Abstract
BackgroundAbattoir 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. 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. We analyzed data collected at all provincially-inspected abattoirs in Ontario, Canada during 2001-2007. During these years 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. Negative binomial models with random intercepts for abattoir, to account for repeated measurements within abattoirs, were created. The relationships between partial carcass condemnation rates for pneumonia and nephritis with year, season, agricultural region, stock price, and abattoir processing capacity were explored. The utility of the spatial scan statistic for detecting clusters of high partial carcass condemnation rates in space, time, and space-time was investigated.ResultsNon-disease factors that were 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 timeframe 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 timeframes during which major respiratory disease outbreaks took place.ConclusionsThis study demonstrated that a number of abattoir-related factors require consideration when using abattoir data for quantitative disease surveillance. 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.
Highlights
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
Other partial condemnation categories that were most frequently listed by meat inspectors were heart adhesions, liver adhesions, cystic kidneys, pneumonic lungs, elbow/hock arthritis, and kidneys due to nephritis [see Additional file 1, Table S1]
When we examined partial condemnation rates for lung condemnations, we found that the rates of lung condemnations were much higher in 2001 compared with all subsequent years, and there was no notable trend in rates over the remainder of the study period (Figure 3)
Summary
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. We analyzed data collected at all provincially-inspected abattoirs in Ontario, Canada during 2001-2007 During these years 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. A major cause of these disease problems was attributed to the the same time as the emergence of PCVAD in 2004, an outbreak of a more severe form of porcine reproductive and respiratory virus (PRRSv) took place in southwestern Ontario [3] This outbreak was closely followed, in the summer of 2005, by the emergence of a triple reassortant subtype H3N2 of swine influenza type A virus (SIV), which swept through Ontario pig herds [4,5]. Disease outbreak detection requires sensitive and specific quantitative surveillance systems based on knowledge of all relevant predictors and their associations
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