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OCR for page 174
B
Analytical Methodology for Estimating
Oncogen~c Risks of Human Exposure to
Agricultural Chemicals in Food Crops
JOHN P. WARGO
The charge to this committee required the ability to characterize and
analyze dietary oncogenic risk associated with exposure to pesticides
through food crops, totaled by individual pesticide, by crop, and by
pesticide-crop combinations. The capability of responding to various
assumptions and questions was also necessary; for example,
· How many pesticide residue tolerances would be affected if a
regulatory threshold Of 10-6 were established for a specific pesticide-crop
combination?
· What if the risk threshold were changed, or if the threshold were
applied only to risk from residues in processed foods?
· 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?
DATA MANAGEMENT SYSTEM
No computerized data management system existed prior to this study
that could respond to these questions. The conceptual framework for this
effort was derived in part from the U.S. Environmental Protection
Agency's (EPA's) Tolerance Assessment System (TAS), which is main-
frame based. The TAS joins pesticide-commodity tolerance data with
174
OCR for page 175
METHODOLOGY FOR ESTIMATING ONCOGENIC RISKS 175
food consumption data to estimate possible chemical intake levels.
Pesticide intake levels can then be converted into oncogenic risk esti-
mates if reliable oncogenic potency estimates are available. Also, the
TAS permits the comparison of possible daily pesticide intake levels with
Acceptable Daily Intakes.
The TAS represents a remarkable breakthrough in analytical capability by
the EPA, yet it is difficult and expensive to operate and update. And because
it is mainframe based, it is accessible only to those with the computers and
the expertise in mainframe operating systems and data analysis software.
The microcomputer-based system designed for this committee report
incorporates TAS food consumption estimates and tolerance data, but it
differs radically from the TAS in several critical respects. It operates on
IBM-compatible microcomputers and uses common statistical (SAS),
data-base management (dBASE III), and spreadsheet (Lotus) software.
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 lev-
els, percentage of acres treated, and oncogenic potency factors, instantly
recalculating risks while graphing the results. In other words, these
variables are linked by formulas in the system so that if any component in
the risk equation is changed, the net effect on oncogenic risk is instantly
demonstrated. The effects of as many as a dozen different tolerance-
setting strategies can be forecast and graphed in 15 minutes.
The structure of the new system differs significantly from the TAS in
the variety of new data fields. For example, the most recent EPA
oncogenic potency estimates are incorporated into the system, permitting
the transformation of estimates of chemical intake into estimates of
oncogenic risk. Raw commodities, processed commodities, and specific
pesticide-crop combinations are uniquely coded for separation into fields,
enabling the calculation of risk by each field. Economic and pesticide-use
data fields were added so that economic effects of different regulatory
scenarios could be forecast.
Also, new fields were created by mathematically transforming existing
fields. For example, the chemical intake field is calculated as the product
of the Code of Federal Regulations (CFR) published tolerance field and
the mean consumption field, and adjusted by the standard error field in a
manner dependent on the desired confidence interval. If desired, the risk
field could be adjusted by the percentage of acres treated. Total risks by
pesticide, by crop, and by pesticide-crop combination also exist in
separate data fields, having been calculated from the individual pesticide
and commodity risk fields.
.
OCR for page 176
76 APPENDIX B
The new system can be updated easily. Tolerance, toxicological,
ecological, and residue data are constantly being submitted to the EPA.
These new data can be added to the system simply by typing the data into
appropriate boxes on the screen.
Finally, a system for rapid electronic transfer of data between the
mainframes and the IBM-compatible microcomputers was designed.
DATA BASE CONSTRUCTION
Tape-to-Mainframe Transfer and Data Transformations
Several transformations of data files were necessary to conduct the
analyses presented within this report. First, several TAS files formatted in
SAS (Statistical Analysis System) were transferred from magnetic tapes
to an IBM 4043 disk, using IBM's Job Control Language. They included
the Mean Consumption file, containing estimates of consumption of the
376 distinct food types by 23 population groups along with associated
standard errors, and similar mean consumption data broken into 691
separate food forms.
Two additional files- the TAS Tolerance file and the TAS Preamble file—
were transferred to the mainframe disk in raw (rather than SAS) format. The
commodity codes in the TAS Tolerance file match the commodity codes in
the TAS Mean Consumption file, but the commodity codes in the CFR
Published Tolerance file do not match commodity codes in the Mean
Consumption file. A code conversion was therefore necessary to relate
current CFR tolerances to TAS-formatted consumption statistics. 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. This merged file was then sorted, first by
commodity code and second by pesticide code, and transformed into raw
(ASCII) format to prepare it for transfer to a microcomputer.
