从汶川地震救援谈灾害医疗救援模式

解放军总医院第309野战医疗所成立于2002年,是一支可以参加多种灾害救援的专业化救援队伍,历经抗击SARS、禽流感、局部战争卫勤保障演练和首都应急卫勤保障,经过     

汶川地震救援工作中的若干问题及其思考

汶川地震发生后,全国上下万众一心取得了抗震救灾的胜利。在应对如此极端性灾难的过程不可避免地存在不尽人意之处,本文通过对云南省赴川救援人员的问卷调查和个人深入访谈,发现其中存在的问题。     

“5.12”汶川特大地震后手机报告症状监测实践

症状监测是较之病例监测更为敏感的疾病监测方法,"5.12"汶川特大地震发生后,在灾后疾病监测中进行了症状监测尝试。现就"5.12"汶川地震后症状监测系统的设计、开发、建立、运行、监测结果、系统评价进行介绍分析和评价,以期对未来症状监测系统的建设提供参考。     

5.12汶川地震后绵阳市症状监测的分析

[目的]早期探查传染病和食物中毒潜在暴发,预防震后突发公共卫生事件发生.[方法]建立震后症状监测系统,对第1个月监测数据进行分析.[结果]5月15日～6月14日,症状监测系统30个报告单位,共监测报告发热10 9914人次,腹泻15 308人次,感冒82 689人次.医疗机构和医疗小组监测点有9个报告单位,报告灾民发热、腹泻和感冒罹患率分别为1.309%、1.430%和14.303%.[结论]症状监测系统的建立,为绵阳市抗震救灾卫生防疫分指挥部动态掌握灾区群众疾病发生状况,早期探查聚集性病例的发生起到了重要的作用,同时也存在数据不够完整和准确的问题,系统运作效果有待于科学评价.     

山区地震医学救援对策

本文结合在汶川地震伤员救治中的实践和教训,提出山区地震医学救援的4个阶段,对救援期间所面临的问题进行了分析,并为山区地震灾害医学救援提出针对性建议,为部队遂行多样化卫勤保障任务提供借鉴。     

浅谈汶川地震的公共卫生救援及体会

地震灾害发生后,自然环境恶化,公共卫生基础设施损坏,给群众的生活带来巨大的影响。及时有效的公共卫生救援应从健康宣教、消杀指导和培训示范、饮用水管理、食品卫生管理、传染病处置等基础公共卫生工作做起,采取综合措施,才能确保大灾之后无大疫目标的实现。公共卫生救援人员应具有较强的业务能力和良好的心理、身体素质,并贯彻以人为本的救援理念,有关部门应对救援工作建立科学的评估考核体系。     

汶川地震妇产儿科伤病员的救治管理

[目的]四川大学华西第二医院作为中国西部规模最大妇女儿童专科医院和疑难重症诊治中心,在汶川地震妇产儿童伤病员医疗救援中,医院医疗救援体系经受了重大考验.医院立即将医疗常态转入双轨应急状态,从分诊、转运、接诊、处置到康复转出管理,建立了运转有序医疗应急体系、环节通畅医疗救援体系,为受灾妇女儿童及时教治、妇产儿专科疾病和并发症有效处理提供了可靠保证.保证了灾区妇产儿科病人第一时间高质量专科救治,无一例孕产妇死亡,有效保证了最高治愈率,最小死亡率和致残率.     

症状监测在大型活动中的应用

近年来,症状监测作为一种新兴的疾病监测手段,被许多城市与地区广泛应用于大型活动的公共卫生保障工作中。本文以2002年盐湖城冬奥会和2005年苏格兰G8峰会开展的症状监测为例,描述症状监测在大型活动中的具体应用,总结、分析该监测手段的特点,为今后同类研究与实践活动提供参考。     

消防部队在地震救援中的人员搜救方法

结合多起国内外地震救援案例, 分析了消防部队在地震救援中进行人员搜救的四种方法, 即询问法、观察法、仪器法和生物法, 指出不同环境下各搜救方法的具体做法和优缺点等, 归纳了各类搜救方法中应注意的问题, 并对未来可能广泛运用的其他一些搜救方法作了展望。     

汶川地震医疗救援分队后勤伴随保障

“5。12”汶川地震发生后,医院紧急抽组两支医疗救援分队于13日分别到达汶川县城、汶川县映秀镇展开紧急医疗救援。截止6月9日,两支医疗救援分队共抢救重危伤病员336名,手术104例,收治伤病员492名,后送伤病员385名,接诊患者2200余人次。后勤人员通过伴随保障实现了运输保障到位,药材保障随时随地补充,分队队员无一伤亡,无一因病撤离,展示了全面建设现代后勤的成效。     

“5．12”大地震紧急医疗救援实践与探讨

2008年5月12日，四川省汶川县发生里氏8．0级大地震，第二军医大学首批7支医疗队受命前往实施紧急医疗救援。本文通过此次紧急医疗救援实践，总结军队机动卫勤力量参与重大灾害事件紧急医疗救援工作的经验，探讨如何在今后类似行动中提高反应速度和救治效率。     

Syndromic Surveillance at the New York State Veterinary Diagnostic Laboratory

Objective

To assess the use and utility of a syndrome check list on the general submission form of a high volume veterinary diagnostic laboratory, and compare to the results of a 2009 pilot study.

