ESSENCE

We started an experimental syndromic surveillance using 1)OTC and 2)outpatients visits, in the last year and included 3)ambulance transfer from this year so as to early detect bioterrorism attack (BTA). 

Currently, Indiana monitors emergency department patient chief complaint data from 73 geographically dispersed hospitals. These data are analyzed using the Electronic Surveillance System for the Early Notification of Community-based Epidemics application. 

While researchers continue to improve syndromic detection methods, there is significant interest among public health practitioners regarding how to most effectively use the currently available tools. The Public Health Emergency Surveillance System (PHESS) staff have developed and refined a daily syndromic alert analysis and response process based on experiences gained since November 2004.

Objective

This paper describes how the Indiana State Department of Health PHESS staff responded to a syndromic surveillance alert related to a bioterrorism preparedness event.

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.

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.

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.

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.

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.

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.

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.