Kite-Powell Aaron

Public health surveillance relies on multiple systems and methodologies for data collection, analysis, and interpretation. Each component provides only part of the picture, such as detection of possible outbreaks or events of concern; geographic profiles or time courses of disease activity; or indicators of clinical severity by age, risk factors, etc. Novel, unstructured data sources like Twitter feeds and aggregated news reports are growing as a source of information about health and disease. What and where are the contributions of these nontraditional, often non-specific, data types to BSV? The answer will depend on the purpose and target population. Different data streams often have greater utility for one BSV function (e.g., outbreak detection) than another (e.g., situation awareness). Furthermore, public health agencies at different levels need and use data differently, as determined by their priorities for public health. New types of data can also be useful for disease prediction and forecasting, pandemic modeling, and developing analytic tools. Before any new data modality can be integrated into standards of surveillance practice, it needs to be evaluated for its contribution to understanding disease activity and the value added when compared to other sources of data with regard to validity, timeliness, accuracy, representativeness, and positive and negative predictive values. Furthermore, questions remain about when novel, unstructured, or nontraditional data sources are acceptable evidence to inform decision-making and public health actions. To address this, the strengths and weaknesses of different types of data for various surveillance functions need to be discussed among stakeholders that bring various perspectives from surveillance research, practice, and policy.

Objective

To gather thought leaders in informatics, public health practice, surveillance research, and strategic decision-making to provide their insights into where and how to effectively integrate novel data streams, such as social media, into biosurveillance (BSV) systems and standards of public health surveillance practice.

Collaborative relationships between academicians and public health practitioners are necessary to ensure that methodologies created in the research setting translate into practice. One barrier to forging these collaborations is restrictions on the sharing and availability of public health surveillance data; therefore, most academics with expertise in method development cannot access 'real world' surveillance data with which to evaluate their approaches. The ISDS Technical Conventions Committee was established in 2013 to facilitate and expedite the development, evaluation, and implementation of technical methods for public health surveillance. The purpose of the committee is to bridge a long-standing gap between technical challenges in public health practice and solution developers needing both understanding of these challenges and representative data.

Objective

The purpose of this panel is to facilitate the dissemination of surveillance-related use cases by public health practitioners with accompanying benchmark datasets to method developers. The panel will present practitioners' experiences with preparing patient-level emergency department data sets to accompany a use case submitted to the ISDS Technical Conventions Committee.

In 2004, the BioDefend (BD) syndromic surveillance (SS) system was implemented in Duval County hospitals (Jacksonville, FL). Daily emergency department chief complaints are manually classified and entered into the BD system by triage personnel. As part of a statewide implementation, the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) began collecting data in the Jacksonville area during the winter of 2007-08. ESSENCE uses an automated data collection, chief complaint parsing and analysis process for data management and analysis. The use of two systems during the same period of time in one area provided a unique opportunity to retrospectively analyze characteristics of the BD and ESSENCE systems.

Objective

To compare detection of a community outbreak of influenza-like illness using two SS systems, one using a clinician’s classification of reason for visit and the other using an automated chief complaint parsing algorithm.

Developing effective data-driven algorithms and visualizations for disease surveillance hinges on the ability to provide application developers with realistic data. However, the sensitivity of the data creates a barrier to its distribution. We have created a tool that assists data providers with de-identifying their data in preparation for sharing. The functions in the tool help data providers comply with the HIPAA 'Safe Harbor' de-identification standard by removing or obscuring information such as names, geographic locations, and identifying numbers.

Objective

To develop a robust, flexible, and easy-to-use data de-identification tool that makes it easier for data providers to create data sets that are sharable with external collaborators.

The success of syndromic surveillance depends on the ability of the surveillance community to quickly and accurately recognize anomalous data. Current methods of anomaly detection focus on sets of syndromic categories and rely on a priori knowledge to map chief complaints to these general syndromic categories. As a result, the mapping scheme may miss key terms and phrases that have not previously been used. Furthermore, analysts do not have a good way of being alerted to these new terms in order to determine if they should be added to the syndromic mapping schema. We use a dynamic dictionary of terms to side-step the downfalls of a priori knowledge in this rapidly evolving field by alerting the analyst to rare and brand new words used in the chief complaint field.

Objective

To automate the detection of very unusual emergency department chief complaints based on a comparison between a trained dictionary of terms and the unstructured chief complaint field.

On March 7th and 8th of 2007 authorities from federal, state, county, and municipal jurisdictions/agencies having mass migration response responsibilities (as per the Department of Homeland Security Operation Vigilant Sentry, as well as State and Local plans) initiated the last of a series of mass migration exercise events. The mission of the exercise was to “unify” a federal, state, and local response to effectively mitigate a catastrophic mass migration incident, similar to the Mariel Boatlift (125,000+ migrants) in 1980. The exercise included volunteers who visited a few local emergency departments with specific scripts describing an acute medical condition.

Objective

Describe the use of the ESSENCE (Electronic Surveillance System for the Early Notification of Community-based Epidemics) system to detect unusual patterns of emergency department use during a full scale mass migration exercise in South Florida.

On October 24, 2005, Hurricane Wilma made landfall on the southwest coast of Florida as a category 3 storm. The storm moved toward the northeast and passed through Palm Beach and Broward Counties before entering the Atlantic Ocean. Hurricane force winds and rain caused extensive damage to electrical infrastructure and traffic lights, and temporarily displaced thousands of residents. Power outages in Broward County affected over 90% of its 1.8 million residents, with some outages lasting >2 weeks. Boil water notices were declared for much of the county. Acute care hospitals remained open during this time, although services provided by health care providers in other settings were interrupted due to structural damage and power outages.

Objective

We used the syndromic surveillance system ESSENCE to describe the morbidity after Hurricane Wilma in Broward County, Florida.