Regulating Pesticides in Food The Delaney Paradox (1987) / Chapter Skim
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Appendix B: Analytical Methodology for Estimating Oncogenic Risks of Human Exposure to Agricultural Chemicals in Food Crops
Appendix B: Analytical Methodology for Estimating Oncogenic Risks of Human Exposure to Agricultural Chemicals in Food Crops
Pages 174-195
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From page 174... ...
· What if regulatory thresholds were established by setting a limit based on total allowable risk by crop, or by pesticide? · How is risk distributed among types of pesticides—for example, apple fungicides or corn herbicides and how might risk be reduced as a result of alternative regulatory scenarios?
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From page 175... ...
Not only does this dramatically increase the types of analyses that can be conducted, it substantially reduces the cost, time, and expertise required to perform the analyses. The new system also contains simulation models that permit the user to change assumptions regarding tolerances, commodity consumption levels, percentage of acres treated, and oncogenic potency factors, instantly recalculating risks while graphing the results.
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From page 176... ...
Once on the mainframe, these files were transformed into a SAS data set. The Mean Consumption file, the Preamble file, and the TAS Tolerance file were then merged so that individual records included pesticide names and codes, commodity names and codes, published tolerance levels, mean consumption estimates, standard errors of these estimates, and a summary of toxicological data.
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From page 177... ...
For example, if column 1 contains data on pesticide type, column 2 contains data on commodity names, column 3 contains data on published tolerances, and column 4 contains data on mean food consumption adjusted by standard errors, it would be possible to place a formula in column 5 that tells Lotus to multiply the value in column 3 by the value in column 4 to obtain an estimate of pesticide intake (Theoretical Maximum Residue Contribution, or TMRC)
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From page 178... ...
Additional data fields were added to these identification codes to indicate the primary uses of each pesticide for example, fungicide, herbicide, or insecticide. Chemical Tolerance Data Tolerance data were derived from two sources: the TAS Tolerance file and the CFR Published Tolerance file, which lists all tolerances in the Code of Federal Regulations.
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From page 179... ...
The TAS Tolerance file contains 342 different pesticides (each assigned a CASWL code by the EPA) and 434 different commodity or food forms (each assigned a Raw Agricultural Code—called an EPARAC by the EPA)
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From page 180... ...
It was derived from the 90,000-record Person-Day Food Consumption file which was then averaged over the three days to produce a 30,000-record Average Daily Individual Food Consumption file. This file was then averaged over individuals to provide mean consumption estimates for the 273 food types reported by survey respondents.
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From page 181... ...
This does not mean, by contrast, that 95 percent of all individuals will consume less than the computed estimate, since the statistic was calculated from the standard error of the population mean consumption estimate, not from the standard deviations of individual consumption data. Pesticide Residue Data In an ideal world, accurate estimates of pesticide residues in foods and in water would be available as the basis for predicting average chemical intake (commonly described by the EPA as a chemical's Theoretical Maximum Residue Contribution, or TMRC)
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From page 182... ...
derived from the food consumption data and assumptions regarding the pesticide residue level in the food at the time of consumption. Regulatory Status Data Tolerances are listed in Titles 21 and 40 of the Code of Federal Regulations and in the TAS Tolerance file for pesticide-commodity combinations.
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From page 183... ...
When the TAS expansion program is run on the CFR Published Tolerance file, a record listing a tolerance for raw tomatoes, for example, is automatically expanded to separate consumption records for tomato paste, juice, catsup, and puree. Each food form carries with it the tolerance and CFR code from the original raw commodity.
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From page 184... ...
The code also made possible the distinction between risk associated with processed-commodity tolerances in the current CFR Published Tolerance file and risk associated with processed-commodity tolerances in the expanded TAS Tolerance file. Chemical Use and Cost Data For eight distinct crop-pesticide combinations, data on acre treatments, percentage of planted acres treated, and expenditures per acre were entered as new fields.
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From page 185... ...
The critical variables that are components of the risk calculation are briefly described below and more thoroughly described under Data Description and Sources, above, and Uncertainty in Oncogenic Risk Estimates, below. CHEMICAL RESIDUES The current tolerances and the residue estimates obtained through the TAS expansion of the CFR Published Tolerance file were used as the basis for estimating "worst-case" pesticide residues in the commodities.
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From page 186... ...
The Qua used by the EPA represents the upper bound of the 95 percent confidence interval surrounding the potency estimates. RISK ESTIMATES The estimate of risk is derived as the product of the estimate of pesticide intake and the estimate of the potency factor.
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From page 187... ...
This number means that an individual would have a 1 in 1 million risk of additional tumor induction above normal probability, assuming lifetime exposure to pesticide residues at the level indicated. PERCENTAGE OF ACRES TREATED In the crop-level analyses in the scenarios below, these risk estimates were adjusted by an additional estimate of the percentage of total acres of any single crop treated with a pesticide.
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From page 188... ...
The ultimate purpose of these scenarios is to estimate and compare the amount of risk; the number of pesticides, crops, and tolerances; and the percentage of total pesticide expenditures that would be affected by the application of the regulatory thresholds described above for each scenario. The calculation of these estimates required the development of several new data fields in the dBASE files: · Chemical-Crop Risk (RISKCCAJ, which is the summation of tolerance-specific risks for all raw- and processed-commodity tolerances associated with any specific pesticide-commodity combination (for example, all tolerances for captafol on apple products)
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From page 189... ...
The effects of the various regulatory scenarios on individual pesticides were estimated by first creating a file of unique pesticide-commodity records. Then, within this file, RISKCCA (total pesticide-commodity risk)
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From page 190... ...
In addition, percent affected acre treatments, and percent affected total pesticide expenditures were calculated for each crop. UNCERTAINTY IN ONCOGENIC RISK ESTIMATES This report includes numeric estimates of dietary oncogenic risk based primarily on tolerance, consumption, oncogenic potency, and percentage of crop acres treated data.
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From page 191... ...
In contrast, the assumption that residues will exist at tolerance levels will likely overestimate risk, except for new compounds for which tolerances have been set close to anticipated residue levels. The method of risk estimation adopted for this study assumes that residues occur at the tolerance levels, which the committee deems reasonable in the absence of other comprehensive and validated data sets on actual residue levels.
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From page 192... ...
This procedure is appropriate for estimating the 95 percent outer-bound level for population consumption means, but the estimate will be far lower than would an estimate of 95 percent outer-bound consumption based on means of individual consumption data adjusted by the standard deviations. For this reason, the consumption estimates used here for individual foods are likely close to the mean.
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From page 193... ...
By incorporating the percentage of acres treated into exposure and risk estimates and by assuming a random national distribution of these treated apples, the EPA would arrive at theoretical average exposure estimates that would tend to overestimate fungicide exposure to the portion of the population living outside the distribution region but underestimate exposure to the portion living within the distribution region. The percentage-acres-treated statistic was used to adjust the risk estimate for the eight crop analyses described above.
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From page 194... ...
Conclusion Worst-Case Scenario The certainty surrounding oncogenic risk estimates is directly related to the uncertainty associated with components of the risk equation: tolerance x consumption factor x potency factor x percent acres treated = risk. In drawing conclusions based upon this methodology, it is useful to remember a fundamental principle of probability: the probability of any outcome is the product of probabilities of independent variables that are believed to influence that outcome.
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From page 195... ...
1985. Nationwide Food Consumption Survey: 1977-78.
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