Syndromic Surveillance for Outbreak Detection and Investigation

Objective

For the purpose of developing a national system of outbreak surveillance, we compared local outbreak signals in three sources of syndromic data – telephone triage of acute gastroenteritis (Swedish Health Care Direct 1177), web queries about symptoms of gastrointestinal illness (Stockholm County’s website for healthcare information), and OTC pharmacy sales of anti-diarrhea medication.

Introduction

A large part of the applied research on syndromic surveillance targets seasonal epidemics, e.g. influenza, winter vomiting disease, rotavirus and RSV, in particular when dealing with preclinical indicators, e.g. web traffic (Hulth et al, 2009). The research on local outbreak surveillance is more limited. Two studies of teletriage data (NHS Direct) have shown positive and negative results respectively (Cooper et al, 2006; Smith et al, 2008). Studies of OTC pharmacy sales have reported similar equivocal performance (Edge et al, 2004; Kirian and Weintraub, 2010). As far as we know, no systematic comparison of data sources with respect to multiple point-source outbreaks has so far been published (cf. Buckeridge, 2007). In the current study, we evaluated the potential of three data sources for syndromic surveillance by analyzing the correspondence between signal properties and point-source outbreak characteristics.

Methods

The extracted data streams were compared with respect to nine waterborne and foodborne outbreaks in Sweden in 2007–2011. The analysis consisted of three parts: (1) the validation of outbreak signals by comparing signal counts during outbreak and baseline periods, (2) the estimation of detection limits by modeling signal rates (signal-to-case ratios), and (3) the evaluation of early warning potential by means of signal detection analysis.

Results

The four largest outbreaks generated strong and clear outbreak signals in the 1177 triage data. The two largest outbreaks produced signals in OTC sales of anti-diarrhea. No signals could be identified in the web query data. The outbreak detection limit based on triage data was about 100–1000 cases. For two outbreaks, triage data on diarrhea provided outbreak signals early on, weeks and months respectively, potentially serving the purpose of early warning.

Conclusions

The sensitivity and specificity were highest for telephone triage data on patient symptoms. It provided the most promising source of syndromic data for surveillance of point-source outbreaks. Currently, a project has been initialized to develop and implement a national system in Sweden for daily syndromic surveillance based on 1177 Health Care Direct, supporting regional and local outbreak detection and investigation.     

Early Detection of Influenza Activity Using Syndromic Surveillance in Missouri

Objective

To assess how weekly percent of influenza-like illness (ILI) reported via Early Notification of Community-based Epidemics (ESSENCE) tracked weekly counts of laboratory confirmed influenza cases in five influenza seasons in order to evaluate the early warning potential of ILI in ESSENCE and improve ongoing influenza surveillance efforts in Missouri.

Introduction

Syndromic surveillance is used routinely to detect outbreaks of disease earlier than traditional methods due to its ability to automatically acquire data in near real-time. Missouri has used emergency department (ED) visits to monitor and track seasonal influenza activity since 2006.

Methods

The Missouri ESSENCE system utilizes data from 84 hospitals, which represents up to 90 percent of all ED visits occurring in Missouri statewide each day. The influenza season is defined as starting during Centers for Disease Control and Prevention (CDC) week number 40 (around the first of October) and ending on CDC week 20 of the following year, which is usually at the end of May.A confirmed influenza case is laboratory confirmed by viral culture, rapid diagnostic tests, or a four-fold rise in antibody titer between acute and convalescent serum samples. Laboratory results are reported on a weekly basis. To assess the severity of influenza activity, all flu seasons were compared with the 2008–09 season, which experienced the lowest influenza activity based on laboratory data. Analysis of variance (ANOVA) was applied for this analysis using Statistical Analysis Software (SAS) (version 9.2).The standard ESSENCE ILI subsyndrome includes ED chief complaints that contain keywords such as “flu”, “flulike”, “influenza” or “fever plus cough” or “fever plus sore throat”. The ESSENCE ILI weekly percent is the number of ILI visits divided by total ED visits.Time series of weekly percent of ILI in ESSENCE were compared to weekly counts of laboratory confirmed influenza cases. Spearman correlation coefficients were calculated using SAS. The baseline refers to the mean of three flu seasons with low influenza activity (2006–07, 2008–09 and 2010–11 seasons). The threshold was calculated as this baseline plus three standard deviations.The early warning potential of the ESSENCE weekly ILI percent was evaluated for five consecutive influenza seasons, beginning in 2006. This was accomplished by calculating the time lag between the first ESSENCE ILI warning versus the first lab confirmed influenza warning. A warning was identified if either lab confirmed case counts or weekly percent of ILI crossed over their respective baselines.

