The National Poison Data System (NPDS) is maintained and operated by the American Association of Poison Control Centers (AAPCC) for the analysis, visualization, and reporting of call data from all 61 regional poison centers (PCs) in cooperation with CDC's National Center for Environmental Health (NCEH). NCEH collaborates with AAPCC toxicologists using NPDS to facilitate early recognition and monitoring of illness due to intentional or unintentional chemical or toxin exposures. NPDS algorithms identify statistically significant increases in callers' reported signs and symptoms - 131 clinical effects (CEs) such as rash and diarrhea - for detection of national poison exposure anomalies. Each day AAPCC toxicologists make decisions about NPDS anomalies' public health importance. Regional PCs are contacted as required for additional information about potentially important anomalies. NPDS also allows for individual case tracking through user-defined 'case-based definitions.' This additional method is especially useful during an outbreak when the agent and/or symptoms of affected persons are known.
Surveillance Systems
Syndromic surveillance systems often classify patients into syndromic categories based on emergency department (ED) chief complaints. There exists no standard set of syndromes for syndromic surveillance, and the available syndromic case definitions demonstrate substantial heterogeneity of findings constituting the definition. The use of fever in the definition of syndromic categories is arbitrary and unsystematic. We determined whether chief complaints accurately represent whether a patient has any of five febrile syndromes: febrile respiratory, febrile gastrointestinal, febrile rash, febrile neurological, or febrile hemorrhagic.
In the past, the media has served a source of data for syndromic surveillance of infectious disease, whether it is outbreaks of disease in animals or humans resulting in illness or death. More often than not, the reverse is true; data based on analyses of syndromic surveillance often flows from hospital to local health departments and federal governmental agencies such as the CDC to the media which then relays it to the public. In both instances, the media may serve as a purveyor of vital information. But, sometimes the media reports are less than ideal; the public may become fearful and panic at the news of a potential outbreak of an emerging infectious disease such as bird flu for which there is a high fatality case rate and no proven available vaccine, or curative therapy. Moreover, supplies of vaccine may be limited, and news of a shortage of antiviral medications such as Tamiflu may lead to stockpiling similar to what occurred with Cipro during the anthrax ‘scare.’
Objective:
This paper explores how the mass media covered bird flu outbreaks overseas in the Fall of 2005, and the nationÃs preparations for a possible bird flu pandemic, and how this period of intense media activity affected sales of antivirals in New City and New York State as monitored by syndromic surveillance techniques.
Syndromic surveillance has been used to detect variation in seasonal viral illnesses such as influenza and norovirus infection (1). Limited information is available on the use of a comprehensive bio-surveillance system, including syndromic surveillance, for detection and situational awareness during a sustained outbreak.
Objective:
To report on surveillance and response activities during the 2006-2007 norovirus season in Boston.
The revised International Health Regulations (IHR) have expanded traditional infectious disease notification to include surveillance diseases of international importance, including emerging infectious diseases. However, there are no clearly established guidelines for how countries should conduct this surveillance, which types of syndromes should be reported, nor any means for enforcement. The commonly established concept of syndromic surveillance in developed regions encompasses the use of pre-diagnostic information in a near real time fashion for further investigation for public health action. Syndromic surveillance is widely used in North America and Europe, and is typically thought of as a highly complex, technology driven automated tool for early detection of outbreaks. Nonetheless, applications of syndromic surveillance using technology appropriate for the setting are being used worldwide to augment traditional surveillance, and may enhance compliance with the revised IHR.
Objective:
To review applications of syndromic surveillance in developing countries
Syndromic surveillance needs to be (1) transparent, (2) actionable, and (3) flexible. Traditional frequentist approaches to syndromic surveillance, such as cusum charts and scan statistics, tend to fail on all three criteria. First, the validity of the assumptions is generally difficult to check and the methods are hard to modify; second, the false positive rate makes it impossible to be both sensitive to true signal and resistant to spurious signal; and third, the implementation usually requires significant hand-tinkering to adjust background rates for known seasonal affects and other identifiable influences.
OBJECTIVE
This paper describes a Bayesian approach to syndromic surveillance. The method provides more interpretable inference than traditional frequentist approaches. Bayesian methods avoid many of the problems associated with alpha levels and multiple comparisons, and make better use of prior information. The technique is illustrated on simulated data.
Foot-and-mouth disease (FMD) is one of the most devastating diseases of farm animals. There is a critical need for countries to have a global FMD situational awareness. Monitoring the online news sources for FMD-related news is an important component of situational awareness. The FMD Lab at UC Davis (http://fmd.ucdavis.edu/) has developed models and systems for global FMD surveillance, including the FMD BioPortal web-based system jointly with the AI Lab at the University of Arizona. They have also been gathering and processing FMD-related news from the FMD World Reference Laboratory, the OIE, the FAO, among others. However, manual searches are necessary identify and integrate the FMD news into their models and systems. This manual work is not only time consuming and labor intensive but may also lead to the loss of some important information.
OBJECTIVE
This paper describes an FMD news monitoring and classification system which can automatically monitor FMD related news from online news data sources, generate news summarization and classify the news into three categories defined by domain experts. The report research is a collaborative effort between the Artificiall Intelligence Lab at the University of Arizona and the FMD Lab at UC Davis.
The statistical process control (SPC) community has developed a wealth of robust, sensitive monitoring methods in the form of control charts [1]. Although such charts have been implemented for a wide variety of health monitoring purposes [2], some implementations monitor data that violate basic assumptions required by the control charts [3] yielding alerting methods with uncertain detection performance. This problem highlights an inherent obstacle to the use of traditional SPC methods for syndromic surveillance: the nature of the data. Syndromic data streams are based not on physical science, as are manufacturing processes, but on changing population behavior and evolving data acquisition and classification procedures. To overcome this obstacle, either more sophisticated detection algorithms must be developed or the data must be preconditioned so that it is appropriate for traditional monitoring tools. Objective: For robust detection performance, alerting algorithms for biosurveillance require input data free of trends, day-of-week effects, and other systematic behavior. Time series forecasting methods may be used to remove this behavior by subtracting forecasts from observations to form residuals for algorithmic input. This abstract examines and compares methods for the automatic preconditioning of health indicator data to enable the timely prospective monitoring required for effective syndromic surveillance.
Objective
For robust detection performance, alerting algorithms for biosurveillance require input data free of trends, day-of-week effects, and other systematic behavior. Time series forecasting methods may be used to remove this behavior by subtracting forecasts from observations to form residuals for algorithmic input. This abstract examines and compares methods for the automatic preconditioning of health indicator data to enable the timely prospective monitoring required for effective syndromic surveillance.
The Los Angeles County (LAC) Bioterrorism Preparedness and Response Unit has made significant progress in automating the syndromic surveillance system. The surveillance system receives electronic data on a daily basis from different hospital information systems, then standardizes and generates analytical results.
OBJECTIVE
This article describes architecture, analytical method, and software applications used in automating the LAC syndromic surveillance system.
OBJECTIVE
Syndromic surveillance systems (SSS) seek early detection of infectious diseases outbreaks by focusing on pre-diagnostic symptoms. We do not yet know which respiratory syndrome should be monitored for a SSS to discover an influenza epidemic as soon as possible. This works compares the delay and workload required to detect an influenza epidemic using a SSS that targets either (1) all cases of acute respiratory infections (ARI) or (2) only those ARI cases that are febrile and satisfy CDC's definition for an influenza-like illness.