医疗机构急性呼吸道传染病症状监测预警研究进展

郭玉清; 林帆; 李柏松; 吴彦霖; 李开明; 郑亚明; 李刚; 王丽萍

doi:10.19837/j.cnki.ahyf.2023.06.001

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安徽预防医学杂志 ›› 2023, Vol. 29 ›› Issue (6) : 441-449. DOI: 10.19837/j.cnki.ahyf.2023.06.001

专家约稿

医疗机构急性呼吸道传染病症状监测预警研究进展

郭玉清¹, 林帆¹, 李柏松^1,2, 吴彦霖¹, 李开明¹, 郑亚明¹, 李刚³, 王丽萍⁴

作者信息 +

Febrile respiratory syndromic surveillance and early warning systems based on medical institutions

GUO Yuqing¹, LIN Fan¹, LI Bosong^1,2, WU Yanlin¹, LI Kaiming¹, ZHENG Yaming¹, LI Gang³, WANG Liping⁴

Author information +

文章历史 +

摘要

目的使用系统综述的方法对全球发热呼吸道症状监测预警系统现状进行梳理,为我国推进医疗机构发热呼吸道症状监测预警工作提供参考。方法系统检索万方数据知识服务平台、中国知网、PubMed、Web of Science数据库中截至2022年10月发表的症状监测预警相关文献。根据预先制定的纳入、排除标准筛选文献并提取信息。对纳入文献的监测预警系统建设情况、发热呼吸道症状病例定义、数据收集方式、预警模型应用与评估等进行分析和总结。结果共纳入48篇文献。根据纳入文献确定发热呼吸道症状监测预警系统27个,其建设应用目的主要是实现疾病早期预警及指导快速启动公共卫生行动,突发传染病疫情应对、大型活动保障及生物恐怖事件处置等加速了系统的建立与探索研究。发热(12/17,70.6%)、咳嗽(14/17,82.4%)、呼吸困难(11/17,64.7%)、咽痛(10/17,58.8%)和胸痛(8/17,47.1%)等为纳入发热呼吸道症状病例定义的首选症状。13个(13/27,48.2%)系统采用自然语言算法或计算机程序等数据自动获取技术,实现对源自医院常规诊疗产生的主诉文本/诊断代码等监测病例信息自动采集与分类,主要分布于欧美国家。全球症状监测预警系统常用的预警模型主要为时间序列预警模型,如累积和模型(CUSUM)、早期异常报告系统(EARS)中的C1、C2、C3模型以及指数加权移动平均(EWMA)模型。结论发热呼吸道症状监测作为急性呼吸道传染病早期预警的有效补充,技术进步使得数据自动提取和分析成为可能,报告更接近“实时”。我国应加强相关研究与实践,以期尽快建立基于医疗机构自动感知的发热呼吸道症状监测预警系统,实现关口前移,进一步提升我国急性严重呼吸道传染病监测预警的敏感性。

Abstract

Objective The aim of this study was to systematically summarize the key characteristics of febrile respiratory syndromic surveillance and early warning systems that have been established and used globally,so as to provide a reference to promote febrile respiratory surveillance and early warning work in medical institutions in China. Methods A systematic search of English databases such as PubMed,Web of Science,and Chinese databases such as CNKI,WanFang was used to screen out the literature about syndromic surveillance and early warning before October 2022.In addition,the relevant articles which met the inclusion criteria were sought further to identify potentially eligible studies.Qualitative description of establishment of surveillance and early warning system,case definition of febrile respiratory syndrome,data collection,model selection,evaluation,and others was conducted. Results Totally 48 studies were identified as eligible for inclusion in the review.According to the included literature,27 surveillance and early warning systems for febrile respiratory syndromic were identified.The main purpose of its construction and application was to achieve early warning of diseases and guide the rapid launch of public health actions.The response to sudden infectious disease outbreaks,large-scale event support,and bioterrorism event response had accelerated the establishment and exploration of the system.Fever (12/17,70.6%),cough (14/17,82.4%),breathing difficulty (11/17,64.7%),sore throat (10/17,58.8%) and chest pain (8/17,47.1%) were first selected in the case definition.13 (13/27,48.2%) systems across America and Europe mainly utilized natural language algorithms or computer programs to automatically collect and classify the information of surveillance cases being from chief complaint/preliminary diagnoses collected during routine patient care.Time seriesearly warning models were still commonly used in global syndromic surveillance and early warning systems,such as cumulative sum (CUSUM) model,the C1,C2,C3 models in the early aberration reporting system (EARS) and the exponentially weighted moving average (EWMA) model. Conclusion Febrile respiratory syndromic surveillance is a relatively effective addition to the early identification of acute respiratory infectious diseases.It is possible that reporting has become closer to “real-time” with automated extraction and analysis due to technological advances.Relevant researches and practices should be strengthened in order to establish an automatic sensing monitoring and early warning system for febrile respiratory symptoms based on medical institutions,to realize the forward movement of warning gate and further enhance the sensitivity of monitoring and early warning of acute severe respiratory infectious diseases in China.

导出引用

郭玉清, 林帆, 李柏松, 吴彦霖, 李开明, 郑亚明, 李刚, 王丽萍. 医疗机构急性呼吸道传染病症状监测预警研究进展[J]. 安徽预防医学杂志. 2023, 29(6): 441-449 https://doi.org/10.19837/j.cnki.ahyf.2023.06.001

GUO Yuqing, LIN Fan, LI Bosong, WU Yanlin, LI Kaiming, ZHENG Yaming, LI Gang, WANG Liping. Febrile respiratory syndromic surveillance and early warning systems based on medical institutions[J]. Anhui Journal of Preventive Medicine. 2023, 29(6): 441-449 https://doi.org/10.19837/j.cnki.ahyf.2023.06.001

中图分类号： R181.8

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