Forskning ved Københavns Universitet - Københavns Universitet


A method for estimating age of medieval sub-adults from infancy to adulthood based on long bone length

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OBJECTIVES: To develop a series of regression equations for estimating age from length of long bones for archaeological sub-adults when aging from dental development cannot be performed. Further, to compare derived ages when using these regression equations, and two other methods.

MATERIAL AND METHODS: A total of 183 skeletal sub-adults from the Danish medieval period, were aged from radiographic images. Linear regression formulae were then produced for individual bones. Age was then estimated from the femur length using three different methods: equations developed in this study, data based on a modern population (Maresh: Human growth and development () pp 155-200), and, lastly, based on archeological data with known ages (Rissech et al.: Forensic Sci Int 180 () 1-9). As growth of long bones is known to be non-linear it was tested if the regression model could be improved by applying a quadratic model.

RESULTS: Comparison between estimated ages revealed that the modern data result in lower estimated ages when compared to the Danish regression equations. The estimated ages using the Danish regression equations and the regression equations developed by Rissech et al. (Forensic Sci Int 180 () 1-9) were very similar, if not identical. This indicates that the growth between the two archaeological populations is not that dissimilar.

DISCUSSION: This would suggest that the regression equations developed in this study may potentially be applied to archaeological material outside Denmark as well as later than the medieval period, although this would require further testing. The quadratic equations are suggested to yield more accurate ages then using simply linear regression equations. Am J Phys Anthropol, 2015. © 2015 Wiley Periodicals, Inc.

TidsskriftAmerican Journal of Physical Anthropology
Udgave nummer1
Sider (fra-til)135-145
Antal sider11
StatusUdgivet - jan. 2016

ID: 147545482