| 王洪琳,孙慧洋,陈凤娟,熊华威,温莹,次仁德吉,张英娈,张洁,吕秋莹,尹凌,张振,彭志行,邹旋.基于传染病就诊和因病缺勤监测的中小学生传染病疫情预警技术及其效能分析[J].中华流行病学杂志,2026,47(3):501-507 |
| 基于传染病就诊和因病缺勤监测的中小学生传染病疫情预警技术及其效能分析 |
| Early warning technology for infectious disease outbreaks in primary and secondary school students based on integrating hospital visit data and monitoring data of school absence due to illness: an effectiveness analysis |
| 收稿日期:2025-07-03 出版日期:2026-03-19 |
| DOI：10.3760/cma.j.cn112338-20250703-00460 |
| 中文关键词: 监测预警|传染病|聚集性疫情|中小学生|评估 |
| 英文关键词: Monitoring and early warning|Infectious diseases|Cluster outbreaks|Primary and secondary school students|Evaluation |
| 基金项目:国家重点研发计划（2024YFC2311500）；国家自然科学基金（82320108018）；广州国家实验室专项（GZNL2024A01025）；广东省基础与应用基础研究基金（2022B1515120064）；深圳市科技计划（SYSPG20241211173921049） |
| 作者 | 单位 | E-mail | | 王洪琳 | 传染病溯源预警与智能决策全国重点实验室, 中国疾病预防控制中心, 北京 102206
深圳市疾病预防控制中心传染病防控科, 深圳 518055 | | | 孙慧洋 | 南华大学公共卫生学院, 衡阳 421000 | | | 陈凤娟 | 深圳市大鹏新区疾病预防控制中心传染病防制科, 深圳 518100 | | | 熊华威 | 深圳市疾病预防控制中心传染病防控科, 深圳 518055 | | | 温莹 | 深圳市疾病预防控制中心传染病防控科, 深圳 518055 | | | 次仁德吉 | 深圳市疾病预防控制中心传染病防控科, 深圳 518055 | | | 张英娈 | 深圳市疾病预防控制中心传染病防控科, 深圳 518055 | | | 张洁 | 深圳市疾病预防控制中心传染病防控科, 深圳 518055 | | | 吕秋莹 | 深圳市疾病预防控制中心传染病防控科, 深圳 518055 | | | 尹凌 | 中国科学院深圳先进技术研究院先进计算与数字工程研究所, 深圳 518055 | | | 张振 | 深圳市疾病预防控制中心传染病防控科, 深圳 518055 | | | 彭志行 | 传染病溯源预警与智能决策全国重点实验室, 中国疾病预防控制中心, 北京 102206 | zhihangpeng@njmu.edu.cn | | 邹旋 | 深圳市疾病预防控制中心传染病防控科, 深圳 518055 | zoux@wjw.sz.gov.cn |
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| 中文摘要: |
| 目的 分析中小学生传染病就诊与因病缺勤监测数据之间的关联，评估不同监测数据在传染病疫情预警中的作用。方法 选取深圳市大鹏新区13所中小学校，收集2015年1月1日至2019年12月31日中国疾病预防控制信息系统中的中小学生传染病就诊监测数据，以及深圳市学生健康监测系统中的因病缺勤监测数据。采用Spearman秩相关分析比较监测数据的时间序列相关性，运用移动平均数法结合固定阈值法建立预警策略，并以深圳市聚集性疫情监测系统中报告的中小学校聚集性疫情数据作为验证基准，通过阳性预测值、灵敏度等指标评估不同预警策略的效能。结果 共收集1 870例因传染病导致缺勤的中小学生病例，以流感、水痘和流行性腮腺炎为主，缺勤原因主要为发热、咳嗽和咽痛。期间共报告52起中小学校聚集性疫情，主要为流感和水痘。传染病就诊与因病缺勤监测数据之间存在滞后相关性（r=0.31，P0.001），后者能较早发现疫情。传染病就诊监测、因病缺勤监测、串联监测和并联监测分别触发预警385、491、298和876个预警信号。传染病就诊监测单独预警的阳性预测值最高（66.49%），串联监测预警的特异度最高（98.42%），而并联监测预警的灵敏度与约登指数均为最高（100.00%和80.52%）。结论 中小学生传染病就诊监测在时间上滞后于因病缺勤监测，采用两者并联监测预警策略能够实现疫情的准确、快速识别，为流行病学调查和防控资源配置提供数据支持。 |
| 英文摘要: |
| Objective To analyze the association between hospital visit data and data of school absence due to illness in primary and secondary school students, and evaluate the performance of different monitoring data in early warning of infectious disease outbreaks. Methods Thirteen primary and secondary schools in Dapeng New District, Shenzhen, were selected to conduct the study. Data of infectious disease-related hospital visits in local students from January 1, 2015 to December 31, 2019 were collected from Chinese Disease Prevention and Control Information System, and the monitoring data of school absence due to illness during the period were collected from Shenzhen Student Health Monitoring System. Spearman rank correlation analysis was used to evaluate the time-series correlation between the two types of data. An early warning strategy was established by using the moving average method combined with a fixed-threshold method. The performance of different early warning strategies were evaluated by using indicators such as positive predictive value (PPV) and sensitivity, the school outbreak data reported in Shenzhen Disease Cluster Surveillance System were used as the gold standard. Results A total of 1 870 cases of school absence due to infectious diseases were found in local primary and secondary school students. The main diseases were influenza, varicella, and mumps, with fever, cough, and sore throat being the most common reasons for school absence. During the study period, 52 infectious disease outbreaks were reported in local primary and secondary schools, which were mainly caused by influenza and varicella. A lag correlation was observed between the hospital visit data and the school absence monitoring data (r=0.31, P0.001), with the latter detecting outbreaks earlier. Hospital monitoring, school absence monitoring, serial monitoring and parallel monitoring triggered 385, 491, 298, and 876 early warning signals, respectively. The hospital monitoring alone achieved the highest positive prediction value (66.49%), the serial monitoring achieved the highest specificity (98.42%), and the parallel monitoring achieved the highest sensitivity (100.00%) and Youden index (80.52%). Conclusions The information about infectious disease obtained from hospital visit data lags behind monitoring data of school absence due to illness in primary and secondary school students. A parallel early warning strategy combining both monitoring methods enables accurate and rapid outbreak detection, providing data support for epidemiological investigation and prevention and control resource allocation. |
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