Estimating Exposure to Direct Food Additives
and Chemical Contaminants in the Diet

Footnotes

1. The FDA acts in conjunction with the US Department of Agriculture (USDA) and the Environmental Protection Agency (EPA), through the Federal Food, Drug, and Cosmetic Act, as amended.

2. CRB is organizationally located in the Division of Product Manufacture and Use, which is in the Office of Premarket Approval of the FDA's Center for Food Safety and Applied Nutrition, Washington, DC. Mail Code HFS-247

3. Typically, animal studies are used to determine an acceptable daily intake (ADI), which must be greater than the estimate of daily intake (EDI) to ensure safety.

4. Code of Federal Regulations, Title 21, Part 184, Subpart B, Section 184.1095 (Abbreviated as 21 CFR 184.1095. In this report, all CFR citations are from Title 21 and will be referred to using the notation, 21 CFR XXX.XXXX.)

5. See Recommendations for Chemistry Data for Indirect Food Additive Petitions, CRB, for approaches to estimating exposure to indirect additives. Copies are available upon request from the Office of Premarket Approval (HFS-200), 200 C St. SW, Washington, DC 20204. (See updated contact information)

6. GRAS ingredients are materials which have been shown, through scientific procedures or history of safe use prior to 1958, to be Generally Recognized As Safe. Prior-sanctioned substances are those materials that the FDA or USDA approved for use in writing before 1958.

7. "Petitioners" refers to any group, individual, or company that submits a petition to amend the regulations concerning food and color additives under "the Act".

8. For a discussion of data sources, and their use in exposure estimation, see:

   1) National Center for Health Statistics, Division of Health Examination Statistics (December 1994) Consensus Workshop on Dietary Assessment: Nutrition Monitoring and Tracking the Year 2000 Objectives, Wright, J.D.; Ervin, B.; and Briefel, R.R., Editors, US Dept. of Health and Human Services, Hyattsville, MD.
   2) Life Sciences Research Office, Federation of American Societies for Experimental Biology (FASEB) (July 1988) Estimation of Exposure to Substances in the Food Supply, Anderson, S.A., Editor, FDA Contract No. 223-84-2059, Life Sciences Research Office, Bethesda, MD.
   3) Life Sciences Research Office, Federation of American Societies for Experimental Biology (December 1986) Guidelines for use of Dietary Intake Data, Anderson, S.A., Editor, Life Sciences Research Office, Bethesda, MD.
   4)WHO Offset Publication No. 87 (1985), Guidelines for the Study of Dietary Intakes of Chemical Contaminants, WHO, Geneva.

9. Pao, E.M., and Cypel, Y.S., Estimation of Dietary Intake. In Present Knowledge in Nutrition, International Life Sciences Institute-Nutrition Foundation, Washington, DC., 1990, pp. 399-406.

10. WHO (1985), Guidelines for the Study of Dietary Intakes of Chemical Contaminants, WHO Offset Publication No. 87, Geneva.

11. FAO (1986), Review of Food Consumption Surveys - 1985, FAO Food and Nutrition Paper No. 35, Rome.

12. FASEB, Estimation of Exposure to Substances in the Food Supply, (1988), Anderson, S.A., Editory, FDA Contract No. 223-84-2059, Life Sciences Research Office, Bethesda, MD.

13. FDA Contract No. 223-87-2088.

14. OPA has access to these data in electronic form from various sources.

15. When food intake data are reported on an eater-only basis, only those individuals who consumed the food have been counted. Total-sample basis food intake data, on the other hand, include the zero intakes of all members of the survey population that did not eat the food being evaluated. A mean, total sample intake is equivalent to a per-capita intake.

16. The Technical Assessment Systems' International Diet Research System, purchased under Contract No. 223-89-2176.

17. Putnam, J.J., and Allshouse, J.E., Food Consumption, Prices, and Expenditures, 1970-90, Economic Research Service, US Dept. of Agriculture, Statistical Bulletin No. 840, 1992.

18. Ibid.

19. Crane, N.T., Lewis, C.L., and Yetley, E.A., (1992) Am. J. Pub. Health, 82, 862-866.

20. Compiled under contract for the US Food and Drug Administration.

21. Pennington, J.A.T., Young, B.E., and Wilson, D.B. (1989) J. Am. Diet. Assoc., 89, 659-664 and references 1-5 therein.

