Lombardo Joseph

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.

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.

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.

National Health IT Initiatives are helping to advance the state of automated disease surveillance through incentives to health care facilities to implement electronic medical records and provide data to health departments and use collaborative systems to enhance quality of care and patient safety. While the emergence of a standard for the transfer of surveillance data is urgently needed, migrating from the current practice to a future standard can be a source of frustration. This project represents collaboration among the CDC BioSense Program, Tarrant County Public Health and the ESSENCE Team at the Johns Hopkins University APL. The objectives of the project are to: develop reusable meaningful use messaging software for ingestion health information exchange data available in Tarrant County, demonstrate the use of this data for supporting surveillance, demonstrate the ability to share data for regional and national surveillance using the messaging guide model, and demonstrate how this model can be proliferated among health departments that use ESSENCE by investigating the potential use of cloud technology. The presentation will outline the steps for achieving this goal.

A number of syndromic surveillance systems include tools that quickly identify potentially large disease outbreak events. However, the high falsepositive rate continues to be a problem in all of these systems. Our earlier work has showed that multi-source information fusion can improve specificity of the syndromic surveillance systems. However, an anomalous health event that presents as only a few cases may remain undetected because the chief complaint data does not contain enough details. New linked data sources need to be used to enhance detection capabilities. The focus of this project examining the incorporation of laboratory, prescription medications and radiology data linked to the patient encounter within syndromic surveillance systems. These data source linkings may enhance the sensitivity of syndromic surveillance.

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.

A pandemic caused by influenza A/H5N1 or another novel strain could kill millions of people and devastate economies worldwide. Recent computer simulations suggest that an emerging influenza pandemic might be contained in Southeast Asia through rapid detection, antiviral distribution, and other interventions [1]. To facilitate containment, the World Health Organization (WHO) has established large, global antiviral stockpiles and called on countries to develop rapid pandemic detection and response protocols [2]. However, developing countries in Southeast Asia would face significant challenges in containing an emerging pandemic. Limited surveillance coverage and diagnostic capabilities; poor communication and transportation infrastructure; and lack of resources to investigate outbreaks could cause critical delays in pandemic recognition. Wealthy countries have committed substantial funds to improve pandemic detection and response in developing countries, but tools to guide system planning, evaluation, and enhancement in such places are lacking.

Objective

We propose a framework for evaluating the ability of syndromic, laboratory-based, and other public health surveillance systems to contain an emerging influenza pandemic influenza in developing countries, and apply the framework to systems in Laos.

The Veterans Health Administration (VHA) operates over 880 outpatient clinics across the nation. The Johns Hopkins Applied Physics Laboratory’s Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) utilizes VHA ICD9 coded outpatient visit data for the detection of abnormal patterns of disease occurrence. The hemorrhagic illness (HI) syndrome category in ESSENCE is comprised of 25 different ICD9 codes, including 12 codes specific for viral hemorrhagic fever (VHF) (e.g., ebola, yellow fever, CrimeanCongo hemorrhagic fever, lassa, etc.) and 13 nonspecific conditions (e.g., purpura not otherwise specified (NOS), thrombocytopathy, and coagulation defect NOS).

Objective

We sought to evaluate the functionality of the diagnosis codes which fall into the syndrome category of hemorrhagic illness.