To evaluate the added value of a syndromic surveillance system in detecting a large severe respiratory disease outbreak with a point-source we used the Legionnaires' disease (LD) outbreak of 1999 in the Netherlands as a case-study. We retrospectively simulated a prospective syndromic surveillance for space-time clusters of patients with pneumonia in hospital records to detect the LD outbreak.
The 2003-2004 influenza season was notable for the early, intense and widespread circulation of a Type A drift variant and a resulting rush on vaccine followed by an abrupt decrease in activity by mid-January. By contrast, the 2004-2005 influenza season began with a national vaccine shortage preceding any influenza activity with the resulting need for close monitoring of influenza activity.
The Connecticut Department of Public Health developed its first syndromic surveillance system in September 2001 to monitor for possible bioterrorism events and emerging infections. This system, known as the Hospital Admissions Surveillance System, receives daily reports from all 32 Connecticut acute care hospitals on their total unscheduled admissions in various diagnostic/syndromic categories. Information from one category, pneumonia admissions, has been tracked throughout the last four years as an indicator of influenza activity. The information has been utilized to supplement data from laboratory-confirmed influenza testing. The contrasts between the 2003-04 and 2004-05 influenza seasons provided an opportunity to further examine the specificity of changes in pneumonia admissions as an index of severe influenza activity.
Objective
This paper examines the continued usefulness through the 2004-05 influenza season of a hospital admissions-based syndromic surveillance system as a supplement to laboratory surveillance to monitor severe influenza.
Seasonal influenza accounts for a high proportion of outpatient morbidity during the winter months. However, influenza case counts are greatly underestimated due to frequently undiagnosed influenza. Electronic medical record (EMR) systems provide a very large, complex data source for influenza surveillance at both the patient and population level. It is important to identify influenza patients for specimen collection, respiratory isolation for school age children, prescription of an appropriate influenza drug, or to identify patients at risk for complications. At a population level, public health agencies monitor the tempo and spread of influenza season for resource management, as well as maintain situational awareness for avian influenza.
Objective
The objective of this work was to evaluate the utility of classification tree methods for syndromic surveillance case definition development using an EMR system as a data source.
Syndromic surveillance systems use residential zip codes for spatial analysis to identify disease clusters. However, the use of emergency medical services can be influenced by geographic proximity, specialty services, and severity of illness. We evaluated zip codes reported to the Boston Public Health Commission’s syndromic surveillance system from 10 Boston emergency departments (EDs).
Objective
To examine the distribution of residential zip codes among patients in Boston EDs over a two month period to better understand how this type of spatial analysis may affect the sensitivity of syndromic surveillance.
There are multiple sources of influenza and influenza-like illness (ILI) surveillance data within the state of Georgia. These include laboratory surveillance for influenza viruses, sentinel providers that report ILI, pneumonia and influenza mortality, influenza-associated hospitalizations, and influenza-associated pediatric deaths. The usefulness of emergency department-based (ED) syndromic surveillance (SS) data as an additional source of ILI surveillance data is currently being evaluated at national, state, and local levels.
Objective
To describe Georgia’s experience using ED-based SS as a source of influenza-like illness surveillance data.
Most of the time, health consequences of heat waves are serious. Heat wave response plans were developed for reducing health effects but even if they are very efficient it is not possible to eliminate all health consequences. It is therefore necessary to develop a flexible health surveillance system capable of rapidly identifying the population health burden of elevated temperature. This study focused on the Year 2006 summer heat wave, which resulted in 2,000 deaths in a 2 week period. This study represents the first opportunity to test the capabilities of a syndromic surveillance system to provide pertinent information and define appropriate indicators.
Objective
The objective of the study is to evaluate the value of a syndromic surveillance system during a heat wave and propose pertinent indicators.
