Surveillance Systems

Regional poison control centers (RPC) receive calls about a variety of poisoning exposures. Callers’ symptoms may not otherwise enter traditional public health (PH) surveillance systems. I report a 16-week pilot study of a new tool to enable the RPC to analyze and integrate call data with the PH, to augment ongoing disease surveillance efforts.

Objective

A new tool allowing analysis of poison control center data and integration of that data into public health surveillance efforts is described.

The North Dakota Veterinary Diagnostic Laboratory (NDVDL) manages animal disease laboratory tests, results and diagnostic services using the software VetStar Animal Disease Diagnostic System (VADDS) (Advanced Technology Corporation, Ramsey, NJ). The North Dakota State Board of Animal Health with the Department of Agriculture, in collaboration with the North Dakota Department of Health (NDDoH), has developed an electronic laboratory reporting system using data streams exported from the VADDS system for statewide animal health and public health surveillance.

Objective

 To describe the North Dakota Electronic Animal Health Surveillance System and data analysis using the CDC EARS V4r5.

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.

Objective

To demonstrate how natural language processing (NLP) of clinical records can contribute to case detection and characterization in biosurveillance.

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.

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.

In the aftermath of September 11th, 2001, the potential for subsequent bioterrorism attacks and more recently, the increased awareness of the threat of Avian flu and other communicable diseases, has compelled the Montana healthcare community to mobilize its diagnostic resources for detecting the presence of toxins or infectious biologic agents at the earliest possible moment. This state-wide, pilot initiative integrates disparate Emergency Room data, making patients’ symptoms and diagnoses available for biosurveillance and achieves interoperability among Montana’s emergency facilities.

Objective

This oral presentation describes a multi-agency and multi-center medical data integration system for syndromic surveillance in the State of Montana. This is a significant public health benefit given the recent threats of bio-terrorism and potential viral epidemics, including Bird-Flu.

Methicillin resistant staphylococcus aureus (MRSA) is a leading cause of skin and soft tissue infections (SSTI). Until recently, S. aureus pneumonia has been considered primarily a nosocomial infection, and was reported infrequently as a cause of severe community-acquired pneumonia. In recent years, there have been several reports of MRSA community-acquired pneumonia cases associated with influenza among healthy individuals resulting in hospitalization or death. During the 2007-08 influenza season, the WA DOH received reports of necrotizing staphylococcus pneumonia associated with flu-like illness and confirmed flu; these included severe cases of pneumonia caused by MRSA. We examined data from our biosurveillance system to describe trends in staphylococcus infection among ED patients and patients hospitalized with pneumonia or influenza in King County, WA.

Objective

We used our biosurveillance system to describe trends in emergency department visits for SSTI as well as staphylococcus pneumonia hospitalization trends.

Drug-related deaths have increased over the past decade throughout the United States. In New York City (NYC), every year there are approximately 900 psychoactive drug-related fatalities with the majority involving opioids. Unintentional drug overdose is the fourth leading cause of early adult death in NYC, and high rates of drug-related morbidity among drug users are evidenced by over 30,000 drug mentions in NYC emergency departments each year. Moreover, nonfatal overdose may be common among chronic drug users. Despite the relationship between fatal and non-fatal overdose clusters and continued increases in drug-related morbidity and mortality, no regular surveillance system currently exists. The implementation of a drug-related early warning system can inform and target a comprehensive public health response addressing the significant health problem of overdose morbidity and mortality.

Objective

This presentation describes how multiple syndromic data sources from emergency medical services ambulance dispatches and emergency department visits can be combined to routinely monitor citywide spatial patterns of adverse drug events and drug morbidity. This information can be used to target information, treatment and prevention services to drug “hotspots,” to provide early warning for drug-related morbidity, and to detect potential increased risk for overdose death.