Real-time Surveillance

National Retail Data Monitor (NRDM) is a public health surveillance tool that collects and analyzes daily sales data for over-the-counter (OTC) health-care products from >15,000 retail stores nationwide. This is a system developed by Real-Time Outbreak and Disease Surveillance Laboratory. NRDM has been in continuous operation since December 2002. The Washoe County District Health Department implemented this system in November 2003. During initial phase of implementation, NRDM was used retrospectively on as-needed basis. Since September 2004, monitoring NRDM for volume of OTC sales for anti-diarrhea medications became a daily routine.

Objective

The objective of this paper is to evaluate the role of NRDM in gastrointestinal illness outbreak investigation in Washoe County, Nevada. The evaluation will focus on usefulness of system, sensitivity, positive predictive value, representativeness, and timeliness followed by updated CDC guidelines.

The development of a real time surveillance system for Forces on duty areas is one of the 5 initiatives of the November 2002 Prague’s NATO meeting. The French Military Health Service has decided to implement a military demonstrator within Forces in operations in a tropical area. This military prototype has three main objectives : i) to study the feasability of real time surveillance system within Forces in operations ii) to evaluate the benefit of such a system and iii) to develop a interoperable system for NATO. This French real time system has been developped by a multidisciplinary team, with military people but also with civilian experts from Pasteur Institute and Mediterranean University of Marseille.

Objective

This paper describes the new real time surveillance system, which has been installed within the French Forces in French Guiana.

Real-time disease surveillance is critical for early detection of the covert release of a biological threat agent (BTA). Numerous software applications have been developed to detect emerging disease clusters resulting from either naturally occurring phenomena or from occult acts of bioterrorism. However, these do not focus adequately on the diagnosis of BTA infection in proportion to the potential risk to public health.

GUARDIAN is a real-time, scalable, extensible, automated, knowledge-based BTA detection and diagnosis system. GUARDIAN conducts real-time analysis of multiple pre-diagnostic parameters from records already being collected within an emergency department. The goal of this system is to move from simple trend anomaly detection to an infectious disease specific expert system in order to assist clinicians in detecting potential BTAs as quickly and effectively as possible. GUARDIAN improves the diagnostic process for BTA infection through the capture and automated application of associated clinical expertise. The automated application of this knowledge provides the focus and accuracy necessary for effective BTA infection diagnosis. The continuity of this process improves the efficiency by which diagnoses of BTA infections can be made.

With the recent emphasis on public health preparedness, health departments are identifying new ways to prepare for emergencies. There has been a significant increase in the number of syndromic surveillance systems operating in recent years. These systems are based on real-time information from hospital emergency departments that is transmitted and analyzed electronically for the purpose of early detection of public health emergencies. Like other states, Rhode Island sought to enhance its traditional surveillance activities through the implementation of such a system. Rhode Island implemented the Real-time Outbreak and Disease Surveillance (RODS) system, developed by the University of Pittsburgh’s Center for Biomedical Informatics. Data from three hospitals were collected as part of the pilot implementation of the Rhode Island RODS system. Personnel at both hospitals and the Department of Health, trained in surveillance-related areas such as infection control and epidemiology, received access to RI RODS. As part of the evaluation framework, Rhode Island desired to assess system user attitudes and opinions towards the new system.

Objective

This paper presents results of a survey assessing syndromic surveillance system initial user satisfaction and attitudes regarding syndromic surveillance.

The interest of medication sales data in Syndromic Surveillance is well recognized. In France, where a real-time computerized surveillance system of frequent communicable diseases based on Sentinel general practitioners (SGPs) provides since 1984 a gold standard to evaluate other indicators, it has been shown that medication sales provided early alerts for influenza. Gastroenteritis surveillance relies in France on the surveillance of acute diarrhea by the SGPs in the general population, since 1991. The main objective of this study is to validate, at a national level, new indicators based on medication sales data to facilitate the detection of gastroenteritis epidemics.

Objective

This study examines how medication sales data can detect gastroenteritis epidemics in France.

