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This variable was modelled on a logarithmic scale for the lung condemnation model in order to linearize the relationship with the outcome.
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On the other hand, condemnation of the model is unjust if it is based on possibly inadequate implementation of the model.
"This is not a condemnation of the model itself but rather its implementation," said Ed Barton, director of digital media at the consultancy Strategy Analytics.
For the model-adjusted scan statistic, standardized morbidity ratios were used in the space-time scan statistic based on the observed number of condemnations and the model predicted counts.
The model-adjusted approach using the ratio of observed versus expected condemnations under the Poisson model would be more appropriate when utilizing relatively rare events, as in the case of abattoir condemnation data.
The unrated abattoirs were unlikely to have influenced the model since the condemnation rates for abattoirs in this category were not significantly different from abattoirs with other ratings.
However, when the event is rare, as in the present example with condemnation rates, the precision of the residuals using the normal model is unstable and may give inaccurate results when applied to the space-time scan statistic [ 20].
In terms of secular changes, results from our multi-level models using lung condemnation data predicted a decline across the study period in all regions.
The variables year, animal class, season and annual audit rating were found to be significantly associated with condemnation rates in both portion models as well as whole carcasses.
It is not surprising that season and animal class were found to be significant factors associated with abattoir condemnation rates in all three models, as many animal diseases tend to have a distinct seasonality and high risk age groups associated with the disease.
To account for concerns regarding biasing factors that may be underlying the spatial clusters of high rates of kidney and lung condemnations, the space-time permutation model adjusts for purely spatial and temporal clusters that might exist in the data [ 39].
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