Query purpose:
To assist state, local, tribal, territorial, and federal public health practitioners in monitoring emergency department (ED) visits for suspected opioid overdoses.
Syndromes
Query purpose:
To assist state, local, tribal, territorial, and federal public health practitioners in monitoring emergency department (ED) visits for suspected overdoses of any drug.
Why the syndrome was created:
Why the syndrome was created:
Why the syndrome was created:
The purpose of the CDC Legionella v1 ESSENCE query is to capture potential visits related to Legionella. It is useful to identify potential cases for follow-up, conduct situation awareness and monitoring of outbreaks, and perform retrospective trend monitoring across geographic regions to identify possible disease hotspots, etc.
Data sources the syndrome was used on (e.g., emergency room, EMS, air quality):
Emergency room
Why the syndrome was created:
The purpose of CDC Medication Refill v1 is to monitor visits for any medication/prescription that a patient may have run out of. Anything repetitive that needs routine refill visits - dialysis, oxygen, heart, BP, cholesterol.
Why was the syndrome created?
Description
In June 2009, the CDC defined a confirmed case of H1N1 as a person with an ILI and laboratory confirmed novel influenza A H1N1 virus infection. ILI is defined by the CDC as fever and cough and/or sore throat, in the absence of a known cause other than influenza. ILI cases are usually reported without accounting for alternate diagnoses (that is, pneumonia). Therefore, evaluation is needed to determine the impact of alternate diagnoses on the accuracy of the ILI case definition.
Objective
This study investigates the impact of alternate diagnoses on the accuracy of the Centers for Disease Control and Prevention’s (CDC) case definition for influenza-like illness (ILI) when used as a screening tool for influenza A (H1N1) virus during the 2009 pandemic, and the implications for public health surveillance.
Referenced File
Description
Emerging event detection is the process of automatically identifying novel and emerging ideas from text with minimal human intervention. With the rise of social networks like Twitter, topic detection has begun leveraging measures of user influence to identify emerging events. Twitter's highly skewed follower/followee structure lends itself to an intuitive model of influence, yet in a context like the Emerging Infections Network (EIN), a sentinel surveillance listserv of over 1400 infectious disease experts, developing a useful model of authority becomes less clear. Who should we listen to on the EIN? To explore this, we annotated a body of important EIN discussions and tested how well 3 models of user authority performed in identifying those discussions. In previous work we proposed a process by which only posts that are based on specific "important" topics are read, thus drastically reducing the amount of posts that need to be read. The process works by finding a set of "bellwether" users that act as indicators for "important" topics and only posts relating to these topics are then read. This approach does not consider the text of messages, only the patterns of user participation. Our text analysis approach follows that of Cataldi et al.[1], using the idea of semantic "energy" to identify emerging topics within Twitter posts. Authority is calculated via PageRank and used to weight each author's contribution to the semantic energy of all terms occurring in within some interval ti. A decay parameter d defines the impact of prior time steps on the current interval.
Objective
To explore how different models of user influence or authority perform when detecting emerging events within a small-scale community of infectious disease experts.
Referenced File
Description
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.
Referenced File