BioSense

BioSense 2.0 has become a platform for technical receipt and analysis of syndromic surveillance data for many jurisdictions nationwide, as well as a collaborative effort that has engaged a larger community of syndromic surveillance practitioners, Governance Group, and federal agencies and organizations. The potential longterm benefits of BioSense 2.0 for resource and data sharing have at times been overshadowed by the short-term limitations of the system and disconnected efforts among the CoP. In May 2014, representatives from 41 jurisdictions attended a 2-day, in-person meeting where four workgroups were formed to address on-boarding, data quality, data sharing and syndrome definition in an effort to advance changes that resonate with actual surveillance practice.

Objective

This roundtable will provide a forum for the syndromic surveillance Community of Practice (CoP) to learn about activities of the BioSense 2.0 User Group (BUG) workgroups that address priority issues in syndromic surveillance. It will be an opportunity to discuss key challenges faced by public health jurisdictions in the era of Meaningful Use and identify further needs and best practices in the areas of data quality, data sharing, onboarding, and developing syndrome definitions.

 

The Louisiana Office of Public Health (OPH) Infectious Disease Epidemiology Section (IDEpi) conducts emergency department (ED) syndromic surveillance using the Louisiana Early Event Detection System (LEEDS). IDEpi has the capability to define and change syndrome definitions in LEEDS based on surveillance needs and quality assurance activities. IDEpi submits all of the ED data to BioSense, which uses different syndrome definitions than LEEDS. Both BioSense and LEEDS use text and ICD code searches in any available chief complaint, admit reason and diagnosis data. The results of LEEDS and BioSense syndrome classifications for influenza-likeillness (ILI), gastrointestinal (GI), and upper respiratory infections (URI) applied to Louisiana’s ED data were compared to examine if the different syndrome definitions yield similar results when applied to the same data.

Objective

To compare the results of BioSense and Louisiana syndrome classifications for influenza-like-illness, gastrointestinal, and upper respiratory infections applied to Louisiana emergency department data.

BioSense 2.0 uses predetermined syndromes based upon ICD-9 codes and chief complaint data to allow users to view and analyze data from emergency department (ED) visits, yet further validations of these syndromes are needed. Previous studies have validated syndromic surveillance syndromes by comparing chief complaint data to discharge diagnosis; however, these efforts are not possible for jurisdictions in which facilities do not submit ICD-9 code data. Currently in Utah, the syndromic surveillance data submitted includes only chief complaint information. Thus, efforts to validate BioSense syndromes, such the “poisoning by medicines” syndrome, can be informed by but not analyzed in accordance with ICD-9 code and discharge diagnosis data in Utah.

Objective

To evaluate the BioSense 2.0 “poisoning by medicines” syndrome by determining chief complaint terms for inclusion and exclusion based upon pre-defined ICD-9 codes and a comparison of binned and unbinned chief complaint data.

BioSense was launched in 2003 by CDC with its primary aim to establish an integrated system of nationwide public health surveillance for the early detection and prompt assessment of potential bioterrorism-related syndromes or other public health emergencies. With the release of CDC’s Surveillance Strategy, BioSense evolved into the National Syndromic Surveillance Program (NSSP). To overcome the challenges experienced throughout the integration of local and state level data to produce a real-time national all-hazards surveillance, CDC sought input from the National Syndromic Surveillance Community of Practice (NSSP CoP). They requested that CDC provide advanced syndromic surveillance functionalities and analytical applications, such as ESSENCE and SAS to improve the BioSense Platform. In response, CDC led this pilot project to: 1) conduct security testing of SAS and ESSENCE in order to identify vulnerabilities; 2) test and improve a limited set of processes that occur before data are transformed; and 3) conduct testing of ESSENCE’s functions to ensure the tool worked as intended, and that it will meet user needs.

Objective

To describe the results of a pilot project that examined selected BioSense 2.0 data processing rules and tested SAS and ESSENCE products in the BioSense platform.

The North Dakota Department of Health (NDDoH) collects outpatient ILI data through North Dakota Influenza-like Illness Network (ND ILINet), providing situational awareness regarding the percent of visits for ILI at sentinel sites across the state. Because of increased clinic staff time devoted to electronic health initiatives and an expanding population, we have found sentinel sites have been harder to maintain in recent years, and the number of participating sentinel sites has decreased. Outpatient sentinel surveillance for influenza is an important component of influenza surveillance because hospital and death surveillance does not capture the full spectrum of influenza illness. Syndromic surveillance (SyS) is another possible source of information for outpatient ILI that can be used for situational awareness during the influenza season; one benefit of SyS is that it can provide more timely information than traditional outpatient ILI surveillance [1,2]. The NDDoH collects SyS data from hospitals (emergency department and inpatient visits) and outpatient clinics, including urgent and primary care locations. Visits include chief complaint and/or diagnosis code data. This data is sent to the BioSense 2.0 SyS platform. We compared our outpatient SyS ILI with our ND ILINet and reported influenza cases, and included hospital and combined SyS ILI for comparison.

Objective

To explore how outpatient and urgent care syndromic surveillance for influenza-like illness (ILI) compare with emergency department syndromic ILI and other seasonal ILI surveillance indicators