22. Wait, A.D. and Bowers, T.S. (1993) Environ. Lab., 5 (Feb/Mar), 20-23. Helsel, D.R. (1990) Environ. Sci. Technol., 24 (12), 1766-1774. Travis, C.C. and Land, M.L. (1990) Environ. Sci. Technol., 24 (7), 961-962. Hornung, R.W. and Reed, L.D. (1990) Appl. Occup. Environ. Hyg., 5 (1), 46-51.

23. Yetley, E.A. and Hanson, E.A. (1984) J. Toxicol. Clin. Toxicol., 21, 181-200.

24. Yetley, E.A., Beloian, A.M., Lewis, C.J. (1992) "Dietary Methodologies for Food and Nutrition Monitoring", in Dietary Methodology Workshop for the Third National Health and Nutrition Examination Survey, DHHS Pub. No. (PHS) 92-1464, pp. 58-67.

25. EPA, Federal Register, 57, 22888-22938, May 29, 1992.

26. Summary data refer to all reports that are derived from raw data. That is, when raw intake data have been collated, weighted, and broken into age/sex, percentile, or other subgroupings, the resulting information is "summary" data.

27. The probability of two independent events occurring simultaneously is the product of the probabilities of each event occurring.

28. In this example, 88% is the theoretical maximum percentage of eaters as in case a.

29. The reverse example, eaters of diet soft drinks always using packets of sweetener, could be true only if the size of the two populations were equal.

30. Monte Carlo modeling has been used to evaluate public health risk assessments, to estimate the variability in soil uptake through the skin, and to explore the effect of uncertainty on risk management approaches. See Thompson, K.M., et al. (1992) Risk Analysis, 12, 53; Burmaster, D.E. and von Stackelberg, K. (1991) J. Exp. Anal. and Env. Epi., 1, 491; McKone, T.E. (1990) Risk Analysis, 10, 407; Finkel, A.M., "Confronting Uncertainty in Risk Management, A Guide for Decision Makers," Resources for the Future, Washington, DC, 1990; Rubinstein, R.Y. "Simulation and the Monte Carlo Method," J. Wiley and Sons, New York, NY, 1981.

31. Commercial software for analysis using Monte Carlo methodology is available from a number of suppliers.

32. Petersen, B.J., Chaisson, C.F., and Douglass, J.S. (1994) Am. J. Clin. Nut., 59 (Supp.), 204S.

33. This assumption is supported by the observation that for each food category the intake at the 90th percentile is approximately twice the mean. See also "Guidelines for the Study of Dietary Intakes of Chemical Contaminants", World Health Organization, Global Environmental Monitoring System, Geneva, (1983), pp. 49-50.

34. A triangular distribution is so named because a minimum, a most likely, and a maximum value is used to delineate a probability function, which, graphically, is triangular in shape.

35. National Marine Fisheries Service, U.S. Department of Commerce, 1993 estimate.

36. Dividing the food intakes of individuals by the number of days, n, in a survey gives distributions of n-day average intakes. Thus, the USDA 3-day survey and MRCA 14-day surveys provide, respectively, 3-day average and 14-day average intake distributions. The intakes at the mean and 90th percentile (for example) of these distributions for infrequently consumed foods will likely overestimate chronic (long term) daily intakes of these foods. This can be illustrated by considering an individual who consumes 28 grams of a food on two occasions during a 6 month period. If the two occasions occurred in the same week, the 14-day average EDI estimated from a 14-day survey capturing the two occasions would be 4 g/p/d (28 x 2 / 14). However, if the survey captured only one occasion it would be 2 g/p/d. For the 6 month period, the 180-day average EDI would be significantly less: (28 x 2) / 180 = 0.31 g/p/d.

37. NMFS reports a national per capita shark consumption figure of 0.0347 lbs/person/year. Weight of shark is dressed weight (ready for consumption), and has been adjusted for dogfish exports and imports of mako and porbeagle shark.

38. Both of these assumptions are reflective of the typical behavior of food intake and environmental contamination data.

39. Tollefson, L. and Cordle, F. (1986) Environmental Health Perspectives, 68, 203.

40. Park, Y.K. and Yetley, E.A. (1990) Am. J. Clin. Nutr., 51, 738-748.

41. Caloric compensation in animals has been shown to occur when up to 40% of caloric material is substituted with inert materials. See Peterson, A.D. and Baumgardt, B.R. (1971) J. Nutr., 101 1057, and references therein.

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