Surveillance Systems

Along with commensurate funding, an increased emphasis on syndromic surveillance systems occurred post September 11, 2001 and the subsequent anthrax attacks. Since then, many syndromic surveillance systems have evolved and have ever-increasing functionality and visualization tools. As outbreak detection using these systems has demonstrated an equivocal track record, epidemiologists have sought out other interesting and unique uses for these systems. Over the numerous years of the International Society for Disease Surveillance (ISDS) conference, many of these studies have been presented, however, there has been a dearth of discussion related to how these systems should be used on a routine basis. As the initial goal of these systems was to provide a near real-time disease surveillance tool, the question of how to most effectively conduct this type of routine surveillance is paramount.

Objective

To discuss how various emergency department based syndromic surveillance systems from across the country and world are being used and to develop best practices for moving forward.

 

In the summer of 2013, the New Jersey Department of Health (NJDOH) began planning for Super Bowl XLVIII to be held on February 2, 2014, in Met Life Stadium, located in the Meadowlands of Bergen County. Surveillance and epidemiology staff in the Communicable Disease Service (CDS) provided expertise in planning for disease surveillance activities leading up to, during, and after the game. A principal component of NJDOH’s Super Bowl surveillance activities included the utilization of an existing online syndromic surveillance system, EpiCenter. EpiCenter is a system developed by Health Monitoring Systems, Inc. (HMS) that incorporates statistical management and analytical techniques to process health-related data in real time. As of February, 2014, 75 of New Jersey’s 81 acute care and satellite emergency departments (EDs) were connected to this system. CDS staff primarily used EpiCenter to monitor ED visits for unusual activity and disease outbreaks during this event. In addition, NJDOH and HMS implemented enhanced reports and expanded monitoring of visit complaints.

Objective

To describe the surveillance planning and activities for a largescale event (Super Bowl XLVIII) using New Jersey’s syndromic surveillance system (EpiCenter).

 

About 60% of Nairobi residents live in slums with higher poverty, population density prevalence diseases and lower health access than the city average. Some residents own livestock or in are in contact with its products. Most slums dwellers work outside slums. Thus, health surveillance in slum area is vital because of potential disease outbreaks and spread. Yet, little is known on practice/challenges of health surveillance in resource-limited slums.

Objective

Disseminate field lessons from a zoonotic disease study nested on the Nairobi Urban Health and Demographic Surveillance System (NUHDSS). The study investigates the emergence and introduction of zoonoses in urban areas

 

An electronic smart-card based school absenteeism surveillance system was introduced to Hong Kong since 2008. The pilot surveillance system initially began with 18 schools in 2008, and expanded to 107 schools in the current academic year of 2013-14. Data on all-cause absenteeism were collected from all participating schools and absenteeism due to sickness such as influenza-like illness, gastroenteritis and hand-foot-and-mouth disease were collected from 39 (36.4%) schools. Data collected were aggregated for the whole territory on a weekly basis for analysis. Temporal trend of influenza activity was disseminated with simple public health advice to each participating schools and the general public through a web-based dashboard [1]. These steps of data aggregation, analysis, and feedback report generation were automated by scripts in the software R which enhanced the timeliness and minimized workload required for maintaining the system.

Objective

This study evaluated the performance of an electronic smart-card based school absenteeism system in Hong Kong, 2008-2014.

Injuries from dog bites affect approximately 4.7 million Americans per year, causing significant societal impact. Currently dog bites are the third leading cause of homeowner insurance claims, and are estimated to cost the insurance industry $489 million annually. When insurance costs are coupled with hospitalizations and lost productivity, dog bites are estimated to cost the United States $2 billion/year. However, the true impact of dog bite injuries remains unknown since discrepancies exist in the number of dog bite injuries being found by various mechanisms, and many bites may actually go unreported. In order to evaluate the true impact of dog bite injuries, the limitations of current surveillance methods must first be delineated and understood.

Objective

To give an overview of the challenges facing dog bite injury surveillance as well as identify some potential solutions for improving surveillance mechanisms.

Biosurveillance systems commonly depend on free-text chief complaints (CC)s for timely situational awareness. However, diagnosis codes may not be available soon enough and may have uncertain value because they are assigned for billing purposes rather than for population monitoring. Existing systems use syndrome categories to classify records based on these free-text fields. A syndromic cluster determination method (TOA) based on patient arrival times has been implemented in versions of ESSENCE and in NCDETECT [1]. While effective for finding case clusters whose CC terms are classifiable into syndromes, TOA implementations do not find clusters whose CC terms share only uncategorized terms. 

Objective

Explain and demonstrate the performance of a statistical method for detection of anomalous terms in pooled, contiguous blocks of freetext chief complaints from a health facility with emergent or urgent care capability.

Public Health England has developed a suite of syndromic surveillance systems, collecting data from a number of health care sources, and linking to public health action to try and improve the public health benefit of the surveillance.1 We aim to describe this national syndromic service, highlighting the flexibility of the systems in responding to a range of environmental incidents.

Objective

To deliver a national syndromic surveillance service, linking analytical and statistical methods with public health action to provide surveillance support for national public health programmes monitoring the spread of infectious diseases and the health impact of environmental incidents in England.