Tokars Jerome

The BioSense system receives patient level clinical data from > 370 hospitals and 1100 ambulatory care Departments of Defense and Veterans Affairs medical facilities. Visits are assigned as appropriate to 78 sub-syndromes, including respiratory syncytial virus (RSV). Among infants and children < 1 year of age, RSV is the most common cause of bronchiolitis and pneumonia; 0.5% to 2% require hospitalization. Increasingly, RSV is also recognized as a major cause of pneumonia in elderly adults.

Objective

To analyze final diagnosis data available to BioSense and determine its potential utility for surveillance of RSV illness.

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.

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

To compare the predictive accuracy of three non-regression methods in analysis of time series count data.

The Centers for Disease Control and Prevention BioSense has developed chief complaint (CC) and ICD9 sub syndrome classifiers for the major syndromes for early event detection and situational awareness. The prevalence of these sub-syndromes in the emergency department population and the performance of these CC classifiers have been little studied. Chart reviews have been used in the past to study this type of question but because of the large number of cases to review, the labor involved would be prohibitive. Therefore, we used an ICD9 code classifier for a syndrome as a surrogate by chart reviews to estimate the performance of a CC classifier.

Objective

To determine the prevalence of the sub-syndromes based on the ICD9 classifiers, and to determine the sensitivity, specificity, positive predictive value and negative predictive value of CC classifiers for the sub-syndromes associated with the respiratory and gastrointestinal syndromes using the ICD9 classifier as the criterion standard.

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. Within NYS excluding New York City, there are a total of 7 DOD and 60 VA hospitals and/or clinics reporting to the BioSense system, located across 41 of 57 counties.

BioSense includes a Sentinel Alert system, which monitors for diagnoses of CDC-classified Category A, B, and C diseases that have been reported from DOD and VA facilities. Sentinel Alerts are issued for single disease records, and can be followed up at local discretion to assess for public health significance and to determine whether the source of the disease might be intentional.

Objective

To describe the NYSDOH's experience with the monitoring of Sentinel Alerts generated for NYS within the CDC’s BioSense application, following up each alert with local health department staff to determine case resolution, and providing user-level feedback to the CDC to effect system improvements.

BioSense is a national system that receives, analyzes, and visualizes electronic health data and makes it available for public health use. In December 2007 CDC added the Influenza Module to the main BioSense application.

Objective

This presentation describes the new BioSense Influenza Module, its performance during the 2007-8 influenza season, and modifications for the 2008-9 influenza season.

West Nile Virus (WNV) is a mosquito-borne virus that can cause meningitis and encephalitis. Since its discovery in New York City during an encephalitis outbreak in 1999, WNV has become endemic in North America. In the United States, 16,000 human WNV disease cases (including West Nile fever, meningitis, encephalitis, and unspecified clinical illness) and over 600 WNV-related deaths have been reported to the Centers for Disease Control from 46 states. Perennial WNV epidemics occur during summer months, peaking during late August. BioSense Early Event Detection and Situation Awareness System receives daily laboratory test order data feed in HL7 from Laboratory Corporation of America. In this study, test orders were studied for their correlation with WNV activity.

Objective

To determine the feasibility of using BioSense laboratory test order data for West Nile disease surveillance in the United States. 

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.

In 2007, the CDC BioSense System received data from 450 non-federal hospitals. Hospitals provide data to Biosense based on their capability and willingness to supply electronic data. As of July 2008, Biosense is receiving data from 550 hospitals. The National Hospital Ambulatory Medical Care Survey (NHAMCS) is an annual national probability sample survey of hospitals that collects data on U.S. emergency department (ED) visits.

Objective

To assess the representativeness of BioSense ED data by comparing it with the NHAMCS results.

BioSense currently receives demographic and chief complaint data from more than 360 hospitals and text radiology reports from 36 hospitals. Detection of pneumonia is an important as several Category A bioterrorism diseases as well as avian influenza can manifest as pneumonia. Radiology text reports are often received within 1-2 days and may provide a faster way to identify pneumonia than coded diagnoses. Objective To study the performance of a simple keyword search of radiology reports for identifying pneumonia.