Introduction

The New York State Veterinary Diagnostic Laboratory (NYSVDL) receives more than 100,000 diagnostic submissions a year that are not currently used in any formal syndromic surveillance system. In 2009, a pilot study of syndrome classification schemes was undertaken and in 2011 a new general submission form was adopted, which includes a check list of syndromes, as part of the clinical history.Monitoring submissions to a veterinary diagnostic laboratory for increases in certain test requests is an established method of syndromic surveillance (1, 2). The new general submission form allows for clinician selected syndromes to be monitored in addition to test request.

Methods

We selected 420 “contract cases” from all submissions for bovines since the implementation of the new form, May 2011, though February 2012. Submissions were reviewed for use of the new form, use of the syndrome check list and tests requested. Test requests were assigned syndromes, if possible, to allow for comparison with the clinician selected syndromes.The selection of cases was weighted towards the end of the period as use of the new form increased with time and to a lesser extent toward the beginning of the period in an attempt to find any early use of the form. “Contract case” refers to New York State Department of Agriculture and Markets subsidization of agricultural animal and herd health testing done under specific guidelines. The benefit of “contract cases” is the guidelines require a complete clinical history, which should include selection of syndromes. Finally, selection was limited bovine submissions as was done in the pilot study.

Results

16% (69/420) of submissions used the new form and of these 23 selected syndromes. As was seen in the pilot study the most commonly occurring syndrome in the clinical history was “Gastrointestinal/Diarrhea” 56% (13/23). The next most common syndromes were “Respiratory” (7/23), “Sudden Death” (6/23) and “Fever” (4/23).Syndromes assigned based on test request followed a similar pattern with “Gastrointestinal/Diarrhea” (166/254) and “Respiratory” (52/254) best represented. An important difference was the syndromes “Sudden Death” and “Fever”, which were never assigned to a test request. These syndromes represent a new source of information for surveillance.These results fit well with the pilot study which found the clinical history was typically incomplete but contained additional information for syndromic surveillance that was not available from monitoring the test request alone and that monitoring syndromes or test requests alone would provide incomplete information nearly a third of the time.

Conclusions

We found monitoring syndromes, in addition to test requests, to be useful and necessary for completeness. Monitoring clinical history provided additional information not available from test requests alone. We recommend the syndromes “Sudden Death” and “Fever” be monitored as these syndromes always provided additional information not available in test requests. Other syndromes that provide new information should be investigated across species and in various clinical scenarios. Accumulating baseline data for all syndromes is recommended to create more accurate models for syndromic surveillance and improve data retrieval for retrospective studies. Despite poor use of the new general submission form and the syndrome check boxes, future compliance is likely to improve significantly with the implementation of online submission and thanks to the continuous training and consultation provided by the NYSVDL staff.     

Utility of System Generated Syndromic Surveillance Alerts to Detect Reportable Disease Outbreaks

Objective

In light of recent outbreaks of pertussis, the ability of Florida Department of Health’s (FDOH) Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL) to detect emergent disease outbreaks was examined. Through a partnership with the Johns Hopkins University Applied Physics Laboratory (JHU/APL), FDOH developed a syndromic surveillance system, ESSENCE-FL, with the capacity to monitor reportable disease case data from Merlin, the FDOH Bureau of Epidemiology’s secure webbased reporting and epidemiologic analysis system for reportable diseases. The purpose of this evaluation is to determine the utility and application of ESSENCE-FL system generated disease warnings and alerts originally designed for use with emergency department chief complaint data to reportable disease data to assist in timely detection of outbreaks in promotion of appropriate response and control measures.

Introduction

Reportable disease case data are entered into Merlin by all 67 county health departments in Florida and assigned confirmed, probable, or suspect case status. De-identified reportable disease data from Merlin are sent to ESSENCE-FL once an hour for further analysis and visualization using tools in the surveillance system. These data are available for ad hoc queries, allowing users to monitor disease trends, observe unusual changes in disease activity, and to provide timely situational awareness of emerging events. Based on system algorithms, reportable disease case weekly tallies are assigned an awareness status of increasing intensity from normal to an alert category. These statuses are constantly scrutinized by county and state level epidemiologists to guide disease control efforts in a timely manner, but may not signify definitive actionable information.

