A Bayesian approach to the evaluation of risk-based microbiological criteria for Campylobacter in broiler meat

Jukka Ranta, Roland Lindqvist, Ingrid Hansson, Pirkko Tuominen, Maarten Nauta

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Shifting from traditional hazard-based food safety management toward risk-based management requires statistical methods for evaluating intermediate targets in food production, such as microbiological criteria (MC), in terms of their effects on human risk of illness. A fully risk-based evaluation of MC involves several uncertainties that are related to both the underlying Quantitative Microbiological Risk Assessment (QMRA) model and the production-specific sample data on the prevalence and concentrations of microbes in production batches. We used Bayesian modeling for statistical inference and evidence synthesis of two sample data sets. Thus, parameter uncertainty was represented by a joint posterior distribution, which we then used to predict the risk and to evaluate the criteria for acceptance of production batches. We also applied the Bayesian model to compare alternative criteria, accounting for the statistical uncertainty of parameters, conditional on the data sets. Comparison of the posterior mean relative risk, $E(\mathit{RR}|\mathrm{data})=E(P(\mathrm{illness}|\mathrm{criterion is met})/P(\mathrm{illness})|\mathrm{data})$, and relative posterior risk, $\mathit{RPR}=P(\mathrm{illness}|\mathrm{data, criterion is met})/P(\mathrm{illness}|\mathrm{data})$, showed very similar results, but computing is more efficient for RPR. Based on the sample data, together with the QMRA model, one could achieve a relative risk of 0.4 by insisting that the default criterion be fulfilled for acceptance of each batch.
Original languageEnglish
JournalThe Annals of Applied Statistics
Volume9
Issue number3
Pages (from-to)1415-1432
Number of pages18
ISSN1932-6157
DOIs
Publication statusPublished - 2015

Keywords

  • Bayesian modeling
  • hierarchical models
  • evidence synthesis
  • uncertainty
  • OpenBUGS
  • 2D Monte Carlo
  • quantitative microbiological risk assessment
  • food safety
  • Campylobacter

Cite this

Ranta, Jukka ; Lindqvist, Roland ; Hansson, Ingrid ; Tuominen, Pirkko ; Nauta, Maarten. / A Bayesian approach to the evaluation of risk-based microbiological criteria for Campylobacter in broiler meat. In: The Annals of Applied Statistics. 2015 ; Vol. 9, No. 3. pp. 1415-1432.
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abstract = "Shifting from traditional hazard-based food safety management toward risk-based management requires statistical methods for evaluating intermediate targets in food production, such as microbiological criteria (MC), in terms of their effects on human risk of illness. A fully risk-based evaluation of MC involves several uncertainties that are related to both the underlying Quantitative Microbiological Risk Assessment (QMRA) model and the production-specific sample data on the prevalence and concentrations of microbes in production batches. We used Bayesian modeling for statistical inference and evidence synthesis of two sample data sets. Thus, parameter uncertainty was represented by a joint posterior distribution, which we then used to predict the risk and to evaluate the criteria for acceptance of production batches. We also applied the Bayesian model to compare alternative criteria, accounting for the statistical uncertainty of parameters, conditional on the data sets. Comparison of the posterior mean relative risk, $E(\mathit{RR}|\mathrm{data})=E(P(\mathrm{illness}|\mathrm{criterion is met})/P(\mathrm{illness})|\mathrm{data})$, and relative posterior risk, $\mathit{RPR}=P(\mathrm{illness}|\mathrm{data, criterion is met})/P(\mathrm{illness}|\mathrm{data})$, showed very similar results, but computing is more efficient for RPR. Based on the sample data, together with the QMRA model, one could achieve a relative risk of 0.4 by insisting that the default criterion be fulfilled for acceptance of each batch.",
keywords = "Bayesian modeling, hierarchical models, evidence synthesis, uncertainty, OpenBUGS, 2D Monte Carlo, quantitative microbiological risk assessment, food safety, Campylobacter",
author = "Jukka Ranta and Roland Lindqvist and Ingrid Hansson and Pirkko Tuominen and Maarten Nauta",
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A Bayesian approach to the evaluation of risk-based microbiological criteria for Campylobacter in broiler meat. / Ranta, Jukka; Lindqvist, Roland; Hansson, Ingrid; Tuominen, Pirkko; Nauta, Maarten.

In: The Annals of Applied Statistics, Vol. 9, No. 3, 2015, p. 1415-1432.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - A Bayesian approach to the evaluation of risk-based microbiological criteria for Campylobacter in broiler meat

AU - Ranta, Jukka

AU - Lindqvist, Roland

AU - Hansson, Ingrid

AU - Tuominen, Pirkko

AU - Nauta, Maarten

PY - 2015

Y1 - 2015

N2 - Shifting from traditional hazard-based food safety management toward risk-based management requires statistical methods for evaluating intermediate targets in food production, such as microbiological criteria (MC), in terms of their effects on human risk of illness. A fully risk-based evaluation of MC involves several uncertainties that are related to both the underlying Quantitative Microbiological Risk Assessment (QMRA) model and the production-specific sample data on the prevalence and concentrations of microbes in production batches. We used Bayesian modeling for statistical inference and evidence synthesis of two sample data sets. Thus, parameter uncertainty was represented by a joint posterior distribution, which we then used to predict the risk and to evaluate the criteria for acceptance of production batches. We also applied the Bayesian model to compare alternative criteria, accounting for the statistical uncertainty of parameters, conditional on the data sets. Comparison of the posterior mean relative risk, $E(\mathit{RR}|\mathrm{data})=E(P(\mathrm{illness}|\mathrm{criterion is met})/P(\mathrm{illness})|\mathrm{data})$, and relative posterior risk, $\mathit{RPR}=P(\mathrm{illness}|\mathrm{data, criterion is met})/P(\mathrm{illness}|\mathrm{data})$, showed very similar results, but computing is more efficient for RPR. Based on the sample data, together with the QMRA model, one could achieve a relative risk of 0.4 by insisting that the default criterion be fulfilled for acceptance of each batch.

AB - Shifting from traditional hazard-based food safety management toward risk-based management requires statistical methods for evaluating intermediate targets in food production, such as microbiological criteria (MC), in terms of their effects on human risk of illness. A fully risk-based evaluation of MC involves several uncertainties that are related to both the underlying Quantitative Microbiological Risk Assessment (QMRA) model and the production-specific sample data on the prevalence and concentrations of microbes in production batches. We used Bayesian modeling for statistical inference and evidence synthesis of two sample data sets. Thus, parameter uncertainty was represented by a joint posterior distribution, which we then used to predict the risk and to evaluate the criteria for acceptance of production batches. We also applied the Bayesian model to compare alternative criteria, accounting for the statistical uncertainty of parameters, conditional on the data sets. Comparison of the posterior mean relative risk, $E(\mathit{RR}|\mathrm{data})=E(P(\mathrm{illness}|\mathrm{criterion is met})/P(\mathrm{illness})|\mathrm{data})$, and relative posterior risk, $\mathit{RPR}=P(\mathrm{illness}|\mathrm{data, criterion is met})/P(\mathrm{illness}|\mathrm{data})$, showed very similar results, but computing is more efficient for RPR. Based on the sample data, together with the QMRA model, one could achieve a relative risk of 0.4 by insisting that the default criterion be fulfilled for acceptance of each batch.

KW - Bayesian modeling

KW - hierarchical models

KW - evidence synthesis

KW - uncertainty

KW - OpenBUGS

KW - 2D Monte Carlo

KW - quantitative microbiological risk assessment

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SN - 1932-6157

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