武汉市症状监测医院医务人员对症状监测工作的认识研究

目的研究医务人员对症状监测工作的认识。方法采取定性研究方法,在武汉市承担的卫生部/WHO"提高突发公共卫生事件早期预警能力项目-提高呼吸道传染病早期发病能力"现场监测工作结束后,就监测点医院项目参与人员对症状监测的认识、项目实施过程中存在的问题等情况等进行了调查。结果医务人员认为症状监测能在早期预警中发挥重要的作用,是传统疾病监测的重要补充;但在完全手工登记、报告的情况下,在门诊量大的医院较难坚持。结论与医院信息系统结合的电子化监测应成为症状监测发展方向。     

症状监测对突发公共卫生事件预警意义

目的突发公共卫生事件传播、进展速度较快,危害群体广泛,从挽救生命和减少经济损失角度讲,应及早识别突发公共卫生事件,以迅速作出有效反应。本研究探讨症状监测对突发公共卫生事件预警意义。方法选取2个县作为监测点,分别依托欧盟项目"中国农村地区传染病症状整合监测系统"(症状监测组)和中国疾病预防控制中心网络直报法定传染病实时报告系统(传统监测组),采集2016-05-01-2018-05-30数据上报员上传至监测系统信息,匹配两组监测条目后,统计两组监测系统运行情况,并对比两组对公共卫生事件预警时间。结果症状监测组小学缺勤记录14 206人次;药店销售记录990 231条;医院门诊急诊176 122人次,监测症状记录52 897条,占门诊急诊总量30.03%;共监测到病例1 402例。传统监测组共上报病例1 365例。症状监测平均监测预警时间为(6.96±2.36)d,长于传统监测的(3.24±1.92)d,差异有统计学意义,t=45.414,P0.001。结论症状监测能早期识别突发公共卫生事件,增强公共卫生监测预警能力,可为公共卫生决策提供指导。     

西安市2015年学生传染病症状监测结果分析

了解西安市学生传染病症状监测系统的建立和运行状况,为探讨学生传染病相关症状监测网上直报工作对传染病早期预警的作用提供依据.方法 选取2015年参加西安市学生传染病相关症状监测网上直报工作的70所试点学校,对网报的学生因病缺课症状和疾病进行统计分析.结果 2015年3-12月网络直报系统共监测因病缺课信息9 396人次,报告症状预警信息75条,其中8条为学校传染病早期预警信息.缺课症状以发热最多(41.2%),其次为咳嗽(15.5%),发热和咳嗽症状均在12月下旬出现高峰.缺课时间以1d最多,占33.9%.缺课主要疾病为其他非传染性疾病(47.3%)、上呼吸道感染(38.9%)和传染病(6.9%);在传染性疾病中以水痘居多,占49.5%.75条症状预警信息中发热症状预警信息最多,占78.7%;其次是出疹,占12.0%.结论 西安市学生传染病症状监测预警系统运行良好,以发热和咳嗽为主要症状的呼吸道传染病是今后学校传染病防控的重点.     

世博期间上海市浦东新区医疗机构症状监测系统的建立与结果分析

目的 以2010年上海世界博览会卫生保障为背景,以医疗机构为数据源,探讨建立浦东新区传染病症状监测系统和早期预警的方法.方法 依托医院信息系统(hospital information system,HIS)嵌入模块,采集上海市浦东新区21家医院就诊患者的相关症状,通过浦东新区卫生信息网导入症状监测系统数据库进行资料分析;并采用累积和预警模型和绝对值预警方法探测报告数异常情况.结果 2010年5月1日～10月31日期间,监测目标病例共计105352例,以发热呼吸道和腹泻症候群最为常见,东方医院和浦东新区人民医院报告病例数最多.监测期内系统总共发出191条预警信号,信号数最多的是发热呼吸道和腹泻症候群,最少的是中枢神经系统症候群,信号强度以1级为主.结论 笔者等成功建立了浦东新区医疗机构传染病症状监测系统,并在2010年上海世界博览会期间成功应用.     

