Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada.
BMC Vet Res. 2012 Jan 6;8:3. doi: 10.1186/1746-6148-8-3.
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. 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.
Non-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.
This 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.
屠宰场数据有可能为地理空间疾病监测应用提供信息，但数据的质量和用于检测疾病暴发的效用尚不清楚。本研究的目的是：1）确定可能使这些数据偏向疾病监测的非疾病因素，以及 2）确定在研究期间发生的重大疾病事件是否可以通过多级建模和扫描统计来捕捉。我们分析了 2001-2007 年期间在加拿大安大略省所有经过省级检查的屠宰场收集的数据。在这些年中，爆发了猪圆环病毒相关疾病（PCVAD）、猪繁殖与呼吸综合征（PRRS）和猪流感，导致该省广泛发生疾病。创建了带有屠宰场随机截距的负二项式模型，以解释屠宰场内的重复测量。探讨了肺炎和肾炎的部分屠体废弃率与年份、季节、农业区、股价和屠宰场加工能力之间的关系。研究了空间扫描统计在检测空间、时间和时空高部分屠体废弃率集群的效用。
与肺和肾废弃率相关的非疾病因素包括屠宰场加工能力、农业区和季节。预测废弃率的年度趋势因农业区而异，两种废弃类型的时间模式也不同。一些与肾炎有关的肾脏高废弃率集群在使用传统实验室数据检测到病例集群的时间段之前出现。与安大略省东部的肾炎病变相关的年度肾脏废弃率与记录的疾病暴发相关的预期趋势最为一致。与重大呼吸道疾病暴发发生的时间框架不对应的年度肺废弃率。
本研究表明，在使用屠宰场数据进行定量疾病监测时，需要考虑一些与屠宰场相关的因素。与肺炎相关的肺部废弃数据并未为预测疾病事件提供有用信息，而肾炎的部分屠体废弃率与预期趋势最为一致。在将聚类检测方法应用于屠宰场数据时，应考虑调整非疾病因素的技术。
