SARS-CoV-2 outbreak in a Canadian suburban tertiary hospital necessitating full facility closure: a descriptive observational study.

BACKGROUND: SARS-CoV-2 can cause outbreaks in community- and hospital-based settings. The aim of this study was to provide a detailed epidemiologic account of a hospital-wide SARS-CoV-2 outbreak and provide a description of case evaluations, transmission networks and the interventions implemented to stem the outbreak. METHODS: We conducted a retrospective descriptive study of a hospital-wide SARS-CoV-2 outbreak at the Misericordia Community Hospital (Edmonton) from June 21 to Aug. 14, 2020. We reviewed hospital chart, public health and occupational health records to determine demographics, case type (community- or hospital-acquired), need for critical care and outcome for each case linked to the outbreak (patients, hospital staff, and community and patient visitors). We developed detailed transmission networks using epidemiologic data to determine what variables may have contributed to transmission. RESULTS: Fifty-eight cases of SARS-CoV-2 infection were linked to this hospital outbreak (31 patients, 25 staff members and 2 visitors; 66% female, age range 19-97 years). One patient required critical care, and 11 deaths were recorded (all among inpatients). Most cases were hospital-acquired (91%), and 28% were asymptomatic at the time of diagnosis. The outbreak was composed of 2 clusters driven by protective equipment breaches, premature removal of precautions, transmission in small staff quarters and infection of a staff member after exposure to a wandering patient with dementia and asymptomatic, undetected SARS-CoV-2 infection. INTERPRETATION: A detailed epidemiologic review of this hospital-wide outbreak shows that a SARS-CoV-2 outbreak can involve complex transmission chains and clusters. Multipronged bundled approaches, aggressive contact tracing, and patient and staff prevalence screening are important to help bring such outbreaks under control, along with ongoing vigilance in detecting delayed cases.

Distribution of Patients at Risk for Complications Related to COVID-19 in the United States: Model Development Study.

BACKGROUND: Coronavirus disease (COVID-19) has spread exponentially across the United States. Older adults with underlying health conditions are at an especially high risk of developing life-threatening complications if infected. Most intensive care unit (ICU) admissions and non-ICU hospitalizations have been among patients with at least one underlying health condition. OBJECTIVE: The aim of this study was to develop a model to estimate the risk status of the patients of a nationwide pharmacy chain in the United States, and to identify the geographic distribution of patients who have the highest risk of severe COVID-19 complications. METHODS: A risk model was developed using a training test split approach to identify patients who are at high risk of developing serious complications from COVID-19. Adult patients (aged ≥18 years) were identified from the Walgreens pharmacy electronic data warehouse. Patients were considered eligible to contribute data to the model if they had at least one prescription filled at a Walgreens location between October 27, 2019, and March 25, 2020. Risk parameters included age, whether the patient is being treated for a serious or chronic condition, and urban density classification. Parameters were differentially weighted based on their association with severe complications, as reported in earlier cases. An at-risk rate per 1000 people was calculated at the county level, and ArcMap was used to depict the rate of patients at high risk for severe complications from COVID-19. Real-time COVID-19 cases captured by the Johns Hopkins University Center for Systems Science and Engineering (CSSE) were layered in the risk map to show where cases exist relative to the high-risk populations. RESULTS: Of the 30,100,826 adults included in this study, the average age is 50 years, 15% have at least one specialty medication, and the average patient has 2 to 3 comorbidities. Nearly 28% of patients have the greatest risk score, and an additional 34.64% of patients are considered high-risk, with scores ranging from 8 to 10. Age accounts for 53% of a patient's total risk, followed by the number of comorbidities (29%); inferred chronic obstructive pulmonary disease, hypertension, or diabetes (15%); and urban density classification (5%). CONCLUSIONS: This risk model utilizes data from approximately 10% of the US population. Currently, this is the most comprehensive US model to estimate and depict the county-level prognosis of COVID-19 infection. This study shows that there are counties across the United States whose residents are at high risk of developing severe complications from COVID-19. Our county-level risk estimates may be used alongside other data sets to improve the accuracy of anticipated health care resource needs. The interactive map can also aid in proactive planning and preparations among employers that are deemed critical, such as pharmacies and grocery stores, to prevent the spread of COVID-19 within their facilities.