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 , 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  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 . 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 . 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 . 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 . 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.  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.