Scientists have utilized many chief complaint (CC) classification techniques in biosurveillance including keyword search, weighted keyword search, and naïve Bayes. These techniques may utilize CC-to-syndrome or CC-to-symptom-to-syndrome classification approaches. In the former approach, we classify a CC directly into syndrome categories. In the latter approach, we first classify a CC into symptom categories. Then, we use a syndrome definition, a combination of one or more symptoms, to determine whether or not a chief complaint belongs in a particular syndrome category. One approach to CC-to-symptom-to-syndrome classification uses manually weighted keyword search and Boolean operations to build syndrome classifiers. A limitation to this approach is that it does not address uncertainty in the data and the system is manually parameterized. A CC-tosymptom-to-syndrome approach that is both probabilistic and utilizes machine learning addresses these limitations.

Objective

Design, build and evaluate a symptom-based probabilistic chief complaint classifier for the Real-time Outbreak and Disease Surveillance System.

When a chemical or biological agent with public health implications is detected in the City of Houston, analysis of syndromic surveillance data is an important tool for investigating the authenticity of the alert, as well as providing information regarding the extent of contamination.

Syndromic surveillance data in Houston is currently provided by the Real-Time Outbreak Disease Surveillance, which collects and synthesizes real-time chief complaint data from 34 area hospitals, representing approximately 70% coverage of licensed ER beds in Harris County. Data collected for each complaint includes patient home and work zip codes, allowing for geographic analysis of the data in the case of a localized environmental contamination.

Historically, when alerted to a contaminant in the Houston area, the Houston Department of Health and Human Services (HDHHS) has analyzed health data for each zip code in the geographic area of interest separately, a time-intensive process.

Recognizing the need for a more accurate and timely response to an environmental alert, HDHHS proposes aggregating zip codes into zones, based on coverage of population and areas of high risk. These “Surveillance Zones” will be used to quickly reference syndromic data in the event of a chemical or biological event.

Objective

This paper discusses the development of zones within the City of Houston in order to more quickly and accurately reference surveillance data in the case of chemical or biological events.

The Automated Hospital Emergency Department Data (AHEDD) System was designed to detect early indicators of bioterrorism and naturally occurring health risks. Initial development includes real-time data collection from four pilot hospitals, an automated syndromic surveillance application, and the capability of raw data analysis for further investigation and follow-up. This automated system frees hospital and State staff from manual reporting and analysis; and has a broad application for Public Health, collecting both chief complaint and diagnosis codes. As the project expands we plan to add the remaining 22 acute care hospitals; include poisoning, asthma, and injury surveillance; and assess electronic disease reporting from diagnosis codes and data linkage with other public health data stores, such as Environmental Health Tracking, and pre-hospital data.

Objective

This paper describes the use of technology to create an automated, real-time surveillance system with the capacity for early detection and alerting of potential health threats, and the capability to facilitate prompt investigation and increased efficiency for both New Hampshire hospital and the Division of Public Health Service resources.

Yearly epidemics of respiratory diseases occur in children. Early recognition of these and of unexpected epidemics due to new agents or as acts of biological/chemical terrorism is desirable. In this study, we evaluate the ordering of chest radiographs as a proxy for early identification of epidemics of lower respiratory tract disease. This has the potential to act as a sensitive real-time surveillance tool during such outbreaks.

Objective:

Create a tool for monitoring respiratory epidemics based on chest radiograph ordering patterns.

Real-time Outbreak and Disease Surveillance (RODS), a syndromic surveillance system created by the University of Pittsburgh has been used in Ohio by the state and local health departments since late 2003. There are currently 133 health care facilities providing 88% coverage of emergency department visits statewide to the RODS system managed by Health Monitoring Systems Inc. (HMS). The system automatically alerts health department jurisdictions when various syndromic thresholds are exceeded.

As part of response protocols, investigators export a case listing in a comma-separated values file which typically includes thousands of lines with each row containing: date admitted, age, gender, zip code, hospital name, visit number, chief complaint, and syndrome. The HMS-RODS web site provides basic graphs and maps, yet lacks the flexibility afforded by ad hoc queries, cross tabulation, and portability enabling off-line analysis.

Objective

This paper describes the integration of open source applications as portable, customizable tools for epidemiologists to provide rapid analysis, visualization, and reporting during surveillance investigations.