Laboratory Data

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.

In the last decade, time series analysis has become one of the most important tools of surveillance systems. Understanding the nature of temporal fluctuations is essential for successful development of outbreak detection algorithms, aberration assessment, and to control for seasonal variations. Typically, in applying the time series methods to health outcomes collected over an extended period of time it is assumed that population profiles remain constant. In practice, such assumptions have been rarely tested. At best, the temporal analysis is performed using stratification by age or other discriminating factors if heterogeneity is suspected. Any community can experience population changes in various forms. Long-term trends of inflow/outflow migration and rapid transient fluctuations associated with specific events are typical examples of changes in population profile. Seasonality, as an intrinsic property of infectious diseases manifestation in a community, is typically attributed to periodic changes in transmissibility of pathogens. To some extent, seasonal fluctuations in the incidence of infectious diseases could also be associated with the changes in population profiles. The ability to detect and describe such changes would provide valuable clues into seasonally changing factors associated with an infection.

Objective

The objective of this communication is two-fold: 1) to introduce an analytical approach for assessing temporal changes in the surveillance reporting with respect to population profile; and 2) to demonstrate the utility of this method using laboratory-confirmed cases for four reportable enteric infections (cryptosporidiosis, giardiasis, shigellosis, and salmonellosis) recorded by the Massachusetts Department of Public Health over the last 12 years. This new approach for assessing seasonal changes is based on comparison of gender-specific single-year age distributions, which constitute population profiles.

Capital Health is a regional health care organization, which provides services for over one million inhabitants in the Edmonton area of Alberta, Canada. Traditionally, disease surveillance under its jurisdiction has been paper-based and records maintained by different departments in several locations. Before the Alberta Real Time Syndromic Surveillance Net (ARTSSN), there was no centralized database or unified approach to surveillance and automated reporting despite rich electronic health data in the region. The existing labor-intensive manual surveillance process is inefficient and inherently susceptible to human error. Its effectiveness is sub-optimal in detecting outbreaks of emerging infectious diseases, and clusters of injuries or toxic exposures. The ultimate objective of ARTSSN is to enhance public health surveillance through earlier and more sensitive detection of clusters and trends, with subsequent tracking and response through an integrated, automated surveillance and reporting system.

Objective

ARTSSN is a pilot public health surveillance project developed for the Capital Health region of Alberta, Canada and funded by Alberta Health and Wellness. This paper describes the advantages of using ARTSSN and comparing information derived from multiple electronic data sources simultaneously for real time syndromic surveillance.

Effective public health response to emerging infectious diseases, natural disasters, and bioterrorism requires access to real-time, accurate information on disease patterns and healthcare utilization. The ESSENCE surveillance system in use by the Department of Defense (DoD) relies primarily on outpatient clinical impression diagnosis, which accurately characterize broad disease syndromes but may not be sufficient for monitoring specific diseases. DoD outpatient military treatment facilities perform nearly 500,000 microbiology laboratory tests annually. Initiated electronically, the ordered test is recorded immediately; most provide specific results in 24 to 72 hours and may prove useful for monitoring population health. Although a syndrome classification has been developed for laboratory tests, the classification cannot be applied directly to the DoD data and no previous study has validated the use of automated laboratory test orders for syndromic surveillance.

Objective

To evaluate the association between military microbiology laboratory test orders and infectious disease patterns.

Electronic laboratory-based surveillance can significantly improve the diagnostic specificity and response time of traditional infectious disease surveillance. Under the project “Models of Infectious Disease Agent Study”, we wished to evaluate the application of space-time outbreak detection algorithms utilizing SaTScan to a national database of routinely collected microbiology laboratory data.

Objective

This paper describes the application of the WHONET software integrated with SaTScan to the detection of Shigella outbreaks in a national database using a space-time cluster detection algorithm in simulated real-time and comparison of findings to outbreaks reported to the Ministry of Health.

To evaluate whether joint monitoring of laboratory test orders and outpatient visits improves the sensitivity and timeliness of alerting during seasonal increases in respiratory illness.

To determine the feasibility of using BioSense laboratory data to do surveillance on Clostridium difficile infection (CDI) and calculate overall and facility rates of disease.

The ability to provide real time syndromic surveillance throughout the Capital Health Region is currently undeveloped. There are limited mechanisms for routine real time surveillance of disease or conditions of public health interest, e.g. communicable diseases, toxic exposure or injury. Toxic exposure and injury while preventable are not notifiable in Alberta and as a consequence there is no real-time surveillance system to identify burden of disease or opportunities for intervention. The notifiable disease system is reliant on paper-based forms which are slow, prone to human error, and labor intensive to convert to electronic database format for flexible analysis and interpretation. Finally there is no system to link the data collected on the same individual in each database without compromising confidentiality. ARTSSN is designed to remedy these deficiencies.

Objective

In this presentation we describe the creation of an IT architecture and infrastructure to integrate data from four sources to support real-time syndromic surveillance for injuries, toxic exposures and notifiable diseases in Capital Health, Alberta, Canada.

The objective of this project was to develop an efficient method to map Logical Observation Identifier Names and Codes (LOINC) to notifiable conditions to accommodate future changes in standard laboratory test codes.