Purpose/Objectives The coronavirus disease 2019 (COVID‐19) pandemic arguably represents the worst public health crisis of the 21st 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. Methods An anonymous online survey was administrated to professional dental students that focused on their experiences related to COVID‐19. The survey included questions about student demographics, protocols for school reopening and student perceptions of institutional responses, student concerns, and psychological impacts. Results Among the 145 respondents, 92.4% were pre‐doctoral dental students and 7.6% were orthodontic residents; 48.2% were female and 12.6% students lived alone during the school closure due to the pandemic. Students’ age ranged from 23 to 39 years. Younger students expressed more concerns about their emotional health (P = 0.01). In terms of the school's overall response to COVID‐19, 73.1% students thought it was effective. The majority (83%) of students believed that social distancing in school can minimize the development of COVID‐19. In general, students felt that clinical education suffered after transitioning to online but responded more positively about adjustments to other online curricular components. Conclusions The COVID‐19 pandemic significantly impacted dental education. Our findings indicate that students are experiencing increased levels of stress and feel their clinical education has suffered. Most students appear comfortable with technology adaptations for didactic curriculum and favor masks, social distancing, and liberal use of sanitizers.

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. Methods This study applied machine learning methods in the field of artificial intelligence to analyze data collected from Twitter. Using tweets originating exclusively in the United States and written in English during the 1-month period from March 20 to April 19, 2020, the study examined COVID-19–related discussions. Social network and sentiment analyses were also conducted to determine the social network of dominant topics and whether the tweets expressed positive, neutral, or negative sentiments. Geographic analysis of the tweets was also conducted. Results There were a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets, and 641,381 mentions in tweets during the study timeframe. Out of 902,138 tweets analyzed, sentiment analysis classified 434,254 (48.2%) tweets as having a positive sentiment, 187,042 (20.7%) as neutral, and 280,842 (31.1%) as negative. The study identified 5 dominant themes among COVID-19–related tweets: health care environment, emotional support, business economy, social change, and psychological stress. Alaska, Wyoming, New Mexico, Pennsylvania, and Florida were the states expressing the most negative sentiment while Vermont, North Dakota, Utah, Colorado, Tennessee, and North Carolina conveyed the most positive sentiment. Conclusions This study identified 5 prevalent themes of COVID-19 discussion with sentiments ranging from positive to negative. These themes and sentiments can clarify the public’s response to COVID-19 and help officials navigate the pandemic.

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.

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.

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. Recommended preventive measures for skin cancer include the use of sunscreen, sun avoidance, and protective clothing. This study used a national database to examine the association of preventive measures with the prevalence of skin cancer, specifically analyzing the preventive measures of sunscreen use, staying in the shade, and wearing long-sleeved shirts. The second aim was to determine which characteristics, if any, correlated with using prevention measures. Methods: This study analyzed data from the National Health and Nutritional Examination Survey 2015–2016 cycle to examine the association of three preventive measures (using sunscreen, staying in the shade, and wearing long-sleeved shirts) with skin cancer. Logistic regression and chi-square tests were utilized to examine the relationship between skin cancer and these prevention methods. Results: Sunscreen use (OR = 3.752; p < 0.05) was statistically associated with a lower prevalence of skin cancer, while wearing long-sleeved shirts (OR = 6.911; p = 0.064) and staying in the shade (OR = 0.646; p = 0.481) did not emerge as factors significantly associated with a lower prevalence after controlling for gender, race/ethnicity, marital status, income, health insurance, and general health. Additionally, men and individuals of color were less likely to use sunscreen. Conclusion: Sunscreen use was associated with a lower prevalence of skin cancer, while wearing long-sleeved shirts and staying in the shade was not significantly linked to lower rates of skin cancer, suggesting that these measures may not be as effective as sunscreen for preventing skin cancer. Men and individuals of color were significantly less likely to use sunscreen. These findings can help guide future education efforts and research regarding skin cancer prevention and suggest the need to develop male-oriented programs to mitigate the gender disparity in employing sun-protection measures.