Dietary patterns in the French adult population: a study from the second French national cross-sectional dietary survey (INCA2) (2006-2007).

Identification and characterisation of dietary patterns are needed to define public health policies to promote better food behaviours. The aim of this study was to identify the major dietary patterns in the French adult population and to determine their main demographic, socio-economic, nutritional and environmental characteristics. Dietary patterns were defined from food consumption data collected in the second French national cross-sectional dietary survey (2006-2007). Non-negative-matrix factorisation method, followed by a cluster analysis, was implemented to derive the dietary patterns. Logistic regressions were then used to determine their main demographic and socio-economic characteristics. Finally, nutritional profiles and contaminant exposure levels of dietary patterns were compared using ANOVA. Seven dietary patterns, with specific food consumption behaviours, were identified: 'Small eater', 'Health conscious', 'Mediterranean', 'Sweet and processed', 'Traditional', 'Snacker' and 'Basic consumer'. For instance, the Health-conscious pattern was characterised by a high consumption of low-fat and light products. Individuals belonging to this pattern were likely to be older and to have a better nutritional profile than the overall population, but were more exposed to many contaminants. Conversely, individuals of Snacker pattern were likely to be younger, consumed more highly processed foods, had a nutrient-poor profile but were exposed to a limited number of food contaminants. The study identified main dietary patterns in the French adult population with distinct food behaviours and specific demographic, socio-economic, nutritional and environmental features. Paradoxically, for better dietary patterns, potential health risks cannot be ruled out. Therefore, this study demonstrated the need to conduct a risk-benefit analysis to define efficient public health policies regarding diet.

Mirabel: an integrated project for risk and cost/benefit analysis of peanut allergy.

Food allergy is a major public health issue. However, no regulatory measures exist when allergens are present at trace levels and the different risk components are poorly described. Thus, knowledge on exposure components such as the allergens present in foods and the consumption behaviour of allergic consumers and models to estimate the related risk need to be enriched. Mirabel proposes for the first time studying each risk component using an integrated approach in order to improve the quality of life of the allergic population. Field surveys were conducted in order to fill in the current gaps in unintentional allergen traces in food, allergic consumers' food behaviour, threshold doses of allergic reaction, allergy symptoms and severity. The aim is also to propose methodological and operational tools to quantify allergic risk, to test management scenarios and to produce a cost/benefit analysis. Medical data on the peanut allergies of 785 patients were collected in the MIRABEL survey and 443 patients answered the food consumption questionnaire. The population surveyed was mostly paediatric - 86% were children under 16 years of age, with a high percentage of males (60%). This project will generate tangible results on peanut allergen exposure and risk which could be used in future risk assessment work and particularly to provide science-based guidance to set up concentration limits for peanut traces on packages.

Probabilistic exposure assessment to food chemicals based on extreme value theory. Application to heavy metals from fish and sea products.

This paper presents new statistical methods in the field of exposure assessment. We focus on the estimation of the probability for the exposure to exceed a fixed safe level such as the provisional tolerable weekly intake (PTWI), when both consumption data and contamination data are independently available. Various calculations of exposure are proposed and compared. For many contaminants, PTWI belongs to the exposure tail distribution, which suggests the use of extreme value theory (EVT) to evaluate the risk. Our approach consists in modelling the exposure tail by a Pareto type distribution characterized by a Pareto index which may be seen as a measure of the risk of exceeding the PTWI. Using propositions by EVT specialists, we correct the bias of the usual Hill estimator to accurately estimate this risk index. We compare the results with an empirical plug-in method and show that the Pareto adjustment is relevant and efficient when exposure is low compared to the PTWI while the plug-in method should be used when exposure is higher. To illustrate our approach, we present some exposure assessment for heavy metals (lead, cadmium, mercury) via sea product consumption.