The Use of a Self-triage Tool to Predict COVID-19 Cases and Hospitalizations in the State of Georgia.

INTRODUCTION: The coronavirus 2019 (COVID-19) pandemic has created significant burden on healthcare systems throughout the world. Syndromic surveillance, which collects real-time data based on a range of symptoms rather than laboratory diagnoses, can help provide timely information in emergency response. We examined the effectiveness of a web-based COVID-19 symptom checking tool (C19Check) in the state of Georgia (GA) in predicting COVID-19 cases and hospitalizations. METHODS: We analyzed C19Check use data, COVID-19 cases, and hospitalizations from April 22-November 28, 2020. Cases and hospitalizations in GA were extracted from the Georgia Department of Public Health data repository. We used the Granger causality test to assess whether including C19Check data can improve predictions compared to using previous COVID-19 cases and hospitalizations data alone. Vector autoregression (VAR) models were fitted to forecast cases and hospitalizations from November 29 - December 12, 2020. We calculated mean absolute percentage error to estimate the errors in forecast of cases and hospitalizations. RESULTS: There were 25,861 C19Check uses in GA from April 22-November 28, 2020. Time-lags tested in Granger causality test for cases (6-8 days) and hospitalizations (10-12 days) were significant (P= <0.05); the mean absolute percentage error of fitted VAR models were 39.63% and 15.86%, respectively. CONCLUSION: The C19Check tool was able to help predict COVID-19 cases and related hospitalizations in GA. In settings where laboratory tests are limited, a real-time, symptom-based assessment tool can provide timely and inexpensive data for syndromic surveillance to guide pandemic response. Findings from this study demonstrate that online symptom-checking tools can be a source of data for syndromic surveillance, and the data may help improve predictions of cases and hospitalizations.

COVID-19-associated venous thromboembolism portends worse survival.

Patients with coronavirus disease 2019 (COVID-19) seem to be at high risk for venous thromboembolism (VTE) development, but there is a paucity of data exploring both the natural history of COVID-19-associated VTE and the risk for poor outcomes after VTE development. This investigation aims to explore the relationship between COVID-19-associated VTE development and mortality. A prospectively maintained registry of patients older than 18 years admitted for COVID-19-related illnesses within an academic health care network between March and September 2020 was reviewed. Codes from the tenth revision of the International Classification of Diseases for VTE were collected. The charts of those patients with a code for VTE were manually reviewed to confirm VTE diagnosis. There were 2,552 patients admitted with COVID-19-related illnesses. One hundred and twenty-six patients (4.9%) developed a VTE. A disproportionate percentage of patients of Black race developed a VTE (70.9% VTE v 57.8% non-VTE; P = .012). A higher proportion of patients with VTE expired during their index hospitalization (22.8% VTE v 8.4% non-VTE; P < .001). On multivariable logistic regression analysis, VTE was independently associated with mortality (odds ratio = 3.17; 95% confidence interval, 1.9-5.2; P < .001). Hispanic/Latinx ethnicity was associated with decreased mortality (odds ratio = 0.45; 95% confidence interval, 0.21-1.00; P = .049). Hospitalized patients of Black race with COVID-19 were more prone to VTE development, and patients with COVID-19 who developed in-hospital VTE had roughly nearly threefold higher odds of mortality. Further emphasis should be placed on optimizing COVID-19 anticoagulation protocols to reduce mortality in this high-risk cohort.