Visualization

The use of syndromic surveillance systems has evolved over the last decade, and increasingly includes both infectious and non- infectious topic areas. Public health agencies at the national, state, and local levels often need to rapidly develop new syndromic categories, or improve upon existing categories, to enhance their public health surveillance efforts. Documenting this development process can help support increased understanding and user acceptance of syndromic surveillance. This presentation will highlight the visualization process being used by CDC’s National Syndromic Surveillance Program (NSSP) program to develop and refine definitions for syndromes of interest to public health programs.

Objective: To describe the use of uni-grams, bi-grams, and tri-grams relationships in the development of syndromic categories.

The Public Health - Seattle & King County syndromic surveillance system has been collecting emergency department (ED) data since 1999. These data include hospital name, age, sex, zip code, chief complaint, diagnoses (when available), disposition, and a patient and visit key. Data are collected for 19 of 20 King County EDs, for visits that occurred the previous day. Over time, various problems with data quality have been encountered, including data drop-offs, missing data elements, incorrect values of fields, duplication of data, data delays, and unexpected changes in files received from hospitals. In spite of close monitoring of the data as part of our routine syndromic surveillance activities, there have occasionally been delays in identifying these problems. Since the validity of syndromic surveillance is dependent on data quality, we sought to develop a visualization to help monitor data quality over time, in order to improve the timeliness of addressing data quality problems.

Objective 

We sought to develop a method for visualizing data quality over time.

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.

Chronic diseases are the leading causes of mortality and morbidity for Americans but public health surveillance for these conditions is limited. Health departments currently use telephone interviews, medical surveys, and death certificates to gather information on chronic diseases but these sources are limited by cost, timeliness, limited clinical detail, and/or poor population coverage. Continual and automated extraction, analysis, and summarization of EHR data could advance surveillance in each of these domains.

Objective

Develop methods for automated chronic disease surveillance and visualization using electronic health record (EHR) data.

BioSense data includes Department of Defense and Veterans Affairs ambulatory care diagnoses and procedures, as well as Laboratory Corporation of America lab test orders. Data are mapped to eleven syndrome categories. SaTScan is a spatio-temporal technique that has previously been applied to surveillance at the metropolitan area level. Visualization of national results involves unique issues, including displaying cluster information that crosses jurisdictions, zip codes with highly variant data volume, and evaluating large multiple state clusters. SaTScan was first implemented in June 2005 in the BioSense application for daily monitoring at CDC’s BioIntelligence Center.

Objective

The objective is to describe the visualization and monitoring of the national spatio-temporal SaTScan results in the BioSense application. This is the first application of this algorithm to a national early event detection and situational awareness system.

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.

When the Chicago Bears met the Indianapolis Colts for Super Bowl XLI in Miami in January, 2007, fans from multiple regions visited South Florida for the game. In the past, public health departments have instituted heightened local surveillance during mass gatherings due to concerns about increased risk of disease outbreaks. For the first time, in 2007, health departments in all three Super Bowl-related regions already practiced daily disease surveillance using biosurveillance information systems (separate installations of the ESSENCE system, developed at JHUAPL). The situation provided an opportunity to explore ways in which separate surveillance systems could be coordinated for effective, short-term, multijurisdictional surveillance.

Objective

This paper describes an inter-jurisdictional surveillance data sharing effort carried out by public health departments in Miami, Chicago, and Indianapolis in conjunction with Super Bowl XLI.