The Edward Mallinckrodt Department of Pediatrics, Washington University in St. Louis School of Medicine, and St. Louis Children's Hospital, St. Louis, MO, USA.
Pediatr Res. 2023 Jan;93(2):342-349. doi: 10.1038/s41390-022-02264-9. Epub 2022 Aug 16.
Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.
儿童健康是由一系列复杂而动态的决定因素构成的，包括遗传、文化、营养、感染和环境等方面，这些决定因素在从受孕前到青春期的不同发育阶段发挥作用。这个网络塑造了儿童的未来、对成年疾病的易感性以及儿童个体的健康结果。进化在胎儿期、婴儿期、儿童期和青春期选择适应可预测和不可预测的暴露/压力的特征，通过创造替代的发育表型轨迹来实现。尽管过去 30 年来，美国和全球的儿童健康状况都有所改善，但要持续改善，就需要获得能够充分反映这些相互作用复杂性的数据，以及新的分析方法。大数据和创新的数据科学方法为整合多个数据维度提供了工具，以便描述最佳的临床、预测和预防实践，减少儿童健康结果中的种族差异，纳入患者和家庭在医疗评估中的意见，并定义个体疾病风险、机制和治疗方法。然而，要利用这些资源，就需要制定新的策略，有针对性地解决机构、伦理、监管、文化、技术和系统障碍，同时与来自不同背景的儿童和家庭建立伙伴关系，承认历史上存在的不信任根源。我们强调现有的儿科大数据计划，并确定未来研究的领域。影响：大数据和数据科学可以改善儿童健康。这篇综述强调了儿童特异性和生命历程大数据和数据科学策略对儿童健康的重要性。本综述为未来儿科大数据和数据科学研究提供了建议。
