Risk Ranking Framework Prototype to Evaluate

Potential High Threat Agents

Don Schaffner, Ph.D.

Rutgers, The State University of New Jersey

Organizations responsible for model development

- This model was developed with funding from the FDA

- The contract was awarded to the Institute of Food Technologists

- IFT assembled an expert panel to do the work of model development

Scientific & Technical Expert Panel

- Frank F. Busta, Ph.D., • Panel Chair, Food

Microbiology

- Joseph Hotchkiss, Ph.D.• Toxicology

- Lee-Ann Jaykus, Ph.D.• Virology, Risk Assessment

- Greg Paoli, M.A.S.• Risk assessment

- Donald Schaffner, Ph.D.• Predictive microbiology, risk

assessment

- Bruce Tompkin, Ph.D.• Food microbiology, Food

processing

- Mary Wagner, Ph.D.• Foodservice, Food

Processing

- Todd Ruthman, B.A.Sc.• Programmer

- Nga Tran, Dr.P.H. • Risk assessment, toxicology

- Barbara Petersen, Ph.D.• Risk assessment

- Rosetta Newsome, Ph.D.• Project Director, Food

microbiology

- Fred R. Shank, Ph.D.• Vice President, IFT Science,

Communications, and Government Relations

What is the primary purpose of the model?

- The primary purpose of the model is to provide a flexible tool for ranking the relative risk of chemical and microbial hazards for all food products regulated by the FDA.

- The framework is primarily, but not exclusively for hazards that are unintentionally added

Modeling software platforms

- Model has been developed in two different platforms• Web-based platform

• Facilitates data sharing (common database of assumptions)

• Provides simple interface, report generation

• Analytica• Transparency of calculations, reproduces

web calculations for single food-hazard pair

Model version

- Current model has not been peer- reviewed.

- FDA has received a preliminary briefing, and a final briefing is scheduled for September 2005.

- Estimated model version = 0.9?

How is the model structured?

- Both by commodity and by hazard• Link hazard to commodity or commodity to

hazard

- The entire food production system is divided into several broadly defined sections, where prevalence and concentration of the hazard can change• Primary production• Processing• Distribution and retail

Intended users/customers

- The primary intended user of the model is FDA, but we hope that the framework is sufficiently robust that others would find it useful

Outcomes used to rank risks

- We use a “simplified” disability adjusted life years concept that we call the Pseudo-DALY

- The pDALY concept allows the user to select from a variety of templates that aggregate impact based on duration and severity

- Users can also design their own template by assigning a fraction of cases to each appropriate health impact

- Using broad templates allows the user to assign the same template to different hazards so that their per case impact is treated identically

- Health impacts can also be added or modified

Scope of Model

- The scope of the model is farm to table, however the resolution is limited to changes in only three stages: primary production; processing; and distribution and end user handling

Source(s) of Dose-response data

- 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. The framework includes places for the user to document the rational and reference for any dose response used.

- 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).

Source(s) of Consumption - Exposure data

- CSFII data are used for consumption/exposure.

- The model does allow the user to view (but not to edit) this information.

- The framework is sufficiently flexible to permit the use of alternate consumption/exposure data.

Principle data gaps

- Since our primary objective was to design a customizable framework, not assess a particular risk, there are many potential data gaps.

- The contract did ask for us to test the framework with a number of food/hazard combinations, and the data gaps are different for each combination.