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Syndrome Definition

State and local jurisdictions have been exploring the use of SyS data to monitor suspected drug overdose outbreaks in their communities. With the increasing awareness and use of SyS systems, staff from the Centers for Disease Control and Prevention (CDC) worked to develop several queries that jurisdictions could use to better capture suspected drug overdose visits. In 2017, CDC released their first two queries on heroin overdose and opioid overdose, followed in 2018 by stimulant and all drug overdose queries. Over time, and with the assistance from the SyS community and the CDC-funded Enhanced State Opioid Overdose Surveillance (ESOOS) state health departments, CDC has revised the queries to address suggestions from jurisdictions. However, it'™s not clear how often and in what way the syndrome definitions are updated over time. This is particularly true as new drugs emerge and the names of those drugs are integrated into syndrome definitions (e.g., recent Spice and œK2 synthetic cannabinoid outbreaks).

Objective: To discuss the process for developing and revising suspected drug overdose queries in syndromic surveillance (SyS) systems.

Submitted by elamb on

The Distribute project began in 2006 as a distributed, syndromic surveillance demonstration project that networked state and local health departments to share aggregate emergency department-based influenza-like illness (ILI) syndrome data. Preliminary work found that local systems often applied syndrome definitions specific to their regions; these definitions were sometimes trusted and understood better than standardized ones because they allowed for regional variations in idiom and coding and were tailored by departments for their own surveillance needs. Originally, sites were asked to send whatever syndrome definition they had found most useful for monitoring ILI. Places using multiple definitions were asked to send their broader, higher count syndrome. In 2008, sites were asked to send both a broad syndrome, and a narrow syndrome specific to ILI.

 

Objective

To describe the initial phase of the ISDS Distribute project ILI syndrome standardization pilot.

Referenced File
Submitted by hparton on

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.

Referenced File
Submitted by elamb on

Domains go through phases of existence, and the electronic disease surveillance domain is no different. This domain has gone from an experimental phase, where initial prototyping and research tried to define what was possible, to a utility phase where the focus was on determining what tools and data were solving problems for users, to an integration phase where disparate systems that solve individual problems are tied together to solve larger, more complex problems or solve existing problems more efficiently. With the integration phase comes the desire to standardize on many aspects of the problem across these tools, data sets, and organizations. This desire to standardize is based on the assumption that if all parties are using similar language or technology then it will be easier for users and developers to move them from one place to another.

Normally the challenge to the domain is deciding on a vocabulary or technology that allows seamless transitions between all involved. The disease surveillance domain has accomplished this by trying to use some existing standards, such as HL7, and trying to develop some of their own, such as chief complaint-based syndrome definitions. However, the standards that are commonly discussed in this domain are easily misunderstood. These misunderstandings are predominantly a communication and/or educational issue, but they do cause problems in the disease surveillance domain. With the increased use of these standards due to meaningful use initiatives, these problems will continue to grow and be repeated without improved understanding and better communication about standards.

 

Objective

This talk will point out the inconsistencies and misunderstandings of the word "standard". Specifically, it will discuss HL7, syndrome definitions, analytical algorithms, and disease surveillance systems.

Referenced File
Submitted by elamb on

The Extended Syndromic Surveillance Ontology (ESSO) is an open source terminological ontology designed to facilitate the text mining of clinical reports in English [1,2]. At the core of ESSO are 279 clinical concepts (for example, fever, confusion, headache, hallucination, fatigue) grouped into eight syndrome categories (rash, hemorrhagic, botulism, neurological, constitutional, influenza-like-illness, respiratory, and gastrointestinal). In addition to syndrome groupings, each concept is linked to synonyms, variant spellings and UMLS Concept Unique Identifiers. ESSO builds on the Syndromic Surveillance Ontology [3], a resource developed by a working group of eighteen researchers representing ten syndromic surveillance systems in North America. ESSO encodes almost three times as many clinical concepts as the Syndromic Surveillance Ontology, and incorporates eight syndrome categories, in contrast to the Syndromic Surveillance Ontology's four (influenza-like-illness, constitutional, respiratory and gastrointestinal). The new clinical concepts and syndrome groupings in ESSO were developed by a board-certified infectious disease physician (author JD) in conjunction with an informaticist (author MC).

Objective

In order to evaluate and audit these new syndrome definitions, we initiated a survey of syndromic surveillance practitioners. We present the results of an online survey designed to evaluate syndrome definitions encoded in the Extended Syndromic Surveillance Ontology.

Referenced File
Submitted by elamb on

Syndromic surveillance systems were designed for early outbreak and bioterrorism event detection. As practical experience shaped development and implementation, these systems became more broadly used for general surveillance and situational awareness, notably influenza-like illness (ILI) monitoring. Beginning in 2006, ISDS engaged partners from state and local health departments to build Distribute, a distributed surveillance network for sharing de-identified aggregate emergency department syndromic surveillance data through existing state and local public health systems. To provide more meaningful cross-jurisdictional comparisons and to allow valid aggregation of syndromic data at the national level, a pilot study was conducted to assess implementation of a common ILI syndrome definition across Distribute.

 

Objective

Assess the feasibility and utility of adopting a common ILI syndrome across participating jurisdictions in the ISDS Distribute project.

Referenced File
Submitted by elamb on
  • Why the syndrome was created? This syndrome was created to monitor tick related emergency room visits using regular expressions in R. 
  • Syndromic surveillance system (e.g., ESSENCE, R STUDIO, RODS, etc.) Data collected from Epicenter, but parsed and analysed in R/Rstudio
  • Data sources the syndrome was used on (e.g., Emergency room, EMS, Air Quality, etc.) Emergency room and Urgent Care
Submitted by Anonymous on

The following syndrome was developed to explore emergency department visit records involving people experiencing homelessness. Trends over time, patient demographics, geographic distribution, and primary reasons for seeking care were explored. Additionally, we have been using this definition, in combination with other illness/injury specific definitions to assess the trends in among people experiencing homelessness (e.g., cold-related illness among people experiencing homelessness during record low temperatures).

Submitted by Anonymous on

Uploaded on behalf of Grace Marx, MD, MPH: Bacterial Diseases Branch, Division of Vector-Borne Diseases, CDC.

 

This syndrome definition was created to explore tick through Syndromic data as an efficient approach to monitor the tick-borne diseases and the utility of tick bite visits to predict the seasonal peak in Lyme disease.

This was created in NSSP ESSENCE, using the Chief Complaint Query Validation (CCQV) data to ensure a broad application across different states and jurisdictions.

Submitted by ZSteinKS on
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