Periodontitis presenting among betel quid users: A case series.

BACKGROUND: Betel leaf chewing habit has been studied extensively, as it has been an ancient practice in many Asian countries. Although betel leaf has been reported to have potential beneficial properties, it has also been shown to have a strong association with oral diseases, including periodontitis. This case series addresses the presentation of periodontitis among betel quid users, to help clinicians identify and manage such patients when they are encountered in settings outside the countries and territories where betel quid use is common. METHODS: Four patients of Asian ethnicity were referred to The Ohio State University College of Dentistry Graduate Periodontology clinic for periodontal treatment. Targeted questioning resulted in all four patients reporting a past and current betel leaf chewing habit, established at a young age, typically in a family setting, and long before their immigration from their native country. RESULTS: All four patients exhibited similar reddish-brown staining on the teeth and generalized radiographic bone loss. Notably, the patients disclosed the betel chewing habit only after different vernacular names for betel quid were used during questioning, which complicated history taking. Severe periodontitis (stage III/IV, grade C) was diagnosed in all four patients, who were given appropriate periodontal treatment plans. CONCLUSION: Betel leaf chewing, prevalent in many Asian countries, is associated with periodontitis and may present with extensive tooth staining. Oral healthcare providers should consider this cultural habit and pursue thorough assessment and history taking for their patients originating from countries where the habit is common practice. KEY POINTS: Clinicians should be aware of the clinical presentation and oral findings in betel quid users. Given the increasing diversity of the patient pool that clinicians are encountering, knowledge of vernacular names for betel quid usage can prove valuable in history taking. A protocol for betel quid cessation should be incorporated into the management strategy for these patients in clinical practice. PLAIN LANGUAGE SUMMARY: Betel quid (paan) chewing is a widespread cultural habit, especially in Asia. The chewing of betel quid, whether alone or with tobacco, has been strongly associated with periodontitis and other oral conditions, such as leukoplakia, oral cancer, and oral submucous fibrosis. This report illustrates the typical presentation of periodontitis patients who habitually chew betel quid, highlights the associated clinical features, such as reddish-brown tooth staining and severe bone loss, and underlines the challenge of obtaining a complete history because of the very many vernacular names used for betel quid in different countries and the fact that for many of the patients, this is considered a routine cultural tradition. It is unusual to encounter patients chewing betel quid in this country because the practice is not common. However, immigrants from countries where betel quid chewing is commonplace may continue the habit after they move to the United States. Oral healthcare providers should be aware of this possibility and elicit the relevant information from the patients. This will help practitioners to properly address this underlying factor and to provide appropriate care to these patients, including cessation advice and support, and establishment of routine oral screenings and regular periodontal maintenance.

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.

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.

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.