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

Kristin Kostka; Talita Duarte-Salles; Albert Prats-Uribe; Anthony G. Sena; Andrea Pistillo; Sara Khalid; Lana Y.H. Lai; Asieh Golozar; Thamir M. Alshammari; Dalia M. Dawoud; Fredrik Nyberg; Adam B. Wilcox; Alan Andryc; Andrew Williams; Anna Ostropolets; Carlos Areia; Chi Young Jung; Christopher A. Harle; Christian G. Reich; Clair Blacketer; Daniel R. Morales; David A. Dorr; Edward Burn; Elena Roel; Eng Hooi Tan; Evan Minty; Frank De Falco; Gabriel De Maeztu; Gigi Lipori; Hiba Alghoul; Hong Zhu; Jason A. Thomas; Jiang Bian; Jimyung Park; Jordi Martínez Roldán; Jose D. Posada; Juan M. Banda; Juan P. Horcajada; Julianna Kohler; Karishma Shah; Karthik Natarajan; Kristine E. Lynch; Li Liu; Lisa M. Schilling; Martina Recalde; Matthew Spotnitz; Mengchun Gong; Michael E. Matheny; Neus Valveny; Nicole G. Weiskopf; Nigam Shah; Osaid Alser; Paula Casajust; Rae Woong Park; Robert Schuff; Sarah Seager; Scott L. Du Vall; Seng Chan You; Seokyoung Song; Sergio Fernández-Bertolín; Stephen Fortin; Tanja Magoc; Thomas Falconer; Vignesh Subbian; Vojtech Huser; Waheed Ul Rahman Ahmed; William Carter; Yin Guan; Yankuic Galvan; Xing He; Peter R. Rijnbeek; George Hripcsak; Patrick B. Ryan; Marc A. Suchard; Daniel Prieto-Alhambra

doi:10.2147/CLEP.S323292

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

Kristin Kostka, Talita Duarte-Salles, Albert Prats-Uribe, Anthony G. Sena, Andrea Pistillo, Sara Khalid, Lana Y.H. Lai, Asieh Golozar, Thamir M. Alshammari, Dalia M. Dawoud, Fredrik Nyberg, Adam B. Wilcox, Alan Andryc, Andrew Williams, Anna Ostropolets, Carlos Areia, Chi Young Jung, Christopher A. Harle, Christian G. Reich, Clair BlacketerDaniel R. Morales, David A. Dorr, Edward Burn, Elena Roel, Eng Hooi Tan, Evan Minty, Frank De Falco, Gabriel De Maeztu, Gigi Lipori, Hiba Alghoul, Hong Zhu, Jason A. Thomas, Jiang Bian, Jimyung Park, Jordi Martínez Roldán, Jose D. Posada, Juan M. Banda, Juan P. Horcajada, Julianna Kohler, Karishma Shah, Karthik Natarajan, Kristine E. Lynch, Li Liu, Lisa M. Schilling, Martina Recalde, Matthew Spotnitz, Mengchun Gong, Michael E. Matheny, Neus Valveny, Nicole G. Weiskopf, Nigam Shah, Osaid Alser, Paula Casajust, Rae Woong Park, Robert Schuff, Sarah Seager, Scott L. Du Vall, Seng Chan You, Seokyoung Song, Sergio Fernández-Bertolín, Stephen Fortin, Tanja Magoc, Thomas Falconer, Vignesh Subbian, Vojtech Huser, Waheed Ul Rahman Ahmed, William Carter, Yin Guan, Yankuic Galvan, Xing He, Peter R. Rijnbeek, George Hripcsak, Patrick B. Ryan, Marc A. Suchard, Daniel Prieto-Alhambra

Medical Informatics and Clinical Epidemiology

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

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 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.

Original language	English (US)
Pages (from-to)	369-384
Number of pages	16
Journal	Clinical Epidemiology
Volume	14
DOIs	https://doi.org/10.2147/CLEP.S323292
State	Published - 2022

Keywords

Descriptive epidemiology
OHDSI
OMOP CDM
Open science
Real world data
Real world evidence

ASJC Scopus subject areas

Epidemiology

Access to Document

10.2147/CLEP.S323292

Cite this

Kostka, K., Duarte-Salles, T., Prats-Uribe, A., Sena, A. G., Pistillo, A., Khalid, S., Lai, L. Y. H., Golozar, A., Alshammari, T. M., Dawoud, D. M., Nyberg, F., Wilcox, A. B., Andryc, A., Williams, A., Ostropolets, A., Areia, C., Jung, C. Y., Harle, C. A., Reich, C. G., ... Prieto-Alhambra, D. (2022). Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS. Clinical Epidemiology, 14, 369-384. https://doi.org/10.2147/CLEP.S323292

