Identifying Fractures in BioSense Radiology Reports

The purposes of this study are to validate a keyword-based text parsing algorithm for identifying fractures and compare radiology results with chief complaint and ICD-9 final diagnoses.

July 30, 2018

Preliminary Findings from the BioSense Evaluation Project

In October 2006, the Centers for Disease Control and Prevention funded four institutions, including Emory University, to conduct evaluations of the BioSense surveillance system.

July 30, 2018

The Predictive Accuracy of Non-Regression Data Analysis Methods

Analysis of time series data requires accurate calculation of a predicted value. Non-regression methods such as the Early Aberration Reporting System CuSum are computationally simple, but most do not adjust for day of week or holiday. Alternately, regression methods require larger counts, more computer resources, and possibly longer baseline periods of data. As increasing volumes of data are reported and analyzed, the predictive accuracy of simpler methods should be assessed and optimized.

 

Objective

July 30, 2018

ICD-9 CM Based Sub-Syndrome Distributions in BioSense Hospital Data

Objective To examine sub-syndrome distributions among BioSense emergency department (ED) chief complaint and final diagnosis based data and to observe patterns by hospital system, age, and gender.

July 30, 2018

Systematic Comparison of Algorithms Used in Syndromic Surveillance

Varied approaches have been used by syndromic surveillance systems for aberration detection. However, the performance of these methods has been evaluated only across a small range of epidemic characteristics.

 

Objective

We conducted a large simulation study to evaluate the detection properties of 6 different algorithms across a range of outbreak characteristics.

July 30, 2018

Monitoring Influenza Activity Using the BioSense System, 2003-2005

One of the standard approaches to public health surveillance for influenza is to monitor the percent of visits to about 2000 sentinel physicians for influenza-like illness (%ILI; fever plus cough or sore throat). The BioSense System currently receives (among other data) ICD-9 discharge diagnoses from Veteran’s Affairs (VA) and Department of Defense (DOD) outpatient clinics. A literature review found that, in addition to ICD-9 code 487 (the code specific for influenza), 29 other codes have been used previously to monitor influenza.

July 30, 2018

Visualizing and Monitoring Data in BioSense Using SaTScan

BioSense data includes Department of Defense and Veterans Affairs ambulatory care diagnoses and procedures, as well as Laboratory Corporation of America lab test orders. Data are mapped to eleven syndrome categories. SaTScan is a spatio-temporal technique that has previously been applied to surveillance at the metropolitan area level. Visualization of national results involves unique issues, including displaying cluster information that crosses jurisdictions, zip codes with highly variant data volume, and evaluating large multiple state clusters.

July 30, 2018

Evaluation of Spatial Estimation Methods for Cluster Detection

CDC’s BioSense system provides near-real time situational awareness for public health monitoring through analysis of electronic health data. Determination of anomalous spatial and temporal disease clusters is a crucial part of the daily disease monitoring task. Spatial approaches depend strongly on having reliable estimated values for counts among the geographic sub-regions. If estimates are poor, algorithms will find irrelevant clusters, and clusters of importance may be missed.

July 30, 2018

The New York State BioSense Sentinel Alert Experience

In addition to monitoring Emergency Department chief complaint data and pharmacy sales as indicators of outbreaks, the New York State Department of Health (NYSDOH) Syndromic Surveillance System also monitors information from the CDC’s Early Event Detection and Situational Awareness System, BioSense. BioSense includes Department of Defense (DOD) and Veterans Affairs (VA) outpatient clinical data (ICD-9-CM diagnoses and CPT procedure codes), and LabCorp test order data.

July 30, 2018

The New York State Department of Health’s Syndromic Surveillance System

The New York State Department of Health (NYSDOH) Syndromic Surveillance System consists of five components: 1. Emergency Department (ED) Phone Call System monitors unusual events or clusters of illnesses in the EDs of participating hospitals; 2. Electronic ED Surveillance System monitors ED chief complaint data; 3. Medicaid data system monitors Medicaid-paid over-the-counter and prescription medica-tions; 4. National Retail Data Monitor/Real-time Outbreak and Disease Surveillance System monitors OTC data; 5.

July 30, 2018

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This Knowledge Repository is made possible through the activities of the Centers for Disease Control and Prevention Cooperative Agreement/Grant #1 NU500E000098-01, National Surveillance Program Community of Practice (NSSP-CoP): Strengthening Health Surveillance Capabilities Nationwide, which is in the interest of public health.

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