ArticleBMJ open2021
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.
Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- What makes administrative data "research-ready"? A systematic review and thematic analysis of published literature.International journal of population data science · 2022Pooled it
- Long-term accrual of conditions following myocardial infarction: a study of disease trajectories in the Wales Multimorbidity e-Cohort.BMC medicine · 2025Article
- Article
- Administrative data linkage to Census 2021 in Wales, UK: A cross-sectional study examining completeness and representativeness for population analytics.International journal of population data science · 2025Observational
- Observational
- Commentary on: Are multimorbidity patterns associated with fear of falling in community-dwelling older adults?Journal of frailty, sarcopenia and falls · 2024Article
- Cardiac interventions in Wales: A comparison of benefits between NHS Wales specialties.PloS one · 2024Article
- Trajectories in chronic disease accrual and mortality across the lifespan in Wales, UK (2005-2019), by area deprivation profile: linked electronic health records cohort study on 965,905 individuals.The Lancet regional health. Europe · 2023Article
- The impact of the COVID-19 pandemic on community prescription of opioid and antineuropathic analgesics for cancer patients in Wales, UK.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2023Observational
- Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study.PloS one · 2023Article
- Developing a linked electronic health record derived data platform to support research into healthy ageing.International journal of population data science · 2023Article
- Ranking sets of morbidities using hypergraph centrality.Journal of biomedical informatics · 2021Article
- Data linkage can reduce the burden and increase the opportunities in the implementation of Value-Based Health Care policy: a study in patients with ulcerative colitis (PROUD-UC Study).International journal of population data science · 2021Article
- Multidisciplinary ecosystem to study lifecourse determinants and prevention of early-onset burdensome multimorbidity (MELD-B) - protocol for a research collaboration.Journal of multimorbidity and comorbidityArticle
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Authors and funding
29 authors at 11 institutions in 1 country.
Funding
Abstract
introductionMultimorbidity 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.