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

Kristin Kostka^{1

2}, Talita Duarte-Salles³, Albert Prats-Uribe⁴, Anthony G Sena^{5

6}, Andrea Pistillo³, Sara Khalid⁴, Lana Y H Lai⁷, Asieh Golozar^{8

9}, Thamir M Alshammari¹⁰, Dalia M Dawoud¹¹, Fredrik Nyberg¹², Adam B Wilcox^{13

14}, Alan Andryc⁵, Andrew Williams¹⁵, Anna Ostropolets¹⁶, Carlos Areia¹⁷, Chi Young Jung¹⁸, Christopher A Harle¹⁹, Christian G Reich^{1

2}, Clair Blacketer^{5

6}, Daniel R Morales²⁰, David A Dorr²¹, Edward Burn^{3

4}, Elena Roel^{3

22}, Eng Hooi Tan⁴, Evan Minty²³, Frank DeFalco⁵, Gabriel de Maeztu²⁴, Gigi Lipori¹⁹, Hiba Alghoul²⁵, Hong Zhu²⁶, Jason A Thomas¹³, Jiang Bian¹⁹, Jimyung Park²⁷, Jordi Martínez Roldán²⁸, Jose D Posada²⁹, Juan M Banda³⁰, Juan P Horcajada³¹, Julianna Kohler³², Karishma Shah³³, Karthik Natarajan^{16

34}, Kristine E Lynch^{35

36}, Li Liu³⁷, Lisa M Schilling³⁸, Martina Recalde^{3

22}, Matthew Spotnitz¹⁴, Mengchun Gong³⁹, Michael E Matheny^{40

41}, Neus Valveny⁴², Nicole G Weiskopf²¹, Nigam Shah²⁹, Osaid Alser⁴³, Paula Casajust⁴², Rae Woong Park^{27

44}, Robert Schuff²¹, Sarah Seager¹, Scott L DuVall^{35

36}, Seng Chan You⁴⁵, Seokyoung Song⁴⁶, Sergio Fernández-Bertolín³, Stephen Fortin⁵, Tanja Magoc¹⁹, Thomas Falconer¹⁶, Vignesh Subbian⁴⁷, Vojtech Huser⁴⁸, Waheed-Ul-Rahman Ahmed^{33

49}, William Carter³⁸, Yin Guan⁵⁰, Yankuic Galvan¹⁹, Xing He¹⁹, Peter R Rijnbeek⁶, George Hripcsak^{16

34}, Patrick B Ryan^{5

16}, Marc A Suchard⁵¹, Daniel Prieto-Alhambra⁴

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.

PMID: 35345821
PMCID: PMC8957305
DOI: 10.2147/CLEP.S323292

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

Kristin Kostka et al. Clin Epidemiol. 2022.

. 2022 Mar 22:14:369-384.

doi: 10.2147/CLEP.S323292. eCollection 2022.

Authors

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.

PMID: 35345821
PMCID: PMC8957305
DOI: 10.2147/CLEP.S323292

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.

Keywords: OHDSI; OMOP CDM; descriptive epidemiology; open science; real world data; real world evidence.

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Conflict of interest statement

