, F.E. ( 1997 ), Life Insurance Theory. Boston: Kluwer.Google Scholar
  5. Elwood, J.M. (1988). Causal Relationships in Medicine. Oxford University Press, Oxford.Google Scholar
  6. Estève, J., E. Benhammou and L. Raymond (1993), Méthodes statistiques en épidémiologie descriptive. Paris: INSERM.Google Scholar
  7. Gourbin, C. (1998), La mortalité foetale: définitions et niveaux. In: Morbidité, mortalité: problèmes de mesure, facteurs d’évolution, essai de prospective. Paris: Presses Universitaires de France, pp. 91–107.Google Scholar
  8. Halley, E. (1693). An Estimate of the Degrees of the Mortality of Mankind, Philosophical Transactions XVII, in D. Smith and N. Keyfitz, Mathematical Demography, Springer, Berlin, 1977, 21–26.CrossRefGoogle Scholar
  9. Hobcraft J., J. Menken, and S. Preston (1982). Age, Period, and Cohort Effects in Demography: a Review. Population Index, 48 (1), 4–43.CrossRefGoogle Scholar
  10. Keyfitz, N. (1968). Introduction to the Mathematics of Population, Addison-Wesley, Reading. Leridon, H. and L. Toulemon ( 1997 ), Démographie. Paris: Economica.Google Scholar
  11. Ni Bhrolchain, M. (1993). Histoire passée, indices synthétiques de fécondité du moment. Population, 48 (2), 427–431.Google Scholar
  12. Nusselder, W. (1998), Compression or Expansion of Morbidity? Rotterdam: Erasmus University.Google Scholar
  13. Ryder, N.B. (1965). The Cohort as a Concept in the Study of Social Change. American Sociological Review, 30 (6), 843–861.CrossRefGoogle Scholar
  14. Smith, D. and N. Keyfitz (1977), Mathematical Demography. Berlin: Springer.CrossRefGoogle Scholar
  15. Wunsch, G., J. Duchéne, E. Thiltgès, and M. Salhi (1996), Socio-economic differences in mortality: a life course approach. European Journal of Population, 12 (2), pp. 167–185.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • Guillaume Wunsch

There are no affiliations available

Personalised recommendations