Speakers will be practicing clinician-researchers discussing needs for and ongoing usage of recent, local epidemiological information for decision support.
Presenters
Andrea Dugas, MD, Attending Physician in the Department of Emergency Medicine, Assistant Professor, Johns Hopkins University School of Medicine
The National Biosurveillance Integration Center (NBIC) serves to enable early warning and shared situational awareness of acute biological events and support better decisions through rapid identification, characterization, localization, and tracking. As part of the U.S. Department of Homeland Security, NBIC integrates biosurveillance information across the domains of human, animal, and plant health, as well as food and the environment using a variety of human and technological sources.
Data from the Emergency Departments (EDs) of 49 hospitals in New York City (NYC) is sent to the Department of Health and Mental Hygiene (DOHMH) daily as part of the syndromic surveillance system. Currently, thirty-four of the EDs transmit data as flat files. As part of the Center for Medicare and Medicaid Services Electronic Health Record Incentive Program, otherwise known as Meaningful Use, many EDs in our system have switched or are in the process of switching to HL7 Messaging Standard Version 2.5.1. Given there may be differences in data completeness, quality, and content between the new HL7 data and legacy data, we evaluated data sent in both formats in parallel by several EDs.
Objective
To evaluate potential changes in emergency department (ED) syndromic surveillance data quality, as hospitals shift from sending data as flat file format (Legacy Data) to real-time/batch HL7 Messaging Standard Version 2.5.1, in compliance with Meaningful Use requirements.
The June 2011 meeting of the Public Health Practice Committee featured a discussion on the June 2011 Report of the National Biosurveillance Advisory Subcommittee (NBAS). Leading the discussion was Pamela S. Diaz, MD, the Director of Biosurveillance Coordination at the Office of Surveillance, Epidemiology and Laboratory Services (OSELS) at the Centers for Disease Control and Prevention (CDC).
Presenter
Presenter
Dr. Andrew Fine, Assistant Professor, Boston Children's Hospital, Harvard Medical School
Hosts
Johns Hopkins University Grand Rounds and ISDS Research Committee
Date
December 14, 2012
Whilst the sensitivity and specificity of traditional laboratory-based surveillance can be readily estimated, the situation is less clear cut for syndromic surveillance. Syndromic surveillance indicators based upon presenting symptoms, chief complaints or preliminary diagnoses are designed to provide public health systems with support to detect multiple potential threats to public health. There is however, no gold standard list of all the possible ‘events’ that should have been detected. This is especially true in emergency response where systems are designed to detect possible events for which there is no directly comparable historical precedent.
Objective
To devise a methodology for validating the effectiveness of syndromic surveillance systems across a range of public health scenarios, even in the absence of historical example datasets.
During the past ten years, the syndromic surveillance has mainly developed thanks to clinical data sources (i.e. emergency department, emergency medical call system, etc.). However, in these systems, the population doesn’t play an active role. It is now important that the population becomes an actor of this surveillance; especially since several European experiences about influenza showed that the population could participate to an internet-based monitoring. In Reunion Island, the population is very sensitive to public health concerns. In this context, the health authorities implemented since April 2014 a web-based surveillance system, called “Koman i lé”, that allows to follow the perceived health among people who don’t systematically see their general practitioner.
Objective
To describe a new surveillance system based on an online selfreported symptoms and to present the first results.
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.
Infectious disease outbreaks during crises can be controlled by detecting epidemics at their earliest possible stages through cost effective and time efficient data analytical approaches. The slow or non reporting is a real gap in existing reporting systems that delays in receiving the disease alerts and outbreaks, and hence delays in response causing high burden of morbidity and mortality, especially during crises situation. As on contrary, the functioning electronic databases for fast and reliable disease early warning and response networks (EWARN) have been found very effective in early detection, confirmation and response to disease outbreaks but launching the implementation of such systems is always time consuming due to resource constraints and other limitations during crises. Hence introduction of time efficient data analytical approaches can serve as a fast and reliable alternative for electronic databases during the launching phases, and may facilitate assessment of epidemics and outbreak situation by ensuring immediate, reliable and fully functional disease reporting and analysis until online database becomes fully functional and adopted by authorities.
Objective
To assess the epidemic and outbreak situations during emergencies through development and application of a data summarization techniques while launching electronic disease early warning systems (eDEWS) in resource poor countries
With economic pressures to shift the care of community-acquired pneumonia (CAP) to the ambulatory setting, there is a need to ensure safety of outpatients with CAP. The use of claims data alone remains the primary strategy for identifying these patients, but billing information often does not match the clinical diagnosis and does not have the ability to find unrecognized cases. In our previous work, an automated pneumonia case detection algorithm (CDA) was able to detect cases of CAP with positive predictive value of 71%. For this study, we begin to illustrate how this type of surveillance system may assist in evaluating the quality of outpatient care for CAP.