Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jan 19;11(1):e047101.
doi: 10.1136/bmjopen-2020-047101.

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  1 , Ashley Akbari  2 , Utkarsh Agrawal  3 , Gill Harper  4 , Amaya Azcoaga-Lorenzo  3 , Rowena Bailey  2 , James Rafferty  2 , Alan Watkins  2 , Richard Fry  2 , Colin McCowan  3 , Carol Dezateux  4 , John P Robson  4 , Niels Peek  5 , Chris Holmes  6 , Spiros Denaxas  7 , Rhiannon Owen  8 , Keith R Abrams  8 , Ann John  2 , Dermot O'Reilly  9 , Sylvia Richardson  10 , Marlous Hall  11 , Chris P Gale  11 , Jan Davies  12 , Chris Davies  12 , Lynsey Cross  2 , John Gallacher  13 , James Chess  14 , Anthony J Brookes  15 , Ronan A Lyons  2
Affiliations

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 et al. BMJ Open. .

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.

Keywords: epidemiology; geriatric medicine; health policy; primary care; public health.

PubMed Disclaimer

Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
WMC flow diagram, based on inclusion criteria. LSOA; lower layer super output area, WDSD; Welsh Demographic Service Dataset, WMC; Wales Multimorbidity e-Cohort, WMML; Welsh Multimorbidity Machine Learning.
Figure 2
Figure 2
WMC pyramid for age (years) at cohort inception. WMC, Wales Multimorbidity e-Cohort.
Figure 3
Figure 3
Heatmap of person years of WMC follow-up, by age group, sex and area-level deprivation at cohort inception. WMC, Wales Multimorbidity e-Cohort.
Figure 4
Figure 4
Number of WMC individuals utilising healthcare services recorded in multisource data sources, 20 most common combinations presented. WMC, Wales Multimorbidity e-Cohort.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. The Academy of Medical Sciences Multimorbidity: a priority for global health research, 2018. Available: https://acmedsci.ac.uk/file-download/82222577
    1. WHO The challenges of a changing world. the world health report 2008—primary health care (now more than ever), 2008. Available: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0...
    1. Barnett K, Mercer SW, Norbury M, et al. . Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet 2012;380:37–43. 10.1016/S0140-6736(12)60240-2 - DOI - PubMed
    1. Cassell A, Edwards D, Harshfield A, et al. . The epidemiology of multimorbidity in primary care: a retrospective cohort study. Br J Gen Pract 2018;68:e245–51. 10.3399/bjgp18X695465 - DOI - PMC - PubMed
    1. Nunes BP, Flores TR, Mielke GI, et al. . Multimorbidity and mortality in older adults: a systematic review and meta-analysis. Arch Gerontol Geriatr 2016;67:130–8. 10.1016/j.archger.2016.07.008 - DOI - PubMed

Publication types