Evaluation of Syndromic Surveillance

An important goal of influenza surveillance is to provide public health decisionmakers with timely estimates of the severity of community-wide influenza. One potential indicator is the number of influenza hospitalizations. In New York City methods for estimating influenza hospitalizations include asking hospitals to self-report, sending field staff to review medical records, and analyzing electronic hospital discharge data available months after influenza season is over. Given the limitations of each of these approaches, we evaluated whether electronic ED data, received daily for syndromic surveillance, could be used to monitor hospitalizations during influenza  epidemics.

Objective

To evaluate whether trends in influenza hospitalizations can be monitored using ED syndromic surveillance data.

The Maryland Department of Health and Mental Hygiene conducts enhanced surveillance using the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE). The current version of ESSENCE for the National Capital Region consists of information from multiple data sources for syndromic surveillance in Maryland, Washington DC, and Virginia. Chief complaint data from emergency department (ED) visits and over-the-counter (OTC) medications are categorized into syndromes and alerts are generated when observed counts are outside the expected range. ESSENCE alerts users to unusual counts of a particular syndrome based on both temporal and spatial distribution for enhanced surveillance of disease activity. While several studies have examined the usefulness of ED data to detect the start of the influenza season, a lack of information exists on the usability of OTC sales to detect influenza. OTC data may provide an earlier alert to illness than other sources, if people self-treat with OTC medications.

Objective

This study examines the ability of syndromic surveillance data to detect seasonal influenza. ED visits for influenza-like illness and OTC flu medication sales are evaluated to determine whether these data sources are useful in the detection of the influenza season. Data sources that can detect seasonal influenza may also be used to help detect the start of pandemic influenza.

Syndromic surveillance has been used been used as method of surveillance for various events in recent years. For example, post September 11th, 2001 anthrax attacks in New York City, World Youth Day in Toronto 2002, Salt Lake City 2002 Olympics, Democratic National Convention Boston 2004, and the G8 Summit in Scotland 2005.

Objective

Historical Emergency Department (ED) visits were examined to characterize ED utilization for the weeks before, during and after Queen’s University Homecoming weekend in Kingston, Ontario, Canada. This information was used to prospectively monitor the 2006 Homecoming period and inform key stakeholders.

In May 2000 accidental contamination of the water supply led to an outbreak of severe gastroenteritis in Walkerton Ontario, Canada. Of 1346 cases associated with exposure to Walkerton water, 65 were admitted to hospital, 27 developed Hemolytic-Uremic Syndrome, and six died. Estimates that 42% of cases were unreported indicate that the actual number of cases was likely 2321.

Objective

This abstract reports preliminary results of a retrospective study of the effectives of ER syndromic surveillance in detecting this outbreak.

The Public Health Agency of Canada is currently utilizing a syndromic surveillance prototype called the Canadian Early Warning System (CEWS). This system monitors several live data feeds, including emergency room chief complaint records from all seven local hospitals, Telehealth (24/7 nurse hotline) calls, and over-the-counter drug sales from a number of the large chain drug stores. Data trends are analysed for aberrations as early indicators of outbreak events. Collaborators on this Winnipeg, Manitoba-based pilot include the Winnipeg Regional Health Authority and IBM Business Solutions. Algorithms currently in CEWS include the 3, 5 and 7-day moving averages, CUSUM and the CDC’s EARS. We seek to investigate the performance of these algorithms in view of the fact that their detection ability may be dependent upon data source and/or the type of outbreak event.

Objective

To determine the sensitivity, specificity and days to detection of several commonly used algorithms in syndromic surveillance systems.

The Death Certificate Surveillance was implemented in October 2001 to enhance New Hampshire's ability to monitor for bioterrorism and other public health threats, such as communicable diseases and chemical exposures. In 2003, this surveillance system was automated. Death certificates become available for review by disease surveillance staff within 24 hours of filing. Learning objectives: 1. Discuss the value of death certificate surveillance in detecting communicable disease 2. Explain the death certificate review process 3. Describe how death certificate surveillance can be automated.

To compare regression models with the modified C2 algorithm for analysis of time series data and real time outbreak detection.

To retrospectively ascertain whether a parallel or consensus monitoring approach is better suited to multistream surveillance of influenza in Ontario.

The objective of this paper is to examine the utility of Emergency Department and Telehealth data for Syndromic Surveillance. This works attempts to minimize false outbreak detection. It also demonstrates that these two data sources contain independent information which is useful for outbreak detection.

To evaluate whether joint monitoring of laboratory test orders and outpatient visits improves the sensitivity and timeliness of alerting during seasonal increases in respiratory illness.