Syndromic Surveillance

Heat waves have serious health impacts such as heat exhaustion, heat stroke, dehydration, and death. Heat illness morbidity and mortality can be reduced with the identification of vulnerable populations and targeted public health interventions. In June and July of 2011, a heat wave occurred in Nebraska in which 28 days reached 90 degrees F or higher. Syndromic surveillance data were used to describe heat-related illness emergency department (ED) visits during this time.

Objective

The purpose of this study was to develop methodology to accurately identify and track heat illness in a timely manner using syndromic surveillance data.

Mining text for real-time syndromic surveillance usually requires a comprehensive knowledge base (KB) which contains detailed information about concepts relevant to the domain, such as disease names, symptoms, drugs, and radiology findings. Two such resources are the Biocaster Ontology [1] and the Extended Syndromic Surveillance Ontology (ESSO) [2]. However, both these resources are difficult to manipulate, customize, reuse and extend without knowledge of ontology development environments (like Protege) and Semantic Web standards (like RDF and OWL). The cKASS software tool provides an easy-to-use, adaptable environment for extending and modifying existing syndrome definitions via a web-based Graphical User Interface, which does not require knowledge of complex, ontology-editing environments or semantic web standards. Further, cKASS allows for - indeed encourages - the sharing of user-defined syndrome definitions, with collaborative features that will enhance the ability of the surveillance community to quickly generate new definitions in response to emerging threats.

Objective

We describe cKASS (clinical Knowledge Authoring & Sharing Service), a system designed to facilitate the authoring and sharing of knowledge resources that can be applied to syndromic surveillance.

For public health surveillance to achieve its desired purpose of reducing morbidity and mortality, surveillance data must be linked to public health response. While there is evidence of the growing popularity of syndromic surveillance (1,2), the impact or value added with its application to public health responses is not well described (3).

Objective

To describe if and how syndromic surveillance data influenced public health decisions made during the 2009 H1N1 pandemic within the context of other existing public health surveillance systems.

In March-April, 2011, Salt Lake Valley Health Department (SLVHD) investigated an outbreak of measles (N=9) resulting from a single imported case from Europe. Syndromic surveillance was used to identify measles-like illness (MLI) and enhance early case detection, which is crucial for proper public health intervention [1].

Objective

To detect measles cases during an outbreak using syndromic surveillance.

Triple-S (Syndromic Surveillance Survey, Assessment towards Guidelines for Europe) was launched in 2010 for a 3-year period. Co-financed by the European Commission through the Executive Agency for Health and Consumers, the project is coordinated by the French Institute for Public Health Surveillance and involves 24 organisations from 13 countries. Both human and veterinary syndromic surveillance are considered.

Objective

The first results of the inventory of syndromic surveillance systems in Europe, conducted in the framework of the European project Triple-S, are presented.

In May of 2001, Boston released a strategic transportation plan to improve bicycle access and safety. [1] According to the Boston Transportation Department, ridership has increased 122% between 2007 and 2009. [2] A collaborative public health and public safety task force was initiated in 2010 to foster a safe and healthy bicycling environment.

Objective

To quantify the injury burden and identify possible risk factors using bicycle related injury (BRI) visits at Boston emergency departments (ED).

The Centers for Disease Control and Prevention case definition of influenza-like illness (ILI) as fever with cough and/or sore throat casts a wide net resulting in lower sensitivity which can have major implications on public health surveillance and response.

Objective

This study investigates additional signs and symptoms to further enhance the ILI case definition for real-time surveillance of influenza.

Informal surveillance systems like HealthMap are effective at the early detection of outbreaks. However, reliance on informal sources such as news media makes the efficiency of these systems vulnerable to newsroom constraints, namely high-profile disease events drawing reporting resources at the expense of other potential outbreaks and diminished staff over weekends and holidays. To our knowledge, this effect on informal or syndromic surveillance systems has yet to be studied.

Objective

Reporting about large public health events may reduce effective disease surveillance by syndromic or informal surveillance systems. The goal is to determine to what extent this problem exists and characterize situations in which it is likely to occur.

The electronic surveillance system for the early notification of community-based epidemics (ESSENCE) is the web-based syndromic surveillance system utilized by the Maryland Department of Health and Mental Hygiene (DHMH). ESSENCE utilizes a secure, automated process for the transfer of data to the ESSENCE system that is consistent with federal standards for electronic disease surveillance. Data sources in the Maryland ESSENCE system include ED chief complaints, poison control center calls, over-the-counter (OTC) medication sales, and pharmaceutical transaction data (specifically for anti-bacterial and anti-viral medications). All data sources have statewide coverage and are captured daily in near real-time fashion.

Objective

To examine the trends in prescription antiviral medication transactions and emergency department (ED) visits for influenza-like illness (ILI) and the relationship between these trends.

Syndromic surveillance of health care data such as the International Classification of Diseases, Ninth Revision (ICD-9), codes related to Influenza-Like-Illness (ILI), was used to track the progression of the 2009 Fall Novel H1N1 Outbreak in the Madison area. Early studies focused on prediction of an outbreak, however further investigation of patient resource utilization would be helpful in developing an action plan for addressing community and patient needs during future outbreaks. There is a paucity of research comparing emergency department (ED) and urgent care utilization rates during the 2009 Novel H1N1 Pandemic, though there is regional data suggesting that urgent care centers bore a larger portion of the burden of H1N1 influenza than emergency departments. Furthermore, one group found that ILI related phone calls to urgent care centers predicted influenza outbreak at least one week ahead of peaks in the ILI hospital care consultation rates. ED data on its own has proven useful for public health disease surveillance and many studies group urgent care and ED care together. The literature is lacking subgroup analysis of these two very different care environments. Understanding the correlation between urgent care and ED utilization rates will provide a more in depth understanding of the stress that the 2009 Fall Novel H1N1 placed on community resources in our geographic region.

Objective

To compare the proportion of patients presenting with ILI to urgent care centers versus the ED during the 2009 Fall Novel H1N1 Outbreak.