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Ramac-Thomas Liane

Description

One objective of public health surveillance is detecting disease outbreaks by looking for changes in the disease occurrence, so that control measures can be implemented and the spread of disease minimized. For this purpose, the Florida Department of Health (FDOH) employs the Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE). The current problem was spawned by a laborintensive process at the FDOH: authentic outbreaks were detected by epidemiologists inspecting ESSENCE time series and derived event lists. The corresponding records indicated that patients arrived at an ED within a short interval, often less than 30minutes. The time-of-arrival (TOA) task was to develop and automate a capability to detect events with clustered patient arrival times at the hospital level for a list of subsyndrome categories of concern to the monitoring counties.

 

Objective

This presentation discusses the approach and results of collaboration to enable a solution of a hospital TOA monitoring problemin syndromic surveillance applied to public health data at the hospital level for county monitoring.

Submitted by hparton on
Description

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.

Submitted by elamb on
Description

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.

Submitted by elamb on
Description

Influenza epidemics occur seasonally but with spatiotemporal variations in peak incidence. Many modeling studies examine transmission dynamics [1], but relatively few have examined spatiotemporal prediction of future outbreaks [2]. Bootsma et al [3] examined past influenza epidemics and found that the timing of public health interventions strongly affected the morbidity and mortality. Being able to predict when and where high influenza incidence levels will occur before they happen would provide additional lead time for public health professionals to plan mitigation strategies. These predictions are especially valuable to them when the positive predictive value is high and subsequently false positives are infrequent.

Objective

Advanced techniques in data mining and integrating evidence from multiple sources are used to predict levels of influenza incidence several weeks in advance and display results on a map in order to help public health professionals prepare mitigation measures.

Submitted by elamb on
Description

Block 3 of the US Military Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE) system affords routine access to multiple sources of data. These include administrative clinical encounter records in the Comprehensive Ambulatory Patient Encounter Record (CAPER), records of filled prescription orders in the Pharmacy Data Transaction Service, developed at the Department of Defense (DoD) Pharmacoeconomic Center, Laboratory test orders and results in HL7 format, and others. CAPER records include a free-text Reason for Visit field, analogous to chief complaint text in civilian records, and entered by screening personnel rather than the treating healthcare provider. Other CAPER data fields are related to case severity. DoD ESSENCE treats the multiple, recently available data sources separately, requiring users to integrate algorithm results from the various evidence types themselves. This project used a Bayes Network approach to create an ESSENCE module for analytic integration, combining medical expertise with analysis of 4 years of data using documented outbreaks.

 

Objective

The project objective was to develop and test a decision support module using the multiple data sources available in the U.S. DoD version of ESSENCE.

Submitted by elamb on
Description

Although rare in the US, the CDC reports 13-14 drinking-water-related disease outbreaks per year, affecting an average of about 1000 people. The US EPA has determined that the distribution system is the most vulnerable component of a drinking water system. Recognizing this vulnerability, water utilities are increasingly measuring disinfectant levels and other parameters in their distribution systems. The US EPA is sponsoring an initiative to fuse this distribution system water quality data with health data to improve surveillance by providing an assessment of the likelihood of the occurrence of a waterborne disease outbreak. This fused analysis capability will be available via a prototype water security module within a population-based public health syndromic surveillance system.

 

Objective

The objective of this paper is to illustrate a technique for combining water quality and population-based health data to monitor for water-borne disease outbreaks.

Submitted by elamb on
Description

A U.S. Department of Defense program is underway to assess health surveillance in resource-poor settings and to evaluate the Early Warning Outbreak Reporting System. This program has included several information-gathering trips, including a trip to Lao PDR in September, 2006.

 

Objective

This modeling effort will provide guidance for policy and planning decisions in developing countries in the event of an acute respiratory illness epidemic, particularly an outbreak with pandemic potential.

Submitted by elamb on
Description

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