title: Development and Application of a Probabilistic Risk-Benefit Assessment Model for Infant Feeding Integrating Microbiological, Nutritional, and Chemical Components.
abstract: A probabilistic and interdisciplinary risk-benefit assessment (RBA) model integrating microbiological, nutritional, and chemical components was developed for infant milk, with the objective of predicting the health impact of different scenarios of consumption. Infant feeding is a particular concern of interest in RBA as breast milk and powder infant formula have both been associated with risks and benefits related to chemicals, bacteria, and nutrients, hence the model considers these three facets. Cronobacter sakazakii, dioxin-like polychlorinated biphenyls (dl-PCB), and docosahexaenoic acid (DHA) were three risk/benefit factors selected as key issues in microbiology, chemistry, and nutrition, respectively. The present model was probabilistic with variability and uncertainty separated using a second-order Monte Carlo simulation process. In this study, advantages and limitations of undertaking probabilistic and interdisciplinary RBA are discussed. In particular, the probabilistic technique was found to be powerful in dealing with missing data and to translate assumptions into quantitative inputs while taking uncertainty into account. In addition, separation of variability and uncertainty strengthened the interpretation of the model outputs by enabling better consideration and distinction of natural heterogeneity from lack of knowledge. Interdisciplinary RBA is necessary to give more structured conclusions and avoid contradictory messages to policymakers and also to consumers, leading to more decisive food recommendations. This assessment provides a conceptual development of the RBA methodology and is a robust basis on which to build upon.
authors.affiliation: Secalim UMR1014, INRA, Oniris, Nantes, France.
authors.forename: Géraldine
authors.initials: G
authors.lastname: Boué
authors.affiliation: UCD School of Biosystems and Food Engineering, Dublin, Ireland.
authors.forename: Enda
authors.initials: E
authors.lastname: Cummins
authors.affiliation: Secalim UMR1014, INRA, Oniris, Nantes, France.
authors.forename: Sandrine
authors.initials: S
authors.lastname: Guillou
authors.affiliation: Laberca USC1329, INRA, Oniris, Nantes, France.
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Antignac
authors.affiliation: Laberca USC1329, INRA, Oniris, Nantes, France.
authors.forename: Bruno
authors.initials: B
authors.lastname: Le Bizec
authors.affiliation: Secalim UMR1014, INRA, Oniris, Nantes, France.
authors.forename: Jeanne-Marie
authors.initials: JM
authors.lastname: Membré
mesh_terms: D003198:Computer Simulation; D044083:Cronobacter sakazakii / Q000302:isolation & purification / Q000472:pathogenicity; D004281:Docosahexaenoic Acids / Q000008:administration & dosage / Q000032:analysis; D005260:Female; D005506:Food Contamination / Q000706:statistics & numerical data; D005516:Food Microbiology / Q000706:statistics & numerical data; D005527:Food, Fortified / Q000032:analysis; D006801:Humans; D007223:Infant; D041943:Infant Formula / Q000009:adverse effects* / Q000737:chemistry / Q000382:microbiology*; D008297:Male; D008895:Milk, Human / Q000737:chemistry* / Q000382:microbiology*; D015233:Models, Statistical; D009010:Monte Carlo Method; D011078:Polychlorinated Biphenyls / Q000633:toxicity; D018570:Risk Assessment / Q000706:statistics & numerical data; D012985:Software Design
chemical_list: D004281:Docosahexaenoic Acids; D011078:Polychlorinated Biphenyls
keywords: Breast milk; infant formula; second-order Monte Carlo simulations