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症状监测:可持续系统的二十年经验——以人为本,而非数据!

Syndromic surveillance: two decades experience of sustainable systems - its people not just data!

机构信息

Real-time Syndromic Surveillance Team,Field Service, National Infection Service,Public Health England,Birmingham,UK.

Health Protection Research Unit in Emergency Preparedness and Response,National Institute for Health Research,London,UK.

出版信息

Epidemiol Infect. 2019 Jan;147:e101. doi: 10.1017/S0950268819000074.

Abstract

Syndromic surveillance is a form of surveillance that generates information for public health action by collecting, analysing and interpreting routine health-related data on symptoms and clinical signs reported by patients and clinicians rather than being based on microbiologically or clinically confirmed cases. In England, a suite of national real-time syndromic surveillance systems (SSS) have been developed over the last 20 years, utilising data from a variety of health care settings (a telehealth triage system, general practice and emergency departments). The real-time systems in England have been used for early detection (e.g. seasonal influenza), for situational awareness (e.g. describing the size and demographics of the impact of a heatwave) and for reassurance of lack of impact on population health of mass gatherings (e.g. the London 2012 Olympic and Paralympic Games).We highlight the lessons learnt from running SSS, for nearly two decades, and propose questions and issues still to be addressed. We feel that syndromic surveillance is an example of the use of 'big data', but contend that the focus for sustainable and useful systems should be on the added value of such systems and the importance of people working together to maximise the value for the public health of syndromic surveillance services.

摘要

症状监测是一种通过收集、分析和解释患者和临床医生报告的常规健康相关症状和临床体征数据,为公共卫生行动提供信息的监测形式,而不是基于微生物学或临床确诊病例。在英国,过去 20 年来,已经开发了一系列国家实时症状监测系统 (SSS),利用来自各种医疗保健环境的数据(远程医疗分诊系统、全科医生和急诊科)。英国的实时系统用于早期检测(例如季节性流感)、了解情况(例如描述热浪对人口健康的影响的规模和人口统计学)以及确保大规模集会对人口健康没有影响(例如 2012 年伦敦奥运会和残奥会)。我们强调了近 20 年来运行 SSS 所吸取的经验教训,并提出了仍需解决的问题和问题。我们认为症状监测是“大数据”应用的一个例子,但认为可持续和有用的系统的重点应该是这些系统的附加值,以及人们共同努力为公众健康最大化症状监测服务价值的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec93/6518508/ac83302a8cea/S0950268819000074_figU1.jpg

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