Veterinary

INDICATOR provides an open source platform for biosurveillance and outbreak detection. Data sources currently include emergency department, patient advisory nurse, outpatient clinic, and school absence activity We are currently working with the University of Illinois College of Veterinary Medicine and will include veterinary data so that animal and human health data can be analyzed together.

Objective

INDICATOR, an existing biosurveillance system, required an updated user interface to support more data sources and more robust reporting and data visualization.

Syndromic surveillance of livestock animals at points of concentration, such as livestock markets, has the potential to provide early detection of endemic, zoonotic, transboundary, environmental, and newly emerging animal diseases and to identify animal health trends. In the United States, inspectors at livestock auction markets routinely observe animals for clinical signs of disease, but do not usually document the number of cattle or clinical signs observed. The purpose of this pilot program was to demonstrate the benefit and feasibility of utilizing inspectors at livestock markets to record the total number of animals observed and the number displaying body system-associated clinical signs/syndromes (BSAS). This project is a Federal and State partnership between the United States Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS) Veterinary Services (VS) and the Texas Animal Health Commission (TAHC). The livestock market syndromic surveillance pilot project is part of a broader effort in VS to develop and monitor non-traditional animal health surveillance data streams. These data streams include clinical sign information from private veterinary practitioners, veterinary diagnostic laboratory test requests, and livestock slaughter facility condemnations.

Objective

To describe the design and implementation of a syndromic surveillance program in selected cattle markets in Texas, USA.

A review of the development of veterinary syndromic surveillance in 2011 indicated that the field was incipient, but fast growing. Many countries are starting to explore different sources of data for syndromic surveillance. Some of the data streams evaluated share similarities with those used in public health syndromic surveillance, such as clinical records and laboratory data. However, many unique animal data sources have arisen, such as abattoir and carcass collection data. We suggest there are three main challenges in the current development of animal syndromic surveillance: The lack of standards in disease classification; The development of statistical methods appropriate to deal with animal data; The creation of ready-to-use tools that employ these statistical methods.

Objective

To summarize the challenges in the development of syndromic surveillance tools in veterinary medicine, and describe the development of an R package to address some of the current gaps.

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) provides early event detection and public health situational awareness to hospital-based and public health users statewide. Authorized users are currently able to view data from emergency departments (n=110), the statewide poison control center, the statewide EMS data system, a regional wildlife center and pilot data from a college veterinary laboratory as well as select urgent care centers. While NC DETECT has over 200 registered users, there are public health officials, hospital clinicians and administrators who do not access the system on a regular basis, but still like to be kept abreast of syndromic trends in their jurisdictions. In order to accommodate this interest and reduce redundant data entry among active users, we developed a summary report that can be easily exported and distributed outside of NC DETECT.

Objective

This paper describes a user driven weekly syndromic report designed and developed to improve the efficiency of sharing syndromic information statewide.

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) serves public health users across NC at the local, regional and state levels, providing early event detection and situational awareness capabilities. At the state level, our primary users are in the General Communicable Disease Control Branch of the NC Division of Public Health. NC DETECT receives 10 different data feeds daily including emergency department visits, emergency medical service runs, poison center calls, veterinary laboratory test results, and wildlife treatment.

In order to fulfill our users’ needs with NC DETECT’s limited staff, business intelligence tools are utilized for the acquisition and processing of our multiple, disparate data sources as well as reporting our findings to our numerous end users. Business intelligence can be described as a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions.

Objective

We report here on how NC DETECT uses business intelligence tools to automate both data capture and reporting in order to run a comprehensive surveillance system with limited resources.

Identifying potential biases and confounders that may affect data quality is an important consideration when evaluating surveillance systems. Having the benefit of predictable temporal trends is a key requirement to improve upon the specificity of detecting outbreaks. Identification of factors that impact on the reliability of the temporal trends observed in the data may provide for the ability to improve the capability to identify aberrations in those trends. During a retrospective study of a dataset of microbiology orders from the veterinary teaching hospital at The Ohio State University for 2003 we noticed regular intervals when increases in the number of culture orders were not accompanied by proportional increases in the number of isolates. These instances appeared to occur at intervals that coincided with the clinical rotation of senior veterinary students within the hospital.

Objective

This paper reports on a potential confounder discovered during an investigation of microbiology orders in a veterinary teaching hospital as a possible data source for outbreak detection.

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.

Current veterinary surveillance systems may be ineffective for timely detection of outbreaks involving non-targeted disease. Earlier detection could enable quicker intervention that might prevent the spread of disease and limit lost revenue. Data sources, similar to those used for early outbreak surveillance in humans, may provide for earlier outbreak detection in animals. Veterinary diagnostic laboratories are a source of data that might be valuable to such efforts.

Objective

To study the value of data from veterinary diagnostic laboratories as an initial step in developing an early outbreak surveillance system for animals.

Animals continue to be recognized as a potential source of surveillance data for detecting emerging infectious diseases, bioterrorism preparedness, pandemic influenza preparedness, and detection of other zoonotic diseases. Detection of disease outbreaks in animals remains mostly dependent upon systems that are disease specific and not very timely. Most zoonotic disease outbreaks are detected only after they have spread to humans. The use of syndromic surveillance methods (outbreak surveillance using pre-diagnostic data) in animals is a possible solution to these limitations. The authors examine microbiology orders from a veterinary diagnostics laboratory (VDL) as a possible data source for early outbreak detection. They establish the species representation in the data, quantify the potential gain in timeliness, and use a CuSum method to study counts of microorganisms, animal species, and specimen collection sites as potential early indicators of disease outbreaks. The results indicate that VDL microbiology orders might be a useful source of data for a surveillance system designed to detect outbreaks of disease in animals earlier than traditional reporting systems.