Kostka, K, Duarte-Salles, T, Prats-Uribe, A, Sena, AG, Pistillo, A, Khalid, S, Lai, LYH, Golozar, A, Alshammari, TM, Dawoud, DM, Nyberg, F, Wilcox, AB, Andryc, A, Williams, A, Ostropolets, A, Areia, C, Jung, CY, Harle, CA, Reich, CG, Blacketer, C, Morales, DR, Dorr, DA, Burn, E, Roel, E, Tan, EH, Minty, E, De Falco, F, De Maeztu, G, Lipori, G, Alghoul, H, Zhu, H, Thomas, JA, Bian, J, Park, J, Roldán, JM, Posada, JD, Banda, JM, Horcajada, JP, Kohler, J, Shah, K, Natarajan, K, Lynch, KE, Liu, L, Schilling, LM, Recalde, M, Spotnitz, M, Gong, M, Matheny, ME, Valveny, N, Weiskopf, NG, Shah, N, Alser, O, Casajust, P, Park, RW, Schuff, R, Seager, S, Du Vall, SL, You, SC, Song, S, Fernández-Bertolín, S, Fortin, S, Magoc, T, Falconer, T, Subbian, V, Huser, V, Ahmed, WUR, Carter, W, Guan, Y, Galvan, Y, He, X, Rijnbeek, PR, Hripcsak, G, Ryan, PB, Suchard, MA & Prieto-Alhambra, D 2022, 'Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS', Clinical Epidemiology, vol. 14, pp. 369-384. https://doi.org/10.2147/CLEP.S323292

@article{b49e32206ec14adb8ae1c4d9e8283d70,

title = "Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS",

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 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.",

keywords = "Descriptive epidemiology, OHDSI, OMOP CDM, Open science, Real world data, Real world evidence",

author = "Kristin Kostka and Talita Duarte-Salles and Albert Prats-Uribe and Sena, {Anthony G.} and Andrea Pistillo and Sara Khalid and Lai, {Lana Y.H.} and Asieh Golozar and Alshammari, {Thamir M.} and Dawoud, {Dalia M.} and Fredrik Nyberg and Wilcox, {Adam B.} and Alan Andryc and Andrew Williams and Anna Ostropolets and Carlos Areia and Jung, {Chi Young} and Harle, {Christopher A.} and Reich, {Christian G.} and Clair Blacketer and Morales, {Daniel R.} and Dorr, {David A.} and Edward Burn and Elena Roel and Tan, {Eng Hooi} and Evan Minty and {De Falco}, Frank and {De Maeztu}, Gabriel and Gigi Lipori and Hiba Alghoul and Hong Zhu and Thomas, {Jason A.} and Jiang Bian and Jimyung Park and Rold{\'a}n, {Jordi Mart{\'i}nez} and Posada, {Jose D.} and Banda, {Juan M.} and Horcajada, {Juan P.} and Julianna Kohler and Karishma Shah and Karthik Natarajan and Lynch, {Kristine E.} and Li Liu and Schilling, {Lisa M.} and Martina Recalde and Matthew Spotnitz and Mengchun Gong and Matheny, {Michael E.} and Neus Valveny and Weiskopf, {Nicole G.} and Nigam Shah and Osaid Alser and Paula Casajust and Park, {Rae Woong} and Robert Schuff and Sarah Seager and {Du Vall}, {Scott L.} and You, {Seng Chan} and Seokyoung Song and Sergio Fern{\'a}ndez-Bertol{\'i}n and Stephen Fortin and Tanja Magoc and Thomas Falconer and Vignesh Subbian and Vojtech Huser and Ahmed, {Waheed Ul Rahman} and William Carter and Yin Guan and Yankuic Galvan and Xing He and Rijnbeek, {Peter R.} and George Hripcsak and Ryan, {Patrick B.} and Suchard, {Marc A.} and Daniel Prieto-Alhambra",

note = "Publisher Copyright: {\textcopyright} 2022 Kostka et al.",

year = "2022",

doi = "10.2147/CLEP.S323292",

language = "English (US)",

volume = "14",

pages = "369--384",

journal = "Clinical Epidemiology",

issn = "1179-1349",

publisher = "Dove Medical Press Ltd.",

}

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

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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DO - 10.2147/CLEP.S323292

M3 - Article

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SN - 1179-1349

VL - 14

SP - 369

EP - 384

JO - Clinical Epidemiology

JF - Clinical Epidemiology

ER -

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

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