Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS
Rattachement africain : us, es, gb, nl, sa, se, kr, au, ca, ps, cn. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
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, real world data, real world evidence, open science
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS
- Date Crossref
- 01/03/2022
- Éditeur
- Informa UK Limited
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
Northeastern University OHDSI Center at The Roux Institute pays non établi dans la noticeUniversité ou école supérieure
-
IQVIA (United States) pays non établi dans la noticeEntreprise
-
Institut Universitari d'Investigació en Atenció Primària Jordi Gol pays non établi dans la noticeInstitution
-
Office for National Statistics pays non établi dans la noticeOrganisme public
-
University of Oxford NDORMS pays non établi dans la noticeUniversité ou école supérieure
-
Erasmus MC pays non établi dans la noticeÉtablissement de santé
-
Janssen (United States) pays non établi dans la noticeEntreprise
-
University of Manchester pays non établi dans la noticeUniversité ou école supérieure
-
Johns Hopkins University pays non établi dans la noticeUniversité ou école supérieure
-
Regeneron (United States) pays non établi dans la noticeEntreprise
-
Riyadh Elm University pays non établi dans la noticeUniversité ou école supérieure
-
National Institute for Health and Care Excellence pays non établi dans la noticeOrganisme public
OHDSI Center at The Roux Institute — Northeastern University, IQVIA (United States) et Institut Universitari d'Investigació en Atenció Primària Jordi Gol, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.