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Food and Waterborne Pathogen Risk Ranking Models: From Theory to Practice

Workshop

FDA/CFSAN Wiley Building Auditorium,
5100 Paint Branch Parkway, College Park, MD 20740
August 18, 2005

Agenda

Opening Session

Model Presentations

Conveners: Tanya Roberts, USDA/ERS and Marianne Miliotis, FDA/CFSAN

Panel discussion

Panel Chairs: Angela Ritzert, FDA/CFSAN and Uday Dessai, USDA/FSIS

Model Demonstrations and Hands-on Experience

Conveners: Steven Schaub, EPA/OW and Wesley Long, FDA/CFSAN

Speakers

Michael Batz

Bio

Michael Batz is a Research Associate at Resources for the Future. His research interests include the application of computer modeling methods to environmental and human health issues. Specific interests include creating risk analysis tools for foodborne disease, valuing human health impacts from environmental risks, applying game theoretic and empirical modeling to spatial land-use decisions such as farming and conservation, and improving regulatory decision making through risk analysis and the quantitative treatment of uncertainty. Batz received a B.Sc. in Electrical and Computer Engineering and Engineering and Public Policy, as well as an M.Sc. in Electrical and Computer Engineering, from Carnegie Mellon University.

Abstract - Resources for the Future

It is the goal of the Food Safety Research Consortium (FSRC) to move towards a food safety system built on science- and risk-based decision making. It is our goal to provide policy makers and stakeholders with the analytical tools necessary in such a system. As a first step, researchers at the University of Maryland School of Medicine (primarily, J. Glenn Morris Jr.) and Resources for the Future (primarily, Michael Taylor, Michael Batz, Alan Krupnick, Sandra Hoffmann) developed the Foodborne Illness Risk Ranking Model (FIRRM) to identify, quantify, and compare the public health impact of the most important microbiological food hazards.

A "top down" Monte Carlo simulation model based on epidemiological approaches and data, FIRRM focuses on 28 foodborne pathogens and their pathways across a comprehensive range of food categories, and produces rankings of food-pathogen combinations. There are three modules in the model: 1) estimation of incidence of each pathogen based on public health surveillance data; 2) economic costs and loss of Quality Adjusted Life Years (QALYs) due to the symptoms, severities, medical treatments (physician visits, hospitalization), fatalities, recovery periods, and chronic sequelae associated with pathogen-specific illnesses; and 3) the attribution of illnesses from pathogens to food-pathogen combinations, based on outbreak data, expert elicitation, risk assessments, case-control studies, and other data. Resulting food-pathogen combinations can be ranked by five measures of public health impact: cases, hospitalizations, deaths, economic costs, and QALYs. Valuation is currently completed for only four pathogens (Salmonella, Campylobacter, E. coli O157:H7, and Listeria monocytogenes), although estimates for remaining FoodNet pathogens, Norovirus, and Toxoplasma are currently underway. The most critical data gap is in food attribution: outbreak data has problems that make it insufficient as a solitary source, and so we attempted to gather expert judgment and other data as alternatives and supplements. These data show large uncertainties in which foods are associated with illness.

Although current results from FIRRM are only preliminary, we are updating many data sources, addressing uncertainty issues attempting to fill data gaps, and developing a web interface. The second version of FIRRM should be completed in October 2006. The model was developed in Analytica, which has a graphical point-and-click user interface; it is likely not "easy to use" for new users, but the eventual web interface should simplify interaction and allow users to organize and save results. The second draft should provide robust rankings useful for broad policy decision-making, such as resource allocation decisions. It will likely only provide partial answers, however, as we still expect to encounter data quality issues.

Donald W. Schaffner, Ph.D.

Bio

Dr. Schaffner is Extension Specialist in Food Science and Professor at Rutgers, The State University of New Jersey.

His research interests include quantitative microbial risk assessment and predictive food microbiology. Dr. Schaffner has authored more than 100 peer-reviewed publications, book chapters and abstracts. He has educated thousands of Food Industry professionals through numerous short courses and workshops in the United States and more than a dozen countries around the world.

Dr. Schaffner has served on expert committees for US National Academy of Sciences, the World Health Organization and Food and Agriculture Organization of the United Nations, and has chaired two expert workshops on microbial risk for WHO/FAO. He is currently a member of Institute of Food Technologists Expert Panel developing a quantitative risk ranking framework for the Food and Drug Administration.

Dr. Schaffner is currently serving a 5 year term as Editor for the journal Applied and Environmental Microbiology. In May 2005 he was also appointed to serve on the National Advisory Committee on Microbial Criteria for Foods (NACMCF).