Mainframe-to-Microcomputer Transfer
The ASCII data files were then electronically transferred to a micro-
computer that is hardwired to the mainframe. A utility called YTERM
was used to transfer the files and to compare the original data and the
copied version, highlighting any transfer errors. The data were stored in
the directory containing the analytical software. (However, they can be
stored in any desired subdirectory on the microcomputer hard drive.)
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METHODOLOG Y FOR ESTIMA TING ONCOGENIC RISKS 177
Raw-to dBASE III Format
dBASE III was used to analyze the master data file. It not only stores
data within individual records but contains its own programming language
which permits mathematical operations such as sums of risks only for
specified criteria, and other operations such as merging, rapid sorting, and
indexing of large files.
Before the raw data set could be loaded into a dBASE file, the dBASE
file structure had to be created. Field names, types (numeric, character,
date, or memo), and lengths had to be specified to precisely match the
location of the data in the stream of numbers and characters lying in raw
data files. When the dBASE file format was established, the raw data set
was appended to the dBASE file "shell." (The only limitation to the size
of the data base is the available disk memory, since dBASE files do not
reside in Random Access Memory fRAM]. A 20-megabyte hard drive was
sufficient for the analyses performed for this study.)
dBASE III-t - Lotus 1-2-3 Transfer
One of the major limitations of dBASE III is the fact that mathematical
transformations of entire data fields must be accomplished within the
context of programs. dBASE is not interactive in a way that allows the
user to change a set of assumptions and to immediately see the effects on
the mathematically related fields.
Lotus 1-2-3 is similar to dBASE III in that data are aligned within records
and fields in a matrix format. The major difference between the two systems
lies in the ability of Lotus to establish formulate relationships between cells
of the data matrix. 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). Once this relationship is established by
formula, any change in one of the cells on which the formula is dependent
will change the visible value or result of the formula.
Lotus was therefore ideal for analyzing the sensitivity of pesticide-crop
risk to changes in assumptions regarding variables such as tolerances,
residues, consumption estimates, potency factors, and acres treated.
Once the risk formulas were established within the spreadsheet, any
changes in assumptions were instantly converted into changes in esti-
mated risk.
The primary limitation of Lotus lies in the fact that the entire package
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178 APPENDIX B
resides in RAM, which is generally limited to 640 kilobytes (kb) by IBM's
Disk Operating System (DOS) unless a special expansion board is
installed to increase memory. This memory constraint limits the size of
analytical spreadsheets. For example, the master dBASE file used in this
study was too large to fit within a single Lotus spreadsheet and, therefore,
had to be broken into smaller units—commonly, groups of pesticides or
crops. For the analyses conducted for this report, the RAM residency of
Lotus was not a problem, since most analyses were performed on
relatively small files.
DATA DESCRIPTION AND SOURCES
Diverse types of data are contained in the microcomputer-based system
used to calculate oncogenic risks for this study. These data, their sources,
and their relevance to the analyses presented are briefly described below.
For clarity, a data file can be thought of as a simple electronic matrix,
with data being stored within individual "cells," defined by the horizontal
and vertical location of the data. The horizontal rows of the file are known
as "records," and the vertical columns are known as "fields." Data can
be entered in character format (e.g., "BENOMYL") or numeric format
(e.g., ".0000435"~. Most computer-based analyses are performed on data
within the cells, through such operations as sorting or merging based upon
specified criteria, statistical analyses, or mathematical transformations
dependent upon user-specified formulas.
Chemical Identification Data
Chemicals are identified by various codes and names. Each chemical
considered in this study was assigned three different alphanumeric codes:
(1) a Chemical Abstracts Service code; (2) a Caswell code (CASWL); and
(3) a Shaughnessy code. Pesticides are also identified by preferred name,
and by as many as five alternate names. These data were derived almost
exclusively from the Preamble file from the EPA's Tolerance Assessment
System. 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.
The TAS Tolerance file contains essentially the same data as the CFR
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METHODOLOGY FOR ESTIMATING ONCOGENIC RISKS 179
Published Tolerance file, but there are several important distinctions. The
TAS file contains 16,526 pesticide-commodity records, as contrasted with
8,477 records'in the CFR file. This difference in file size can be explained
as follows. In the CFR file, for example, one tolerance might be listed for
captan on fresh tomatoes and another for captan in processed tomato
products. In the TAS file, however, each possible processed-food form-
catsup, juice, puree, and paste, for example is listed as a separate
record. This expansion was accomplished to more accurately represent
dietary exposure to the various food forms and to enable the estimation of
average daily pesticide intakes by food form.
