Center for Healthcare Research in Pediatrics, Department of Population Medicine, Harvard Medical School, Harvard University and Harvard Pilgrim Health Care, Boston, Massachusetts
Department of Sociology and Criminology, Population Research Institute, Pennsylvania State University, University Park, Pennsylvania.
Pediatrics. 2021 Jul;148(Suppl 1):s25-s32. doi: 10.1542/peds.2021-050693F.
Advances in new technologies, when incorporated into routine health screening, have tremendous promise to benefit children. The number of health screening tests, many of which have been developed with machine learning or genomics, has exploded. To assess efficacy of health screening, ideally, randomized trials of screening in youth would be conducted; however, these can take years to conduct and may not be feasible. Thus, innovative methods to evaluate the long-term outcomes of screening are needed to help clinicians and policymakers make informed decisions. These methods include using longitudinal and linked-data systems to evaluate screening in clinical and community settings, school data, simulation modeling approaches, and methods that take advantage of data available in the digital and genomic age. Future research is needed to evaluate how longitudinal and linked-data systems drawing on community and clinical settings can enable robust evaluations of the effects of screening on changes in health status. Additionally, future studies are needed to benchmark participating individuals and communities against similar counterparts and to link big data with natural experiments related to variation in screening policies. These novel approaches have great potential for identifying and addressing differences in access to screening and effectiveness of screening across population groups and communities.
新技术的进步，如果融入到常规健康筛查中，将有望极大地造福儿童。健康筛查测试的数量急剧增加，其中许多是使用机器学习或基因组学开发的。为了评估健康筛查的效果，理想情况下，应该对年轻人进行筛查的随机试验；然而，这些试验可能需要数年时间，并且可能不可行。因此，需要创新的方法来评估筛查的长期结果，以帮助临床医生和决策者做出明智的决策。这些方法包括使用纵向和关联数据系统来评估临床和社区环境中的筛查，利用学校数据、模拟建模方法以及利用数字和基因组时代可用数据的方法。需要未来的研究来评估如何利用社区和临床环境中的纵向和关联数据系统来对筛查对健康状况变化的影响进行强有力的评估。此外，还需要未来的研究来将参与的个人和社区与类似的对照组进行基准测试，并将大数据与与筛查政策变化相关的自然实验联系起来。这些新方法具有很大的潜力，可以识别和解决不同人群和社区之间筛查机会和筛查效果的差异。
