Evaluation of Syndromic Surveillance

To evaluate whether syndromic surveillance can detect localized outbreaks of lower-respiratory infections (LRI'­s) with limited numbers of alarms in time.

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.

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. 

The Early Aberration Reporting System was developed at the Centers for Disease Control and Prevention to help assist local and state health officials to focus limited resources on appropriate activities of public health surveillance. Outbreaks of

infectious diseases are indicated in multiple spatial and temporal data sources, such as emergency department visits, drug store sales, and ambulatory clinic visits. Based on this premise, we provided correlated data sets and investigated disease clusters.

Objective

We present a pilot study of simulation of correlated outbreak signals for early aberration reporting and evaluating detection methods.

The use of syndromic surveillance systems to assist with the timely detection of unusual health events first occurred prior to the events of September 11, 2001. In the State of Michigan a pilot project with emergency departments began collecting syndromic data in 2004. Little research has been done in rural settings which have unique characteristics such as having one medical facility for a large geographic region. In addition to being rural, the community in which the following study was done is a resort com-munity where the population differs between the summer and winter months in number and composi-tion. Another unique factor in this study is that there is little published literature utilizing triage and dis-charge syndromic groups as a means for determining system sensitivity and specificity.

Objective

This paper describes the analysis of sensitivity and specificity of an ICD-9 based syndromic surveillance system in a rural emergency department located in Northern Lower Michigan.

Previously we developed an “Ngram” classifier for syndromic surveillance of emergency department (ED) chief complaints (CC) in Turkish for bioterrorism. The classifier is developed from a set of ED visits for which both the ICD diagnosis code and CC are available. A computer program calculates the associations of text fragments within the CC (e.g. 3 characters for a “3-gram”) with a syndromic group of ICD codes. The program then generates an algorithm which can be deployed to evaluate chief complaint data in real-time. However, the N-gram method differs from most other classifiers in that it assigns a probability that each visit falls within the syndrome rather than ruling the visit “in” or “out” of the syndrome. It is possible to dichotomize visits “in” or “out” using N-grams by choosing a cut-off sensitivity for the n-grams used, but this affects the specificity of the method. The effect of this trade-off is best measured by a receiveroperator curve.

Objective

Our objective was to determine the sensitivity and specificity of the Ngram CC classifier for individual ED visits. We also wish to compare these results to those obtained when we substituted anglicized characters for 6 problematic Turkish characters.

This paper describes the evaluation of an electronic disease surveillance system implemented in the Peruvian Army following the 2001 CDC surveillance evaluation guidelines.

Disease surveillance provides essential information for control and response planning1. Emergency Room (ER) syndromic surveillance data can help to identify changes in disease incidence and affected group thereby providing valuable additional time for public health interventions1. The current study explored the relationship between ER syndromic surveillance data and influenza notification to the Houston Department of Health and Human services (HDHHS).

In 2003, with the advent of SARS, the Ontario Ministry of Health and Long-Term Care (MOHLTC) released a document mandating the use of a clinical screening tool to detect patients at high risk for having a febrile respiratory illness (FRI), defined as a temperature of > 38ºC and a new or worsening cough or shortness of breath (1). The FRI screening tool is available in all Ontario Emergency Departments (ED), and is utilized in 86% of them (2). Any patient who meets all of the criteria is designated FRI positive, treated with droplet precautions and is instructed to wear a mask and undergo frequent hand-washing (1). The FRI screening tool was created as a response to the SARS outbreaks, and while it is used to identify any FRI, its sensitivity has not been documented. We attempt to determine the utility of FRI as a defining element of clinical influenza.

Objective

 (1) To determine if patients who are found to be positive for influenza or parainfluenza by culture or antigen detection are all detected by the Ontario Ministry of Health and Long-Term Care's Febrile Respiratory Illness (FRI) screening tool, and thereby treated with appropriate respiratory precautions to prevent spread. (2) To determine if syndromic surveillance or another clinical predictor would be a more effective screening tool than FRI.