Results

For each influenza season evaluated, weekly ILI rates reported via ESSENCE were significantly correlated with weekly counts of laboratory-confirmed influenza cases (Figure 1 shows that two influenza seasons (2007–08 and 2009–10) were more severe than others examined based on the ESSENCE percent ILI threshold analysis, this result is consistent with the examination of severity of influenza activity based on lab confirmed influenza data (p<0.05).Open in a separate windowFigure 1Number and baseline of lab confirmed influenza cases, ESSENCE weekly ILI percent and baseline, and ESSENCE ILI threshold for five consecutive influenza seasons, 2006–2011.

Table 1

Correlation between laboratory confrmed influenza cases and ESSENCE ILI weekly percent in five influenza seasons, 2006–2011

Enabling Syndromic Surveillance in Pakistan

Objective

This work presents our first steps in developing a Global Real-time Infectious Disease Surveillance System (GRIDDS) employing robust and novel infectious disease epidemiology models with real-time inference and pre/exercise planning capabilities for Lahore, Pakistan. The objective of this work is to address the infectious disease surveillance challenges (specific to developing countries such as Pakistan) and develop a collaborative capability for monitoring and managing outbreaks of natural or manmade infectious diseases in Pakistan.

Methods

Utilizing our partner hospitals in the Lahore, Punjab area, we have begun developing a theoretical model of patient hospital visits with respect to diseases and syndromes within Pakistan. Our first thrust has focused on the collection, categorization and cleansing of data based on expert knowledge from our partnering institutions in Pakistan. Data consists of a patient’s home address and chief complaint which is then categorized into syndromes. Home addresses are geocoded utilizing the Google API with a resultant 72% accuracy. Unknown geolocations are aggregated only at the hospital level. Using this cleaned data, we employ methods similar to our previous work [1] on syndromic surveillance for early disease detection. Currently, we have collected over 600,000 patient records over 1.5 years.We employ the use of choropleth maps, isopleth maps utilizing kernel density estimation of patient addresses, traditional control chart methods such as exponentially weighted moving averages (EWMA), and a non-parametric time series analysis approach (seasonal trend decomposition using loess smoothing (STL) [2]) which requires only 90 days of historical data to be put into operation. The time series models are deployed as part of a real-time surveillance system in which temporal anomalies over regions can be analyzed and disease outbreaks reported.

Results

Figure 1 illustrates our visual analytics toolkit in operation. Here we see the location of our partner hospital in the Lahore region. The hospital coverage is in the most populous location of the city, providing data as a sentinel site for the overall health of the city. Currently, our system employs the use of interactive filters and linked isopleth or choropleth maps with time series analysis on mouse over.

Conclusions

Currently our research has focused on one partner location within the city of Lahore. Our ongoing work is focusing on the adoption of such a system to other regions of the country and the development of disease spread simulations (particularly Dengue Fever) utilizing baseline data collected by our partners. We plan to integrate these models into our visual analytics system for real-time planning and simulation.Open in a separate window     

Innovative Uses for Syndromic Surveillance

To determine if expanded queries can be used to identify specific reportable diseases/conditions not detected by using automated syndrome categories, we developed new categories to use with the Electronic Surveillance System for the Early Notification of Community Based Epidemics. Results suggest innovative queries can enhance clinicians’ compliance with reportable disease requirements.     

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.     

Syndromic Surveillance Based on Emergency Visits: A Reactive Tool for Unusual Events Detection

Objective

To show with examples that syndromic surveillance system can be a reactive tool for public health surveillance.

Introduction

The late health events such as the heat wave of 2003 showed the need to make public health surveillance evolve in France. Thus, the French Institute for Public Health Surveillance has developed syndromic surveillance systems based on several information sources such as emergency departments (1). In Reunion Island, the chikungunya outbreak of 2005–2006, then the influenza pandemic of 2009 contributed to the implementation and the development of this surveillance system (2–3). In the past years, this tool allowed to follow and measure the impact of seasonal epidemics. Nevertheless, its usefulness for the detection of minor unusual events had yet to be demonstrated.

Methods

In Reunion Island, the syndromic surveillance system is based on the activity of six emergency departments. Two types of indicators are constructed from collected data:

• - Qualitative indicators for the alert (every visit whose diagnostic relates to a notifiable disease or potential epidemic disease);
• - Quantitative indicators for the epidemic/cluster detection (number of visits based on syndromic grouping).

