Aging methods and age‐at‐death distributions: Does transition analysis call for a re‐examination of bioarchaeological data?

Clark, Melissa A.; Simon, Allyson; Hübbe, Mark

doi:10.1002/oa.2848

Cited by 16 publications

(26 citation statements)

References 73 publications

(126 reference statements)

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“…This is supported by the improved ability of TA to estimate the age of older adults (Lopez‐Cerquera & Casallas, 2019). However, such a conclusion is suspect because in a study comparing survivorship in skeletons from multiple sites in post‐medieval Ireland, Clark et al (2020) found that survivorship was lowest among individuals from Coombe/Cork St. and North King St. It is perhaps more likely that the greater survivorship of the individuals from St. Luke and St. Michan, as estimated from the skeletons from Coombe/Cork St. and North King St., could be a result of user error or a tendency in the method itself to overestimate age‐at‐death.…”

Section: Discussionmentioning

confidence: 99%

“…Bioarchaeologists should continue to exercise caution when drawing conclusions about survivorship in past populations. Second, bioarchaeologists should avoid making direct comparisons between survivorship estimated using different age methods (Clark et al, 2020), including survivorship distributions calculated from known‐age‐at‐death samples, unless the differences are acknowledged and discussed at length. Additionally, bioarchaeologists should identify socioeconomic factors that could contribute to differences in survivorship even in a single population.…”

Section: Discussionmentioning

confidence: 99%

“…( n = 206) (Bennett, 2000) (Table 1). Scores were entered into the software ADBOU to produce a maximum‐likelihood age‐at‐death estimate, as well as a 95% confidence interval range, and were corroborated with secondary age estimation methods (e.g., Brooks & Suchey, 1990) after Clark et al (2020).…”

Section: Methodsmentioning

confidence: 99%

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The effectiveness of skeletal age‐estimation in the reconstruction of population survivorship in post‐medieval Dublin

Clark

2021

Intl J of Osteoarchaeology

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Bioarchaeologists have long used age estimates from skeletal remains to draw conclusions about past lifeways and sociobiological phenomena such as health, stress, fertility, and frailty, among others. However, the estimation of age from skeletal remains is complicated by a multitude of well‐documented challenges including age mimicry, variation in degeneration, preservation, and accuracy between anatomical regions, individuals, and populations. The application of transition analysis to bioarchaeological age estimation was introduced nearly two decades ago to address some of these challenges, and the newest version promise further improvements. However, given the time lag between the introduction of a new technology and its adoption, continued evaluation of existing methods remains important. The purpose of this study was to evaluate the effectiveness of skeletal age‐at‐death estimation through transition analysis in the reconstruction of population survivorship in 18th century Dublin, Ireland. It was hypothesized that there would be no differences between the survival distributions calculated from 18th‐century parish records and survival distributions estimated from skeletal remains rom approximately the same time and location. Results show that survivorship estimated from transition analysis was approximately equal to the survivorship calculated from burial records in early‐ to mid‐adulthood, despite the fact that the samples do not represent the same individuals. However, transition analysis consistently produced higher survival curves in later adulthood, despite the lower socioeconomic statuses of the individuals represented in the skeletal sample relative to the individuals in the burial records, though these differences were not statistically significant for females. These findings suggest that bioarchaeologists should continue to exercise caution when drawing conclusions about populations based on skeletons.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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The effectiveness of skeletal age‐estimation in the reconstruction of population survivorship in post‐medieval Dublin

Clark

2021

Intl J of Osteoarchaeology

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“…The Bayesian transition analysis is a suitable method for modeling a sequential variable in which the order of the steps is determined by a skeletal trait and its morphological changes are constantly changing (17). This analysis is based on a cumulative probability model in which age can be considered as an independent variable and estimated for a person transitioning from one age to another (18)(19)(20)(21). By estimating the regression slope for age and tracking each transition of the regression model, a cumulative probability was obtained for each subgroup.…”

Section: Bayesian Transition Analysismentioning

confidence: 99%

Age-Dependent Three-Dimensional Evaluation of the Spheno-Occipital Synchondrosis Using Computed Tomography

