Association between Household Exposure to Secondhand Smoke and Dental Caries among Japanese Young Adults: A Cross-Sectional Study

Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning

Background: Dental caries is a prevalent, complex, chronic illness that is avoidable. Better dental health outcomes are achieved as a result of accurate and early caries risk prediction in children, which also helps to avoid additional expenses and repercussions. In recent years, artificial intelligence (AI) has been employed in the medical field to aid in the diagnosis and treatment of medical diseases. This technology is a critical tool for the early prediction of the risk of developing caries. Aim: Through the development of computational models and the use of machine learning classification techniques, we investigated the potential for dental caries factors and lifestyle among children under the age of five. Design: A total of 780 parents and their children under the age of five made up the sample. To build a classification model with high accuracy to predict caries risk in 0–5-year-old children, ten different machine learning modelling techniques (DT, XGBoost, KNN, LR, MLP, RF, SVM (linear, rbf, poly, sigmoid)) and two assessment methods (Leave-One-Out and K-fold) were utilised. The best classification model for caries risk prediction was chosen by analysing each classification model’s accuracy, specificity, and sensitivity. Results: Machine learning helped with the creation of computer algorithms that could take a variety of parameters into account, as well as the identification of risk factors for childhood caries. The performance of the classifier is almost unbiased, making it generalizable. Among all applied machine learning algorithms, Multilayer Perceptron and Random Forest had the best accuracy, with 97.4%. Support Vector Machine with RBF Kernel (with an accuracy of 97.4%) was better than Extreme Gradient Boosting (with 94.9% accuracy). Conclusion: The outcomes of this study show the potential of regular screening of children for caries risk by experts and finding the risk scores of dental caries for any individual. Therefore, in order to avoid dental caries, it is possible to concentrate on each individual by utilizing machine learning modelling.

New Method of Avoiding Underestimation of Caries Incidence and Its Association with Possible Risk Factors in Japanese University Students: A Prospective Cohort Study

The objective of this three-year prospective cohort study was to investigate the association between a new definition of an increase in dental caries and risk factors in Japanese young adults. Data of Okayama University students who volunteered to undergo oral examinations and answer questionnaires in 2015 and 2018 were analyzed. The status of filled teeth and the status of occlusal/proximal surfaces of filled or decayed teeth were recorded. An increase in dental caries was defined as a change in the status of filled teeth and/or an increase in dental caries of occlusal and proximal surfaces. A total of 393 participants (18.2 ± 0.8 years) were analyzed. First and second molars showed a high prevalence of dental caries. Of the participants, 144 (36.6%) showed an increase in dental caries. In all the participants and in the females, the decayed, missing, and filled teeth (DMFT) score and history of orthodontic treatment at baseline were significantly associated with an increase in dental caries (p < 0.05) in logistic regression analyses. In the males, the DMFT score and the daily frequency of snacking (≥2) at baseline were significantly associated with an increase in dental caries (p = 0.04). The DMFT score and history of orthodontic treatment at baseline can be risk factors for an increase in dental caries using the new definition in young adults.

A survey of oral health status, subjective oral symptoms and oral health behaviors among first-year dental students at a Japanese university

PURPOSE

The rapid deterioration of oral health in young adults is an alarming problem in Japan. The aim of the present study is to investigate the oral health status, subjective oral symptoms and oral health behaviors of dental students.

METHODS

Participants were 108 first-year students attending dental school in 2018-2019. Oral examinations were performed to assess dental caries indices, oral hygiene status, gingival bleeding on probing (BOP) and pocket depth. A self-administered questionnaire was used to assess subjective oral symptoms and oral health behaviors.

RESULTS

The prevalence of decayed teeth (DT) and gingivitis (BOP ≥ 10%) were 43.5% and 50.0%, respectively. Having DT and gingivitis were significantly associated with poorer oral hygiene. No association was observed between DT and subjective symptoms. Having gingivitis was significantly associated with xerostomia, mouth-breathing and less use of interdental cleaning tools. Multiple logistic regression analysis for gingivitis yielded an odds ratio of 1.41 (95% confidence interval: 1.19-1.67) for plaque score, and 2.75 (1.27-5.98) for xerostomia.

CONCLUSION

Since a relatively high ratio of students had DT and gingivitis without clear subjective symptoms, they require regular dental visits for early treatment and oral hygiene maintenance from the start of their time at university.

Association between exposure to secondhand smoking at home and tooth loss in Japan: A cross-sectional analysis of data from the 2016 National Health and Nutrition Survey

INTRODUCTION

Tooth loss affects oral health and physical and social functions. With widespread population aging, its prevalence is increasing. Secondhand smoking is a risk factor for oral diseases; however, in Japan, there are currently no regulations restricting exposure to secondhand smoke at home. This cross-sectional study examined the association between secondhand smoking at home and tooth loss among Japanese adults.

METHODS

The study examined secondary data from the 2016 National Health and Nutrition Survey, Japan. The self-reported responses of 18812 non-smokers aged ≥20 years were analyzed. The association between exposure to secondhand smoke at home and number of teeth was examined through multiple linear regression with multiple imputation. To obtain a more normal distribution, logarithmic transformation was applied to the number of teeth that deviated substantially from a normal distribution. Adjustments were made for sex, age, occupation, household size, dental check-up within the past year, and exposure to secondhand smoke outside the home.

RESULTS

Overall, 8.4% of the respondents were exposed to secondhand smoking at home almost every day; this percentage was larger among younger respondents, who also had more teeth than the older respondents. Although the univariate linear regression did not show a positive association between exposure to secondhand smoke and tooth loss, the multivariate-adjusted analysis revealed that respondents who were exposed to secondhand smoke at home almost every day had fewer teeth (β= -0.04; 95% CI: -0.07 – -0.01).

CONCLUSIONS

The present results suggest that exposure to secondhand smoke at home increases the risk of tooth loss.