Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS

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Abstract

Purpose

Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.

Patients and Methods

We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.

Results

We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.

Conclusion

We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.

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Most cited references 22

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OpenSAFELY: factors associated with COVID-19 death in 17 million patients

Elizabeth J Williamson, Alex J Walker, Krishnan Bhaskaran … (2020)

COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.

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Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers.

George Hripcsak, Jon D Duke, Nigam Shah … (2015)

The vision of creating accessible, reliable clinical evidence by accessing the clincial experience of hundreds of millions of patients across the globe is a reality. Observational Health Data Sciences and Informatics (OHDSI) has built on learnings from the Observational Medical Outcomes Partnership to turn methods research and insights into a suite of applications and exploration tools that move the field closer to the ultimate goal of generating evidence about all aspects of healthcare to serve the needs of patients, clinicians and all other decision-makers around the world.

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The National COVID Cohort Collaborative (N3C): Rationale, Design, Infrastructure, and Deployment

Haendel Melissa, Chute Christopher, Gersing Kenneth (2020)

Abstract Objective COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Methods The Clinical and Translational Science Award (CTSA) Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Organized in inclusive workstreams, in two months we created: legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Discussion The N3C has demonstrated that a multi-site collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multi-organizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19. LAY SUMMARY COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though medical records are abundant, they are largely inaccessible to outside researchers. Statistical, machine learning, and causal research are most successful with large datasets beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many clinical centers to reveal patterns in COVID-19 patients. To create N3C, the community had to overcome technical, regulatory, policy, and governance barriers to sharing patient-level clinical data. In less than 2 months, we developed solutions to acquire and harmonize data across organizations and created a secure data environment to enable transparent and reproducible collaborative research. We expect the N3C to help save lives by enabling collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care needs and thereby reduce the immediate and long-term impacts of COVID-19.

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Author and article information

Journal

Journal ID (nlm-ta): Clin Epidemiol

Journal ID (iso-abbrev): Clin Epidemiol

Journal ID (publisher-id): clep

Title: Clinical Epidemiology

Publisher: Dove

ISSN (Electronic): 1179-1349

Publication date (Electronic): 22 March 2022

Publication date Collection: 2022

Volume: 14

Pages: 369-384

Affiliations

[1 ]IQVIA , Cambridge, MA, USA

[2 ]OHDSI Center at The Roux Institute, Northeastern University , Portland, ME, USA

[3 ]Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol) , Barcelona, Spain

[4 ]Centre for Statistics in Medicine, NDORMS, University of Oxford , Oxford, UK

[5 ]Janssen Research & Development , Titusville, NJ, USA

[6 ]Department of Medical Informatics, Erasmus University Medical Center , Rotterdam, The Netherlands

[7 ]School of Medical Sciences, University of Manchester , Manchester, UK

[8 ]Regeneron Pharmaceuticals , Tarrytown, NY, USA

[9 ]Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD, USA

[10 ]College of Pharmacy, Riyadh Elm University , Riyadh, Saudi Arabia

[11 ]National Institute for Health and Care Excellence , London, UK

[12 ]School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg , Gothenburg, Sweden

[13 ]Department of Biomedical Informatics and Medical Education, University of Washington , Seattle, WA, USA

[14 ]Unviersity of Washington Medicine , Seattle, WA, USA

[15 ]Tufts Institute for Clinical Research and Health Policy Studies , Boston, MA, USA

[16 ]Department of Biomedical Informatics, Columbia University Irving Medical Center , New York, NY, USA

[17 ]Nuffield Department of Clinical Neurosciences, University of Oxford , Oxford, UK

[18 ]Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center , Daegu, South Korea

[19 ]University of Florida Health , Gainesville, FL, USA

[20 ]Division of Population Health and Genomics, University of Dundee , Dundee, UK

[21 ]Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University , Portland, OR, USA

[22 ]Universitat Autònoma de Barcelona , Barcelona, Spain

[23 ]O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary , Calgary, Canada

[24 ]IOMED , Barcelona, Spain

[25 ]Faculty of Medicine, Islamic University of Gaza , Gaza, Palestine

[26 ]Nanfang Hospital, Southern Medical University , Guangzhou, People’s Republic of China

[27 ]Department of Biomedical Sciences, Ajou University Graduate School of Medicine , Suwon, South Korea

[28 ]Director of Innovation and Digital Transformation, Hospital del Mar , Barcelona, Spain

[29 ]Department of Medicine, School of Medicine, Stanford University , Redwood City, CA, USA

[30 ]Georgia State University, Department of Computer Science , Atlanta, GA, USA

[31 ]Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d’Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona, Universitat Pompeu Fabra , Barcelona, Spain

[32 ]United States Agency for International Development , Washington, DC, USA

[33 ]Botnar Research Centre, NDORMS, University of Oxford , Oxford, UK

[34 ]New York-Presbyterian Hospital , New York, NY, USA

[35 ]VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System , Salt Lake City, UT, USA

[36 ]Department of Internal Medicine, University of Utah School of Medicine , Salt Lake City, UT, USA

[37 ]Biomedical Big Data Center, Nanfang Hospital, Southern Medical University , Guangzhou, People’s Republic of China

[38 ]Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus , Aurora, CO, USA

[39 ]Institute of Health Management, Southern Medical University , Guangzhou, People’s Republic of China

[40 ]Tennessee Valley Healthcare System, Veterans Affairs Medical Center , Nashville, TN, USA

[41 ]Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, TN, USA

[42 ]Real-World Evidence, TFS , Barcelona, Spain

[43 ]Massachusetts General Hospital, Harvard Medical School , Boston, MA, USA

[44 ]Department of Biomedical Informatics, Ajou University School of Medicine , Suwon, South Korea

