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ESSENCE

ESSENCE receives and analyzes data for the Military Health System’s (MHS) 9 million beneficiaries resulting in approximately 90,000 daily outpatient and emergency department visits worldwide. In May 2008, MHS released ESSENCE Version 2.0, a system-wide upgrade which includes the following enhancements: improved system security, additional reporting and display capabilities, laboratory orders, radiology orders, and the ability for users to define their own syndrome groups.

 

Objective

As an evolving syndromic surveillance system, ESSENCE has recently undergone some significant improvements and new additional capabilities. We present three of these impactful enhancements and evaluate their added value to military public health and preventive medicine providers and system users. Specific Version 2.0 enhancements include: (1) laboratory orders (2) radiology orders and (3) the ability for users to create their own syndrome groups for outbreak classification and detection.

Referenced File
Essence_Version_2_0_The_Department_Of_Defense's_Worldwide_Syndromic_Surveillance_System_Receives_Several_Enhancements.pdf
Submitted by elamb on

The threat of terrorism and high-profile disease outbreaks has drawn attention to public health syndromic surveillance systems for early detection of natural or man-made disease events. In this sense, the Miami-Dade County Health Department has implemented ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics) in 2005; which has been developed and updated by the Johns Hopkins University.

 

Objective 

This paper describes the dual monitoring process of Influenza-like Illness (ILI) syndrome in Miami-Dade County using the ESSENCE syndromic surveillance system, and their potential use as part of the seasonal influenza and pandemic influenza surveillance strategies.

Referenced File
Dual_Monitoring_Of_Ili_Syndrome_Using_The_Essence_System.pdf
Submitted by elamb on

Patient’s chief complaint (CC) is often used for syndromic surveillance for bioterrorism and outbreak detection, but little is known about the inter-hospital variability in the sensitivity of this method. Objective: Our objective was to characterize the variability of a gastrointestinal (GI) CC text-matching algorithm.

Submitted by elamb on

We describe the development and implementation of a protocol for identifying syndromic signals and for assessing their value to public health departments for routine (non-bioterrorism) purposes. The specific objectives of the evaluation are to determine the predictive value positive, sensitivity, and timeliness of the surveillance system, as well as its costs and benefits to public health.

Referenced File
Assessing_The_Impact_Of_Syndromic_Surveillance_Systems_On_Routine_Public_Health_Practice_Identifying_And_Evaluating_Syndromic_Signals.pdf
Submitted by elamb on

Many disease-outbreak detection algorithms, such as control chart methods, use frequentist statistical techniques. We describe a Bayesian algorithm that uses data D consisting of current day counts of some event (e.g., emergency department (ED) chief complaints of respiratory disease) that are tallied according to demographic area (e.g., zip codes).

Objective

We introduce a disease-outbreak detection algorithm that performs complete Bayesian Model Averaging (BMA) over all possible spatial distributions of disease, yet runs in polynomial time.

Referenced File
An_Outbreak_Detection_Algorithm_That_Efficiently_Performs_Complete_Bayesian_Model_Averaging_Over_All_Possible_Spatial_Distributions_Of_Disease.pdf
Submitted by elamb on

As major disease outbreaks are rare, empirical evaluation of statistical methods for outbreak detection requires the use of modified or completely simulated health event data in addition to real data. Comparisons of different techniques will be more reliable when they are evaluated on the same sets of artificial and real data. To this end, we are developing a toolkit for implementing and evaluating outbreak detection methods and exposing this framework via a web services interface.

