Objective: Social distancing policies are key in curtailing COVID-19 infection spread, but their effectiveness is heavily contingent on public understanding and collective adherence. We sought to study public perception of social distancing through organic, large-scale discussion on Twitter. Design: Retrospective cross-sectional study. Methods: Between March 27 and April 10, 2020, we retrieved English-only tweets matching two trending social distancing hashtags, #socialdistancing and #stayathome. We analyzed the tweets using natural language processing and machine learning models, conducting a sentiment analysis to identify emotions and polarity. We evaluated subjectivity of tweets and estimated frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and associated sentiments. Results: We studied a sample of 574,903 tweets. For both hashtags, polarity was positive (mean, 0.148; SD, 0.290); only 15% of tweets had negative polarity. Tweets were more likely to be objective (median, 0.40; IQR, 0 to 0.6) with approximately 30% of tweets labeled as completely objective (labeled as 0 in range from 0 to 1). Approximately half (50.4%) of tweets primarily expressed joy and one-fifth expressed fear and surprise. Each correlated well with topic clusters identified by frequency including leisure and community support (i.e., joy), concerns about food insecurity and quarantine effects (i.e., fear), and unpredictability of COVID and its implications (i.e., surprise). Conclusions: The positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms led us to believe that Twitter users generally supported social distancing in the early stages of their implementation.

The COVID-19 pandemic has challenged the United States’ existing national public health informatics infrastructure. This report details the factors that have contributed to COVID-19 data inaccuracies and reporting delays and their effect on the modeling and monitoring of the COVID-19 pandemic.

Background COVID-19 has been associated with increased risk of thromboembolism in critically ill patients. Objectives We sought to examine the association of SARS-CoV-2 test positivity and subsequent acute vascular thrombosis, including venous thromboembolism (VTE) or arterial thrombosis (AT) in, a large nationwide registry of emergency department patients tested with a nucleic acid test for suspected SARS-CoV-2. Methods The RECOVER registry includes 155 emergency departments across the US. We performed a retrospective cohort study to produce odds ratios for COVID+ versus COVID- status as a predictor of 30 day VTE or AT, adjusting for age, biological sex, active cancer, intubation, hospital length of stay (LOS), and ICU care. Results Comparing 14,056 COVID+ patients with 12,995 COVID- patients, the overall 30-day prevalence of VTE events was 1.4% versus 1.3%, respectively (p=0.44, χ 2 ). Multivariable analysis identified that testing positive for SAR-CoV-2 status was negatively associated with both VTE (OR 0.76, 95% CI: 0.61-0.94) and AT (0.51, 0.32-0.80), whereas intubation, ICU care, and age>=50 were positively associated with both VTE and AT. Conclusions In contrast to other reports, results from this large, heterogenous national sample of ED patients tested for SARS-CoV-2, showed no association between vascular thrombosis and COVID-19 test positivity.

The objective of this study was to investigate the interaction between ethanol and dextroamphetamine with regard to psychomotor performance. Twelve healthy, male, paid volunteers, moderate users of ethanol and amphetamines, participated in this study. Ethanol (0.85 g/kg or placebo) was administered over a 30-min interval. Five minutes before the termination of ethanol or placebo ingestion, dextroamphetamine elixir (0.09 mg/kg, 0.18 mg/kg or placebo) diluted in 50 ml of orange juice was administered. Subjects were tested in a single-blind, latin-square, crossover design with each of the following six conditions: placebo ethanol/placebo dextroamphetamine; placebo ethanol/low-dose dextroamphetamine; placebo ethanol/high-dose dextroamphetamine; ethanol/placebo dextroamphetamine; ethanol/low-dose dextroamphetamine; and ethanol/high-dose dextroamphetamine. The variables measured in this study were: subjective rating of ethanol and dextroamphetamine intoxication, accuracy and latency of response in the Simulator Evaluation of Drug Impairment (SEDI task), blood ethanol concentration by breath analyzer, and plasma concentrations of dextroamphetamine by gas chromatography. Results indicate ethanol induced decrements in performance of the skills necessary to drive an automobile were significantly decreased by dextroamphetamine in a dose-response fashion. The administration of dextroamphetamine did not decrease the subjective ratings of ethanol intoxication.

Objectives The COVID‐19 pandemic has placed acute care providers in demanding situations in predicting disease given the clinical variability, desire to cohort patients, and high variance in testing availability. An approach to stratify patients by likelihood of disease based on rapidly available emergency department (ED) clinical data would offer significant operational and clinical value. The purpose of this study was to develop and internally validate a predictive model to aid in the discrimination of patients undergoing investigation for COVID‐19. Methods All patients greater than 18 years presenting to a single academic ED who were tested for COVID‐19 during this index ED evaluation were included. Outcome was defined as the result of COVID‐19 PCR testing during the index visit or any positive result within the following 7 days. Variables included chest radiograph interpretation, disease specific screening questions, and laboratory data. Three models were developed with a split‐sample approach to predict outcome of the PCR test utilizing logistic regression, random forest, and gradient boosted decision‐tree methods. Model discrimination was evaluated comparing AUC and point statistics at a predefined threshold. Results 1026 patients were included in the study collected between March and April 2020. Overall, there was disease prevalence of 9.6% in the population under study during this time frame. The logistic regression model was found to have an AUC of 0.89 (95% CI 0.84 ‐ 0.94) when including four features: exposure history, temperature, WBC, and chest radiograph result. Random forest method resulted in AUC of 0.86 (95% CI 0.79 ‐ 0.92) and gradient boosting had an AUC of 0.85 (95% CI 0.79‐0.91). With a consistently held negative predictive value, the logistic regression model had a positive predictive value of 0.29 (0.2‐0.39) compared to 0.2 (0.14‐0.28) for random forest and 0.22 (0.15 – 0.3) for the gradient boosted method. Conclusion The derived predictive models offer good discriminating capacity for COVID‐19 disease and provide interpretable and usable methods for those providers caring for these patients at the important crossroads of the community and the health system. We found utilization of the logistic regression model utilizing exposure history, temperature, WBC, and Chest XR result had the greatest discriminatory capacity with the most interpretable model. Integrating a predictive model‐based approach to COVID‐19 testing decisions and patient care pathways and locations could add efficiency and accuracy to decrease uncertainty.