Abattoir data have the potential to provide information for geospatial disease surveillance applications, but the quality of the data and utility for detecting disease outbreaks is not well understood, according to Andrea L. Thomas-Bachli and colleagues at the University of Guelph in Canada.

In a paper published recently in BMC Veterinary Research, they explain that the objectives of this study were to 1) identify non-disease factors that may bias these data for disease surveillance and 2) determine if major disease events that took place during the study period would be captured using multi-level modelling and scan statistics.

The researchers analysed data collected at all provincially-inspected abattoirs in Ontario, Canada during 2001-2007. Over this period, there were outbreaks of porcine circovirus-associated disease (PCVAD), porcine reproductive and respiratory syndrome (PRRS) and swine influenza that produced widespread disease within the province.

They created negative binomial models with random intercepts for abattoir, to account for repeated measurements within abattoirs.

The relationships between partial carcass condemnation rates for pneumonia and nephritis with year, season, agricultural region, stock price and abattoir processing capacity were explored.

Finally, the Guelph group also investigated the utility of the spatial scan statistic for detecting clusters of high partial carcass condemnation rates in space, time and space-time.

Non-disease factors that they found to be associated with lung and kidney condemnation rates included abattoir processing capacity, agricultural region and season. Yearly trends in predicted condemnation rates varied by agricultural region, and temporal patterns were different for both types of condemnations. Some clusters of high condemnation rates of kidneys with nephritis in time and space-time preceded the time-frame during which case clusters were detected using traditional laboratory data.

Yearly kidney condemnation rates related to nephritis lesions in eastern Ontario were most consistent with the trends that were expected in relation to the documented disease outbreaks. Yearly lung condemnation rates did not correspond with the time-frames during which major respiratory disease outbreaks took place.

This study demonstrated that a number of abattoir-related factors require consideration when using abattoir data for quantitative disease surveillance, concluded Thomas-Bachli and colleagues. Data pertaining to lungs condemned for pneumonia did not provide useful information for predicting disease events, while partial carcass condemnations of nephritis were most consistent with expected trends. Techniques that adjust for non-disease factors should be considered when applying cluster detection methods to abattoir data.

Reference

Thomas-Bachli A.L., D.L. Pearl, R.M. Friendship and O.Berk. 2012. Suitability and limitations of portion-specific abattoir data as part of an early warning system for emerging diseases of swine in Ontario. BMC Veterinary Research, 8:3. doi:10.1186/1746-6148-8-3

Further Reading

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You can view the full report (as a provisional PDF) by clicking here.

Further Reading

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Find out more information on the diseases mentioned in this article by clicking here.

February 2012