Objective
To study if syndromic surveillance would have an added value over existing surveillance systems, we retrospectively evaluated whether known trends in respiratory pathogens are reflected in medical registrations in the Netherlands when using respiratory syndrome groupings.
As public health surveillance is becoming more and more prevalent, new sources of data collection are more evident. One such data source is school absenteeism. By monitoring the symptoms of illness recorded when students are absent, health departments ideally can pinpoint potential outbreaks prior to their existence, all at little to no cost. The symptoms reported may not amount to disease, but their increase in incidence may indicate the preliminary spread of illness. This surveillance tool is also used to develop community intervention containment practices.
Objective
This paper describes the application of syndromic surveillance data from area school districts to detect influenza epidemics in a county setting.
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.
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.
One of the common tasks faced by the U.S. Department of Agriculture (USDA) food safety analysts is to estimate the risk of observing positive outcomes of microbial tests of food samples collected at the slaughter and food processing establishments. Resulting risk estimates can be used, among other criteria, to drive allocation of FSIS investigative resources. The Activity From Demographics and Links (AFDL) algorithm is a computationally efficient method for estimating activity of unlabeled entities in a graph from patterns of connectivity of known active entities, and from their demographic profiles. It has been successfully used in social network analysis and intelligence applications. In order to test its utility in the food safety context, we treat a co-occurrence of the same strain of bacteria (in particular a specific serotype of Salmonella) in samples taken at different establishments at roughly the same time, as a link in the graph spanning all of the USDA controlled establishments. Now, given the historical patterns of linkage and the information about the distribution of the currently observed microbial positives (which make the corresponding establishments “active” in the AFDL terminology), we aim at predicting which of the remaining establishments are likely to also report positive results of tests. Even though such definition of a link produces uncertain data given that the co-occurrences of specific test results at different establishments may be purely coincidental and our analysis does not attempt to distinguish them from truly correlated instances, we expect that using this inherently noisy data in combination with demographic features of establishments, would lead to useful predictability of microbial events.
Objective
The objective of the research summarized in this paper is to evaluate utility of the AFDL in predicting likelihood of positive isolates obtained from microbial testing of food samples collected at the USDA controlled establishments.
Medical surveillance in the military can be improved through the use of clinical laboratory results collected within the Military Health System. This presentation describes an effort to establish Electronic Laboratory Reporting in the military using existing Health Level 7 (HL7) messages. HL7 data is being evaluated for data integrity, completeness, reliability and validity. In addition, initial efforts to evaluate, standardize, and use this data to support investigations of interest over the past year are presented.
Objective
This presentation describes the HL7 clinical lab results dataset and how it can and has been used for medical surveillance in the military.
Objective
Understanding the baseline dynamics of syndrome counts is essential for use in prospective syndromic surveillance. Therefore we studied to what extent the known seasonal dynamics of gastro-intestinal (GI) pathogens explain the dynamics in GI syndrome in general practitioner and hospital data.
Syndromic surveillance systems can detect increases in respiratory and gastrointestinal illness, but diagnosis of etiologic agents can be delayed due to difficult, time-consuming identification and low rates of testing for viral pathogens. Rapid diagnostic (RD) assays may aid in early identification and characterization of large outbreaks by allowing decision makers to “rule in” or “rule out” potential etiologic agents.
Objective
This paper describes preliminary results and implementation lessons learned from a RD testing pilot project. The project’s purpose is to prospectively collect diagnostic data on common causes of community-wide illness in order to supplement syndromic surveillance in New York City.
In the Northern part of Norway, all general practitioners (GPs) and hospitals use electronic health record systems. They are all connected via an independent secure IP-network called the Norwegian Health Network which enables electronic communication between all institutions involved in disease prevention and healthcare.
Objective
The Norwegian Centre for Telemedicine plans to establish a peer-to-peer based surveillance network between all GPs, laboratories, accident and emergency units, and other relevant health providers and authorities in Northern Norway. This paper briefly describes the architecture and components of the system and the motivation for using this approach.
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.