EFSA's framework for evidence-based scientific assessments: A case study on uncertainty analysis

ALTEX. 2022;39(3):451–462. doi: 10.14573/altex.2004211. Epub 2021 Jun 28.

Abstract

To provide sound scientific advice in support of the European decision-making process in food and feed safety, the European Food Safety Authority (EFSA) has defined the principles for producing “evidence-based scientific assessments” (impartiality, methodological rigor, transparency, and engagement) and, to help fulfil them, has developed cross-cutting methodological approaches. This paper focusses on two of these approaches: conducting scientific assessments in four steps – with an emphasis on developing a protocol for the assessment a priori – and analyzing uncertainty. An overview of the 4-step approach and of the methods for addressing uncertainty is given, and a case study on uncertainty analysis, developed in collaboration with the German Federal Institute for Risk Assessment, is illustrated. The main advantage related to the implementation of protocols and uncertainty analysis is improvement of the scientific value of the outputs. However, experience and further capacity-building is needed to better incorporate uncertainty analysis into the planning phase (protocol) of the scientific assessment process. The case study is based on exposure in humans. Nonetheless it provides an example of a framework for evidence-based scientific assessments that is applicable also to other types of evidence, including evidence arising from new approach methodologies. Adopting the proposed framework, which covers an analysis of uncertainties in the planning and implementation phase, is expected to foster the integration of multiple evidence sources, including alternative methods and testing strategies, in the regulatory scientific assessment process.

Keywords: food and feed safety; methodological rigour; protocols; scientific assessments; transparency.

Publication types

  • Editorial

MeSH terms

  • Animal Testing Alternatives*
  • Animals
  • Humans
  • Risk Assessment / methods
  • Uncertainty