Daily and weekly analyses are carried out. A decision algorithm allows to validate the signal and to organize an epidemiological investigation if necessary.

Results

Each year, about 150 000 visits are registered in the six emergency departments that is 415 consultations per day on average. Several unusual health events on small-scale were detected early.In August 2011, the surveillance system allowed to detect the first autochthonous cases of measles, a few days before this notifiable disease was reported to health authorities (Figure 1). In January 2012, the data of emergency departments allowed to validate the signal of viral meningitis as well as to detect a cluster in the West of the island and to follow its trend. In June 2012, a family foodborne illness was detected from a spatio-temporal cluster for abdominal pain by the surveillance system and was confirmed by epidemiological investigation (Figure 2).Open in a separate windowFigure 1Epidemic curve of measles casesOpen in a separate windowFigure 2Line-list of patient characteristics in an abdominal pain cluster.

Conclusions

Despite the improvement of exchanges with health practitioners and the development of specific surveillance systems, health surveillance remains fragile for the detection of clusters or unusual health events on small scale. The syndromic surveillance system based on emergency visits has proved to be relevant for the identification of signals leading to health alerts and requiring immediate control measures. In the future, it will be necessary to develop these systems (private practitioners, sentinel schools) in order to have several indicators depending on the degree of severity.     

Syndromic Surveillance Data for Accidental Fall Injury

BackgroundFall injuries (FI) are a priority for public health planning. Syndromic surveillance (SS) is used to detect outbreaks, environmental exposures, and bioterrorism in real time. Since information is gathered on patients, the utility of using this system for FI should be evaluated.MethodsStrategies to integrate FI medical and SS data were compared using a cohort versus case control (CC) study design.ResultsThe CC study was accurate 77.7% (57.7-91.3) of the time versus 100% for a cohort design. The CC study design found FI increased for older age groups, female gender, November, and December months. Dates with any freezing temperature had a higher case fatality rate. Repeat acute care visits increased the risk of FI diagnosis by over 6% and trended upward with each visit (R=.333, p<.001).ConclusionsThe CC diagnostic quality of FI were better for age and gender than for area. The CC study found the indicators of increased risk of FI including freezing temperature, repeat acute care visits, older age groups, female gender, November, and December months. A gradient of increasing odds of FI with the number of acute care visits provides proof that community fall prevention programs should focus on those most likely to fall. A CC design of SS data can quickly identify indicators of FI with a lower accuracy but with less cost than a full cohort study, thus providing a method to focus local public health interventions.Key words: Accidental Falls, Public Health Surveillance, Case Control, Risk Factors     

症状监测与预警系统建立及应用

目的在2010上海世博会期间,于上海市浦东新区建立一个基于多数据源的症状监测与自动预警信息系统。方法选择医院、学校、宾馆和药店4类监测点,对已有的数据采集传输渠道进行修改、完善,实现监测数据每日自动化、电子化采集和传输,形成症状监测数据库,部署CUSUM模型和固定阈值两种方法对数据进行计算分析,实现自动预警,即时发布预警信号。结果系统持续运行了184 d,共采集数据近60万条,发出预警信号约800个,发现疾病聚集性事件11起,均得到及时响应与处理,未发展成突发公共卫生事件。结论症状监测与预警系统建立运行顺利,为上海世博会的召开提供了公共卫生保障,可在今后国内开展的类似大型活动中推广应用。     

Evaluating Syndromic Data for Surveillance of Non-infectious Disease

Objective

To evaluate several non-infectious disease related syndromes that are based on chief complaint (cc) emergency department (ED) syndromic surveillance (SS) data by comparing these with the New York Statewide Planning and Research Cooperative System (SPARCS) clinical diagnosis data. In particular, this work compares SS and SPARCS data for total ED visits and visits associated with three non-infectious disease syndromes, namely asthma, oral health and hypothermia.

Introduction

Syndromic surveillance data has predominantly been used for surveillance of infectious disease and for broad symptom types that could be associated with bioterrorism. There has been a growing interest to expand the uses of syndromic data beyond infectious disease. Because many of these conditions are specific and can be swiftly diagnosed (as opposed to infectious agents that require a lab test for confirmation) there could be added value in using the ICD9 ED discharge diagnosis field collected by SS. However, SS discharge diagnosis data is not complete or as timely as chief complaint data. Therefore, for the time being SS chief complaint data is relied on for non-infectious disease surveillance.SPARCS data are based on clinical diagnoses and include information on final diagnosis, providing a means for comparing the chief complaint (from SS) to a diagnosis code (from SPARCS), for evaluating how well the syndrome is captured by SS and for assessing if it would be advantageous to get SS ED diagnosis codes in a more timely and complete manner.

