COVID-19-related state-wise racial and ethnic disparities across the USA: an observational study based on publicly available data from The COVID Tracking Project

  1. Zhaoying Xian
  2. Anshul Saxena
  3. Zulqarnain Javed
  4. John E Jordan
  5. Safa Alkarawi
  6. Safi U Khan
  7. Karan Shah
  8. Farhaan S Vahidy
  9. Khurram Nasir
  10. Prachi Dubey

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Abstract

To evaluate COVID-19 infection and mortality disparities in ethnic and racial subgroups in a state-wise manner across the USA.

Methods

Publicly available data from The COVID Tracking Project at The Atlantic were accessed between 9 September 2020 and 14 September 2020. For each state and the District of Columbia, % infection, % death, and % population proportion for subgroups of race (African American/black (AA/black), Asian, American Indian or Alaska Native (AI/AN), and white) and ethnicity (Hispanic/Latino, non-Hispanic) were recorded. Crude and normalised disparity estimates were generated for COVID-19 infection (CDI and NDI) and mortality (CDM and NDM), computed as absolute and relative difference between % infection or % mortality and % population proportion per state. Choropleth map display was created as thematic representation proportionate to CDI, NDI, CDM and NDM.

Results

The Hispanic population had a median of 158% higher COVID-19 infection relative to their % population proportion (median 158%, IQR 100%–200%). This was followed by AA, with 50% higher COVID-19 infection relative to their % population proportion (median 50%, IQR 25%–100%). The AA population had the most disproportionate mortality, with a median of 46% higher mortality than the % population proportion (median 46%, IQR 18%–66%). Disproportionate impact of COVID-19 was also seen in AI/AN and Asian populations, with 100% excess infections than the % population proportion seen in nine states for AI/AN and seven states for Asian populations. There was no disproportionate impact in the white population in any state.

Conclusions

There are racial/ethnic disparities in COVID-19 infection/mortality, with distinct state-wise patterns across the USA based on racial/ethnic composition. There were missing and inconsistently reported racial/ethnic data in many states. This underscores the need for standardised reporting, attention to specific regional patterns, adequate resource allocation and addressing the underlying social determinants of health adversely affecting chronically marginalised groups.

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  1. Version published to 10.1136/bmjopen-2020-048006
  2. ScreenIT

    SciScore for 10.1101/2020.12.03.20243360: (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 study has several limitations, mostly stemming from inconsistent and incomplete reporting of race/ethnicity data. There were inconsistencies in race/ethnic categorization across states. The categories were not mutually exclusive, with overlap between race and ethnicity. Many states don’t clarify the composition of the category labeled as “Others”, or may include Asian as “pan-racial,” effectively combining Asian, Pacific Islander and Native American subgroups. Some states reported race/ethnicity data on only a minority of cases at the time of data query. Some groups such as AI/AN and Asian populations had low representation in many states, (<1%) and had to be excluded from analyses. We do not have detailed clinical and socio-demographic information to precisely understand the impact of SDOH, age and other risk factors. In conclusion, African American, Hispanic, AI/AN, and Asian American populations are disproportionately impacted by COVID-19 both in terms of burden of infection and mortality. We provide a state-wise summary of COVID-19 associated disparities across the United States. Hispanics and AAs tend to experience the greatest disparities in infection while AAs tend to have the greatest disparities in mortality nationwide. We also observed higher infection and mortality in AI/AN and Asians in some states. We acknowledge that the assessment of disparities in groups such as in AI/AN and Asians can be underestimated in states with <1% population representation of these...

    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.

  3. Version published to 10.1101/2020.12.03.20243360 on medRxiv