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.

As of October 1, 2015, all HIPAA covered entities transition from the use of International Classification of Diseases version 9 (ICD-9-CM) to version 10 (ICD-10-CM/PCS). Many Public Health surveillance entities receive, interpret, analyze, and report ICD-9 encoded data, which will all be significantly impacted by the transition. Public health agencies will need to modify existing database structures, extraction rules, and messaging guides, as well as revise established syndromic surveillance definitions and underlying analytic and business rules to accommodate this transition. Implementation challenges include resource, funding, and time constraints for code translation and syndrome classification, and developing statistical methodologies to accommodate changes to coding practices.

To address these challenges, the International Society for Disease Surveillance (ISDS), in consultation with the Centers for Disease Control and Prevention (CDC) and the Council of State and Territorial Epidemiologists (CSTE), has conducted a project to develop consensus-driven syndrome definitions based on ICD- 10-CM codes. The goal was to have the newly created ICD-9-CM to-ICD-10-CM mappings and corresponding syndromic definitions fully reviewed and vetted by the syndromic surveillance community, which relies on these codes for routine surveillance, as well as for research purposes. The mappings may be leveraged by other federal, state, and local public health entities to better prepare and improve the surveillance, analytics, and reporting activities impacted by the ICD-10-CM transition.

Objective

To describe the process undertaken to translate syndromic surveillance syndromes and sub-syndromes consisting of ICD-9 CM diagnostic codes to syndromes and sub-syndromes consisting of ICD-10-CM codes, and how these translations can be used to improve syndromic surveillance practice.

The Risk Identification Unit (RIU) of the US Dept. of Agriculture’s Center for Epidemiology and Animal Health (CEAH) conducts weekly surveillance of national livestock health data and routine coordination with agricultural stakeholders. In an initiative to increase the monitored species, health issues, and data sources, CEAH epidemiologists are building a surveillance system based on weekly counts of laboratory test orders along with Colorado State Univ. laboratorians and statistical analysts from the Johns Hopkins Univ. Applied Physics Lab. Initial efforts used 12 years of equine test records from 3 state labs covering most Colorado horse testing. Trial syndrome groups were formed based on RIU experience and published articles. Data analysis, stakeholder input, and discovery of laboratory workflow details were needed to modify these groups and filter test records to eliminate alerting bias. Customized statistical monitoring methods were sought based on specialized lab information characteristics and on likely presentation and health significance of syndrome-associated diseases.

Public health practitioners endeavor to expand and refine their syndromic and other advanced surveillance systems which are designed to supplement their existing laboratory testing and disease surveillance toolkit. While much of the development and widespread implementation of these systems was previously supported by public health preparedness funding, the reduction of these monies has greatly constrained the ability of public health agencies to staff and maintain these systems. The appearance of highly-pathogenic avian influenza (HPAI) H3N2v, and other novel influenza A viruses required agencies to carefully identify systems which provide the most cost-effective data to support their public health practice. The global emergence of influenza A (H7N9), Ebola virus strains, Middle East Respiratory Syndrome Coronavirus (MERS-CoV), and other viruses associated with high mortality, emphasize the importance of maintaining vigilance for the presence of emerging diseases.

Objective

To continue efforts in characterizing the challenges experienced by influenza surveillance coordinators and other practitioners conducting surveillance for the presence of avian influenza, novel respiratory diseases, and other globally emerging viruses in an era of limited resources among public health agencies.