Methods

Syndromes previously developed by the DOHMH were used for this work. Syndrome definitions are based on querying the cc field in SS data for terms associated with asthma, oral health and hypothermia. The asthma syndrome consists of search terms for ‘ASTHMA’, ‘WHEEZING’ and ‘COPD’. The oral health syndrome uses (‘TOOTH’ or ‘GUM’) and (‘ACHE’, ‘HURT’) and excludes visits resulting from trauma (e.g., ‘INJURY’, ‘ACCIDENT’). The hypothermia syndrome is limited to search for the word ‘HYPOTHERMIA’. For the purpose of comparison of the SS data with SPARCS data for the three syndromes, the following ICD9 diagnosis codes were considered in SPARCS: 493 for asthma, 521–523, 525, 528–529 for oral health and 991 for hypothermia.SS and SPARCS data for 2007 were used for this work as this was the most recent and complete SPARCS ED dataset that was available. Overall city-wide daily counts and hospital-level annual counts for total ED, asthma-, oral health- and hypothermia-related visits were computed for SS ED data and SPARCS ED data. A comparison of daily and hospital trends for SS and SPARCS for total and syndrome-related counts were conducted using correlation coefficients.

Results

There is a high correlation between total ED SS and SPARCS daily counts (r=0.98, p-value<0.001). On average, SPARCS daily counts are higher by approximately 75 visits (range: −674, 591) per day. Correlations between SS and SPARCS daily counts for asthma, oral health and hypothermia were 0.96 (p-value<0.001), 0.66 (p-value<0.001) and 0.45 (p-value<0.001), respectively. Correlations between SS and SPARCS hospital-level annual counts for asthma, oral health and hypothermia were 0.89 (p<0.001), 0.87 (p<0.001) and 0.07 (p=0.61). In 2007, less than 8% of individual SS records had a discharge diagnosis, and this was found to vary between hospitals (0–69%); therefore, a comparison between SS discharge diagnosis and SPARCS diagnosis data was not possible.

Conclusions

Overall, syndromic surveillance data was found to be a useful data source for public health surveillance of non-infectious disease. Total ED visits were found to be comparable between SS and SPARCS. While direct comparison of counts for syndromes is not possible, the daily syndrome counts between SS and SPARCS correlated well. However, the strength of correlation varied depending on the syndrome, with a better correlation for syndromes with larger volume of visits to the ED (e.g., asthma) and with more commonly used terms in the cc search (e.g., ‘tooth ache’) compared to syndromes with very specific search terms (e.g., ‘hypothermia’).In certain instances, it is hypothesized that SS discharge diagnosis would provide more reliable and representative estimates than cc for tracking non-infectious disease. Future work will consider a period with more complete SS ED discharge diagnosis data for further comparisons and to test the hypothesis that more complete and timely SS ED discharge diagnosis data could improve surveillance efforts.     

Usefulness of Syndromic Surveillance for Early Outbreak Detection in Small Islands: The Case of Mayotte

Objective

To present the usefulness of syndromic surveillance for the detection of infectious diseases outbreak in small islands, based on the experience of Mayotte.

Introduction

Mayotte Island, a French overseas department of around 374 km2 and 200 000 inhabitants is located in the North of Mozambique Channel in the Indian Ocean (Figure 1).Open in a separate windowFigure 1Map of the western Indian Ocean featuring Mayotte IslandIn response to the threat of the pandemic influenza A(H1N1)2009 virus emergence, a syndromic surveillance system has been implemented in order to monitor its spread and its impact on public health (1). This surveillance system which proved to be useful during the influenza pandemic, has been maintained in order to detect infection diseases outbreaks.

Methods

Data are collected daily directly from patients’ computerized medical files that are filled in during medical consultations at the emergency department (ED) of the hospital Center of Mayotte (2). Among the collected variables, the diagnosis coded according to ICD-10 is used to categorize the syndromes. Several syndromes are monitored including the syndromic grouping for conjunctivitis and unexplained fever.For early outbreak detection, a control chart is used based on an adaptation of the Cusum methods developed by the CDC within the framework of the EARS program (3).

