Rationale: Black race and Hispanic ethnicity are associated with increased risks for coronavirus disease (COVID-19) infection and severity. It is purported that socioeconomic factors may drive this association, but data supporting this assertion are sparse. Objectives: To evaluate whether socioeconomic factors mediate the association of race/ethnicity with COVID-19 incidence and outcomes. Methods: We conducted a retrospective cohort study of adults tested for (cohort 1) or hospitalized with (cohort 2) COVID-19 between March 1, 2020, and July 23, 2020, at the University of Miami Hospital and Clinics. Our primary exposure was race/ethnicity. We considered socioeconomic factors as potential mediators of our exposure’s association with outcomes. We used standard statistics to describe our cohorts and multivariable regression modeling to identify associations of race/ethnicity with our primary outcomes, one for each cohort, of test positivity (cohort 1) and hospital mortality (cohort 2). We performed a mediation analysis to see whether household income, population density, and household size mediated the association of race/ethnicity with outcomes. Results: Our cohorts included 15,473 patients tested (29.0% non-Hispanic White, 48.1% Hispanic White, 15.0% non-Hispanic Black, 1.7% Hispanic Black, and 1.6% other) and 295 patients hospitalized (9.2% non-Hispanic White, 56.9% Hispanic White, 21.4% non-Hispanic Black, 2.4% Hispanic Black, and 10.2% other). Among those tested, 1,256 patients (8.1%) tested positive, and, of the hospitalized patients, 47 (15.9%) died. After adjustment for demographics, race/ethnicity was associated with test positivity—odds-ratio (95% confidence interval [CI]) versus non-Hispanic White for Non-Hispanic Black: 3.21 (2.60–3.96), Hispanic White: 2.72 (2.28–3.26), and Hispanic Black: 3.55 (2.33–5.28). Population density mediated this association (percentage mediated, 17%; 95% CI, 11–31%), as did median income (27%; 95% CI, 18–52%) and household size (20%; 95% CI, 12–45%). There was no association between race/ethnicity and mortality, although this analysis was underpowered. Conclusions: Black race and Hispanic ethnicity are associated with an increased odds of COVID-19 positivity. This association is substantially mediated by socioeconomic factors.

Rationale Sequential organ failure assessment (SOFA) scores are commonly used in crisis standards of care policies to assist in resource allocation. The relative predictive value of SOFA by coronavirus disease (COVID-19) infection status and among racial and ethnic subgroups within patients infected with COVID-19 is unknown. Objectives To evaluate the accuracy and calibration of SOFA in predicting hospital mortality by COVID-19 infection status and across racial and ethnic subgroups. Methods We performed a retrospective cohort study of adult admissions to the University of Miami Hospital and Clinics inpatient wards (July 1, 2020–April 1, 2021). We primarily considered maximum SOFA within 48 hours of hospitalization. We assessed accuracy using the area under the receiver operating characteristic curve (AUROC) and created calibration belts. Considered subgroups were defined by COVID-19 infection status (by severe acute respiratory syndrome coronavirus 2 polymerase chain reaction testing) and prevalent racial and ethnic minorities. Comparisons across subgroups were made with DeLong testing for discriminative accuracy and visualization of calibration belts. Results Our primary cohort consisted of 20,045 hospitalizations, of which 1,894 (9.5%) were COVID-19 positive. SOFA was similarly accurate for COVID-19–positive (AUROC, 0.835) and COVID-19–negative (AUROC, 0.810; P = 0.15) admissions but was slightly better calibrated in patients who were positive for COVID-19. For those with critical illness, maximum SOFA score accuracy at critical illness onset also did not differ by COVID-19 status (AUROC, COVID-19 positive vs. negative: intensive care unit admissions, 0.751 vs. 0.775; P = 0.46; mechanically ventilated, 0.713 vs. 0.792, P = 0.13), and calibration was again better for patients positive for COVID-19. Among patients with COVID-19, SOFA accuracy was similar between the non-Hispanic White population (AUROC, 0.894) and racial and ethnic minorities (Hispanic White population: AUROC, 0.824 [ P vs. non-Hispanic White = 0.05]; non-Hispanic Black population: AUROC, 0.800 [ P = 0.12]; Hispanic Black population: AUROC, 0.948 [ P = 0.31]). This similar accuracy was also found for those without COVID-19 (non-Hispanic White population: AUROC, 0.829; Hispanic White population: AUROC, 0.811 [ P = 0.37]; Hispanic Black population: AUROC, 0.828 [ P = 0.97]; non-Hispanic Black population: AUROC, 0.867 [ P = 0.46]). SOFA was well calibrated for all racial and ethnic groups with COVID-19 but estimated mortality more variably and performed less well across races and ethnicities without COVID-19. Conclusions SOFA accuracy does not differ by COVID-19 status and is sim...

