SOS teeth with advanced caries and sociodemographic indicators, health-related habits and dental attendance patterns: data from the Dental, Oral, Medical Epidemiological (DOME) nationwide records-based study

Abramovitz, Itzhak; Zini, Avraham; Baruch, Ortal Kessler; Kedem, Ron; Protter, Noam; Shay, Boaz; Yavnai, Nirit; Zur, Dorit; Mijiritsky, Eitan; Almoznino, Galit

doi:10.1186/s12903-021-01751-5

Cited by 6 publications

(5 citation statements)

References 42 publications

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“…This study is a part of the Dental, Oral, Medical Epidemiological (DOME) record-based big data study [ 25 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. These previous publications have used and described the DOME study, including a paper dedicated to the describe the protocol and study methods of the DOME [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

Obstructive Sleep Apnea, Metabolic Dysfunction, and Periodontitis—Machine Learning and Statistical Analyses of the Dental, Oral, Medical Epidemiological (DOME) Big Data Study

et al. 2023

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This study aimed to analyze the associations of obstructive sleep apnea (OSA) with dental parameters while controlling for socio-demographics, health-related habits, and each of the diseases comprising metabolic syndrome (MetS), its consequences, and related conditions. We analyzed data from the dental, oral, and medical epidemiological (DOME) cross-sectional records-based study that combines comprehensive socio-demographic, medical, and dental databases of a nationally representative sample of military personnel for one year. Analysis included statistical and machine learning models. The study included 132,529 subjects; of these, 318 (0.2%) were diagnosed with OSA. The following parameters maintained a statistically significant positive association with OSA in the multivariate binary logistic regression analysis (descending order from highest to lowest OR): obesity (OR = 3.104 (2.178–4.422)), male sex (OR = 2.41 (1.25–4.63)), periodontal disease (OR = 2.01 (1.38–2.91)), smoking (OR = 1.45 (1.05–1.99)), and age (OR = 1.143 (1.119–1.168)). Features importance generated by the XGBoost machine learning algorithm were age, obesity, and male sex (located on places 1–3), which are well-known risk factors of OSA, as well as periodontal disease (fourth place) and delivered dental fillings (fifth place). The Area Under Curve (AUC) of the model was 0.868 and the accuracy was 0.92. Altogether, the findings supported the main hypothesis of the study, which was that OSA is linked to dental morbidity, in particular to periodontitis. The findings highlight the need for dental evaluation as part of the workup of OSA patients and emphasizes the need for dental and general medical authorities to collaborate by exchanging knowledge about dental and systemic morbidities and their associations. The study also highlights the necessity for a comprehensive holistic risk management strategy that takes systemic and dental diseases into account.

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Section: Methodsmentioning

confidence: 99%

Obstructive Sleep Apnea, Metabolic Dysfunction, and Periodontitis—Machine Learning and Statistical Analyses of the Dental, Oral, Medical Epidemiological (DOME) Big Data Study

et al. 2023

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“…This study is a records-based cross-sectional study. The study is a part of the dental, oral, and medical epidemiological (DOME) study [ 27 , 28 , 29 , 30 , 31 ]. Complete information on the methods of the DOME has been depicted in our previous publication [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

“…Complete information on the methods of the DOME has been depicted in our previous publication [ 27 ]. The DOME is a structured comprehensive study based on big data, including the demographics, dental, and medical profiles of military personnel ranging from young to middle-aged [ 27 , 28 , 29 , 30 , 31 ]. Thus, the DOME is a powerful tool that provides an exceptional opportunity to cross-reference dental outcomes with BMI measurements to investigate the associations between BMI categories and caries, whilst also adjusting for socio-demographics, health-associated practices, and systemic conditions related to metabolic syndrome.…”

