To estimate of food and nutrient intakes, 24-h recalls are frequently used in dietary assessment. However intake data collected for a short period are a limited estimator of long-term usual intake. An important limitation of such data is that the within-person variability tends to inflate the intake distribution leading to a biased estimation of extreme percentiles. Statistical models, named usual-intake models, that separate the within-person variability from the between-persons variability, have lately been implemented. The main objectives of this study were to highlight the potential impact that usual-intake models can have on exposure estimate and risk assessment and to point out which are the key aspects to be considered in order to run these models properly and be sure to interpret the output correctly. To achieve the goal we used the consumption data obtained by the French dietary survey INCA2 and the concentration data collected during the French TDS2, using Monte Carlo Risk Assessment (MCRA) software, release 8.0. For the three substances included in this study (cadmium, acrylamide and sulphites), the exposure of the upper percentiles was significantly reduced when using usual-intake models in comparison with the results obtained in the observed individual mean models, even if in terms of risk assessment the impact of using usual-intake models was limited. From the results it appears that the key aspects to consider when using usual-intake models are: (1) the normality of the log-transformed intake distribution, (2) the contribution per single food group to the total exposure, and (3) the independency of food consumption data on multiple days. In conclusion, usual-intake models may have an impact on exposure estimates although, referring to the results, it did not bring any changes in terms of risk assessment, but further investigations are needed.
Keywords: acrylamide; cadmium; exposure assessment; risk assessment; sulphites; usual-intake models.