Comorbidities and Disparities in Outcomes of COVID-19 Among African American and White Patients

  1. Ahmad Khan
  2. Arka Chatterjee
  3. Shailendra Singh

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Abstract

Initial surveillance data suggests a disproportionately high number of deaths among Black patients with COVID-19. However, high-risk comorbidities are often over-represented in the Black population, and understanding whether the disparity is entirely secondary to them is essential. We performed a retrospective cohort study using real-time analysis of electronic medical records (EMR) of patients from multiple healthcare organizations in the United States. Our results showed that Black patients with COVID-19 have a significantly higher risk of mortality, hospitalization, and invasive mechanical ventilation compared to White patients. The incremental risk of poor outcomes in Blacks persists despite accounting for a higher prevalence of comorbidities. This may point to the disparities in socioeconomic determinants of health affecting Blacks and the need for an improvement in the care of this vulnerable population.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.10.20090167: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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:
    Our analysis carries the limitations of using an EMR database despite the ability of TriNetX to aggregate the data directly from the EMRs in a real-time fashion minimizing the risk of data collection errors. Patient counts were rounded up to the nearest 10 in our analysis to guard Protected Health Information (PHI). Rounding may influence measures of association results for small cohorts and infrequent outcomes; however, most of our outcomes have a relatively large number of patients. Exposure history, incubation time, and dynamic changes in patients’ clinical condition could not be estimated. Socioeconomic factors, geographical variations, delivery, and access to health care during COVID-19 were beyond the scope of our study; however, they have a significant influence on differences in high-risk characteristics and outcomes for COVID-19. We did not include other minorities in our study because of the relatively small sample size. In conclusion, our study provides evidence of poor outcomes among Black patients affected by COVID-19. Our results indicate the role of disproportionate comorbidities in the Black population but also shows an unexplained significant residual difference that persists after careful propensity matching. This highlights the role socioeconomic factors and healthcare disparities have played in the COVID-19 pandemic and the need for a vast improvement in the care of Black population.

    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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2020.05.10.20090167 on medRxiv