ISDS Conference

Although many syndromic surveillance (SS) systems have been developed and implemented, few have included response protocols to guide local health jurisdictions when alerts occur [1,2]. SS was first implemented in GA during the 2004 G-8 Summit. Six EDs in the Coastal Public Health District (PHD), 1 of 18 GA PHDs (Figure 1), conducted SS during that “national security special event.” Since that time, EDs in other PHDs have been actively recruited to participate in GA’s SS system. In GA, the PHD has the responsibility for monitoring SS data. Likewise, the PHD responds to alerts and initiates public health investigations and interventions; the state Division of Public Health (DPH) assists, if requested. To address these responsibilities, the Coastal PHD informally developed their own response practices.

Objective

To develop a template protocol to guide local response to syndromic surveillance alerts generated through analyses of emergency department (ED) visit data.

Syndromic surveillance may be suited for detection of emerging respiratory disease elevations that could pass undiagnosed. The syndromes under surveillance should then retrospectively reflect known respiratory pathogen activity. To validate this for respiratory syndromes we analyzed dutch medical registration data from 1999-2003 (national hospital discharge diagnoses and causes of death). We assume that syndromes with a good reflection of pathogen activity have the potential ability to reflect unexpected respiratory pathogen activity in prospective surveillance.

Objective

As a validation for syndromic surveillance we studied whether respiratory syndromes indeed reflect the activity of respiratory pathogens. Therefore we retrospectively estimated the temporal trend of two respiratory syndromes by the seasonal dynamics of common respiratory pathogens.

To date, most syndromic surveillance systems rely heavily on complicated statistical algorithms to identify aberrations. The assumption is that when the statistics identify something unusual, follow-up should occur. However, with multiple strata analyzed, small numbers for some strata, and wide variances in daily counts, the statistical algorithms will generate flags too often. Experience has shown that these flags usually have little or no public health significance. As a result, syndromic surveillance systems suffer from the ‘boy who cried wolf’ syndrome. It is clear that the analyst’s ability to use professional judgment to sift through multitudes of flags is very important to the success of the system, which suggests that statistics alone cannot identify issues of public health importance from ED data.

Objective

This study's aim was to refine an automated biosurveillance system in order to better suit the daily monitoring capabilities and resources of a health department.

In 2007, the CDC BioSense System received data from 450 non-federal hospitals. Hospitals provide data to Biosense based on their capability and willingness to supply electronic data. As of July 2008, Biosense is receiving data from 550 hospitals. The National Hospital Ambulatory Medical Care Survey (NHAMCS) is an annual national probability sample survey of hospitals that collects data on U.S. emergency department (ED) visits.

Objective

To assess the representativeness of BioSense ED data by comparing it with the NHAMCS results.

Disease surveillance provides essential information for control and response planning1. Emergency Room (ER) syndromic surveillance data can help to identify changes in disease incidence and affected group thereby providing valuable additional time for public health interventions1. The current study explored the relationship between ER syndromic surveillance data and influenza notification to the Houston Department of Health and Human services (HDHHS).

In 2003, with the advent of SARS, the Ontario Ministry of Health and Long-Term Care (MOHLTC) released a document mandating the use of a clinical screening tool to detect patients at high risk for having a febrile respiratory illness (FRI), defined as a temperature of > 38ºC and a new or worsening cough or shortness of breath (1). The FRI screening tool is available in all Ontario Emergency Departments (ED), and is utilized in 86% of them (2). Any patient who meets all of the criteria is designated FRI positive, treated with droplet precautions and is instructed to wear a mask and undergo frequent hand-washing (1). The FRI screening tool was created as a response to the SARS outbreaks, and while it is used to identify any FRI, its sensitivity has not been documented. We attempt to determine the utility of FRI as a defining element of clinical influenza.

Objective

 (1) To determine if patients who are found to be positive for influenza or parainfluenza by culture or antigen detection are all detected by the Ontario Ministry of Health and Long-Term Care's Febrile Respiratory Illness (FRI) screening tool, and thereby treated with appropriate respiratory precautions to prevent spread. (2) To determine if syndromic surveillance or another clinical predictor would be a more effective screening tool than FRI.

