Risk Factors for COVID-19-associated hospitalization: COVID-19-Associated Hospitalization Surveillance Network and Behavioral Risk Factor Surveillance System

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Abstract

Background

Identification of risk factors for COVID-19-associated hospitalization is needed to guide prevention and clinical care.

Objective

To examine if age, sex, race/ethnicity, and underlying medical conditions is independently associated with COVID-19-associated hospitalizations.

Design

Cross-sectional.

Setting

70 counties within 12 states participating in the Coronavirus Disease 2019-Associated Hospitalization Surveillance Network (COVID-NET) and a population-based sample of non-hospitalized adults residing in the COVID-NET catchment area from the Behavioral Risk Factor Surveillance System.

Participants

U.S. community-dwelling adults (≥18 years) with laboratory-confirmed COVID-19-associated hospitalizations, March 1- June 23, 2020.

Measurements

Adjusted rate ratios (aRR) of hospitalization by age, sex, race/ethnicity and underlying medical conditions (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).

Results

Our sample included 5,416 adults with COVID-19-associated hospitalizations. Adults with (versus without) 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) had higher rates of hospitalization, after adjusting for age, sex, and race/ethnicity. In models adjusting for the presence of an individual underlying medical condition, higher hospitalization rates were observed for adults ≥65 years, 45-64 years (versus 18-44 years), males (versus females), and non-Hispanic black and other race/ethnicities (versus non-Hispanic whites).

Limitations

Interim analysis limited to hospitalizations with underlying medical condition data.

Conclusion

Our findings elucidate groups with higher hospitalization risk that may benefit from targeted preventive and therapeutic interventions.

Article activity feed

  1. SciScore for 10.1101/2020.07.27.20161810: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: This analysis was exempt from CDC’s Institutional Review Board, as it was considered part of public health surveillance and emergency response.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All other analyses were performed using SAS v.9.4 (SAS Institute, Cary, NC).
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: This study has several limitations. First, this analysis is based on data as of June 23, 2020 from COVID-NET, a surveillance system designed first to provide hospitalization rates. Additional data such as underlying medical conditions is reliant on medical chart abstraction; approximately 60% of the total hospitalized cases have yet to be abstracted for underlying medical condition. Thus, included cases represent a convenience sample of hospitalizations with underlying medical conditions, which may have resulted in biased estimates of risk. However, bi-weekly updates of this analysis over a 2-month period with the most recently available COVID-NET data (i.e., additional chart abstractions) suggests consistent estimates of the frequency and distribution of underlying conditions and resulting rate ratios. Second, these data did not include institutionalized adults. Third, estimates of risk are restricted to the COVID-NET catchment area; the interpretation of rate ratios as risk in this analysis assumes that risk of SARS-CoV-2 infection is consistent across all groups. Fourth, we were unable to assess the association of more granular race/ethnicity categories or co-occurring underlying health conditions due to small cell sizes from the COVID-NET catchment area; further investigation on both aspects is important. Fifth, COVID-NET likely under-ascertains COVID-19 cases as testing for SARS-CoV-2 is performed at treating health care providers’ discretion and is subject ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. SciScore for 10.1101/2020.07.27.20161810: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementThis analysis was exempt from CDC’s Institutional Review Board, as it was considered part of public health surveillance and emergency response.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variableIn models adjusting for the presence of an individual underlying medical condition, higher hospitalization rates were observed for adults ≥65 years, 45-64 years (versus 18-44 years), males (versus females), and non-Hispanic black and other race/ethnicities (versus non-Hispanic whites).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All other analyses were performed using SAS v.9.4 (SAS Institute, Cary, NC).
    SAS Institute
    suggested: (Statistical Analysis System, SCR_008567)
    Finally, we used BRFSS to obtain estimates for underlying medical conditions in the COVID-NET catchment area.
    BRFSS
    suggested: None

    Data from additional tools added to each annotation on a weekly basis.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.