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1.1 General framework
The overall framework is similar to the quantitative risk-benefit analysis described in Hoekstra et al. (Hoekstra et al. 2008) , but in more generalised form (facilitating its application to a wide range of risk-benefit questions) and adding the capability to quantify uncertainties at each stage of the process.
Since 1995 a generic risk (safety) assessment model for food standards issues had been agreed upon (FAO/WHO Expert Consultation, 1995) . It is increasingly recognized, among others at a EFSA Science colloquium that a similar paradigm can – and should – be constructed for the benefit assessment or rather in an integrated risk-benefit assessment approach (EFSA, 2006a) . Consequently, risk-benefit assessment can be divided into four analogous steps, i.e. 1) hazard and benefit identification, 2) hazard and benefit characterization through dose-response functions, 3) exposure assessment, and 4) risk-benefit characterization including integration of risks and benefits.
To perform an integrated risk-benefit assessment new elements have to be developed. Both hazardous and beneficial effects need to be taken into account and potential risks and benefits must be balanced by use of a common measure such as disability-adjusted-life-years (DALYs), quality-adjusted-life-years (QALYs), or healthy-life-expectancy (HALE) (Havelaar et al. 2000; Ezzati et al. 2003; Ponce et al. 2000) . Consequently, the Qalibra framework includes the following elements, described in detail later in this document:
· Define the question – a pre-assessment phase, sometimes referred to as problem definition or problem formulation.
· Identify the beneficial and adverse health effects to consider, the compounds that may influence these effects, and the affected population
· Assess the level of exposure to the hazardous and beneficial compounds
· Quantify the level of hazards and benefits that may result from this exposure via dose-response curves
· Integrate the adverse and beneficial health effects using a common metric
· Identify unquantified uncertainties and evaluate their implications for interpretation of the results. Also quantifiable uncertainties and variabilities must be taken into account. Although this is stated as the last step, it is an ongoing process within each of the earlier steps.