ObjectivesWe describe a hospital’s implementation of predictive models to optimise emergency response to the COVID-19 pandemic.MethodsWe were tasked to construct and evaluate COVID-19 driven predictive models to identify possible planning and resource utilisation scenarios. We used system dynamics to derive a series of chain susceptible, infected and recovered (SIR) models. We then built a discrete event simulation using the system dynamics output and bootstrapped electronic medical record data to approximate the weekly effect of tuning surgical volume on hospital census. We evaluated performance via a model fit assessment and cross-model comparison.ResultsWe outlined the design and implementation of predictive models to support management decision making around areas impacted by COVID-19. The fit assessments indicated the models were most useful after 30 days from onset of local cases. We found our subreports were most accurate up to 7 days after model run.DiscusssionOur model allowed us to shape our health system’s executive policy response to implement a ‘hospital within a hospital’—one for patients with COVID-19 within a hospital able to care for the regular non-COVID-19 population. The surgical scheduleis modified according to models that predict the number of new patients withCovid-19 who require admission. This enabled our hospital to coordinateresources to continue to support the community at large. Challenges includedthe need to frequently adjust or create new models to meet rapidly evolvingrequirements, communication, and adoption, and to coordinate the needs ofmultiple stakeholders. The model we created can be adapted to other health systems,provide a mechanism to predict local peaks in cases and inform hospitalleadership regarding bed allocation, surgical volumes, staffing, and suppliesone for COVID-19 patients within a hospital able to care for the regularnon-COVID-19 population.ConclusionPredictive models are essential tools in supporting decision making when coordinating clinical operations during a pandemic.

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Background In vitro studies suggesting that REGEN-COV (casirivimab plus imdevimab monoclonal antibodies) had poor efficacy against Omicron-variant SARS-CoV-2 infection led to amendment of REGEN-COV’s Emergency Use Authorization to recommend use only in regions without high Omicron prevalence. REGEN-COV’s relative clinical effectiveness for Omicron is unknown. Methods and findings We conducted a retrospective cohort study of non-hospitalized adults who tested positive for SARS-CoV-2 by polymerase chain reaction at the University of Miami Health System from July 19 –November 21, 2021 (Delta period) and December 6, 2021 –January 7, 2022 (Omicron period). Subjects were stratified be REGEN-COV receipt within 72h of test positivity and by time period of infection. We constructed multivariable logistic regression models to assess the differential association of REGEN-COV receipt with hospitalization within 30 days (primary outcome) and ED presentation; all models included three exposure terms (REGEN-COV receipt, Omicron vs Delta period, interaction of REGEN-COV with time period) and potential confounders (vaccination status, vaccine boosting, cancer diagnosis). Our cohort consisted of 2,083 adults in the Delta period (213 [10.2%] received REGEN-COV) and 4,201 in the Omicron period (156 [3.7%] received REGEN-COV). Hospitalization was less common during the Omicron period than during Delta (0.9% vs 1.7%, p = 0.78) and more common for patients receiving REGEN-COV than not (5.7% vs 0.9%, p<0.001). After adjustment, we found no differential association of REGEN-COV use during Omicron vs Delta with hospitalization within 30d (adjusted odds ratio [95% confidence interval] for the interaction term: 2.31 [0.76–6.92], p = 0.13). Similarly, we found no differential association for hospitalization within 15d (2.45 [0.63–9.59], p = 0.20) or emergency department presentation within 30d (1.43 [0.57–3.51], p = 0.40) or within 15d (1.79 [0.65–4.82], p = 0.30). Conclusions Within the limitations of this study’s power to detect a difference, we identified no differential effectiveness of REGEN-COV in the context of Omicron vs Delta SARS-CoV-2 infection.

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