上海市长宁区重点传染病症状监测预警信息系统介绍

[目的]建立上海市长宁区重点传染性疾病症状监测预警信息系统,探索数据采集、阈值建立、异常信号识别的方法与机制。[方法]选择医院、学校作为监测点,以不明原因肺炎、流感样病例、菌痢、霍乱、感染性腹泻、麻疹、水痘、手足口病和乙脑为监测目标疾病,开展相关症候群监测。采用移动百分位数法,计算各症候群的预警阈值。[结果]设计开发了"重点传染病症状监测预警信息系统",具有症候群信息采集、数据导入、预警参数设置、每日预警值计算、异常自动预警、异常信息报告、预警数据查询、用户管理、权限管理等功能。[结论]通过研究,开发了一套电子数据源采集和自动分析预警的软件,形成了系统数据采集、阈值建立、异常信号识别的一套方法与机制。所建立的"重点传染病症状监测预警信息系统"适用于疾病预防控制机构开展症状监测,对于增强传染病预警预测水平,及早发现疫情爆发苗子具有积极意义。     

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,     

症状监测预警技术研究进展

症状监测与传统疾病监测在监测疾病暴发流行的及时性、敏感性、特异性等方面各有所长。科学有效的疾病监测应借鉴传统疾病监测的经验,利用已获得的数据,建立一套整合传统疾病监测和症状监测的联合系统,取长补短,长期协调发展。区域卫生信息平台的建设,为两种疾病监测方式的信息共享与业务协调提供了可能,也为联合传统疾病监测和症状监测系统的实现提供了极大的便利。我国急需加强症状监测领域的相关研究与实践,弥补现有疾病监测系统的缺陷,增强对传染病和突发公共卫生事件的早期预警、监测和态势感知的能力。     

传染病智慧化症状监测的进展与挑战

智慧化症状监测是我国传染病智慧化预警多点触发和多渠道预警体系的重要组成部分,是传统症状监测在新技术发展下的必然路径。智慧化症状监测不仅包括医院就诊患者的大量医疗数据,还囊括未在医院就诊、海量的非医疗数据。然而,发展的同时也迎来了挑战,如信息爆炸、成本-效益问题以及数据共享、数据安全和隐私问题。本文通过对智慧化症状监测相关概念发展的思考和总结,为传染病智慧化预警方法与技术的发展提供参考,为我国和全球传染病防控提供新思路。     

症状监测在新发传染病和暴发疫情预警中应用的进展

近来,症状监测作为疾病监测的补充方法,以近于实时的速度对新发传染病和重大传染病暴发疫情进行预警,大大提高了疫情发现的敏感性和公共卫生预警能力,在国内外得到高度的重视和推广,在我国也取得很好的应用效果。笔者通过大量的国内外文献检索,描述了症状监测在新发传染病和暴发疫情预警中应用的进展及优势,并结合我国实际情况分析了目前症状监测在基层应用中存在推广应用成本昂贵、医疗机构缺乏信息平台、未能建立数字化的实时预警系统,以及需要建立多方位的公共数据交流平台等问题和困难。提出了在未来工作中需要结合实际情况适时推广症状监测,需立足现有条件,提高监测效率,并与传统的疾病监测系统和先进的实验室网络体系有机结合,迫切需要建立数字化信息系统来实现实时预警等策略建议。     

国内外药品销售监测系统介绍

随着突发公共卫生事件的不断增多,提高对公共卫生事件的早期预警能力显得尤为重要。传统的疾病监测系统在症状报告和疾病的最后诊断之间通常存在一段时间的滞后期,为提高疾病或疫情的早期预警能力,症状监测在20世纪90年代开始得以广泛应用。作为症状监测的一种,药品销售监测已开始在国外应用。虽然药品销售监测还存在某些问题,但国外初步应用结果表明,对药品销售量进行监测可能会比传统的监测系统较早发现疾病的存在。目前,国内借助药品销售监测来预警疾病的应用较少,而且未能将药品销售监测与其他症状监测较好地结合,有关数据种类与来源、分析方法、信息反馈和评估体系等内容还有更大的发展空间。     

Integration of syndromic surveillance data into public health practice at state and local levels in North Carolina

Objectives

We sought to describe the integration of syndromic surveillance data into daily surveillance practice at local health departments (LHDs) and make recommendations for the effective integration of syndromic and reportable disease data for public health use.

Methods

Structured interviews were conducted with local health directors and communicable disease nursing staff from a stratified random sample of LHDs from May through September 2009. Interviews captured information on direct access to the North Carolina syndromic surveillance system and on the use of syndromic surveillance information for outbreak management, program management, and the creation of reports. We analyzed syndromic surveillance system data to assess the number of signals resulting in a public health response.

