Demographic Disparities in Clinical Outcomes of COVID-19: Data From a Statewide Cohort in South Carolina

  1. Xueying Yang
  2. Jiajia Zhang
  3. Shujie Chen
  4. Bankole Olatosi
  5. Larisa Bruner
  6. Abdoulaye Diedhiou
  7. Cheryl Scott
  8. Ali Mansaray
  9. Sharon Weissman
  10. Xiaoming Li

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Abstract

Background

Current literature examining the clinical characteristics of coronavirus disease 2019 (COVID-19) patients under-represent COVID-19 cases who were either asymptomatic or had mild symptoms.

Methods

We analyzed statewide data from 280 177 COVID-19 cases from various health care facilities during March 4–December 31, 2020. Each COVID-19 case was reported using the standardized Case Report Form (CRF), which collected information on demographic characteristics, symptoms, hospitalization, and death. We used multivariable logistic regression to analyze the associations between sociodemographics and disease severity, hospitalization, and mortality.

Results

Among a total of 280 177 COVID-19 cases, 5.2% (14 451) were hospitalized and 1.9% (5308) died. Older adults, males, and Black individuals had higher odds of hospitalization and death from COVID-19 (all P < 0.0001). In particular, individuals residing in rural areas experienced a high risk of death (odds ratio [OR], 1.16; 95% CI, 1.08–1.25). Regarding disease severity, older adults (OR, 1.06; 95% CI, 1.03–1.10) and Hispanic or Latino patients (OR, 2.06; 95% CI, 1.95–2.18) had higher odds of experiencing moderate/severe symptoms, while male and Asian patients, compared with White patients, had lower odds of experiencing moderate/severe symptoms.

Conclusions

As the first statewide population-based study using data from multiple health care systems with a long follow-up period in the United States, we provide a more generalizable picture of COVID-19 symptoms and clinical outcomes. The findings from this study reinforce the fact that rural residence and racial/ethnic social determinants of health, unfortunately, remain predictors of adverse health outcomes for COVID-19 patients.

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  1. Version published to 10.1093/ofid/ofab428
  2. ScreenIT

    SciScore for 10.1101/2021.05.19.21257489: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The study protocol received approval from the institutional review board in University of South Carolina and relevant SC state agencies.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All statistical analysis were performed using SAS software version 9.4 (SAS Institute, Inc., Cary, NC) and R software (version 3.6.2).
    SAS
    suggested: (SASqPCR, RRID:SCR_003056)
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    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:
    The findings in this report are subject to several limitations. First, there were missing values for the outcomes, such as hospitalization and mortality. The missing values might cause inaccurate estimation in clinical outcomes. For example, if more hospitalized or dead patients were misclassified into missing data, the outcomes might be underestimated in this study. Second, some important variables, such as underlying conditions, were not included in the current analysis as these conditions would merit separate analyses because of their clinical significance to COVID-19 research. Despite of the limitations, this study is still one of the first US statewide population-based study using the entire population to investigate the presenting symptoms and clinical outcomes of COVID-19 patients. Such a population-based study can minimize sampling selection bias and is more representative of all COVID-19 cases. Our results revealed that severe illness was strongly associated with hospitalization and mortality. However, the differences in the symptom distribution are not reflected in disparities in hospitalization and mortality in certain gender- and racial- minority groups. The findings from this study reinforce the fact that underlying health system disparities remain a challenge. South Carolina is often reflective of the “Deep South” states. Preexisting structural disparities were exacerbated during COVID-19 and put the already vulnerable populations at more risk. Rural residence, ...

    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

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  3. Version published to 10.1101/2021.05.19.21257489v1 on medRxiv