Risk-benefit assessment for plant food supplements (PFS) Jouni Tuomisto, THL

New context: shared understanding A large group of people who try to understand –What issues are on the table –Where are the agreements and disagreements –What are the reasons for disagreements  The aim to understand everyone’s point of view leads to inclusiveness. ”Scientific” in this context means that a hypothesis can be openly criticised based on observations and relevance.

Guidance based on inclusiveness Benefits and risks separately or combined? –Do both if you can. Should DALYs or QALYs be used? –Do both if you can.

What impacts to include? Health risks of compounds in products. Health risks of behavioural changes due to consumption. Health benefits of compounds in products. Health benefits of placebo effect. Health benefits of the habit of consumption (tradition of use).

Burden of proof? Wide use of a product in a society implies health or other benefits in that society.  Requirement of substantial evidence of risk before banning a product. If there is no use, burden of proof is rather not on risk side.

What do consumers expect? Large variation between individuals for sure. Probably those who consume products expect the products to be net beneficial. Consumer values and expectations should be studied and used as a basis for assessments and regulation. The motto of THL / Environmental health: –”People must be able to drink, eat, breathe, and use consumer products trusting that their health is not in danger.” –  This should be applied to PFS as a default.

Sharing is learning People mostly think about one assessment at a time. We should systematically collect and share data about all important and interesting compounds. We should try to learn about groups of assessments and groups of chemicals. Big data: e.g. Google’s influenza predictions..

Sharing is learning (2) We will never have enough data about many single chemicals, but we do have good data about eg. cancer impacts of all chemical exposures combined. –  We should aim at a systematic assessment of all exposures. That is a huge task, though.

Is it technically feasible? The null hypothesis is usually: ”No impact” But it could also be: P(X > x) –Probability that the true impact X is actually larger than value x.  It would be possible to assess the highest plausible impacts to see whether conclusions can be made. Bayesian approaches do this naturally. Quantitative assessment is feasible more often than thought.