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). (See Pesticide-Commodity Codes, below, for a description of these
codes.) All tolerances are expressed in parts per million. For this study,
use cancellations or suspensions were identified by adding a new data
field to each record. Canceled or suspended uses were deleted before any
risk calculations were performed.
The CFR Published Tolerance file mirrors the data contained in the
Code of Federal Regulations. The 1985 file includes 351 distinct chemi-
cals, 32 of which are listed as "Exempt" (40 CFR Part 180, Subpart D),
and 19 of which are listed as Generally Regarded As Safe (GRAS) (40
CFR § 180.21. Together, the Exempt and GRAS categories account for 97
of the 8,477 records.2 Each record contains the pesticide name and unique
pesticide code, the commodity name and unique code, the 1985 tolerance,
the CFR citation, and an EPA-assigned petition number.
Food Consumption Data
The food consumption (Mean Consumption) file was transferred di-
rectly from the TAS, and was based on a survey conducted during
1977-1978 by the Nutrition Monitoring Division of the Human Nutrition
Information Service (HNIS) of the U.S. Department of Agriculture
(USDA).3 Data were collected from 30,770 individuals who were asked to
recall food eaten the previous day and to record food eaten during the day
of the interview and the day that followed. Each individual reported the
food ingested, an estimate of the amount, the eating "event" (such as
breakfast or lunch), water consumption, general health status, height and
weight, and socioeconomic and geographic characteristics.4 The primary
sample was a multistage, stratified probability sample of all households in
the coterminous United States. Within this sample, four independent,
interpenetrating samples were drawn in four successive seasonal quarters
between April 1, 1977, and March 31, 1978. A similar survey is currently
OCR for page 180
i 80 APPENDIX B
being conducted by the FINIS; however, its results will not be available
until 1989 (Bruce Gray, personal communication, 19861.
The Research Triangle Institute (RTI) transformed the USDA con-
sumption survey into a Raw Agricultural Commodity Data Base, defining
376 unique food types.5 These foods included commodities for which the
EPA would be most likely to establish pesticide residue tolerances,
thereby permitting the estimation of pesticide intake by commodity. It
was not intended to include all edible foods. The list was developed from
various sources, including the EPA's Food Factor list, the CFR listing of
then-current tolerances, and the USDA food consumption survey
codebook. Although the list includes 376 unique food types, only 273 of
these foods were mentioned by the USDA survey respondents.
That survey did not include food components such as spices, estimated
to constitute less than 0.1 percent of a given food consumed. Estimates of
consumption for the 103 food types not reported by survey respondents
were derived from studies by the USDA,6 Magness et al.,7 the National
Academy of Sciences,8 and the International Tariff Commission.9 Finally,
an "arbitrary consumption value" (lo-6 g/kg body weight/day) was
assigned to foods for which tolerances do not exist, or in cases where
information could not be obtained.~°
RTI also developed a set of "food form" codes that distinguish
between raw, cooked, fresh, frozen, canned, dried, baked, broiled, fried,
pickled, corned, or salt-cured forms of the 376 food types. The Food
Form file includes 691 records, yet does not include any new food types.
Four records exist in the Food Form file for each of the 30,770 survey
respondents. The first record contains descriptive information (such as
age, sex, weight, census region); the second, third, and fourth records for
each respondent indicate food consumption for each of the three days
surveyed. Consumption is recorded in grams of food form per day.
Basic transformations of data conducted by RTI include
· Converting consumption from grams/day to grams/kilogram of body
weight/day;
· Aggregating consumption over foods; and
· Averaging consumption over days and/or individuals.
The USDA Average Food Consumption file was used as the primary
basis of this analysis. 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 respon-
dents. 12
Since this mean consumption estimate is essentially an arithmetic mean
OCR for page 181
METHODOLOG Y FOR ESTIMA TING ONCOGENIC RISKS I 8 1
derived from the sample, it was adjusted, or weighted, on the basis ot
sample size to derive an estimate of the population mean consumption for
each food type.~3
Standard errors of the mean consumption estimates for each of the
commodities were computed by RTI using a first-order Taylor-series
approximation of the deviations of the estimates from their expected
values. (Formulas used by SESUDAAN, a statistical package developed
by RTI, have also been reported by RTI.~4) These data were used to
calculate various confidence intervals that bound the estimated popula-
tion mean consumption.
