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Protocol for the development of the Wales multimorbidity e-Cohort (WMC) : data sources and methods to construct a population-based research platform to investigate multi-morbidity

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Date
01/2021
Author
Lyons, Jane
Akbari, Ashley
Agrawal, Utkarsh
Harper, Gill
Azcoaga-Lorenzo, Amaya
Bailey, Rowena
Rafferty, James
Watkins, Alan
Fry, Richard
McCowan, Colin
Dezateux, Carol
Robson, John P
Peek, Niels
Holmes, Chris
Denaxas, Spiros
Owen, Rhiannon
Abrams, Keith R
John, Ann
O'Reilly, Dermot
Richardson, Sylvia
Hall, Marlous
Gale, Chris P
Davies, Jan
Davies, Chris
Cross, Lynsey
Gallacher, John
Chess, James
Brookes, Anthony J
Lyons, Ronan A
Keywords
RA0421 Public health. Hygiene. Preventive Medicine
ZA4050 Electronic information resources
3rd-DAS
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Abstract
Introduction  Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence. We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity. Methods and analysis  The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity. Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation. Ethics and dissemination  The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.
Citation
Lyons , J , Akbari , A , Agrawal , U , Harper , G , Azcoaga-Lorenzo , A , Bailey , R , Rafferty , J , Watkins , A , Fry , R , McCowan , C , Dezateux , C , Robson , J P , Peek , N , Holmes , C , Denaxas , S , Owen , R , Abrams , K R , John , A , O'Reilly , D , Richardson , S , Hall , M , Gale , C P , Davies , J , Davies , C , Cross , L , Gallacher , J , Chess , J , Brookes , A J & Lyons , R A 2021 , ' Protocol for the development of the Wales multimorbidity e-Cohort (WMC) : data sources and methods to construct a population-based research platform to investigate multi-morbidity ' , BMJ Open , vol. 11 , no. 1 , e047101 . https://doi.org/10.1136/bmjopen-2020-047101
Publication
BMJ Open
Status
Peer reviewed
DOI
https://doi.org/10.1136/bmjopen-2020-047101
ISSN
2044-6055
Type
Journal article
Rights
Copyright information: © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
Description
This work was supported by Health Data Research UK (HDR-9006; CFC0110) and the Medical Research Council (MR/S027750/1). Health Data Research UK is funded by: UK Medical Research Council; Engineering and Physical Sciences Research Council; Economic and Social Research Council; National Institute for Health Research (England); Chief Scientist Office of the Scottish Government Health and Social Care Directorates; Health and Social Care Research and Development Division (Welsh Government); Public Health Agency (Northern Ireland); British Heart Foundation and Wellcome Trust.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/21481

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