Results

Each week, about 700 patients attend the ED of the hospital. The syndromic surveillance system allowed to detect an outbreak of conjunctivitis from week 10 (Figure 2). During the epidemic peak on week 12, conjunctivitis consultations represented 5% of all consultations. The data of the sentinel practitioner network confirmed this epidemic and the laboratory isolated Enterovirus (4). At the same time, an unusual increase of unexplained fever was detected.Open in a separate windowFigure 2Weekly number of conjonctivitis and unexplained fever consultations and statistical alarms detected

Conclusions

Due to its geographical and socio-demographical situation, the population of Mayotte is widely exposed to infectious diseases. Even on a small island, syndromic surveillance can be useful to detect outbreak early leading to alerts and to mobilize a rapid response in addition to others systems.     

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.     

症状监测在汶川地震救援部队中的应用

目的探索症状监测在地震救援部队中及早发现和控制重要传染病和其他疾病流行的作用。方法2008—05．15／06．14以某部参加救灾全体人员为监测对象,确立发热、腹泻、咽痛、皮疹、皮肤外伤、眼结膜红肿等6种症状为监测指标。结果共监测2726人,在31d监测期内,6种症状的罹患率分别为1．28％～16．62％,皮疹和外伤在救援初期分别有1个发病高峰,发热在第3周有1个发病高峰。荨麻疹、湿疹、急性上呼吸道感染、急性扁桃体炎、外伤等为主要疾病。结论在地震救援部队执行任务期间进行症状监测是可行的,监测的6种主要症状代表性强,可起到预警作用。     

Adaptation of GUARDIAN for Syndromic Surveillance During the NATO Summit

Objective

To develop and implement a framework for special event surveillance using GUARDIAN, as well as document lessons learned post-event regarding design challenges and usability.

Introduction

Special event driven syndromic surveillance is often initiated by public health departments with limited time for development of an automated surveillance framework, which can result in heavy reliance on frontline care providers and potentially miss early signs of emerging trends. To address timelines and reliability issues, automated surveillance system are required.

Methods

The North Atlantic Treaty Organization (NATO) summit was held in Chicago, IL, May 19–21, 2012. During the NATO summit, the Chicago Department of Public Health (CDPH) was charged with collecting and analyzing syndromic surveillance data from emergency department (ED) visits that may indicate a man-made or naturally occurring infectious disease threat.Ten days prior to the NATO summit surveillance period, Rush University Medical Center (RUMC) received a guidance document from CDPH outlining the syndromes for systematic surveillance, specifically febrile rash illness, localized cutaneous lesion, acute febrile respiratory illness, gastrointestinal illness, botulism-like illness, hemorrhagic illness, along with unexplained deaths or severe illness potentially due to infectious disease and cases due to toxins or suspected poisoning. RUMC researchers collected relevant ICD-9 codes for each syndrome category.GUARDIAN (1), an automated surveillance system, was programmed to scan patient charts and match free text using National Library of Medicine free-text term to unique medical concept, which were further mapped to relevant ICD-9 codes. The baselines were developed using ED patient data from 1/1/2010 to 12/31/2011. Statistical references were established for unsmoothed, 24 hour counts (Baseline = Average; Threshold = +2 standard deviations).During the NATO surveillance timeframe (May 13–26, 2012) automated results with prior reporting period’s counts, reference statistics, and charts were electronically sent to CDPH. In addition, ED charge nurses made manual surveillance reports by telephone at least daily. Open lines of communication were maintained between RUMC and CDPH during the event to discuss potential positive cases. In addition, a post-event debriefing was conducted to document lessons learned.

Results

The automated GUARDIAN surveillance reports not only provided timely counts of potentially positive cases for each syndrome but also provided trend analysis with baseline measures. The GUARDIAN User Interface was used to explain what data points could trigger positive cases. The Epic system was used to review patient charts, if further explanation was necessary. The observed counts never exceeded +2 standard deviations during the NATO surveillance period for any of the syndromes.Based on the debriefing meeting between RUMC and CDPH, the top three achievements and lessons learned were as follows:

1. Quick turnaround time (∼ 10 days) from surveillance concept development to automated implementation using GUARDIAN
2. Surveillance data was timely and reliable
3. Additional statistical information was beneficial to put trends in context
4. System may be too sensitive resulting in false alarms and additional investigative burden on public health departments
5. Need for development of user-interfaces with drill down capabilities to patient level data
6. Clinicians don’t necessarily utilize exact terminology used in ICD-9 codes which could result in undetected cases.

Conclusions

This exercise successfully highlights rapid development and implementation of special event driven automated surveillance as well as collaborative approach between front-line entities such as emergency departments, surveillance researchers, and the department of public health. In addition, valuable lessons learned with potential solutions are documented for further refinements of such surveillance activities.     

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。