Measures aimed at controlling epidemics of infectious diseases critically benefit from early outbreak recognition [1]. SSS seek early detection by focusing on pre-diagnostic symptoms that by themselves may not alarm clinicians. We have previously determined the performance of various Case Detector (CD) algorithms at finding cases of influenza-like illness (ILI) recorded in the electronic medical record of the Veterans Administration (VA) health system. In this work, we measure the impact of using CDs of increasing sensitivity but decreasing specificity on the time it takes a VA-based SSS to identify a modeled community-wide influenza outbreak. Objective This work uses a mathematical model of a plausible influenza epidemic to test the influence of different case-detection algorithms on the performance of a real-world syndromic surveillance system (SSS).

The Automated Hospital Emergency Department Data (AHEDD) System was designed to detect early indicators of bioterrorism and naturally occurring health risks. Initial development includes real-time data collection from four pilot hospitals, an automated syndromic surveillance application, and the capability of raw data analysis for further investigation and follow-up. This automated system frees hospital and State staff from manual reporting and analysis; and has a broad application for Public Health, collecting both chief complaint and diagnosis codes. As the project expands we plan to add the remaining 22 acute care hospitals; include poisoning, asthma, and injury surveillance; and assess electronic disease reporting from diagnosis codes and data linkage with other public health data stores, such as Environmental Health Tracking, and pre-hospital data.

Objective

This paper describes the use of technology to create an automated, real-time surveillance system with the capacity for early detection and alerting of potential health threats, and the capability to facilitate prompt investigation and increased efficiency for both New Hampshire hospital and the Division of Public Health Service resources.

Wetter and stormier weather is predicted in the UK as global temperatures rise. It is likely there will be increases in river and coastal flooding. The known short and medium term health effects of flooding are drowning, injury, acute asthma, skin rashes and outbreaks of gastrointestinal and respiratory disease. Longer term health effects of flooding are thought to be psychological stress and increased rates of mental illness. Reacher et al. conducted a retrospective study of illness in a population affected by flooding in Lewes, South-East England during 2000 [1]. They found a significant raised risk of earache (RR=2.2) and gastroenteritis (RR=1.7) for flooded households. More striking was the higher level of psychological distress experienced by these residents (RR=4.1), which may have also explained some of the excess physical illness.

Objective

This paper describes the results of prospective real time syndromic surveillance conducted during a national flooding incident during 2007 in the UK.

Prehospital  EMS  data  is  rarely  mentioned  in  discus-sions  surrounding  syndromic  surveillance  for  covert  bio-terrorism  attacks  or  for  the  monitoring  of  syn-dromic  illness  such  as  bird  flu.    However,  EMS  dis-patch data may serve as the very first marker in such an event.  EMS dispatch data has many useful advan-tages  in  syndromic  surveillance.    These  include  the  ability to monitor across wide areas of geography and a  single  data  collection  source.    Additionally,  EMS  dispatchers  are  a  medically  trained  core  group  of  in-dividuals that use a single standardized set of interro-gation  questions  and  methods  with  specific  dispatch  codes  regarding  patient  conditions.    This  data  would  arguably be a more reliable source of data than mul-tiple  different  inputs  from  multiple  individuals  at  various clinics and hospitals emergency departments.  EMS  data  is  also  able  to  look  at  a  much  broader  group  of  individuals  both  by  volume  of  calls  and  by  geography,  since  they  are  instantaneously  able  to  capture  the  location  of  the  callers  when  dialing  911. EMS  dispatch  is  also  able  to  monitor  patient  move-ment to different accepting facilities.

Objective

This paper describes how the surveillance of actual EMS real time events occurring during normal operations were analyzed using a syndromic surveillance system and how these events can be used as surrogate markers for how a bio-surveillance system would act if an actual covert or overt terrorist event or pandemic illness were to occur