Relationship Between Socio-Demographic Profile, Parity and Dental Caries Among a Group of Nursing Mothers in South East, Nigeria

Parity, dental caries and implications for maternal depletion syndrome in northern Nigerian Hausa women

BACKGROUND

Female reproductive history, especially high parity, affects general health and may impact negatively on oral health. While parity has been positively linked to tooth loss, the specific association between parity and caries has not been adequately investigated.

AIM

To determine the association between parity and caries in a population of higher parity women. Influences of likely confounders (age, socio-economic status, reproductive parameters, oral health practices and sugar consumption between meals) were considered.

METHODS

This was a cross-sectional study involving 635 Hausa women of varying parity aged 13-80 years. Socio-demographic status, oral health practices and sugar consumption were obtained using a structured interviewer-administered questionnaire. All decayed, missing and filled teeth due to caries (excluding third molars) were noted, and tooth loss etiology was queried. Associations with caries were evaluated through correlation, ANOVA, post hoc analyses and Student's t tests. Effect sizes were considered for magnitude of differences. Multiple regression (binomial model) was used to investigate predictors of caries.

RESULTS

Hausa women had a high prevalence of caries (41.4%) despite low sugar consumption; nonetheless the overall mean DMFT score was very low (1.23 ± 2.42). Older, higher parity women experienced more caries, as did those with longer reproductive spans. Additionally, poor oral hygiene, use of fluoride toothpaste and frequency of sugar consumption were significantly associated with caries.

CONCLUSION

Higher parity (>6 children) was associated with higher DMFT scores. These results suggest that a form of maternal depletion, expressed as heightened caries susceptibility and subsequent tooth loss, occurs with higher parity.

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.