Data management

Ordering-based approaches [1,2] and quadtrees [3] have been introduced recently to detect multiple spatial clusters in point event datasets. The Autonomous Leaves Graph (ALG) [4] is an efficient graph-based data structure to handle the communication of cells in discrete domains. This adaptive data structure was favorably compared to common tree-based data structures (quad-trees). An additional feature of the ALG data structure is the total ordering of the component cells through a modified adaptive Hilbert curve, which links sequentially the cells (the orange curve in the example of Figure 1).

Objective

To detect multiple significant spatial clusters of disease in case-control point event data using the Autonomous Leaves Graph and the spatial scan statistic.

There is national recognition of the need for cross-programmatic data and system coordination and integration for surveillance, prevention, response, and control implementation. To accomplish this public health must develop an informatics competency and create an achievable roadmap, supported by performance measures, for the future. Within the New York State Department of Health, Office of Public Health (OPH), a cross-organizational and cross-functional Public Health Information Management Workgroup (PHIM-WG) was formed to align public health information and technology goals, objectives, strategies, and resources across OPH. In June 2011, the OPH Performance Management Initiative, funded by the Centers for Disease Control and Prevention, offered strategic planning workshops to PHIM-WG.

Objective 

To develop strategic objectives necessary to optimize the collection, integration, and use of information across public health programs and internal and external partners for improving the overall health and safety of people and their communities.

Life science and biotechnology advances have provided transforming capabilities that could be leveraged for integrative global biosurveillance. Global infectious disease surveillance holds great promise as a tool to mitigate the endemic and pandemic infectious disease impacts, and remains an area of broad international interest. All nations have significant needs for addressing infectious diseases that impact human health and agriculture, and concerns for bioenergy research and environmental protection. In January 2011, Los Alamos National Laboratory, Department of State, and the Defense Threat Reduction Agency co-hosted the "Global Biosurveillance Enabling Science and Technology" Conference. Guided by the National Strategy for Countering Biological Threats, and joined by major government stakeholders, the primary objective was to bring together the international technical community to discuss the scientific basis and technical approaches to an effective and sustainable InGBSV system and develop a research agenda enabling a long-term, sustainable capability. The overall objective of the conference was to develop a technology road map for InGBSV, with three underlying components: 1) Identify opportunities for integrating existing biosurveillance systems, the near-term technological advancements that can support such integration, and the priority of future research and development areas; 2) Identify the required technical infrastructure to support InGBSV, such as methodologies and standards for technology evaluation, validation and transition; 3) Identify opportunities, and the challenges that must be overcome, for partnerships and collaborations.

Objective

To review observations and conclusions from a recent Global Biosurveillance conference, provide an assessment of the scientific and technical capabilities and gaps to achieve an effective and sustainable integrative global biosurveillance (InGBSV) system, and recommend research and development priorities enabling InGBSV.

The Voronoi Based Scan (VBScan)[1] is a fast method for the detection and inference of point data set space-time disease clusters. A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance Minimum Spanning Tree (MST) linking the cases. The successive removal of its edges generates sub-trees which are the potential space-time clusters, which are evaluated through the scan statistic [2]. Monte Carlo replications of the original data are used to evaluate cluster significance. In the present work we modify VBScan to find the best partition dividing the map into multiple low and high risk regions.

Objective

We describe a method to determine the partition of a map consisting of point event data, identifying all the multiple significant anomalies, which may be of high or low risk, thus monitoring the existence of possible outbreaks.

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) serves public health users across NC at the local, regional and state levels, providing early event detection and situational awareness capabilities. At the state level, our primary users are in the General Communicable Disease Control Branch of the NC Division of Public Health. NC DETECT receives 10 different data feeds daily including emergency department visits, emergency medical service runs, poison center calls, veterinary laboratory test results, and wildlife treatment.

In order to fulfill our users’ needs with NC DETECT’s limited staff, business intelligence tools are utilized for the acquisition and processing of our multiple, disparate data sources as well as reporting our findings to our numerous end users. Business intelligence can be described as a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions.

Objective

We report here on how NC DETECT uses business intelligence tools to automate both data capture and reporting in order to run a comprehensive surveillance system with limited resources.

One of the challenges facing developers and users of automated disease surveillance systems is being able to accurately evaluate the performance of their systems for the wide variety of public health threats that are possible. A variety of methods have been used in the past to create data sets for use in testing algorithm performance. Synthetic data has been created using agent-based simulations where data is created based on the hypothesized activity of individuals with contagious diseases. This data is only as accurate as the social models and variety of assumptions which must be made permit. Real data containing elevated levels of respiratory and gastrointestinal activity have been used to evaluate the ability of algorithms to detect the elevated levels. Routine unvalidated outbreaks are typically not public health emergencies and may not represent signals of interest. Another approach is to use real background data and inject a variety of different types of synthetic cases representing various types of outbreaks on top of that background.

