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Objectives To estimate the burden and severity of suspected reinfection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods A retrospective cohort of members of Kaiser Permanente Southern California with PCR-positive SARS-CoV-2 infection between 1st March 2020 and 31st October 2020 was followed through electronic health records for subsequent positive SARS-CoV-2 tests (suspected reinfection) ≥90 days after initial infection, through 31st January 2021. Incidence of suspected reinfection was estimated using the Kaplan–Meier method. Cox proportional hazards models estimated the association of suspected reinfection with demographic and clinical characteristics, hospitalization, and date of initial infection. Results The cohort of 75 149 was predominantly Hispanic (49 648/75 149, 66.1%) and included slightly more females than males (39 736, 52.9%), with few immunocompromised patients (953, 1.3%); 315 suspected reinfections were identified, with a cumulative incidence at 270 days of 0.8% (95% confidence interval (CI) 0.7–1.0%). Hospitalization was more common at suspected reinfection (36/315, 11.4%) than initial infection (4094/75 149, 5.4%). Suspected reinfection rates were higher in females (1.0%, CI 0.8–1.2% versus 0.7%, CI 0.5–0.9%, p 0.002) and immunocompromised patients (2.1%, CI 1.0–4.2% versus 0.8%, CI 0.7–1.0%, p 0.004), and lower in children than adults (0.2%, CI 0.1–0.4% versus 0.9%, CI 0.7–1.0%, p 0.023). Patients hospitalized at initial infection were more likely to have suspected reinfection (1.2%, CI 0.6–1.7% versus 0.8%, CI 0.7–1.0%, p 0.030), as were those with initial infections later in 2020 (150-day incidence 0.4%, CI 0.2–0.5% September–October versus 0.2%, CI 0.1–0.3% March–May and 0.3%, CI 0.2–0.3% June–August, p 0.008). In an adjusted Cox proportional hazards model, being female (hazard ratio (HR) 1.44, CI 1.14–1.81), adult (age 18–39, HR 2.71, CI 1.38–5.31, age 40–59 HR 2.22, CI 1.12–4.41, age ≥60 HR 2.52, CI 1.23–5.17 versus <18 years), immunocompromised (HR 2.48, CI 1.31–4.68), hospitalized (HR 1.60, CI 1.07–2.38), and initially infected later in 2020 (HR 2.26, CI 1.38–3.71 September–October versus March–May) were significant independent predictors of suspected reinfection. Conclusions Reinfection with SARS-CoV-2 is uncommon, with suspected reinfections more likely in women, adults, immunocompromised subjects, and those previously hospitalized for coronavirus 2019 (COVID-19). This suggests a need for continued precautions and vaccination in patients with COVID-19 to prevent reinfection.

Objective Develop and validate a risk score using variables available during an Emergency Department (ED) encounter to predict adverse events among patients with suspected COVID-19. Methods A retrospective cohort study of adult visits for suspected COVID-19 between March 1 – April 30, 2020 at 15 EDs in Southern California. The primary outcomes were death or respiratory decompensation within 7-days. We used least absolute shrinkage and selection operator (LASSO) models and logistic regression to derive a risk score. We report metrics for derivation and validation cohorts, and subgroups with pneumonia or COVID-19 diagnoses. Results 26,600 ED encounters were included and 1079 experienced an adverse event. Five categories (comorbidities, obesity/BMI ≥ 40, vital signs, age and sex) were included in the final score. The area under the curve (AUC) in the derivation cohort was 0.891 (95% CI, 0.880–0.901); similar performance was observed in the validation cohort (AUC = 0.895, 95% CI, 0.874–0.916). Sensitivity ranging from 100% (Score 0) to 41.7% (Score of ≥15) and specificity from 13.9% (score 0) to 96.8% (score ≥ 15). In the subgroups with pneumonia ( n = 3252) the AUCs were 0.780 (derivation, 95% CI 0.759–0.801) and 0.832 (validation, 95% CI 0.794–0.870), while for COVID-19 diagnoses ( n = 2059) the AUCs were 0.867 (95% CI 0.843–0.892) and 0.837 (95% CI 0.774–0.899) respectively. Conclusion Physicians evaluating ED patients with pneumonia, COVID-19, or symptoms suspicious for COVID-19 can apply the COVAS score to assist with decisions to hospitalize or discharge patients during the SARS CoV-2 pandemic.

BACKGROUND:The demands for healthcare resources following a COVID-19 diagnosis are substantial, but not currently quantified. OBJECTIVE: To describe trends in healthcare utilization within 180 days for patients diagnosed with COVID-19 and identify patient factors associated with increased healthcare use. DESIGN: Observational cohort study. PATIENTS: A total of 64,011 patients with a testconfirmed COVID-19 diagnosis from March to September 2020 in a large integrated healthcare system in Southern California. MAIN MEASURES: Overall healthcare utilization during the 180 days following COVID-19 diagnosis, as well as encounter types and reasons for visits during the first 30 days. Poisson regression was used to identify patient factors associated with higher utilization. Analyses were performed separately for patients who were and were not hospitalized for COVID-19. KEY RESULTS: Healthcare utilization was about twice as high for hospitalized patients compared to nonhospitalized patients in all time periods. The average number of visits was highest in the first 30 days (hospitalized: 12.3 visits/30 person-days; non-hospitalized: 6.6) and gradually decreased over time. In the first 30 days, the majority of healthcare visits were telehealth encounters (hospitalized: 9.0 visits; non-hospitalized: 5.6 visits), and the most prevalent reasons for visits were COVIDrelated diagnoses, COVID-related symptoms, and respiratory-related conditions. For hospitalized patients, older age (≥65: RR 1.27, 95% CI 1.15-1.41), female gender (RR 1.07, 95% CI 1.05-1.09), and higher BMI (≥40: RR 1.07, 95% CI 1.03-1.10) were associated with higher total utilization. For non-hospitalized patients, older age, female gender, higher BMI, non-white race/ethnicity, former smoking, and greater number of pre-existing comorbidities were all associated with increased utilization. CONCLUSIONS: Patients with COVID-19 seek healthcare frequently within 30 days of diagnosis, placing high demands on health systems. Identifying ways to support patients diagnosed with COVID-19 while adequately providing the usual recommended care to our communities will be important as we recover from the pandemic.

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