Results

Syndromic surveillance data were used for outbreak investigation (19% of respondents) and program management and report writing (43% of respondents); a minority reported use of both syndromic and reportable disease data for these purposes (15% and 23%, respectively). Receiving data from frequent system users was associated with using data for these purposes (p=0.016 and p=0.033, respectively, for syndromic and reportable disease data). A small proportion of signals (<25%) resulted in a public health response.

Conclusions

Use of syndromic surveillance data by North Carolina local public health authorities resulted in meaningful public health action, including both case investigation and program management. While useful, the syndromic surveillance data system was oriented toward sensitivity rather than efficiency. Successful incorporation of new surveillance data is likely to require systems that are oriented toward efficiency.Effective use of surveillance data is essential to good public health practice. In recent years, public health agencies have experienced a significant increase in the amount of data available for surveillance (e.g., data used for syndromic surveillance), and this increase is likely to continue. For example, the federal Health Information Technology for Economic and Clinical Health Act (HITECH Act) supports forwarding medical record data to public health agencies. Published work demonstrates that better data are needed for communicable disease surveillance; communicable disease reporting is not complete,¹ and many cases are reported later than is necessary for public health action.² While the medical record data that may be provided to public health have the potential to improve completeness and timeliness, these datasets are likely to have many records that are not usable for public health purposes.³ Furthermore, limited staff are available to review these data.^4,5 Effective use of these new data for public health surveillance will require efficient identification of and access to the usable data elements present in new datasets.The implementation of syndromic surveillance is an example of the incorporation of new data sources. Syndromic surveillance systems were established to facilitate early detection of events requiring a rapid response, such as outbreaks caused by bioterrorism agents. Events that may require public health intervention are identified using aberration detection algorithms and individual record review. Most states have a system of this type,⁶ and their value for public health event detection and characterization has been demonstrated.^6–10 Lessons learned from attempts to integrate syndromic data for public health surveillance and response can inform future management of new data.While syndromic surveillance data can be valuable to public health practice, the design of these systems frequently limits their use to jurisdictions with greater capacity. Alerts created by system algorithms are often of low positive predictive value,^11,12 and these systems can require a high level of staff time for detecting events that require public health action.⁸ Therefore, syndromic surveillance data are most commonly used by state and large city public health departments that have enough staff time for reviewing alerts and individual case records.^6,13,14 Although these data can be useful to health departments of all sizes, little is known about how best to make these data usable in situations with limited surveillance staff. The use of syndromic surveillance data in smaller population settings, such as most local health departments (LHDs), has not been described.North Carolina can provide an example of the integration of syndromic surveillance data into public health surveillance practice. Current electronic surveillance for communicable disease in the state includes a population-based syndromic surveillance system, the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT), and a population-based reportable communicable disease surveillance system, the North Carolina Electronic Disease Surveillance System (NC EDSS). Both syndromic and reportable disease data have been used for public health surveillance since 2006. NC DETECT use is the responsibility of syndromic surveillance staff, which includes two state-level epidemiologists and 11 hospital-based epidemiologists. All other public health agency staff may use NC DETECT. NC EDSS use is required for and restricted to staff responsible for communicable disease reporting at state and local levels.The objectives of this study were to quantitatively assess the use of syndromic surveillance data at state and local public health agencies in North Carolina, to describe how syndromic surveillance is incorporated into public health practice in the state, and to make recommendations for the effective integration of syndromic and reportable disease data for public health use.     

症状监测的研究进展

随着新发传染病的频频出现、旧传染病的卷土重来以及生物恐怖主义威胁的加剧,传统的传染病监测系统面临新的挑战.症状监测以其能及时发现异常或疾病暴发信号的特点而得到发展.此文系统阐述了症状监测的概念、发展、特点以及面临的问题,为开展相关研究提供参考.     

Review of syndromic surveillance: implications for waterborne disease detection

Syndromic surveillance is the gathering of data for public health purposes before laboratory or clinically confirmed information is available. Interest in syndromic surveillance has increased because of concerns about bioterrorism. In addition to bioterrorism detection, syndromic surveillance may be suited to detecting waterborne disease outbreaks. Theoretical benefits of syndromic surveillance include potential timeliness, increased response capacity, ability to establish baseline disease burdens, and ability to delineate the geographical reach of an outbreak. This review summarises the evidence gathered from retrospective, prospective, and simulation studies to assess the efficacy of syndromic surveillance for waterborne disease detection. There is little evidence that syndromic surveillance mitigates the effects of disease outbreaks through earlier detection and response. Syndromic surveillance should not be implemented at the expense of traditional disease surveillance, and should not be relied upon as a principal outbreak detection tool. The utility of syndromic surveillance is dependent on alarm thresholds that can be evaluated in practice. Syndromic data sources such as over the counter drug sales for detection of waterborne outbreaks should be further evaluated.     

