COVID-19 test positivity: predictive value of various symptoms in a large community-based testing program in California

BackgroundMuch of the early data on COVID-19 symptomatology was captured in the hospital setting. In a community setting the symptoms most predictive of SARS-CoV-2 positivity may be different. Data from the California sites of a COVID-19 community testing program are presented here. MethodsPrior to being tested, participants in the Baseline COVID-19 Testing Program completed an online screener, in which they self-reported basic demographics and the presence or absence of 10 symptoms. Both positive and negative COVID-19 RT-PCR tests were linked back to the screener data. A multivariable model of positivity was fit using generalized estimating equations, adjusting for month of testing as a fixed effect and accounting for clustering of data within each test site. ResultsAmong 547,018 first-time tests in California in 2020, positivity rates were 3.4%, 9.9%, and 19.8% for participants with no symptoms, 1 symptom, or 2 or more symptoms at the time of screening, respectively. All ten symptoms were individually associated with higher positivity rates, but only six of ten symptoms were associated with higher positivity when adjusting for other symptoms. Major symptoms with highest predictive value were recent loss of taste or smell, fever, and coughing with ORs of 3.27, 1.97, and 1.95, respectively. Shortness of breath and vomiting or diarrhea were negatively associated with positivity adjusting for other symptoms and, absent other symptoms, participants with these symptoms did not have significantly higher positivity rates than asymptomatic participants. ConclusionsRecent loss of taste and smell should be elevated to a major symptom along with fever and coughing in public health messaging and in our community approach to testing and surveillance, while mild to moderate shortness of breath should be de-emphasized as a sensitive early predictor of COVID-19 positivity.

A holistic approach for suppression of COVID-19 spread in workplaces and universities

As society has moved past the initial phase of the COVID-19 crisis that relied on broad-spectrum shutdowns as a stopgap method, industries and institutions have faced the daunting question of how to return to a stabilized state of activities and more fully reopen the economy. A core problem is how to return people to their workplaces and educational institutions in a manner that is safe, ethical, grounded in science, and takes into account the unique factors and needs of each organization and community. In this paper, we introduce an epidemiological model (the "Community-Workplace" model) that accounts for SARS-CoV-2 transmission within the workplace, within the surrounding community, and between them. We use this multi-group deterministic compartmental model to consider various testing strategies that, together with symptom screening, exposure tracking, and nonpharmaceutical interventions (NPI) such as mask wearing and social distancing, aim to reduce disease spread in the workplace. Our framework is designed to be adaptable to a variety of specific workplace environments to support planning efforts as reopenings continue. Using this model, we consider a number of case studies, including an office workplace, a factory floor, and a university campus. Analysis of these cases illustrates that continuous testing can help a workplace avoid an outbreak by reducing undetected infectiousness even in high-contact environments. We find that a university setting, where individuals spend more time on campus and have a higher contact load, requires more testing to remain safe, compared to a factory or office setting. Under the modeling assumptions, we find that maintaining a prevalence below 3% can be achieved in an office setting by testing its workforce every two weeks, whereas achieving this same goal for a university could require as much as fourfold more testing (i.e., testing the entire campus population twice a week). Our model also simulates the dynamics of reduced spread that result from the introduction of mitigation measures when test results reveal the early stages of a workplace outbreak. We use this to show that a vigilant university that has the ability to quickly react to outbreaks can be justified in implementing testing at the same rate as a lower-risk office workplace. Finally, we quantify the devastating impact that an outbreak in a small-town college could have on the surrounding community, which supports the notion that communities can be better protected by supporting their local places of business in preventing onsite spread of disease.

Cause of Death and Predictors of All-Cause Mortality in Anticoagulated Patients With Nonvalvular Atrial Fibrillation: DataFrom ROCKET AF

Atrial fibrillation is associated with higher mortality. Identification of causes of death and contemporary risk factorsfor all-cause mortality may guide interventions. Methods and Results-—In the Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation (ROCKET AF) study, patients with nonvalvular atrial fibrillation were randomized to rivaroxaban or dose-adjusted warfarin. Cox proportional hazards regression with backward elimination identifiedfactors at randomization that were independently associated with all-cause mortality in the 14 171 participants in the intentionto-treat population. The median age was 73 years, and the mean CHADS2 score was 3.5. Over 1.9 years of median follow-up,1214 (8.6%) patients died. Kaplan–Meier mortality rates were 4.2% at 1 year and 8.9% at 2 years. The majority of classified deaths (1081) were cardiovascular (72%), whereas only 6% were nonhemorrhagic stroke or systemic embolism. No significant difference in all-cause mortality was observed between the rivaroxaban and warfarin arms (P=0.15). Heart failure (hazard ratio1.51, 95% CI 1.33–1.70, P<0.0001) and age ≥75 years (hazard ratio 1.69, 95% CI 1.51–1.90, P<0.0001) were associated with higher all-cause mortality. Multiple additional characteristics were independently associated with higher mortality, with decreasing creatinine clearance, chronic obstructive pulmonary disease, male sex, peripheral vascular disease, and diabetes being among the most strongly associated (model C-index 0.677). Conclusions-—In a large population of patients anticoagulated for nonvalvular atrial fibrillation, 7 in 10 deaths were cardiovascular, whereas <1 in 10 deaths were caused by nonhemorrhagic stroke or systemic embolism. Optimal prevention and treatment of heart failure, renal impairment, chronic obstructive pulmonary disease, and diabetes may improve survival...