title: Campylobacter QMRA: A Bayesian Estimation of Prevalence and Concentration in Retail Foods Under Clustering and Heavy Censoring.
abstract: A Bayesian statistical temporal-prevalence-concentration model (TPCM) was built to assess the prevalence and concentration of pathogenic campylobacter species in batches of fresh chicken and turkey meat at retail. The data set was collected from Finnish grocery stores in all the seasons of the year. Observations at low concentration levels are often censored due to the limit of determination of the microbiological methods. This model utilized the potential of Bayesian methods to borrow strength from related samples in order to perform under heavy censoring. In this extreme case the majority of the observed batch-specific concentrations was below the limit of determination. The hierarchical structure was included in the model in order to take into account the within-batch and between-batch variability, which may have a significant impact on the sample outcome depending on the sampling plan. Temporal changes in the prevalence of campylobacter were modeled using a Markovian time series. The proposed model is adaptable for other pathogens if the same type of data set is available. The computation of the model was performed using OpenBUGS software.
authors: Finnish Food Safety Authority Evira, Risk Assessment Research Unit, Helsinki, Finland.
authors: Antti
authors: A
authors: Mikkelä
authors: Finnish Food Safety Authority Evira, Risk Assessment Research Unit, Helsinki, Finland.
authors: Jukka
authors: J
authors: Ranta
authors: Finnish Food Safety Authority Evira, Risk Assessment Research Unit, Helsinki, Finland.
authors: Manuel
authors: M
authors: González
authors: Finnish Food Safety Authority Evira, Food and Feed Microbiology Research Unit, Helsinki, Finland.
authors: Marjaana
authors: M
authors: Hakkinen
authors: Finnish Food Safety Authority Evira, Risk Assessment Research Unit, Helsinki, Finland.
authors: Pirkko
authors: P
authors: Tuominen
keywords: Bayesian hierarchical modeling; campylobacter; clustering; heavy censoring; quantitative microbiological risk assessment (QMRA)