Olson Don

Syndromic surveillance systems were designed for early outbreak and bioterrorism event detection. As practical experience shaped development and implementation, these systems became more broadly used for general surveillance and situational awareness, notably influenza-like illness (ILI) monitoring. Beginning in 2006, ISDS engaged partners from state and local health departments to build Distribute, a distributed surveillance network for sharing de-identified aggregate emergency department syndromic surveillance data through existing state and local public health systems. To provide more meaningful cross-jurisdictional comparisons and to allow valid aggregation of syndromic data at the national level, a pilot study was conducted to assess implementation of a common ILI syndrome definition across Distribute.

Objective

Assess the feasibility and utility of adopting a common ILI syndrome across participating jurisdictions in the ISDS Distribute project.

Epidemic acute gastroenteritis (AGE) is a major contributor to the global burden of morbidity and mortality. Rotavirus and norovirus epidemics present a significant burden annually, with their predominant impact in temperate climates occurring during winter periods. Annually, epidemic rotavirus causes an estimated 600,000 deaths worldwide, and 70,000 hospitalizations in the US, primarily among children <5 years of age. The US burden from norovirus is estimated at 71,000 hospitalizations annually, with the impact more generally across age groups. Changes in rotavirus vaccine use have significantly reduced the impact of epidemic rotavirus.

Objective 

We describe the initial phase of the ISDS Distribute pilot for monitoring AGE syndromic emergency department visits, and present preliminary analysis of age-specific trends documenting a dramatic shift in AGE consistent with US rotavirus vaccine policy and use.

Cross-jurisdictional sharing of public health syndrome data is useful for many reasons, among them to provide a larger regional or national view of activity and to determine if unusual activity observed in one jurisdiction is atypical. Considerable barriers to sharing of public health data exist, including maintaining control of potentially sensitive data and having informatics systems available to take and view data. The Distribute project [1,2] has successfully enabled cross-jurisdictional sharing of ILI syndrome data through a community of practice approach to facilitate control and trust, and a distributed informatics solution. The Gossamer system [3] incorporates methods used in several UW projects including Distribute. Gossamer has been designed in a modular fashion to be hosted using virtual or physical machines, including inside cloud environments. Two modules of the Gossamer system are designed for aggregate data sharing, and provide a subset of the Distribute functionality. The Distribute and Gossamer systems have been used for ad-hoc sharing in three different contexts; sharing of common ILI data for research into syndrome standardization, sharing syndromic data for specific events (2010 Olympics) and for pilot regional sharing of respiratory lab results. Two additional projects are underway to share specific syndromes of recent interest: alcohol related and heat related ED visits.

Objective

To demonstrate how rapid adhoc sharing of surveillance data can be achieved through informatics methods developed for the Distribute project.

Distribute is a national emergency department syndromic surveillance project developed by the International Society for Disease Surveillance for influenza-like-illness (ILI) that integrates data from existing state and local public health department surveillance systems. The Distribute project provides graphic comparisons of both ILI-related clinical visits across jurisdictions and a national picture of ILI. Unlike other surveillance systems, Distribute is designed to work solely with summarized (aggregated) data which cannot be traced back to the un-aggregated 'raw' data. This and the distributed, voluntary nature of the project creates some unique data quality issues, with considerable site to site variability. Together with the ISDS, the University of Washington has developed processes and tools to address these challenges, mirroring work done by others in the Distribute community.

Objective

To present exploratory tools and methods developed as part of the data quality monitoring of Distribute data, and discuss these tools and their applications with other participants.

Distribute is a national emergency department syndromic surveillance project developed by the International Society for Disease Surveillance (ISDS) for influenza-like-illness (ILI) that integrates data from existing state and local public health department surveillance systems. The Distribute is a national emergency department syndromic surveillance project developed by the International Society for Disease Surveillance (ISDS) for influenza-like-illness (ILI) that integrates data from existing state and local public health department surveillance systems. The Distribute project provides graphic comparisons of both ILI-related clinical visits across jurisdictions and a national picture of ILI. Unlike other surveillance systems, Distribute is designed to work solely with summarized (aggregated) data which cannot be traced back to the un-aggregated 'raw' data. This and the distributed, voluntary nature of the project create some unique data quality issues, with considerable site to site variability. Together with the ISDS, the University of Washington has developed processes and tools to address these challenges, mirroring work done by others in the Distribute community.

Objective

The goal of this session will be to briefly present two methods for comparing aggregate data quality and invite continued discussion on data quality from other surveillance practitioners, and to present the range of data quality results across participating Distribute sites.

There has been much recent interest in using disease signatures to better recognize disease outbreaks. Conversely, the metrics used to describe these signatures can also be used to better characterize the outbreaks. Recent work at the New York City Department of Health has shown the ability to identify characteristic age-specific patterns during influenza outbreaks. One issue that remains is how to implement a search for such patterns using prospective outbreak detection tools such as SatScan.

A potential approach to this problem arises from another currently active research area: the simultaneous use of multiple datastreams. One form of this is to disaggregate a data stream with respect to a third variable such as age. Two drawbacks to this approach are that the categories used to make the streams have to be defined a priori and that relationships between the streams cannot be exploited. Furthermore, the resulting description is less rich as it describes outbreaks in a few non-overlapping age-specific streams. It would be desirable to look for age specific patterns with the age groupings implicitly defined.

Objective

This paper presents an implementation of a citywide SatScan analysis that uses age as a one-dimensional spatial variable. The resulting clusters identify age-specific clusters of respiratory and fever/flu syndromes in the New York City Emergency Department Data.