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

Jane Lyons, Ashley Akbari, Utkarsh Agrawal, Gill Harper, Amaya Azcoaga-Lorenzo, Rowena Bailey, James Rafferty, Alan Watkins, Richard Fry, Colin McCowan, Carol Dezateux, John P. Robson, Niels Peek, Chris Holmes, Spiros Denaxas, Rhiannon Owen, Keith R. Abrams, Ann John, Dermot O'Reilly, Sylvia RichardsonMarlous Hall, Chris P. Gale, Jan Davies, Chris Davies, Lynsey Cross, John Gallacher, James Chess, Anthony J. Brookes, Ronan A. Lyons*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Downloads (Pure)

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.

Original languageEnglish
Article numbere047101
Number of pages8
JournalBMJ Open
Volume11
DOIs
Publication statusPublished - 19 Jan 2021

Bibliographical note

Funding Information:
Funding Two UK-wide collaborative efforts have been formed to address several of the AMS report priorities: Measuring and Understanding Multimorbidity using Routine Data in the UK – (MUrMuRUK) and Application of machine learning to discover new multimorbidity phenotypes associated with poorer outcomes (WMML). 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.

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

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • epidemiology
  • geriatric medicine
  • health policy
  • primary care
  • public health

ASJC Scopus subject areas

  • Medicine(all)

Fingerprint

Dive into the research topics of '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'. Together they form a unique fingerprint.

Cite this