Mancini Francesca Romana, Sirot Véronique, Busani Luca, Volatier Jean-Luc, Hulin Marion
a Department of Veterinary Public Health and Food Safety , Istituto Superiore di Sanità , Rome , Italy.
Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 2015;32(7):1065-74. doi: 10.1080/19440049.2015.1041428. Epub 2015 May 12.
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.
为了估计食物和营养素摄入量，24小时回顾法在膳食评估中经常被使用。然而，短期内收集的摄入量数据对于长期通常摄入量而言是有限的估计指标。此类数据的一个重要局限性在于，个体内部变异性往往会使摄入量分布膨胀，从而导致对极端百分位数的估计出现偏差。最近已经实施了一些统计模型，即通常摄入量模型，这些模型将个体内部变异性与个体间变异性区分开来。本研究的主要目的是强调通常摄入量模型可能对暴露估计和风险评估产生的潜在影响，并指出为了正确运行这些模型并确保正确解释输出结果需要考虑的关键方面。为了实现这一目标，我们使用了法国膳食调查INCA2获得的消费数据以及法国TDS2期间收集的浓度数据，使用的是蒙特卡罗风险评估（MCRA）软件8.0版。对于本研究中包含的三种物质（镉、丙烯酰胺和亚硫酸盐），与观察到的个体均值模型的结果相比，使用通常摄入量模型时，高百分位数的暴露量显著降低，即使在风险评估方面，使用通常摄入量模型的影响有限。从结果来看，使用通常摄入量模型时需要考虑的关键方面是：（1）对数转换后的摄入量分布的正态性，（2）每个单一食物组对总暴露的贡献，以及（3）多天食物消费数据的独立性。总之，通常摄入量模型可能会对暴露估计产生影响，尽管根据结果，它在风险评估方面没有带来任何变化，但仍需要进一步研究。
