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Racial/Ethnic, Biomedical, and Sociodemographic Risk Factors for COVID-19 Positivity and Hospitalization in the San Francisco Bay Area

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Abstract

Background

The COVID-19 pandemic has uncovered clinically meaningful racial/ethnic disparities in COVID-19-related health outcomes. Current understanding of the basis for such an observation remains incomplete, with both biomedical and social/contextual variables proposed as potential factors.

Purpose

Using a logistic regression model, we examined the relative contributions of race/ethnicity, biomedical, and socioeconomic factors to COVID-19 test positivity and hospitalization rates in a large academic health care system in the San Francisco Bay Area prior to the advent of vaccination and other pharmaceutical interventions for COVID-19.

Results

Whereas socioeconomic factors, particularly those contributing to increased social vulnerability, were associated with test positivity for COVID-19, biomedical factors and disease co-morbidities were the major factors associated with increased risk of COVID-19 hospitalization. Hispanic individuals had a higher rate of COVID-19 positivity, while Asian persons had higher rates of COVID-19 hospitalization. The excess hospitalization risk attributed to Asian race was not explained by differences in the examined biomedical or sociodemographic variables. Diabetes was an important risk factor for COVID-19 hospitalization, particularly among Asian patients, for whom diabetes tended to be more frequently undiagnosed and higher in severity.

Conclusion

We observed that biomedical, racial/ethnic, and socioeconomic factors all contributed in varying but distinct ways to COVID-19 test positivity and hospitalization rates in a large, multi-racial, socioeconomically diverse metropolitan area of the United States. The impact of a number of these factors differed according to race/ethnicity. Improving overall COVID-19 health outcomes and addressing racial and ethnic disparities in COVID-19 outcomes will likely require a comprehensive approach that incorporates strategies that target both individual-specific and group contextual factors.

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Acknowledgements

The authors wish to thank Ashly Dyke for assistance with the literature review. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. Computing resources were granted through the HPC Consortium for access to Pittsburgh Supercomputer Center’s Bridges, Bridges-2, and Bridges-AI.

Funding

This research was supported in part by unrestricted grants to DGH from All May See (formerly That Man May See) and to DGH from Research to Prevent Blindness.

Author information

Authors and Affiliations

  1. Department of Ophthalmology, School of Medicine, University of California San Francisco, San Francisco, CA, USA

    Wendy K. Tam Cho & David G. Hwang

  2. Department of Political Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Wendy K. Tam Cho

  3. Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Wendy K. Tam Cho

  4. Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Wendy K. Tam Cho

  5. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Wendy K. Tam Cho

  6. Department of Asian American Studies, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Wendy K. Tam Cho

  7. College of Law, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Wendy K. Tam Cho

  8. National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Wendy K. Tam Cho

Authors
  1. Wendy K. Tam Cho
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Contributions

Concept and design: both authors. Acquisition, analysis, or interpretation of data: both authors. Drafting of the manuscript: both authors. Critical revision of the manuscript for intellectual content: both authors.

Corresponding author

Correspondence to Wendy K. Tam Cho.

Ethics declarations

Ethics Approval

This study was approved by the University of California San Francisco Human Research Protection Program Institutional Review Board (IRB #2030987. Reference #324788).

Conflict of Interest

The authors declare no competing interests.

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Appendix

Appendix

Table 3 Odds ratios with 95% confidence intervals for logistic regression models of COVID-19 positivity
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Table 4 Odds ratios with 95% confidence intervals for logistic regression models of COVID-19 hospitalization
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Table 5 Odds ratios with 95% confidence intervals for biomedical logistic regression models of COVID-19 positivity
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Cho, W.K.T., Hwang, D.G. Racial/Ethnic, Biomedical, and Sociodemographic Risk Factors for COVID-19 Positivity and Hospitalization in the San Francisco Bay Area. J. Racial and Ethnic Health Disparities 10, 1653–1668 (2023). https://doi.org/10.1007/s40615-022-01351-1

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  • DOI: https://doi.org/10.1007/s40615-022-01351-1

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