Semi-parametric modeling of SARS-CoV-2 transmission using tests, cases, deaths, and seroprevalence data.

Mechanistic models fit to streaming surveillance data are critical to understanding the transmission dynamics of an outbreak as it unfolds in real-time. However, transmission model parameter estimation can be imprecise, and sometimes even impossible, because surveillance data are noisy and not informative about all aspects of the mechanistic model. To partially overcome this obstacle, Bayesian models have been proposed to integrate multiple surveillance data streams. We devised a modeling framework for integrating SARS-CoV-2 diagnostics test and mortality time series data, as well as seroprevalence data from cross-sectional studies, and tested the importance of individual data streams for both inference and forecasting. Importantly, our model for incidence data accounts for changes in the total number of tests performed. We model the transmission rate, infection-to-fatality ratio, and a parameter controlling a functional relationship between the true case incidence and the fraction of positive tests as time-varying quantities and estimate changes of these parameters nonparametrically. We compare our base model against modified versions which do not use diagnostics test counts or seroprevalence data to demonstrate the utility of including these often unused data streams. We apply our Bayesian data integration method to COVID-19 surveillance data collected in Orange County, California between March 2020 and February 2021 and find that 32-72% of the Orange County residents experienced SARS-CoV-2 infection by mid-January, 2021. Despite this high number of infections, our results suggest that the abrupt end of the winter surge in January 2021 was due to both behavioral changes and a high level of accumulated natural immunity.

Patients with coronary disease fail observation status at higher rates than patients without coronary disease.

BACKGROUND: Few studies have evaluated emergency department (ED) observation unit chest pain protocols for optimal patient characteristics and admission rates. At our 35 000-visits/y ED, we implemented a chest pain protocol for our observation unit that allowed emergency physicians to admit patients with known coronary artery disease (CAD). METHODS: We performed a retrospective chart review of all observation unit patients admitted under the chest pain protocol from April 1, 2006, to May 31, 2007. We compared the outcomes of patients who had a history of CAD with those who did not. RESULTS: Five hundred thirty-one patients were admitted to the observation unit under the chest pain protocol for the 14-month study period. Of these patients, 125 (23.5%) had a history of CAD. Patients with a history of CAD had a higher inpatient admission rate ( 24% vs 8.6%; P < .001), higher rate of a positive stress test or positive coronary computed tomographic scan (32.3% vs 6.9%; P < .001), a higher rate of cardiac catheterization (12% vs 5.9%; P = .02), and a higher rate of stent placement or coronary artery bypass graft (CABG) (7.2% vs 2.2%; P = .007). In multivariate analysis, patient history of CAD was an independent predictor of hospital admission (P = .005) and stent placement or CABG (P = .030). CONCLUSION: Patients with known CAD who were admitted to the ED observation unit failed observation status (ie, required hospitalization) and had higher rates of positive testing than those without CAD.

Emergency department patients with psychiatric complaints return at higher rates than controls.

STUDY OBJECTIVE: At our 35,000 visit/year emergency department (ED), we studied whether patients presenting to the ED with psychiatric complaints were admitted to the hospital at a higher rate than non-psychiatric patients, and whether these patients had a higher rate of reevaluation in the ED within 30 days following the index visit. METHODS: We reviewed the electronic records of all ED patients receiving a psychiatric evaluation from January to February 2007 and compared these patients to 300 randomly selected patients presenting during the study period for non-psychiatric complaints. Patients were followed for 30 days, and admission rates and return visits were compared. RESULTS: Two hundred thirty-four patients presented to the ED and were evaluated for psychiatric complaints during the study period. Twenty-four point seven percent of psychiatric patients were admitted upon initial presentation versus 20.7% of non-psychiatric patients (p = 0.258). Twenty-one percent of discharged psychiatric patients returned to the ED within 30 days versus 13.4% of discharged non-psychiatric patients (p=0.041). Patients returning to the ED within 30 days had a 17.1% versus 21.6% admission rate for the psychiatric and non-psychiatric groups, respectively (p=0.485). CONCLUSION: Patients presenting to this ED with psychiatric complaints were not admitted at a significantly higher rate than non-psychiatric patients. These psychiatric patients did, however, have a significantly higher return rate to the ED when compared to non-psychiatric patients.