数据系统中儿童的不可见性

M. Rigby, S. Deshpande, D. Luzi, F. Pecoraro, Oscar Tamburis, I. Rocco, Barbara Corso, Nadia Mimnicuci, H. Liyanage, U. Hoang, F. Ferreira, S. Lusignan, Ekelechi MacPepple, H. Gage
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引用次数: 5

摘要

为了评估欧洲儿童的健康状况,或评估医疗保健提供的系统和模式,关于儿童的数据是必不可少的,并尽可能精确和准确地按小群体特征进行。不幸的是,儿童健康评估模型(MOCHA)项目及其科学家的经验表明,这一理想很少得到满足,因此评估或规划工作的准确性受到损害。在这个项目中,我们对欧洲和全球使用的多个数据库收集的儿童数据进行了研究,发现尽管4个五岁年龄组是常见的,但在统计分析中不可能将0-17岁的儿童作为一个法律定义的群体。尤其是青少年,他们是最不被人注意的年龄组,尽管他们正处于一个迅速变化并面临越来越多挑战的人生阶段。在测量和监测方面,与近20年前建议建立信息系统的工作相比,进展甚微,也没有关注建立政策和道德框架,以允许对保存真实世界中基于人的数据的丰富匿名数据库进行协作分析。在数据系统和监测方面,欧洲社会几乎所有的系统都口头上承认儿童的重要性,但没有在实际和统计意义上照顾他们。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Invisibility of Children in Data Systems
In order to assess the state of health of Europe’s children, or to appraise the systems and models of healthcare delivery, data about children are essential, with as much precision and accuracy as possible by small group characteristic. Unfortunately, the experience of the Models of Child Health Appraised (MOCHA) project and its scientists shows that this ideal is seldom met, and thus the accuracy of appraisal or planning work is compromised. In the project, we explored the data collected on children by a number of databases used in Europe and globally, to find that although the four quinquennial age bands are common, it is impossible to represent children aged 0–17 years as a legally defined group in statistical analysis. Adolescents, in particular, are the most invisible age group despite this being a time of life when they are rapidly changing and facing increasing challenges. In terms of measurement and monitoring, there is little progress from work of nearly two decades ago that recommended an information system, and no focus on the creation of a policy and ethical framework to allow collaborative analysis of the rich anonymised databases that hold real-world people-based data. In respect of data systems and surveillance, nearly all systems in European society pay lip service to the importance of children, but do not accommodate them in a practical and statistical sense.
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