Referenced File
An_Open_Source_Web_Services_Toolkit_For_Event_Detection_Algorithms.pdf
Submitted by elamb on

Automated disease surveillance systems that analyze data by syndrome categories have been used to look for outbreaks of disease for about 10 years. Most of these systems notify users of increases in the prevalence of reports in syndrome categories and allow users to view patient level data related to the increase. For most situations this level of investigation is sufficient, but occasionally a more dynamic level of control is required to properly understand an emerging illness in a community. During the SARS outbreak, for example, the respiratory syndrome was defined too broadly to allow users to track SARS. However, some systems, allowed users to build dynamic queries that allowed them to search their data by using the SARS case definition [1]. Users could perform free-text queries that identified records containing specific keywords in the chief complaint or specific combinations of ICD9 codes. This advanced querying capability has proven to be one of the most used features used by monitors of disease surveillance systems. Objective: The objective of this project is to build a new, more flexible query interface that allows users to define and build their query as if they were writing a logical expression for a mathematical computation. The interface is designed so that it can be easily adapted to fit into nearly any syndromic surveillance system.The interface will be evaluated in future versions of the ESSENCE and BioSense Systems.

Referenced File
Advanced_Querying_Features_For_Disease_Surveillance_Systems.pdf
Submitted by elamb on

Reportable disease case data are entered into Merlin by all 67 county health departments in Florida and assigned confirmed, probable, or suspect case status. De-identified reportable disease data from Merlin are sent to ESSENCE-FL once an hour for further analysis and visualization using tools in the surveillance system. These data are available for ad hoc queries, allowing users to monitor disease trends, observe unusual changes in disease activity, and to provide timely situational awareness of emerging events. Based on system algorithms, reportable disease case weekly tallies are assigned an awareness status of increasing intensity from normal to an alert category. These statuses are constantly scrutinized by county and state level epidemiologists to guide disease control efforts in a timely manner, but may not signify definitive actionable information.

 

Objective

In light of recent outbreaks of pertussis, the ability of Florida Department of Health’s (FDOH) Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL) to detect emergent disease outbreaks was examined. Through a partnership with the Johns Hopkins University Applied Physics Laboratory (JHU/APL), FDOH developed a syndromic surveillance system, ESSENCE-FL, with the capacity to monitor reportable disease case data from Merlin, the FDOH Bureau of Epidemiology’s secure webbased reporting and epidemiologic analysis system for reportable diseases. The purpose of this evaluation is to determine the utility and application of ESSENCE-FL system generated disease warnings and alerts originally designed for use with emergency department chief complaint data to reportable disease data to assist in timely detection of outbreaks in promotion of appropriate response and control measures.

Submitted by hparton on

Automated Electronic Disease Surveillance has become a common tool for most public health practitioners. Users of these systems can analyze and visualize data coming from hospitals, schools, and a variety of sources to determine the health of their communities. The insights that users gain from these systems would be valuable information for emergency managers, law enforcement, and other nonpublic health officials. Disseminating this information, however, can be difficult due to lack of secure tools and guidance policies. This abstract describes the development of tools necessary to support information sharing between public health and partner organizations.

Objective

The objective of this project is to provide a technical mechanism for information to be easily and securely shared between public health ESSENCE users and non-public health partners; specifically, emergency management, law enforcement, and the first responder community. This capability allows public health officials to analyze incoming data and create interpreted information to be shared with others. These interpretations are stored securely and can be viewed by approved users and captured by authorized software systems. This project provides tools that can enhance emergency management situational awareness of public health events. It also allows external partners a mechanism for providing feedback to support public health investigations.

Submitted by uysz on

The ESSENCE demonstration module was built to help DoD health monitors make routine decisions based on disparate evidence sources such as daily counts of ILI-related chief complaints, ratios of positive lab tests for influenza, patient age distribution, and counts of antiviral prescriptions [1]. The module was a population-based (rather than individual-based) Bayesian network (PBN) in that inputs were algorithmic results from these multiple aggregate data streams, and output was the degree of belief that the combined evidence required investigation. The module reduced total alerts substantially and retained sensitivity to the majority of documented outbreaks while clarifying underlying sources of evidence. The current effort was to advance the prototype to production by refining components of the fusion methodology to improve sensitivity while retaining the reduced alert rate.

Objective

The project involves analytic combination of multiple evidence sources to monitor health at hundreds of care facilities. A demonstration module featuring a population-based Bayes Network [1] was refined and expanded for application in the Department of Defense Electronic Surveillance System for Community-Based Epidemics (ESSENCE).

Submitted by uysz on
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