Differences in COVID-19 Risk by Race and County-Level Social Determinants of Health among Veterans

  1. Hoda S. Abdel Magid
  2. Jacqueline M. Ferguson
  3. Raymond Van Cleve
  4. Amanda L. Purnell
  5. Thomas F. Osborne

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Abstract

COVID-19 disparities by area-level social determinants of health (SDH) have been a significant public health concern and may also be impacting U.S. Veterans. This retrospective analysis was designed to inform optimal care and prevention strategies at the U.S. Department of Veterans Affairs (VA) and utilized COVID-19 data from the VAs EHR and geographically linked county-level data from 18 area-based socioeconomic measures. The risk of testing positive with Veterans’ county-level SDHs, adjusting for demographics, comorbidities, and facility characteristics, was calculated using generalized linear models. We found an exposure–response relationship whereby individual COVID-19 infection risk increased with each increasing quartile of adverse county-level SDH, such as the percentage of residents in a county without a college degree, eligible for Medicaid, and living in crowded housing.

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  1. Version published to 10.3390/ijerph182413140
  2. ScreenIT

    SciScore for 10.1101/2021.10.20.21265149: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: This quality assessment project received a Determination of Non-Research from Stanford Institutional Review Board as well as by VA determination.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We conducted all statistical analyses using Stata Version 15 (StataCorp LLC).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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 evaluation is focused on evaluating the association of area-level county-level SDH and COVID-19 test and test positivity of our unique Veteran population, who are on average are male, older, and have more comorbidities than the general US population, which limits generalizability.13 Furthermore, our evaluation does not assign weights to the county-level SDH relative to each other since there is no strong evidence to rigorously assign importance across categories.1 The association between COVID-19 infection risk and Veterans’ county-level SDH may be stronger than the estimated results presented here owing to the fact that some of the covariates adjusted for in this analysis may likely be mediators in the pathway, which would attenuate risk. Lastly, Veterans’ home address may not fully capture where Veterans spend most of their time which may result in exposure misclassification, however, we anticipate misclassification would be attenuated by county-level aggregation.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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.

  3. Version published to 10.1101/2021.10.20.21265149v1 on medRxiv