Jahanbin¹,

Shirazi²,

Kamyabnezhad³

et al. 2021

Iran J Ortho

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Background: The spheno‐occipital synchondrosis (SOS) is an important growth center which plays an important role in cranial base development and can define the final relation of SOS with the upper and lower jaws. The morphology and fusion degree of SOS varies in different ages among different populations. Objectives: Thus, the aim of this study was to evaluate the age and sex dependence of ossification of the spheno-occipital synchondrosis in the Iranian population using CBCT. Methods: This cross-sectional retrospective study was performed on 517 CBCT images of 6- to 18-year-old patients in Mashhad, Iran. Patients with obvious pathology or fractures were excluded from the study. The SOS ossification was classified into four stages from 0 to 3. Two independent observers scored the images. The ossification stage of the SOS was classified according to the age and sex of the patients. Kruskal-Wallis and Kendall’s tau-b used for statistical analysis. Results: In this study, 517 samples of 260 females (50.3%) and 257 males (49.7%) with a mean age of 12.03 ± 3.74 years were examined. A significant difference did not exist between males and females regarding their stage of SOS ossification, although males were younger at the SOS transitional stages of 0 to 1, and 1 to 2 compared to females. Also, the SOS starts the ossification process at the age of 11 in females and 10.8 in males. Conclusions: The present study showed in the mean age of 16.81 ± 1.12 years, most subjects were in developmental stage 3. Due to the fact that in developmental stage 3 SOS closes, there were no passive maxillary growth after this time. In none of the SOS stages, females and males differed significantly in mean age (P > 0.05). The age of the onset of SOS closure in females was 11 years and in males it was about 10.8 years.

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“…Taking the estimates as the best approximations to true chronological age, studies are using them as data to investigate secondary factors, such as the effects of indirect indicators of social disparities, differential access to resources, and pathophysiological stress (e.g., body size, sex, socioeconomic stature, linear enamel hypoplasia), which create mortality disparities between groups (DeWitte, 2009, 2010, 2014; DeWitte & Hughes‐Morey, 2012; Fojas, 2016; Wilson, 2014; Yaussy et al, 2016). Moreover, some studies have even called for the re‐evaluation of previously studied archeological skeletal samples with TA2, expecting more meaningful estimates and greater insights (Clark et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Age‐at‐death patterns and transition analysis trends for three Asian populations: Implications for [paleo]demography

Kim

Algee‐Hewitt

2021

American Journal of Biological Anthropology

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Objectives Age‐at‐death estimation provides biological anthropologists with fundamental biographic information to help resolve various theoretical and practical questions about both individual expression and populational trends in present and past contexts. Particularly, researchers interested in reconstructing past populations' mortality profiles have favored using Boldsen and colleagues' Transition Analysis (TA2), as the method provides both methodological and statistical solutions for optimally treating traditional ordinal, categorical score data from multiple age indicators. In this article, we present an assessment of TA2 to better understand its age estimation patterns and trends when applied to a large‐scale, documented Asian skeletal sample. Materials and methods Specifically, this study examines the patterns of error and bias of TA2 when applied to three Asian populations. We assess cumulative effects of the age estimates in mortality reconstruction, and TA2's ability to accurately characterize the effects of covariates (e.g., sex, stature) that are linked to inequal social status and differential access to resources and known to further cause mortality disparities. Results The results show that age estimates of three age indicators (the pubic symphysis, auricular surface, and cranial sutures) combined with an informed prior produced the most accurate age estimates across all three Asian samples. Thus, the age composition of a target skeletal sample dictates the accuracy of TA2 estimates, and therefore demographic parameters. Discussion Although the exact parameter values may differ from the true values and a consistent underestimation of female age‐at‐death and survivorship is evident, TA2 recovers a realistic approximation of a mortality pattern by revealing (previously hidden) elderly individuals.

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Aging methods and age‐at‐death distributions: Does transition analysis call for a re‐examination of bioarchaeological data?

Cited by 16 publications

References 73 publications

The effectiveness of skeletal age‐estimation in the reconstruction of population survivorship in post‐medieval Dublin

The effectiveness of skeletal age‐estimation in the reconstruction of population survivorship in post‐medieval Dublin

Age-Dependent Three-Dimensional Evaluation of the Spheno-Occipital Synchondrosis Using Computed Tomography

Age‐at‐death patterns and transition analysis trends for three Asian populations: Implications for [paleo]demography

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