For this study the high end of the 95 percent confidence interval, or two
standard errors above the population mean consumption, was chosen by
the committee to represent the U.S. average commodity consumption for
each food type. Thus, if a similar-sized sample were taken from the
population of the contiguous United States, there is a 95 percent proba-
bility that the population mean consumption estimate derived from the
second sample would lie within the same confidence interval as the first.
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). The FDA is responsible for
enforcing tolerances for all pesticides used in food. To do this, it samples
7,000 domestic and 5,000 imported shipments of food each year.
There are several potential problems with relying on the FDA's sampling
results to estimate food residues, most of which are associated with the
agency's declining budgetary resources. First, the number of samples tested
for each pesticide-crop combination is not large enough to develop popula-
tion consumption estimates with meaningful confidence intervals. Second,
detection of residues in raw agricultural commodities is often a meaningful
indicator of residues in processed-food forms. Drying, oil extraction, cook-
ing, and other processing techniques can dramatically alter pesticide residue
levels. Third, the EPA is currently requiring a battery of residue tests prior
to pesticide reregistration. Complete residue data are available for only a
small percentage of the pesticides that are currently registered, leading to
gaps and significant variation in quality of data on pesticides. Fourth, the
multichemical detection technology used by the FDA cannot detect residues
OCR for page 182
I 82 APPENDIX B
of all registered pesticides. Fifth, this multichemical detection technology is
not sufficiently sensitive to simultaneously detect residues of all the chemi-
cals that can be detected individually.
Together, these conditions led the committee to base its estimate of the
human pesticide intake (TMRC) on the assumption that the chemical is in
or on the food at the current tolerance level. This choice was made to
standardize the quality of data on chemicals, even though it will likely
overestimate chemical TMRCs since most FDA samples contained resi-
dues at levels substantially below established tolerances.~5
Toxicological Data
The pesticide oncogenic potency factors in this study were estimated by the
EPA's Hazard Evaluation Division. In no instance did the staff of the Board on
Agriculture or the authors of this report interpret the EPA potency estimates.
The potential for tumor induction is indicated by a "potency factor" or
Q*. The Q* is an estimate of the number of additional tumors that can be
expected to develop within a human population, based upon the dose
response results of animal bioassays. Laboratory animals are generally
exposed at much higher doses over their lifetimes than the average human
would normally encounter in his or her lifetime. Because animal life-spans
are much shorter than human life-spans, dose response data must be
extrapolated to predict human tumor incidence at the lower doses that
humans are likely to encounter in food or water.
With only several exceptions, the 28 potency estimates used in these
analyses were derived from the linearized multistage model of low-dose
extrapolation. The potency factor used by the EPA is the slope of the line
at the 95 percent upper confidence limit representing the dose response
relationship—(change in lifetime probability of extra tumor incidence)/
(unit of exposure of dose). The expression of the oncogenic potency
factor as a linear extra tumor incidence/dose ratio enables the prediction
of tumor incidence based on estimates of human chemical exposure. The
potency factor (estimated extra tumor incidence/dose) is simply multi-
plied by the pesticide intake estimate (dose) 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. A separate data field was created in the data base to
indicate the status of tolerances. However, risk calculations were based
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METHODOLOGY FOR ESTIMATING ONCOGENIC RISKS I 83
only on active tolerances, under the assumption that residues of canceled
or suspended pesticides would not be relevant to analyses designed to
measure the impact of theoretical regulatory policies. These data were
provided by the EPA in January 1986.~6
Food Processing Data
The TAS Tolerance file is an expanded version of the CFR Published
Tolerance file, as described above. Risk estimates based solely on the
CFR file would underestimate the risk for commodities for which toler-
ances are listed only for the raw form, even though residues could also be
expected in processed forms. The expansion program was developed by
the EPA to more acurately reflect probable sources of exposure to
chemicals in processed-food forms. 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. Where processed-food tolerances existed in the CFR
file, the correct tolerance and CFR citation are carried into the TAS
Tolerance file. Duplicates created by the expansion are automatically
deleted.
Since neither the TAS nor the CFR Tolerance files code individual
commodities by their raw or processed state, a new data field had to be
created. A new code Phantom was designed to distinguish CFR toler-
ances from TAS tolerances and to distinguish between processed forms of
commodities (where residue concentration might occur) and raw agricul-
tural commodities. The following Phantom codes were assigned to each
chemical-commodity record that existed in the TAS Tolerance file:
0 = New TAS raw-commodity tolerance created by TAS expansion;
1 = New TAS processed-commodity tolerance created by TAS expan-
s~on;
2 = 01d CFR processed-commodity tolerance;
3 = 01d CFR raw-commodity tolerance.
