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Background <p>The Observed Individual Means (OIM) methodology, based on the non-parametric bootstrap, is usually employed to perform basic probabilistic dietary chronic exposure assessment, and assumes independence and identical distribution of occurrence data within food category. However, this assumption may not be valid if several expected distributions of occurrence can be a priori identified within food category. Moreover, OIM assumes each analysed food sample to equally contribute to mean occurrence, as information about relevance of each food item cannot be incorporated into exposure assessment.</p> Objective <p>In this paper we address the above-mentioned violations and develop two statistical methodologies to accommodate for them into OIM.</p> Methods <p>The stratified non-parametric bootstrap and weighted mean occurrence are employed to correct for such violations. As a case study, we compare the methodologies by estimating the exposure of the adult Italian population to the process contaminant 3-monochloropropane-1,2-diol.</p> Results <p>We propose strategies to interpret their results and show their relevance in conducting exposure assessment.</p> Impact statement <p>For the first time in the literature, we critically examine a widely used methodology for Probabilistic Dietary Exposure Assessment from a statistical perspective, focusing on the underlying assumptions and their potential violations in real-world scenarios. We then develop techniques to address these violations, providing a more accurate and robust approach to exposure assessment. This work is particularly relevant for risk assessors and managers, since it offers a refined toolset for more precise exposure assessments.</p>

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Refined methodologies for probabilistic dietary exposure assessment for food contaminants based on the observed individual means methodology

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月29日

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  • Simone Stefano,
  • Alessia Lanno,
  • Sofia Ghironi,
  • Alice Passoni,
  • Renzo Bagnati,
  • Alessandra Roncaglioni,
  • Enrico Davoli,
  • Elena Fattore

摘要

Background

The Observed Individual Means (OIM) methodology, based on the non-parametric bootstrap, is usually employed to perform basic probabilistic dietary chronic exposure assessment, and assumes independence and identical distribution of occurrence data within food category. However, this assumption may not be valid if several expected distributions of occurrence can be a priori identified within food category. Moreover, OIM assumes each analysed food sample to equally contribute to mean occurrence, as information about relevance of each food item cannot be incorporated into exposure assessment.

Objective

In this paper we address the above-mentioned violations and develop two statistical methodologies to accommodate for them into OIM.

Methods

The stratified non-parametric bootstrap and weighted mean occurrence are employed to correct for such violations. As a case study, we compare the methodologies by estimating the exposure of the adult Italian population to the process contaminant 3-monochloropropane-1,2-diol.

Results

We propose strategies to interpret their results and show their relevance in conducting exposure assessment.

Impact statement

For the first time in the literature, we critically examine a widely used methodology for Probabilistic Dietary Exposure Assessment from a statistical perspective, focusing on the underlying assumptions and their potential violations in real-world scenarios. We then develop techniques to address these violations, providing a more accurate and robust approach to exposure assessment. This work is particularly relevant for risk assessors and managers, since it offers a refined toolset for more precise exposure assessments.