How old are you now? A new ageing method for nonadults based on dental wear

Abstract

The main aim of this study is to present a novel method of nonadult (ca. 1–19 years) age‐at‐death estimation using the dental wear of deciduous, mixed deciduous‐permanent, and permanent dentitions, including the incisors, canines, premolars, and first and second molars. The stage‐based method is derived from degrees of dental wear in known‐age (n = 39) and estimated‐age (n = 11) nonadults containing 951 teeth from the predominately 19th century cemetery of Middenbeemster, The Netherlands. The need for such a method is warranted in cases where dental development and/or eruption cannot be assessed for age‐at‐death estimation. As well, by establishing a baseline for normal age‐related nonadult tooth wear, users may better document wear that could be due to extramasticatory behaviours. The regression analysis reveals a strong quadratic correlation—F(2, 47) = 555.1, p < .001, R2 = .95, standard error of the estimate = 1.14, residual sum of squares (RSS) = 68.89, predicted residual error sum of squares (PRESS) = 77.67—between age and wear and multivariate adaptive regression splines (R2 = .95, generalised cross validation = 1.67, RSS = 67.68, PRESS = 89.34), which are used to develop an R‐package that users may employ to estimate age‐at‐death from dental wear. The accuracy of this method (78–98%) is evaluated using leave‐one‐out cross‐validation. Analyses of males versus females, deciduous versus permanent, upper versus lower, and anterior versus posterior teeth revealed no apparent reason to warrant separate methods for these groups of separated dentitions. This method fills a disciplinary gap in the understudied area of deciduous and nonadult dental wear and hopes to stimulate much future research. With the R‐package, we also provide the foundation and framework for the development of additional reference populations across different spatiotemporal contexts, to make the method more widely applicable.

Getting Rid of Dichotomous Sex Estimations: Why Logistic Regression Should be Preferred Over Discriminant Function Analysis

“…The cemetery was in use from AD 1612 to 1866, and a cemetery ledger is available for individuals interred between 1829 and 1866, which includes name, sex, date of birth, and date of death. More information on the population can be found elsewhere (18–20). The sample consists of 48 (57.14%) females with mean age 48.73 years (SD = 18.51), and 36 (42.86%) males with mean age 52.89 years (SD = 21.01).…”

Estimation of Age by Evaluating the Occlusal Tooth Wear in Molars: A Study on Dakshina Kannada Population

“…Tooth wear assessment between various assessors was precise. 23 Bartholdy et al (2019) 29 n= 951 Age: 1–19 years The Netherlands (post-medieval cemetery) Smith’s scoring r =0.964 SEE: 1.4 years Recording of non-adult dental wear. 24 The present study (2020) n = 136 Age: 20–70 years Dakshina Kannada population Li and Ji method r = 0.631 SEE: 9.231 years In younger age group <30 years more accurate.…”

Developing the protocol for paleoauxological study

“…Ñõîaeèé ïîäõîä èñïîëüçîâàëñÿ â íåäàâíåé ðàáîòå Á. Áàðòîëüäè ñ ñîàâòîðàìè [Bartholdy et al, 2019] ïðè ðàçðàáîòêå ìåòîäà îöåíêè âîçðàñòà ïî ñòåðòîñòè êîðîíîê çóáîâ ó äåòåé è ïîäðîñòêîâ. Àâòîðû èñïîëüçîâàëè ìîäèôèöèðîâàííóþ ñõåìó áàëëîâîé îöåíêè ñòåðòîñòè ïî Õ. Ñìèò, ïðèìåíèâ å¸ îäíîâðåìåííî ê ìîëî÷íûì è ïîñòîÿííûì çóáàì â âûáîðêå èíäèâèäîâ 1-19 ëåò.…”

“…Ïðåäñòàâèòåëè âòîðîãî ïîäõîäà -À. Êëåìåíò è Ý. Ôðåéí [Clement, Freyne, 2012] ïðåäëîaeèëè íà öèôðîâûõ ôîòîãðàôèÿõ êî-ëè÷åñòâåííî îöåíèâàòü ïëîùàäü îáíàaeèâøåãîñÿ äåíòèíà ïî îòíîøåíèþ ê ïëîùàäè ïîâåðõíîñòè çóáà. Ñîãëàñíî òðåòüåìó ïîäõîäó, ñòåðòîñòü îöåíèâàåòñÿ ïî ïîðÿäêîâîé øêàëå [íàïð., Bartholdy et al, 2019].…”

“…Áëàíê, ïðåäñòàâëåííûé â «Ñòàíäàðòàõ…» äëÿ çóáíîé ñèñòåìû, ïðåäïîëàãàåò ðåãèñòðàöèþ [Moorrees et al, 1963a] è ïîñòîÿííûõ [Moorrees et al, 1963b] et al, 1963a, b] îòíîñèòåëüíî øèðîêîóïîòðåáèòåëüíà è ðàññ÷èòàíà, â òîì ÷èñëå, íà èññëåäîâàíèå ìîëî÷íûõ çóáîâ (âêëþ÷àÿ ñòàäèè èõ ðåçîðáöèè). Íåñìîòðÿ íà íåêîòîðûå íåäîñòàòêè, íàèáîëåå îïòèìàëüíîé ñèñòåìîé ôèêñàöèè ñòåðòîñòè ó äåòåé â íàñòîÿùåå âðåìÿ ÿâëÿåòñÿ ñõåìà Á. Áàðòîëüäè ñ ñîàâòîðàìè [Bartholdy et al, 2019]. Íåäîñòàòîê çàêëþ÷àåòñÿ â ïðîåöèðîâàíèè ñòåðòîñòè ïîñòîÿííûõ çóáîâ íà ìîëî÷íûå.…”