The Phantom codes were assigned to the food type codes known as
EPARACs, which are uniquely assigned to all food types contained in the
food consumption survey (see Pesticide-Commodity Codes, below, for
more information). The proper form (raw or processed) of each of the 376
commodities listed within the survey was determined by matching
EPARAC codes (explained below) in the CFR Tolerance file with
EPARAC codes in the TAS Tolerance file and through information
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I 84 APPENDIX B
provided by the Residue Chemistry Branch of the Office of Pesticide
Programs (OPP).
The development and assignment of the Phantom code made possible
the estimation of oncogenic risk based on the raw or processed form of
the commodity. 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. These combinations are corn- and soybean-
herbicides, cotton-insecticides, and apple-, tomato-, potato-, peanut-, and
grape-fungicides. The primary sources for these data included the Eco-
nomic Analysis Branch of the Office of Pesticide Programs and the USDA
Economic Research Service.
The addition of these data fields facilitates the analysis of pesticide-
commodity risk by acres treated, acre treatments, and expenditures.
Comparison of expenditures per acre for likely substitute pesticides
allows the development of analyses that estimate the cost per unit risk
reduction, assuming various patterns of pesticide substitution.
Pesticide-Commodity Codes
The TAS data sets include unique codes assigned to unique pesticides
(CASWL codes) and to unique commodities (EPARAC codes). Because
most analyses relevant to this study involved calculation of risk by
pesticide-commodity combination, it was necessary to develop a code
that was also unique to pesticide-commodity combinations. The CASRAC
code was designed for this purpose and is a combination of the CASWL
code (for unique pesticides) and the EPARAC code (for unique commod-
ities).
The EPA-designed EPARAC code was not suitable for the committee's
analyses. Each code has a five-digit numeric prefix and a two-digit
character suffix. For most commodities, all numeric suffixes are identical
for basic commodity forms, such as tomatoes or apples. In several cases,
such as peanuts, this was not the case, and the codes were redefined so
that all peanut records began with the same numeric prefix. This was
absolutely crucial to the development of a methodology to sum risks by
pesticide-crop combination. Now all distinct crop groups (apples, pea-
nuts, corn, grapes, etc.) have distinct EPARAC codes (renamed
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METHODOLOGY FOR ESTIMATING ONCOGENIC RISKS ~ 85
EPARAC 11. The CASRAC field, which is unique to each distinct
pesticide-crop combination, permitted the calculation of total pesticide-
commodity risks that might be affected if, for example, tolerances for
each pesticide-commodity combination exceeding a predefined risk
threshold were revoked.
ANALYTICAL METHODS
Risk Calculation
The method of oncogenic risk estimation used in this study is a minor
variant of the Routine Chronic Analysis used by the EPA and described
in the documentation of the Tolerance Assessment System. )7 The
oncogenic risk associated with any individual chemical can be calculated
only if a reliable estimate of oncogenic potency (Q*) has been developed
for that chemical. The committee's risk estimates are derived from 28 of
30 compounds for which oncogenic potencies were provided by the EPA.
In these cases, risk estimates were calculated for all distinctive food types
in which residues could be anticipated. For example, a separate risk
estimate was calculated for the residues of alachlor in raw corn as well as
alachlor in corn oil. Of the 16,500 tolerances that exist in the TAS
Tolerance file, risk estimates were derived for only 2,306 pesticide-
commodity combinations. The limited number of potency factors reflects
findings of non-oncogenicity for many compounds and the absence of
valid oncogenic or chronic feeding studies for numerous other pesticides.
For several of the scenario analyses presented below, calculation of the
number of pesticides and crops that would be affected by different
regulatory thresholds was based on a pool of 53 chemicals that the EPA
believes to be oncogenic, despite the absence of potency factors for 25 of
them.
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.
Although a far more accurate representation of likely exposure might be
developed through statistically valid commodity and residue sampling
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I 86 APPENDIX B
techniques, these data were not available for this study. Tolerances are
expressed in parts per million.
CONSUMPTION ESTIMATES
Commodity consumption estimates were based on the mean consump-
tion data developed from the 1977-1978 food consumption survey of
individuals within the 48 contiguous states. The consumption estimate is
a U.S. average statistic and could vary significantly beyond the mean for
individuals. This estimate was used to calculate the high end of the 95
percent confidence interval. Consumption estimates are expressed in
grams of commodity per kilogram of body weight per day.
