Disease Outbreaks

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.

In 2003, the need for a system to track and manage patient status and location was identified by Boston Emergency Medical Services (Boston EMS) and the Conference of Boston Teaching Hospitals. After consultation with EMS (municipal, fire based, and private), hospital, local and state public health and emergency management stakeholders, a core group from Boston EMS and Boston Public Health Commission (BPHC) developed guidelines for a Metro Boston Patient Tracking System. The goal was to provide a system to reunite family members and serve as a tool for managing short term/high impact mass casualty incidents and protracted disease outbreaks.

Since 2004, BPHC Communicable Disease Control Division (CDC) has effectively managed several mass prophylaxis clinics in response to infectious disease outbreaks. However patient data was largely collected on paper based forms, limiting the availability of real-time clinic data to incident command. To address these challenges BPHC CDC began meeting with Boston EMS to define the business processes and information needs during public health emergencies.

Objective

To describe the electronic patient tracking system configured by Boston EMS and the BPHC CDC to address information needs during public health emergencies.

The practice of real-time disease surveillance, sometimes called syndromic surveillance, is widespread at local, state, and national levels. Diseases ignore legal boundaries, so situations frequently arise where it is important to share surveillance information between public health jurisdictions. There are currently two fundamental ways for systems to share public health data and information related to disease outbreaks: sharing data, or sharing information. Data refers to patient level and aggregate counts of patients, and can be difficult to share legally because of privacy issues. Information refers to summaries, opinions or conclusions about data. There are few if any legal barriers to sharing information, and by definition it includes interpretation of data by knowledgeable local personnel which is vital during outbreak investigation. Currently most shared information is unstructured text, and this format makes it difficult for computers to use the information in any meaningful way. The only thing a system can do with this unstructured information is allow users to read each message.

Objectives

Alternate methods are needed to facilitate communication between jurisdictions during potential disease outbreaks. One alternative is to share structured information. Defined at the appropriate level, information sharing can avoid traditional data sharing barriers while capturing valuable local knowledge. The key is to identify the types of surveillance information that are neither so highly interpreted as to lose their value nor so loosely interpreted as to face traditional data sharing barriers. The objective of this work is to identify the level at which surveillance information sharing can be both feasible and beneficial, and to create a vocabulary standard that supports the exchange of structured information between diverse surveillance systems. 

While early event detection systems aim to detect disease outbreaks before traditional means, following up on the many alerts generated by these systems can be time-consuming and a drain on limited resources.

Authorized users at local, regional and state levels in North Carolina rely on the North Carolina Disease Event Tracking and Epidemiologic Collection Tool's (NC DETECT) Java-based Web application to monitor and follow-up on signals based on the CDC’s EARS CUSUM algorithms. The application provides users with access to aggregate syndrome-based reports as well as to patient-specific line listing reports for three data sources: emergency departments, ambulance runs and the statewide poison control center. All NC DETECT Web functionality is developed in a user-centered, iterative process with user feedback guiding enhancements and new development. This feedback, along with the need for improved situational awareness and the desire to improve communication among users drove the development of the Annotation Reports and the Custom Event Report.

Objective

We describe the addition of two reports to NC DETECT designed to improve NC public health situational awareness capability.

HealthMap (www.healthmap.org) is a freely accessible, automated real-time system that monitors, organizes, integrates, filters, and maps online news about emerging diseases. The system performs geographic parsing (“geo-parsing”) of disease outbreaks by assigning incoming alerts to low resolution geographic descriptions, such as  country, with the help of a purposely crafted gazetteer. However, the system is limited by the size of the gazetteer, precluding high resolution assignment of place. In this study, we use the prior knowledge encoded in the gazetteer to expand the capabilities of the geo-parsing system.

 

Objective

Discovering geographic references in text is a task that human readers perform using both their lexical and contextual knowledge. Automating this task for real-time surveillance of informal sources on epidemic intelligence therefore requires efforts beyond dictionary-based pattern matching. Here, we describe an automated approach to learning the particular context in which outbreak locations appear and by this means extending prior knowledge encoded in a gazetteer.

Current veterinary surveillance systems may be ineffective for timely detection of outbreaks involving non-targeted disease. Earlier detection could enable quicker intervention that might prevent the spread of disease and limit lost revenue. Data sources, similar to those used for early outbreak surveillance in humans, may provide for earlier outbreak detection in animals. Veterinary diagnostic laboratories are a source of data that might be valuable to such efforts.

Objective

To study the value of data from veterinary diagnostic laboratories as an initial step in developing an early outbreak surveillance system for animals.

Rhode Island implemented the Real-time Outbreak and Disease Surveillance (RODS) system, developed in 1999 by the University of Pittsburgh’s Center for Biomedical Informatics. This system is based on real-time information from hospital emergency departments that is transmitted and analyzed electronically for the purpose of early detection of and situational awareness for public health emergencies. Through this system, chief complaint is reported in real-time. Diagnoses, coded in the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), are reported to the RI RODS system as they become available. Three hospitals are currently participating in a pilot implementation of the RI RODS system.

Preliminary work by a CDC Working Group (CDCWG) developed recommendations for syndrome definitions for use in syndromic surveillance programs. Ten syndromes, based on ICD-9-CM diagnosis codes, identified diseases associated with critical bioterrorism-associated agents or indicative of naturally occurring infectious disease outbreaks. As a component of the evaluation of the RI RODS system, we evaluated the RI RODS chief complaint classifier (CoCo) using ICD-9-CM codes and the CDCWG work as the gold standard.

Objective

This paper presents findings related to the evaluation of the CoCo used in the pilot implementation of a syndromic surveillance system in Rhode Island.

 Syndromic surveillance systems often classify patients into syndromic categories based on emergency department (ED) chief complaints. There exists no standard set of syndromes for syndromic surveillance, and the available syndromic case definitions demonstrate substantial heterogeneity of findings constituting the definition. The use of fever in the definition of syndromic categories is arbitrary and unsystematic. We determined whether chief complaints accurately represent whether a patient has any of five febrile syndromes: febrile respiratory, febrile gastrointestinal, febrile rash, febrile neurological, or febrile hemorrhagic.

Our toolkit adds statistical trend analysis, interactive plots, and kernel density estimation to an existing spatio-temporal visualization platform. The goal of these tools is to provide both a quick assessment of the current syndromic levels across a large area and then allow the analyst to view the actual data for a specific region or hospital over a period of time along with an indication as to whether or not a given data point is statistically significant. The sample data used for this toolkit come from over 70 emergency rooms throughout the state of Indiana.

Objective

This paper presents a toolkit designed to aid in the assessment of disease outbreak by visualizing spatiotemporal trends and interactively displaying detailed statistical data.