The Food Standards Agency is an independent government department responsible for food safety and hygiene across the UK. We work with businesses to help them produce safe food, and with local authorities to enforce food safety regulations.
Everything we do reflects our vision of ‘Safer food for the nation’. We aim to ensure that food produced or sold in the UK is safe to eat, consumers have the information they need to make informed choices about where and what they eat and that regulation and enforcement is risk-based and focused on improving public health.
Genstat has been deployed in many important areas of FSA activity, including the following:-
1. Exposure Assessment
There are many types of chemical whose presence in food can pose some level of risk for consumers. These include environmental contaminants (e.g. lead or cadmium), as well as certain pesticides and food additives. The FSA needs to estimate the likely level of human exposure to such chemicals, either from individual foods, or from the diet as a whole. Our exposure assessment models combine occurrence data (on the level of a given contaminant in foods) with extensive human intake data (from individual food diaries). Genstat provides excellent tools for the necessary data import, indexing, manipulation and analysis and produces fast, reliable results. It has the flexibility to perform more complex modelling when required: e.g. probabilistic assessments of risk that allow for variability and uncertainty in some of the key input variables.
2. Risk Assessment
The FSA uses Hierarchical Generalized Linear Models to model the probability of elevated levels of naturally occurring biotoxins in shellfish. These estimates are used to prioritise biotoxin sampling activity around the shores of the UK, indicating where, and at what times of year, the level of sampling should be raised, and also where and when it can be reduced. Genstat has excellent procedures for performing models of this kind and for extracting model coefficients in a form that can be used to make a range of policy-relevant inferences.