ISDS Conference

The use of syndromic surveillance in Tulsa County began as an attempt to identify symptoms associated with Category A agents, namely Anthrax. The underlying premise for adopting the system was the hope that an astute clinician, upon observing clusters of cases exhibiting certain symptoms, would rapidly notify the local health department so that an epidemiological investigation could be initiated. The system is also designed to send spatial and temporal alerts when cases of pre-defined syndromes are observed. Since 2002, when the system was first implemented, Tulsa Health Department has looked for other ways to integrate syndromic surveillance into its daily operations, and to expand its focus from an exclusive bioterrorism tool, to one that is broader in scope. One such way has been to  utilize the system to identify other syndromes and conditions. Collected emergency data has therefore, been used to identify occurrences of animal bites, mental conditions etc. This paper addresses the use of syndromic surveillance for the identification of heat-related illnesses during the hot Oklahoma summer months.

Objective

This paper describes the application of syndromic surveillance methodologies to identify nonbioterrorism syndromes particularly, the incidence of heat-related syndromes during the hot Oklahoma summer months.

The United States Environmental Protection Agency (U.S. EPA) has developed a prototype contamination warning system (CWS) for drinking water in response to Homeland Security Presidential Directive 9 (HSPD9). The goal of HSPD9 and the CWS is to expedite contamination containment and emergency response, thereby minimizing public health and economic impacts.

U.S. EPA’s conceptual CWS system, named WaterSentinel, is currently being pilot tested by U.S. EPA and its research partners. WaterSentinel is a multi-faceted approach involving water quality monitoring at optimal locations throughout the drinking water distribution system, enhanced security monitoring at key water utility infrastructure assets, consumer complaint surveillance, and innovative uses of public health surveillance data streams.

Objective

This paper summarizes the use and evaluation of various types of public health surveillance data for the early detection of chemical and biological contamination of drinking water.

Safe drinking water is essential for all communities. Intentional or unintentional contamination of drinking water requires water utilities and local public health to act quickly. The Water Security (WS) initiative of the U.S. Environmental Protection Agency is a multi-faceted approach involving water utilities and local public health officials (LPH) to identify, communicate, contain, and mitigate a drinking water contamination event. Components of WS include: online water quality monitoring, enhanced security monitoring, consumer complaint surveillance, and innovative uses of public health surveillance data streams. LPH already use multiple surveillance data systems to recognize disease events in a timely manner. However, few of these systems can be integrated or specifically designed for detection of drinking water contamination incidents.

Objective

This poster describes the integration of public health surveillance data as a component of an early warning system for detection of a drinking water contamination incident.

The threat of pandemic and seasonal influenza has drawn attention to syndromic surveillance systems for early detection of influenza-like illness. Since 2005, the Miami-Dade County Health Department has implemented ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics) to monitor emergency department data for influenza-like Illness (ILI) using chief complaint information. This study evaluates the ability of the ESSENCE ILI chief complaint grouping for identifying true ICD-9 diagnosed influenza.

Objective

Previous studies have examined the utility of different methods of syndromic grouping. This study evaluates the utility of ESSENCE for ILI surveillance.

On 12/14/06, a windstorm in western Washington caused 4 million residents to lose power; within 24 hours, a surge in patients presented to emergency departments (EDs) with carbon monoxide (CO) poisoning. As previously described, records of all patients presenting to King County EDs with CO poisoning between 12/15/06 to 12/24/06 (n=279) were abstracted, of which 249 met the case definition and eligibility requirements. We attempted to identify each of the 249 confirmed cases of CO poisoning in our syndromic ED data set by comparing the hospital name, date, time, age, sex, zip code, chief complaint, and diagnoses across the two data sets. We designated each record as an exact match, likely match, possible match, or unmatched on the basis of the available fields.

Objective

We evaluated ED and emergency medical services data for describing an outbreak of CO poisoning following a windstorm, and determined whether loss of power was followed by an increase in other health conditions.

ICD-9-CM codes have been proposed to be used as adjuncts to existing public health reporting systems and are commonly used for public health surveillance and research purposes. However these codes have been found to have variable accuracy for both healthcare billing as well as for disease classification due to both coding and physician errors, and these codes have never been comprehensively validated for their use for surveillance. Quantification of the positive predictive value for ICD-9 CM diagnosis codes is crucial for assessing their utility for public health disease surveillance and research.

Objective

To quantify the positive predictive values of ICD-9 CM diagnosis codes for public health surveillance of communicable diseases.

Although the majority of work in syndromic surveillance has been its application to bioterrorism and infectious diseases, one of the emerging priorities for its use is for the monitoring of environmental health conditions. Heat-related illness (HRI) is of growing public health importance, especially with global warming concerns and increased frequency of heat waves. Ambient temperatures are responsible for significant morbidity and mortality, as was demonstrated during the 1995 heat wave in Chicago that resulted in over 700 excess deaths and 33,000 emergency room visits due to HRI. A syndromic surveillance system that is able to detect early indications of excess HRI may start the public health response earlier, and thus reduce associated morbidity and mortality. The utility of 911 ambulance dispatch data for the early detection of heat-related illness was explored.

Objective

This paper describes the use of 911 ambulance dispatch data for the early detection of HRI in Toronto, Ontario, Canada.

The variability of free text emergency department (ED) data is problematic for biosurveillance, and current methods of identifying search terms for symptoms of interest are inefficient as well as time- and labor-intensive. Our ad hoc approach to term identification for the North Carolina Disease and Epidemiologic Collection Tool (NC DETECT) begins with development of clinical case definitions from which we build automated syndrome queries in standard query language. The queries are used to search free text clinical data from EDs, with the goal of identifying free text terms to match the case definitions. The free text search terms were initially collected from epidemiologists and clinical and technical staff at NC DETECT through informal review of ED data. Over time, we reviewed individual cases missed by our queries and identified additional search terms. We also manually reviewed records to find misspellings, abbreviations and acronyms for known search terms (e.g., dypnea, diff. br. and SHOB for dyspnea), and developed a pre-processor to clean text prior to syndromic classification. The purpose of this project was to develop and test a more standardized approach to search term identification.

Objective

This paper describes and applies a new method for identifying biosurveillance search terms using the Semantic Network of the Unified Medical Language System.

Abbreviation, misspellings, and site specific terminology may misclassify chief complaints syndromes. The Emergency Medical Text Processor (EMT-P) is system that cleans emergency department chief complaints and returns standard terms. However, little information is available on the implementation of EMT-P in a syndromic surveillance system.

Objective

To describe the implementation and baseline evaluation of EMT-P developed by the University of North Carolina.

Measures aimed at controlling epidemics of infectious diseases critically benefit from early outbreak recognition. Through a manual electronic medical record (EMR) review of 5,127 outpatient encounters at the Veterans Administration health system (VA), we previously developed single-case detection algorithms (CDAs) aimed at uncovering individuals with influenza-like illness (ILI). In this work, we evaluate the impact of using CDAs of varying statistical performance on the time and workload required to find a community-wide influenza outbreak through a VA-based syndromic surveillance system (SSS). The CDAs utilize various logical arrangements of EMR data, including ICD-9 codes, structured clinical parameters, and/or an automated analysis of the free-text of the full clinical note. The 18 ILI CDAs used here are limited to the most successful representatives of ICD-9-only and EMR-based case detectors.

Objective

This work uses a mathematical model of a plausible influenza epidemic to begin to test the influence of CDAs on the performance of a SSS.