A National Study Exploring the Association Between Fluoride Levels and Dental Fluorosis.

Importance: While the effects of fluoride on health have been widely researched, fewer high-quality studies examine the association of fluoride levels in water and dental fluorosis.

Objective: To investigate the association between fluoride exposure from drinking water and dental fluorosis.

Design, Setting, And Participants: This cross-sectional study used the 2013-2014 and 2015-2016 National Health and Nutrition Examination Survey (NHANES) data (January 1, 2013, through December 31, 2016).

An Exploration of the Use and Impact of Preventive Measures on Skin Cancer.

Background: Skin cancer is the most common form of cancer, and both clinical and epidemiological data link cumulative solar dosages and the number of sunburns to skin cancer. Each year, more than 5.4 million new cases of skin cancer are diagnosed, incurring a significant health and financial burden.

The association of adolescent e-cigarette harm perception to advertising exposure and marketing type.

Background: Despite controversy over their possible health consequences, manufacturers of e-cigarettes employ a variety of marketing media to increase their popularity among adolescents. This study analyzed the relationship between adolescent e-cigarette harm perception and five types of e-cigarette advertising exposures: social media, radio, billboard, newspaper, and television.

Methods: This study used data from Wave 4.

Using machine learning to identify factors associated with practice location of the healthcare workforce.

Introduction: Past studies examined factors associated with rural practice, but none employed newer machine learning (ML) methods to explore potential predictors. The primary aim of this study was to identify factors related to practice in a rural area. Secondary aims were to capture a more precise understanding of the demographic characteristics of the healthcare professions workforce in Utah (USA) and to assess the viability of ML as a predictive tool.

Health Disparities Associated with Females Reporting Human Papillomavirus Infection in the United States.

Few studies provide detailed findings about the health disparities of women being told by a physician whether they have ever had a human papillomavirus (HPV) infection. This study sought to characterize the prevalence and characteristics associated with women age 18 to 59 years in the United States who report being told they were infected with HPV. This study used data from the National Health and Nutritional Examination Survey.

Examination of orthodontic expenditures and trends in the United States from 1996 to 2016: disparities across demographics and insurance payers.

Background: Orthodontics prevent and treat facial, dental, and occlusal anomalies. Untreated orthodontic problems can lead to significant dental public health issues, making it important to understand expenditures for orthodontic treatment. This study examined orthodontic expenditures and trends in the United States over 2 decades.

Predicting all-cause 90-day hospital readmission for dental patients using machine learning methods.

Introduction: Hospital readmission rates are an indicator of the health care quality provided by hospitals. Applying machine learning (ML) to a hospital readmission database offers the potential to identify patients at the highest risk for readmission. However, few studies applied ML methods to predict hospital readmission.

Artificial intelligence in dentistry: Harnessing big data to predict oral cancer survival.

Background: Oral cancer is the sixth most prevalent cancer worldwide. Public knowledge in oral cancer risk factors and survival is limited.

Aim: To come up with machine learning (ML) algorithms to predict the length of survival for individuals diagnosed with oral cancer, and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival.

Prediction of 30-Day Hospital Readmissions for All-Cause Dental Conditions using Machine Learning.

Introduction: It is unknown whether patients admitted for all-cause dental conditions (ACDC) are at high risk for hospital readmission, or what are the risk factors for dental hospital readmission.

Objective: We examined the prevalence of, and risk factors associated with, 30-day hospital readmission for patients with an all-cause dental admission. We applied artificial intelligence to develop machine learning (ML) algorithms to predict patients at risk of 30-day hospital readmission.

Machine Learning Approach to Predict Risk of 90-Day Hospital Readmissions in Patients With Atrial Fibrillation: Implications for Quality Improvement in Healthcare.

Background: Atrial fibrillation (AF) in the elderly population is projected to increase over the next several decades. Catheter ablation shows promise as a treatment option and is becoming increasingly available. We examined 90-day hospital readmission for AF patients undergoing catheter ablation and utilized machine learning methods to explore the risk factors associated with these readmission trends.

Exploring the Intersection between Social Determinants of Health and Unmet Dental Care Needs Using Deep Learning.

The goals of this study were to develop a risk prediction model in unmet dental care needs and to explore the intersection between social determinants of health and unmet dental care needs in the United States. Data from the 2016 Medical Expenditure Panel Survey were used for this study. A chi-squared test was used to examine the difference in social determinants of health between those with and without unmet dental needs.

In an era of uncertainty: Impact of COVID-19 on dental education.

Purpose/objectives: The coronavirus disease 2019 (COVID-19) pandemic arguably represents the worst public health crisis of the 21 century. However, no empirical study currently exists in the literature that examines the impact of the COVID-19 pandemic on dental education. This study evaluated the impact of COVID-19 on dental education and dental students' experience.

Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence.

Background: The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments, and individuals navigate the pandemic.

Objective: The aim of this study is to analyze discussions on Twitter related to COVID-19 and to investigate the sentiments toward COVID-19.

Health and dental care expenditures in the United States from 1996 to 2016.

Introduction: As total health and dental care expenditures in the United States continue to rise, healthcare disparities for low to middle-income Americans creates an imperative to analyze existing expenditures. This study examined health and dental care expenditures in the United States from 1996 to 2016 and explored trends in spending across various population subgroups.

Methods: Using data collected by the Medical Expenditure Panel Survey, this study examined health and dental care expenditures in the United States from 1996 to 2016.