Kanya Godde

Sex estimation methods from the pelvis have been well‐studied in research settings to estimate accuracy, error, and bias. However, patterns in casework are minimally described. We uniquely examine forensic anthropology casework in the United States retrospectively for the Phenice and Klales et al.'s sex estimation methods. Our hypothesis is that casework patterns will reflect the greater literature derived from research settings that show Phenice's method is more accurate and has lower error and sex bias. We use the publicly available Forensic Anthropology Database for Assessing Methods Accuracy. A sample of 229 cases from the United States reported the outcomes of applying these methods. McNemar's tests evaluate whether estimated sex is consistent with documented sex, and a Fisher's exact test compared the performance of the two methods. We further calculated accuracy, error, and sex biases of the methods. The McNemar's and Fisher's exact tests were not statistically significant, which indicates that both methods estimated sex at a rate close to the documented sex and to each other. Phenice's method displayed an accuracy of 99.4%, an error of 0.6%, and a sex bias of −2.4%. Alternatively, the Klales et al.'s method performed slightly lower with a 97.5% accuracy, 2.5% error, and 3.5% sex bias. Forensic anthropology casework in the United States reflects broader patterns in accuracy, error, and bias in the research setting literature, where Phenice outperforms the Klales et al.'s method, despite the values from casework probably reflecting practitioners using information beyond the method reported to make a final sex estimate.

The study of human skeletal remains from historic contexts is uniquely positioned to explore inequality over time by linking the underlying sociocultural factors that enforce marginalization with lived experiences and health outcomes. We analyzed mortality rates among historic individuals of low socioeconomic status from a documented human skeletal collection, to examine how culture and identity become biologically embodied. Because pre-existing health conditions affect mortality risk, we examined whether individuals with short stature experienced earlier mortality. Kaplan–Meier analyses and log rank tests indicated significant differences in survivorship over time and among social race groups, indicating that African American individuals showed significantly higher mortality, but no sex differences were observed within population groups. Logistic regressions tested for the effects of age-at-death, combined sex and population group, and birthdate on the outcome of bone length. Age and birthdate were not significant, suggesting no relationship between short stature and age-at-death, which also did not change over time. However, odds ratios indicated fewer African Americans were surviving up to adulthood. While many individuals in the collection likely suffered some poverty and marginalization, survivorship was significantly worse for African Americans. The likely effects of systematic racism resulting in further socioeconomic marginalization significantly affected the health of the African American community.

Objectives: This study investigates how demographic factors influenced mortality risk during the Black Death. Uniquely, we examine how specific ages‐at‐death given sex (e.g., 18–26 years old, female) experienced the Black Death and whether the intersection of those identities further compounded vulnerability. We hypothesize that individuals who were at the intersection of being younger and male faced higher mortality risk. Materials and Methods: We analyzed skeletal data from seven medieval London cemeteries using the Wellcome Osteological Research Database (WORD). The variables analyzed were the interaction of sex and age‐at‐death, and we controlled for heterogeneity in the attritional burials with a variable identifying cemeteries as being composed of laypersons/religious clergy/combination, urban/rural context, and frailty markers. Logistic regression assessed plague mortality risk, with survivorship compared via Kaplan–Meier curves. Missing data were imputed with missForest. Results: Only one category in the interaction term, males aged 52–64, was statistically significant and protective, showing lower odds of being buried in the plague cemetery. Urban/rural context and layperson/religious/combination were not statistically significant, but the presence of frailty was. Discussion: Our findings indicate that the Black Death amplified biological and social vulnerabilities: supporting our hypothesis, middle‐aged males had higher mortality risk in the Black Death cemetery, possibly due to differences in social buffering and biological resilience. These results demonstrate the complex intersection of social and biological factors in shaping plague mortality, where biological susceptibility may have outweighed social disadvantage. These findings show how pandemics are shaped by existing social inequalities, offering important insight for understanding health disparities.

