OBJECTIVE
A “whole-system facsimile” recreates a complex automated biosurveillance system running prospectively on real historical datasets. We systematized this approach to compare the performance of otherwise identical surveillance systems that used alternative statistical outbreak detection approaches, those used by CDC’s BioSense syndromic system or a popular scan statistics.
The North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS) receives daily emergency department (ED) data from 33 (29%) of the 114 EDs in North Carolina. These data are available via a Web-based portal and the Early Aberration Reporting System to authorized NC public health users for the purpose of syndromic surveillance (SS). Users currently monitor several syndromes including: gastrointestinal severe, fever/rash illness and influenza-like illness. The syndrome definitions are based on the infection-related syndrome definitions of the CDC and search the chief complaint (CC) and, when available, triage note (TN) and initial temperature fields. Some EDs record a TN, which is a brief text passage that describes the CC in more detail. Most research on the utility of ED data for SS has focused on the use of CC. The goal of this study was to determine the sensitivity, specificity, and both positive and negative predictive value of including TN in the syndrome queries.
Objective
This study evaluates the addition of TN to syndrome queries used in the NC BEIPS.
While there has been some work to evaluate different data sources for syndromic surveillance of influenza, no one has yet assessed the utility of simultaneously restricting data to specific visit settings and patient age-groups using data drawn from a single source population. Furthermore, most studies have been limited to the emergency departments (ED), with few evaluating the timeliness of data from community-based primary care.
Objective
Using physician billing data from a single source population, we aimed to compare age-group and visit setting specific patterns in the timing of patients presenting to community-based healthcare settings and hospital ED for influenza-like-illnesses (ILI). We thus evaluate the utility of focusing on particular age-groups and care settings for syndromic surveillance of ILI in ambulatory care.
Objective
There were two objectives of this analysis. First, apply text-processing methods to free-text clinician notes extracted from the VA electronic medical record for automated detection of Influenza-Like-Illness. Secondly, determine if use of data from free-text clinical documents can be used to enhance the predictive ability of case detection models based on coded data.
National surveillance is used to detect the emergence and spread of influenza virus variants and to monitor influenza-related morbidity and mortality. Nurse telephone triage (“call”) data may serve as a useful complement to traditional influenza surveillance, especially at times or in places traditional surveillance is not operating. It may also be useful to detect increased occurrence of non-influenza respiratory infection.
Objective
We compared state-level nurse call data to CDC national influenza surveillance data to determine how well call data performed relative to CDC sentinel provider and viral isolate data. This quantitative analysis extends an earlier semiquantitative regional analysis of the same data.
Crude mortality could be valuable for infectious disease surveillance if available in a complete and timely fashion. Such data can be of used for detecting, and tracking the impact of unusual health events (e.g. pandemic influenza) or other unexpected or unknown events of infectious nature.
To evaluate whether these goals can be achieved with crude mortality monitoring in the Netherlands, a pilot study was set up in 2008 in which death counts were received from Statistics Netherlands.
The aims of this pilot are: 1) Setting up communication and data transmission. 2) Calculating expected mortality counts (depending on the season) and a prediction interval. 3) Detecting deviations in mortality counts above the threshold. 4) Comparing such deviations (and lags hereof) with other public health information (such as sentinel influenza-like-illness surveillance, and web-based selfreported ILI). 4) Evaluating the additional value of such a system for infectious disease public health.
Objective
To evaluate the potential use of mortality data in the Netherlands for real-time surveillance of infectious disease events through a pilot study.
The University of Washington has been working since 2000 with partners in Washington State to advance bioterrorism (BT) detection and preparedness. This project collects data on patients presenting with influenza-like illnesses and other potentially BT-related syndromes at emergency departments and primary care clinics (Kitsap, Clallam, and Jefferson counties) using a secure automated informatics approach. Local health jurisdiction epidemiologists use a web-based interface to view de-identified data and use a version of CDC’s EARS disease detection algorithms to watch for variances in patterns of diagnoses, volume, time and space as part of the public health real-time disease surveillance system. This processed hospital data is also made available back to the officials and administrators at the reporting hospital.
Objective
To understand GIS issues in a rural-tourban setting and demonstrate limitations of ZIPcode-only approaches compared to census tract and block approaches.
There are multiple sources of influenza and influenza-like illness (ILI) surveillance data within the state of Georgia. These include laboratory surveillance for influenza viruses, sentinel providers that report ILI, pneumonia and influenza mortality, influenza-associated hospitalizations, and influenza-associated pediatric deaths. The usefulness of emergency department-based (ED) syndromic surveillance (SS) data as an additional source of ILI surveillance data is currently being evaluated at national, state, and local levels.
Objective
To describe Georgia’s experience using ED-based SS as a source of influenza-like illness surveillance data.
The Automated Hospital Emergency Department Data System is designed to detect early indicators of bioterrorism events and naturally occurring public health threats. Four investigatory tools have been developed with drill-down detail reporting: 1. Syndromic Alerting, 2. Chief Complaint Data Mining, 3. ICD9 Code Disease, and 4. Influenza-Like-Illness Tracking.
All analysis processing runs on the server in seconds using ORACLE PL/SQL stored procedures and arrays.
Objective
This paper details the development of electronic surveillance tools by Communicable Disease Surveillance, which have increased detection and investigation capabilities.
During influenza season, the Boston Public Health Commission uses syndromic surveillance to monitor Emergency Department visits for chief complaints indicative of influenza-like illness (ILI). We created three syndrome definitions for ILI to capture variable presentations of disease, and compared the trends with Boston pneumonia and influenza mortality data, and onset dates for reported cases of influenza.
Objective
To evaluate the impact of different syndrome definitions for ILI by comparing weekly trends with other data sources during the 2005-2006 influenza season in Boston.