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Age at Death: Current Approaches and Methods

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Handbook of Palaeodemography

Part of the book series: INED Population Studies ((INPS,volume 2))

Abstract

To propose a palaeodemographic reconstitution from skeletal data, criteria accessible from historical and archaeological data must be used and, as a demographic study directly based upon archaeological data is not possible, tools must be defined to take account of osteological constraints. This chapter examines available tools and proposes some adaptations.

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Notes

  1. 1.

    It is not possible to enter them directly into the life tables established by demographers.

  2. 2.

    This method corresponds to what Daniel Courgeau in Chap. 12 calls the “Proportional Fitting Procedure (PFP)” or “ALK method” (Age Length Key), without the I for Iterative, because when these methods are applied in palaeodemography they are not really iterative and need to stop after the first loop.

  3. 3.

    On the basis of isotope analyses, Estelle Herrscher (2003) estimates that the mediaeval children buried at Saint-Laurent, Grenoble, were weaned between ages 2.6 and 3.3 years.

  4. 4.

    Since the Middle Ages, confirmation has occurred at various ages, usually between 12 and 14 years.

  5. 5.

    Using the age of 17 as the upper limit for the immature population makes it possible, in a site study, to link the results obtained with those calculated for the adult population (ages 18 and above) with the probability vector method.

  6. 6.

    Using 1-year age classes for adults whose ages cover 77 years (from ages 18 to 94) would have given only a dozen individuals on average per age class. Together with the fine detail of the biological observations, segmented into 41 suture closure coefficients, this would have caused an unacceptable fragmentation of the data.

  7. 7.

    The first applications concerned archaeological sites recent enough for us to have accurate demographic information on the mother population (Buchet and Séguy 1999, 2003; Buchet et al. 2003; Signoli et al. 2005).

  8. 8.

    Chapter 13 also shows how specific situations can be simply and effectively taken into account by the estimation method it proposes.

  9. 9.

    In the sense of proven demographic situations that can be re-used in similar contexts.

  10. 10.

    Half the tables before the twentieth century and some more contemporary ones concern countries that had not yet begun their demographic and public health transition. The sample was extended to 45 tables in 2002 (Bocquet-Appel 2002).

  11. 11.

    The problem can be solved by applying the properties of a normal distribution: starting from estimated age and its standard deviation, many software packages can calculate the likelihood of belonging to one age class or the other.

  12. 12.

    Bocquet-Appel and Masset propose a relationship based on the juvenility index \( \left(\frac{D_{5-14}}{D_{20+}}\right) \) and the senility index \( \left(\frac{D_{60-\omega }}{D_{20+}}\right) \). Unfortunately, the latter is hard to define by anthropological analysis. Since there are currently no criteria for assessing the growth rate of buried populations or for distinguishing its sign, only assumptions can be made, which is why the authors proposed regressions for various annual growth rates.

  13. 13.

    Calculated like Halley’s table by considering all individuals deceased after age x + a to be survivors at age x. Thus 5q5 represents the ratio \( \left(\frac{D_{5-9}}{D_{5+}}\right) \).

  14. 14.

    Although this name does not correspond to the mathematical method used (see Chap. 12).

  15. 15.

    Adult mean age at death correlates weakly with the earliest probabilities of death, unlike the juvenility index. Combining two age indicators that contain different information provides a better estimate of demographic parameters.

  16. 16.

    We prefer to denote it P 5–19 to avoid any confusion with the probability of survival at ages 5–19.

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Séguy, I., Buchet, L. (2013). Age at Death: Current Approaches and Methods. In: Handbook of Palaeodemography. INED Population Studies, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-01553-8_5

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