The monitoring and surveillance of emerging infectious and zoonotic diseases in food animals are important components of our food safety system. In recent years, emerging zoonotic diseases have been of increased concern to both public and animal health, following the emergence of H5N1 influenza and bovine spongiform encephalopathy (BSE) [1]. Consequently, researchers are turning their attention to novel approaches, such as syndromic surveillance, for detecting emerging diseases in food animals at various points along the farm-to-fork continuum [2, 3].

Abattoirs have played an important role in the surveillance of various diseases of human and animal health importance [4–6]. Surveillance at the abattoir allows for all animals passing into the human food chain to be examined for unusual signs, lesions or specific diseases. For instance, a study evaluating surveillance systems for bovine tuberculosis in Switzerland found that surveillance during meat inspection at the slaughterhouse had the highest sensitivity for identifying the disease compared to passive clinical surveillance of humans or cattle on farms [5]. A relatively new application of surveillance methods for animal health, food safety and public health is syndromic surveillance of food animals.

Syndromic surveillance is the grouping of large numbers of signs/symptoms and data regarding non-traditional sources of information. These groups of signs/symptoms are loosely designated as 'syndromes'. This information is then used to track disease trends in a population and signal putative outbreaks that warrant further investigation [7]. Syndromic surveillance has been primarily used in public health practice [8–10] and has had some success at the early detection of disease outbreaks in humans [11]. Recently, syndromic surveillance has been applied to animal health using data from farms, sales-yards, veterinary practitioners, and abattoir condemnation data [2, 3, 12]. Changes in the incidence of lesions at slaughter may provide important information for syndromic surveillance of diseases of animal, public health, and food safety significance.

The application of appropriate quantitative methods is important for any surveillance system. A variety of statistical methods has been developed and is frequently used for disease surveillance, including spatial, temporal and spatio-temporal methods [7]. In general, these methods use various statistical detection algorithms to analyze a continuous stream of data and raise an alarm when the count is significantly greater than expected, suggesting a possible disease outbreak [13]. Depending on the method used and the data collected, one might be able to identify the area and/or time of the disease outbreak. Although, these methods have been shown to be useful, surveillance systems are only as good as the data provided to the system. Consideration of the quality of the data and naturally occurring covariates need to be taken into account in the selection, application and interpretation of quantitative methods for disease surveillance.

Recent literature has suggested the need for model-based approaches for surveillance in order to include other variables into the specification of expected disease incidence [14]. For many diseases, the incidence may vary with biological factors such as sex and season. In addition, factors associated with the reporting of disease may also impact the apparent incidence of disease. For example, price of the commodity may be associated with the quality of animals being shipped to slaughter, which then in turn will affect the condemnation rate in abattoirs. Being able to account for these known factors prior to the application of cluster detection methods, may improve the sensitivity and specificity of a quantitative syndromic surveillance system [15, 16]. The quality of spatial data is also important to consider prior to the application of space or space-time cluster detection methods [17]. These issues may be particularly true for Ontario provincial abattoir data where factors, such as the capacity of an abattoir may be correlated with the quality of animals they receive, and the location of the abattoir can only approximately reflect the spatial location of an animal's farm of origin.

It was hypothesized that a variety of factors may be associated with the condemnation rates seen in abattoirs, and that these effects may vary in space and/or time. Abattoir characteristics, such as the number of weeks abattoirs processed animals and the number of animals processed, may be associated with condemnation rates, as the speed of processing may impact inspection. An abattoir's audit rating may reflect a plant's compliance to regulations and/or willingness to accept animals of poorer quality. Region of the abattoir may be associated with condemnation rates in provincial abattoirs due to regional differences in animal density and disease prevalence. Season and year may also be associated with condemnation rates as many diseases in animals are thought to have seasonal and secular variability. Animal class may be associated with condemnation rates, as older animals are generally at higher risk of disease. Economic factors, such as commodity price fluctuate greatly and may be associated with the quality of animals being sent to slaughter, as producers consider shipping costs against the possible return of shipping an animal of suspect health.

Consequently, the objectives of this study were to identify biological and non-biological factors that may be associated with abattoir condemnations and possibly influence cluster detection methods for quantitative syndromic surveillance systems. Specifically, provincially inspected abattoirs which slaughter cattle in Ontario were characterized in terms of number of abattoirs, the number of weeks abattoirs processed cattle, geographical distribution, types of condemnations reported, and distance animals are shipped to provincial abattoirs. Secondly, this study will determine how abattoir characteristics, season, year and commodity price, and animal class may be associated with whole carcass condemnation rates in provincial abattoirs. In addition, the suitability of Ontario provincial abattoir data for spatial and spatio-temporal analyses will be considered based on the results.

Provincial abattoir data are useful for surveillance because they can provide a more regionally specific picture of emerging diseases in Ontario. However, various biological and non-biological factors were found to have an effect on condemnation rates. Consequently, careful consideration should be given to how these factors may influence quantitative methods designed for outbreak detection. If these variables are ignored, quantitative methods designed to identify trends or disease clusters may not provide valid results.

Results from this study regarding the distance animals are shipped to slaughter indicated that the majority of cattle are shipped less than 100 km. Within the spatial scale of the province of Ontario, which is approximately one million square kilometres [21], we can conclude that cattle sent to provincial abattoirs come from relatively local farms. This result is important for the application of quantitative spatial surveillance methods, as the assumption that abattoir data reflects disease rates among locally slaughtered animals appears to be valid.

