Evaluation

Current veterinary surveillance systems may be ineffective for timely detection of outbreaks involving non-targeted disease. Earlier detection could enable quicker intervention that might prevent the spread of disease and limit lost revenue. Data sources, similar to those used for early outbreak surveillance in humans, may provide for earlier outbreak detection in animals. Veterinary diagnostic laboratories are a source of data that might be valuable to such efforts.

Objective

To study the value of data from veterinary diagnostic laboratories as an initial step in developing an early outbreak surveillance system for animals.

Research evaluating the use of spatial data for surveillance purposes is ongoing and evolving. As spatial methods evolve, it is important to characterize their effectiveness in real-world settings. Assessing the performance of surveillance systems has been difficult because there has been a paucity of data from real bioterrorism events. Recent efforts to assess surveillance system performance have focused on injecting synthetic outbreak data (signal) into actual background visit data. These studies focused on either temporal data, a single syndrome category, or a single bioterrorism agent. We are unaware of prior studies evaluating the performance of spatial outbreak detection for multiple syndrome categories in an operational surveillance system.

Objective

To characterize the performance of a spatial scan statistic, we used SaTScan to measure the sensitivity and positive predictive value for detecting simulated outbreaks having varying size, case density, and syndrome type.

The utility of syndromic surveillance systems to augment health departments’ traditional surveillance for naturally occurring disease has not been prospectively evaluated.

Objective

In this interim report we describe the signals detected by a real-time ambulatory care-based syndromic surveillance system and discuss their relationship to true outbreaks of illness.

Current syndromic surveillance systems run multiple simultaneous univariate procedures, each focused on detecting an outbreak in a single data stream. Multivariate procedures have the potential to better detect some types of outbreaks, but most of the existing methods are directionally invariant and are thus less relevant to the problem of syndromic surveillance. This article develops two directionally sensitive multivariate procedures and compares the performance of these procedures both with the original directionally invariant procedures and with the application of multiple univariate procedures using both simulated and real syndromic surveillance data. The performance comparison is conducted using metrics and terminology from the statistical process control (SPC) literature with the intention of helping to bridge the SPC and syndromic surveillance literatures. This article also introduces a new metric, the average overlapping run length, developed to compare the performance of various procedures on limited actual syndromic surveillance data. Among the procedures compared, in the simulations the directionally sensitive multivariate cumulative sum (MCUSUM) procedure was preferred, whereas in the real data the multiple univariate CUSUMs and the MCUSUM performed similarly. This article concludes with a brief discussion of the choice of performance metrics used herein versus the metrics more commonly used in the syndromic surveillance literature (sensitivity, specificity, and timeliness), as well as some recommendations for future research.

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.

Communicable diseases are underreported by physicians, especially diseases without laboratory tests. The goals of our study were to determine reporting levels for clinical chickenpox, describe clinical data elements common to chickenpox, and assess ability of an electronic syndromic surveillance system, BioSense, to capture chickenpox cases.

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.

This paper describes three years of electronic health record (EHR) data from a network of urban ambulatory care clinics in New York City.

To compare the ability to detect disease outbreaks of separate and combined data streams from ambulatory care and emergency department from Harvard Pilgrim Health Care.

We evaluated several classifications of emergency department (ED) syndromic data to ascertain best syndrome classifications for ILI.