ISDS Conference

The South Carolina (SC) Department of Health and Environmental Control uses multiple surveillance systems to monitor influenza activity from October to May of each year, including participating in the U.S. Influenza Sentinel Providers Surveillance Network. A percentage of influenza-like-illness surpassing the national 2.5% baseline is considered evidence of increased influenza activity by the CDC; this baseline is historical and does not change throughout the influenza season. Though not a part of the national influenza surveillance, SC also requires health care providers in the state to report positive rapid influenza tests, by number, on a weekly basis. Currently, only a trend analysis is used on weekly reports of positive rapid influenza test data for SC. A more robust method for determining statistically significant increases in activity for these two influenza surveillance systems is needed, and would provide a more accurate assessment of the status of seasonal influenza activity in SC.

Objective

Use the Early Aberration Reporting System (EARS) to analyze influenza sentinel provider surveillance data and positive rapid influenza test reports to identify weeks where influenza activity was significantly increased in South Carolina. Demonstrate the utility of using EARS to detect increases in influenza activity using existing surveillance systems.

Syndromic surveillance can be a useful tool for the early recognition of outbreaks and trends in emergency department (ED) data. In addition, as a more timely data source than traditional disease reporting, syndromic data may also be leveraged to identify individual disease cases, increasing the utility for first time or redundant case recognition.

San Diego County (COSD) performs daily ED syndromic surveillance. In order to assess the utility for early identification of specific conditions of public health interest (e.g., salmonellosis, meningitis, hazardous exposures, heat-related illness), a novel process entitled Priority Infectious Conditions Capture, was developed.

Objective

This paper describes an assessment of an enhanced surveillance process used to identify reportable diseases and conditions of public health importance from ED chief complaint data in COSD.

In February of 2007, the Bureau of Epidemiology (BOE) received a request from Houston Department of Public Works to investigate a possible rise in gastrointestinal (GI) illness associated with complaints about poor water quality in a Northeastern Houston neighborhood. To investigate this complaint, BOE combined case report data with syndromic data from our Real-Time Outbreak Disease Surveillance (RODS). The Houston RODS collects and synthesizes real-time chief complaint data from 34 area hospitals and health facilities, representing approximately 70% coverage of licensed ER beds in Harris County. The system uses a Naïve Bayes Classifier to categorize ER chief complaints into 7 different syndromes, including GI illness.

Objective

To investigate public concern over a possible increase in GI illness associated with water quality complaints in Northeast Houston.

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.

In this study, we compare two methods of generating grid points to enable efficient geographic cluster detection when the original geographical data are prohibitively numerous. One method generates uniform grid points, and the other employs quad trees to generate non-uniform grid points. We observe differences in the results of the spatial scan approach to cluster detection for both of these grid generation schemes. In both our simulated experiment, and our analysis of real data, the grid generation schemes produce different results. Generally speaking, the quad tree scheme is more sensitive to detecting high resolution spatial clusters than the uniform scheme. The quad tree grid point scheme may be a useful alternative to the uniform (and other) grid point generation schemes when it is important to set up a surveillance system sensitive to clusters at unspecified spatial resolutions. The quad tree grid scheme may also be useful in a number of other geographic surveillance applications.

The Miami-Dade County Health Department currently utilizes Emergency Department based Syndromic surveillance data, 911 Call Center data, and more recently Public School Absenteeism data. Daily monitoring of school absenteeism data may enhance early outbreak detection in Miami-Dade County in conjunction with the use of other syndromic systems. These systems were employed to detect any possible outbreaks resulting from a large outdoor festival occurring March 11th, 2007. This event had an estimated 1 million visitors and it ended at 7:00 p.m.

Objective

Utility of school absenteeism data to enhance syndromic surveillance activities for unusual public health events or outbreak detection.

North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) is the Web-based early event detection and timely public health surveillance system in the North Carolina Public Health Information Network. At the present time NC DETECT monitors five data sources: emergency departments, the statewide poison center, the statewide EMS data collection system, a regional wildlife center and laboratories from the NC State College of Veterinary Medicine for suspicious patterns. NC DETECT receives Carolinas Poison Control Center (CPC) data every 24 hours as of August, 2005. CPC provides the poison hotline for the entire state and handles over 105,000 calls a year 24/7/365. Seventy-five percent of calls are from the general public, with the remainder originating from healthcare providers, pharmacists, law enforcement, etc. CPC is staffed by registered nurses and pharmacists specially trained to provide diagnostic and treatment advice for acute and chronic poisonings to the public and healthcare professionals, backed up by board-certified medical toxicologists.

Objective

This paper describes the use of CPC data for early detection of chemical and environmental events and the follow up protocol development process.

Outbreak detection algorithms for syndromic surveillance data are becoming increasingly complex. Initial algorithms focused on temporal data but newer methods incorporate geospatial dimensions. As methods evolve, it is important to understand the effects on detection of both algorithm parameters and population characteristics. Intensive, iterative data analyses are required to accomplish this. Even with leading-edge computer hardware, it can take weeks or months to complete analyses using advanced signal detection techniques such as the space-time scan statistic in the SaTScan program.

Given the strategic significance and national security implications of timely and accurate detection, proper tools for studying and thus improving increasingly complex surveillance algorithms are warranted.

Objective

We describe a method to perform computationally intensive analyses on large volumes of syndromic surveillance data using open-source grid computing technology.

A major goal of biosurveillance is the timely detection of an infectious disease outbreak. Once a disease has been identified, another very important goal is to find all known cases of the disease to assist public health investigators. Natural language processing (NLP) systems may be able to assist in identifying epidemiological variables and decrease time-consuming manual review of records.

Objective

To identify epidemiologically important factors such as infectious disease exposure history, travel or specific variables from unstructured data using NLP methods.

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.