The (mis)use of adult age estimates in osteology

Abstract

Context: Adult age-at-death is presented in a number of different ways by anthropologists. Ordinal categories predominate in osteoarchaeology, but do not reflect individual variation in ageing, with too many adults being classified as 'middle adults'. In addition, mean ages (derived from reference samples) are overly-relied upon when developing and testing methods. In both cases, 'age mimicry' is not adequately accounted for.Objectives: To highlight the many inherent biases created when developing, testing and applying age-estimation methods without fully considering the impact of 'age mimicry' and individual variation.Methods: The paper draws on previously published research (Web of Science, Pub Med, Google Scholar) on age estimation methods and their use in anthropology.Results and Conclusions: There is a lack of consistency in the methods used to estimate age, and for the mode of combining them. Ordinal categories are frequently used in osteoarchaeology, whereas forensic anthropologists are more likely to produce case-specific age ranges. Mean ages reflect the age structure of reference samples, and should not be used to estimate age for individuals from populations with a different age-at-death structure. Individual-specific age ranges and/or probability densities should be used to report individual age. Further research should be undertaken on how to create unbiased, combined method age estimates. 2Age estimation of adults remains one of the most complex, yet essential, aspects of human skeletal analysis (Aykroyd et al. 1997;Bocquet-Appel and Masset 1982; Hoppa and Vaupel 2002a;Konigsberg and Frankenberg 1992;Milner et al. 2008). In a society ruled by numbers, we are eager to ascribe a specific age to skeletonised remains, either to facilitate the identification of unidentified remains in a forensic situation, or to better interpret the life, death and burial of archaeological humans. Yet many methods of age estimation, especially for adults but also for non-adults, are woefully inadequate at producing point age estimates. Over the last few decades, research into the development, refinement and testing of adult age-estimation methods has expanded, resulting in a plethora of techniques, and variants of techniques, available for application, with the aim of improving accuracy and precision.Age estimation in non-adults, where dental and skeletal maturation are employed to estimate chronological age, typically produces estimates that are both accurate and precise. Dental development in particular has been shown to be minimally affected by external factors such as disease or malnutrition (Elamin and Liversidge 2013) and thus less variation in age for a specific stage of development. Once skeletal maturity has been reached, age is typically estimated using degenerative changes of the skeleton, dental wear and (less frequently) microscopic analysis of bone and cementum. All of these, except tooth cementum annulations, have only a broad relationship with chronological age (Gauthier and Schutkowski 2013;Gro...

A biocultural analysis of mortuary practices in the later Anglo‐Saxon to Anglo‐Norman Black Gate Cemetery, Newcastle‐upon‐Tyne, England

“…The age categories employed were foetal/neonate (0–1 month), infant (1–12 months), young child (1–5 years), older child (6–11 years), adolescent (12–17 years), young adult (18–24 years), prime adult (c. 25–34 years), mature adult (c. 35–44 years), and older adult (c. 45+ years). The use of such age categories has its limitations (Buckberry, ) but is consistent with published literature (Falys & Lewis, ).…”

Magnetic resonance imaging of third molars: developing a protocol suitable for forensic age estimation

“…Although previous research has already revealed many aspects of skeletal and dental development, authors keep stressing the importance of further investigation to enable scientists and authorities to optimise and standardise age estimation practices (Buckberry, 2015, Larsen et al, 2015.…”

DXAGE: A New Method for Age at Death Estimation Based on Femoral Bone Mineral Density and Artificial Neural Networks

“…BMD at different ROI shows a sharp decline with age at death, a pattern observed in both epidemiological and anthropological research . BMD has a multifactorial etiology and, like most skeletal indicators of age in adults, varies between individuals and between populations, reflecting the complexity of the senescence process . In general, circa 60% of the variation in skeletal age indicators is related with features other than age : compare it with the percent variance of BMD Ward that is explained by age at death (57.8%).…”