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Ising Amy

Animal bites may have potentially devastating consequences, including physical and emotional trauma, infection, rabies exposure, hospitalization, and, rarely, death. NC law requires animal bites be reported to local health directors. However, methods for recording and storing bite data vary among municipalities. NC does not have a statewide system for reporting and surveillance of animal bites. Additionally, many animal bites are likely not reported to the appropriate agencies. NC DETECT provides near-real-time statewide surveillance capacity to local, regional, and state level users with twice daily data feeds from NC EDs. Between 2008 and 2010, 110 to 113 EDs were submitting visit data to NC DETECT. Several animal bite-related on-line reports are available and provide aggregate and visit-level analyses customized to users' respective jurisdictions. The NC DETECT ED visit database currently provides the most comprehensive and cost-effective source of animal bite data in NC.

Objective

We describe the use of emergency department (ED) visit data collected through the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) for surveillance of animal bites in North Carolina (NC). Animal bite surveillance using ED visit data provides useful and timely information for public health practitioners involved in bite surveillance and prevention in NC.

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Submitted by elamb on

Presented December 6, 2016

Amy Ising presented these slides during the ISDS Pre-Conference Workshop as part of the 2016 ISDS Annual Conference in Atlanta, Georgia. This presentation provices an introduction to Syndromic Surveillance, an overview of key data elements involved in the analysis, sample use cases, and guidance on presenting syndromic surveillance data to stakeholders.

 

Presenter: Amy Ising, University of Chapel Hill, Chapel Hill, NC

Submitted by elamb on

TOA identifies clusters of patients arriving to a hospital ED within a short temporal interval. Past implementations have been restricted to records of patients with a specific type of complaint. The Florida Department of Health uses TOA at the county level for multiple subsyndromes (1). In 2011, NC DPH, CCHI and CDC collaborated to enhance and evaluate this capability for NC DETECT, using NC DETECT data in BioSense 1.0 (2). After this successful evaluation based on exposure complaints, discussions were held to determine the best approach to implement this new algorithm into the production environment for NC DETECT. NC DPH was particularly interested in determining if TOA could be used for identifying clusters of ED visits not filtered by any syndrome or sub-syndrome. In other words, can TOA detect a cluster of ED visits relating to a public health event, even if symptoms from that event are not characterized by a predefined syndrome grouping? Syndromes are continuously added to NC DETECT but a syndrome cannot be created for every potential event of public health concern. This TOA approach is the first attempt to address this issue in NC DETECT. The initial goal is to identify clusters of related ED visits whose keywords, signs and/or symptoms are NOT all expressed by a traditional syndrome, e.g. rash, gastrointestinal, and flu-like illnesses. The goal instead is to identify clusters resulting from specific events or exposures regardless of how patients present – event concepts that are too numerous to pre-classify.

Objective:

To describe a collaboration with the Johns Hopkins Applied Physics Laboratory (JHU APL), the North Carolina Division of Public Health (NC DPH), and the UNC Department of Emergency Medicine Carolina Center for Health Informatics (CCHI) to implement time-of-arrival analysis (TOA) for hospital emergency department (ED) data in NC DETECT to identify clusters of ED visits for which there is no pre-defined syndrome or sub-syndrome.

 

Submitted by Magou on

NC DETECT is the Web-based early event detection and timely public health surveillance system in the North Carolina Public Health Information Network. The reporting system also provides broader public health surveillance reports for emergency department visits related to hurricanes, injuries, asthma,  vaccine-preventable diseases, environmental health and others. NC DETECT receives data on at least a daily basis from four data sources: emergency departments, the statewide poison center, the statewide EMS data collection system, a regional wildlife center and laboratory data from the NC State College of Veterinary Medicine. Data from select urgent care centers are in pilot testing.

