Assessment of food intake input distributions for use in probabilistic exposure assessments of food additives

A key component of a food chemical exposure assessment using probabilistic analysis is the selection of the most appropriate input distribution to represent exposure variables. The study explored the type of parametric distribution that could be used to model variability in food consumption data likely to be included in a probabilistic exposure assessment of food additives. The goodness-of-fit of a range of continuous distributions to observed data of 22 food categories expressed as average daily intakes among consumers from the North–South Ireland Food Consumption Survey was assessed using the BestFit® distribution fitting program. The lognormal distribution was most commonly accepted as a plausible parametric distribution to represent food consumption data when food intakes were expressed as absolute intakes (16/22 foods) and as intakes per kg body weight (18/22 foods). Results from goodness-of-fit tests were accompanied by lognormal probability plots for a number of food categories. The influence on food additive intake of using a lognormal distribution to model food consumption input data was assessed by comparing modelled intake estimates with observed intakes. Results from the present study advise some level of caution about the use of a lognormal distribution as a mode of input for food consumption data in probabilistic food additive exposure assessments and the results highlight the need for further research in this area.

[1]  J. Brown,et al.  Miscellaneous foods: Fourth supplement to the Fifth Edition of McCance and Widdowson's The Composition of Foods. , 1994 .

[2]  J. Lambe,et al.  The use of food consumption data in assessments of exposure to food chemicals including the application of probabilistic modelling , 2002, Proceedings of the Nutrition Society.

[3]  M. Gibney,et al.  Irish National Food Ingredient Database: application for assessing patterns of additive usage in foods , 2002, Food additives and contaminants.

[4]  D. Burmaster,et al.  Using lognormal distributions and lognormal probability plots in probabilistic risk assessments , 1997 .

[5]  J. Brown,et al.  Meat, poultry and game: fifth supplement to the fifth edition of McCance and Widdowson's The composition of foods. , 1995 .

[6]  B. Holland,et al.  Milk Products and Eggs: Fourth Supplement to McCance and Widdowson's the Composition of Foods , 1989 .

[7]  D. Vose.  Risk Analysis: A Quantitative Guide , 2000 .

[8]  M. Gibney,et al.  Comparison of stochastic modelling of the intakes of intentionally added flavouring substances with theoretical added maximum daily intakes (TAMDI) and maximized survey-derived daily intakes (MSDI) , 2002, Food additives and contaminants.

[9]  Max Henrion,et al.  Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis , 1990 .

[10]  W. T. Barton.  Response from Palisade Corporation , 1989 .

[11]  J Kleiner,et al.  Assessment of intake from the diet. , 2002, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[12]  B S Binkowitz,et al.  Disparity in Quantitative Risk Assessment: A Review of Input Distributions , 2001, Risk analysis : an official publication of the Society for Risk Analysis.

[13]  C. Harris,et al.  The variation of pesticide residues in fruits and vegetables and the associated assessment of risk. , 1999, Regulatory toxicology and pharmacology : RTP.

[14]  A. C. Taylor.  Using objective and subjective information to develop distributions for probabilistic exposure assessment. , 1993, Journal of exposure analysis and environmental epidemiology.

[15]  M. Gibney,et al.  Validation analysis of probabilistic models of dietary exposure to food additives , 2003, Food additives and contaminants.

[16]  B. Holland.  Fruit and nuts , 1992 .

[17]  G. Birch.  Meat products and dishes: Supplement to McCance and Widdowson's The composition of foods. RSC. 1996. W. Chan, J. Brown, S. M. Church & D. H. Buss. ISBN 085404809 x ca. 150 pp. £25.95 , 1997 .

[18]  Taylor Ac.  Using objective and subjective information to develop distributions for probabilistic exposure assessment. , 1993 .

[19]  D E Burmaster,et al.  Principles of good practice for the use of Monte Carlo techniques in human health and ecological risk assessments. , 1994, Risk analysis : an official publication of the Society for Risk Analysis.

[20]  W D Shaw,et al.  Short communication: selecting input distributions for use in Monte Carlo simulations. , 1995, Regulatory toxicology and pharmacology : RTP.

[21]  B J Petersen,et al.  Probabilistic modelling: theory and practice , 2000, Food additives and contaminants.

[22]  Options for Development of Parametric Probability Distributions for Exposure Factors , 2000 .

[23]  Vicki M. Bier,et al.  B. Extremes, Extrapolation, And Surprise , 1999 .

[24]  M. Day,et al.  UK consumption databases relevant to acute exposure assessment , 2000, Food Additives and Contaminants.

[25]  B. Holland,et al.  Fish and fish products: third supplement to the fifth edition of McCance and Widdowson's 'The composition of foods'. , 1993 .

[26]  David E. Burmaster,et al.  Lognormal Distributions for Fish Consumption by the General U.S. Population , 1994 .

[27]  B. Holland,et al.  Fruit and nuts : first supplement to the fifth edition of McCance and Widdowsons's The composition of foods , 1992 .

[28]  J H Driver,et al.  Estimation of dietary exposure to chemicals: a case study illustrating methods of distributional analyses for food consumption data. , 1996, Risk analysis : an official publication of the Society for Risk Analysis.

[29]  P. Robson,et al.  The North/South Ireland Food Consumption Survey: survey design and methodology , 2001, Public Health Nutrition.

[30]  U. Epa,et al.  Guiding Principles for Monte Carlo Analysis , 1997 .

[31]  H. Christopher Frey,et al.  Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs , 1999 .

[32]  F. A. Seiler,et al.  On the Selection of Distributions for Stochastic Variables , 1995 .

[33]  D. Burmaster,et al.  Estimated Distributions for Average Daily Consumption of Total and Self-Caught Fish for Adults in Michigan Angler Households , 1994 .