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BackgroundCommunity-onset bacterial coinfection in adults hospitalized with coronavirus disease 2019 (COVID-19) is reportedly uncommon, though empiric antibiotic use has been high. However, data regarding empiric antibiotic use and bacterial coinfection in children with critical illness from COVID-19 are scarce.MethodsWe evaluated children and adolescents aged ResultsOf the 532 patients, 63.3% received empiric antibiotics, but only 7.1% had bacterial coinfection, and only 3.0% had respiratory bacterial coinfection. In multivariable analyses, empiric antibiotics were more likely to be prescribed for immunocompromised patients (adjusted relative risk [aRR], 1.34 [95% confidence interval {CI}, 1.01–1.79]), those requiring any respiratory support except mechanical ventilation (aRR, 1.41 [95% CI, 1.05–1.90]), or those requiring invasive mechanical ventilation (aRR, 1.83 [95% CI, 1.36–2.47]) (compared with no respiratory support). The presence of a pulmonary comorbidity other than asthma (aRR, 2.31 [95% CI, 1.15–4.62]) was associated with bacterial coinfection.ConclusionsCommunity-onset bacterial coinfection in children with critical COVID-19 is infrequent, but empiric antibiotics are commonly prescribed. These findings inform antimicrobial use and support rapid de-escalation when evaluation shows coinfection is unlikely.

Background Risk factors for MIS-C, a rare but serious hyperinflammatory syndrome associated with SARS-CoV-2 infection, remain unclear. We evaluated household, clinical, and environmental risk factors potentially associated with MIS-C. Methods This investigation included MIS-C cases hospitalized in 14 US pediatric hospitals in 2021. Outpatient controls were frequency-matched to case-patients by age group and site and had a positive SARS-CoV-2 viral test within 3 months of the admission of their matched MIS-C case (Figure 1). We conducted telephone surveys with caregivers and evaluated potential risk factors using mixed effects multivariable logistic regression, including site as a random effect. We queried regarding exposures within the month before hospitalization for MIS-C cases or the month after a positive COVID-19 test for controls. Figure.Patient enrollment timeline. Enrollment scheme for MIS-C case-patients and SARS-CoV-2-positive outpatient controls. MIS-C case-patients were identified through hospital electronic medical records, while two outpatient controls per case were identified through registries of outpatient SARS-CoV-2 testing logs at facilities affiliated with that medical center. Caregivers of outpatient controls were interviewed at least four weeks after their positive test to ensure they did not develop MIS-C after their infection. Results We compared 275 MIS-C case-patients with 494 outpatient SARS-CoV-2-positive controls. Race, ethnicity and social vulnerability indices were similar. MIS-C was more likely among persons who resided in households with >1 resident per room (aOR=1.6, 95% CI: 1.1–2.2), attended a large (≥10 people) event with little to no mask-wearing (aOR=2.2, 95% CI: 1.4–3.5), used public transportation (aOR=1.6, 95% CI: 1.2–2.1), attended school >2 days per week with little to no mask wearing (aOR=2.1, 95% CI: 1.0–4.4), or had a household member test positive for COVID-19 (aOR=2.1, 95% CI: 1.3–3.3). MIS-C was less likely among children with comorbidities (aOR=0.5, 95% CI: 0.3–0.9) and in those who had >1 positive SARS-CoV-2 test at least 1 month apart (aOR=0.4, 95% CI: 0.2–0.6). MIS-C was not associated with a medical history of recurrent infections or family history of underlying rheumatologic disease. Conclusion Household crowding, limited masking at large indoor events or schools and use of public transportation were associated with increased likelihood of developing MIS-C after SARS-CoV-2 infection. In contrast, decreased likelihood of MIS-C was associated with having >1 SARS-CoV-2 positive test separated by at least a month. Our data suggest that additional studies are needed to determine if viral load, and/or recurrent infections in the month prior to MIS-C contribute to MIS-C risk. Medical and family history were not associated with MIS-C in our analysis. Disclosures Natasha B. Halasa, MD, Quidel: Grant/Research Support|Quidel: equipment donation|Sanofi: Grant/Research Support|Sanofi: HAI testing and vaccine donation Mark Hall, MD, Abbvie: Service on a Data Safety Monitoring Board|Kiadis: Licensing income unrelated to the current submission Mary A. Staat, MD, MPH, Centers for Disease Control and Prevention: Grant/Research Support|Cepheid: Grant/Research Support|National Institute of Health: Grant/Research Support|Uptodate: Royalties Pia S. Pannaraj, MD, MPH, AstraZeneca: Grant/Research Support|Pfizer: Grant/Research Support|Sanofi-Pasteur: Advisor/Consultant|Seqirus: Advisor/Consultant Charlotte V. Hobbs, MD, Biofire (Biomerieux): Advisor/Consultant.

Background Test-negative design studies for evaluating influenza vaccine effectiveness (VE) enroll patients with acute respiratory infection. Enrollment typically occurs before influenza status is determined, resulting in over-enrollment of influenza-negative patients. With availability of rapid and accurate molecular clinical testing, influenza status could be ascertained before enrollment, thus improving study efficiency. We estimate potential biases in VE when using clinical testing. Methods We simulate data assuming 60% vaccinated, 25% of those vaccinated are influenza positive, and VE of 50%. We show the effect on VE in 5 scenarios. Results Vaccine effectiveness is affected only when clinical testing preferentially targets patients based on both vaccination and influenza status. Vaccine effectiveness is overestimated by 10% if nontesting occurs in 39% of vaccinated influenza-positive patients and 24% of others. VE is also overestimated by 10% if nontesting occurs in 8% of unvaccinated influenza-positive patients and 27% of others. Vaccine effectiveness is underestimated by 10% if nontesting occurs in 32% of unvaccinated influenza-negative patients and 18% of others. Conclusions Although differential clinical testing by vaccine receipt and influenza positivity may produce errors in estimated VE, bias in testing would have to be substantial and overall proportion of patients tested would have to be small to result in a meaningful difference in VE.