| Literature DB >> 33468531 |
Jane Lyons1, Ashley Akbari2, Utkarsh Agrawal3, Gill Harper4, Amaya Azcoaga-Lorenzo3, Rowena Bailey2, James Rafferty2, Alan Watkins2, Richard Fry2, Colin McCowan3, Carol Dezateux4, John P Robson4, Niels Peek5, Chris Holmes6, Spiros Denaxas7, Rhiannon Owen8, Keith R Abrams8, Ann John2, Dermot O'Reilly9, Sylvia Richardson10, Marlous Hall11, Chris P Gale11, Jan Davies12, Chris Davies12, Lynsey Cross2, John Gallacher13, James Chess14, Anthony J Brookes15, Ronan A Lyons2.
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. © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.Entities:
Keywords: epidemiology; geriatric medicine; health policy; primary care; public health
Mesh:
Year: 2021 PMID: 33468531 PMCID: PMC7817800 DOI: 10.1136/bmjopen-2020-047101
Source DB: PubMed Journal: BMJ Open ISSN: 2044-6055 Impact factor: 2.692