A Bayesian Approach to Estimate Skeletal Age‐at‐Death Utilizing Dental Wear

Abstract

In the forensic context, teeth are often recovered in mass disasters, armed conflicts, and mass graves associated with human rights violations. Therefore, for victim identification, techniques utilizing the dentition to estimate the first parameters of identity (e.g., age) can be critical. This analysis was undertaken to apply a Bayesian statistical method, transition analysis, based on the Gompertz-Makeham (GM) hazard model, to estimate individual ages-at-death for Balkan populations utilizing dental wear. Dental wear phases were scored following Smith's eight-phase ordinal scoring method and chart. To estimate age, probability density functions for the posterior distributions of age for each tooth phase are calculated. Transition analysis was utilized to generate a mean age-of-transition from one dental wear phase to the next. The age estimates are based on the calculated age distribution from the GM hazard analysis and the ages-of-transition. To estimate the age-at-death for an individual, the highest posterior density region for each phase is calculated. By using a Bayesian statistical approach to estimate age, the population's age distribution is taken into account. Therefore, the age estimates are reliable for the Balkan populations, regardless of population or sex differences. The results showed that a vast amount of interpersonal variation in dental wear exists within the current sample and that this method may be most useful for classifying unknown individuals into broad age cohorts rather than small age ranges.