ISDS Conference

In September 2004, Kingston, Frontenac and Lennox and Addington Public Health began a 2-year pilot project to develop and evaluate an Emergency Department Chief Complaint Syndromic Surveillance System in collaboration with the Ontario Ministry of Health and Long Term Care – Public Health Branch, Queen’s University, Public Health Agency of Canada, Kingston General Hospital and Hotel Dieu Hospital. At this time, the University of Pittsburgh’s Real-time Outbreak and Disease Surveillance (RODS, Version 3.0) was chosen as the surveillance tool best suited for the project and modifications were made to meet Canadian syndromic surveillance requirements.

Objective

This poster provides an overview of a RODS-based syndromic surveillance system as adapted for use at a Public Health unit in Kingston, Ontario Canada. The poster will provide a complete overview of the technical specifications, the capture, classification and management of the data streams, and the response protocols developed to respond to system alerts. It is hoped that the modifications described here, including the addition of unique data streams, will provide a benchmark for Canadian syndromic surveillance systems of the future.

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.

The Bioterrorism Surveillance Unit of the Los Angeles County (LAC) Department of Public Health, Acute Communicable Disease Control (ACDC) program analyzes Emergency Department (ED) data daily. Currently capturing over 40% of the ED visits in LAC, the system categorizes visits into syndrome groups and analyzes the data for aberrations in count and spatial distribution. Typical usage of the system may be extended for various enhanced surveillance activities by creating additional syndrome categories tailored to specific illnesses or conditions. This report describes how ED data was utilized for enhanced surveillance regarding: (1) a sustained heat wave in California that broke temperature and duration records, (2) a 30,000 gallon raw sewage spill that prompted the closure of two miles of beach, and (3) an alert to ACDC of a high school student who attended school while symptomatic for meningitis.

Objective

To describe enhanced surveillance provided by the LAC Department of Public Health’s syndromic surveillance system for monitoring health events in 2006.

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.

Data quality for syndromic surveillance extends beyond validating and evaluating syndrome results. Data aggregators and data providers can take additional steps to monitor and ensure the accuracy of the data. In North Carolina, hospitals are mandated to transmit electronic emergency department data to the North Carolina Disease Event Tracking and Epidemiologic Tool (NC DETECT) system at least every 24 hours. Protocols have been established to ensure the highest level of data quality possible. These protocols involve multiple levels of data validity and reliability checks by NC DETECT staff as well as feedback from end-users concerning data quality. Hospitals also participate in the data quality processes by providing metadata including historical trends at each facility.

Objective

The purpose of this project is to describe the initiatives used by the NC DETECT to ensure the quality of ED data for surveillance.

Many cities in the US and the Center for Disease Control and Prevention have deployed biosurveillance systems to monitor regional health status. Biosurveillance systems rely on algorithms that analyze data in temporal domain (e.g., CuSUM) and/or spatial domain (e.g., SaTScan). Spatial domain-based algorithms often require population information to normalize the counts (e.g., emergency department visits) within a geographic region. This paper presents a new algorithm Ellipse-based Clustering Analysis (ECA) that analyzes data in both temporal and spatial domains--using time series analysis for each of zip codes with abnormal counts and using pattern recognition methods for spatial clusters.

Objective

This paper describes a new clustering algorithm ECA, which uses a time series algorithm to identify zip codes with abnormal counts, and uses a pattern recognition method to identify spatial clusters in ellipse shapes. Using ellipses could help detect elongated clusters resulting from wind dispersion of bio-agents. We applied the ECA to over-the-counter medicine sales. The pilot study demonstrated the potential use of the algorithm in detection of clustered outbreak regions that could be associated with aerosol release of bio-agents.

Clinician initiated reporting of notifiable conditions is often delayed, incomplete, and lacking in detail. We report on the deployment of Electronic medical record Support for Public health (ESP), a system we have created to automatically screen electronic medical record (EMR) systems for evidence of reportable diseases, to securely transmit disease reports to health authorities, and to respond to queries from health departments for clinical details about laboratory detected cases. ESP consists of software that constructs and analyzes a temporary database that is regularly populated with comprehensive codified encounter data from a medical practice's EMR system. The ESP database resides within the host medical practice's firewall, configured on either a central workstation to service large multi-site, multi-physician practices or as a software module running alongside a small practice's EMR system on a personal computer. The encounter data sent to ESP includes patient demographics, diagnostic codes, laboratory test results, vital signs, and medication prescriptions. ESP regularly analyzes its database for evidence of notifiable diseases. When a case is found, the server initiates a secure Health Level 7 message to the health department. The server is also able to respond to queries from the health department for demographic data, treatment information, and pregnancy status on cases independently reported by electronic laboratory systems. ESP is designed to be compatible with any EMR system with export capability: it facilitates translation of proprietary local codes into standardized nomenclatures, shifts the analytical burden of disease identification from the host electronic medical record system to the ESP database, and is built from open source software. The system is currently being piloted in Harvard Vanguard Medical Associates, a multi-physician practice serving 350,000 patients in eastern Massachusetts. Disease detection algorithms are proving to be robust and accurate when tested on historical data. In summary, ESP is a secure, unobtrusive, flexible, and portable method for bidirectional communication between EMR systems and health departments. It is currently being used to automate the reporting of notifiable conditions but has promise to support additional public health objectives in the future.

The Threat Agent Detection and (TADR) Network currently supports the U.S. Government’s (USG) strategy for strengthening Biological Weapons Convention (BWC) compliance through focus on disease surveillance and investigations of suspicious outbreaks of disease in the Republics of Kazakhstan, Uzbekistan, Georgia and Azerbaijan. TADR is a comprehensive approach to achieving the USG’s overall BWC compliance, and consists of

several components.

Objective

This paper describes the Electronic Integrated Disease Surveillance System being deployed in Uzbekistan, Kazakhstan, Georgia, and Azerbaijan under the Biological Threat Reduction Program (BTRP) as part of the TADR Network.

The effectiveness of public health interventions during a disease outbreak depends on rapid, accurate characterization of the initial outbreak and spread of the pathogen. Computer-based simulation using mathematical models provides a means to characterize both and enables practitioners to test intervention strategies. While compartmental differential equation models can be used to represent epidemics, they are unsuitable for early time simulations (first few days) when a small number of people are infected (and even fewer symptomatic), nor are they capable of representing spatial disease spread. Numerous models for disease propagation have been explored, including national scale network models for influenza and social network-based and probabilistic models for smallpox. To be useful in a public health context, a model for disease propagation should be efficient (e.g., simulating several weeks of real time in an hour) and flexible enough to simultaneously represent multiple diseases and attack scenarios.

Objective

This paper describes biologically-based mathematical models and efficient methods for early epoch simulation of disease outbreaks and bioterror attacks.

Effective public health response to emerging infectious diseases, natural disasters, and bioterrorism requires access to real-time, accurate information on disease patterns and healthcare utilization. The ESSENCE surveillance system in use by the Department of Defense (DoD) relies primarily on outpatient clinical impression diagnosis, which accurately characterize broad disease syndromes but may not be sufficient for monitoring specific diseases. DoD outpatient military treatment facilities perform nearly 500,000 microbiology laboratory tests annually. Initiated electronically, the ordered test is recorded immediately; most provide specific results in 24 to 72 hours and may prove useful for monitoring population health. Although a syndrome classification has been developed for laboratory tests, the classification cannot be applied directly to the DoD data and no previous study has validated the use of automated laboratory test orders for syndromic surveillance.

Objective

To evaluate the association between military microbiology laboratory test orders and infectious disease patterns.