Developing syndrome definitions based on consensus and current use.

OBJECTIVE: Standardized surveillance syndromes do not exist but would facilitate sharing data among surveillance systems and comparing the accuracy of existing systems. The objective of this study was to create reference syndrome definitions from a consensus of investigators who currently have or are building syndromic surveillance systems. DESIGN: Clinical condition-syndrome pairs were catalogued for 10 surveillance systems across the United States and the representatives of these systems were brought together for a workshop to discuss consensus syndrome definitions. RESULTS: Consensus syndrome definitions were generated for the four syndromes monitored by the majority of the 10 participating surveillance systems: Respiratory, gastrointestinal, constitutional, and influenza-like illness (ILI). An important element in coming to consensus quickly was the development of a sensitive and specific definition for respiratory and gastrointestinal syndromes. After the workshop, the definitions were refined and supplemented with keywords and regular expressions, the keywords were mapped to standard vocabularies, and a web ontology language (OWL) ontology was created. LIMITATIONS: The consensus definitions have not yet been validated through implementation. CONCLUSION: The consensus definitions provide an explicit description of the current state-of-the-art syndromes used in automated surveillance, which can subsequently be systematically evaluated against real data to improve the definitions. The method for creating consensus definitions could be applied to other domains that have diverse existing definitions.

Bridging radiology and public health: the emerging field of radiologic public health informatics.

Radiology and public health have an emerging opportunity to collaborate, in which radiology's vast supply of imaging data can be integrated into public health information systems for epidemiologic assessments and responses to population health problems. Fueling the linkage of radiology and public health include (i) the transition from analog film to digital formats, enabling flexible use of radiologic data; (ii) radiology's role in imaging across nearly all medical and surgical subspecialties, which establishes a foundation for a consolidated and uniform database of images and reports for public health use; and (iii) the use of radiologic data to characterize disease patterns in a population occupying a geographic area at one time and to characterize disease progression over time via follow-up examinations. The backbone for this integration is through informatics projects such as Systematized Nomenclature of Medicine Clinical Terms and RadLex constructing terminology libraries and ontologies, as well as algorithms integrating data from the electronic health record and Digital Imaging and Communications in Medicine Structured Reporting. Radiology's role in public health is being tested in disease surveillance systems for outbreak detection and bioterrorism, such as the Electronic Surveillance System for the Early Notification of Community-based Epidemics. Challenges for radiologic public health informatics include refining the systems and user interfaces, adhering to privacy regulations, and strengthening collaborative relations among stakeholders, including radiologists and public health officials. Linking radiology with public health, radiologic public health informatics is a promising avenue through which radiology can contribute to public health decision making and health policy.

Event communication in a regional disease surveillance system.

When real-time disease surveillance is practiced in neighboring states within a region, public health users may benefit from easily sharing their concerns and findings regarding potential health threats. To better understand the need for this capability, an event communications component (ECC) was added to the National Capital Region Disease Surveillance System, an operational biosurveillance system employed in the District of Columbia and in surrounding Maryland and Virginia counties. Through usage analysis and user survey methods, we assessed the value of the enhanced system in daily operational use and during two simulated exercises. Results suggest that the system has utility for regular users of the system as well as suggesting several refinements for future implementations.

A systems overview of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II).

The Electronic Surveillance System for the Early Notification of Community-Based Epidemics, or ESSENCE II, uses syndromic and nontraditional health information to provide very early warning of abnormal health conditions in the National Capital Area (NCA). ESSENCE II is being developed for the Department of Defense Global Emerging Infections System and is the only known system to combine both military and civilian health care information for daily outbreak surveillance. The National Capital Area has a complicated, multijurisdictional structure that makes data sharing and integrated regional surveillance challenging. However, the strong military presence in all jurisdictions facilitates the collection of health care information across the region. ESSENCE II integrates clinical and nonclinical human behavior indicators as a means of identifying the abnormality as close to the time of onset of symptoms as possible. Clinical data sets include emergency room syndromes, private practice billing codes grouped into syndromes, and veterinary syndromes. Nonclinical data include absenteeism, nurse hotline calls, prescription medications, and over-the-counter self-medications. Correctly using information marked by varying degrees of uncertainty is one of the more challenging aspects of this program. The data (without personal identifiers) are captured in an electronic format, encrypted, archived, and processed at a secure facility. Aggregated information is then provided to users on secure Web sites. When completed, the system will provide automated capture, archiving, processing, and notification of abnormalities to epidemiologists and analysts. Outbreak detection methods currently include temporal and spatial variations of odds ratios, autoregressive modeling, cumulative summation, matched filter, and scan statistics. Integration of nonuniform data is needed to increase sensitivity and thus enable the earliest notification possible. The performance of various detection techniques was compared using results obtained from the ESSENCE II system.