Over the last decade dramatic advances have been made in both the technology and data available to better understand the multifactorial influences on child and adolescent health and development. This paper seeks to clarify methods that can be used to link information from health, education, social care and research datasets. Linking these different types of data can facilitate epidemiological research that investigates mental health from the population to the patient; enabling advanced analytics to better identify, conceptualise and address child and adolescent needs. The majority of adolescent mental health research is not able to maximise the full potential of data linkage, primarily due to four key challenges: confidentiality, sampling, matching and scalability. By presenting five existing and proposed models for linking adolescent data in relation to these challenges, this paper aims to facilitate the clinical benefits that will be derived from effective integration of available data in understanding, preventing and treating mental disorders.

中文翻译：

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英国儿童和青少年数据链接的五种模型：对现有和拟议方法的审查。


在过去的十年中，技术和数据都取得了巨大的进步，可以更好地了解对儿童和青少年健康和发展的多因素影响。本文旨在阐明可用于链接健康、教育、社会关怀和研究数据集信息的方法。将这些不同类型的数据联系起来可以促进流行病学研究，调查从人群到患者的心理健康状况；实现高级分析，以更好地识别、概念化和满足儿童和青少年的需求。大多数青少年心理健康研究无法最大限度地发挥数据链接的全部潜力，这主要是由于四个关键挑战：保密性、抽样、匹配和可扩展性。通过提出五个现有和拟议的模型来链接与这些挑战相关的青少年数据，本文旨在促进有效整合现有数据在理解、预防和治疗精神障碍方面所带来的临床益处。