Estimation of age structure in anthropological demography

Konigsberg, Lyle W.; Frankenberg, Susan R.

doi:10.1002/ajpa.1330890208

Cited by 198 publications

(180 citation statements)

References 56 publications

(69 reference statements)

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“…Age 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.…”

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The (mis)use of adult age estimates in osteology

Buckberry

2015

Annals of Human Biology

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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...

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The (mis)use of adult age estimates in osteology

Buckberry

2015

Annals of Human Biology

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“…34 In order to test bias and accuracy, several important features of both target and reference sample should be fulfi lled such as suffi cient number, a suffi ciently wide age range in order to include early and late maturing individuals and a uniform age distribution. 36 Having similar numbers across the age range ensures that accuracy is consistent over the entire age immature target sample. The inclusion of individuals with mature M3s is something that previous studies of bias and accuracy have failed to clarify.…”

Section: Accuracy Of Age Estimationmentioning

confidence: 99%

Estimating age and the likelihood of having attained 18 years of age using mandibular third molars

2010

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by accuracy? Do population differences in dental maturity infl uence accuracy? There is an urgent need for an evidencebased reference to address some of these questions. The fi rst aim of this study was to calculate the bias (difference between dental and real age) of age-estimating methods that use mandibular third molar (M3) root formation. The probability of being at least 18 given M3 root stage is also of interest. The second aim was to apply diagnostic tests of accuracy showing how root stage discriminates between individuals at least 18 years of age and those younger and to apply this knowledge to predict the likelihood of age 18 for a single individual. For this part of this study we used a separate reference sample of 1,663 radiographs. We propose an age interval for M3 root stages to aid interpretation of the term 'on the balance of probabilities' . We highlight the similarity in M3 apex maturity between world groups from published data and illustrate how a small group difference in average age has little impact on the confi dence interval of estimated age for an individual.

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“…Bayesian modeling of mortality and uncertain age estimates Konigsberg and Frankenberg (1992) noted that much of prior paleodemographic analysis had an ersatz Bayesian flavor that followed from conditioning "age on stage." The problem with these previous approaches is that they essentially hid the prior distribution for age-at-death.…”

Section: Bayes In Bioarchaeologymentioning

confidence: 99%

Bayes in biological anthropology

Konigsberg

Frankenberg

2013

American J Phys Anthropol

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In this article, we both contend and illustrate that biological anthropologists, particularly in the Americas, often think like Bayesians but act like frequentists when it comes to analyzing a wide variety of data. In other words, while our research goals and perspectives are rooted in probabilistic thinking and rest on prior knowledge, we often proceed to use statistical hypothesis tests and confidence interval methods unrelated (or tenuously related) to the research questions of interest. We advocate for applying Bayesian analyses to a number of different bioanthropological questions, especially since many of the programming and computational challenges to doing so have been overcome in the past two decades. To facilitate such applications, this article explains Bayesian principles and concepts, and provides concrete examples of Bayesian computer simulations and statistics that address questions relevant to biological anthropology, focusing particularly on bioarchaeology and forensic anthropology. It also simultaneously reviews the use of Bayesian methods and inference within the discipline to date. This article is intended to act as primer to Bayesian methods and inference in biological anthropology, explaining the relationships of various methods to likelihoods or probabilities and to classical statistical models. Our contention is not that traditional frequentist statistics should be rejected outright, but that there are many situations where biological anthropology is better served by taking a Bayesian approach. To this end it is hoped that the examples provided in this article will assist researchers in choosing from among the broad array of statistical methods currently available. Am J Phys Anthropol 57: 2013. V C 2013 Wiley Periodicals, Inc."The Bayesian approach can have a clarifying effect on one's thinking about evidence." (Koehler and Saks, 1991, p 364) Traditional training in statistical methods for those who go on to become practicing biological anthropologists has focused primarily on classical hypothesis testing. This is apparent in both textbooks geared toward anthropologists in general (Thomas, 1986;Madrigal, 1998;Bernard, 2011) and specialized texts for biological anthropologists (Slice, 2005;D'Aoãut and Vereecke, 2011). While Bayes' Theorem may be mentioned in passing in introductory statistics courses, this is typically restricted to examples of such limited interest that the student has little motivation to recall the theorem, and even less motivation to assume that there may be future value in having learned about Bayes' Theorem. In Bayesian terms, the prior probability that the student will retain Bayes' Theorem is quite low. In contrast, the student and eventual practitioner is likely to learn about confidence intervals, Type I and Type II errors in hypothesis testing, and P-values, and to blithely assume that what they have learned represents the near totality of what is available and useful within modern statistical practice. This represents an unfortunate omission of Ba...

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Estimation of age structure in anthropological demography

Cited by 198 publications

References 56 publications

The (mis)use of adult age estimates in osteology

The (mis)use of adult age estimates in osteology

Estimating age and the likelihood of having attained 18 years of age using mandibular third molars

Bayes in biological anthropology

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