Population Data Science, Swansea University Medical School, Swansea, UK
Population Data Science, Swansea University Medical School, Swansea, UK.
BMJ Open. 2021 Jan 19;11(1):e047101. doi: 10.1136/bmjopen-2020-047101.
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.
多种疾病广泛被认为是两种或多种同时存在的慢性疾病,但尽管其患病率不断上升,它仍然是一个理解不足的全球性问题。我们创建了威尔士多种疾病电子队列(Wales Multimorbidity e-Cohort,WMC),以提供一个易于访问的研究就绪的数据资产,以进一步了解多种疾病。我们的目标是创建一个平台,支持研究,以帮助了解多种疾病的患病率、轨迹和决定因素,描述导致个人和医疗保健服务负担最高的疾病集群,并评估和向国家卫生服务和研究社区提供新的多种疾病表型和算法,以支持预防、医疗保健规划和多种疾病患者的管理。
WMC 是从与威尔士人口相关的多源人口统计学、行政和电子健康记录数据中创建和衍生而来的,这些数据位于安全匿名信息链接(Secure Anonymised Information Linkage,SAIL)数据库中。WMC 包括 290 万 2000 年 1 月 1 日在世并居住在威尔士的人,并跟踪到 2019 年 12 月 31 日,或威尔士居民离开或死亡。将使用已发表的合并症指数和表型代码列表来衡量和概念化多种疾病。研究结果将包括:(1)使用已发表的数据表型算法/本体描述多种疾病,(2)研究基线人口统计学因素与多种疾病之间的关联,(3)确定疾病集群和多种疾病的时间轨迹,以及(4)研究与死亡率和高医疗服务利用率等不良结局相关的多种疾病集群。
SAIL 数据库独立信息治理审查小组已批准本研究(SAIL 项目:0911)。研究结果将提交给政策小组、公开会议、国家和国际会议,并在同行评议期刊上发表。