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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 datab...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.2147/clep.s323292

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
NDORMS
Oxford college:
Green Templeton College
Role:
Author
ORCID:
0000-0003-2595-8736
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Role:
Author
ORCID:
0000-0002-8274-0357
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Role:
Author
ORCID:
0000-0003-1202-9153
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Role:
Author
ORCID:
0000-0002-4467-0220
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Publisher:
Dove Press Publisher's website
Journal:
Clinical Epidemiology Journal website
Volume:
14
Pages:
369-384
Publication date:
2022-03-22
Acceptance date:
2022-01-27
DOI:
EISSN:
1179-1349
ISSN:
1179-1349
Pmid:
35345821
Language:
English
Keywords:
Pubs id:
1249042
Local pid:
pubs:1249042
Deposit date:
2022-06-16

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