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


Journal

BMJ open
ISSN: 2044-6055
Titre abrégé: BMJ Open
Pays: England
ID NLM: 101552874

Informations de publication

Date de publication:
19 01 2021
Historique:
entrez: 20 1 2021
pubmed: 21 1 2021
medline: 27 4 2021
Statut: epublish

Résumé

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. 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. 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.

Identifiants

pubmed: 33468531
pii: bmjopen-2020-047101
doi: 10.1136/bmjopen-2020-047101
pmc: PMC7817800
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e047101

Subventions

Organisme : Medical Research Council
ID : MC_UU_00002/10
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S027750/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 206470/Z/17/Z
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/L023784/2
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/K006584/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : G0901530
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_17215
Pays : United Kingdom

Informations de copyright

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

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Auteurs

Jane Lyons (J)

Population Data Science, Swansea University Medical School, Swansea, UK j.lyons@swansea.ac.uk.

Ashley Akbari (A)

Population Data Science, Swansea University Medical School, Swansea, UK.

Utkarsh Agrawal (U)

School of Medicine, University of St Andrews, St Andrews, Fife, UK.

Gill Harper (G)

Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.

Amaya Azcoaga-Lorenzo (A)

School of Medicine, University of St Andrews, St Andrews, Fife, UK.

Rowena Bailey (R)

Population Data Science, Swansea University Medical School, Swansea, UK.

James Rafferty (J)

Population Data Science, Swansea University Medical School, Swansea, UK.

Alan Watkins (A)

Population Data Science, Swansea University Medical School, Swansea, UK.

Richard Fry (R)

Population Data Science, Swansea University Medical School, Swansea, UK.

Colin McCowan (C)

School of Medicine, University of St Andrews, St Andrews, Fife, UK.

Carol Dezateux (C)

Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.

John P Robson (JP)

Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.

Niels Peek (N)

Health e-Research Centre, Institute of Population Health, University of Manchester, Manchester, UK.

Chris Holmes (C)

Department of Statistics, Oxford University, Oxford, Oxfordshire, UK.

Spiros Denaxas (S)

Institute of Health Informatics, University College London, London, London, UK.

Rhiannon Owen (R)

Department of Health Sciences, University of Leicester, Leicester, Leicestershire, UK.

Keith R Abrams (KR)

Department of Health Sciences, University of Leicester, Leicester, Leicestershire, UK.

Ann John (A)

Population Data Science, Swansea University Medical School, Swansea, UK.

Dermot O'Reilly (D)

Epidemiology and Public Health, Queens University Belfast, Belfast, UK.

Sylvia Richardson (S)

Department of Epidemiology and Public Health, MRC Biostatistics Unit, Cambridge, UK.

Marlous Hall (M)

School of Medicine, University of Leeds, Leeds, UK.

Chris P Gale (CP)

School of Medicine, University of Leeds, Leeds, UK.

Jan Davies (J)

Members of the public, Swansea, UK.

Chris Davies (C)

Members of the public, Swansea, UK.

Lynsey Cross (L)

Population Data Science, Swansea University Medical School, Swansea, UK.

John Gallacher (J)

Department of Psychiatry, Oxford University, Oxford, UK.

James Chess (J)

Renal Unit, Swansea Bay University Health Board, Swansea, UK.

Anthony J Brookes (AJ)

Department of Genetics, University of Leicester, Leicester, UK.

Ronan A Lyons (RA)

Population Data Science, Swansea University Medical School, Swansea, UK.

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Classifications MeSH