Evidence map›Paper›PMID 36751466›Full record

ArticleJAMIA open2023

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.

Nader Zidan, Vishal Dey, Katie Allen, John Price, Sarah Renee Zappone, Courtney Hebert, Titus Schleyer, Xia Ning

Abstract read
In one paragraph

Article in JAMIA open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
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  6. A Multi-Layered GRU Model for COVID-19 Patient Representation and Phenotyping from Large-Scale EHR Data.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Nader ZidanDepartment of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA.
Vishal DeyDepartment of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA.
Katie AllenRegenstrief Institute, Indianapolis, Indiana, USA.ORCID https://orcid.org/0000-0002-6058-6280
John PriceRegenstrief Institute, Indianapolis, Indiana, USA.
Sarah Renee ZapponeRegenstrief Institute, Indianapolis, Indiana, USA.
Courtney HebertDepartment of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA.
Titus SchleyerRegenstrief Institute, Indianapolis, Indiana, USA.
Xia NingDepartment of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA.ORCID https://orcid.org/0000-0002-6842-1165

Funding

Enhancing information retrieval in electronic health records through collaborative filteringR01LM012605 · NLM · INDIANA UNIVERSITY INDIANAPOLIS · PI NING, XIA, SCHLEYER, TITUS K. · 2018 to 2020
$1.7M
COVID-19 disease course analysis using multi-site large-scale EHR dataR21LM013678 · NLM · OHIO STATE UNIVERSITY · PI NING, XIA, SCHLEYER, TITUS K. · 2021 to 2022
$400k
NLM NIH HHS R01 LM012605NLM NIH HHS R21 LM013678
6 · The paper itself

Abstract

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.

Indexed as

comorbidityCOVID-19health disparity analysispredictive analysisrisk factor analysis

Identifiers

PMID36751466
PMCPMC9893867

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.