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).
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).
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
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.
In the past year, three major health care organizations â the American Veterinary Medical Association, the American Medical Association and the Society for Tropical Veterinary Medicine â have officially endorsed the concept of âOne Healthâ 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.)
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.
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.
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.
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.