The application of advanced statistical methods to investigate the epidemiology and improve the surveillance of companion animal poisonings using data from a North American poison control call centre

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The American Society for the Prevention of Cruelty to Animals' (ASPCA) Animal Poison Control Center (APCC) operates currently a toxicology call centre that receives over 100,000 calls each year from across Canada and the United States. The APCC maintains a large database concerning animal cases involving pesticide, drug, plant, metal and other toxin exposures in a variety of species including companion animals like cats and dogs. Their database includes information concerning the characteristics of the animal involved (age, breed, and species), the type of caller (veterinarian or general public), clinical signs, probable organ systems involved, suspected toxin, and the geographical location and time of the call. Outbreaks of poisonings, such as those associated with contaminated pet food, have been documented and might be detected using this database for surveillance purposes with advanced statistical and epidemiological methods. Similarly, these data could offer a great deal of epidemiological information concerning regional and seasonal differences in the nature of poisoning events and the reporting of these events. This research program will conduct the following analytical studies using these data over the next five years to investigate the epidemiology of these poisoning events in cats and dogs, and the potential of these data for surveillance purposes:***1. Describe the temporal and spatial distribution of the rate of calls involving specific poisons, clinical signs, and organ systems for cats and dogs based on data reported from the general public and veterinary clinics.***2. Use multilevel statistical models designed to evaluate risk factors at different organizational levels to assess how factors including pet characteristics, geographical region, socio-economic factors, season, and source of data are associated with the rates of calls concerning specific poisons, clinical signs, and organ systems.***3. Use space, time, and space-time cluster detection methods to identify outbreaks of poisoning cases and high risk areas and times for specific toxins and for clinical signs or groups of clinical signs (i.e., syndromes). We will also assess the impact of statistically controlling for known regional and temporal factors on the data prior to running these analyses to understand the impact of removing systematic "noise" on the performance of these space-time cluster detection methods.***4. Investigate the ability of machine learning techniques and multivariate statistical approaches to identify syndromes that might predict the poison/toxin associated with a call when the source of the toxin is unclear.***These studies will improve our understanding of factors that influence poisonings in companion animals, identify communities and times to target specific information to prevent poisoning events, and identify the best approaches to use these and other data for disease surveillance.**