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Space-time scan statistics are often used to identify emerging spatial clusters of disease cases [1,2]. They operate by maximizing a score function (likelihood ratio statistic) over multiple spatio-temporal regions. The temporal component is typically incorporated by aggregating counts across a given time window, thus assuming that the affected region does not change over time. To relax this hard constraint on spatial-temporal “shape” and increase detection power and accuracy when tracking spreading outbreaks, we implement a new graph-based event detection approach which enables identification of dynamic clusters while enforcing temporal consistency constraints between temporally-adjacent spatial regions.

Objective:

We describe a novel graph-based event detection approach which can accurately identify and track dynamic outbreaks (where the affected region changes over time). Our approach enforces soft constraints on temporal consistency, allowing detected regions to grow, shrink, or move while penalizing implausible region dynamics. Using simulated contaminant plumes diffusing through a water distribution system, we demonstrate that our method improves both detection time and spatial-temporal accuracy when tracking dynamic waterborne outbreaks.

 

Submitted by Magou on

The State of Ohio, as well as the country, has experienced an increasing incidence of drug ODs over the last three decades [3]. Of the increased number of unintended drug OD deaths in 2008, 9 out of 10 were caused by medications or illicit drugs [1]. In Ohio, drug ODs surpassed MVCs as the leading cause of injury death in 2007. This trend has continued through the most current available data [3]. Using chief complaint data to quickly track changes in the geographical distribution, demographics, and volume of drug ODs may aid public health efforts to decrease the number of associated deaths.

Objective:

Preliminary analysis was completed to define, identify, and track the trends of drug overdoses (OD), both intentional and unintentional, from emergency department (ED) and urgent care (UC) chief complaint data.

 



 

Submitted by Magou on

TOA identifies clusters of patients arriving to a hospital ED within a short temporal interval. Past implementations have been restricted to records of patients with a specific type of complaint. The Florida Department of Health uses TOA at the county level for multiple subsyndromes (1). In 2011, NC DPH, CCHI and CDC collaborated to enhance and evaluate this capability for NC DETECT, using NC DETECT data in BioSense 1.0 (2). After this successful evaluation based on exposure complaints, discussions were held to determine the best approach to implement this new algorithm into the production environment for NC DETECT. NC DPH was particularly interested in determining if TOA could be used for identifying clusters of ED visits not filtered by any syndrome or sub-syndrome. In other words, can TOA detect a cluster of ED visits relating to a public health event, even if symptoms from that event are not characterized by a predefined syndrome grouping? Syndromes are continuously added to NC DETECT but a syndrome cannot be created for every potential event of public health concern. This TOA approach is the first attempt to address this issue in NC DETECT. The initial goal is to identify clusters of related ED visits whose keywords, signs and/or symptoms are NOT all expressed by a traditional syndrome, e.g. rash, gastrointestinal, and flu-like illnesses. The goal instead is to identify clusters resulting from specific events or exposures regardless of how patients present – event concepts that are too numerous to pre-classify.

Objective:

To describe a collaboration with the Johns Hopkins Applied Physics Laboratory (JHU APL), the North Carolina Division of Public Health (NC DPH), and the UNC Department of Emergency Medicine Carolina Center for Health Informatics (CCHI) to implement time-of-arrival analysis (TOA) for hospital emergency department (ED) data in NC DETECT to identify clusters of ED visits for which there is no pre-defined syndrome or sub-syndrome.

 

Submitted by Magou on

Irregularly shaped spatial disease clusters occur commonly in epidemiological studies, but their geographic delineation is poorly defined. Most current spatial scan software usually displays only one of the many possible cluster solutions with different shapes, from the most compact round cluster to the most irregularly shaped one, corresponding to varying degrees of penalization parameters imposed to the freedom of shape. Even when a fairly complete set of solutions is available, the choice of the most appropriate parameter setting is left to the practitioner, whose decision is often subjective.

 

Objective

We propose a novel approach to the delineation of irregularly shaped disease clusters, treating it as a multi-objective optimization problem. We present a new insight into the geographic meaning of the cluster solution set, providing a quantitative approach to the problem of selecting the most appropriate solution among the many possible ones.

Referenced File
Submitted by elamb on

NC DETECT is the Web-based early event detection and timely public health surveillance system in the North Carolina Public Health Information Network. The reporting system also provides broader public health surveillance reports for emergency department visits related to hurricanes, injuries, asthma,  vaccine-preventable diseases, environmental health and others. NC DETECT receives data on at least a daily basis from four data sources: emergency departments, the statewide poison center, the statewide EMS data collection system, a regional wildlife center and laboratory data from the NC State College of Veterinary Medicine. Data from select urgent care centers are in pilot testing.

