Quantitative risk assessment for Escherichia coli O157:H7 in ground beef hamburgers

Quantitative Risk Assessment (QRA) is a methodology used to organize and analyze scientific information to estimate the probability and severity of an adverse event. Applied to microbial food safety, the methodology can also help to identify those stages in the manufacture, distribution, handling, a...

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Veröffentlicht in:International journal of food microbiology 1998-05, Vol.41 (1), p.21-44
Hauptverfasser: Cassin, Michael H., Lammerding, Anna M., Todd, Ewen C.D., Ross, William, McColl, R.Stephen
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container_end_page 44
container_issue 1
container_start_page 21
container_title International journal of food microbiology
container_volume 41
creator Cassin, Michael H.
Lammerding, Anna M.
Todd, Ewen C.D.
Ross, William
McColl, R.Stephen
description Quantitative Risk Assessment (QRA) is a methodology used to organize and analyze scientific information to estimate the probability and severity of an adverse event. Applied to microbial food safety, the methodology can also help to identify those stages in the manufacture, distribution, handling, and consumption of foods that contribute to an increased risk of foodborne illness, and help focus resources and efforts to most effectively reduce the risk of foodborne pathogens. The term Process Risk Model (PRM) is introduced in this paper to describe the integration and application of QRA methodology with scenario analysis and predictive microbiology to provide an objective assessment of the hygienic characteristics of a manufacturing process. The methodology was applied to model the human health risk associated with Escherichia coli O157:H7 in ground beef hamburgers. The PRM incorporated two mathematical submodels; the first was intended to described the behaviour of the pathogen from the production of the food through processing, handling, and consumption to predict human exposure. The exposure estimate was then used as input to a dose–response model to estimate the health risk associated with consuming food from the process. Monte Carlo simulation was used to assess the effect of the uncertainty and variability in the model parameters on the predicted human health risk. The model predicted a probability of Hemolytic Uremic Syndrome of 3.7×10 −6 and a probability of mortality of 1.9×10 −7 per meal for the very young. These estimates are likely high for all hamburger meals, but may be reasonable for the home-prepared hamburgers described by this model. The efficacy of three risk mitigation strategies were evaluated by modifying the values of the predictive factors and comparing the new predicted risk. The average probability of illness was predicted to be reduced by 80% under a hypothetical mitigation strategy directed at reducing microbial growth during retail storage through a reduction in storage temperature. This strategy was predicted to be more effective than a hypothetical intervention which estimated a plausible reduction in the concentration of E. coli O157:H7 in the feces of cattle shedding the pathogen and one aimed at convincing consumers to cook hamburgers more thoroughly. The conclusions of this approach are only accurate to the extent that the model accurately represents the process. Currently, uncertainty and ignorance about the hygienic effe
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Applied to microbial food safety, the methodology can also help to identify those stages in the manufacture, distribution, handling, and consumption of foods that contribute to an increased risk of foodborne illness, and help focus resources and efforts to most effectively reduce the risk of foodborne pathogens. The term Process Risk Model (PRM) is introduced in this paper to describe the integration and application of QRA methodology with scenario analysis and predictive microbiology to provide an objective assessment of the hygienic characteristics of a manufacturing process. The methodology was applied to model the human health risk associated with Escherichia coli O157:H7 in ground beef hamburgers. The PRM incorporated two mathematical submodels; the first was intended to described the behaviour of the pathogen from the production of the food through processing, handling, and consumption to predict human exposure. 