大数据能否解决大问题？根据前瞻性肥胖系统图报告肥胖数据现状。
Can big data solve a big problem? Reporting the obesity data landscape in line with the Foresight obesity system map.
机构信息
Leeds Institute for Data Analytics and School of Medicine, University of Leeds, Leeds, LS2 9JT, UK.
School of Sport, Leeds Beckett University, Leeds, LS6 3QQ, UK.
出版信息
Int J Obes (Lond). 2018 Dec;42(12):1963-1976. doi: 10.1038/s41366-018-0184-0. Epub 2018 Sep 21.
BACKGROUND
Obesity research at a population level is multifaceted and complex. This has been characterised in the UK by the Foresight obesity systems map, identifying over 100 variables, across seven domain areas which are thought to influence energy balance, and subsequent obesity. Availability of data to consider the whole obesity system is traditionally lacking. However, in an era of big data, new possibilities are emerging. Understanding what data are available can be the first challenge, followed by an inconsistency in data reporting to enable adequate use in the obesity context. In this study we map data sources against the Foresight obesity system map domains and nodes and develop a framework to report big data for obesity research. Opportunities and challenges associated with this new data approach to whole systems obesity research are discussed.
METHODS
Expert opinion from the ESRC Strategic Network for Obesity was harnessed in order to develop a data source reporting framework for obesity research. The framework was then tested on a range of data sources. In order to assess availability of data sources relevant to obesity research, a data mapping exercise against the Foresight obesity systems map domains and nodes was carried out.
RESULTS
A reporting framework was developed to recommend the reporting of key information in line with these headings: Background; Elements; Exemplars; Content; Ownership; Aggregation; Sharing; Temporality (BEE-COAST). The new BEE-COAST framework was successfully applied to eight exemplar data sources from the UK. 80% coverage of the Foresight obesity systems map is possible using a wide range of big data sources. The remaining 20% were primarily biological measurements often captured by more traditional laboratory based research.
CONCLUSIONS
Big data offer great potential across many domains of obesity research and need to be leveraged in conjunction with traditional data for societal benefit and health promotion.
背景
人群层面的肥胖研究具有多方面和复杂性。英国的前瞻性肥胖系统图谱对此进行了描述，确定了超过 100 个变量，分布在七个被认为影响能量平衡和随后肥胖的领域。传统上，缺乏考虑整个肥胖系统的数据。然而，在大数据时代，新的可能性正在出现。了解可用的数据可能是第一个挑战，其次是数据报告的不一致，以使其在肥胖背景下得到充分利用。在这项研究中，我们根据前瞻性肥胖系统图谱的领域和节点对数据源进行了映射，并开发了一个报告肥胖研究大数据的框架。讨论了这种针对整个系统肥胖研究的新数据方法的机会和挑战。
方法
利用 ESRC 肥胖战略网络的专家意见，开发了一个肥胖研究数据源报告框架。然后，该框架在一系列数据源上进行了测试。为了评估与肥胖研究相关的数据源的可用性，对前瞻性肥胖系统图谱的领域和节点进行了数据映射练习。
结果
开发了一个报告框架，建议按照以下标题报告关键信息：背景；要素；范例；内容；所有权；聚合；共享；时间性（BEE-COAST）。新的 BEE-COAST 框架成功应用于来自英国的八个范例数据源。使用广泛的大数据源，可以实现前瞻性肥胖系统图谱的 80%的覆盖。其余的 20%主要是生物学测量值，通常由更传统的基于实验室的研究捕获。
结论
大数据在肥胖研究的许多领域都具有巨大的潜力，需要与传统数据结合使用，以造福社会和促进健康。
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