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ORIGINAL RESEARCH article
Front. Public Health
Sec. Infectious Diseases: Epidemiology and Prevention
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1513508
Development of the ECHOES national dataset: A resource for monitoring post-acute and long-term COVID-19 health outcomes in England
Provisionally accepted
Hester Allen 1,2*
Katie Hassell 3 Christopher Rawlinson 2
Owen Pullen 3 Colin Campbell 2
Annika Jödicke 3
Martí Català 3
Albert Prats-Uribe 3 Gavin Dabrera 2
Daniel Prieto-Alhambra 3,4 Ines Campos-Matos 2- 1 Health Data Sciences, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS),, University of Oxford, Oxford, United Kingdom
- 2 Immunisation and Vaccine Preventable Diseases Division, UK Health Security Agency (UKHSA), London, United Kingdom
- 3 Student EPSRC Centre for Doctoral Training in Health Data Science, Department of Computer Science, University of Oxford, Oxford, England, United Kingdom
- 4 5Department of Medical Informatics, Erasmus Medical Center, Rotterdam, Netherlands
The dataset contains comprehensive COVID-19 testing data and demographic, socio economic and health related information for 44 million individuals, who tested for SARS-CoV-2 between March 2020 and April 2022, representing 15,720,286 individuals who tested positive and 42,351,016 individuals who tested negative.With the application of epidemiological and statistical methods, this dataset allows a range of clinical outcomes to be investigated, including pre-specified health conditions and mortality. Furthermore, understanding of potential determinants of health outcomes can be gained, including pre-existing health conditions, acute disease characteristics, SARS-CoV-2 vaccination status and genomic variant.
Keywords: COVID-19, Post-acute Covid-19, Electronic Health Data, data linkage, Health Outcomes
Received: 18 Oct 2024; Accepted: 12 Feb 2025.
Copyright: © 2025 Allen, Hassell, Rawlinson, Pullen, Campbell, Jödicke, Català, Prats-Uribe, Dabrera, Prieto-Alhambra and Campos-Matos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hester Allen, Health Data Sciences, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS),, University of Oxford, Oxford, United Kingdom
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