Analysis on unidentified cases in which dental information was collected from 2014 to 2019 in Miyagi Prefecture, Japan.

This study aimed to summarize the characteristics of unidentified cases in which dental information was collected during 2014-2019 in Miyagi Prefecture and to discuss the challenges and social characteristics of dental identification in routine forensic work. Cases were selected and examined from the database of dental charts collected at Tohoku University and the database of the Miyagi Prefectural Police. The annual percentage of cases with matched dental findings ranged from 19.2 - 37.1%, and 80.6% of the cases were identified within four days using dental information. The most common age group was the 50-70 s. Males were more common in the 20-70 s, whereas females were more frequent in the 80-90 s age group. Utilization of dental information was lower for cases involving young people and those over 90 years of age. The number of unidentified cases and unspecified death cases in the central block was significantly higher than other regions. However, there were no significant differences in sex, age, and season between each region. Spearman's rank-order analysis of the unspecified deaths showed a strong correlation with the monthly average temperature (ρ = 0.89, p < 0.01). Within the limitations of this study, the results provide valuable information on the long-term progress of routine dental identification work and could be useful as a reference for a statistical survey. Future research must examine the role of dental information in forensic identification by exploring the role of more specific background characteristics among autopsy cases.

Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis.

Prevalence of tooth loss has increased due to population aging. Tooth loss negatively affects the overall physical and social well-being of older adults. Understanding the role of socio-demographic and other predictors associated with tooth loss that are measured in non-clinical settings can be useful in community-level prevention. We used high-dimensional epidemiological data to investigate important factors in predicting tooth loss among older adults over a 6-year period of follow-up. Data was from participants of 2010 and 2016 waves of the Japan Gerontological Evaluation Study (JAGES). A total of 19,407 community-dwelling functionally independent older adults aged 65 and older were included in the analysis. Tooth loss was measured as moving from a higher number of teeth category at the baseline to a lower number of teeth category at the follow-up. Out of 119 potential predictors, age, sex, number of teeth, denture use, chewing difficulty, household income, employment, education, smoking, fruit and vegetable consumption, community participation, time since last health check-up, having a hobby, and feeling worthless were selected using Boruta algorithm. Within the 6-year follow-up, 3013 individuals (15.5%) reported incidence of tooth loss. People who experienced tooth loss were older (72.9 ± 5.2 vs 71.8 ± 4.7), and predominantly men (18.3% vs 13.1%). Extreme gradient boosting (XGBoost) machine learning prediction model had a mean accuracy of 90.5% (±0.9%). A visual analysis of machine learning predictions revealed that the prediction of tooth loss was mainly driven by demographic (older age), baseline oral health (having 10-19 teeth, wearing dentures), and socioeconomic (lower household income, manual occupations) variables. Predictors related to wide a range of determinants contribute towards tooth loss among older adults. In addition to oral health related and demographic factors, socioeconomic factors were important in predicting future tooth loss. Understanding the behaviour of these predictors can thus be useful in developing prevention strategies for tooth loss among older adults.