Gaps in the type 2 diabetes care cascade: a national perspective using South Africa's National Health Laboratory Service (NHLS) database.

BACKGROUND: Research out of South Africa estimates the total unmet need for care for those with type 2 diabetes mellitus (diabetes) at 80%. We evaluated the care cascade using South Africa's National Health Laboratory Service (NHLS) database and assessed if HIV infection impacts progression through its stages. METHODS: The cohort includes patients from government facilities with their first glycated hemoglobin A1c (HbA1c) or plasma glucose (fasting (FPG); random (RPG)) measured between January 2012 to March 2015 in the NHLS. Lab-diagnosed diabetes was defined as HbA1c ≥ 6.5%, FPG ≥ 7.0mmol/l, or RPG ≥ 11.1mmol/l. Cascade stages post diagnosis were retention-in-care and glycaemic control (defined as an HbA1c < 7.0% or FPG < 8.0mmol/l or RPG < 10.0mmol/l) over 24-months. We estimated gaps at each stage nationally and by people living with HIV (PLWH) and without (PLWOH). RESULTS: Of the 373,889 patients tested for diabetes, 43.2% had an HbA1c or blood glucose measure indicating a diabetes diagnosis. Amongst those with lab-diagnosed diabetes, 30.9% were retained-in-care (based on diabetes labs) and 8.7% reached glycaemic control by 24-months. Prevalence of lab-diagnosed diabetes in PLWH was 28.6% versus 47.3% in PLWOH. Among those with lab-diagnosed diabetes, 34.3% of PLWH were retained-in-care versus 30.3% PLWOH. Among people retained-in-care, 33.8% of PLWH reached glycaemic control over 24-months versus 28.6% of PLWOH. CONCLUSIONS: In our analysis of South Africa's NHLS database, we observed that 70% of patients diagnosed with diabetes did not maintain in consistent diabetes care, with fewer than 10% reaching glycemic control within 24 months. We noted a disparity in diabetes prevalence between PLWH and PLWOH, potentially linked to different screening methods. These differences underscore the intricacies in care but also emphasize how HIV care practices could guide better management of chronic diseases like diabetes. Our results underscore the imperative for specialized strategies to bolster diabetes care in South Africa.

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.

Application of machine learning for diagnostic prediction of root caries.

OBJECTIVE: This study sought to utilise machine learning methods in artificial intelligence to select the most relevant variables in classifying the presence and absence of root caries and to evaluate the model performance. BACKGROUND: Dental caries is one of the most prevalent oral health problems. Artificial intelligence can be used to develop models for identification of root caries risk and to gain valuable insights, but it has not been applied in dentistry. Accurately identifying root caries may guide treatment decisions, leading to better oral health outcomes. METHODS: Data were obtained from the 2015-2016 National Health and Nutrition Examination Survey and were randomly divided into training and test sets. Several supervised machine learning methods were applied to construct a tool that was capable of classifying variables into the presence and absence of root caries. Accuracy, sensitivity, specificity and area under the receiver operating curve were computed. RESULTS: Of the machine learning algorithms developed, support vector machine demonstrated the best performance with an accuracy of 97.1%, precision of 95.1%, sensitivity of 99.6% and specificity of 94.3% for identifying root caries. The area under the curve was 0.997. Age was the feature most strongly associated with root caries. CONCLUSION: The machine learning algorithms developed in this study perform well and allow for clinical implementation and utilisation by dental and nondental professionals. Clinicians are encouraged to adopt the algorithms from this study for early intervention and treatment of root caries for the ageing population of the United States, and for attaining precision dental medicine.

COVID-19 lockdown: Impact on college students' lives.

Objective: In light of COVID-19, leaders issued stay-at-home orders, including closure of higher-education schools. Most students left campus, likely impacting their employment and social network. Leaders are making decisions about opening universities and modality of instruction. Understanding students' psychological, physiological, academic, and financial responses to the shut-down and reopening of campuses can help leaders make informed decisions. Participants: 654 students from a large western university enrolled during the pandemic shutdown. Methods: Students were invited via email to complete an online survey. Results: Students reported stress, depression, loneliness, lack of motivation, difficulty focusing on schoolwork, restless sleep, appetite changes, job loss concerns, and difficulties coping. Most wanted to return to campus and felt social/physical distancing was effective but were mixed in terms of testing or masks. Conclusions: Moving to remote learning created physical and psychological stress. Students want to return to campus but do not want to take risk-reducing measures.

Record linkage without patient identifiers: proof of concept using data from South Africa's national HIV program.