Background/Objectives: The identification of a decedent through skeletal analysis is dependent on accurate estimation of demographic characteristics, including biological sex. The most well-known sex estimation technique using the pubic bone is the Phenice method. In 2012, it was revised by Klales and colleagues and a logistic regression equation to predict sex was applied. Later, a program that estimates sex from Klales’ scoring with a random forest model, MorphoPASSE, was developed by Klales. Methods: Here we compare the accuracy of the original and revised methods, along with MorphoPASSE, using a contemporary sample of Northern Italians with documented sex. We further test the assertions by Phenice that his method is easy to employ for new observers and that ambiguity can be applied when characteristics do not morphologically fit into the categories of the method. Accuracy, error, bias, sensitivity, and specificity were calculated for each approach, along with McNemar’s tests for paired data, which compared documented sex and estimated sex. A linear weighted Cohen’s Kappa measured the differences in scoring between a new observer and an experienced observer. Results: Phenice’s method achieved higher accuracy (97%) than the Klales method and MorphoPASSE (86% each), as well as higher sensitivity and specificity, and lower error and bias. All McNemar’s tests conducted were not significant. The new observer demonstrated a similar accuracy (93%) to the experienced observer (97%). Furthermore, comparisons of Phenice’s scoring with ambiguity indicate its superior performance for capturing variation over the Klales method and MorphoPASSE. Conclusions: Phenice’s method is recommended in forensic anthropology and bioarchaeological contexts, particularly in Milan.

Objectives: Intracemetery patterns at the Qinifab School site, a late Meroitic to Christian period cemetery (c. 250–1400 CE) between the fourth and fifth cataracts of the Nile River in Sudan, are modeled to explore the role of kinship in cemetery organization during significant sociopolitical and religious shifts.Materials and Methods: Sixty‐eight cranial and 36 dental nonmetric traits were examined among 67 adults. A machine‐learning clustering method (k‐modes clustering algorithm), new to biological anthropology, was used to detect patterning among biological, demographic, and temporal data and validated with inter‐individual Mahalanobis distances and mixed data principal components analysis using only biological data.Results: When three validated clusters were examined, a pattern emerged that aligned with the archaeological context of the cemetery when time period is considered. One cluster concentrated at the oldest end of the site appeared to be a founding group, with the other two groups equally comprising later periods. Some mortuary patterning by sex was visually identified; a few late to Post‐Meroitic males grouped together in one cluster.Discussion: Individuals from Clusters 1 and 3 were buried near each other infrequently, which suggests two different kin groups may have intermarried with the third, or one group held a status that allowed identification with the other clusters. Kinship was not a determinant for males with archery items or individuals with incisor avulsion, as they were found in all three clusters. The Qinifab School cemetery reflects temporal organization, a multilocal residence pattern, and a lack of sex stratification for the medieval period.

Although it is well established that psychological disorders and osteoporosis risk are linked, how the relationship manifests is not. This study examines depressive symptoms and a history of psychological problems as potential risk factors for osteoporosis diagnosis, adjudicating between 4 theoretical models rarely tested together. We analyze these models across multiple domains (i.e., demographic, socioeconomic, and health-related), while accounting for bone mineral density (BMD) scans, which have been shown to improve health equity across sex and racial/ethnic identities. Data from the 2012–2016, nationally representative, population-based, cohort Health and Retirement Study (N = 18,224–18,359) were used to estimate 4 logistic regression models with the outcome of osteoporosis diagnosis. Approximately 50% of the sample identified as female and 50% as male, while about 81% identified as White/European American, 11% as Black/African American, and 8% as another race/ethnicity. The key independent variables were depressive symptoms—measured using two common scales—and a history of psychological problems. A history of psychological problems and one depressive symptoms measure were associated with the odds of osteoporosis diagnosis in the presence of other known risk factors for osteoporosis. Support for the theoretical models was limited. Evidence suggests possible directionality; a history of psychological distress may be a risk factor for osteoporosis, though we cannot rule out the other direction. Public health professionals and healthcare providers should consider a history of psychological problems as a risk factor for osteoporosis when deciding whether to recommend a BMD scan.