Section: Methodsmentioning

confidence: 99%

Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study

et al. 2022

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The objectives of the research were to analyze the association between Body Mass Index (BMI) and dental caries using novel approaches of both statistical and machine learning (ML) models while adjusting for cardiovascular risk factors and metabolic syndrome (MetS) components, consequences, and related conditions. This research is a data-driven analysis of the Dental, Oral, Medical Epidemiological (DOME) big data repository, that integrates comprehensive socio-demographic, medical, and dental databases of a nationwide sample of dental attendees to military dental clinics for 1 year aged 18–50 years. Obesity categories were defined according to the World Health Organization (WHO): under-weight: BMI < 18.5 kg/m2, normal weight: BMI 18.5 to 24.9 kg/m2, overweight: BMI 25 to 29.9 kg/m2, and obesity: BMI ≥ 30 kg/m2. General linear models were used with the mean number of decayed teeth as the dependent variable across BMI categories, adjusted for (1) socio-demographics, (2) health-related habits, and (3) each of the diseases comprising the MetS definition MetS and long-term sequelae as well as associated illnesses, such as hypertension, diabetes, hyperlipidemia, cardiovascular disease, obstructive sleep apnea (OSA) and non-alcoholic fatty liver disease (NAFLD). After the statistical analysis, we run the XGBoost machine learning algorithm on the same set of clinical features to explore the features’ importance according to the dichotomous target variable of decayed teeth as well as the obesity category. The study included 66,790 subjects with a mean age of 22.8 ± 7.1. The mean BMI score was 24.2 ± 4.3 kg/m2. The distribution of BMI categories: underweight (3113 subjects, 4.7%), normal weight (38,924 subjects, 59.2%), overweight (16,966, 25.8%), and obesity (6736, 10.2%). Compared to normal weight (2.02 ± 2.79), the number of decayed teeth was statistically significantly higher in subjects with obesity [2.40 ± 3.00; OR = 1.46 (1.35–1.57)], underweight [2.36 ± 3.04; OR = 1.40 (1.26–1.56)] and overweight [2.08 ± 2.76, OR = 1.05 (1.01–1.11)]. Following adjustment, the associations persisted for obesity [OR = 1.56 (1.39–1.76)] and underweight [OR = 1.29 (1.16–1.45)], but not for overweight [OR = 1.11 (1.05–1.17)]. Features important according to the XGBoost model were socioeconomic status, teeth brushing, birth country, and sweetened beverage consumption, which are well-known risk factors of caries. Among those variables was also our main theory independent variable: BMI categories. We also performed clinical features importance based on XGBoost with obesity set as the target variable and received an AUC of 0.702, and accuracy of 0.896, which are considered excellent discrimination, and the major features that are increasing the risk of obesity there were: hypertension, NAFLD, SES, smoking, teeth brushing, age as well as our main theory dependent variable: caries as a dichotomized variable (Yes/no). The study demonstrates a positive association between underweight and obesity BMI categories and caries, independent of the socio-demographic, health-related practices, and other systemic conditions related to MetS that were studied. Better allocation of resources is recommended, focusing on populations underweight and obese in need of dental care.

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“…It is critical considering the flaws in the official accounting papers when analyzing and evaluating the preventative and therapeutic activity results to lower the frequency of conditions amongst the people in the dental profile [22,23]. As a result, the work outcomes are summarized every year by creating an analytical assessment of the Voronezh Region's dental service [24,25]. This research allows a dentist operating in outpatient clinics to develop a measure to manage their work and handling the health care vertical at a regional level.…”

Section: Methodsmentioning

confidence: 99%

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2022

J. Med. Chem. Sci.

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Oral diseases such as dental caries, pharyngeal cancers, and periodontitis are the global health problems, particularly in developing nations. In several developing nations, oral health care utilization is limited. It should also be mentioned that due to the high use of drugs in patients in need of dental treatment and the possibility of drug interactions in drugs used with dental anesthetics, a dentist should be fully familiar with the mechanism of action of various drugs possible emergencies. Within the framework of this work, the formation method was studied, and a mechanism for calculating the integral indicator of the provision of dentists with the dental profile of the people of the Voronezh area was developed. In the formulation of the problem, it was determined to obtain a complex integral indicator in a graphical and analytical form, characterizing the dynamics of changes in the provision of dentists with the dental profile of the population of the region of Voronezh. When fulfilling the task, a database of various indicators of the dental service staff of the region of Voronezh for 2014-2020 was utilized. As a result of the work conducted, mathematical and algorithmic support was developed, and a software module that provides the user with the ability to work with various data on the staff of the Voronezh region's dental service; based on these data, the calculation of a complex integral indicator for 2014-2020 and a visual presentation of all data.

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SOS teeth with advanced caries and sociodemographic indicators, health-related habits and dental attendance patterns: data from the Dental, Oral, Medical Epidemiological (DOME) nationwide records-based study

Cited by 6 publications

References 42 publications

Obstructive Sleep Apnea, Metabolic Dysfunction, and Periodontitis—Machine Learning and Statistical Analyses of the Dental, Oral, Medical Epidemiological (DOME) Big Data Study

Obstructive Sleep Apnea, Metabolic Dysfunction, and Periodontitis—Machine Learning and Statistical Analyses of the Dental, Oral, Medical Epidemiological (DOME) Big Data Study

Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study

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