Updated estimates of comorbidities associated with risk for COVID-19 complications based on US data

  1. Mary L. Adams
  2. Joseph Grandpre
  3. David L. Katz

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Abstract

We updated previous estimates ( wwwnc.cdc.gov/eid/article/26/8/20-0679_article ) of adults with any underlying condition increasing risk of complications from COVID-19 using recent US hospitalization data instead of mortality data from China. This substitutes obesity for cancer in the definition and increased the percentage of adults reporting ≥1 condition to 56.0% (95% CI 55.7-56.4). When controlled for all measures listed, factors increasing odds of reporting any of the underlying conditions include being male, older, African American, American Indian, household income <$25,000, < high school education, underinsurance, living in the South or Midwest (vs. West), plus the risk factors of ever smoking, sedentary lifestyle, and inadequate fruit and vegetable consumption. Population-attributable risk for the listed risk factors was 13.0%, 12.6%, and 15.0% respectively. Results have potential implications for policies based on risk-stratification of the population and for improvement of risk status through lifestyle change. National support for a “health promotion” campaign would be timely.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.02.20088781: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationWe used publicly available 2017 Behavioral Risk Factor Surveillance System (BRFSS) data (5) from telephone surveys of 444,649 randomly selected adults ages 18 and older in the 50 states and the District of Columbia (DC).
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Reliability and validity of the BRFSS have been found to be moderate to high for many survey measures, in particular those used here which can be checked versus medical records (7).
    BRFSS
    suggested: None
    Demographic measures included age group (18-29, 30-39, 40-49, 50-59, 60-69, 70-79, and 80+ years, which was created by combining 5 year age groups provided in the data set), self-reported race/ethnicity (non-Hispanic white, Black or African American, Hispanic of any race, American Indian/Alaska native, Asian/Pacific Islander, and other), less than a high school education (vs. high school graduate or higher), household income < $25,000 (vs. all other incomes including unknown), being underinsured (having no health insurance coverage or reporting a time in the past year when the needed health care but could not get it due to cost), state of residence which included DC, whether or not they lived within the center city of a Metropolitan Statistical Area (MSA), and census region (Northeast, Midwest, South, or West)(8).
    Islander
    suggested: (Islander, RRID:SCR_007758)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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. Version published to 10.1101/2020.05.02.20088781v1 on medRxiv