Population based estimates of comorbidities affecting risk for complications from COVID-19 in the US

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

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Abstract

We used 2017 Behavioral Risk Factor Surveillance System (BRFSS) data (N=444,649) to estimate the proportion of US adults who report comorbidities that suggest heightened risk of complications from COVID-19. Co-morbidities included cardiovascular disease, chronic obstructive pulmonary disease (COPD), diabetes, asthma, hypertension, and/or cancer other than skin, based on data from China. Overall 45.4% (95% CI 45.1-45.7) of adults reported any of the 6 comorbidities, increasing from 19.8% (19.1-20.4) for ages 18-29 years to 80.7% (79.5-81.8) for ages 80+ years. State rates ranged from 37.3% (36.2-38.5) in Utah to 58.7% (57.0-60.4) in West Virginia. Rates also varied by race/ethnicity, health insurance status, and employment. Excluded were residents of nursing homes or assisted living facilities. Although almost certainly an underestimate of all adults at risk due to these exclusions, these results should help in estimating healthcare needs for adults with COVID-19 complications living in the community.

Article Summary Line

Overall, 45.4% of US adults were estimated to be at heightened risk of COVID-19 complications due to co-morbidities, increasing from 19.8% for ages 18-29 years to 80.7% for ages 80+ years, with state-to-state variation.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.30.20043919: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    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 (6).
    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), health insurance coverage (any kind of health care coverage, including health insurance, prepaid plans such as HMOs, or government plans such as Medicare, or Indian Health Service), employment status (employed or self-employed, out of work, homemaker, student, retired, or unable to work), and state of residence which included the District of Columbia. Analysis: Stata version 14.1 (Stata Corp LP, College Station, TX) was used for data analysis to account for the complex sample design of the BRFSS.
    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: We detected the following sentences addressing limitations in the study:
    Limitations: Our study does not address possible differences in contracting the disease, only the risk of developing complications among those with COVID-19, based on results from China (1-3). Only non-institutionalized adults are surveyed so 1.3 million adults in nursing homes (13) were excluded which almost certainly underestimates risk, as the first death in the US from COVID-19 was a nursing home resident (14). Data are self-reported and reliability and validity can vary for different measures tested (6). But as long as a respondent was told they had a chronic condition, validity was high. Low response rates could introduce bias but, as noted, validity appears high for the measures used in this study. Results are specific for this coronavirus in the US.

    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. ScreenIT

    SciScore for 10.1101/2020.03.30.20043919: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.RandomizationMethods We used publicly available 2017 Behavioral Risk Factor Surveillance System ( BRFSS ) data ( 4 ) 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 variableAmong all 444,649 survey respondents , 48.7 % were male , 13.9 % were ages 70+ , 63.3 % were white , 18.2 % were retired , and 12.1 % were uninsured , with similar results for the study sample with 11,508 records with missing values removed.

    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 ( 6) .
    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) , selfreported race/ethnicity ( non-Hispanic white , Black or African American , Hispanic of any race , American Indian/Alaska native , Asian/Pacific Islander , and other) , health insurance coverage ( any kind of health care coverage , including health insurance , prepaid plans such as HMOs , or self-employed , out of work , homemaker , student , retired , or unable to work) , and state of residence which included the District of Columbia .
    Islander
    suggested: (Islander, 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).

    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.

  3. Version published to 10.1101/2020.03.30.20043919v1 on medRxiv