ESTIMATE OF CHEMICAL INTAKE
An estimate of mean pesticide intake was developed by multiplying the
tolerance and the mean consumption estimate for each pesticide-
commodity record. This exposure estimate is called the Theoretical
Maximum Residue Contribution. The TMRC assumes that residues are
present at the tolerance level on every crop that has a tolerance, and that
all acres of all crops with tolerances are treated. Exposure to these
residues is expressed in milligrams of chemical per kilogram of body
weight per day. This method of calculating exposure to residues overes-
timates actual dietary exposure across the whole population, but it is
preferred by the EPA as an initial step in risk assessment because it
incorporates a prudent safety factor into the risk assessment process.
POTENCY FACTOR OR Q*
The potency factor, as calculated by the EPA, is the slope of the dose
response curve or line from animal oncogenic tests. This slope represents
the change in Y (tumor incidence) over the change in X (dose). Potency
therefore increases with the steepness of the slope. The units of the
potency factor are tumors per milligram of pesticide per kilogram of body
weight per day. The potency factor assumes that this average level of
exposure over a 70-year human life span is necessary for tumor induction.
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. Thus,
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METHODOLOGY FOR ESTIMATING ONCOGENIC RISKS ~ 87
TMRC x
(ma pesticide/
kg body wt/day)
Q* or potency factor
(excess tumor
x incidence/mg pesticide/
kg body wt/day)
excess tumor
incidence/unit dose
excess tumor
incidence/unit dose.
This amount is commonly very small, i.e., 0.000001 or 10-6, assuming
daily exposure at this level for a 70-year period. 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 resi-
dues 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. The percentage of acres treated
represents a national average which does not take into account regional
and local pesticide application and food distribution patterns. Ideally,
critical components of exposure analysis would include where the pesti-
cide is applied as well as the distributional pattern of produce within the
country.
Scenario Analyses
The analysis of changes in the distributions of risks and benefits
associated with alternative regulatory scenarios is a critical component of
this report. Four scenarios are considered and distinguished by various
risk levels that trigger regulatory prohibitive action. The threshold risk
level may vary between tolerances for raw and processed foods within
any scenario. The scenario analyses were performed using dBASE files
containing all pesticide-commodity combinations for the 53 compounds
identified by the EPA as potential oncogens. Scenarios dependent on
quantitative risk levels were conducted only on the 28 compounds with
Q*'s.
As noted in Chapters 2 and 3 of the report, it is virtually impossible to
distinguish which portion of the raw commodity will be sent to the fresh
produce market and which portion will be processed. In designing these
scenarios, the committee adopted the EPA's assumption that a regulatory
strategy is impractical if it denies processed-food tolerances while allow-
ing raw-commodity tolerances, in the expectation that residues in the raw
foods will somehow not make their way into the processed-food forms.
OCR for page 188
I88 APPENDIX B
Scenario 1 revokes all tolerances for all oncogenic pesticides. Scenarios
2, 3, and 4 require the identification of combined raw- and processed-
commodity risks associated with any individual pesticide under carefully
defined conditions that vary among scenarios. For example, Scenario 2
requires the cancellation of all processed-commodity tolerances with a
risk greater than zero, along with the cancellation of all raw commodity
tolerances that are associated with the canceled processed-commodity
tolerances. In this case, if the risk from pesticide X in apple juice
exceeded zero, then all tolerances for pesticide X in all apple products
would be canceled.
Scenario 3, by contrast, requires the cancellation of both raw- and
processed-commodity tolerances for any individual pesticide, if the
combination of raw-commodity and processed-commodity risks exceeds
a threshold probability of 1 x 10-6. Finally, Scenario 4 requires the
revocation of both raw- and processed-commodity tolerances for any
individual pesticide if and only if the risk associated with the processed
commodity tolerances exceeds the threshold probability of 1 x 10-6.
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 sce-
nario.
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 exam-
ple, all tolerances for captafol on apple products);
· Chemical-Crop Processed Risk (RISKCCPJ, which is the summation
of tolerance-specific risks for all processed-commodity tolerances for any
specific pesticide-commodity combination;
· Tolerances Affected (TOLAFFJ, which is the summation of toler-
ances affected by applying the regulatory standard in each scenario; and
· Crops Affected (CROPAFFJ, which is the summation of crops
affected by applying the regulatory standard of each scenario.
The creation of these data fields required the design of a new uniquely
defined chemical-commodity code (CASRAC), which is a combination of
the unique chemical code CASWL and the commodity code EPARAC.