With the introduction of the American Health Information Community (AHIC) Minimum Data Set (MDS), the public health surveillance community should have the potential to obtain greater specificity for alerts generated in automated systems. The introduction of these additional data elements increases the complexity of algorithms using linked data elements. Creating synthetic data sets that accurately estimate relationships among chief complaint, pharmacy, laboratory and radiology is an added complexity in creating synthetic outbreaks for performance evaluation.

Objective

The objectives of this presentation are to describe the need for synthetic data containing the elements of the AHIC MDS. Approaches for creating synthetic data with MDS data elements will be presented and methods for insuring maintenance of confidentiality will be discussed.

CDC is building a public health information grid to enable controlled distribution of data, services and applications for researchers, Federal authorities, local and state health departments nationwide, enabling efficient controlled sharing of data and analytical tools. Federated aggregate analysis of distributed data sources may detect clusters that might be invisible to smaller, isolated systems. Success of the public health grid is contingent upon the number of participating agencies and the quantity, quality, and utility of data and applications available for sharing. Grid protocols allow data owners to control data access, but requires a model to control the level of identifiability of depending upon the user’s permissions. Here, we describe a work currently in progress involving the design and implementation of an ambulatory syndromic surveillance data stream generator for the public health grid. The project is intended to broadly disseminate aggregate syndrome counts for general use by the public health community, to develop a model for sharing varying levels of identifiable data on cases depending upon the user, and to facilitate ongoing development of the grid.

Objective

To implement a syndromic surveillance system on CDC’s public health information grid, capable of securely distributing syndromic data streams ranging from aggregate case counts to individual case details, to appropriate personnel.

Real-time Outbreak and Disease Surveillance (RODS), a syndromic surveillance system created by the University of Pittsburgh has been used in Ohio by the state and local health departments since late 2003. There are currently 133 health care facilities providing 88% coverage of emergency department visits statewide to the RODS system managed by Health Monitoring Systems Inc. (HMS). The system automatically alerts health department jurisdictions when various syndromic thresholds are exceeded.

As part of response protocols, investigators export a case listing in a comma-separated values file which typically includes thousands of lines with each row containing: date admitted, age, gender, zip code, hospital name, visit number, chief complaint, and syndrome. The HMS-RODS web site provides basic graphs and maps, yet lacks the flexibility afforded by ad hoc queries, cross tabulation, and portability enabling off-line analysis.

Objective

This paper describes the integration of open source applications as portable, customizable tools for epidemiologists to provide rapid analysis, visualization, and reporting during surveillance investigations.

In September 2004, Kingston, Frontenac and Lennox and Addington Public Health began a 2-year pilot project to develop and evaluate an Emergency Department Chief Complaint Syndromic Surveillance System in collaboration with the Ontario Ministry of Health and Long Term Care – Public Health Branch, Queen’s University, Public Health Agency of Canada, Kingston General Hospital and Hotel Dieu Hospital. At this time, the University of Pittsburgh’s Real-time Outbreak and Disease Surveillance (RODS, Version 3.0) was chosen as the surveillance tool best suited for the project and modifications were made to meet Canadian syndromic surveillance requirements.

Objective

This poster provides an overview of a RODS-based syndromic surveillance system as adapted for use at a Public Health unit in Kingston, Ontario Canada. The poster will provide a complete overview of the technical specifications, the capture, classification and management of the data streams, and the response protocols developed to respond to system alerts. It is hoped that the modifications described here, including the addition of unique data streams, will provide a benchmark for Canadian syndromic surveillance systems of the future.

Data quality for syndromic surveillance extends beyond validating and evaluating syndrome results. Data aggregators and data providers can take additional steps to monitor and ensure the accuracy of the data. In North Carolina, hospitals are mandated to transmit electronic emergency department data to the North Carolina Disease Event Tracking and Epidemiologic Tool (NC DETECT) system at least every 24 hours. Protocols have been established to ensure the highest level of data quality possible. These protocols involve multiple levels of data validity and reliability checks by NC DETECT staff as well as feedback from end-users concerning data quality. Hospitals also participate in the data quality processes by providing metadata including historical trends at each facility.

Objective

The purpose of this project is to describe the initiatives used by the NC DETECT to ensure the quality of ED data for surveillance.