珠海市2015-2019年学校传染病症状监测系统数据分析

描述2015-2019年珠海市学校传染病症状监测系统监测数据的时间分布并评估监测系统的准确性及预警作用,为完善珠海市学校传染病症状监测系统及传染病防控提供依据.方法 从珠海市学校传染病症状监测系统导出2015-2019年全市学校的症状监测数据,描述近5年学校学生发病症状的时间分布特点,结合相关传染病疫情的时间分布,评估监测系统的准确性;结合学校发生的突发公共卫生事件,比较事件报告日期间隔天数,评估监测系统的预警作用.结果 监测系统中监测到呼吸道疾病症状(发热、咳嗽、咽痛)的出现高峰为每年3,5和12月,消化道疾病症状(呕吐和腹泻)的出现高峰为每年的3和6月,皮疹的出现高峰为每年的5月.突发公共卫生事件报告日期间隔天数的中位数为(3.0±1.4)d,各年之间的报告日期间隔天数差异无统计学意义(F=1.01,P>0.05).不同类型学校的报告日期间隔天数不同(F=8.18,P相似文献   

学校传染病症状监测及暴发疫情分析

目的对学校传染病症状监测系统进行评价,对疫情暴发情况进行分析。方法天津市滨海新区汉沽疾控中心于2012年9月起在辖区34年学校中选取7所监测点学校,开展学生因病缺课传染病症状监测工作。并与监测点医院数据进行比对,对症状监测系统进行效果评价。结果 2012年9月3日—2013年1日15日(1个学期),监测点学校症状监测系统共报告缺课学生人数540例,符合流感样病例人数255例,占47.22%,普通感冒244例,腹痛、腹泻13例,肺炎11例。7所学校及时发现5所学校流感暴发事件,均为甲3型季节流感。其中2所中学,3所小学。5所学校累计报告流感样病例170例,波及人数506例,平均罹患率为3.09%。哨点医院监测发现学校流感样病例暴发疫情较学校监测时间晚了17 d。结论通过对学校症状监测,可对传染病疫情及时预警,补充哨点医院监测系统中的不足,及时发现暴发疫情,及时处置。     

传染病症状监测系统的设计要点与方法

近十年来,症状监测作为一种新兴的公共卫生监测手段引起了普遍的关注.相对于基于病例诊断的传统公共卫生监测手段,症状监测是对临床诊断前患者相关的非特异性信息进行监测[1].相关研究表明,症状监测可应用于公共危机应对(如生物恐怖事件早期发现[2,3]、自然灾害传染病应急监测[4]),早期探测新发传染病[5],掌握疾病发病水平与流行趋势(如急性迟缓性麻痹综合征监测和流感样病例监测[6,7]),以及大型体育活动与政治集会等大规模人群聚集活动公共卫生保障[8,9].     

常州市传染病症状监测系统应用的初步分析

从1988年上海甲肝流行,1998年南通的猪链球菌感染,2003年非典爆发到2005年禽流感疫情,这些事件在一次次促进传染病防治发展的同时,也一次次暴露出对急性传染病预警及应急处理能力的严重不足。在急性传染病爆发的应急处理过程中,一个敏感的信息系统和反应灵活的应急机制是有效的     

浦东新区学校传染病症状监测信息系统的建立与初步分析

目的对上海世博会期间浦东新区学校传染病症状监测信息系统的建立与运作情况进行分析评价,探讨该系统在学校传染病疫情预防控制中发挥的作用。方法开发"学生症状监测信息系统",从2010年5月1日起,学校通过信息系统网络报告发热伴呼吸道、胃肠道腹泻和发热伴出疹等3个症状群因病缺课数据以及异常情况事件。结果 478家中小学校及托幼机构加入该监测信息系统,2010年184天世博会活动期间,450家学校及托幼机构坚持每天及时上报症状监测信息;57家(11.9%)学校及托幼机构报告基于暴发疫情早期的异常情况事件409起,共发现11起学校/托幼机构传染病集聚性事件,平均每起事件报告病例3.5例。结论 "学校症状监测信息系统"在本地区成功建立并正常运作,对中小学校及托幼机构传染病预防控制及疫情早期预警防控起到了积极的作用。     

沈阳市重大呼吸道传染病症状监测分析

在全球生物恐怖威胁与传染病暴发形式日益严峻的今天．如何发现重大传染病、新发传染病的暴发或流行的征兆,尤其是在症状出现早期发现疾病的异常动态,一直是公共卫生急需解决的问题。症状监测近几年在国外得到了广泛的应用．在一定程度上促进了对疾病及其相关事件预警能力的提高。减少了损失。我国的症状监测系统尚处于探索运行的雏形阶段。部分省份都在探讨控制症状监测系统,即多采用前5年的历史疫情资料,采用控制图法,当报告病例超过虚线上限（虚线代表距离平均值2个标准差）即表示控制到异常情况来实现预警。为此,沈阳市开展疾病预防控制机构派出流行病学医生进驻医院有关临床科室,建立传染病的现场监测体系,并实行日报告制度,以达到早期排查由禽流感病毒、SAILS病毒引起的发热患者或不明原因肺炎患者,     