 

Objective

Managers of the NC DETECT surveillance system wanted to augment standard tabular Web-based access with a Web-based spatial-temporal interface to allow users to see spatial and temporal characteristics of the surveillance data. Users need to see spatial and temporal patterns in the data to help make decisions about events that require further investigation. The innovative solution using Adobe Flash and Web services to integrate the mapping component with the backend database will be described in this paper.

Referenced File
Submitted by elamb on

Syndromic Surveillance has been in use in New York City since 2001, with 2.5 million visits reported from 39 participating emergency departments, covering an estimated 75% of annual visits. As syndromic surveillance becomes increasingly spatial and tied to geography, the resulting spatial analysis is also evolving to provide new methodology and tools. In late 2004, the New York City Department of Health and Mental Hygiene (DOHMH) created the geographic information systems (GIS) Center of Excellence to identify ways in which GIS could enhance programs like syndromic surveillance. The DOHMH uses the SaTScan program for much of its spatial analysis (i.e. cluster analysis).

 

Objective

This paper describes a series of visualization enhancements and automation processes to efficiently depict syndromic surveillance data in GIS. Modelling the portrayal of events when merging existing syndromic surveillance with GIS can standardize and expedite results.

Referenced File
Submitted by elamb on

The variability of free text emergency department (ED) data is problematic for biosurveillance, and current methods of identifying search terms for symptoms of interest are inefficient as well as time- and labor-intensive. Our ad hoc approach to term identification for the North Carolina Disease and Epidemiologic Collection Tool (NC DETECT) begins with development of clinical case definitions from which we build automated syndrome queries in standard query language. The queries are used to search free text clinical data from EDs, with the goal of identifying free text terms to match the case definitions. The free text search terms were initially collected from epidemiologists and clinical and technical staff at NC DETECT through informal review of ED data. Over time, we reviewed individual cases missed by our queries and identified additional search terms. We also manually reviewed records to find misspellings, abbreviations and acronyms for known search terms (e.g., dypnea, diff. br. and SHOB for dyspnea), and developed a pre-processor to clean text prior to syndromic classification. The purpose of this project was to develop and test a more standardized approach to search term identification.

 

Objective

This paper describes and applies a new method for identifying biosurveillance search terms using the Semantic Network of the Unified Medical Language System.

Referenced File
Submitted by elamb on

Abbreviation, misspellings, and site specific terminology may misclassify chief complaints syndromes. The Emergency Medical Text Processor (EMT-P) is system that cleans emergency department chief complaints and returns standard terms. However, little information is available on the implementation of EMT-P in a syndromic surveillance system.

 

Objective

To describe the implementation and baseline evaluation of EMT-P developed by the University of North Carolina.

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Submitted by elamb on

In this study, we compare two methods of generating grid points to enable efficient geographic cluster detection when the original geographical data are prohibitively numerous. One method generates uniform grid points, and the other employs quad trees to generate non-uniform grid points. We observe differences in the results of the spatial scan approach to cluster detection for both of these grid generation schemes. In both our simulated experiment, and our analysis of real data, the grid generation schemes produce different results. Generally speaking, the quad tree scheme is more sensitive to detecting high resolution spatial clusters than the uniform scheme. The quad tree grid point scheme may be a useful alternative to the uniform (and other) grid point generation schemes when it is important to set up a surveillance system sensitive to clusters at unspecified spatial resolutions. The quad tree grid scheme may also be useful in a number of other geographic surveillance applications.

Referenced File
Submitted by elamb on

Outbreak detection algorithms for syndromic surveillance data are becoming increasingly complex. Initial algorithms focused on temporal data but newer methods incorporate geospatial dimensions. As methods evolve, it is important to understand the effects on detection of both algorithm parameters and population characteristics. Intensive, iterative data analyses are required to accomplish this. Even with leading-edge computer hardware, it can take weeks or months to complete analyses using advanced signal detection techniques such as the space-time scan statistic in the SaTScan program.

Given the strategic significance and national security implications of timely and accurate detection, proper tools for studying and thus improving increasingly complex surveillance algorithms are warranted.

 

Objective

We describe a method to perform computationally intensive analyses on large volumes of syndromic surveillance data using open-source grid computing technology.

Referenced File
Submitted by elamb on
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