A Case–Control Study of Socio-Economic and Nutritional Characteristics as Determinants of Dental Caries in Different Age Groups, Considered as Public Health Problem: Data from NHANES 2013–2014

Abstract

One of the principal conditions that affects oral health worldwide is dental caries, occurring in about 90% of the global population. This pathology has been considered a challenge because of its high prevalence, besides being a chronic but preventable disease which can be caused by a series of different demographic, dietary, among others. Based on this problem, in this research a demographic and dietary features analysis is performed for the classification of subjects according to their oral health status based on caries, according to the age group where the population belongs, using as feature selector a technique based on fast backward selection (FBS) approach for the development of three predictive models, one for each age range (group 1: 10–19; group 2: 20–59; group 3: 60 or more years old). As validation, a net reclassification improvement (NRI), AUC, ROC, and OR values are used to evaluate their classification accuracy. We analyzed 189 demographic and dietary features from National Health and Nutrition Examination Survey (NHANES) 2013–2014. Each model obtained statistically significant results for most features and narrow OR confidence intervals. Age group 2 obtained a mean NRI = −0.080 and AUC = 0.933; age group 3 obtained a mean NRI = −0.024 and AUC = 0.787; and age group 4 obtained a mean NRI = −0.129 and AUC = 0.735. Based on these results, it is concluded that these specific demographic and dietary features are significant determinants for estimating the oral health status in patients based on their likelihood of developing caries, and the age group could imply different risk factors for subjects.

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

“…ML techniques can be used to identify variables associated with an outcome as the number of variables increase. ML techniques have been applied to survey data to identify variables that are associated with different psychological and disease conditions [ 41 , 42 , 43 , 44 , 45 , 46 ].…”

Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013–2014

“…It is important to mention that this specific database was chosen in order to show the consistency of the data and the results obtained in the relationship to its background work. In this work [ 21 ] three different models obtained through a fast backward selection (FBS) of features are presented, each model corresponding to a different age group, then a posterior evaluation using a net reclassification improvement (NRI) technique is performed, besides the AUC parameter, obtaining a maximum true positive-true negative rate of 0.787. On the other hand, this work [ 22 ] presents a multivariate model obtained through a FBS method based on the p -value, in order to classify between three different classes, ”caries”, ”restorations” and ”control”; this model was evaluated using a statistical analysis, obtaining a maximum AUC value of 0.664.…”

Prevalence and Severity of Dental Caries and Fluorosis in 8 Year-old Children With or Without Fluoride Supplementation

“…Dental caries is a multifactorial, polymicrobial, localized and progressive disease that affects hard tissues on teeth and constitutes a serious public health issue in all countries, including developed ones (Carounanidy & Sathyanarayanan, 2009;Quock, 2017;Zanella-Calzada et al, 2018). In Chile, this pathology has the highest prevalence amongst children and adolescents (Ministerio de Salud, 2010).…”