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Margaret E Coleman

Publications and source records attributed to Margaret E Coleman.

4 recordsLinked to original sources
PubMed2006

Overview and summary of the Food Safety and Inspection Service risk assessment for Salmonella enteritidis in shell eggs, October 2005.

In 1998, the United States Department of Agriculture's Food Safety and Inspection Service (FSIS) and the Food and Drug Administration completed a risk assessment that indicated multiple interventions along the farm-to-table chain were needed to reduce the risk of human illness from Salmonella Enteritidis in shell eggs. Based on newly available data and improved modeling techniques, FSIS completed an updated risk assessment to examine the effect of pasteurization and refrigeration on reducing human illnesses from S. Enteritidis in shell eggs. The risk assessment model was written in Visual Basic for Applications (Microsoft, Redmond, WA) and run using Monte Carlo methods. The model estimated that if all shell eggs produced in the United States were pasteurized for a 3-log10 reduction of S. Enteritidis, the annual number of illnesses from S. Enteritidis in eggs would decrease from approximately 130,000 to 40,000. Pasteurization for a 5-log10 reduction of S. Enteritidis was estimated to reduce the annual number of illnesses to 19,000. The model also estimated that if all eggs produced in the United States were stored and held at 7.2 degrees C within 12 hours of lay, the annual number of illnesses from S. Enteritidis in eggs would decrease from 130,000 to 28,000. As a result, rapid cooling and pasteurization of shell eggs were predicted to be highly effective mitigations for reducing illnesses from consumption of S. Enteritidis in shell eggs.

Animals↗
PubMed2003

Impact of microbial ecology of meat and poultry products on predictions from exposure assessment scenarios for refrigerated storage.

A novel extension of traditional growth models for exposure assessment of food-borne microbial pathogens was developed to address the complex interactions of competing microbial populations in foods. Scenarios were designed for baseline refrigeration and mild abuse of servings of chicken broiler and ground beef Our approach employed high-quality data for microbiology of foods at production, refrigerated storage temperatures, and growth kinetics of microbial populations in culture media. Simple parallel models were developed for exponential growth of multiple pathogens and the abundant and ubiquitous nonpathogenic indigenous microbiota. Monte Carlo simulations were run for unconstrained growth and growth with the density-dependent constraint based on the "Jameson effect," inhibition of pathogen growth when the indigenous microbiota reached 10(9) counts per serving. The modes for unconstrained growth of the indigenous microbiota were 10(8), 10(10), and 10(11) counts per serving for chicken broilers, and 10(7), 10(9) and 10(11) counts per serving for ground beef at respective sites for backroom, meat case, and home refrigeration. Contamination rates and likelihoods of reaching temperatures supporting growth of the pathogens in the baseline refrigeration scenario were rare events. The unconstrained exponential growth models appeared to overestimate L. monocytogenes growth maxima for the baseline refrigeration scenario by 1500-7233% (10(6)-10(7) counts/serving) when the inhibitory effects of the indigenous microbiota are ignored. The extreme tails of the distributions for the constrained models appeared to overestimate growth maxima 110% (10(4)-10(5) counts/serving) for Salmonella spp. and 108% (6 x 10(3) counts/serving) for E. coli O157:H7 relative to the extremes of the unconstrained models. The approach of incorporating parallel models for pathogens and the indigenous microbiota into exposure assessment modeling motivates the design of validation studies to test the modeling assumptions, consistent with the analytical-deliberative process of risk analysis.

Animals↗
PubMedDate not supplied

Discerning strain effects in microbial dose-response data.

In order to estimate the risk or probability of adverse events in risk assessment, it is necessary to identify the important variables that contribute to the risk and provide descriptions of distributions of these variables for well-defined populations. One component of modeling dose response that can create uncertainty is the inherent genetic variability among pathogenic bacteria. For many microbial risk assessments, the "default" assumption used for dose response does not account for strain or serotype variability in pathogenicity and virulence, other than perhaps, recognizing the existence of avirulent strains. However, an examination of data sets from human clinical trials in which Salmonella spp. and Campylobacter jejuni strains were administered reveals significant strain differences. This article discusses the evidence for strain variability and concludes that more biologically based alternatives are necessary to replace the default assumptions commonly used in microbial risk assessment, specifically regarding strain variability.

Campylobacter Infections↗