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Society For Risk Analysis Annual Meeting 2008

Risk Analysis: the Science and the Art

Session Schedule & Abstracts

* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.

Common abbreviations

Public Health Risk Assessment

Room: Commonwealth B   2:00-3:30 PM

Chair(s): Eric Erel

W3-E.1  14:00  Simplified Framework for Microbial Risk Assessments: A Multiplier Approach. Williams, Michael S. MSW*, Ebel, Eric D. EDE; Risk Analysis and Residue Division, Food Safety and Inspection Service, USDA

Abstract: Farm-to-table risk assessment models have traditionally tracked changes in pathogen prevalence and levels up to consumption, at which point a dose-response model is applied to the pathogen distribution to predict the number of human illnesses. The effect of uncertainty regarding the true parameterization of the model is incorporated using Monte Carlo simulation wherein random draws from the uncertainty distribution are used as input to the model. However, when uncertainty about multiple parameters is added to the model, combinations of multiple parameters can produce predictions of the number of illnesses that are either unlikely or impossible to observe. FSIS is investigating a new modeling framework where the number of human illnesses observed through public health surveillance is treated as data that inform the model, rather than being a model output. Estimation of model parameters is carried out using Markov-Chain Monte-Carlo techniques. This framework has a number of advantages over a traditional Farm-to-table approach. For example: • Information from outbreak investigations regarding the probability of illness given a person was exposed can be incorporated in the model as prior information. • New information is readily incorporated into the model using Bayes’ theorem. • Variability and uncertainty of parameters are easily incorporated. • Constraining the model with the observed number of human illnesses prevents unrealistic combinations of input parameters. An overview of the modeling framework is provided and an example of its application with Escherichia coli O157:H7 in ground beef is presented.

W3-E.2  14:20  The Risk of Trypanosoma cruzi Infections from Blood Transfusions in the U.S. Forshee RA*, Anderson SA, Walderhaug MO, Yang H; U.S. Food and Drug Administration

Abstract: Trypanosoma cruzi is a flagellated protozoan parasite and the causative agent of Chagas’ disease. Infected individuals usually experience an acute phase lasting 1-3 months followed by a long asymptomatic phase of the disease. Eventually, 10% to 30% of persons chronically infected may develop more serious symptoms arising from irreversible damage to the heart and nervous system. The T. cruzi parasite can be transmitted by blood transfusion. As the number of immigrants entering the U.S. from endemic areas has increased, the risk of transmission via transfusion in the U.S. is thought to be increasing. T. cruzi is not currently classified as a Relevant Communicable Disease Agent or Disease (RCDAD), so blood donations are not required to be tested for the agent. However, the risks associated with the presence of T. cruzi in blood cell products have been discussed at recent meetings of FDA’s Blood Products Advisory Committee. The FDA is considering whether to make T. cruzi a RCDAD and to recommend testing all blood donors for T. cruzi. This presentation describes a quantitative probabilistic model for predicting the risks of T. cruzi transmission via transfusion of blood products in the U.S. and the loss of uninfected donors as a result of the screening questionnaire. The model is comprised of two main modules: Immigration and Donation. The Immigration Module contains a model of the estimated number of foreign born persons in the U.S. who may be infected with the T. cruzi parasite. The Donation Module contains a model of the number of units of whole blood and blood cell products that may contain the T. cruzi parasite and associated parameters. The model predicts that blood transfusions are likely to be responsible for some new T. cruzi infections in the U.S. The magnitude of the new infections is related to several factors including the effectiveness of screening questionnaires and number of donors born in areas where Chagas’ Disease is endemic.

W3-E.3  14:40  Trade-Offs between Blood Availability and Safety Associated with Malaria Antibody Screening and Donor Deferral Policies . Yang *, Walderhaug O, Forshee A, Anderson A; Center for Biologics Evaluation and Research, US Food and Drug Administration

Abstract: Five cases of transfusion-transmitted malaria (TTM) have been reported in the United States in the past decade. The dominant risk of TTM is attributed to blood donors who may have been exposed to malaria during a visit or residence in malarious regions of the world. Current policy defers blood donations by travelers who have recently visited malaria endemic regions for 1 year and defers immigrants from those regions for 3 years. Donor deferral effectively reduces the risk of TTM, however, it also causes a loss of >100,000 donors each year. Antibody testing can be used to detect malaria infection in donors and has been implemented in the United Kingdom, France and Australia for donor screening in lieu of a year long deferral. We present a probabilistic model to evaluate the risks (TTM) and benefits (donor recovery) of using the malaria antibody test to screen all blood donors (scenario 1), currently deferrable donors (scenario 2), or donors currently deferrable because of travel to Mexico (scenario 3). The model consists of four modules, “at-risk populations”, “blood donation”, “donor deferral”, and “antibody testing”. The model considers donation rate, disease prevalence, probability of being asymptomatic when donating, efficiency of deferral, and sensitivity and specificity of malaria antibody testing. The model estimates that with 99% sensitivity and 99.98% specificity of antibody testing, scenarios 1 and 2 would reduce risk of TTM by ~50%, and recover ~110,000 donors each year. Scenario 3 would recover ~10,000 donors, while maintaining the blood supply at a level of safety comparable to the current deferral policy. This model has a user friendly interface. It provides a tool to inform risk management of TTM associated with the US blood supply, and also allows risk managers to modify inputs and assumptions for different risk management questions.

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