ESSENCE

Recent years' informatics advances have increased availability of various sources of health-monitoring information to agencies responsible for disease surveillance. These sources differ in clinical relevance and reliability, and range from streaming statistical indicator evidence to outbreak reports. Information-gathering advances have outpaced the capability to combine the disparate evidence for routine decision support. In view of the need for analytical tools to manage an increasingly complex data environment, a fusion module based on Bayesian networks (BN) was developed in 2011 for the Dept. of Defense (DoD) Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). In 2012 this module was expanded with syndromic queries, data-sensitive algorithm selection, and hierarchical fusion network training [1]. Subsequent efforts have produced a full fusion-enabled version of ESSENCE for beta testing, further upgrades, and a software specification for live DoD integration. Beta test reviewers cited the reduced alert burden and the detailed evidence underlying each alert. However, only 39 reported historical events were available for training and calibration of 3 networks designed for fusion of influenza-like-illness, gastrointestinal, and fever syndrome categories. The current presentation describes advances to formalize the network training, calibrate the component alerting algorithms and decision nodes together for each BN, and implement a validation strategy aimed at both the ESSENCE public health user and machine learning communities.

Objective

This presentation aims to reduce the gap between multivariate analytic surveillance tools and public health acceptance and utility. We developed procedures to verify, calibrate, and validate an evidence fusion capability based on a combination of clinical and syndromic indicators and limited knowledge of historical outbreak events.

The Armed Forces Health Surveillance Center (AFHSC) supports the development of new analytical tools to improve alerting in the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) disease-monitoring application used by the Department of Defense (DoD). Developers at the Johns Hopkins University Applied Physics Laboratory (JHU/APL) have added an analytic capability to alert the user when corroborating evidence exists across syndromic and clinical data streams including laboratory tests and filled prescription records. In addition, AFHSC epidemiologists have guided the addition of data streams related to case severity for monitoring of events expected to require expanded medical resources. Evaluation of the multi-level fusion capability for both accuracy and utility is a challenge that requires feedback from the user community before implementation and deployment so that changes to the design can be made to save both time and money. The current effort describes the design and results of a large evaluation exercise.

Objective

To evaluate, prior to launch, a surveillance system upgrade allowing analytical combination of weighted clinical and syndromic evidence with multiple severity indicators.

In light of recent communicable disease outbreaks, the ability of Florida Department of HealthÕs (FDOH) syndromic surveillance system, ESSENCE-FL, to identify emergent disease outbreaks using reportable disease data and algorithms originally designed for emergency department chief complaint data was examined. Preliminary work on this analysis presented last year was recently updated and expanded to include additional diseases, further levels of locale, and detector algorithm comparisons. Cases are entered into Merlin, the Bureau of EpidemiologyÕs secure web-based reporting and epidemiologic analysis system, by all 67 county health departments and the de-identified case data are sent hourly to ESSENCE-FL. These data are then available for ad hoc queries, allowing users to observe unusual changes in disease activity and assist in timely identification of infectious disease outbreaks. Based on system algorithms, weekly case tallies are assigned an increasing intensity awareness status from normal to alert and are monitored by county and state epidemiologists to guide timely disease control efforts, but may not by themselves be definitive actionable information.

Objective

To determine if there is an association between known outbreak activity and ESSENCE generated alerts. 

Data streams related to case severity have been added to the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE), a disease-monitoring application used by the Department of Defense (DoD), as an additional analytic capability to alert the user when indications for events requiring expanded medical resources exist in clinical data streams. Commonly used indicators are admission and death, but fatalities are rare and many DoD clinics lack admitting capability, so we sought to derive additional severity indicators from outpatient records. This abstract describes the technical details and the thought process behind two novel derived indicators: Sick-in-Quarters (SIQ) and Escalating Care.

Objective

To evaluate new severity indicators that mimic a public health professional or clinician's judgment in determining the severity of a public health event when detected by a surveillance system.

