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Syndromic Surveillance

The mission of the Maricopa County Department of Public Health (MCDPH; Arizona) is to protect and promote the health and well-being of its residents and visitors. Surveillance efforts allow epidemiologists to quantify and characterize public health threats, but traditional methods take time. In an effort to enhance situational awareness, the Office of Epidemiology dedicated resources to begin developing a robust syndromic surveillance program. This abstract outlines steps for enhancing syndromic surveillance at a local public health department.

Objective

To demonstrate how a local public health department used the Centers for Disease Control and Prevention (CDC) Framework for Program Evaluation and a logic model to enhance its syndromic surveillance program.

Submitted by teresa.hamby@d… on

The EpiCenter syndromic surveillance platform currently uses Java libraries for time series analysis. Expanding the data quality capabilities of EpiCenter requires new analysis methods. While the Java ecosystem has a number of resources for general software engineering, it has lagged behind on numerical tools. As a result, including additional analytics requires implementing the methods de novo.

The R language and ecosystem has emerged as one of the leading platforms for statistical analysis. A wide range of standard time series analysis methods are available in either the base system or contributed packages, and new techniques are regularly implemented in R. Previous attempts to integrate R with EpiCenter were hampered by the limitations of available R/Java interfaces, which were not actively developed for a long time.

An alternative bridge is via the PostgreSQL database used by EpiCenter on the backend. An R extension for PostgreSQL exists, which can expose the entire R ecosystem to EpiCenter with minimal development effort.

Objective To demonstrate the broader analytical capabilities available by making the R language available to EpiCenter reporting

Submitted by teresa.hamby@d… on

Traditional disease surveillance systems suffer from several disadvantages, including reporting lags and antiquated technology, that have caused a movement towards internet-based disease surveillance systems. Recently, Wikipedia access logs (e.g., McIver 2014, Generous 2014) have been shown to be effective in this arena. Much richer Wikipedia data are available, though, including the entire Wikipedia article content and edit histories.

We study two different aspects of Wikipedia content as it relates to unfolding disease events: 1) we demonstrate how to capture case, death, and hospitalization counts from the article text, and 2) we show there are valuable time series data present in the tables found in certain articles.

We argue that Wikipedia data cannot only be used for disease surveillance but also as a centralized repository system for collecting disease-related data in near real-time.

Objective

To improve traditional outbreak surveillance systems by utilizing the content of Wikipedia articles.

Submitted by teresa.hamby@d… on

Emergency medicine is a recognized specialty in the United Kingdom (UK), with formal training and accreditation conducted and governed by the Royal College of Emergency Medicine. Health care in the UK is publicly funded and provided by the National Health Service (NHS) through a residence-based (rather than insurance-based) system. Emergency care within emergency departments (EDs) is currently provided free at the point of delivery for everyone, including non-UK residents.

Submitted by elamb on

Fifteen years have passed since the Public Health Security and Bioterrorism Preparedness and Response Act of 2002 called for the establishment of nationwide surveillance and reporting mechanisms to detect bioterrorism-related events. In the 1990s, several health departments established surveillance systems to detect prediagnostic (ie, before diagnoses are confirmed) signs and symptoms for the early identification of disease occurrences.

Submitted by elamb on

The BioSense program was launched in 2003 with the aim of establishing a nationwide integrated public health surveillance system for early detection and assessment of potential bioterrorism-related illness. The program has matured over the years from an initial Centers for Disease Control and Prevention–centric program to one focused on building syndromic surveillance capacity at the state and local level.

Submitted by elamb on

Thirteen surveillance professionals from seven state and local public health agencies in the U.S. Department of Health and Human Service (HHS) Region 5 planned and participated in the 2-day Workshop. The participants selected data sharing for heatrelated illness surveillance using BioSense 2.0 as a use case to focus Workshop activities and discussions.

Submitted by elamb on

A Regional Syndromic Surveillance Data Sharing Workshop was held in Health and Human Services (HHS) Region 4 on May 12-13, 2015 at the Emory University Rollins School of Public Health in Atlanta, GA. This was the seventh workshop in a series, with the ultimate aim to reach all ten HHS regions.

Submitted by elamb on

A Regional Syndromic Surveillance Data Sharing Workshop was held in Health and Human Services (HHS) Region 3 on June 9-10, 2015 at the offices of the District of Columbia Department of Health. This was the eighth workshop in a series, with the ultimate aim to reach all ten HHS regions.

Submitted by elamb on

A planning team that included staff from ISDS, ASTHO, and Charlie Ishikawa of Ishikawa Associates, LLC created and implemented the HHS Region 2+ workshop. Charlie led the workshop facilitation and design of workshop artifacts. The workshop was based on a model that utilizes a non-formal education (NFE) approach2, which features self-directed learning and peer-to-peer problem solving, and actively engages participants in identifying their learning needs and methods with guidance by a facilitator.

Submitted by elamb on
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