Departamento de Medicina Veterinária Preventiva, Faculdade de Veterinária, Universidade Federal do Rio Grande do Sul, Brazil; Division of Epidemiology and Microbial Genomics, National Food Institute, Technical University of Denmark, Denmark.
Division of Epidemiology and Microbial Genomics, National Food Institute, Technical University of Denmark, Denmark.
Int J Food Microbiol. 2015 Jul 16;205:1-6. doi: 10.1016/j.ijfoodmicro.2015.03.023. Epub 2015 Apr 2.
In microbiological surveys, false negative results in detection tests precluding the enumeration by MPN may occur. The objective of this study was to illustrate the impact of screening test failure on the probability distribution of Salmonella concentrations in pork using a Bayesian method. A total of 276 swab samples in four slaughter steps (69 samples in each slaughter step: after dehairing, after singeing, after evisceration, and before chilling) were screened for Salmonella and enumerated by the MPN method. Salmonella contamination data were fitted to a lognormal distribution by using a Bayesian model that uses the number of positive tubes at each dilution in an MPN analysis to estimate the parameters of the concentration distribution. With Salmonella paired data, three data sets were used for each slaughter step: one that includes the positives in the screening test only, a second one that includes false negative results from the screening, and a third that considers the entire data set. The relative sensitivity of the screening test was also calculated assuming as gold standard samples with confirmed Salmonella. Salmonella was confirmed by a reference laboratory in 29 samples either by screening or MPN method. The relative sensitivity of the screening test was 69% (CI 95%: 52%-85%). The data set that included enumerations from screen-negative samples (false negative results) tended to have higher μ̂ and smaller σ̂ in comparison with the data set that discards false negative results, suggesting that the lack of sensitivity of the screening test affects the distribution that describes the contamination across the population. Numerous surveys on fitting distribution methods of microbial censored data have been published and discuss source of bias due to fitting method. Results of this survey contribute with that discussion by illustrating another possible source of bias due to failure of the screening methods preceding the MPN.
在微生物调查中，可能会出现检测试验出现假阴性结果，从而无法通过最大可能数（MPN）进行计数。本研究的目的是使用贝叶斯方法说明筛选试验失败对猪肉中沙门氏菌浓度概率分布的影响。对四个屠宰步骤中的 276 个拭子样本（每个屠宰步骤 69 个样本：去毛后、烧燎后、去内脏后和冷藏前）进行沙门氏菌筛选和 MPN 计数。使用贝叶斯模型对数正态分布进行拟合，该模型使用 MPN 分析中每个稀释度的阳性管数来估计浓度分布的参数。对于沙门氏菌配对数据，每个屠宰步骤使用三个数据集：一个仅包含筛选试验中的阳性结果，第二个包含筛选试验中的假阴性结果，第三个包含整个数据集。还假设筛选试验的假阳性和假阴性数据，以确认沙门氏菌，以此来计算筛选试验的相对灵敏度。通过参考实验室，在 29 个样本中确认了沙门氏菌，这些样本通过筛选或 MPN 方法进行确认。筛选试验的相对灵敏度为 69%（95%CI：52%-85%）。与排除假阴性结果的数据集相比，包含筛检阴性样本（假阴性结果）的计数数据集中的 μ̂较高，σ̂较小，这表明筛选试验的灵敏度不足会影响描述整个种群污染的分布。已经发表了许多关于拟合微生物有偏数据分布方法的调查，并讨论了拟合方法引起的偏倚来源。本调查的结果通过说明在 MPN 之前的筛选方法失败可能导致的另一个偏倚来源，为该讨论做出了贡献。
