Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
Research Triangle Institute, Research Triangle Park, NC, USA.
Pediatr Res. 2024 Jun;95(7):1726-1733. doi: 10.1038/s41390-024-03039-0. Epub 2024 Feb 16.
The United States (U.S.) National Institutes of Health-funded Environmental influences on Child Health Outcomes (ECHO)-wide Cohort was established to conduct high impact, transdisciplinary science to improve child health and development. The cohort is a collaborative research design in which both extant and new data are contributed by over 57,000 children across 69 cohorts. In this review article, we focus on two key challenging issues in the ECHO-wide Cohort: data collection standardization and data harmonization. Data standardization using a Common Data Model and derived analytical variables based on a team science approach should facilitate timely analyses and reduce errors due to data misuse. However, given the complexity of collaborative research designs, such as the ECHO-wide Cohort, dedicated time is needed for harmonization and derivation of analytic variables. These activities need to be done methodically and with transparency to enhance research reproducibility. IMPACT: Many collaborative research studies require data harmonization either prior to analyses or in the analyses of compiled data. The Environmental influences on Child Health Outcomes (ECHO) Cohort pools extant data with new data collection from over 57,000 children in 69 cohorts to conduct high-impact, transdisciplinary science to improve child health and development, and to provide a national database and biorepository for use by the scientific community at-large. We describe the tools, systems, and approaches we employed to facilitate harmonized data for impactful analyses of child health outcomes.
美国国立卫生研究院(NIH)资助的环境对儿童健康结果(ECHO)全队列研究旨在开展具有重大影响的跨学科科学研究,以改善儿童健康和发育。该队列是一种合作研究设计,其中 69 个队列中的 57000 多名儿童贡献了既有数据和新数据。在这篇综述文章中,我们重点关注 ECHO 全队列研究中的两个关键挑战问题:数据采集标准化和数据协调。使用通用数据模型和基于团队科学方法的衍生分析变量进行数据标准化,应有助于及时分析并减少因数据误用而导致的错误。然而,考虑到 ECHO 全队列等合作研究设计的复杂性,需要专门的时间来协调和衍生分析变量。这些活动需要有条不紊地进行,并具有透明度,以提高研究的可重复性。影响:许多合作研究需要在分析前或在编译数据的分析中进行数据协调。环境对儿童健康结果(ECHO)队列研究汇集了 69 个队列中 57000 多名儿童的既有数据和新数据收集,以开展具有重大影响的跨学科科学研究,改善儿童健康和发育,并为整个科学界提供国家数据库和生物库。我们描述了我们用于促进协调数据以进行有影响力的儿童健康结果分析的工具、系统和方法。