农村地区建立传染病症状监测系统的可行性——以江西省2县为例

目的：从村卫生室服务能力入手,探索在农村地区建立传染病症状监测系统的可行性。方法：通过问卷调查和小组访谈分析江西省2个县15个乡镇155家村卫生室的资源配置以及253名村医开展传染病症状监测的能力,以及相应的期望和建议。结果：“一村一所”管理模式下的村卫生室门诊量大,病人集中,更适合症状监测的开展;网络直报是症状监测数据报告的首选方式,但有12．5％的村卫生室负责人不会使用电脑;村医接触最多的五种传染性疾病是上感、其他感染性腹泻、流行性腮腺炎、水痘和痢疾,分别有84．6％和71．5％的村医能够通过临床症状诊断流行性腮腺炎和水痘;75．9％的村医发现传染病人后会立即报告乡镇卫生院,77．1％的村医参与过传染病的调查核实。结论：依托村卫生室构建传染病症状监测系统具有可行性,但需完善村卫生室管理模式,提高卫生服务可及性;明确目标监测疾病,促进资源的有效利用;充分利用信息网络技术,搭建症状监测报告平台;大力推进乡村一体化管理,完善监测信号响应机制。     

学校传染病症状监测疫情暴发实例分析

学校是传染病突发公共卫生事件的高发场所,为尽早预警学校传染病疫情,前移学校传染病防治工作,辽宁省自2010年4月起在7个城市选取部分监测点校,开展学生因病缺课传染病症状监测工作。现以本溪市某监测点校发生腮腺炎疫情为     

美国疾病预防控制中心和世界卫生组织传染病监测系统评价策略分析

目的 对比分析美国疾病预防控制中心(CDC)及WHO的监测系统评价方案,探讨适合中国的传染病监测系统评价策略.方法 系统收集美国CDC及WHO历年来提出的各版本监测系统评价方案,分析其主要思想和对中国的适用性;以现况分析和可行性分析探讨适合中国的传染病监测系统评价策略.结果 美国CDC提出的评价方案适于评价专病监测系统,重在对系统特征进行分析.WHO提出的评价方案适于评价国家或区域的整体监测体系,重在对系统功能进行分析.中国现阶段开展的监测系统评价多应用了特征评价的思想,也已初步具备功能评价的条件.结论 现阶段制定传染病监测系统评价策略时应参考美国CDC及WHO的各版本监测方案,并结合中国国情加以制定.     

1991年河南省疾病监测系统法定传染病监测报告分析

西洱河梯级电站是我院设计、１４局施工的中型水电站，位于云南省大理市著名的西洱河上。她的开发建成不仅促进了我省经济建设的发展，而且，对合理开发高原湖的水电资源提供了成功经验。本文对西洱河梯级电站建设中的几个技术问题进行回顾、分析，以期总结经验与教训。     

武汉市2005-2007年学校和幼儿园传染病监测结果分析

目的 了解武汉市中小学、幼儿园儿童常见传染病发病特点.为采取有效控制措施提供科学依据.方法 采用分层抽样法,将武汉市学校分为中学、小学、幼儿园3层,结合学校学生规模,共抽取117所中小学、幼儿园作为全市的监测点,进行常见传染病监测.下载＜中国疾病预防控制信息系统＞中的有关信息,对学龄儿童和托幼儿童常见传染病进行分析,并与监测数据进行比较.结果 学校和幼儿园传染病发病以水痘、流行性腮腺炎等呼吸道传染病为主;全市2005-2007年突发公共卫生事件有66.67%发生在学校和幼儿园;学校传染病爆发疫情中呼吸道传染病占85.71%,有64.29%发生在小学;加强医疗机构传染病报告卡的登记也是发现学校传染病爆发疫情的重要途径.结论 学校和幼儿园常见传染病监测结果 能够较真实反应出学校传染病流行情况.弥补疫情报告系统之不足.加强儿童呼吸道传染病疫苗的接种,提高免疫覆盖率,是保护儿童健康成长经济有效的手段.     

Use of Control Bar Matrix for Outbreak Detection in Syndromic Surveillance System

Objective

To develop and test the method of incorporating different control bars for outbreak detection in syndromic surveillance system.

Introduction

Aberration detection methods are essential for analyzing and interpreting large quantity of nonspecific real-time data collected in syndromic surveillance system. However, the challenge lies in distinguishing true outbreak signals from a large amount of false alarm (1). The joint use of surveillance algorithms might be helpful to guide the decision making towards uncertain warning signals.