[45 ]Department of Preventive Medicine, Yonsei University College of Medicine , Seoul, South Korea

[46 ]Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine , Daegu, South Korea

[47 ]College of Engineering, The University of Arizona , Tucson, AZ, USA

[48 ]National Library of Medicine, National Institutes of Health , Bethesda, MD, USA

[49 ]College of Medicine and Health, University of Exeter, St Luke’s Campus , Exeter, UK

[50 ]DHC Technologies Co. Ltd ., Beijing, People’s Republic of China

[51 ]Departments of Biostatistics, Computational Medicine, and Human Genetics, University of California , Los Angeles, CA, USA

Author notes

Correspondence: Daniel Prieto-Alhambra, Botnar Research Centre , Windmill Road, Oxford, OX37LD, UK, Email daniel.prietoalhambra@ndorms.ox.ac.uk

Author information

Kristin Kostka http://orcid.org/0000-0003-2595-8736

Talita Duarte-Salles http://orcid.org/0000-0002-8274-0357

Albert Prats-Uribe http://orcid.org/0000-0003-1202-9153

Andrea Pistillo http://orcid.org/0000-0002-4467-0220

Thamir M Alshammari http://orcid.org/0000-0002-5630-2468

Fredrik Nyberg http://orcid.org/0000-0003-0892-5668

Alan Andryc http://orcid.org/0000-0002-2663-2298

Christian G Reich http://orcid.org/0000-0002-3641-055X

Edward Burn http://orcid.org/0000-0002-9286-1128

Hiba Alghoul http://orcid.org/0000-0001-8234-5843

Jimyung Park http://orcid.org/0000-0002-6998-2546

Juan M Banda http://orcid.org/0000-0001-8499-824X

Juan P Horcajada http://orcid.org/0000-0001-9873-5459

Karthik Natarajan http://orcid.org/0000-0002-9066-9431

Li Liu http://orcid.org/0000-0002-5265-4159

Neus Valveny http://orcid.org/0000-0003-1105-5018

Paula Casajust http://orcid.org/0000-0003-2733-5436

Scott L DuVall http://orcid.org/0000-0002-4898-3865

Seokyoung Song http://orcid.org/0000-0002-5653-7005

Tanja Magoc http://orcid.org/0000-0001-8213-2266

Thomas Falconer http://orcid.org/0000-0001-6904-1394

Waheed-Ul-Rahman Ahmed http://orcid.org/0000-0003-0880-0355

William Carter http://orcid.org/0000-0001-5346-3453

Marc A Suchard http://orcid.org/0000-0001-9818-479X

Daniel Prieto-Alhambra http://orcid.org/0000-0002-3950-6346

Article

Publisher ID: 323292

DOI: 10.2147/CLEP.S323292

PMC ID: 8957305

PubMed ID: 35345821

SO-VID: 3c71d755-5895-4362-96ec-ed98a005ccc8

License:

This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

History

Date received : 18 June 2021

Date accepted : 27 January 2022

Page count

Figures: 2, Tables: 6, References: 28, Pages: 16

Funding

Funded by: the Innovative Medicines Initiative 2 Joint Undertaking (JU);

Funded by: the European Union’s Horizon 2020 research and innovation programme and EFPIA;

Funded by: the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), US National Institutes of Health, US Department of Veterans Affairs, the Health Department from the Generalitat de Catalunya;

Funded by: the Bill & Melinda Gates Foundation;

Funded by: National Key Research & Development Program of China;

Funded by: the University of Oxford;

Funded by: Gates Foundation;

Funded by: the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health;

Funded by: the Bill & Melinda Gates Foundation;

Funded by: the Bill & Melinda Gates Foundation (Investment ID INV-016284);

Funded by: the Bio Industrial Strategic Technology Development Program (20003883);

Funded by: the Ministry of Trade, Industry & Energy, and from the Korea Health Technology R&D Project;

Funded by: the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea;

The European Health Data & Evidence Network has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 806968. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This research received partial support from the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), US National Institutes of Health, US Department of Veterans Affairs, the Health Department from the Generalitat de Catalunya with a grant for research projects on SARS-CoV-2 and COVID-19 disease organized by the Direcció General de Recerca i Innovació en Salut, Janssen Research & Development, IQVIA, TFS and IOMED. The University of Oxford received funding related to this work from the Bill & Melinda Gates Foundation (Investment ID INV-016201 and INV-019257). This study was supported by National Key Research & Development Program of China (Project No.2018YFC0116901). TFS received funding related to this work from the University of Oxford. OHSU received support from Gates Foundation, INV-016910 and the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number UL1TR002369. The University of Washington received a grant related to this work from the Bill & Melinda Gates Foundation (INV-016910). No funders had a direct role in this study. The views and opinions expressed are those of the authors and do not necessarily reflect those of the Clinician Scientist Award programme, NIHR, Department of Veterans Affairs or the United States Government, NHS, National Institute for Health and Care Excellence (NICE) or the Department of Health, England. The Ajou University received funding related to this work from the Bill & Melinda Gates Foundation (Investment ID INV-016284), from the Bio Industrial Strategic Technology Development Program (20003883), funded by the Ministry of Trade, Industry & Energy, and from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (HR16C0001).

Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS

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Abstract

Purpose

Patients and Methods

Results

Conclusion

Related collections

Novel Coronavirus Disease COVID-19

Most cited references 22

OpenSAFELY: factors associated with COVID-19 death in 17 million patients

Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers.

The National COVID Cohort Collaborative (N3C): Rationale, Design, Infrastructure, and Deployment

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