An example of the file structure is shown in Table B-1.
To perform the scenario analyses, the file is sorted on the CASRAC
field and the records affected by a particular scenario are displayed.
Affected risks, tolerances, and crops are summed within this file, based
OCR for page 189
METHODOLOGY FOR ESTIMATING ONCOGENIC RISKS al S&9
TABLE B-1 Sample of Fields Created to Analyze Distribution of Risks
Associated with Alternative Regulatory Scenarios
File structure used to determine effects of regulatory threshold changes
TOL TOL CROP
CASRAC CHEM COMMODITY TYPE RISKCCA RISKCCP AFF AFF
04001075 CHEMX APPL RAW RAW .000008 .000007 3 1
04001075 CHEMX APPL JUI PROC .000008 .000007 3 1
04001075 CHEMX APPL DRY PROC .000008 .000007 3 1
11004093 CHEMY SOY RAW RAW .000006 .000004 2 1
11004093 CHEMY SOY OIL PROC .000006 .000004 2 1
File sorted on CASRAC field
CASRAC CHEM COMMODITY
04001075 CHEMX
11004093 CHEMY
TOL
TYPE RISKCCA RISKCCP
.000007
.000004
APPL RAW RAW .000008
SOY RAW RAW .000006
TOL CROP
AFF AFF
3
2
on the specific conditions defined by the scenarios. For example, scenario
2 would require RISKCCA, TOLAFF, and CROPAFF to be summed
conditionally for instances where processed-commodity risk (RISKCCP)
exceeds zero. In the example in Table B-1, since both cases meet the
condition specified, total affected risk would be 0.000014, total number of
tolerances affected would be 5, and total crops affected would be 2-
apples and soybeans. Scenario 3 would require that RISKCCA,
TOLAFF, and CROPAFF be summed conditionally for cases where total
pesticide-commodity risk exceeds the threshold probability of 0.000001.
Scenario 4 would require that RISKCCA, TOLAFF, and CROPAFF be
summed conditionally for cases in which RISKCCP (processed-
commodity risk) exceeds the threshold probability of 0.000001.
The scenario criteria were applied to three subsets of the master
dBASE file:
1. Chemical type analyses. The effects of applying each regulatory
scenario to types of pesticides (herbicides, fungicides, and insecticides)
were estimated by performing the calculations described in the previous
paragraph with the added condition that only the specific chemical class
of interest be used as the basis of calculation.
2. Individual chemical analyses. 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) was summed conditionally based on the
scenario-specific criteria wherein RISKCCP > 0 (scenario 21; RISKCCA
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190 APPENDIX B
~ 0.000001 (31; RISKCCP > 0.000001 (41. Again, affectedrisk, affected
tolerances, and affected crops were all calculated for each pesticide.
3. Crop analyses. Finally, the effects of applying the regulatory sce-
nario were estimated for eight crop groups of particular interest: apples,
corn, cotton, grapes, peanuts, potatoes, soybeans, and tomatoes. The
scenario-specific risk thresholds were again applied to calculate affected
risk and affected numbers of tolerances, as in the previous two cases. 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. This section briefly describes the types and
ranges of uncertainty that surround these estimates.
All risk estimates other than the crop-level estimates adjusted by the
percentage of crop acres treated represent a conservative upper-bound
calculation of the additional oncogenic risk across the U.S. population
from exposure to any oncogenic agent. Conservative upper-bound esti-
mates or "worst-case" estimates are used primarily to allow for uncer-
tainties in the independent variables that determine the risk estimate
(residues/tolerances, consumption, acres treated, and chemical potency).
Residue Estimates
All risk estimates in this report assume that chemicals exist in com-
modities at tolerance levels when consumed. The results of the FDA's
Market Basket surveys indicate that residues very rarely occur in raw
commodities at the tolerance level; they are more commonly at levels of
less than 50 percent of the tolerance. Similarly, residue levels can be
affected by the method of food preparation; for example, boiling certain
vegetables can volatilize water-soluble chemical residues. Therefore, it
seems reasonable to conclude that the assumption of exposure at full
tolerance is a highly conservative, very-low-probability event. However,
this is not always the case. An October 1986 report by the U.S. General
Accounting Office (GAO) found that 3 to 4 percent of all foods sampled by
the FDA contained violative residues.' Further, some pesticide metab-
olites and conversion products are known to increase during food storage
and cooking.
One of two assumptions could have been made when calculating
risks that residues exist in commodities at a level detected by the FDA's
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METHODOLOGYFOR ESTIMATING ONCOGENIC RISKS 191
sampling surveys, or that residues exist at the tolerance level in commod-
ities.
