Mother and Infant Research Electronic Data Analysis (MIREDA): A protocol for creating a common data model for federated analysis of UK birth cohorts and the life course.

IF 2.2 Q3 HEALTH CARE SCIENCES & SERVICES
International Journal of Population Data Science Pub Date : 2024-09-12 eCollection Date: 2024-01-01 DOI:10.23889/ijpds.v9i2.2406
Mike Seaborne, Hope Jones, Neil Cockburn, Stevo Durbaba, Arturo González-Izquierdo, Amy Hough, Dan Mason, Carlos Sánchez-Soriano, Chris Orton, Armando Méndez-Villalon, Tom Giles, David Ford, Phillip Quinlan, Krish Nirantharakumar, Lucilla Poston, Rebecca Reynolds, Gillian Santorelli, Sinead Brophy
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引用次数: 0

Abstract

Introduction: 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.

Objectives: 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.

Methods: 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.

Ethics: Each cohort has approval from their TRE through compliance with their project application processes and information governance.

Dissemination: 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.

Abstract Image

母婴研究电子数据分析(MIREDA):为联合分析英国出生队列和生命历程创建通用数据模型的协议。
出生队列是研究早期生活、健康、疾病和发育的决定因素的宝贵资源。它们对于学习人生历程是必不可少的。动态纵向电子队列使用常规收集的数据,是实时的,可以减少与传统出生队列直接招募相关的选择偏差。然而,这些数据仅限于卫生和行政数据,可能缺乏背景信息。母婴研究电子数据分析(MIREDA)伙伴关系通过调整现有的电子出生队列,使其具有相同的结构、内容和词汇表,创建了一个全英国范围的出生队列,从而实现了全英国范围的联合分析。目标:使用通用数据模型(CDM)创建一个核心动态、全英国范围内的实时电子出生队列,其中每年约有50万新生儿。从临床实践研究数据链(CPRD)、MuM-PreDiCT和布拉德福德、威尔士、苏格兰和南伦敦的“出生”计划中,为出生的长期随访提供数据链接和自动化,以进行比较分析。方法:建立核心数据内容,整理可链接数据。一套提取、转换和加载(ETL)工具将用于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。元数据将为公众上传到健康数据研究(HDRUK)创新网关。我们将为研究人员开发一个单一的在线数据访问请求。研究人员将编制一份队列资料以供参考。道德规范:每个队列通过遵守其项目申请流程和信息治理,获得其TRE的批准。传播:我们将与该领域的研究人员合作,通过伙伴关系网络、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。
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
386
审稿时长
20 weeks
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