Dr. Schaffner is active in several scientific associations including the International Association for Food Protection, the Institute of Food Technologists, the Society for Risk Analysis, and the American Society for Microbiology. He holds a B.S. in Food Science from Cornell University and a M.S. and Ph.D. in Food Science and Technology from the University of Georgia.

Abstract - Rutgers University

The IFT/FDA risk ranking framework or model was developed by an expert panel, coordinated by the Institute of Food Technologists.  The panel was composed of experts in microbial and chemical risk assessment, as well as industry and academic food scientists with expertise in microbiology and toxicology.  The funding for the project was primarily provided by the FDA, with funding for some of the modeling provided by the Rutgers Food Risk Analysis Initiative and IFT.  The primary purpose of the model is to provide the FDA with a flexible tool for ranking the relative risk of chemical and microbial hazards (primarily, but not exclusively those that are unintentional added) for all food products regulated by the agency.  The framework has been implemented on two separate and distinct platforms.  An internet browser accessible version was developed to permit storage of data in a central repository, and to allow a potentially large number of users to add data and generate reports.  An Analytica version of the model has also been developed to allow interested users to see a detailed explanation of the inner working of the model and calculations not possible over the web.  The model allows the user to input data by hazard (linking hazards to certain commodities) or by commodity (linking commodities to certain hazards).  Once a particular food and hazard are identified, the user then estimates the effect of various stages (primary production, processing, distribution and end user handling) on the prevalence and concentration of the risk.  The framework uses a concept called the Pseudo-DALY which allows the user to select from a variety of templates that aggregate impact based on duration and severity, or design their own template.  The framework allows for the selection of a variety of dose response models needed to capture the impact of microbial as well as chemical hazards and includes places for the user to document the rational and reference for any dose response used.  NHANES data are used to estimate consumption/exposure.  The framework allows the user to select from 4 different population groups (entire US, females 16-49 yrs, children 1-6 yrs and the elderly 65+ yrs).  The model generates reports which allow for grouping risk by hazard or food, sorting by rank or name, and the exclusion of foods hazard or combinations that are still in development.

David Kendall

Bio

David Kendall is Professor of Economics and Finance at the University of Virginia’s College at Wise. His 26-year professional career includes university teaching, research, and administration. Before returning to academia in 2004, he was a research economist with RTI International’s Food and Nutrition Policy and Consumer Behaviors research program. Throughout his career he has specialized in economic analysis of federal regulations, statistical analysis, simulation modeling, and primary data collection methods—including survey research, in-depth interviewing, and expert elicitation. Dr. Kendall has developed a variety of simulation models for use in economic impact analysis of federal regulations, including the Food Handling Practices Model.

Elaine Scallan

Bio

Elaine Scallan is a Senior Epidemiologist with the Foodborne Diseases Active Surveillance Network (FoodNet), Foodborne and Diarrheal Diseases Branch, Centers for Disease Control and Prevention. Her main areas of focus are determining the burden of foodborne diseases and attributing foodborne illness to specific foods. Prior to arriving at CDC, Elaine worked with the Food Safety Authority of Ireland. She has a PhD in Epidemiology from the Department of Public Health Medicine and Epidemiology, University College Dublin, Ireland.

Abstract - CDC Approach to Food Attribution

The Foodborne and Diarrheal Diseases Branch (FDDB), Division of Bacterial and Mycotic Diseases (DBMD), National Center for Infectious Diseases, has provided national estimates on the human health burden of acute foodborne diseases. More recently, FDDB has been working towards analyzing information currently available to gain better information on the proportion of human illnesses that can be attributed to specific foods.

Foodborne illnesses may be attributed to the contamination of foods at any point from consumption (‘the fork) to the pathogen reservoir (‘the farm’). While thousands of individual foods have been associated with human illness, FDDB is working on several attribution projects that will estimate the proportion of illness due to foods prepared with major food commodities such as dairy products, eggs, and fish.  To facilitate discussion of attribution, it is useful to consider three different but overlapping approaches and data sources for attribution analyses:

(1) Information concerning food vehicles identified in outbreak investigations and in case-control studies of sporadic infections can be used in an analysis we call “point-of-consumption” attribution

(2) Information concerning the prevalence and characteristics of pathogens detected in food processing sources can be compared with similar information on pathogens isolated from ill persons in an analysis we call “point-of-processing” attribution

(3) Information concerning the prevalence and characteristics of pathogens detected in foods before harvest can be compared with similar information on pathogens isolated from ill persons in an analysis we call “pre-harvest” attribution. 