Linking syndromic surveillance with virological self-sampling

Calls to a UK national telephone health helpline (NHS Direct) have been used for syndromic surveillance, aiming to provide early warning of rises in community morbidity. We investigated whether self-sampling by NHS Direct callers could provide viable samples for influenza culture. We recruited 294 NHS Direct callers and sent them self-sampling kits. Callers were asked to take a swab from each nostril and post them to the laboratory. Forty-two per cent of the samples were returned, 16.2% were positive on PCR for influenza (16 influenza A(H3N2), three influenza A (H1N1), four influenza B) and eight for RSV (5.6%). The mean time between the NHS Direct call and laboratory analysis was 7.4 days. These samples provided amongst the earliest influenza reports of the season, detected multiple influenza strains, and augmented a national syndromic surveillance system. Self-sampling is a feasible method of enhancing community-based surveillance programmes for detection of influenza.     

Simulated anthrax attacks and syndromic surveillance

We measured sensitivity and timeliness of a syndromic surveillance system to detect bioterrorism events. A hypothetical anthrax release was modeled by using zip code population data, mall customer surveys, and membership information from HealthPartners Medical Group, which covers 9% of a metropolitan area population in Minnesota. For each infection level, 1,000 releases were simulated. Timing of increases in use of medical care was based on data from the Sverdlovsk, Russia, anthrax release. Cases from the simulated outbreak were added to actual respiratory visits recorded for those dates in HealthPartners Medical Group data. Analysis was done by using the space-time scan statistic. We evaluated the proportion of attacks detected at different attack rates and timeliness to detection. Timeliness and completeness of detection of events varied by rate of infection. First detection of events ranged from days 3 to 6. Similar modeling may be possible with other surveillance systems and should be a part of their evaluation.     

试论开展突发公共卫生事件症状监测的必要性和困难

症状监测是致力于早期发现突发公共卫生事件的主动监测系统，目前正日益受到世界各国的重视。本文通过分析症状监测的定义、应用、优点和局限性，结合我国现状，探讨在我国开展症状监测的必要性和困难。     

The public health value of emergency department syndromic surveillance following a natural disaster

During a recent natural disaster public health staff required timely and comprehensive surveillance of priority health conditions, including injury, mental health disorders and selected infectious diseases, to inform response and recovery activities. Although traditional surveillance is of value in such settings it is constrained by a focus on notifiable conditions and delays in reporting. The application of an electronic emergency department syndromic surveillance system proved valuable and timely in informing public health activities following a natural disaster in New South Wales.     

Reducing false alarms in syndromic surveillance

Algorithms for identifying public health threats or disease outbreaks are vulnerable to false alarms arising from sudden shifts in health-care utilization or data participation. This paper describes a method of reducing false alerts in automated public health surveillance algorithms, and in particular, automated syndromic surveillance algorithms, that rely on health-care utilization data. The technique is based on monitoring syndromic counts with reference to a suitable background, or reference, series of counts. The suitability of the background time series in decreasing the false-alarm rate will be shown to be related mathematically to the so-called mutual information that exists between the random variables representing the syndromic and background time series of counts. The method can be understood as a noise cancellation filter technique in which one noisy (reference) channel is used to cancel the background noise of the monitored (measured) channel. The issues discussed here may also be relevant to the appropriate use of rates in epidemiology and biostatistics.     

Medication sales and syndromic surveillance, France

Although syndromic surveillance systems using nonclinical data have been implemented in the United States, the approach has yet to be tested in France. We present the results of the first model based on drug sales that detects the onset of influenza season and forecasts its trend. Using weekly lagged sales of a selected set of medications, we forecast influenzalike illness (ILI) incidence at the national and regional level for 3 epidemic seasons (2000-01, 2001-02, and 2002-03) and validate the model with real-time updating on the fourth (2003-04). For national forecasts 1-3 weeks ahead, the correlation between observed ILI incidence and forecast was 0.85-0.96, an improvement over the current surveillance method in France. Our findings indicate that drug sales are a useful additional tool to syndromic surveillance, a complementary and independent source of information, and a potential improvement for early warning systems for both epidemic and pandemic planning.