Community Water Fluoridation in Focus: A Comprehensive Look at Fluoridation Levels across America.

Objective: This study reports on the number and percentage of community water systems (CWSs) meeting fluoride concentration standards set by the U.S. Department of Health and Human Services (DHHS). The study also explored changes in the population exposed to optimally fluoridated water in these systems between 2006 and 2020. Methods: This study analyzed U.S. Centers for Disease Control and Prevention data from 2006 to 2020, tabulating state-specific CWS fluoridation rates, ranking them, and calculating the percent change. Results: In 2020, 72.7% of the US population received CWS water, with 62.9% of those individuals served by a CWS system meeting DHHS fluoridation standards. This compares to 69.2% receiving CWS water in 2006 and 74.6% in 2012. The overall change in those receiving fluoridated water was 1.4%, from 61.5% in 2006 to 62.9% in 2020. State-specific percentages ranged from 8.5% in Hawaii to 100% in Washington DC in 2020 (median: 76.4%). Conclusions: Although endorsed by the American Dental Association, the percentage of individuals receiving fluoridated water did not increase substantially from 2006 to 2020, indicating that there has not been much progress toward meeting the Healthy People 2030 goal that 77.1% of Americans receive water with enough fluoride to prevent tooth decay.

Relationships between ENDS-Related Familial Factors and Oral Health among Adolescents in the United States.

The influence of familial and social environments plays a significant role in Electronic Nicotine Delivery System (ENDS) use and may contribute to poor oral health among adolescents. This study utilized the Population Assessment of Tobacco and Health (PATH) database and included youths aged 12 to 17 years who reported no history of dental health issues at baseline. Adjusted odds ratios (AOR) were used to examine the association between END-related familial factors and oral health among adolescents in the United States, with statistical significance set at p < 0.05. The sample consisted of 3892 adolescents (weighted N = 22,689,793). Parents' extremely negative reaction towards ENDS when they found their children using ENDS (AOR = 0.309) was connected to a lower risk of oral health issues. The findings suggest that clinicians and policymakers need to consider the roles of these factors when developing strategies to improve oral health outcomes.

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. METHODS: We used the Surveillance, Epidemiology, and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables. Four ML techniques in the area of artificial intelligence were applied for model training and validation. Model accuracy was evaluated using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R 2 and adjusted R 2. RESULTS: The most important factors predictive of oral cancer survival time were age at diagnosis, primary cancer site, tumor size and year of diagnosis. Year of diagnosis referred to the year when the tumor was first diagnosed, implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past. The extreme gradient boosting ML algorithms showed the best performance, with the MAE equaled to 13.55, MSE 486.55 and RMSE 22.06. CONCLUSION: Using artificial intelligence, we developed a tool that can be used for oral cancer survival prediction and for medical-decision making. The finding relating to the year of diagnosis represented an important new discovery in the literature. The results of this study have implications for cancer prevention and education for the public.

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. Trends in spending were displayed graphically and spending across subgroups examined. All expenditures were adjusted for inflation or deflation to the 2016 dollar. RESULTS: Both total health and dental expenditures increased between 1996 and 2016 with total healthcare expenditures increasing from $838.33 billion in 1996 to $1.62 trillion in 2016, a 1.9-fold increase. Despite an overall increase, total expenditures slowed between 2004 and 2012 with the exception of the older adult population. Over the study period, expenditures increased across all groups with the greatest increases seen in older adult health and dental care. The per capita geriatric dental care expenditure increased 59% while the per capita geriatric healthcare expenditure increased 50% across the two decades. For the overall US population, the per capita dental care expenditure increased 27% while the per capita healthcare expenditure increased 60% over the two decades. All groups except the uninsured experienced increased dental care expenditure over the study period. CONCLUSIONS: Healthcare spending is not inherently bad since it brings benefits while exacting costs. Our findings indicate that while there were increases in both health and dental care expenditures from 1996 to 2016, these increases were non-uniform both across population subgroups and time. Further research to understand these trends in detail will be helpful to develop strategies to address health and dental care disparities and to maximize resource utilization.

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. Machine learning was used to determine top predictors of unmet dental care needs and to build a risk prediction model to identify those with unmet dental care needs. Age was the most important predictor of unmet dental care needs. Other important predictors included income, family size, educational level, unmet medical needs, and emergency room visit charges. The risk prediction model of unmet dental care needs attained an accuracy of 82.6%, sensitivity of 77.8%, specificity of 87.4%, precision of 82.9%, and area under the curve of 0.918. Social determinants of health have a strong relationship with unmet dental care needs. The application of deep learning in artificial intelligence represents a significant innovation in dentistry and enables a major advancement in our understanding of unmet dental care needs on an individual level that has never been done before. This study presents promising findings and the results are expected to be useful in risk assessment of unmet dental care needs and can guide targeted intervention in the general population of the United States.