TY - JOUR
T1 - Unraveling COVID-19
T2 - A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS
AU - Kostka, Kristin
AU - Duarte-Salles, Talita
AU - Prats-Uribe, Albert
AU - Sena, Anthony G.
AU - Pistillo, Andrea
AU - Khalid, Sara
AU - Lai, Lana Y.H.
AU - Golozar, Asieh
AU - Alshammari, Thamir M.
AU - Dawoud, Dalia M.
AU - Nyberg, Fredrik
AU - Wilcox, Adam B.
AU - Andryc, Alan
AU - Williams, Andrew
AU - Ostropolets, Anna
AU - Areia, Carlos
AU - Jung, Chi Young
AU - Harle, Christopher A.
AU - Reich, Christian G.
AU - Blacketer, Clair
AU - Morales, Daniel R.
AU - Dorr, David A.
AU - Burn, Edward
AU - Roel, Elena
AU - Tan, Eng Hooi
AU - Minty, Evan
AU - De Falco, Frank
AU - De Maeztu, Gabriel
AU - Lipori, Gigi
AU - Alghoul, Hiba
AU - Zhu, Hong
AU - Thomas, Jason A.
AU - Bian, Jiang
AU - Park, Jimyung
AU - Roldán, Jordi Martínez
AU - Posada, Jose D.
AU - Banda, Juan M.
AU - Horcajada, Juan P.
AU - Kohler, Julianna
AU - Shah, Karishma
AU - Natarajan, Karthik
AU - Lynch, Kristine E.
AU - Liu, Li
AU - Schilling, Lisa M.
AU - Recalde, Martina
AU - Spotnitz, Matthew
AU - Gong, Mengchun
AU - Matheny, Michael E.
AU - Valveny, Neus
AU - Weiskopf, Nicole G.
AU - Shah, Nigam
AU - Alser, Osaid
AU - Casajust, Paula
AU - Park, Rae Woong
AU - Schuff, Robert
AU - Seager, Sarah
AU - Du Vall, Scott L.
AU - You, Seng Chan
AU - Song, Seokyoung
AU - Fernández-Bertolín, Sergio
AU - Fortin, Stephen
AU - Magoc, Tanja
AU - Falconer, Thomas
AU - Subbian, Vignesh
AU - Huser, Vojtech
AU - Ahmed, Waheed Ul Rahman
AU - Carter, William
AU - Guan, Yin
AU - Galvan, Yankuic
AU - He, Xing
AU - Rijnbeek, Peter R.
AU - Hripcsak, George
AU - Ryan, Patrick B.
AU - Suchard, Marc A.
AU - Prieto-Alhambra, Daniel
N1 - Funding Information:
The European Health Data and 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 and Development, IQVIA, TFS and IOMED. The University of Oxford received funding related to this work from the Bill and Melinda Gates Foundation (Investment ID INV-016201 and INV-019257). This study was supported by National Key Research and 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 and 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 and Melinda Gates Foundation (Investment ID INV-016284), from the Bio Industrial Strategic Technology Development Program (20003883), funded by the Ministry of Trade, Industry and Energy, and from the Korea Health Technology RandD Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea (HR16C0001).
Funding Information:
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).
Publisher Copyright:
© 2022 Kostka et al.
PY - 2022
Y1 - 2022
N2 - 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 nonmutually 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.
AB - 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 nonmutually 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.
KW - Descriptive epidemiology
KW - OHDSI
KW - OMOP CDM
KW - Open science
KW - Real world data
KW - Real world evidence
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U2 - 10.2147/CLEP.S323292
DO - 10.2147/CLEP.S323292
M3 - Article
AN - SCOPUS:85129810843
SN - 1179-1349
VL - 14
SP - 369
EP - 384
JO - Clinical Epidemiology
JF - Clinical Epidemiology
ER -