Curating a longitudinal research resource using linked primary care EHR data - a UK Biobank case study

Philip Darke; Sophie Cassidy; Michael Catt; Roy Taylor; Paolo Missier; Jaume Bacardit

doi:10.1093/jamia/ocab260

Curating a longitudinal research resource using linked primary care EHR data - a UK Biobank case study

Philip Darke^*, Sophie Cassidy, Michael Catt, Roy Taylor, Paolo Missier, Jaume Bacardit

^*Corresponding author for this work

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Abstract

Primary care EHR data are often of clinical importance to cohort studies however they require careful handling. Challenges include determining the periods during which EHR data were collected. Participants are typically censored when they deregister from a medical practice, however, cohort studies wish to follow participants longitudinally including those that change practice. Using UK Biobank as an exemplar, we developed methodology to infer continuous periods of data collection and maximize follow-up in longitudinal studies. This resulted in longer follow-up for around 40% of participants with multiple registration records (mean increase of 3.8 years from the first study visit). The approach did not sacrifice phenotyping accuracy when comparing agreement between self-reported and EHR data. A diabetes mellitus case study illustrates how the algorithm supports longitudinal study design and provides further validation. We use UK Biobank data, however, the tools provided can be used for other conditions and studies with minimal alteration.

Original language	English
Pages (from-to)	546-552
Number of pages	7
Journal	Journal of the American Medical Informatics Association
Volume	29
Issue number	3
Early online date	13 Dec 2021
DOIs	https://doi.org/10.1093/jamia/ocab260
Publication status	Published - Mar 2022

Bibliographical note

Funding Information:
The data used were provided under UK Biobank application 12184. The authors are grateful to all UK Biobank participants who generously contributed their time to the study. PD would like to thank Dr Peter Philipson at Newcastle University, Dr Sam Hodgson at University of Southampton, Dr Sarah Finer at Queen Mary University of London, and Professor Naomi Allen and Dr Rishi Caleyachetty at Oxford University/UK Biobank for helpful discussions.

Publisher Copyright:
© 2021 The Author(s).

Keywords

diabetes mellitus
electronic health records
longitudinal studies
medical record linkage
phenotype

ASJC Scopus subject areas

Health Informatics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1093/jamia/ocab260Licence: Creative Commons: Attribution (CC BY)

DarkeP2021CuratingFinal published version, 625 KBLicence: Creative Commons: Attribution (CC BY)

Cite this

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Curating a longitudinal research resource using linked primary care EHR data - a UK Biobank case study

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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Cite this