There is a saying in EMS that if you've "seen one EMS system, then you've seen one EMS system". Many EMS systems have good or even great data for surveillance and research, but while there are some standards developing for EMS data formats and sharing, very few systems have the capability to share data using them.Presenters discussed the current state of 9-1-1/EMS dispatch and field electronic medical records systems, and the changing impact of the official and informal standards and variations of data seen in different communities.
Influenza-Like-Illness (ILI)
Edward Goldstein, PhD, Harvard School of Public Health, Senior Research Scientist, Department of Epidemiology discusses his paper "Predicting the Epidemic Sizes of Influenza A/H1N1, A/H3N2, and B: A Statistical Method." Published in PLoS Med. 2011 Jul;8(7): e1001051.
Presenter
Edward Goldstein, PhD, Harvard School of Public Health
Date and Time
Tuesday, November 29, 2011
Host
ISDS Research Committee
Globally, disease surveillance systems suffer from a number of resource constraints. These constraints are more pronounced in developing countries, which bear the greatest burden of disease and where pathogens are more likely to emerge, reemerge, and mutate into drug-resistant strains (US-GAO August 2001). It has traditionally been difficult to monitor disease burden and trends in India, and even more difficult to detect, diagnose, and control outbreaks until they had become quite large (Suresh June 2003).
Krista Kniss and Scott Epperson, from the CDC Influenza Division, will be joining the ISDS Public Health Practice Committee this coming Monday, October 22, for a two-part discussion of influenza surveillance in the United States and abroad. The first presentation by Krista Kniss will discuss the differences between the U.S. influenza surveillance system and how influenza surveillance is conducted in other countries, specifically those with few resources. The second presentation by Scott Epperson will discuss the evolution and current status of influenza A viruses both in humans and swine.
Follow these instructions on how to run ILI R code on your BioSense data locker using R studio.
Description
Mapping ILI surveillance data can be useful in identifying the direction and speed of an outbreak and for focusing control measures for an efficient public health response. The Centers for Disease Control and Prevention’s (CDC) ILINet currently displays weekly ILI geographic data at a national/regional/state level, but this visual data could also be useful at the local level.
Objective
To create a local geographic influenza-like illness (ILI) activity report.
Description
ILINet is used nationwide by sentinel healthcare providers for reporting weekly outpatient visit numbers for influenza-like illness to CDC. The Florida Department of Health receives urgent care center (UCC) data through ESSENCE from participating facilities. Seminole County is unique in that its four sentinel providers located in separate UCCs report into both systems, and all their discharge diagnoses are available through ESSENCE. However, the reported number of patients being discharged from those providers with diagnoses of influenza is not equivalent to the number of cases reported into ILINet. Data from the two systems were therefore compared both among and between the individual sentinel providers in order to determine the extent of the variation over four influenza seasons.
Objective
To compare influenza-like illness (ILI) data reported to the Centers for Disease Control and Prevention (CDC) U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet) with discharge diagnosis data for influenza from the same reporting source obtained through the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) in Seminole County, Florida.
Description
Effective real-time surveillance of infectious diseases must strike a balance between reliability and timeliness for early detection. Traditional syndromic surveillance utilizes limited sections of the EMR, such as chief complaints and/or diagnosis. However, other sections of the EMR may contain more pertinent information than what is captured in a brief chief complaint. These other EMR sections may provide relevant information earlier in the patient encounter than at the diagnosis or disposition stage, which can appear in the EMR up to 24 hours after the patient’s discharge. Comprehensive analysis may identify the most relevant section of EMRs for surveillance of all major infectious diseases, including ILI.
Objective
To investigate which section(s) of a patient’s electronic medical record (EMR) contains the most relevant information for timely detection of influenza-like illness (ILI) in the emergency department (ED).
Each season in the United States a multi-component influenza surveillance system monitors and describes influenza activity. This presentation will describe the overall picture of influenza virus circulation and compare data from each of the surveillance components to previous years to better understand what turned out to be a season with high levels of activity. Also, to provide a global context for this season, data from the U.S. will be compared to other Northern Hemisphere countries, and a selection of vaccine strains for the 2013-2014 will be covered.
Description
In recent years, the threat of pandemic influenza has drawn extensive attention to the development and implementation of syndromic surveillance systems for early detection of ILI. Emergency department (ED) data are key components for syndromic surveillance systems. However, the lack of standardization for the content in chief complaint (CC) free-text fields may make it challenging to use these elements in syndromic surveillance systems. Furthermore, little is known regarding how ED data sources should be structured or combined to increase sensitivity without elevating false positives. In this study, we constructed two different models of ED data sources and evaluated the resulting ILI rates obtained in two different institutions.
Objective
To compare the influenza-like illness (ILI) rates in the emergency departments (ED) of a community hospital versus a large academic medical center (AMC).