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We wanted to compute a global distance between condemned cattle based on both demographic and condemnation data and to balance the influence of these two sets of variables on this computation.
Principal component method such as MFA allowed the definition of a distance between condemned cattle based on several sets of categorical variables (demographic and condemnation data) through the computation of the Euclidean distance from the individual principal coordinates from MFA.
By having a more specific outcome, it is hypothesized that portion condemnation data should be more sensitive than whole carcass data because inspectors condemn a carcass for one reason.
Condemnation data were available for several animal categories.
Portion condemnation data may be particularly useful, as these data can provide more specific information on health outcomes than whole carcass condemnation data.
It was unclear whether the same factors would also be significantly associated with condemnation data when applied to portion condemnation data.
Previous research has investigated the use of whole carcass condemnation data for syndromic surveillance [ 17].
Farm location information is not routinely recorded for provincial abattoir condemnation data.
Partial condemnation data pertaining to kidneys with nephritis did reflect the outbreaks more closely.
This study identified non-disease factors that may bias partial carcass condemnation data for cluster detection.
However, this method has not been applied to animal condemnation data for disease surveillance.
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