Methods

A syndromic surveillance project (ISSC) has been implemented in rural Jiangxi Province of China since August 2011. Doctors in the healthcare surveillance units of ISSC used an internet-based electronic system to collect information of daily outpatients, which included 10 infectious related symptoms. From ISSC database, we extracted data of fever patients reported from one township hospital in GZ town between August 1st and December 31st, 2011 to conduct an exploratory study. Six different control bar algorithms, which included Shewart, Moving Average (MA), Exponentially Weighted Moving Average (EWMA) and EARS’ C1, C2, C3, were prospectively run among historical time series of daily fever count to simulate a real-time outbreak detection. Each control bar used 7 days’ moving baseline with a lag of 2 days [the baseline for predicting Day(t) starts from Day(t-9) to Day(t-3), C1 method used a lag of zero day]. We set the threshold of μ+2σ for Shewart and MA, and 2.1 for EWMA C1, C2 and C3. An alarm was triggered when the observed data exceeded threshold, and the detailed information of each patient were checked for signal verification. Microsoft Excel 2007 was used to calculate the simulation results.

Results

During the 5 months, GZ township hospital reported 514 outpatients with fever symptom, with an average of 3.4 per day. All control bars were simultaneously operated among daily counts of fever cases. Of the 153 days on surveillance, 29 triggered alarms by at least one of the control bars. Nine days triggered alarms from >= 3 control bars while on one day (12/30) all 6 algorithms raised alarms. Figure 1 shows the date, fever count, algorithm and warning level (color) of each alarm, which we called a control bar matrix. It can be seen that C3 and EWMA present a higher sensitiveness towards tiny data change whereas C1, C2 and MA focus on large increase of data. C3 also had a memory effect on recent alarms. No infectious disease epidemic or outbreak event was confirmed within the signals. Most fever patients on the nine high-warning days (red and purple) were diagnosed as upper level respiratory infection. However, we discovered that the sharp increase of fever cases on 12/30 was attributed to 5 duplicate records mistakenly input by the staff in GZ hospital.Open in a separate windowFigure 1:Detailed information of alarm signals generated by control bar matrix (No-alarm days were omitted).

Conclusions

By combining control bars with different characteristics, the matrix has potential ability to improve the specificity of detection while maintaining a certain degree of sensitivity. With alarms categorized into hierarchical warning levels, public health staffs can decide which alarm to investigate according to the required sensitivity of surveillance system and their own capacity of signal verification. Though we did not find any outbreak event in the study, the possibility of localized influenza epidemic on high-warning days cannot be wiped out, and the matrix’s ability to detect abnormal data change was apparent. The proper combination, baseline and threshold of control bars will be further explored in the real-time surveillance situation of ISSC.     

Selecting Targeted Symptoms/Syndromes for Syndromic Surveillance in Rural China

Objective

To select the potential targeted symptoms/syndromes as early warning indicators for epidemics or outbreaks detection in rural China.

Introduction

Patients’ chief complaints (CCs) as a common data source, has been widely used in syndromic surveillance due to its timeliness, accuracy and availability (1). For automated syndromic surveillance, CCs always classified into predefined syndromic categories to facilitate subsequent data aggregation and analysis. However, in rural China, most outpatient doctors recorded the information of patients (e.g. CCs) into clinic logs manually rather than computers. Thus, more convenient surveillance method is needed in the syndromic surveillance project (ISSC). And the first and important thing is to select the targeted symptoms/syndromes.

Methods

Epidemiological analysis was conducted on data from case report system in Jingmen City (one study site in ISSC) from 2004 to 2009. Initial symptoms/syndromes were selected by literature reviews. And finally expert consultation meetings, workshops and field investigation were held to confirm the targeted symptoms/syndromes.

Results

10 kinds of infectious diseases, 6 categories of emergencies, and 4 bioterrorism events (i.e. plague, anthrax, botulism and hemorrhagic fever) were chose as specific diseases/events for monitoring (

Utility of Syndromic Surveillance Using Novel Clinical Data Sources

Objective

To document the current evidence base for the use of electronic health record (EHR) data for syndromic surveillance using emergency department, urgent care clinic, hospital inpatient, and ambulatory clinical care data.

Introduction

Historically, syndromic surveillance has primarily involved the use of near real-time data sent from hospital emergency department (EDs) and urgent care (UC) clinics to public health agencies. The use of data from inpatient and ambulatory settings is now gaining interest and support throughout the United States, largely as a result of the Stage 2 and 3 Meaningful Use regulations [1]. Questions regarding the feasibility and utility of applying a syndromic approach to these data sources are hampering the development of systems to collect, analyze, and share this potentially valuable information. Solidifying the evidence base and communicating the results to the public health surveillance community may help to initiate and build support for using these data to advance surveillance functions.