 

Objective

Managers of the NC DETECT surveillance system wanted to augment standard tabular Web-based access with a Web-based spatial-temporal interface to allow users to see spatial and temporal characteristics of the surveillance data. Users need to see spatial and temporal patterns in the data to help make decisions about events that require further investigation. The innovative solution using Adobe Flash and Web services to integrate the mapping component with the backend database will be described in this paper.

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Submitted by elamb on

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) provides early event detection and public health situational awareness to hospital-based and public health users statewide. Authorized users are currently able to view data from emergency departments (n=110), the statewide poison control center, the statewide EMS data system, a regional wildlife center and pilot data from a college veterinary laboratory as well as select urgent care centers. While NC DETECT has over 200 registered users, there are public health officials, hospital clinicians and administrators who do not access the system on a regular basis, but still like to be kept abreast of syndromic trends in their jurisdictions. In order to accommodate this interest and reduce redundant data entry among active users, we developed a summary report that can be easily exported and distributed outside of NC DETECT.

 

Objective

This paper describes a user driven weekly syndromic report designed and developed to improve the efficiency of sharing syndromic information statewide.

Referenced File
Submitted by elamb on

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) serves public health users across NC at the local, regional and state levels, providing early event detection and situational awareness capabilities. At the state level, our primary users are in the General Communicable Disease Control Branch of the NC Division of Public Health. NC DETECT receives 10 different data feeds daily including emergency department visits, emergency medical service runs, poison center calls, veterinary laboratory test results, and wildlife treatment.

In order to fulfill our users’ needs with NC DETECT’s limited staff, business intelligence tools are utilized for the acquisition and processing of our multiple, disparate data sources as well as reporting our findings to our numerous end users. Business intelligence can be described as a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions.

 

Objective

We report here on how NC DETECT uses business intelligence tools to automate both data capture and reporting in order to run a comprehensive surveillance system with limited resources.

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Submitted by elamb on

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.

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Submitted by elamb on

States and localities are using biosurveillance for a variety purposes including event detection, situational awareness, and response. However, little is known about the impact of biosurveillance on the operational components and functioning of the public health system and the added value of biosurveillance to traditional surveillance methods. A deeper understanding of how state and local public health systems use biosurveillance data and the factors that facilitate and impede its utility are needed to inform efforts to improve public health surveillance.

 

Objectives

A goal of the case studies was to assess the impact of biosurveillance on public health system preparedness, detection and response for a range of public health threats.

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Submitted by elamb on

While early event detection systems aim to detect disease outbreaks before traditional means, following up on the many alerts generated by these systems can be time-consuming and a drain on limited resources.

Authorized users at local, regional and state levels in North Carolina rely on the North Carolina Disease Event Tracking and Epidemiologic Collection Tool's (NC DETECT) Java-based Web application to monitor and follow-up on signals based on the CDC’s EARS CUSUM algorithms. The application provides users with access to aggregate syndrome-based reports as well as to patient-specific line listing reports for three data sources: emergency departments, ambulance runs and the statewide poison control center. All NC DETECT Web functionality is developed in a user-centered, iterative process with user feedback guiding enhancements and new development. This feedback, along with the need for improved situational awareness and the desire to improve communication among users drove the development of the Annotation Reports and the Custom Event Report.

 

Objective

We describe the addition of two reports to NC DETECT designed to improve NC public health situational awareness capability.

Referenced File
Submitted by elamb on

NC BEIPS is a system designed and developed by the NC Division of Public Health (DPH) for early detection of disease and bioterrorism outbreaks or events. It analyzes emergency department (ED) data on a daily basis from 33 (29%) EDs in North Carolina. With a new mandate requiring the submission of ED data to DPH, NC BEIPS will soon have data from all 114 EDs. NC BEIPS also receives data on a daily basis from the Carolinas Poison Center, the Prehospital Medical Information System and the Piedmont Wildlife Center, although syndromic surveillance output from these data sources is still in testing.

Objective

 This paper describes the North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS). NC BEIPS is the syndromic surveillance arm of NC PHIN.

Referenced File
Submitted by elamb on
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