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Living conditions ; Escherichia coli ; Escherichia coli Infections - epidemiology ; Escherichia coli Infections - prevention &amp; control ; Escherichia coli O157 - growth &amp; development ; Escherichia coli O157 - pathogenicity ; Feces - microbiology ; Food Handling - methods ; Food Handling - standards ; Food industries ; Food Microbiology ; FOOD SAFETY ; Food-Processing Industry - standards ; Foodborne Diseases - epidemiology ; Foodborne Diseases - prevention &amp; control ; FORECASTING ; Fundamental and applied biological sciences. Psychology ; Ground beef ; Hamburger ; Hemolytic-Uremic Syndrome - epidemiology ; Hemolytic-Uremic Syndrome - mortality ; Hot Temperature ; Humans ; INNOCUITE DES PRODUITS ALIMENTAIRES ; INOCUIDAD ALIMENTARIA ; Meat - microbiology ; Meat - standards ; Meat and meat product industries ; Medical sciences ; MODELE ; MODELOS ; Models, Biological ; Monte Carlo Method ; Monte Carlo simulation ; Predictive microbiology ; Prevalence ; Probabilistic modelling ; Probability ; Public health. Hygiene ; Public health. 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Applied to microbial food safety, the methodology can also help to identify those stages in the manufacture, distribution, handling, and consumption of foods that contribute to an increased risk of foodborne illness, and help focus resources and efforts to most effectively reduce the risk of foodborne pathogens. The term Process Risk Model (PRM) is introduced in this paper to describe the integration and application of QRA methodology with scenario analysis and predictive microbiology to provide an objective assessment of the hygienic characteristics of a manufacturing process. The methodology was applied to model the human health risk associated with Escherichia coli O157:H7 in ground beef hamburgers. The PRM incorporated two mathematical submodels; the first was intended to described the behaviour of the pathogen from the production of the food through processing, handling, and consumption to predict human exposure. The exposure estimate was then used as input to a dose–response model to estimate the health risk associated with consuming food from the process. Monte Carlo simulation was used to assess the effect of the uncertainty and variability in the model parameters on the predicted human health risk. The model predicted a probability of Hemolytic Uremic Syndrome of 3.7×10 −6 and a probability of mortality of 1.9×10 −7 per meal for the very young. These estimates are likely high for all hamburger meals, but may be reasonable for the home-prepared hamburgers described by this model. The efficacy of three risk mitigation strategies were evaluated by modifying the values of the predictive factors and comparing the new predicted risk. The average probability of illness was predicted to be reduced by 80% under a hypothetical mitigation strategy directed at reducing microbial growth during retail storage through a reduction in storage temperature. This strategy was predicted to be more effective than a hypothetical intervention which estimated a plausible reduction in the concentration of E. coli O157:H7 in the feces of cattle shedding the pathogen and one aimed at convincing consumers to cook hamburgers more thoroughly. The conclusions of this approach are only accurate to the extent that the model accurately represents the process. Currently, uncertainty and ignorance about the hygienic effects of the individual operations during production, processing, and handling limit the applicability of a PRM to specify HACCP criteria in a quantitative manner. However, with continuous improvement through stimulated research, a PRM should encompass all available information about the process, food, and pathogen and should be the most appropriate decision-support tool since it represents current knowledge.</description><subject>Air. Soil. Water. Waste. Feeding</subject><subject>ANALISIS CUANTITATIVO</subject><subject>ANALYSE QUANTITATIVE</subject><subject>Animals</subject><subject>BEEF</subject><subject>Biological and medical sciences</subject><subject>CARNE DE RES</subject><subject>Cattle</subject><subject>Colony Count, Microbial</subject><subject>Computer Simulation</subject><subject>E. coli O157:H7</subject><subject>Environment. Living conditions</subject><subject>Escherichia coli</subject><subject>Escherichia coli Infections - epidemiology</subject><subject>Escherichia coli Infections - prevention &amp; control</subject><subject>Escherichia coli O157 - growth &amp; development</subject><subject>Escherichia coli O157 - pathogenicity</subject><subject>Feces - microbiology</subject><subject>Food Handling - methods</subject><subject>Food Handling - standards</subject><subject>Food industries</subject><subject>Food Microbiology</subject><subject>FOOD SAFETY</subject><subject>Food-Processing Industry - standards</subject><subject>Foodborne Diseases - epidemiology</subject><subject>Foodborne Diseases - prevention &amp; control</subject><subject>FORECASTING</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Ground beef</subject><subject>Hamburger</subject><subject>Hemolytic-Uremic Syndrome - epidemiology</subject><subject>Hemolytic-Uremic Syndrome - mortality</subject><subject>Hot Temperature</subject><subject>Humans</subject><subject>INNOCUITE DES PRODUITS ALIMENTAIRES</subject><subject>INOCUIDAD ALIMENTARIA</subject><subject>Meat - microbiology</subject><subject>Meat - standards</subject><subject>Meat and meat product industries</subject><subject>Medical sciences</subject><subject>MODELE</subject><subject>MODELOS</subject><subject>Models, Biological</subject><subject>Monte Carlo Method</subject><subject>Monte Carlo simulation</subject><subject>Predictive microbiology</subject><subject>Prevalence</subject><subject>Probabilistic modelling</subject><subject>Probability</subject><subject>Public health. Hygiene</subject><subject>Public health. Hygiene-occupational medicine</subject><subject>QUANTITATIVE ANALYSIS</subject><subject>Quantitative risk assessment</subject><subject>RIESGO</subject><subject>RISK</subject><subject>Risk analysis</subject><subject>Risk Assessment</subject><subject>RISQUE</subject><subject>TECHNIQUE DE PREVISION</subject><subject>TECNICAS DE PREDICCION</subject><subject>VIANDE BOVINE</subject><issn>0168-1605</issn><issn>1879-3460</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1998</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkUtvFDEQhC0ECpvAT4jkA0LkMOD32FwQigJBWhGhwNnyeHp2DfMI7plI_Hu82dVyDBf7UF-Xy12EnHP2ljNu3t2Ww1bcMP3G2QvGmLCVeEJW3Nauksqwp2R1RJ6TU8SfBdJSshNy4ozkUuoVuf22hHFOc5jTPdCc8BcNiIA4wDjTbsr0CuMWcorbFGic-kRvuK7fX9c0jXSTp2VsaQPQ0W0YmiVvIOML8qwLPcLLw31Gfny6-n55Xa1vPn-5_LiuotZmrrgGbbgxygpVR1M7baXoWihxbWtZwzqhTFTBga3rhgUrHXcMbPmoMTU4eUZe733v8vR7AZz9kDBC34cRpgW9cJZradl_gFoaLfWjIDdalXSqgHoPxjwhZuj8XU5DyH88Z35Xj3-ox-927531D_V4UebODw8szQDtcerQR9FfHfSAMfRdDmNMeMSE0LWQ7p9NFyYfNqU2_3XNnbOMKcV3Nh_2OpT13yfIHmOCMUKbMsTZt1N6JOhf0j2xjA</recordid><startdate>19980505</startdate><enddate>19980505</enddate><creator>Cassin, Michael H.</creator><creator>Lammerding, Anna M.</creator><creator>Todd, Ewen C.D.</creator><creator>Ross, William</creator><creator>McColl, R.Stephen</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>FBQ</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7T2</scope><scope>7T7</scope><scope>7U1</scope><scope>7U2</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>7TB</scope><scope>F28</scope></search><sort><creationdate>19980505</creationdate><title>Quantitative risk assessment for Escherichia coli O157:H7 in ground beef hamburgers</title><author>Cassin, Michael H. ; Lammerding, Anna M. ; Todd, Ewen C.D. ; Ross, William ; McColl, R.Stephen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c556t-15e5616648247c6795832fde6058d80b0f246c4a9e877b0a839190e8028667e93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Air. Soil. Water. Waste. Feeding</topic><topic>ANALISIS CUANTITATIVO</topic><topic>ANALYSE QUANTITATIVE</topic><topic>Animals</topic><topic>BEEF</topic><topic>Biological and medical sciences</topic><topic>CARNE DE RES</topic><topic>Cattle</topic><topic>Colony Count, Microbial</topic><topic>Computer Simulation</topic><topic>E. coli O157:H7</topic><topic>Environment. Living conditions</topic><topic>Escherichia coli</topic><topic>Escherichia coli Infections - epidemiology</topic><topic>Escherichia coli Infections - prevention &amp; control</topic><topic>Escherichia coli O157 - growth &amp; development</topic><topic>Escherichia coli O157 - pathogenicity</topic><topic>Feces - microbiology</topic><topic>Food Handling - methods</topic><topic>Food Handling - standards</topic><topic>Food industries</topic><topic>Food Microbiology</topic><topic>FOOD SAFETY</topic><topic>Food-Processing Industry - standards</topic><topic>Foodborne Diseases - epidemiology</topic><topic>Foodborne Diseases - prevention &amp; control</topic><topic>FORECASTING</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Ground beef</topic><topic>Hamburger</topic><topic>Hemolytic-Uremic Syndrome - epidemiology</topic><topic>Hemolytic-Uremic Syndrome - mortality</topic><topic>Hot Temperature</topic><topic>Humans</topic><topic>INNOCUITE DES PRODUITS ALIMENTAIRES</topic><topic>INOCUIDAD ALIMENTARIA</topic><topic>Meat - microbiology</topic><topic>Meat - standards</topic><topic>Meat and meat product industries</topic><topic>Medical sciences</topic><topic>MODELE</topic><topic>MODELOS</topic><topic>Models, Biological</topic><topic>Monte Carlo Method</topic><topic>Monte Carlo simulation</topic><topic>Predictive microbiology</topic><topic>Prevalence</topic><topic>Probabilistic modelling</topic><topic>Probability</topic><topic>Public health. Hygiene</topic><topic>Public health. 