The electronic surveillance system for the early notification of community-based epidemics (ESSENCE) is the web-based syndromic surveillance system utilized by the Maryland Department of Health and Mental Hygiene (DHMH). ESSENCE utilizes a secure, automated process for the transfer of data to the ESSENCE system that is consistent with federal standards for electronic disease surveillance. Data sources in the Maryland ESSENCE system include ED chief complaints, poison control center calls, over-the-counter (OTC) medication sales, and pharmaceutical transaction data (specifically for anti-bacterial and anti-viral medications). All data sources have statewide coverage and are captured daily in near real-time fashion.

Objective

To examine the trends in prescription antiviral medication transactions and emergency department (ED) visits for influenza-like illness (ILI) and the relationship between these trends.

In development for over fourteen years, ESSENCE is a disease surveillance system utilized by public health stakeholders at city, county, state, regional, national, and global levels. The system was developed by a team from the Johns Hopkins University Applied Physics Laboratory (JHU/APL) with substantial collaborations with the US Department of Defense Global Emerging Infections Surveillance and Response System (DoD GEIS), US Department of Veterans Affairs (VA), and numerous public health departments. This team encompassed a broad range of individuals with backgrounds in epidemiology, mathematics, computer science, statistics, engineering and medicine with significant and constant influence from many public health collaborators.

Objective

This talk will describe the history and events that influenced the design and architecture decisions of the Electronic Surveillance System for Community-based Epidemics (ESSENCE)(1). Additionally, it will discuss the current functionality and capabilities of ESSENCE and the future goals and planned enhancements of the system.

The electronic surveillance system for the early notification of community-based epidemics (ESSENCE) is the web-based syndromic surveillance system utilized by DHMH. ESSENCE utilizes a secure, automated process for the transfer of data to the ESSENCE system. Data sources in the Maryland ESSENCE system include emergency department (ED) chief complaints, poison control center calls, over-the-counter (OTC) medication sales, and pharmaceutical transaction data (for certain classes of anti-bacterial and anti-viral medication). All data sources have statewide coverage and are captured daily in near real-time fashion. OIT developed a web based application in conjunction with OP&R to allow the epidemiologists involved in the ESSENCE program to monitor and audit the transfer of this data. The application allows the user to indicate whether or not each data file has been consumed into ESSENCE for any date of the year. The user can edit these daily entries at any time to update the status of the data that has been received. The user may also query the database by data source, date, and date range to generate a report. The database also contains contact information for technical and infection control staff at the hospitals that participate in the ESSENCE program. Finally, the application can also generate reports that detail which users have logged into ESSENCE, when the log-in occurred, and which pages within ESSENCE were visited.

Objective

To describe the application and process developed by the Maryland Department of Health and Mental Hygiene (DHMH) Office of Preparedness and Response (OP&R) and Office of Information Technology (OIT) for monitoring and auditing the transfer of syndromic surveillance data.

CVD is one of the leading causes of death in the US, with 800,000 deaths being linked to CVD every year. Recently, the CDC reported that 1 in 4 of these deaths could be prevented by lifestyle changes, creating healthier living spaces, as well as managing high cholesterol, blood pressure and diabetes levels [1]. The report also stressed the importance of electronic health records (EHR) in identifying patients with CVD risk factors [1]. Surveillance is a critical component of national effort to prevent CVD [2]. The Nebraska Department of Health and Human Services (NDHHS) has traditionally tracked the burden of chronic diseases by retrospective analysis of hospital discharge data (HDD). However, HDD is limited by its lack of immediate availability and its limited amount of data. Timeliness of detection and analysis of CVD events could be improved with syndromic surveillance. To enhance CVD surveillance in Nebraska, NDHHS implemented a near-real_time IP surveillance system in 2011. This surveillance system facilitates near-real_time assessment of CVD risk factors, outcomes, and prevention program efficacy.

Objective

The main objective of this project is to expand inpatient syndromic surveillance in Nebraska to include indicators of Cardiovascular Disease (CVD).