Painter Ian

Cost-effective, flexible and innovative tools that integrate disparate data sets and allow sharing of information between geographically dispersed collaborators are needed to improve public health surveillance practice. Gossamer Health (Good Open Standards System for Aggregating, Monitoring and Electronic Reporting of Health), http://gossamerhealth.org, is an open source system, suitable for server or "cloud" deployment, that is designed for the collection, analysis, interpretation and visualization of syndromic surveillance data and other indicators to monitor population health. The Gossamer Health system combines applied public health informatics research conducted at the University of Washington Center for Public Health Informatics and Washington State Department of Health, in collaboration with other state and local health jurisdictions, the International Society for Disease Surveillance and the Centers for Disease Control and Prevention.

Objective

The goal of this work is to make available to the public health community an open source system that makes available in a standards-based, modular fashion the basic tools required to conduct automated indicator-based population health surveillance. These tools may be deployed in a flexible fashion on health department servers, in the Amazon EC2 cloud, or in any combination, and are coupled through well-defined standards-based interfaces.

Syndromic surveillance systems use electronic health-related data to support near-real time disease surveillance. Over the last 10 years, the use of ILI syndromes defined from emergency department (ED) data has become an increasingly accepted strategy for public health influenza surveillance at the local and national levels. However, various ILI definitions exist and few studies have used patient-level data to describe validity for influenza specifically.

Objective

Estimate and compare the accuracy of various ILI syndromes for detecting lab-confirmed influenza in children.

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.

This paper describes several interactive data exploration tools for examining surveillance data.

A common problem in syndromic surveillance using ED department data is temporary gaps in the data received from individual ED departments caused by delays in receiving the data.

Currently most syndromic surveillance systems provide information about the status of the data sources feeding into the system, for example on the home page of the system, but do not show the effects of any missing data sources on individual derived data elements (except in that graphs may show obvious drops in counts on days when data sources are missing).

The utility of specific sources of data for surveillance, and the quality of those data, are an ingoing issue in public health(1). Syndromic surveillance is typically conducted as a secondary use of data collected as part of routine clinical practice, and as such the data can be of high quality for the clinical use but of lower quality for the purpose of surveillance. A major data quality issue with surveillance data is that of timeliness. Data used in surveillance typically arrive as a periodic process, inherently creating a delay in the availability of the data for surveillance purposes. Surveillance data are often collected from multiple sources, each with their own processes and delays, creating a situation where the data available for surveillance are accrued piecemeal.

Objective

This abstract discusses the quality issues identified in using Distribute. From 2006 to 2012, the ISDS ran Distribute (2), a surveillance system for monitoring influenza like illness (ILI) and gastroenteritis (GI) ED visits on a nationwide basis. This system collected counts for ILI, GI and total ED visits, aggregated to the level of jurisdiction. The primary data quality issue faced with the Distribute system was that of timeliness due to accrual lag; variable delays in the receipt of surveillance data from sources by jurisdictions together with variable delays in the reporting of aggregate data from jurisdictions to Distribute resulted in data which accrued over time(3).