BioSense

BioSense 2.0 protects the health of the American people by providing timely insight into the health of communities, regions, and the nation by offering a variety of features to improve data collection, standardization, storage, analysis, and collaboration. BioSense 2.0 is the result of a partnership between the Centers for Disease Control and Prevention (CDC) and the public health community to track the health and well-being of communities across the country. As part of the redesign effort, new fat pipe system architecture has recently been implemented to improve the features and capabilities of the system.

Objective

The objective of this presentation is to provide an overview of the technical architecture of BioSense 2.0.

Biosurveillance systems typically receive free- text chief complaint and coded diagnosis data, however this data has limited specificity for notifiable disease surveillance. The Biosense System receives chief complaint and/or diagnosis data from over 360 hospitals and laboratory results from 24 hospitals in 7 states using the Public Health Information Network Messaging System (PHINMS) and HL7 standards. BioSense also receives final diagnosis from Veterans’ Affairs and Department of Defense outpatient clinics, but these clinics do not currently report laboratory findings. Chief complaints and diagnoses are assigned, as appropriate, to 11 syndromes (e.g., Gastrointestinal [GI]) (1) and to 78 more granular categories termed sub-syndromes (e.g., abdominal pain, nausea and vomiting, diarrhea) Surveillance for Salmonella infection is important since this agent is both a commonly- reported notifiable disease and a Category B bioterrorist agent.

Objective

To describe visits reported from BioSense hospitals with non-typhoidal Salmonella infections.

Use of robust and broadly applicable statistical alerting methods is essential for a public health Biosurveillance system. We compared several algorithms related to the Early Aberration Reporting System C2 (adaptive control chart) method for practical detection sensitivity and timeliness using a realistic but stochastic signal inject strategy with a variety of data streams. The comparison allowed detail examination of strategies for adjusting daily syndromic counts for day-of-week effects and the total daily volume of facility visits. Adjustment for the total visit volume allows monitoring of surrogate rates instead of just counts, and the use of real data with both syndromic and total visit counts enables this adjustment.

Objective

We compared several aberration detection algorithms using a set of syndromic data streams from a large number of treatment facilities in the CDC Biosense 1.0 system. A realistic signal injection strategy was devised to compare different ways of adjusting for total facility visits and background day-of-week effects.

Inter-jurisdictional data sharing can enhance disease surveillance capabilities for local, state, regional and national public health situational awareness and response. BioSense 2.0, a cloud-based computing platform for syndromic surveillance, provides participating local, state and federal health jurisdictions with the ability to share aggregated data; a functionality that is easily activated by selecting an administrative checkbox within the BioSense application. Checking the data-sharing box, however, is a considerable decision that comes with benefits and consequences. On May 20-21, 2013, nine city, county, and state public health department jurisdictions (mainly from the mid-western region of the U.S.) met to explore data sharing for Heat Related Illness (HRI) surveillance using BioSense 2.0. During the workshop, all participants agreed to share data (using the BioSense 2.0 front-end application) in real-time to investigate HRI trends in regional populations during May-August 2012, evaluated HRI case-definitions, and documented benefits and barriers to inter-jurisdictional data sharing. The workshop was convened by ISDS, in collaboration with the Association of State and Territorial Health Officials (ASTHO), with the support of the U.S. Centers for Disease Control and Prevention. Staff from BioSense programmatic and technical teams were also present for the workshop.

Objective

Build upon the findings of a Regional Data Sharing workshop with the larger surveillance community to more clearly describe the benefits, barriers, and needs for data sharing on the BioSense 2.0 platform.

The CDC's BioSense 2.0 system is designed with a user-centered approach, where the needs and requests of the users are part of its continued development. User requirements were gathered extensively to help design BioSense 2.0 and users continue to submit feedback which is used to make improvements to the system. However, in order to ensure that these needs are gathered in a formal and ongoing way, the BioSense 2.0 Governance Group, comprised primarily of state and local public health representatives, was established to advise the CDC on the development of BioSense 2.0. The Governance Group (GG) understands that to make recommendations having direct relevance and utility to the community, they must engage public health jurisdictions which use BioSense 2.0. To that end, the GG has conducted three surveys of the BioSense 2.0 community. The survey results will help inform the group's prioritized recommendations to the CDC.

Objective

In this presentation we discuss the findings and lessons learned from these surveys.

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. SaTScan was first implemented in June 2005 in the BioSense application for daily monitoring at CDC’s BioIntelligence Center.

Objective

The objective is to describe the visualization and monitoring of the national spatio-temporal SaTScan results in the BioSense application. This is the first application of this algorithm to a national early event detection and situational awareness system.

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.

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.