ESSENCE

ESSENCE is a web-based syndromic surveillance system utilized by DHMH to detect and track outbreaks, suspicious patterns of illness, public health emergencies, and biological threats. ESSENCE ED chief complaint data is collected daily from 47 emergency departments in Maryland (all 45 acute care hospitals and 2 freestanding emergency medical facilities). A chief complaint in ESSENCE is a free text field that lists the patient’s reason for the ED visit upon arrival at the hospital. Chief complaint data may be comprehensive or abbreviated and may include a single reason or multiple reasons for the ED visit. Medical history may be included in chief complaint data, which can create low specificity (false positive cases). Chief complaint data alone may yield less accurate modeling and lower outbreak detection sensitivity. This analysis evaluates whether counts of chief complaints are appropriate indicators of disease burden for several specific illnesses, by comparing chief complaints to their corresponding discharge diagnoses.

Objective

The state of Maryland has incorporated chief complaint data from 100% of its acute care emergency departments (ED) into the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). In 2012, the Maryland Department of Health and Mental Hygiene (DHMH) began using this statewide disease surveillance system to track several specific disease measures including certain chronic diseases. The validity of using ESSENCE ED data to track and analyze these health outcomes was evaluated.

The Electronic Surveillance System for the Early Notification of Community-based Epidemics in Florida (ESSENCE-FL) is a web-based application for use by public health professionals within the Florida Department of Health (FDOH). The main source of data for ESSENCE-FL is emergency department (ED) data. Ten hospitals in Hillsborough County, Florida send their data to the ESSENCE-FL server. ESSENCE-FL requires only a limited data set to be sent by the hospital which includes patient chief complaint (CC) and discharge diagnosis (DD). These fields can be searched individually, in separate queries, to identify possible records of interest. These two fields have been concatenated to create the single chief complaint and discharge diagnosis (CCDD) field, allowing both fields to be searched with a single query.

Objective

While syndromic surveillance systems were originally designed for the detection of outbreaks and clusters of illness, they have been found to be useful at identifying unreported conditions of public health importance. Within the Florida Department of Health in Hillsborough County (FDOH-Hillsborough), these conditions of public health importance have primarily focused on the reportable diseases and conditions that fall under the responsibility of the Epidemiology Program and have not included tuberculosis. A specific query has been developed to search for and identify possible tuberculosis patients and exposed contacts. This study is designed to determine the usefulness of specific-term chief complaint and discharge diagnosis (CCDD) queries in identifying tuberculosis patients and exposed contacts.

Although Electronic Surveillance System for the Early Notification of Community Based Epidemics (ESSENCE) provides tools to detect a significant alert regarding an unusual public health event, combining that information with other surveillance data, such as 911 calls, school absenteeism and poison control records, has proved to be more sensitive in detecting an outbreak. On Monday, June 16, Florida Poison Information Network, which takes after-hours and weekend calls for Miami-Dade County Health Department (MDCHD), contacted the Office of Epidemiology and Disease Control about five homeless persons that visited the same hospital simultaneously with gastrointestinal symptoms on Saturday, June 14. Poison control staff asked MDCHD to investigate further to determine whether it was an outbreak.

Objective

To illustrate how MDCHD utilized ESSENCE in order to track a gastrointestinal outbreak in a homeless shelter.

On March 7th and 8th of 2007 authorities from federal, state, county, and municipal jurisdictions/agencies having mass migration response responsibilities (as per the Department of Homeland Security Operation Vigilant Sentry, as well as State and Local plans) initiated the last of a series of mass migration exercise events. The mission of the exercise was to “unify” a federal, state, and local response to effectively mitigate a catastrophic mass migration incident, similar to the Mariel Boatlift (125,000+ migrants) in 1980. The exercise included volunteers who visited a few local emergency departments with specific scripts describing an acute medical condition.

Objective

Describe the use of the ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics) system to detect unusual patterns of emergency department use during a full scale mass migration exercise in South Florida.

Syndromic surveillance is an investigational approach used to monitor trends of illness in communities. It relies on pre-diagnostic health data rather than laboratory-confirmed clinical diagnoses. Its primary purpose is to detect disease outbreaks, incidents and unusual public health events earlier than possible with traditional public health surveillance methods.

Objective

To describe how epidemiological principles are utilized to distinguish a real alert from statistically significant alerts in order to monitor and create daily reports in the Miami-Dade County Health Department using Electronic Surveillance System for the Early Notification of Community Based Epidemics. 

Early and reliable detection of anomalies is a critical challenge in disease surveillance. Most surveillance systems collect data from multiple data streams but the majority of monitoring is performed at univariate time series level. Purely statistical methods used in disease surveillance look at each time series separately and tend to generate a large number of false alarms. Support Vector Machines can be used to develop rich multivariate models that allow detecting abnormal relationships between different time series leading to greatly reduced number of false alarms.

Objective

This paper depicts a novel method for reliable detection of disease outbreaks. The methodology and initial results obtained on ESSENCE data are presented.

Recognizing the threat of pandemic influenza and new or emerging disease such as SARS, the U.S. Department of Health and Human Services has recommended that schools work in partnership with their local health departments “to develop a surveillance system that would alert the local health department to substantial increases in absenteeism among students.”3 Tarrant County’s pilot project system meets that need and transcends absenteeism data; it seeks to quantify ILI in schools and lets school nurses view daily maps of changing disease patterns, access flu prevention resources, and receive and respond to action items suggested by TCPH. While the focus is on seasonal flu, best practices for mitigating seasonal flu also apply to pandemic flu. Because the system uses open source software4 , it’s affordable and replicable for other public health agencies seeking to strengthen their school partnerships as well as their local or regional biosurveillance capabilities.

Objective

This oral presentation will share key findings and next steps following the first year of a pilot project in which Tarrant County, Texas schools used a Web-based system to share their daily health data with Tarrant County Public Health (TCPH) epidemiologists, who can use ESSENCE1 to analyze the data. The projectís ongoing goal is to reduce the magnitude of flu outbreaks by focusing on school-aged children and youth, where infectious diseases often emerge first and spread rapidly.2

To illustrate how ESSENCE can been utilized to collaborate with healthcare practitioners in order to improve communicable disease reporting

Real-time disease surveillance is critical for early detection of the covert release of a biological threat agent (BTA). Numerous software applications have been developed to detect emerging disease clusters resulting from either naturally occurring phenomena or from occult acts of bioterrorism. However, these do not focus adequately on the diagnosis of BTA infection in proportion to the potential risk to public health.

GUARDIAN is a real-time, scalable, extensible, automated, knowledge-based BTA detection and diagnosis system.  GUARDIAN conducts real-time analysis of multiple pre-diagnostic parameters from records already being collected within an emergency department (ED).  The goal of this system is to assist clinicians in detecting potential BTAs as quickly and effectively as possible in order to better respond to and mitigate the effects of a large-scale outbreak.  

GUARDIAN improves the diagnostic process by moving away from simple trend anomaly detection and towards the development of a BTA-specific infectious disease expert system [1].  Through the capture and automated application of specific clinical expertise, GUARDIAN provides the focus and accuracy necessary for effective BTA infection diagnosis.  The continuity of this process improves the efficiency by which diagnoses of BTA infections can be made.