Skip to main content

Rainisch Gabriel

Description

Early Aberration Reporting System (EARS, US Centers for Disease Control and Prevention, EARS Program, MS C-18, Atlanta, GA, USA) is a freeware surveillance tool that can be downloaded from the Center for Disease Control and Prevention’s website (http://emergency.cdc.gov/surveillance/ears/). It was designed for quick set-up and customization for automated monitoring of emergency department and other syndromic data sources, including, but not limited to, 911 calls, school absenteeism,

and over-the-counter medication sales. The United States’ city, county, state health departments, and various international public health organizations, use EARS software to conduct daily, near-real time surveillance of conditions easily defined by patient-reported complaints, and physician diagnoses (for example, influenza-like illness, gastroenteritis, asthma, heat-related illness). It is also used to conduct suspect case finding during outbreaks, natural disaster responses, verify that potential threats are not manifested in communities, and for supporting ad hoc analyses and research.

 

Objective

The objective of this poster is to highlight recent upgrades to the EARS software, and identify features planned for future releases.

Submitted by hparton on
Description

The BioSense system currently receives real-time data from more than 370 hospitals, as well as national daily batched data from over 1100 Department of Defense and Veterans Affairs medical facilities. BioSense maps chief complaint and diagnosis data to 11 syndromes and 78 sub-syndromes (indicators). One of the 11 syndromes is gastrointestinal (GI) illness and 6 of the subsyndromes (abdominal pain; anorexia, diarrhea, food poisoning, intestinal infections, ill-defined; and nausea and vomiting) represent gastrointestinal concepts.

 

Objective

To describe the potential use of BioSense chief complaint and final diagnosis data for GI illness surveillance.

Submitted by elamb on
Description

The eleven syndrome classifications for clinical data records monitored by BioSense include rare events such as death or lymphadenitis and also common occurrences such as respiratory infections. BioSense currently uses two statistical methods for prediction and alerting with respect to the eleven syndromes. These are a modified CUSUM; and small area regression and testing (SMART), described by Ken Kleinman. At the inception of BioSense, these prediction methods were implemented as one-model-fits-all, and they remain largely unmodified. An evaluation of the predictive value of these methods is required. The SMART method, as used in BioSense, uses long-term data. As covariate predictors, day-of-week, a holiday indicator, day after holiday, and sine/cosine seasonality variables are used. Lengthy, stable historical data is not always available in BioSense data sources, and this obstacle is expected to grow as data sources are added. We wish to test regression methods of surveillance that use shorter time periods, and different sets of predictors.

 

Objective

This paper compares the prediction accuracy of regression models with different covariates and baseline periods, using a subset of data from CDC’s BioSense initiative. Accurate predictions are needed to achieve sensitivity at practical false alarm rates in anomaly detection for biosurveillance.

Submitted by elamb on
Description

BioSense is a Centers for Disease Control and Prevention (CDC) national near real-time public health surveillance system. CDC’s BioIntelligence Center (BIC) analysts monitor, analyze, and interpret BioSense data daily and provide support to BioSense users at state and local health departments and facilities sending data. The BioSense Application is continually being enhanced in concordance with public health and clinical partners. Ongoing dialogue between the BIC and these partners is required to gather user feedback, understand what would improve system utility, build collaborative relationships, and develop appropriate jurisdictionspecific communication protocols. In May 2006, BioSense hosted a face-to-face meeting in Atlanta with approximately 50 users to solicit recommendations for the program in general and the application. Also, every 1 to 2 months, a teleconference (“Real Time, Real Talk”) is held for all BioSense users. Because of confidentially issues, jurisdiction-specific data and issues can not be raised during such meetings, thus warranting the need for a forum in which such topics could be addressed.

Objective

To present lessons learned from the BioSense jurisdiction-specific webinars conducted in 2007.

Submitted by elamb on
Description

Each year, more than two-thirds of all fireworksrelated injuries occur during June 16-July 16 [1]. During the 2006 July 4th holiday weekend, thousands of people were treated in emergency departments (EDs) for fireworks-related injuries [2]. Over 50% of these injuries were burns, most often occurring on the extremities and face. CDC’s BioSense System receives near real-time data from >11% of total U.S. ED visits. Most data is sent to BioSense by state or local systems. The system includes >540 hospital EDs; 522 facilities send patient chief complaints and 182 facilities also send physician diagnoses.  BioSense maps chief complaint and diagnosis data to 11 syndromes and 78 sub-syndromes; burns are one of 13 injury-related sub-syndromes.

Objective:

To describe burn injuries reported to the BioSense System during the 2008 Independence Day holiday.

Submitted by elamb on
Description

BioSense is a national automated surveillance system designed to enhance the nation's capability to rapidly detect and quantify public health emergencies, by accessing and analyzing diagnostic and prediagnostic health data. The BioSense system currently receives near real-time data from more than 540 civilian hospitals, as well as national daily batched data from over 1100 Department of Defense and Veterans Affairs medical facilities. BioSense maps chief complaint and diagnosis data to 11 syndromes and 78 sub-syndromes. This project was spurred by the recent detection of several clusters with chief complaints containing the term “exposure” only some of which map to current BioSense sub-syndromes. BioSense currently does not have a generic “exposure” sub-syndrome.

 

OBJECTIVE

To identify hospital visits with chief complaints concerning exposures, characterize them, and develop methods for detecting exposure clusters.

Submitted by elamb on
Description

In 2006, approximately 6.8 million children and 16.1 million adults were reported to have asthma in the US. The CDC BioSense System currently receives data from >540 hospital emergency departments (EDs; 522 send patient chief complaints and 182 send physician diagnoses), and captures about 11% of all U.S. ED visits.

 

OBJECTIVE

To describe the potential utility of BioSense data for surveillance of asthma.

Submitted by elamb on
Description

Since July 2004 the BioSense program at the Centers for Disease Control and Prevention (CDC) has received data from DoD military and VA outpatient clinics (not in real time). In January 2006 real-time hospital data (e.g. chief complaints and diagnoses) was added. Various diagnoses from all sources are binned into one or more of 11 syndrome categories.

Objective

This paper'­s objective is to compare syndromic categorization of newly acquired real-time civilian hospital data with existing BioSense data sources.

Submitted by elamb on