Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA
Open accessAbstract
Objective: To characterize COVID-19 patients in Indiana, United States, and to evaluate their demographics and comorbidities as risk factors to COVID-19 severity. Materials and Methods: EHR data of 776 936 COVID-19 cases and 1 362 545 controls were collected from the COVID-19 Research Data Commons (CoRDaCo) in Indiana. Data regarding county population and per capita income were obtained from the US Census Bureau. Statistical analysis was conducted to determine the association of demographic and clinical variables with COVID-19 severity. Predictive analysis was conducted to evaluate the predictive power of CoRDaCo EHR data in determining COVID-19 severity. Results: Chronic obstructive pulmonary disease, cardiovascular disease, and type 2 diabetes were found in 3.49%, 2.59%, and 4.76% of the COVID-19 patients, respectively. Such COVID-19 patients have significantly higher ICU admission rates of 10.23%, 14.33%, and 11.11%, respectively, compared to the entire COVID-19 patient population (1.94%). Furthermore, patients with these comorbidities have significantly higher mortality rates compared to the entire COVID-19 patient population. Health disparity analysis suggests potential health disparities among counties in Indiana. Predictive analysis achieved F1-scores of 0.8011 and 0.7072 for classifying COVID-19 cases versus controls and ICU versus non-ICU cases, respectively. Discussion: Black population in Indiana was more adversely affected by COVID-19 than the White population. This is consistent to findings from existing studies. Our findings also indicate other health disparities in terms of demographic and economic factors. Conclusion: This study characterizes the relationship between comorbidities and COVID-19 outcomes with respect to ICU admission across a large COVID-19 patient population in Indiana.
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CoRDaCo enables easier participation in national collaborations such as the NCATS N3C initiative.
“Important secondary uses of CoRDaCo include easier participation in national collaborations, such as the NCATS N3C initiative [49, 52, 53, 54], which will greatly expand the pool of individuals who can directly access this data.”
From Evolution of clinical Health Information Exchanges to population health resources: a case study of the Indiana network for patient care (2025) · cites this paper as [54]
The Introduction (Background and significance; page unavailable) describes N3C as a national COVID-19 cohort, and Materials and Methods (page unavailable) describes CoRDaCo as a research data resource. Neither passage says CoRDaCo makes participation in N3C or other national collaborations easier; searches for that connection found no supporting passage. (LLM, conf=0.72)
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- Authors
- Zidan, N., Dey, V., Allen, K., Price, J., Zappone, S. R., Hebert, C., Schleyer, T., & Ning, X.
- Affiliations
- The Ohio State University (US)
- Regenstrief Institute (US)
- Corresponding author
- Ning, Xia
- Venue
- JAMIA Open
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-01-06
- Volume
- 6
- Issue
- 1
- Pages
- ooad002-ooad002
- ISSN
- 2574-2531 (ISSN-L)
- PMC
- PMC9893867
- OpenAlex
- W4318947300
- Cited by
- 11
- References
- 40
- Citations by year
- 2025: 4 · 2024: 4 · 2023: 3
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- JAMIA Open (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Library of Medicine (3R01LM012605-03S1, 1R21LM013678-01)
- Indiana Clinical and Translational Sciences Institute
- National Institutes of Health
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Oncology › COVID-19 and healthcare impacts
- Keywords
- COVID-19 pandemic, comorbidities, health disparities, disease severity, chronic obstructive pulmonary disease, cardiovascular disease, type 2 diabetes, intensive care unit admission, mortality, racial disparities, Black patients, per capita income, county-level disparities, Indiana, electronic health records
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