Surveillance Systems

Although norovirus (NoV) is the most common cause of acute gastroenteritis (ewinter vomiting diseaseí), its contribution to mortality remains unknown and may be an unrecognized problem [1]. In Europe a genetic shift in circulating NoV strains was observed in 2002 which coincided with an unusually high number of NoV outbreaks in all but one country participating in the European NoV-surveillance network [2]. Covering a time period which included this outbreak peak, we used general practitioner (GP), hospital, and death-cause data in combination with NoV surveillance data to explore the association between NoV outbreaks and morbidity and mortality.

The Indiana Public Health Emergency Surveillance System (PHESS) currently receives approximately 5,000 near real-time chief complaint messages from 55 hospital emergency departments daily.  The ISDH partners with the Regenstrief Institute to process, batch, and transmit data every three hours.  The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) tool is utilized to analyze these chief complaint data and visualize generated alerts.1   

 

The ISDH syndromic surveillance team discovered that certain chief complaints of interest were coded into the “other” syndrome and not visible in typical daily alert data.  Staff determined that even a single chief complaint containing keywords related to specific reportable diseases could be of significant public health value and should be made available to investigating epidemiologists.2 

 

In addition, data quality is critical to the success of the program and must be evaluated to ensure optimal system performance.  Metrics related to data flow and completeness were identified to serve as indicators of hospital connectivity or coding problems.  These measures included the percent change in daily admits and the proportion of chief complaints missing the patient address.

Objective

This paper describes the development of targeted query tools and processes designed to maximize the extraction of information from, and improve the quality of, the hospital emergency department chief complaint data stream utilized by the Indiana State Department of Health (ISDH) for syndromic surveillance.

Versatile, user-friendly visualization tools are required to organize the wealth of information available to users of large, regional surveillance systems into a coherent view of population health status. Communications components must allow multiple users of the same system to share information about the health of their populations in an organized fashion and facilitate communications among jurisdictions.

The Johns Hopkins University Applied Physics Laboratory has developed a communications tool to be used within the regional disease surveillance system in the National Capital Region. This abstract describes this new communications component that is designed to encourage and facilitate communication between multiple jurisdictions using a common surveillance system.

Objective

The objective is to create a capability within an existing regional disease surveillance system that allows event information to be shared easily, thoroughly, and in a timely manner, while gathering the knowledge needed to improve the entire system in the future. The functionality of this communication component must balance the utility of immediate situational awareness with the long term benefits of capturing critical information, such as system usage patterns and user response behavior, which can be used to develop future system enhancements. 

Objective This presentation discusses the problem of detecting small-scale events in biosurveillance data that are relatively sparse in the sense that the median count of monitored time series values is zero. Research goals are to understand conditions when methods adapted for sparseness are warranted, to examine adaptations of control charts and other algorithms under these scenarios, and to compare the detection performance of these algorithms.

Advanced surveillance systems require expertise from the fields of medicine, epidemiology, biostatistics, and information technology to develop a surveillance application that will automatically acquire, archive, process and present data to the user. Additionally, for a surveillance system to be most useful, it must adapt to the changing environment in which it operates to accommodate emerging public health events that could not be conceived of when the initial system was developed.

Objective

The objective of this presentation is to describe both within-discipline and across-discipline changes to standard methods and operating procedures that must be adopted to achieve automated systems that will be an effective complement and extension to traditional disease surveillance. This presentation describes adaptations already in place, as well as those still needed to rapidly recognize and respond to public health emergencies.

We apply recently developed spatial biosurveillance techniques to the law enforcement domain, with the goal of helping local police departments to rapidly detect and respond to (or better yet, to predict and prevent) emerging spatial patterns of crime.

This paper describes the syndromic networks paradigm and its application to various surveillance settings.

The University of Washington's Center for Public Health Informatics, in collaboration with the Kitsap County Health District and the UW Clinical Informatics Research Group, has developed the Peninsula Syndromic Surveillance Information Collection System (SSIC), a complex second-generation [1,2] distributed database system which collects heterogeneous data from three emergency department / urgent care facilities computerized electronic admission and discharge diagnosis data. We transform heterogeneous institution-specific data to a standardized XML (eXtensible Markup Language) format, which is then transmitted to and integrated into a central database. Aberration detection algorithms are used to analyze this data so that public health officials can detect higher-than-usual incidences of the clinical syndromes under surveillance.

A comprehensive definition of a syndrome is composed of direct (911 calls, emergency departments, primary care providers, sensor, veterinary, agricultural and animal data) and indirect evidence (data from schools, drug stores, weather etc.). Syndromic surveillance will benefit from quickly integrating such data. There are three critical areas to address to build an effective syndromic surveillance system that is dynamic, organic and alert, capable of continuous growth, adaptability and vigilance: (1) timely collection of high quality data (2) timely integration and analysis of information (data in context) (3) applying innovative thinking and deriving deep insights from information analysis. In our view there is excessive emphasis on algorithms and applications to work on the collected data and insufficient emphasis on solving the integration challenges. Therefore, this paper is focused on information integration.

Objective

EII is the virtual consolidation of data from multiple systems into a unified, consistent and accurate representation. An analyst working in an EII environment can simultaneously view and analyze data from multiple data sources as if it were coming from one large local data warehouse. This paper posits that EII is a viable solution to implement a system covering large areas and disparate data sources for syndromic surveillance and discusses case studies from environments external to health.

To examine data from 911/Emergency Medical Services (EMS) and determine whether these data provide a useful addition to syndromic surveillance (SS) when used with emergency department (ED) chief complaint (CC) data.