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Background Estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence in young children and risk factors for seropositivity are scarce. Using data from a prospective cohort study of households during the pre-coronavirus disease 2019 (COVID-19) vaccine period, we estimated SARS-CoV-2 seroprevalence by age and evaluated risk factors for SARS-CoV-2 seropositivity. Methods The SARS-CoV-2 Epidemiology and Response in Children (SEARCh) study enrolled 175 Maryland households (690 participants) with ≥1 child aged 0–4 years during November 2020–March 2021; individuals vaccinated against COVID-19 were ineligible. At enrollment, participants completed questionnaires about sociodemographic and health status and work, school, and daycare attendance. Participants were tested for SARS-CoV-2 antibodies in sera. Logistic regression models with generalized estimating equations (GEE) to account for correlation within households assessed predictors of individual- and household-level SARS-CoV-2 seropositivity. Results Of 681 (98.7%) participants with enrollment serology results, 55 (8.1%; 95% confidence interval [CI], 6.3%–10.4%) participants from 21 (12.0%) households were seropositive for SARS-CoV-2. Among seropositive participants, fewer children than adults reported being tested for SARS-CoV-2 infection before enrollment (odds ratio [OR] = 0.23; 95% CI,.06–.73). Seropositivity was similar by age (GEE OR vs 0–4 years: 1.19 for 5–17 years, 1.36 for adults; P =.16) and was significantly higher among adults working outside the home (GEE adjusted OR = 2.2; 95% CI, 1.1–4.4) but not among children attending daycare or school. Conclusions Before study enrollment, children and adults in this cohort had similar rates of SARS-CoV-2 infection as measured by serology. An adult household member working outside the home increased a household's odds of SARS-CoV-2 infection, whereas a child attending daycare or school in person did not. [ABSTRACT FROM AUTHOR]

Longitudinal trajectories of vital signs and biomarkers during hospital admission of patients with COVID-19 remain poorly characterized despite their potential to provide critical insights about disease progression. We studied 1884 patients with severe acute respiratory syndrome coronavirus 2 infection from April 3, 2020, to June 25, 2020, within 1 Maryland hospital system and used a retrospective longitudinal framework with linear mixed-effects models to investigate relevant biomarker trajectories leading up to 3 critical outcomes: mechanical ventilation, discharge, and death. Trajectories of 4 vital signs (respiratory rate, ratio of oxygen saturation (Sp o 2) to fraction of inspired oxygen (Fi o 2), pulse, and temperature) and 4 laboratory values (C-reactive protein (CRP), absolute lymphocyte count (ALC), estimated glomerular filtration rate, and D-dimer) clearly distinguished the trajectories of patients with COVID-19. Before any ventilation, log(CRP), log(ALC), respiratory rate, and Sp o 2-to-Fi o 2 ratio trajectories diverge approximately 8–10 days before discharge or death. After ventilation, log(CRP), log(ALC), respiratory rate, Sp o 2-to-Fi o 2 ratio, and estimated glomerular filtration rate trajectories again diverge 10–20 days before death or discharge. Trajectories improved until discharge and remained unchanged or worsened until death. Our approach characterizes the distribution of biomarker trajectories leading up to competing outcomes of discharge versus death. Moving forward, this model can contribute to quantifying the joint probability of biomarkers and outcomes when provided clinical data up to a given moment. [ABSTRACT FROM AUTHOR]

Background Males experience increased severity of illness and mortality from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) compared with females, but the mechanisms of male susceptibility are unclear. Methods We performed a retrospective cohort analysis of SARS-CoV-2 testing and admission data at 5 hospitals in the Maryland/Washington DC area. Using age-stratified logistic regression models, we quantified the impact of male sex on the risk of the composite outcome of severe disease or death (World Health Organization score 5–8) and tested the impact of demographics, comorbidities, health behaviors, and laboratory inflammatory markers on the sex effect. Results Among 213 175 SARS-CoV-2 tests, despite similar positivity rates, males in age strata between 18 and 74 years were more frequently hospitalized. For the 2626 hospitalized individuals, clinical inflammatory markers (interleukin-6, C-reactive protein, ferritin, absolute lymphocyte count, and neutrophil:lymphocyte ratio) were more favorable for females than males (P <.001). Among 18–49-year-olds, male sex carried a higher risk of severe outcomes, both early (odds ratio [OR], 3.01; 95% CI, 1.75 to 5.18) and at peak illness during hospitalization (OR, 2.58; 95% CI, 1.78 to 3.74). Despite multiple differences in demographics, presentation features, comorbidities, and health behaviors, these variables did not change the association of male sex with severe disease. Only clinical inflammatory marker values modified the sex effect, reducing the OR for severe outcomes in males aged 18–49 years to 1.81 (95% CI, 1.00 to 3.26) early and 1.39 (95% CI, 0.93 to 2.08) at peak illness. Conclusions Higher inflammatory laboratory test values were associated with increased risk of severe coronavirus disease 2019 for males. A sex-specific inflammatory response to SARS-CoV-2 infection may underlie the sex differences in outcomes. [ABSTRACT FROM AUTHOR]

Background: Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission.Objective: To develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https://rsconnect.biostat.jhsph.edu/covid_trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization.Design: Retrospective observational cohort study.Settings: Five hospitals in Maryland and Washington, D.C.Patients: Patients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease.Measurements: A clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization.Results: Among 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively.Limitation: The SCARP tool was developed by using data from a single health system.Conclusion: Using the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information.Primary Funding Source: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response. [ABSTRACT FROM AUTHOR]