Background: Linkage between health databases typically requires identifiers such as patient names and personal identification numbers. We developed and validated a record linkage strategy to combine administrative health databases without the use of patient identifiers, with application to South Africa's public sector HIV treatment program. Methods: We linked CD4 counts and HIV viral loads from South Africa's HIV clinical monitoring database (TIER.Net) and the National Health Laboratory Service (NHLS) for patients receiving care between 2015-2019 in Ekurhuleni District (Gauteng Province). We used a combination of variables related to lab results contained in both databases (result value; specimen collection date; facility of collection; patient year and month of birth; and sex). Exact matching linked on exact linking variable values while caliper matching applied exact matching with linkage on approximate test dates (± 5 days). We then developed a sequential linkage approach utilising specimen barcode matching, then exact matching, and lastly caliper matching. Performance measures were sensitivity and positive predictive value (PPV); share of patients linked across databases; and percent increase in data points for each linkage approach. Results: We attempted to link 2,017,290 lab results from TIER.Net (representing 523,558 unique patients) and 2,414,059 lab results from the NHLS database. Linkage performance was evaluated using specimen barcodes (available for a minority of records in TIER.net) as a "gold standard". Exact matching achieved a sensitivity of 69.0% and PPV of 95.1%. Caliper-matching achieved a sensitivity of 75.7% and PPV of 94.5%. In sequential linkage, we matched 41.9% of TIER.Net labs by specimen barcodes, 51.3% by exact matching, and 6.8% by caliper matching, for a total of 71.9% of labs matched, with PPV=96.8% and Sensitivity = 85.9%. The sequential approach linked 86.0% of TIER.Net patients with at least one lab result to the NHLS database (N=1,450,087). Linkage to the NHLS Cohort increased the number of laboratory results associated with TIER.Net patients by 62.6%. Conclusions: Linkage of TIER.Net and NHLS without patient identifiers attained high accuracy and yield without compromising patient privacy. The integrated cohort provides a more complete view of patients' lab history and could yield more accurate estimates of HIV program indicators.

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.

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.

Using Machine Learning to Predict 30-Day Hospital Readmissions in Patients with Atrial Fibrillation Undergoing Catheter Ablation.

Atrial fibrillation (AF) cases are expected to increase over the next several decades, due to the rise in the elderly population. One promising treatment option for AF is catheter ablation, which is increasing in use. We investigated the hospital readmissions data for AF patients undergoing catheter ablation, and used machine learning models to explore the risk factors behind these readmissions. We analyzed data from the 2013 Nationwide Readmissions Database on cases with AF, and determined the relative importance of factors in predicting 30-day readmissions for AF with catheter ablation. Various machine learning methods, such as k-nearest neighbors, decision tree, and support vector machine were utilized to develop predictive models with their accuracy, precision, sensitivity, specificity, and area under the curve computed and compared. We found that the most important variables in predicting 30-day hospital readmissions in patients with AF undergoing catheter ablation were the age of the patient, the total number of discharges from a hospital, and the number of diagnoses on the patient's record, among others. Out of the methods used, k-nearest neighbor had the highest prediction accuracy of 85%, closely followed by decision tree, while support vector machine was less desirable for these data. Hospital readmissions for AF with catheter ablation can be predicted with relatively high accuracy, utilizing machine learning methods. As patient age, the total number of hospital discharges, and the total number of patient diagnoses increase, the risk of hospital readmissions increases.

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.

Oral health as a gateway to overall health and well-being: Surveillance of the geriatric population in the United States.

AIMS: Little evidence exists to confirm that better oral health is associated with better overall health and well-being. The present study aimed to examine the impact of oral health on the overall health of the population greater than 65-year old in the entire United States. METHODS AND RESULTS: Data from National Health and Nutrition Examination Survey (NHANES) 2015-2016 were used. Variables included demographics and perceptions of oral health and overall health and well-being. Weighted prevalence estimates were calculated using mean, standard deviation, and percentage as appropriate. Chi-square tests and logistic regressions were performed to examine the association of oral health with physical health, mental health, general health, and systemic disease conditions. Analyses showed statistically significant relationships between oral health, physical, mental and general health, energy levels, work limitation, depression, and appetite. Out of the 10 systemic diseases being investigated, six of them were directly related to oral health outcome. CONCLUSION: This study provided strong empirical evidence that oral health is directly associated with different disease conditions and contributes largely to an individual's general health, particularly in the elderly. In the current landscape of patient-centered and value-based care, addressing the oral health needs of the elderly, who generally find themselves with limited access to care, should be a priority.