A Minimum Data Set of Animal Health Laboratory Data to allow for Collation and Analysis across Jurisdictions for the Purpose of Surveillance

Abstract

A minimum data set consisting of 15 data elements originating from laboratory submissions and results was formulated by a national committee of epidemiologists in Canada for the purposes of disease reporting, disease detection and analysis. The data set consists of both data that are filled out on the submission form as well as the results of the laboratory testing. The elements in the data set are unique identifier, premises identification, date submitted, geographic location, species, farm type, group type, total population of tested species on the farm, number sick, number dead, test(s) performed, disease agent, test result, disease classification by submitter and final laboratory diagnosis. The data set was designed to be concise while allowing for domestic and international disease reporting, effective analysis, including geographic, temporal and prevalence outputs, and syndromic surveillance to enable disease detection. The selected data elements do not identify the producer as specific geographic and nominal information is not included in the data set. The data elements selected, thus, allow for voluntary collaboration and data sharing by avoiding issues associated with privacy legislation.

Systems Biology and Ratio-Based, Real-Time Disease Surveillance

“…This method revealed to be applicable for individualized decisions, that is, discrimination did not depend on sample size: even when only one subject was investigated, temporal changes were detected. Such feature distinguished this method from alternatives: other models have not addressed the minimal number of observations such data sets may possess (Kloeze et al., ). Other features, expected to be shown by Systems Biology methods, were demonstrated, such as (i) data inflections, (iii) dynamics, (iii) ‘functional data integrity’ and (iv) directionality.…”

“…The fact that most surveillance methods are not designed for early detection (Bravata et al., ) justifies the development of new methods. To that end, new systems could be designed to facilitate prompt responsiveness – a concept associated with both a minimal data set (the smallest data set that can support diagnosis and decisions) and a ‘critical response time’ (the minimal time required to deploy interventions, Rivas et al., ; Kloeze et al., ).…”

Integrating Novel Data Streams to Support Biosurveillance in Commercial Livestock Production Systems in Developed Countries: Challenges and Opportunities

“…Data standardization and consistency are also needed within diagnostic laboratories, including the collection, naming convention, and coding of data within LIMS systems. A working group of epidemiologists in Canada recently published a list of minimum data requirements to support disease surveillance using diagnostic laboratory submissions ( 98 ). These included the (1) unique laboratory submission identifier, (2) unique premises identifier, (3) sample submission date, (4) geographic location for the premises, (5) species tested, (6) main farm type, (7) production type of animals tested, (8) total population of the species tested on the farm, (9) total number sick, (10) total number dead, (11) diagnostic test(s) performed, (12) disease agent(s) screened, (13) test results, (14) syndromic classification, and (15) final diagnosis.…”

The Ontario Animal Health Network: enhancing disease surveillance and information sharing through integrative data sharing and management

“…However, laboratory submission forms frequently lack information that is considered necessary for disease detection, analysis, and reporting, such as history, geographic location, farm type, group type, and the number of animals that are at risk, sick, and dead. 7 Cases lacking premises identification numbers make it difficult to determine if multiple cases have been submitted from a single premises. When working with a small number of test results, the ability to group submissions by premises is particularly critical because multiple submissions from a single premises experiencing a disease outbreak can falsely be interpreted as a more widespread increase in disease.…”