Each of these approaches may be used to help estimate the attribution of human illness to major food commodities. We view these three approaches as complimentary; each is useful for informing public health policy.

John Painter

Bio

Jois a medical epidemiologist with the CDC Foodborne and Diarrheal Disease Branch.  He had his medical training in veterinary medicine and public health training through the CDC Epidemiology Intelligence Service.  He specializes in foodborne outbreak surveillance and response.  In addition, he is responsible for the CDC's Cholera and Other Vibrio Surveillance System.

Abstract - CDC Approach to Food Attribution

The Foodborne and Diarrheal Diseases Branch (FDDB), Division of Bacterial and Mycotic Diseases (DBMD), National Center for Infectious Diseases, has provided national estimates on the human health burden of acute foodborne diseases. More recently, FDDB has been working towards analyzing information currently available to gain better information on the proportion of human illnesses that can be attributed to specific foods.

Foodborne illnesses may be attributed to the contamination of foods at any point from consumption (‘the fork) to the pathogen reservoir (‘the farm’). While thousands of individual foods have been associated with human illness, FDDB is working on several attribution projects that will estimate the proportion of illness due to foods prepared with major food commodities such as dairy products, eggs, and fish.  To facilitate discussion of attribution, it is useful to consider three different but overlapping approaches and data sources for attribution analyses:

(1) Information concerning food vehicles identified in outbreak investigations and in case-control studies of sporadic infections can be used in an analysis we call “point-of-consumption” attribution

(2) Information concerning the prevalence and characteristics of pathogens detected in food processing sources can be compared with similar information on pathogens isolated from ill persons in an analysis we call “point-of-processing” attribution

(3) Information concerning the prevalence and characteristics of pathogens detected in foods before harvest can be compared with similar information on pathogens isolated from ill persons in an analysis we call “pre-harvest” attribution. 

Each of these approaches may be used to help estimate the attribution of human illness to major food commodities. We view these three approaches as complimentary; each is useful for informing public health policy.

Andrew M. Jaine

Bio

Andrew M. Jaine is co-founder and Chief Technology Officer for BTSafety, LLC, a privately-held technology company that develops advanced software systems designed to help government and industry to enhance rapid and effective response to food incidents. Dr. Jaine received his B.S. in Physics and Mathematics and his Ph.D. in Theoretical Physics from the University of Reading, England. In his career that spans more than 30 years of experience in all facets of technology and information systems, Dr. Jaine has led the development of a range of computer technology products, including computer programming languages, operating systems and online banking systems, and has helped build a business from start-up through its public offering. As computer systems gained in power and sophistication, he focused his career on harnessing that power to develop computer-based systems that simplify and automate complex processes, and has developed advanced “expert systems” across a wide range of domains, including advertising planning, life insurance underwriting, banking, personal wellness, medical triage, safety, health and the environment. For the last four years Dr. Jaine has focused primarily in the area of food protection.

Abstract - The BT Safety Consequence Management System

The Foodborne and Diarrheal Diseases Branch (FDDB), Division of Bacterial and Mycotic Diseases (DBMD), National Center for Infectious Diseases, has provided national estimates on the human health burden of acute foodborne diseases. More recently, FDDB has been working towards analyzing information currently available to gain better information on the proportion of human illnesses that can be attributed to specific foods.

Foodborne illnesses may be attributed to the contamination of foods at any point from consumption (‘the fork) to the pathogen reservoir (‘the farm’). While thousands of individual foods have been associated with human illness, FDDB is working on several attribution projects that will estimate the proportion of illness due to foods prepared with major food commodities such as dairy products, eggs, and fish.  To facilitate discussion of attribution, it is useful to consider three different but overlapping approaches and data sources for attribution analyses:

(1) Information concerning food vehicles identified in outbreak investigations and in case-control studies of sporadic infections can be used in an analysis we call “point-of-consumption” attribution

(2) Information concerning the prevalence and characteristics of pathogens detected in food processing sources can be compared with similar information on pathogens isolated from ill persons in an analysis we call “point-of-processing” attribution

(3) Information concerning the prevalence and characteristics of pathogens detected in foods before harvest can be compared with similar information on pathogens isolated from ill persons in an analysis we call “pre-harvest” attribution. 

Each of these approaches may be used to help estimate the attribution of human illness to major food commodities. We view these three approaches as complimentary; each is useful for informing public health policy.