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

利用北美中毒控制呼叫中心的数据，应用先进的统计方法来调查流行病学并改进伴侣动物中毒的监测

• 批准号: RGPIN-2016-04386
• 负责人: Pearl, David
• 金额: $ 2.55万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=763607
• 关键词: application; advanced; statistical; methods; investigate

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.

美国防止虐待动物协会(ASPCA)动物毒物控制中心(APCC)目前运营着一个毒理学呼叫中心，每年接到来自加拿大和美国各地的10万多个电话。APCC维护着一个大型数据库，涉及各种物种中涉及杀虫剂、药物、植物、金属和其他毒素暴露的动物病例，包括猫和狗等同伴动物。他们的数据库包括有关涉及的动物的特征(年龄、品种和物种)、呼叫者的类型(兽医或普通公众)、临床症状、可能涉及的器官系统、疑似毒素以及呼叫者的地理位置和时间的信息。中毒的暴发，例如与受污染的宠物食品有关的中毒暴发已被记录在案，并可利用该数据库通过先进的统计和流行病学方法进行监测。同样，这些数据可以提供大量关于中毒事件性质的区域和季节差异以及这些事件的报告的流行病学信息。这项研究计划将在未来五年利用这些数据进行以下分析研究，以调查猫和狗的这些中毒事件的流行病学，以及这些数据用于监测目的的可能性：1.根据普通公众和兽医诊所报告的数据，描述猫和狗涉及特定毒药、临床症状和器官系统的呼叫率的时空分布。使用旨在评估不同组织级别的风险因素的多级别统计模型，评估宠物特征、地理区域、社会经济因素、季节和数据来源等因素与特定毒物、临床症状和器官系统的呼叫率之间的关系。利用空间、时间和时空聚类检测方法，识别中毒病例的暴发和特定毒素的高危区域和时间，以及临床体征或临床体征组(即证候)。在运行这些分析之前，我们还将评估对已知区域和时间因素的统计控制对数据的影响，以了解去除系统噪声对这些时空簇检测方法性能的影响。研究机器学习技术和多元统计方法在毒素来源不明的情况下识别可能预测与呼叫相关的毒物/毒素的症状的能力。这些研究将提高我们对影响同伴动物中毒的因素的理解，确定针对特定信息的社区和时间以防止中毒事件，并确定使用这些数据和其他数据进行疾病监测的最佳方法。