| Summary: | Estimating sex and age at death are two crucial processes during the creation of a biological profile for a set of skeletal remains. Whether the remains are archaeological or forensic, estimating the sex and age of the individual is necessary for further analysis and interpretation. Specifically, in a medicolegal context, knowing the biological sex and approximate age of the remains assists law enforcement or government agencies in identifying unknown individuals. Since the inception of the field of forensic anthropology, practitioners have been developing methods to perform the aforementioned tasks. It is crucial that these methods be consistent, repeatedly tested, validated, and improved for multiple reasons. Firstly, to conform to Daubert (1993) standards, and additionally, to make sure that they are accurate and applicable to modern forensic cases. The present study was performed to validate the efficacy of the method for estimating sex from the os pubis originally proposed in Klales et al. (2012), as well as the efficacy of the “transition analysis” method for estimating age, originally outlined in Boldsen et al. (2002).
Considering the recent popularity of using these methods to create a biological profile for forensic cases, it is necessary to develop error rates on a large, modern, American autopsy sample. These two methods are not only being readily utilized, but are additionally being taught to students in training. The utilization of these models involves a “logistic regression model” created by Klales et. al (2012) to process ordinal scores, and the Bayesian statistics software program “ADBOU” that was created for processing data collected using the method in Boldsen et. al (2002). These statistical systems which produced age estimates are relatively young compared to methods developed for the same purpose. The new generation of forensic anthropologists is fully responsible for objectively critiquing and validating these methods that are being disseminated by their professors and senior practitioners. The goal of the present study is to do just that. A skeletal reference sample of 630 pubic bones, all removed from modern autopsy cases and housed at the Maricopa County Forensic Science Center in Phoenix, Arizona, was utilized for data collection in the present study. Each pubic bone was assessed and scored according to the exact instructions outlined in the materials for each method, which was the Klales et al. (2012) paper for sex estimation, and the UTK Data Collection Procedures for Forensic Skeletal Material 2.0 for age estimation (Langley et al. 2016). Additionally, the observers recorded their “gestalt” estimates for sex using the Phenice (1969) system, as well as Brooks and Suchey (1990) and Hartnett (2010a) phases for each pubis. Demographic information labels were hidden, and the collection demographic information was not viewed until the completion of data collection.
The null hypothesis in the present study is that both methods (the Klales et al. method (2012) and “transition analysis” method (Boldsen et al. 2002) will perform as well as they did in the original studies. The alternate hypothesis is that they do not result in the same accuracy rates reported in the original studies. Statistical analysis of the data indicates that there is sufficient evidence to reject the null hypothesis as it applies to the Klales et al. (2012) method. The classification accuracies achieved applying the logistic regression equation to the sample of pubic bones was found to be significantly lower than reported in the original study (86.2%), averaging around 70% between observers. The level of both intraobserver and interobserver agreement was only moderate for this method. It was also found that asymmetry occurred in some individuals, producing differing estimates of sex when the left and right pubes were scored separately. When utilizing the Boldsen et al. (2012) method and the ADBOU software package on only pubic symphyseal components to estimate age, the method was found to perform reasonably well. The majority (about 82%) of individuals had actual ages at death that fell within the predicted range produced by the statistical analysis. The majority of the symphyseal component scores showed moderate to good levels of interobserver agreement, and the estimated maximum likelihood (point estimate) of age at death predicted by the software package correlated moderately well with the actual age of death of the individual. These methods did not perform as well as reported in the original studies, and they should be further validated and recalibrated to improve their accuracy and reliability.
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