Racial/Ethnic Disparities in COVID-19 Hospital Admissions

  1. Violeta Álvarez Retamales
  2. Oswaldo Madrid Suarez
  3. Odalys E. Lara-Garcia
  4. Suhayb Ranjha
  5. Ruby Maini
  6. Susan Hingle
  7. Vidya Sundareshan
  8. Robert L. Robinson

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Importance

COVID-19 has affected millions of people worldwide. Furthermore, with its increasing incidence, more has been learned about the risk factors that can make certain groups more at risk of contracting the disease or have worse outcomes. We aim to identify any discrepancy in the hospitalization rate by race/ethnicity of patients who tested positive for COVID-19, and through this, analyze the risks of these groups in an effort to call out for attention to the circumstances that make them more vulnerable and susceptible to disease.

Observations

Analysis indicates that patients identified as non-Hispanic White and Asian/Pacific Islander in hospital admission data are underrepresented in COVID-19 admissions. Patients identified as non-Hispanic Black, Hispanic/Latino, and American Indian have a disproportionate burden of hospital admissions, suggesting an increased risk of more severe disease.

Conclusions and Relevance

There is a disproportionate rate of COVID-19 hospitalizations found among non-Hispanic Blacks. Further investigation is imperative to identify and remediate the reason(s) for increased vulnerability to COVID-19 infections requiring hospital admission. These efforts would likely reduce the COVID-19 morbidity and mortality in the non-Hispanic Black population.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.07.12.20152017: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Institutional review board review for this study was obtained from the Springfield Committee for Research Involving Human Subjects.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analyses were performed using SPSS version 25 (SPSS Inc., Chicago, IL, USA).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    There are many limitations to this study. We assume that the patients admitted met hospitalization criteria based on medical necessity. Yet, we are unable to corroborate this as we are not able to incorporate the population symptoms or severity of illness. At the same time, we can establish the demographics of the admitted population. Still, we cannot identify specific determinants to these results as we are not able to prove it is solely due to Health disparities. We are yet to determine if there is a population protected from COVID-19 due to their race/ethnicity, which could also impact their hospitalization.

    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 10.1101/2020.07.12.20152017v1 on medRxiv