Public health practice

Despite the number of infections, hospitalizations, and deaths from influenza each year, developing the ability to predict the timing of these outbreaks has remained elusive. Public health practitioners have lacked a reliable, easy-to-implement method for predicting the onset of a period of elevated influenza incidence in a community. We (a team of statisticians, epidemiologists, and clinicians) have developed a model to help public health practitioners develop simple, adaptable, data-driven rules to define a period of increased disease incidence in a given location. We call this method the Above Local Elevated Respiratory illness Threshold (ALERT) algorithm. The ALERT algorithm is a simple method that defines a period of elevated disease incidence in a community or hospital that systematically collects surveillance data on a particular disease.

Objective

Our objective was to develop a simple, easy-to-use algorithm to predict the onset of a period of elevated influenza incidence in a community using surveillance data.

BioSense 2.0 protects the health of the American people by providing timely insight into the health of communities, regions, and the nation by offering a variety of features to improve data collection, standardization, storage, analysis, and collaboration. BioSense 2.0 is the result of a partnership between the Centers for Disease Control and Prevention (CDC) and the public health community to track the health and well-being of communities across the country. As part of the redesign effort, new fat pipe system architecture has recently been implemented to improve the features and capabilities of the system.

Objective

The objective of this presentation is to provide an overview of the technical architecture of BioSense 2.0.

The HEDSS system was implemented in 2004 to monitor disease activity [1]. Twenty of 32 emergency departments (ED) and 1 urgent care clinic provide data. Chief complaints are routinely categorized into 8 syndromes. Although previous studies have shown that ED syndomic surveillance is not useful for early detection of GI outbreaks [2], it has demonstrated utility in monitoring trends in seasonal norovirus activity[3]. An evaluation to assess the utility of HEDSS to characterize endemic and out-break levels of GI illness has not been previously conducted in Connecticut.

Objective

To evaluate the utility of the Connecticut Hospital Emergency Department Syndromic Surveillance System (HEDSS) to monitor gastrointestinal (GI) illness in the community.

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.

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 (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.

As system users develop queries within ESSENCE, they step through the user-interface to select data sources and parameters needed for their query. Then they select from the available output options (e.g., time series, table builder, data details). These activities execute a SQL query on the database, the majority of which are saved in a log so that system developers can troubleshoot problems. Secondarily, these data can be used as a form of web analytics to describe user query choices, query volume, query execution time, and develop an understanding of ESSENCE query patterns.

Objective:

The objective of this work is to describe the use and performance of the NSSP ESSENCE system by analyzing the structured query language (SQL) logs generated by users of the National Syndromic Surveillance Program'™s (NSSP) Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE).

Syndromic surveillance uses near-real-time Emergency Department healthcare and other data to improve situational awareness and inform activities implemented in response to public health concerns. The National Syndromic Surveillance Program (NSSP) is a collaboration among state and local health departments, the Centers for Disease Control and Prevention (CDC), other federal organizations, and other entities, to strengthen the means for and the practice of syndromic surveillance. NSSP thus strives to strengthen syndromic surveillance at the national and the state, and local levels through the coordinated activities of the involved partners and the development and use of advanced technologies, such as the BioSense platform. Evaluation and performance measurement are crucial to ensure that the various strategies and activities implemented to strengthen syndromic surveillance capacity and practice are effective. Evaluation activities will be discussed at this session and feedback from audience will be sought with the goal to further strengthen evaluation activities in the future. 

Objective:

The objective of this session is to discuss syndromic surveillance evaluation activities. Panel participants will describe contexts and importance of selected evaluation and performance measurement activities in NSSP. Discussions will explore ways to strengthen evaluation in syndromic surveillance activities in the future.