Methods

Within the ESSENCE-FL query portal, the Merlin Reportable Diseases Data Source was selected with a weekly time resolution by event date. Case Classification included all confirmed, probable and suspect cases, reported and not yet reported, during the time period of week 35, 2011, to week 35, 2012. The ESSENCE Weighted Moving Average (EWMA 1.2) detector was used to classify weekly counts as either of normal, warning or alert status based on previous weeks’ counts, indicating the possibility of an emerging outbreak. These weekly statuses were then compared with outbreaks reported in Merlin’s fully integrated outbreak reporting system and with outbreak reports submitted to EpiCom, Florida’s EpiX or health alert network. An ESSENCE-FL generated warning or alert was considered valid if a corresponding outbreak of 2 or more epi-linked pertussis cases were reported in either Merlin’s outbreak module or in EpiCom. For the sake of brevity in this abstract, the analysis of pertussis is presented, while other reportable disease conditions of immediate interest will be presented at the conference.

Results

Examination of 494 pertussis cases reported from September 2011 to September 2012 showed that of 53 weeks, 38 weeks contained normal case counts, 11 weeks generated warnings, and 4 weeks produced alerts. The number of warnings that corresponded to actual outbreaks was 6 of 11, whereas 2 of the 4 alerts matched reported outbreaks. Of the remaining 38 weeks, 12 had outbreaks reported with no warning or alert generated by ESSENCE-FL. When comparing confirmed outbreak status with ESSENCE-FL weekly data count status, warning/alert versus normal, it was found that the sensitivity of ESSENCE-FL to detect a true outbreak was 40.0% while the specificity was 78.8%. This comparison generated a positive-predictive value of 53.3% and a negative predictive value of 68.4%.

Conclusions

The ability of ESSENCE-FL to act as a first alert system for emerging disease events using Merlin reportable disease data should be considered with constraint. While warnings or alerts about potential pertussis outbreaks were generated correctly about half the time, the nearly one-third of reported outbreaks with no warning or alert makes the utility of the alerts questionable as far as initiating immediate action without prior verification of the alert. Florida does not currently have a requirement for centrally documenting all outbreaks, so it is likely that outbreaks occurred but were not recorded, precluding verification of all outbreaks.     

军队地震医学救援应急卫勤统计平台研究与设计

目的：为提高地震医学救援应急卫勤统计效率,设计一套基于军事综合信息网和远程医学卫星网的应急卫勤统计平台。方法：根据四川芦山4·207．0级强烈地震军队医学救援应急卫勤统计特点,对应急统计平台进行需求分析,从平台框架结构、工作流程及各功能模块等方面进行设计。结果：该平台可实现卫勤信息远程上报、快速传输、海量存储、分级汇总、科学分析、全面共享等功能。结论：该平台为统计人员提供了方便快捷的上报及统计途径,为决策人员提供了及时准确的统计信息,具有良好的推广应用前景。     

Using an Emergency Department Syndromic Surveillance System to investigate the impact of extreme cold weather events

This report describes the development of novel syndromic cold weather public health surveillance indicators for use in monitoring the impact of extreme cold weather on attendances at EDs, using data from the 2010–11 and 2011–12 winters.     

Nontraumatic Oral Health Classification for Alternative Use of Syndromic Data

Objective

To develop a nontraumatic oral health classification that could estimate the burden of oral health-related visits in North Carolina (NC) Emergency Departments (EDs) using syndromic surveillance system data.

Introduction

Lack of access to regular dental care often results in costly, oral health visits to EDs that could otherwise have been prevented or managed by a dentist (1). Most studies on oral health-related visits to EDs have used a wide range of classifications from different databases, but none have used syndromic surveillance data. The volume, frequency, and included details of syndromic data enabled timely burden estimates of nontraumatic oral health visits for NC EDs.

Methods

Literature review, input by subject matter experts (SMEs), and analysis of syndromic data was used to create the nontraumatic oral health classification. BioSense, a near real-time, national-level, electronic health surveillance system was the source of the NC ED syndromic data. Visits with at least one oral health-related ICD-9-CM code were extracted for NC fiscal years 2008–2010. Univariate analyses of chief complaint (CC) and final diagnosis data along with SME consultation were used to determine the CC substrings and ‘white list’ of ICD-9-CM codes used as inclusion criteria to classify visits as oral health-related. These analyses and consultations also determined the trauma-related codes and substrings used to exclude visits.

Results