Seaborne Mike, Jones Hope, Cockburn Neil, Durbaba Stevo, González-Izquierdo Arturo, Hough Amy, Mason Dan, Sánchez-Soriano Carlos, Orton Chris, Méndez-Villalon Armando, Giles Tom, Ford David, Quinlan Phillip, Nirantharakumar Krish, Poston Lucilla, Reynolds Rebecca, Santorelli Gillian, Brophy Sinead
National Centre for Population Health and Wellbeing Research, Swansea University Medical School, Swansea, UK.
Data Lab, National Centre for Population Health and Wellbeing Research, Swansea University Medical School, Swansea, UK.
Int J Popul Data Sci. 2024 Sep 12;9(2):2406. doi: 10.23889/ijpds.v9i2.2406. eCollection 2024.
Birth cohorts are valuable resources for studying early life, the determinants of health, disease, and development. They are essential for studying life course. Dynamic longitudinal electronic cohorts use routinely collected data, are live, and can reduce selection bias specifically associated with direct recruitment in traditional birth cohorts. However, they are limited to health and administrative data and may lack contextual information.The MIREDA (Mother and Infant Research Electronic Data Analysis) partnership creates a UK-wide birth cohort by aligning existing electronic birth cohorts to have the same structure, content, and vocabularies, enabling UK-wide federated analyses.
Create a core dynamic, live UK-wide electronic birth cohort with approximately 500,000 new births per year using a common data model (CDM).Provide data linkage and automation for long-term follow up of births from the Clinical Practice Research Datalink (CPRD), MuM-PreDiCT and the 'Born in' initiatives of Bradford, Wales, Scotland, and South London for comparable analyses.
We will establish core data content and collate linkable data. A suite of extraction, transformation, and load (ETL) tools will be used to transform data for each birth cohort into the CDM. Transformed datasets will remain within each cohort's trusted research environment (TRE). Metadata will be uploaded for the public to the Health Data Research (HDRUK) Innovation Gateway. We will develop a single online data access request for researchers. A cohort profile will be developed for researchers to reference the resource.
Each cohort has approval from their TRE through compliance with their project application processes and information governance.
We will engage with researchers in the field to promote our resource through partnership networking, publication, research collaborations, conferences, social media, and marketing communications strategies.
出生队列是研究早期生活、健康、疾病和发育决定因素的宝贵资源。它们对于研究生命历程至关重要。动态纵向电子队列使用常规收集的数据,实时存在,并且可以减少与传统出生队列中直接招募相关的选择偏倚。然而,它们仅限于健康和行政数据,可能缺乏背景信息。MIREDA(母婴研究电子数据分析)合作项目通过使现有的电子出生队列具有相同的结构、内容和词汇,创建了一个全英国范围的出生队列,从而实现全英国范围的联合分析。
使用通用数据模型(CDM)创建一个核心动态、实时的全英国范围电子出生队列,每年约有50万例新生儿。为来自临床实践研究数据链(CPRD)、MuM-PreDiCT以及布拉德福德、威尔士、苏格兰和南伦敦的“出生于”计划的出生记录提供数据链接和自动化功能,以便进行可比分析。
我们将确定核心数据内容并整理可链接的数据。将使用一套提取、转换和加载(ETL)工具将每个出生队列的数据转换为CDM。转换后的数据集将保留在每个队列的可信研究环境(TRE)中。元数据将上传至健康数据研究(HDRUK)创新网关供公众使用。我们将为研究人员开发一个单一的在线数据访问请求。将为研究人员开发一个队列简介以供参考该资源。
每个队列已通过其TRE根据其项目申请流程和信息治理获得批准。
我们将与该领域的研究人员合作,通过合作网络、出版物、研究合作、会议、社交媒体和营销传播策略来推广我们的资源。