Syndromic surveillance using over the counter (OTC) sales has been shown to provide earlier signals of diarrheal and respiratory disease outbreaks than hospital diagnoses. Under normal circumstances, sales patterns of OTC sales related to gastrointestinal illness (GI) are high in the winter and low in the summer. The Canadian laboratory-based surveillance system that provides weekly counts of reportable bacterial, parasitic and viral isolates by province, has shown that bacterial and parasitic infections tend to be higher in summer and early fall, whereas viral infections (particularly Norovirus and Rotavirus) appear to peak in winter and spring. This suggests that the OTC sales reflect underlying community viral infections rather than bacterial or parasitic infections. If OTC sales are to be considered for use in syndromic surveillance of community GI, the nature of this relationship needs to be clarified. The main objective of this study was to compare temporal distributions of GI-related OTC sales to laboratory-isolate patterns of bacterial, parasitic and viral cases of human GI infections.
Objective
To assess if OTC sales of GI related medications are associated with temporal trends of reportable community viral, bacterial and parasitic infections.
The eleven syndrome classifications for clinical data records monitored by BioSense include rare events such as death or lymphadenitis and also common occurrences such as respiratory infections. BioSense currently uses two statistical methods for prediction and alerting with respect to the eleven syndromes. These are a modified CUSUM; and small area regression and testing (SMART), described by Ken Kleinman. At the inception of BioSense, these prediction methods were implemented as one-model-fits-all, and they remain largely unmodified. An evaluation of the predictive value of these methods is required. The SMART method, as used in BioSense, uses long-term data. As covariate predictors, day-of-week, a holiday indicator, day after holiday, and sine/cosine seasonality variables are used. Lengthy, stable historical data is not always available in BioSense data sources, and this obstacle is expected to grow as data sources are added. We wish to test regression methods of surveillance that use shorter time periods, and different sets of predictors.
Objective
This paper compares the prediction accuracy of regression models with different covariates and baseline periods, using a subset of data from CDC’s BioSense initiative. Accurate predictions are needed to achieve sensitivity at practical false alarm rates in anomaly detection for biosurveillance.
Scientists have utilized many chief complaint (CC) classification techniques in biosurveillance including keyword search, weighted keyword search, and naïve Bayes. These techniques may utilize CC-to-syndrome or CC-to-symptom-to-syndrome classification approaches. In the former approach, we classify a CC directly into syndrome categories. In the latter approach, we first classify a CC into symptom categories. Then, we use a syndrome definition, a combination of one or more symptoms, to determine whether or not a chief complaint belongs in a particular syndrome category. One approach to CC-to-symptom-to-syndrome classification uses manually weighted keyword search and Boolean operations to build syndrome classifiers. A limitation to this approach is that it does not address uncertainty in the data and the system is manually parameterized. A CC-tosymptom-to-syndrome approach that is both probabilistic and utilizes machine learning addresses these limitations.
Objective
Design, build and evaluate a symptom-based probabilistic chief complaint classifier for the Real-time Outbreak and Disease Surveillance System.
Syndromic surveillance is the surveillance of healthrelated data that precedes diagnosis to detect a disease outbreak or other health related event that warrants a public health response. Though syndromic surveillance is typically utilized to detect infectious disease outbreaks, its utility to detect bioterrorism events is increasingly being explored by public health agencies. Many agencies believe that syndromic surveillance holds great promise in enhancing our ability to detect both planned and unplanned outbreaks of disease and have made significant investments to develop syndromic surveillance capabilities.
For instance, the Centers for Disease Control and Prevention has invested in Biosense and the Department of Defense has invested in the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) which it has deployed in partnership with the Department of Veterans Affairs. The Department of Homeland Security has invested heavily in the National Bio-surveillance Integration System which integrates a broad spectrum of bio-surveillance information including data from Biosense and ESSENCE. The University of Pittsburgh has also developed a prominent tool and is considered a thought leader in this space.
Despite the significant investments in the area of syndromic surveillance, the technology is young and the relatively small field remains fragmented. As a result, there is limited public information that addresses the field as a whole.
Objective
The objective of this assessment is to research, develop and maintain a national syndromic surveillance registry that describes each system’s configuration. By collecting current information on the leading systems we will gain a greater understanding of the syndromic surveillance landscape and capabilities.