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Testing the Applicability of Shape-based Computational Age-at-Death Estimation Methods Using Pubic Symphyseal Surface scans of Asian Origin

NCJ Number
253898
Journal
American Journal of Physical Anthropology Volume: 168 Dated: 2019 Pages: 124-125
Date Published
2019
Length
2 pages
Annotation

This study assessed the applicability of recently tested computational methods for age-at-death estimation to a mixed sex sample from Asia.

Abstract

Recent computational age-at-death estimation methods developed using 3D laser scans of the pubic symphysis have been shown to provide robust estimates of age for documented White individuals. Although validation testing has demonstrated reduced within-/between-observer error, improved objectivity, invariance to asymmetry, and equal applicability to female and male pubic symphyses, no study has explored the issue of population diversity. Concerns over broad applicability arise from the fact that these methods were developed using a reference sample composed of modern American White males and that the same sample was used to produce the final age estimate in the associated software, forAge, which implements the shape algorithms and multiple regression analyses; therefore, there is a need to determine their utility for people from different geographic regions. In the current Three shape-based measures that captured the gradual flattening of the face and changes in the ventral margin of the symphysis were used to build a series of regression models. The final age estimates were assessed for error and bias. Preliminary results from 69 scans suggest statistically significant relationships existed between ages-at-death and the three shape measures (p<0.05). R-squared values indicated that 30-50 percent of the shape variation can be explained by age. RMSE values of 10-11 years were lower than those originally reported. These preliminary results suggest the utility of these methods for Asia and support additional investigation for the rest of the region. (publisher abstract modified)

Date Published: January 1, 2019