Syndromes

The LAC SSS has been in existence since 2004. Currently, the system collects data from over 50 hospitals daily and performs a chief complaint-based syndrome classification analysis of all ED visits. The keyword “fever” is of special interest due to its inclusion within several syndrome category definitions such as influenza, meningitis, etc. However, inclusion of such terms in syndrome definitions may be a disadvantage as such keyword searches would depend upon the consistency in which the term “fever” is reported. In 2014, several LAC syndromic surveillance hospital data connections were upgraded to include notes recording patient body temperature. To evaluate the newly added temperature information, analyses were conducted on those observations that included body temperature, chief complaint, and diagnosis information.

Objective

The Los Angeles County (LAC) Emergency Department (ED) Syndromic Surveillance System (SSS) classifies patients into syndrome categories based on stated chief complaints. In an effort to evaluate the accuracy of patient- stated chief complaints and final diagnoses, both “fever” chief complaints and diagnoses were compared with patient body temperature readings.

In the event of a large-scale public health crisis, successfully detecting and assessing health threats and monitoring population health status over a sustained period of time is likely to require integration of information from multiple sources. In addition, this information must be shared at varying levels of detail both among different agencies or organizations within an affected locality and among response participants at local, state, and federal levels of government. In early 2007, the International Society for Disease Surveillance (ISDS) proposed a project to support member initiated consultations on priority unresolved questions in the field of syndromic surveillance (SS) research, development, or practice. The Duval County Health Department sought and obtained ISDS support to address the use of SS data in combination with other human health and veterinary surveillance data, environmental sampling data, and plume modeling results in the event of an airborne bioterrorist (BT) attack. To date, the development of SS in Florida has mainly focused on systems that monitor information from emergency department (ED) visits. In addition, because SS development was decentralized and managed primarily by county health departments, various systems were used in Florida, including ESSENCE, STARS, EARS and BioDefend.

Objective

The objective of this consultation was to develop expert, consensus-based recommendations for use of SS in combination with other human health, animal health, and environmental data sources to improve situational awareness in the event of a large-scale public health emergency. The consultation, convened by the Duval County, Florida, Health Department, involved other local and state public health offi cials from Florida who addressed this question in the context of a hypothetical BT attack scenario in Duval County. Insights arising from the consultation will be used to strengthen public health surveillance capacities as part of both local and state emergency preparedness efforts in Florida. The approach used by the consultation may be useful to other health departments seeking to enhance their emergency situational awareness capacity.

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.

Syndromic surveillance refers to the monitoring of disease related events, sets of clinical features (i.e. syndromes), or other indicators in a population. Tennessee obtains emergency department data for syndromic surveillance in standardized HL7 format following the field and value set standards published by the Public Health Information Network. Messages contain information previously unavailable to syndromic surveillance systems, including quantitative values such as recorded temperature. Data are received daily and processed by a Tennessee ESSENCE application and the national BioSense platform.

These systems use chief complaint keywords, ICD9 codes, and other algorithms to assign syndromes for each record. The differences between the BioSense and ESSENCE syndrome assignments have not been well defined. Detailed comparisons of syndrome assignment across tools are difficult to perform due to the intensity of the manual review required. However, definitions of fever can be easily confirmed in HL7 messages when the recorded temperature is provided. Currently, both the BioSense and ESSENCE syndrome definitions exclude recorded temperature from consideration when assigning syndromes.

To compare the performance of the fever syndromes used by BioSense and ESSENCE, recorded temperature data was used as the gold standard.

Objective

To objectively compare the BioSense and ESSENCE fever syndromes using recorded temperature as a gold standard.