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Background Currently, the United States has the largest number of reported coronavirus disease 2019 (COVID-19) cases and deaths globally. Using a geographically diverse surveillance network, we describe risk factors for severe outcomes among adults hospitalized with COVID-19. Methods We analyzed data from 2,491 adults hospitalized with laboratory-confirmed COVID-19 during March 1–May 2, 2020 identified through the Coronavirus Disease 2019-Associated Hospitalization Surveillance Network comprising 154 acute care hospitals in 74 counties in 13 states. We used multivariable analyses to assess associations between age, sex, race and ethnicity, and underlying conditions with intensive care unit (ICU) admission and in-hospital mortality. Results Ninety-two percent of patients had ≥1 underlying condition; 32% required ICU admission; 19% invasive mechanical ventilation; and 17% died. Independent factors associated with ICU admission included ages 50-64, 65-74, 75-84 and ≥85 years versus 18-39 years (adjusted risk ratio (aRR) 1.53, 1.65, 1.84 and 1.43, respectively); male sex (aRR 1.34); obesity (aRR 1.31); immunosuppression (aRR 1.29); and diabetes (aRR 1.13). Independent factors associated with in-hospital mortality included ages 50-64, 65-74, 75-84 and ≥85 years versus 18-39 years (aRR 3.11, 5.77, 7.67 and 10.98, respectively); male sex (aRR 1.30); immunosuppression (aRR 1.39); renal disease (aRR 1.33); chronic lung disease (aRR 1.31); cardiovascular disease (aRR 1.28); neurologic disorders (aRR 1.25); and diabetes (aRR 1.19). Conclusion In-hospital mortality increased markedly with increasing age. Aggressive implementation of prevention strategies, including social distancing and rigorous hand hygiene, may benefit the population as a whole, as well as those at highest risk for COVID-19-related complications.

This report has been corrected. The definition of pediatric obesity was incorrectly stated in the text of the report and in the Table footnote; however, the analysis was correct and used the CDC definition of pediatric obesity for children aged ≥2 years (body mass index [kg/m2] ≥95th percentile for age and sex based on CDC growth charts).

Background Data on risk factors for COVID-19-associated hospitalization are needed to guide prevention efforts and clinical care. We sought to identify factors independently associated with COVID-19-associated hospitalizations Methods U.S. community-dwelling adults (≥18 years) hospitalized with laboratory-confirmed COVID-19 during March 1–June 23, 2020 were identified from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET), a multi-state surveillance system. To calculate hospitalization rates by age, sex, and race/ethnicity strata, COVID-NET data served as the numerator and Behavioral Risk Factor Surveillance System estimates served as the population denominator for characteristics of interest. Underlying medical conditions examined included hypertension, coronary artery disease, history of stroke, diabetes, obesity [BMI ≥30 kg/m 2], severe obesity [BMI≥40 kg/m 2], chronic kidney disease, asthma, and chronic obstructive pulmonary disease. Generalized Poisson regression models were used to calculate adjusted rate ratios (aRR) for hospitalization Results Among 5,416 adults, hospitalization rates were higher among those with ≥3 underlying conditions (versus without)(aRR: 5.0; 95%CI: 3.9, 6.3), severe obesity (aRR:4.4; 95%CI: 3.4, 5.7), chronic kidney disease (aRR:4.0; 95%CI: 3.0, 5.2), diabetes (aRR:3.2; 95%CI: 2.5, 4.1), obesity (aRR:2.9; 95%CI: 2.3, 3.5), hypertension (aRR:2.8; 95%CI: 2.3, 3.4), and asthma (aRR:1.4; 95%CI: 1.1, 1.7), after adjusting for age, sex, and race/ethnicity. Adjusting for the presence of an individual underlying medical condition, higher hospitalization rates were observed for adults aged ≥65, 45-64 (versus 18-44 years), males (versus females), and non-Hispanic black and other race/ethnicities (versus non-Hispanic whites) Conclusion Our findings elucidate groups with higher hospitalization risk that may benefit from targeted preventive and therapeutic interventions

We have developed a prototype three-channel microfluidic chip that is capable of generating a linear concentration gradient within a microfluidic channel and is useful in the study of bacterial chemotaxis. The linear chemical gradient is established by diffusing a chemical through a porous membrane located in the side wall of the channel and can be established without through-flow in the channel where cells reside. As a result, movement of the cells in the center channel is caused solely by the cells chemotactic response and not by variations in fluid flow. The advantages of this microfluidic chemical linear gradient generator are (i) its ability to produce a static chemical gradient, (ii) its rapid implementation, and (iii) its potential for highly parallel sample processing. Using this device, wildtype Escherichia coli strain RP437 was observed to move towards an attractant (e.g., l-asparate) and away from a repellent (e.g., glycerol) while derivatives of RP437 that were incapable of motility or chemotaxis showed no bias of the bacteria's distribution. Additionally, the degree of chemotaxis could be easily quantified using this assay in conjunction with fluorescence imaging techniques, allowing for estimation of the chemotactic partition coefficient (CPC) and the chemotactic migration coefficient (CMC). Finally, using this approach we demonstrate that E. coli deficient in autoinducer-2-mediated quorum sensing respond to the chemoattractant l-aspartate in a manner that is indistinguishable from wildtype cells suggesting that chemotaxis is insulated from this mode of cell-cell communication.