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Syndromic Surveillance

Description

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

Submitted by elamb on
Description

Sixty-one percent of known disease-causing agents that infect humans can also infect animals [1]. While humans are the primary reservoir for only 3% of zoonoses, detection of zoonotic disease outbreaks remains mostly dependant on the identification of human cases [2]. Very few of the diseases that are a threat to humans are reportable in pets. Over onethird of American households include at least one pet [3]. Pets can present with clinical signs of disease earlier than people after becoming infected at the same time [4]. Pets can also become infected first and act as a source of infection for humans [5]. Detection of an outbreak in pets may then provide for warning of an outbreak that could affect humans.

Objective

This paper describes occurrences of possible co-morbidity in pets and humans discovered in a retrospective study of veterinary microbiology records and through the application of syndromic surveillance methods in a prospective outbreak detection system using veterinary laboratory orders.

Submitted by elamb on
Description

In the past year, three major health care organizations – the American Veterinary Medical Association, the American Medical Association and the Society for Tropical Veterinary Medicine – have officially endorsed the concept of “One Health” recognizing the continuum of communicable infectious disease from humans to animals and animals to humans. Further, there is widespread recognition that continuous robust surveillance of animals is beneficial not only to animal health but to food safety for humans and for early warning of naturally-occurring novel diseases (all of significance have been zoonotic for the past 20 years in the US and elsewhere) and for detecting bioterrorism events (with only one exception, all human bioterrorism agents are animal diseases.)

Submitted by elamb on
Description

NC BEIPS is a system designed and developed by the NC Division of Public Health (DPH) for early detection of disease and bioterrorism outbreaks or events. It analyzes emergency department (ED) data on a daily basis from 33 (29%) EDs in North Carolina. With a new mandate requiring the submission of ED data to DPH, NC BEIPS will soon have data from all 114 EDs. NC BEIPS also receives data on a daily basis from the Carolinas Poison Center, the Prehospital Medical Information System and the Piedmont Wildlife Center, although syndromic surveillance output from these data sources is still in testing.

Objective

 This paper describes the North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS). NC BEIPS is the syndromic surveillance arm of NC PHIN.

Submitted by elamb on
Description

The Texas Department of State Health Services (DSHS) Health Service Region 8 (HSR 8) encompasses 28 counties in South Central Texas. Of these, 5 counties are covered by a local health department syndromic surveillance system while the remaining counties fall under HSR 8 syndromic surveillance coverage. Of the 23 counties covered by HSR 8, 15 have hospitals with emergency departments. HSR 8 began receiving emergency department data from 3 hospitals for RedBat® syndromic surveillance monitoring in May of 2006. Four syndromes are monitored daily; Influenza-like Illness, Gastrointestinal Illness (GI), Rash-Illness, and Neurologic-Toxicologic Illness. Aberrations are detected by the Gustav algorithm using RedBat’s ‘Automatic Threshold Alert’ feature. The Gustav algorithm [patent pending], developed by ICPA, Inc., is an advanced variation of the cumulative sum method commonly used for aberration detection. The Gustav algorithm does not require an extended baseline level of illness and is very sensitive to small outbreaks; the algorithm also adjusts for weekly periodicity of medical visits.

Objective

This abstract describes the use of syndromic surveillance at a regional health department to detect an outbreak of norovirus in a nursing home facility.

Submitted by elamb on
Description

Syndromic surveillance has traditionally been used by public health in disease epidemiology. Partnerships between hospital-based and public health systems can improve efforts to monitor for disease clusters. Greenville Hospital System operates a syndromic surveillance system, which uses EARS-X to monitor chief complaint, lab, and radiological data for the four emergency departments within the hospital system. Combined, the emergency departments have approximately 145,000 visits per year. During March 2007 an increase in invasive group A Streptococcus (GAS) disease in the community lead to the use of syndromic surveillance to determine if there was a concomitant increase in Scarlet Fever within the community.

Objective

 Demonstrate the utility of collaboration between hospital-based and public health syndromic surveillance systems in disease investigation. Demonstrate the ability of syndromic surveillance in identification and evaluation of process improvements.

Submitted by elamb on
Description

Clinicians can pursue the clinical findings for specific patients until reaching a diagnosis in real time.  When using electronic ED complaints, one relies on symptoms volunteered by patients in the triage setting.  Patients seek emergency care at different stages of disease and there is scant information detailing how they respond when allowed only 2-3 complaints.  Our emergency department (ED) clinical data warehouse includes date, demographics, complaints, diagnosis, laboratory results, and disposition. We used a process similar to reverse engineering to augment our ability to detect chief complaints and test results consistent with MEE.  We started with the diagnosis of MEE and examined the chief complaints and diagnostic findings in patients diagnosed with MEE to develop expanded algorithms.

Objective

Our research questions were:

1.) could we use existing data to empirically improve our syndrome surveillance algorithms?

2.) Is it feasible to combine disparate data sources to detect the same event? We studied these questions using the meningoencephali-tis (MEE) syndrome and the West Nile Virus Chicago outbreak in 2002.

Submitted by elamb on
Description

NC DETECT receives data on at least a daily basis from five data sources: emergency departments (ED), the statewide poison center (CPC), the statewide EMS data collection system, a regional wildlife center and laboratories from the NC State College of Veterinary Medicine.  A Web portal is available to users at state, regional and local levels and provides syndromic surveillance reports as well as reports for broader public health surveillance such as injury, occupational health, and post-disaster.  The current portal is built on access controls initially designed in 2002 for hospital-based users only.  The role-based access was modified slightly in 2004 to accommodate public health epidemiologists (PHEs) at the local, regional and state levels who wanted county-based report access.  The design used, however, was shortsighted and limited.  For example, the controls cannot accommodate certain users’ access to non-ED data sources as well as the ability to retrieve protected health information (PHI) via the portal when needed for investigation.  These evolving user needs have led to a full system redesign with a much more robust security model.

Objective

This paper describes the role-based access used in the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) Web portal for early event detection and timely public health surveillance.

Submitted by elamb on
Description

This abstract describes Missouriís experience with syndromic surveillance. Missouri has expanded from acquiring pre-tabulated data from volunteers to receiving patient-level data via electronic feeds from 85 hospitals across the state processed through multiple analysis, visualization, and reporting tools. Missouri and its partners use these data for early event detection and situational awareness at the state and local levels.

Submitted by elamb on