Methods

We conducted a literature search in the published and grey literature that scanned for relevant articles in the Google Scholar, Pub Med, and EBSCO Information Services databases. Search terms included: “inpatient/ambulatory electronic health record”; “ambulatory/inpatient/hospital/outpatient/chronic disease syndromic surveillance”; and “EHR syndromic surveillance”. Information gleaned from each article included data use, data elements extracted, and data quality indicators. In addition, several stakeholders who provided input on the September 2012 ISDS Recommendations [2] also provided articles that were incorporated into the literature review.ISDS also invited speakers from existing inpatient and ambulatory syndromic surveillance systems to give webinar presentations on how they are using data from these novel sources.

Results

The number of public health agencies (PHAs) routinely receiving ambulatory and inpatient syndromic surveillance data is substantially smaller than the number receiving ED and UC data. Some health departments, private medical organizations (including HMOs), and researchers are conducting syndromic surveillance and related research with health data captured in these clinical settings [2].In inpatient settings, many of the necessary infrastructure and analytic tools are already in place. Syndromic surveillance with inpatient data has been used for a range of innovative uses, from monitoring trends in myocardial infarction in association with risk factors for cardiovascular disease [3] to tracking changes in incident-related hospitalizations following the 2011 Joplin, Missouri tornado [3].In contrast, ambulatory systems face a need for new infrastructure, as well as pose a data volume challenge. The existing systems vary in how they address data volume and what types of encounters they capture. Ambulatory data has been used for a variety of uses, from monitoring gastrointestinal infectious disease [3], to monitoring behavioral health trends in a population, while protecting personal identities [4].

Conclusions

The existing syndromic surveillance systems and substantial research in the area indicate an interest in the public health community in using hospital inpatient and ambulatory clinical care data in new and innovative ways. However, before inpatient and ambulatory syndromic surveillance systems can be effectively utilized on a large scale, the gaps in knowledge and the barriers to system development must be addressed. Though the potential use cases are well documented, the generalizability to other settings requires additional research, workforce development, and investment.     

Using Medications Sales from Retail Pharmacies for Syndromic Surveillance in Rural China

Objective

To use an unconventional data - pharmaceutical sales surveillance for the early detection of respiratory and gastrointestinal epidemics in rural China.

Introduction

Drug sales data as an early indicator in syndromic surveillance has attracted particular interest in recent years (1, 2), however previous studies were mostly conducted in developed countries or areas. In China, many people (around 60%) choose self-medication as their first option when they encounter a health problem (3), and electronic sales information system is gradually used by retail pharmacies, which makes drug sales data become a promising data source for syndromic surveillance in China.

Methods

This experimental study was conducted in four rural counties in central China. From Apr. 1st 2012, there are 56 retail pharmacies joined the study, including 21 county pharmacies and 35 township pharmacies. 123 drugs were selected under surveillance based on the analysis of local historical sales volume and consultation with local pharmacists, including 19 antibiotics, 15 antidiarrheal medications, 9 antipyretics, 41 compound cold medicine, and 39 cough suppressants. Daily sales volume of the selected drugs was recorded into the database by pharmacy staff at each participating unit via electronic file importing or manual entering. Figure 1 showed the user interface for data viewing, query and export. Field training and supervision were regularly conducted to ensure the data quality.Open in a separate windowFigure 1User interface in the system for data viewing, query and export

Results

From Apr. 1st to Jun. 30th 2012, there were 103814 sales records reported in the system, including 44464 (42.83%) records from county pharmacies and 59350 (57.17%) from township pharmacies. Among all surveillance drugs, the sales of compound cold medicine accounted for the largest proportion (43.42%), followed by antibiotics (22.52 %), cough suppressants (18.50%), antidiarrheal drugs (9.49%) and antipyretics (6.06 %). More than 80% data were reported into the system within 24 hours after the sales date, and the reporting timeliness of county pharmacies improved with time (Report time after sales dateAprilMayJuneCounty N(%)Township N(%)County N(%)Township N(%)County N(%)Township N(%)within 24 h11359(75.84)16653(85.6)12483(81.80)17749(87.99)12225(85.93)15977(81.01)24 h-48h1523(10.25)1001(5.15)1487(9.74)1760(8.72)1321(9.29)2224(11.28)later than 48h2084(13.91)1801(9.26)1290(8.45)663(3.29)680(4.78)1522(7.72)Sum149781945515260201721422619723Open in a separate window

Conclusions

Although the current reporting timeliness and completeness are satisfying, it is noteworthy the quality of data is not stable during the beginning phase of the implementation. Further validation of the data will be required. To ensure the accuracy of data and the effective and sustainable deployment of the system, it is imperative to establish a data sharing policy between pharmacies and public health agencies, and achieve automated data collection to avoid additional human labor involvement.