E-cigarette addiction and harm perception: Does initiation flavor choice matter?

INTRODUCTION: The 21st century was marked by a dramatic increase in adolescent e-cigarette use in the United States (US). The popularity of non-traditional flavor types, including fruit and pastry, is thought to contribute toward growing product use nationally, leading to a variety of federal and state regulations limiting the use of non-traditional flavors in the US. The relationship between flavor type and increased adolescent use suggests a possible link between flavor use and addiction and harm perception. This study assessed if the flavor type used when initiating e-cigarette use predicted addiction and harm perceptions. METHODS: The study utilized data from the multi-wave youth Population Assessment of Tobacco Health Study. It explored the impact initiating e-cigarette use with traditional versus non-traditional flavor types among cigarette users on the outcome variables: e-cigarette addiction and harm perception. Both e-cigarette addiction and harm perception were measured using self-report, Likert scale questionnaires. Descriptive statistics characterized the study variables and linear regression analyses performed to test whether flavor initiation type is associated with addiction and harm perception. RESULTS: The study sample consisted of 1,043 youth (weighted N = 1,873,617) aged 12 to 17 years who reported at least one instance of e-cigarette use. After adjusting for age, age of onset, sex, race and annual household income there was no statistically significant difference in addiction levels between those initiating with traditional versus non-traditional flavors (p = 0.294). Similarly, traditional versus non-traditional flavor initiation did not show a statistically significant difference in adolescent e-cigarette harm perceptions (p = 0.601). CONCLUSIONS: Traditionally flavored e-cigarette initiation produces similar risk for addiction and harm perceptions as non-traditionally flavored initiation. These findings suggest that banning non-traditional flavors alone may be ineffective in curbing e-cigarette addiction and harm perception. Additional research is needed to better understand which e-cigarette product characteristics and behaviors may be associated with greater addiction and reduced harm perceptions.

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. METHODS: This study incorporated four datasets - the 2017 dental workforce, the 2016 physician workforce, the 2014 nursing workforce and the 2017 pharmacy workforce - collected by the Utah Medical Education Council. Supervised ML techniques were used to identify factors associated with practice location, the outcome variable of interest. RESULTS: The study sample consisted of 11 259 healthcare professionals with an average age of 46.6 years, of which 36.6% were males and 94.5% Caucasian. Four ML methods were applied to assess model performance by comparing accuracy, sensitivity, specificity and area under the receiver operating characteristic (ROC) curve. Of the methods used, support vector machine performed the best (accuracy 99.7%, precision 100%, sensitivity 100%, specificity 99.4% and ROC 0.997). The models identified income and rural upbringing as the top factors associated with rural practice. CONCLUSION: By far, income emerged as the most important factor associated with rural practice, suggesting that attractive income offers might help rural communities address health professional shortages. Rural upbringing was the next most important predictive factor, validating and updating earlier research. The performance of the ML algorithms suggests their usefulness as a tool to model other databases for individualized prediction.

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. METHODS: This study used data collected by the Medical Expenditure Panel Survey to examine orthodontic expenditures in the United States from 1996 to 2016. Descriptive statistics for orthodontic expenditures were computed and graphed across various groups. Trends in orthodontic expenditures were adjusted to the 2016 United States dollar to account for inflation and deflation over time. Sampling weights were applied in estimating per capita and total expenditures to account for non-responses in population groups. RESULTS: Total orthodontic expenditures in the United States almost doubled from $11.5 billion in 1996 to $19.9 billion in 2016 with the average orthodontic expenditure per person increasing from $42.69 in 1996 to $61.52 in 2016. Black individuals had the lowest per capita orthodontic visit expenditure at $30.35. Out-of-pocket expenses represented the highest total expenditure and although the amount of out-of-pocket expenses increased over the years, they decreased as a percentage of total expenditures. Public insurance increased the most over the study period but still accounted for the smallest percentage of expenditures. Over the course of the study, several annual decreases were interspersed with years of increased spending CONCLUSION: While government insurance expenditure increased over the study period, out of pocket expenditures remained the largest contributor. Annual decreases in expenditure associated with economic downturns and result from the reliance on out-of-pocket payments for orthodontic care. Differences in spending among groups suggest disparities in orthodontic care among the US population.