Ms. Kostka was an employee of IQVIA during the conduct of this study and received grant funding from the NIH NCATS National COVID Cohort Collaborative and the Bill and Melinda Gates Foundation. Mr. Sena is an employee and holds stock at Janssen Research & Development, a Johnson and Johnson family of companies. Dr. Golozar reports personal fees from Regeneron Pharmaceuticals, outside the submitted work. She is a full-time employee at Regeneron Pharmaceuticals. This work was not conducted at Regeneron Pharmaceuticals. Dr. Nyberg was an employee of AstraZeneca until 2019 and hold some shares. Dr. Wilcox reports grants from Bill and Melinda Gates Foundation, grants from National Institute of Health, during the conduct of the study. Mr. Andryc is an employee of Janssen Research & Development, a subsidiary of Johnson & Johnson. Dr. Reich is an employee of IQVIA. Dr. Blacketer reports she is an employee and holds stock at Janssen Research & Development, a Johnson and Johnson family of companies. Dr. Morales is supported by a Wellcome Trust Clinical Research Development Fellowship (Grant 214588/Z/18/Z) and reports grants from Chief Scientist Office (CSO), grants from Health Data Research UK (HDR-UK), grants from National Institute of Health Research (NIHR), outside the submitted work. Mr. DeFalco reports he is an employee and holds stock at Janssen Research & Development, a Johnson and Johnson family of companies. Mr. Thomas reports grants from Bill and Melinda Gates Foundation (INV-016910), grants from National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number UL1TR002369 to his institution, during the conduct of the study. Dr Jiang Bian reports grants from NIH/NIEHS (R21ES032762), during the conduct of the study. Dr. Posada reports grants from National Library of Medicine, during the conduct of the study. Dr. Natarajan reports grants from US NIH, during the conduct of the study. Dr. Matheny reports grants from US NIH, grants from US VA HSR&D, during the conduct of the study. Dr. Weiskopf reports personal fees from Merck, during the conduct of the study and outside the submitted work. Dr. Shah reports grants from National Library of Medicine, during the conduct of the study. Dr. Park reports grants from Ministry of Trade, Industry & Energy, Republic of Korea, grants from Ministry of Health & Welfare, Republic of Korea, grants from Bill & Melinda Gates Foundation, during the conduct of the study. Mr Robert Schuff reports grants from Gates Foundation, grants from NIH-NCATS, during the conduct of the study. Ms. Seager is an employee of IQVIA. Dr. DuVall reports grants from Anolinx, LLC, Astellas Pharma, Inc, AstraZeneca Pharmaceuticals LP, Boehringer Ingelheim International GmbH, Celgene Corporation, Eli Lilly and Company, Genentech Inc., Genomic Health, Inc., Gilead Sciences Inc., GlaxoSmithKline PLC, Innocrin Pharmaceuticals Inc., Janssen Pharmaceuticals, Inc., Kantar Health, Myriad Genetic Laboratories, Inc., Novartis International AG, and Parexel International Corporation through the University of Utah or Western Institute for Veteran Research outside the submitted work. Dr. Fortin is an employee of Janssen R&D, a subsidiary of Johnson and Johnson. Dr. Subbian reports grants from State of Arizona; Arizona Board of Regents, during the conduct of the study; grants from National Science Foundation (grant# 1838745), grants from Agency for Healthcare Research and Quality, grants from National Institutes of Health, outside the submitted work. Dr. Rijnbeek reports grants from Innovative Medicines Initiative, Janssen Research and Development, during the conduct of the study. He also works for a research institute which receives/received unconditional research grants from Yamanouchi, Pfizer-Boehringer Ingelheim, GSK, Amgen, UCB, Novartis, Astra-Zeneca, Chiesi, Janssen Research and Development, none of which relate to the content of this work. Dr. Hripcsak reports grants from US NIH and Janssen Research. Dr. Ryan is an employee of Janssen Research and Development and shareholder of Johnson & Johnson. Dr. Suchard reports grants from US National Institutes of Health, Department of Veterans Affairs, during the conduct of the study; grants and/or personal fees from IQVIA, Janssen Research and Development, US Food and Drug Administration, and Private Health Management, outside the submitted work. Dr. Prieto-Alhambra reports grants, non-financial support, speaker/consultancy services and/or advisory board membership from AMGEN, UCB Biopharma, and Les Laboratoires Servier, outside the submitted work; and Janssen, on behalf of IMI-funded EHDEN and EMIF consortiums, and Synapse Management Partners have supported training programmes organised by DPA’s Department and open for external participants. The views expressed are those of the authors and do not necessarily represent the views or policy of the Department of Veterans Affairs or the United States Government. No other relationships or activities that could appear to have influenced the submitted work. The authors report no other conflicts of interest in this work.

Figures

**Figure 1**
COVID-19 cases across the OHDSI COVID-19 network.

**Figure 2**
Distribution of diagnosed, hospitalized and requiring intensive services COVID-19 cases by age and sex across the OHDSI COVID-19 network in the United States.

See this image and copyright information in PMC

Update of

Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS.
Prieto-Alhambra D, Kostka K, Duarte-Salles T, Prats-Uribe A, Sena A, Pistillo A, Khalid S, Lai L, Golozar A, Alshammari TM, Dawoud D, Nyberg F, Wilcox A, Andryc A, Williams A, Ostropolets A, Areia C, Jung CY, Harle C, Reich C, Blacketer C, Morales D, Dorr DA, Burn E, Roel E, Tan EH, Minty E, DeFalco F, de Maeztu G, Lipori G, Alghoul H, Zhu H, Thomas J, Bian J, Park J, Roldán JM, Posada J, Banda JM, Horcajada JP, Kohler J, Shah K, Natarajan K, Lynch K, Liu L, Schilling L, Recalde M, Spotnitz M, Gong M, Matheny M, Valveny N, Weiskopf N, Shah N, Alser O, Casajust P, Park RW, Schuff R, Seager S, DuVall S, You SC, Song S, Fernández-Bertolín S, Fortin S, Magoc T, Falconer T, Subbian V, Huser V, Ahmed WU, Carter W, Guan Y, Galvan Y, He X, Rijnbeek P, Hripcsak G, Ryan P, Suchard M. Prieto-Alhambra D, et al. Res Sq [Preprint]. 2021 Mar 1:rs.3.rs-279400. doi: 10.21203/rs.3.rs-279400/v1. Res Sq. 2021. Update in: Clin Epidemiol. 2022 Mar 22;14:369-384. doi: 10.2147/CLEP.S323292. PMID: 33688639 Free PMC article. Updated. Preprint.

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Update of

References

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