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Powell M.R.,Office of Risk Assessment and Cost Benefit Analysis
Risk Analysis | Year: 2013

A recent paper by Ferrier and Buzby provides a framework for selecting the sample size when testing a lot of beef trim for Escherichia coli O157:H7 that equates the averted costs of recalls and health damages from contaminated meats sold to consumers with the increased costs of testing while allowing for uncertainty about the underlying prevalence of contamination. Ferrier and Buzby conclude that the optimal sample size is larger than the current sample size. However, Ferrier and Buzby's optimization model has a number of errors, and their simulations failed to consider available evidence about the likelihood of the scenarios explored under the model. After correctly modeling microbial prevalence as dependent on portion size and selecting model inputs based on available evidence, the model suggests that the optimal sample size is zero under most plausible scenarios. It does not follow, however, that sampling beef trim for E. coli O157:H7, or food safety sampling more generally, should be abandoned. Sampling is not generally cost effective as a direct consumer safety control measure due to the extremely large sample sizes required to provide a high degree of confidence of detecting very low acceptable defect levels. Food safety verification sampling creates economic incentives for food producing firms to develop, implement, and maintain effective control measures that limit the probability and degree of noncompliance with regulatory limits or private contract specifications. Published 2013. This article is a U.S. government work and is in the public domain for the USA. Source


Powell M.R.,Office of Risk Assessment and Cost Benefit Analysis
Risk Analysis | Year: 2013

Since the 1997 EC - Hormones decision, World Trade Organization (WTO) Dispute Settlement Panels have wrestled with the question of what constitutes a negligible risk under the Sanitary and Phytosanitary Agreement. More recently, the 2010 WTO Australia - Apples Panel focused considerable attention on the appropriate quantitative model for a negligible probability in a risk assessment. The 2006 Australian Import Risk Analysis for Apples from New Zealand translated narrative probability statements into quantitative ranges. The uncertainty about a "negligible" probability was characterized as a uniform distribution with a minimum value of zero and a maximum value of 10-6. The Australia - Apples Panel found that the use of this distribution would tend to overestimate the likelihood of "negligible" events and indicated that a triangular distribution with a most probable value of zero and a maximum value of 10-6 would correct the bias. The Panel observed that the midpoint of the uniform distribution is 5 × 10-7 but did not consider that the triangular distribution has an expected value of 3.3 × 10-7. Therefore, if this triangular distribution is the appropriate correction, the magnitude of the bias found by the Panel appears modest. The Panel's detailed critique of the Australian risk assessment, and the conclusions of the WTO Appellate Body about the materiality of flaws found by the Panel, may have important implications for the standard of review for risk assessments under the WTO SPS Agreement. © 2012 Society for Risk Analysis. Source


Powell M.R.,Office of Risk Assessment and Cost Benefit Analysis
Risk Analysis | Year: 2014

Much of the literature regarding food safety sampling plans implicitly assumes that all lots entering commerce are tested. In practice, however, only a fraction of lots may be tested due to a budget constraint. In such a case, there is a tradeoff between the number of lots tested and the number of samples per lot. To illustrate this tradeoff, a simple model is presented in which the optimal number of samples per lot depends on the prevalence of sample units that do not conform to microbiological specifications and the relative costs of sampling a lot and of drawing and testing a sample unit from a lot. The assumed objective is to maximize the number of nonconforming lots that are rejected subject to a food safety sampling budget constraint. If the ratio of the cost per lot to the cost per sample unit is substantial, the optimal number of samples per lot increases as prevalence decreases. However, if the ratio of the cost per lot to the cost per sample unit is sufficiently small, the optimal number of samples per lot reduces to one (i.e., simple random sampling), regardless of prevalence. In practice, the cost per sample unit may be large relative to the cost per lot due to the expense of laboratory testing and other factors. Designing effective compliance assurance measures depends on economic, legal, and other factors in addition to microbiology and statistics. © 2013 Society for Risk Analysis Published 2013. This article is a U.S. Government work and is in the public domain for the U.S.A. Source


Sampling plans are specified by the Codex Alimentarius Commission microbiological criteria for Listeria monocytogenes in ready-to-eat foods. This case study evaluates the direct food safety impact of the Codex sampling plans as estimated by the FAO/WHO web-based microbiological sampling plan analysis tool under different assumptions about the pathogen distribution, test procedures, and the fraction of lots tested. The case study uses L. monocytogenes concentration data available for deli-type salads to empirically illustrate application of the sampling tool. The results indicate that the estimated impact of the sampling plans is dependent on the partitioning of total observed variance into its within- and between-lot components. The presence-absence based sampling plan is relatively insensitive to the substantial uncertainty and variability of the sensitivity of the reference method for detection of L. monocytogenes. The analytical sample size for enumeration impacts the ability of the concentration-based sampling plan to discriminate between compliant and non-compliant lots. Reducing the frequency of lot testing dramatically changes the statistical properties of the sampling schemes. Skip-lot sampling places greater importance on compliance assurance than on the direct, curative impact of lot acceptance sampling. Source

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