| Age estimation techniques have utilized cranial suture closure, the sternal rib ends, the auricular surface, and the pubic symphysis, each with varying degrees of success. Although recent research has attempted to advance methodologies for age estimation, little progress has been made in discerning forensic age ranges that are beyond general estimates, especially in the old adult (50+) cohort. Since the accuracy of current aging methods decreases as chronological age increases, degenerative changes within the skeleton could potentially yield useful data for establishing and narrowing age estimates for older individuals, especially where only limited or fragmentary remains are recovered. The purpose of the present study was to conduct a visual examination of joint surfaces typically found to be affected by osteoarthritis (OA) by the fourth decade of life using a modified version of the OA scoring system proposed by Buikstra and Ubelaker (1994).;According to archaeological, forensic, and clinical research, OA is most commonly found in the shoulder, hip, and knee, making these joints ideal for use in the present study. Within these three joints, ten osseous surfaces were examined: the acromial facet of the scapula, the glenoid fossa of the scapula, the lateral clavicle, the humeral head, the acetabulum of the os coxa, the femoral head, the medial and lateral femoral condyles, and the medial and lateral facets of the patella. Evidence of lipping, surface porosity, osteophyte formation, and eburnation were recorded on an ordinal scale, along with the percentage of the joint surface that was covered by each of the aforementioned traits. The data gathered from this examination were used to create a composite scoring system for age-at-death estimation using a modern North American sample of 206 White individuals from the W. M. Bass Donated Skeletal Collection and the Boston University Donated Osteological Collection.;Significance testing indicated that sex differences were not present in the current analysis. A paired-sample t-test determined that the sample was affected with statistically significant levels of bilateral asymmetry. In addition, the current method is affected by low levels of intraobserver error, with only 5% of the sample being affected.;Data from multiple joint surfaces were combined for statistical analysis to create composite variables for each joint. The composite variables are combinations of traits for each joint that stepwise regression demonstrated as the best indictors for narrowing prediction intervals. This created a series of composite scores for the left shoulder, right shoulder, left hip, right hip, left knee and right knee. Individual prediction intervals at the 90% confidence interval were generated to create age ranges for each composite score. The mean age and frequency of each composite score was also recorded. This multifactorial approach demonstrated that the left and right shoulders provided the narrowest prediction intervals and also possessed the highest predictive power for estimating age-at-death. Relative predictive power was determined using R2. The R 2 value for the right shoulder was the highest at 0.603, followed closely by the left shoulder with an R2 value of 0.567. The R2 value for all remaining variables was less than 0.3, indicating weak predictive power.;The results of the present study were then compared to the four traditional macroscopic aging techniques: suture closure, morphology of the sternal rib ends, morphology of the auricular surface and the pubic symphysis. Sample distribution, correlation data, derived age ranges and error rates were compared between previous research and the results of the present study. All age estimation techniques demonstrated a positive correlation with age. Age ranges that were derived using Bayesian statistics or individual prediction intervals are more accurate at predicting actual age than those that were generated using confidence intervals of the mean, which provide information for mean age rather than actual age. (Abstract shortened by UMI.). |
