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Benefit-risk assessment for food: an iterative value-of-information approach

Final Report Summary - BENERIS (Benefit-risk assessment for food: An iterative value-of-information approach)

The general objective of the BENERIS project was to create a framework for handling complicated benefit risk situations and apply it for the analysis of benefits and risks of certain foods. The first food commodity that was used in the development of the methodology was fish, while vegetable consumption by infants was also examined. More specifically, the project partial objectives were to:
1. develop Bayesian belief networks (BBN) to handle complicated benefit-risk situations and create a Decision support system (DSS) based on BBN;
2. propose improved methods for dose-response assessment and apply them in combining information on fish contaminants;
3. develop an integrated repository of surveillance, nutrient and food consumption data that would be capable of receiving, analysing and disseminating the accumulated information to key stakeholders;
4. estimate average nutrient intakes and food consumption in various subgroups;
5. analyse the health benefits of fish and understand the effect of fish on different population subgroups;
6. establish the association between external dose and internal dose by analysing contaminants from 100 to 200 placentas;
7. determine the effects of certain policy options on dietary habits and on intake of important nutrient and contaminants;
8. integrate the obtained results into updated assessments and evaluate the remaining uncertainties and their importance for decision making;
9. develop an internet tool for publishing risk assessment results, as well as to develop a method to publish entire models over the internet;
10. disseminate the results and evaluate the relevance and usefulness of the project from the perspective of an end user or authority.

The work related to combining existing databases into an integrated repository led to several important conclusions that affected the plans of further work. Firstly, it was very difficult and time consuming to integrate food consumption data between three different countries, as was initially planned. Therefore, the collection of data for benefit-risk analyses was designed so that there was particular emphasis on the applicability and simplicity of the data structure. This applied to both existing databases and the data produced within BENERIS. Secondly, the new benefit-risk assessment method imposed several database requirements. These considerations were taken into account when the BENERIS data repository was designed and resulted in the development of the Opasnet base, which was an internet tool to collect, organise and distribute information on issues relevant for Benefit-risk analyses (BRA) of food. Interested parties were welcome to contribute to the case studies with their own information, as long as it was offered under a proper copyright. Opasnet also had a feedback and discussion functionality to facilitate contribution.

The BENERIS team established a collaboration effort through clustering with the QALIBRA project and these two consortia collaborated from the start of both projects. Furthermore, the two teams collaborated with the BRAFO project, which developed a tiered framework for risk-benefit assessments. In addition, the INTARESE and some other projects on environmental health risk assessments contributed significantly to the Opasnet and open assessment development.

The methods and tools that were developed by BENERIS were offered to other projects and real life benefit-risk assessments. Some of the projects undertook tasks to develop new functionalities to the website and thus put additional resources in the BENERIS work. The proposed methods were believed to be able to serve policy making in a better way than the previously implemented practices, since they made the production of credible assessments easier, they were more likely to tackle questions of real interest to stakeholders or decision makers and they were anticipated to produce decisions that would be better informed.