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. 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.

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). NHANES uses a complex sampling technique to develop nationally representative sample estimates of the US population that consists of interviews and physical assessments. Children and adolescents aged 6 to 15 years were included because NHANES contains their data for all 3 forms of fluoride measures: plasma fluoride levels, water levels of fluoride, and dietary fluoride supplementation. Data were analyzed from January 1 to April 30, 2023. Exposures: Water and plasma fluoride levels were measured by laboratory personnel. Dietary fluoride supplement data were self-reported. Main Outcomes and Measures: The Dean's Fluorosis Index was used to evaluate fluorosis status for each tooth. The dental fluorosis severity value was based on the second most affected tooth. Independent variables included plasma and water fluoride concentrations and dietary fluoride supplementation. An independent samples t test was used to compare fluoride exposures between groups, and Pearson correlation assessed the association between plasma and water fluoride levels. To assess whether fluoride exposures were associated with dental fluorosis, logistic regression analyses were conducted. Results: There were 1543 participants in the 2013-2014 NHANES cycle (weighted proportion male, 51.9%; mean [SD] age, 11.0 [2.7] years) and 1452 in the 2015-2016 cycle (weighted proportion male, 52.6%; mean [SD] age, 11.1 [2.8] years). A weighted 87.3% exhibited some degree of fluorosis in the 2013-2014 cycle and 68.2% in the 2015-2016 cycle. Higher fluoride levels in water and plasma were significantly associated with higher odds of dental fluorosis (adjusted odds ratios, 2.378 [95% CI, 2.372-2.383] in the 2013-2014 cycle and 1.568 [95% CI, 1.564-1.571] in the 2015-2016 cycle). Conclusions and Relevance: The findings of this cross-sectional study suggest that exposure to higher concentrations of fluoride in water and having higher plasma levels of fluoride were associated with a greater risk of dental fluorosis. Further research can help policy makers develop policies that balance substantial caries prevention with the risk of dental fluorosis.

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. 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.

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 4.5 of the Population Assessment of Tobacco and Health Study (PATH). PATH collects demographic data and interview individuals about issues pertaining to tobacco use, health outcomes, attitudes, and behaviors. This study applied factor analysis to three individual PATH harm perception items to develop a composite harm perception score. Using linear regression, the study explored the relationship of harm perception and participant responses to their recalled viewing of five different types (i.e., newspaper, radio, billboard, television and social media) of advertisements within the past 30 days. A second analysis explored if adjusting for exposure to anti-tobacco messaging and environmental factors such as family approval mitigated the association of harm perception and advertisement types. RESULTS: The study sample consisted of 12,570 (weighted N = 23,993,149) individuals aged 12 to 17 years old. Unadjusted past 30-day exposure to newspaper, radio, billboard, and social media advertising all correlated with a reduced harm perception, but only the associations for newspaper and social media were statistically significant (p<0.05). After adjusting for environmental support factors, exposure to warning labels, and anti-tobacco advertisements, the analysis yielded statistically significant associations between increased e-cigarette harm perception and exposure to radio, billboard, and television advertisements (p<0.05). Adjusting for covariates also reduced the association of marketing and harm perception for all forms of media. CONCLUSION: E-cigarette advertising influences adolescent perceptions of harm in e-cigarette use, particularly for social media and newspaper advertisements. This association weakens when adjusted for covariates such as environmental support and exposure to anti-tobacco marketing. These findings provide evidence for policy makers to continue anti-tobacco marketing and incorporate environmentally supportive strategies such as holistic, family-centered educational approaches to reduce e-cigarette use among adolescents.

