Background/purposeThe number of dietary exposure assessment studies focussing on children is very limited. Children are however a vulnerable group due to their higher food consumption level per kg body weight. Therefore, the EXPOCHI project aims [1] to create a relational network of individual food consumption databases in children, covering different geographical areas within Europe, and [2] to use these data to assess the usual intake of lead, chromium, selenium and food colours.MethodsEXPOCHI includes 14 food consumption databases focussed on children (1-14 y old). The data are considered representative at national/regional level: 14 regions covering 13 countries. Since the aim of the study is to perform long-term exposure assessments, only data derived from 24 hr dietary recalls and dietary records recorded on at least two non-consecutive days per individual were included in the dietary exposure assessments. To link consumption data and concentration data of lead, chromium and selenium in a standardised way, categorisation of the food consumption data was based on the food categorisation system described within the SCOOP Task report 3.2.11. For food colours, the food categorisation system specified in the Council Directive 94/36/EC was used.ConclusionThe EXPOCHI project includes a pan-European long-term exposure assessment of lead, chromium, selenium and food colours among children living in 13 different EU countries. However, the different study methods and designs used to collect the data in the different countries necessitate an in-depth description of these different methods and a discussion about the resulting limitations.

Suggested citation: Ocké M., de Boer E., Brants H., van der Laan J., Niekerk M., van Rossum C., Temme L., Freisling H., Nicolas G., Casagrande C., Slimani N., Trolle E., Ege M., Christensen T., Vandevijvere S., Bellemans M., De Maeyer M., Defourny S., Rupich J., Dofkova M., Rehurkova I., Jakubikova M., Blahova J., Piskackova Z., Maly M.; PANCAKE -Pilot study for the Assessment of Nutrient intake and food Consumption Among Kids in Europe. Supporting Publications

Studies using 24 h urine collections need to incorporate ways to validate the completeness of the urine samples. Models to predict urinary creatinine excretion (UCE) have been developed for this purpose; however, information on their usefulness to identify incomplete urine collections is limited. We aimed to develop a model for predicting UCE and to assess the performance of a creatinine index using paraaminobenzoic acid (PABA) as a reference. Data were taken from the European Food Consumption Validation study comprising two nonconsecutive 24 h urine collections from 600 subjects in five European countries. Data from one collection were used to build a multiple linear regression model to predict UCE, and data from the other collection were used for performance testing of a creatinine indexbased strategy to identify incomplete collections. Multiple linear regression (n 458) of UCE showed a significant positive association for body weight (b ¼ 0·07), the interaction term sex £ weight (b ¼ 0·09, reference women) and protein intake (b ¼ 0·02). A significant negative association was found for age (b ¼ 20·09) and sex (b ¼ 23·14, reference women). An index of observed-to-predicted creatinine resulted in a sensitivity to identify incomplete collections of 0·06 (95 % CI 0·01, 0·20) and 0·11 (95 % CI 0·03, 0·22) in men and women, respectively. Specificity was 0·97 (95 % CI 0·97, 0·98) in men and 0·98 (95 % CI 0·98, 0·99) in women. The present study shows that UCE can be predicted from weight, age and sex. However, the results revealed that a creatinine index based on these predictions is not sufficiently sensitive to exclude incomplete 24 h urine collections.

In this document, we report on the long-term dietary exposure to 40 different food colours in young children living in 11 European countries. Food consumption data of children aged 1 up to 10 years (in Cyprus 11 up to 14 years) were combined with food colour concentrations as supplied by EFSA using statistical models to calculate exposure. Food consumption data were all categorised according to a harmonised system to allow for linkage with food colour concentration data in a standardised way. Two different exposure scenarios were calculated for each food colour: the maximum permitted usage levels in the relevant Community legislation (here called the "MPL scenario") and the actual maximum reported usage patterns (here called the "maximum usage level scenario"). For some food colours extra scenarios were considered depending on the percentage of coating (5 or 20%), e.g. food colours permitted in the coating of chocolate products. For lycopene, five different scenarios were considered. Although the results showed differences in exposure between countries, such differences should be interpreted with caution, as different methodological limitations rather than true exposure differences might be partly responsible for such findings. It is clear from this exercise that food consumption data in different countries can be categorised in a standard way to allow for harmonised exposure modelling. Methodological issues related to exposure 1 EFSA-Q-

High dietary Na intake is associated with multiple health risks, making accurate assessment of population dietary Na intake critical. In the present study, reporting accuracy of dietary Na intake was evaluated by 24 h urinary Na excretion using the EPIC-Soft 24 h dietary recall (24-HDR). Participants from a subsample of the European Food Consumption Validation study (n 365; countries: Belgium, Norway and Czech Republic), aged 45-65 years, completed two 24 h urine collections and two 24-HDR. Reporting accuracy was calculated as the ratio of reported Na intake to that estimated from the urinary biomarker. A questionnaire on salt use was completed in order to assess the discretionary use of table and cooking salt. The reporting accuracy of dietary Na intake was assessed using two scenarios: (1) a salt adjustment procedure using data from the salt questionnaire; (2) without salt adjustment. Overall, reporting accuracy improved when data from the salt questionnaire were included. The mean reporting accuracy was 0·67 (95 % CI 0·62, 0·72), 0·73 (95 % CI 0·68, 0·79) and 0·79 (95 % CI 0·74, 0·85) for Belgium, Norway and Czech Republic, respectively. Reporting accuracy decreased with increasing BMI among male subjects in all the three countries. For women from Belgium and Norway, reporting accuracy was highest among those classified as obese (BMI $30 kg/m 2 : 0·73, 95 % CI 0·67, 0·81 and 0·81, 95 % CI 0·77, 0·86, respectively). The findings from the present study showed considerable underestimation of dietary Na intake assessed using two 24-HDR. The questionnaire-based salt adjustment procedure improved reporting accuracy by 7 -13 %. Further development of both the questionnaire and EPIC-Soft databases (e.g. inclusion of a facet to describe salt content) is necessary to estimate population dietary Na intakes accurately.Key words: Diet surveys: Self-reports: Biological markers/urine: Dietary sodium: European Food Consumption ValidationThe current WHO guidelines strongly recommend a reduction in Na intake to ,2 g/d (i.e. , 5 g salt/d) (1) . There is conclusive evidence that reduction in Na intake reduces blood pressure (2 -5) . The relationship between Na consumption and the risk of CVD and stroke is less clear than that of hypertension. For example, a systematic review of randomised controlled trials has found no relationship between Na intake and CVD risk (6) ; however, a meta-analysis of thirteen cohort studies has concluded that there is a direct relationship between increased Na consumption and the subsequent risk of CVD and stroke (7) . Data from an international epidemiological study on dietary factors in the aetiology of unfavourable blood pressure (the INTERMAP study) showed that sources of dietary Na can vary considerably. For instance, processed foods contribute heavily to Na intake in the USA and the UK (70-95 %), whereas in China, most dietary Na intake (76 %) is from salt added during home cooking (8) . Another study, in which Li