BioSense

The CDC's BioSense Program receives near real-time health care utilization data from a number of sources, including Department of Defense (DoD) healthcare facilities from around the globe and non-federal hospital emergency departments (EDs) in the US, to support all-hazards surveillance and situation awareness. Following the tsunami in Japan on March 11, 2011, the BioSense Program modified its surveillance protocols to monitor: 1) injuries and possible radiation-associated health effects in Japan-based DoD facilities and 2) potential adverse health effects associated with the consumption of potassium iodide (KI), a salt used to prevent injury to the thyroid gland in the event of radiation exposure, among persons attending participating EDs in the US. We present the findings from that enhanced surveillance.

Objective

To demonstrate the utility of the BioSense Program for post-disaster response surveillance.

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.

Syndromic surveillance can provide early warning of potential public health emergencies and acute health events in a population. The sharing and aggregation of syndromic data among jurisdictions can provide more comprehensive situational awareness and improve coordination and decision-making. The BioSense 2.0 Program supports increased syndromic data-sharing among a nationwide network of local and state public health agencies. Most users of this application utilize the main web site front-door interface due to its user-friendly features for query and analysis. However, this interface currently has a limited number of analytic tools, export functions, and provides access only to binned data. The back-door interface, with access to additional data lockers containing raw and exception data, represents a potentially rich source of untapped and underutilized information. In this presentation, we discuss our ongoing development and early success of a capacity (consisting of code libraries, a parser, and an implementation guide) that allows users to tailor a program-specific, automated process for generating surveillance reports from their BioSense 2.0 data locker. The product will soon be available to members of the BioSense User Community.

Objective

The purpose of this project is to develop a capacity to facilitate implementation of a user-driven enhanced process for generating program-specific surveillance reports from BioSense 2.0 locker data.

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.

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.