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Applied to microbial food safety, the methodology can also help to identify those stages in the manufacture, distribution, handling, and consumption of foods that contribute to an increased risk of foodborne illness, and help focus resources and efforts to most effectively reduce the risk of foodborne pathogens. The term Process Risk Model (PRM) is introduced in this paper to describe the integration and application of QRA methodology with scenario analysis and predictive microbiology to provide an objective assessment of the hygienic characteristics of a manufacturing process. The methodology was applied to model the human health risk associated with Escherichia coli O157:H7 in ground beef hamburgers. The PRM incorporated two mathematical submodels; the first was intended to described the behaviour of the pathogen from the production of the food through processing, handling, and consumption to predict human exposure. The exposure estimate was then used as input to a dose–response model to estimate the health risk associated with consuming food from the process. Monte Carlo simulation was used to assess the effect of the uncertainty and variability in the model parameters on the predicted human health risk. The model predicted a probability of Hemolytic Uremic Syndrome of 3.7×10 −6 and a probability of mortality of 1.9×10 −7 per meal for the very young. These estimates are likely high for all hamburger meals, but may be reasonable for the home-prepared hamburgers described by this model. The efficacy of three risk mitigation strategies were evaluated by modifying the values of the predictive factors and comparing the new predicted risk. The average probability of illness was predicted to be reduced by 80% under a hypothetical mitigation strategy directed at reducing microbial growth during retail storage through a reduction in storage temperature. This strategy was predicted to be more effective than a hypothetical intervention which estimated a plausible reduction in the concentration of E. coli O157:H7 in the feces of cattle shedding the pathogen and one aimed at convincing consumers to cook hamburgers more thoroughly. The conclusions of this approach are only accurate to the extent that the model accurately represents the process. Currently, uncertainty and ignorance about the hygienic effects of the individual operations during production, processing, and handling limit the applicability of a PRM to specify HACCP criteria in a quantitative manner. However, with continuous improvement through stimulated research, a PRM should encompass all available information about the process, food, and pathogen and should be the most appropriate decision-support tool since it represents current knowledge.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><pmid>9631335</pmid><doi>10.1016/S0168-1605(98)00028-2</doi><tpages>24</tpages></addata></record>
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ispartof International journal of food microbiology, 1998-05, Vol.41 (1), p.21-44
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subjects Air. Soil. Water. Waste. Feeding
ANALISIS CUANTITATIVO
ANALYSE QUANTITATIVE
Animals
BEEF
Biological and medical sciences
CARNE DE RES
Cattle
Colony Count, Microbial
Computer Simulation
E. coli O157:H7
Environment. Living conditions
Escherichia coli
Escherichia coli Infections - epidemiology
Escherichia coli Infections - prevention & control
Escherichia coli O157 - growth & development
Escherichia coli O157 - pathogenicity
Feces - microbiology
Food Handling - methods
Food Handling - standards
Food industries
Food Microbiology
FOOD SAFETY
Food-Processing Industry - standards
Foodborne Diseases - epidemiology
Foodborne Diseases - prevention & control
FORECASTING
Fundamental and applied biological sciences. Psychology
Ground beef
Hamburger
Hemolytic-Uremic Syndrome - epidemiology
Hemolytic-Uremic Syndrome - mortality
Hot Temperature
Humans
INNOCUITE DES PRODUITS ALIMENTAIRES
INOCUIDAD ALIMENTARIA
Meat - microbiology
Meat - standards
Meat and meat product industries
Medical sciences
MODELE
MODELOS
Models, Biological
Monte Carlo Method
Monte Carlo simulation
Predictive microbiology
Prevalence
Probabilistic modelling
Probability
Public health. Hygiene
Public health. Hygiene-occupational medicine
QUANTITATIVE ANALYSIS
Quantitative risk assessment
RIESGO
RISK
Risk analysis
Risk Assessment
RISQUE
TECHNIQUE DE PREVISION
TECNICAS DE PREDICCION
VIANDE BOVINE
title Quantitative risk assessment for Escherichia coli O157:H7 in ground beef hamburgers
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