Demographic and early‐life socioeconomic and parental investment factors may influence later‐life health and development of chronic and progressive diseases, including osteoporosis, a costly condition common among women. The “long arm of childhood” literature links negative early‐life exposures to lower socioeconomic attainment and worse adult health. We build on a small literature linking childhood socioeconomic status (SES) and bone health, providing evidence of whether associations exist between lower childhood SES and maternal investment and higher risk of osteoporosis diagnosis. We further examine whether persons identifying with non‐White racial/ethnic groups experience underdiagnosis. Data from the nationally representative, population‐based cohort Health and Retirement Study (N = 5,490–11,819) were analyzed for participants ages 50–90 to assess these relationships. Using a machine learning algorithm, we estimated seven survey‐weighted logit models. Greater maternal investment was linked to lower odds of osteoporosis diagnosis (odds ratio [OR] = 0.80, 95% confidence interval [CI] = 0.69, 0.92), but childhood SES was not (OR = 1.03, 95% CI = 0.94, 1.13). Identifying as Black/African American (OR = 0.56, 95% CI = 0.40, 0.80) was associated with lower odds, and identifying as female (OR = 7.22, 95% CI = 5.54, 9.40) produced higher odds of diagnosis. There were differences in diagnosis across intersectional racial/ethnic and sex identities, after accounting for having a bone density scan, and a model predicting bone density scan receipt demonstrated unequal screening across groups. Greater maternal investment was linked to lower odds of osteoporosis diagnosis, likely reflecting links to life‐course accumulation of human capital and childhood nutrition. There is some evidence of underdiagnosis related to bone density scan access. Yet results demonstrated a limited role for the long arm of childhood in later‐life osteoporosis diagnosis. Findings suggest that (1) clinicians should consider life‐course context when assessing osteoporosis risk and (2) diversity, equity, and inclusivity training for clinicians could improve health equity. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Socioeconomic status (SES) is an important social determinant of health inequities that has been linked to chronic conditions, including osteoporosis, but research tends to focus on socioeconomic disadvantage rather than how socioeconomic advantage may facilitate these inequities. This study accounts for structural inequities and assesses the relationship between early-life and later-life SES, and risk of osteoporosis diagnosis. Data come from the nationally representative, population-based cohort Health and Retirement Study and include individuals ages 50 to 90. The outcome variable is osteoporosis diagnosis. Logistic regression models of the relationship between SES and osteoporosis diagnosis are estimated, accounting for demographic, health, and childhood variables. Higher levels of childhood and adult SES link to lower odds of osteoporosis diagnosis. Structural inequities in income and underdiagnosis of osteoporosis among persons identifying as Black/African American were detected. Accounting for bone density scan access, inequities in osteoporosis diagnosis appear to stem from barriers to accessing health care due to financial constraints. The important role of SES and evidence of structural inequities leading to underdiagnosis suggest the critical importance of clinicians receiving Diversity, Equity, and Inclusion training to reduce health inequities.

Age estimation from human skeletal remains is a critical component of the biological profile for unidentified decedents. Using a Bayesian approach, we examine two popular methods (Lovejoy–LJ, and Buckberry zand Chamberlain–BC) for estimating age from the auricular surface of the ilium. Ages of transition are generated from a modern Portuguese skeletal sample (n = 466) and are coupled with an informative prior from historic Spitalfields, London (n = 179) to estimate age in a sample of modern Americans from the Bass Donated collection (n = 639). The Bass collection was challenging to statistically model, potentially due to higher morbidity and mortality characteristics of the central southern United States. The highest posterior density ranges provide a realized accuracy between 84–89% for males and 85–91% for females using the LJ method, and a realized accuracy between 79–82% for males and 65–71% for females using the BC method. Both methods worked well for older individuals. Cumulative binomials showed that both methods significantly underperformed; however, results were better for the LJ method, which also showed lower bias. Reference tables for aging modern American samples are provided, and the data meet Daubert guidelines, i.e., legal criteria for acceptable scientific evidence in a court of law in the United States.