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. This study sought to assess ML as a tool to develop prediction models for all-cause 90-day hospital readmission for dental patients. METHODS: Using the 2013 Nationwide Readmissions Database (NRD), the study identified 9260 cases for all-cause 90-day index admission for dental patients. Five ML classification algorithms including decision tree, logistic regression, support vector machine, k-nearest neighbors, and artificial neural network (ANN) were implemented to build predictive models. The model performance was estimated and compared by using area under the receiver operating characteristic curve (AUC), and accuracy, sensitivity, specificity, and precision. RESULTS: Hospital readmission within 90 days occurred in 1746 cases (18.9%). Total charges, number of diagnosis, age, number of chronic conditions, length of hospital stays, number of procedures, primary expected payer, and severity of illness emerged as the top eight important features in all-cause 90-day hospital readmission. All models had similar performance with ANN (AUC = 0.743) slightly outperforming the rest. CONCLUSION: This study demonstrates a potential annual saving of over $500 million if all of the 90-day readmission cases could be prevented for 21 states represented in the NRD. Among the methods used, the prediction model built by ANN exhibited the best performance. Further testing using ANN and other methods can help to assess important readmission risk factors and to target interventions to those at the greatest risk.

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. Descriptive statistics were computed on study variables and multiple logistic regression analyses were conducted to explore the association of the study variables with the outcome variable. Sampling weights were applied to produce national estimates of prevalence. The sample consisted of 1,669 females, representative of 75,107,170 females in the United States population. Around 11.5% reported being told that they had an HPV infection, of which 60.9% were White, and 82.9% were born in the United States. White women are 2.0 times more likely to be told they have HPV than Asian women and 2.8 times more likely than Black women. United States-born women were 2.1 times more likely told they had an HPV infection than those foreign born. This study found that among U.S. women, less than 12% reported ever having been told they have had an HPV infection. Epidemiologic findings suggest gaps between ever being told of a previous infection and being diagnosed with a clinically relevant HPV infection. Despite epidemiologic data indicating higher HPV prevalence among those less educated and women of color, these groups were less likely to report ever being told they have an HPV infection than White women, and those with a college degree suggesting communication gaps among these subgroups about HPV infection that might exist. Strategies to address potential gaps in communication among these subgroups can potentially reduce the economic burden and health disparities related to HPV infection.

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 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.

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. METHODS: Data from the 2013 Nationwide Readmissions Database on AF cases were used to predict 90-day readmissions for AF with catheter ablation. Multiple machine learning methods such as k-Nearest Neighbors, Decision Tree, and Support Vector Machine were employed to determine variable importance and build risk prediction models. Accuracy, precision, sensitivity, specificity, and area under the curve were compared for each model. RESULTS: The 90-day hospital readmission rate was 17.6%; the average age of the patients was 64.9 years; 62.9% of patients were male. Important variables in predicting 90-day hospital readmissions in patients with AF undergoing catheter ablation included the age of the patient, number of diagnoses on the patient's record, and the total number of discharges from a hospital. The k-Nearest Neighbor had the best performance with a prediction accuracy of 85%. This was closely followed by Decision Tree, but Support Vector Machine was less ideal. CONCLUSIONS: Machine learning methods can produce accurate models in predicting hospital readmissions for patients with AF. The likelihood of readmission to the hospital increases as the patient age, total number of hospital discharges, and total number of patient diagnoses increase. Findings from this study can inform quality improvement in healthcare and in achieving patient-centered care.

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.

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. METHODS: This study used data extracted from the 2013 Nationwide Readmissions Database (NRD). There were a total of 11,341 cases for all-cause index admission for dental patients admitted to the hospitals. Descriptive statistics were used to analyze patient characteristics. This study applied five techniques to build risk prediction models and to identify risk factors. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), and accuracy, sensitivity, specificity and precision. RESULTS: There were 11% of patients admitted for ACDC readmitted within 30 days of hospital discharge. On average, the total charge per patient was $131,004 for those with 30-day readmission (n=1254) and $69,750 for those without readmission (n=10,087). Factors significantly associated with 30-day hospital readmission included total charges, number of diagnoses, age, number of chronic conditions, length of hospital stays, number of procedures, Medicare insurance and Medicaid insurance, and severity of illness. Model performance from all methods was similar with the artificial neural network showing the highest AUC of 0.739. CONCLUSION: Our results demonstrate that readmission after hospitalization with ACDC is fairly common. If one-third of the 30-day readmission cases can be avoided, there is a potential annual saving of over $25 million among the twenty-one states represented in the NRD. The ML algorithms can predict hospital readmission in dental patients and should be further tested to aid the reduction of hospital readmission and enhancement of patient-centered care.

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.