Seasonal effects were noted in the results of the multivariable analysis with summer and fall having lower condemnation rates compared to winter. This may be due to a change in the quality of animals being submitted over the year. Animals which are shipped during the winter may reflect those animals which grew slower due to certain health issues causing delayed market readiness, thus, resulting in more condemnations at slaughter during this season. More research on quality of animals and point in production cycle is needed to confirm if this trend reflects "poor-doers". It is important to identify seasonal trends before the application of quantitative surveillance methods, as many diseases have seasonal variability, and without correcting for this trend, any results from temporal or spatial-temporal quantitative methods will be biased.

Commodity class and year were found to be associated with condemnation rates in provincial abattoirs. It is suspected that the discovery of BSE in Alberta, Canada in May 2003 [22] had an impact on the patterns of condemnations in Ontario provincial abattoirs. Within hours of the confirmation of the first Canadian case, the United States (US) government announced an immediate ban of all imports of Canadian beef. On July 24, 2003, new processing regulations were implemented in Canadian abattoirs outlining that specified risk materials (SRMs), such as brain and spinal cord, must be removed from cattle older than 30 months. After 26 months, the US ban on Canadian cattle imports was lifted [23]. The effects of BSE in Canada, and subsequent changes to trade and processing regulations are mirrored in the descriptive data, which showed a decreasing trend over the study period in the number of abattoirs processing cattle, as well as overall condemnation rates and condemnation rates by animal class. The decreasing trend levels off in 2004 - 2006 where rates begin to increase again. Condemnation rates in cows showed the largest drop, coinciding with regulations implemented for cattle over 30 months [22]. These trends are also mirrored in the interaction effect between year and animal class seen in the multivariable model. Condemnation rates for cows ranged from approximately 3 to 8 times greater than calves throughout the study period, with cows during 2005 - 2006 having the most prominent decrease in condemnation rates in abattoirs compared to calves in 2001. Being older, cows are at higher risk for disease and thus are condemned more frequently. The decline in cow condemnation rates during this period occurred while the total number of cows being processed by provincial abattoirs increased. In the case of cows, the decline in the rate of condemnations most likely resulted from younger and healthier cows being shipped for slaughter to provincial abattoirs, which ship their products intra-provincially, rather than federal plants, which ship their products inter-provincially and internationally. Commodity class is an important factor, which must be accounted for in a food animal syndromic surveillance system. Older animals are not only more likely to be condemned due to an increased incidence of disease, but are more likely to be processed in certain abattoirs that specialize in this animal class. Consequently, accounting for animal class is important in determining the expected or baseline condemnation rates at an abattoir.

Economic factors, such as commodity sales price, also appear to play a role in condemnation rates, as we found that condemnation rates in cattle were higher when the sales price was above the yearly median. This may be due to a difference in the quality of animals being sent to slaughter depending on the sales price. If the price were too low, the cost of shipping animals of questionable quality may have exceeded the potential return for the producer. There was also an association found between condemnation rates and audit rating. Condemnation rates tended to be higher in C- rated abattoirs compared to those abattoirs with higher ratings. This may be due to certain abattoirs accepting a larger proportion of older or poorer quality cattle; however, there were only a small number of C-rated abattoirs during the study period. 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. These results demonstrate the need to consider not only biological factors, but also non-biological factors, which may be associated with condemnation rates.

This study did not find a statistical association between condemnation rates and factors dealing with abattoir throughput. However, it is also important to consider abattoir characteristics, such as the number of weeks and/or the number of animals processed at abattoirs. There was great variability in the number of weeks and the number of animals processed in Ontario provincial abattoirs, which may affect quantitative data analysis. For instance, a spatio-temporal surveillance method such as the spatio-temporal scan statistic using a space-time permutation model assumes the background population remains relatively stable over time [24]. This is not the case in Ontario provincial abattoir data, as only 19% of abattoirs consistently process cattle throughout the year. If the background population increases or decreases faster in certain areas, there is a risk of population shift bias, which can cause biased p-values [24]. This bias may cause abattoirs to be identified in a high rate cluster solely due to the irregular timing of processing at certain abattoirs, not due to a true disease outbreak. It may be more appropriate to select sentinel abattoirs from regions throughout Ontario, which have a more consistent and stable processing capacity throughout the year to more accurately capture disease clusters among abattoirs.

It is important to understand the factors affecting and even biasing abattoir surveillance data, before any quantitative methods can be chosen in the design of a surveillance system. This study identified and discussed the implications of biological and non-biological factors, which may affect quantitative surveillance methods. Once these factors are identified, appropriate adjustments can be made to the quantitative methods being used for outbreak detection. For instance, a study by Kleinman et al. [15] compared the performance of the space-time scan statistic using unadjusted data for lower respiratory complaints and model-adjusted data for day of week, month, holidays and local history of illness. The study found significantly lower false detection rates in the model-adjusted analysis compared to the unadjusted.

This study has identified various seasonal, secular, biological and non-biological factors that may be associated with the expected incidence of disease and indicates the potential importance of adjusting for these factors when applying quantitative methods for any disease surveillance system. Specifically, this study found that animal class, year, season, price, and audit rating impact condemnation rates from provincially inspected cattle abattoirs in Ontario. Similarly, the quality of spatial data should also be part of the assessment of potential biases that may arise from surveillance data prior to decisions concerning the application of spatial and/or spatial-temporal surveillance techniques.