If one were primarily interested in calculating probable past exposure
and resulting risk, use of a residue survey, if statistically valid, would
seem reasonable. A problem then arises, however, when attempting to
use past residue data to project future risk, particularly for pesticides with
tolerances far in excess of residues actually detected. Use of such survey
data could underestimate risk in some cases; further, residues could
theoretically rise to the tolerance level without triggering a regulatory
response.
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.
Food Consumption Estimates
In all cases, the U.S. mean consumption estimate has been used to
calculate risk in this report. The mean consumption estimate has been
adjusted by the standard error, so that it actually represents the outer
bound of the 95 percent confidence interval for the population mean
consumption estimate. One can therefore conclude that there is a 95
percent probability that if a similar-sized sample were surveyed from the
same population at the same time, the estimate of the population mean
consumption level would not exceed the original outer-bound estimate.
However, one cannot conclude that 95 percent of the individual consump-
tion reports will fall below this level. That outer bound would likely be far
higher.
It is clear that using the U.S. average consumption estimate alone will
inaccurately estimate food consumption for many population subgroups.
For example, infants have a low level of diversity in their diets, and their
consumption of fruits and fruit juices (in grams/kilogram body weight/day)
is far higher than the U.S. average consumption estimate. Table B-2
demonstrates these differences for fresh apple and apple juice consumption.
As can be seen, some population groups may consume eight times the
U.S. average estimate for certain foods if measured on a milligram/
kilogram body weight/day basis. The certainty that U.S. mean consump-
tion will rarely be exceeded therefore seems quite low. Specific age
group-commodity combinations may exceed the U.S. mean consumption
estimate by as much as an order of magnitude.
OCR for page 192
~ 92 APPENDIX B
TABLE B-2 Apple Consumption by Age Class (g/kg body weight/day)
U.S. Nursing Nonnursing Children Children
Average Infants Infants Ages 2~ Ages 7-12
Fresh apples 0.457 2.203 2.854 1.228 0.762
Apple juice 0.222 2.517 3.464 0.994 0.198
NOTE: Differences in mean consumption estimates between subpopulations will result
in differences in chemical intake estimates or TMRC.
SOURCE: USDA 1977-1978 food consumption survey.
On the other hand, the exposure model used for risk assessment (the
TMRC method described above) also assumes that every individual
consumes some portion of every food form every day for 70 years.
Benomyl, for example, has nearly 100 food tolerances, and the exposure
model assumes that each individual is exposed to benomyl as a residue in
each of these 100 different foods every day for an average lifetime. While
it is prudent to consider this a possibility, its probability is extremely
remote, since most people do not eat all these foods and most chemicals
are used for far less than 70 years.
Finally, the estimate of outer-bound consumption used in this study is
based on a standard error adjustment of the mean consumption data. 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 devia-
tions. For this reason, the consumption estimates used here for individual
foods are likely close to the mean. Given a sample size of over 30,000
people, something approaching a normal distribution can be assumed,
suggesting that roughly 45 percent of the population will be consuming
higher levels than estimated in this study.
Acreage Treated
An additional source of uncertainty in risk estimation is the fact that
pesticide exposure estimates are not adjusted for likely geographical
patterns of pesticide application and food distribution. For all but the
above-described analyses of chemical levels in crops, the committee
assumed that all acres of all crops were treated with all pesticides for
which tolerances were available. In most cases this method of risk
estimation is also used by the EPA. In certain cases, however, an average
percentage of total acres treated is incorporated into the risk estimate and
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METHODOLOG Y FOR ESTIMA TING ONCOGENIC RISKS 193
risk assessment process. For example, there may be instances in which a
particular apple fungicide is used only in one part of the country, and the
apples from this area are distributed only within a several-state region
surrounding the production site. 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. These percentages
represent an average of three years, generally between 1981 and 1984 but
occasionally including 1985 use data. For soybeans, cotton, and corn,
1983 data were not used because of acreage reductions under the
Payment-in-Kind program. The following method was used for these
analyses: if 500,000 acres were planted with apples and 20 percent of
those acres were treated with benomyl, the apple-benomyl risk estimate
would be reduced by 80 percent (that is, the risk would be multiplied by
0.2~. To assume that the risk to all individuals is actually reduced by 80
percent requires an assumption that all benomyl-treated apples are evenly
distributed throughout the population. This is obviously a gross oversim-
plification of probable exposure.
If percentage-of-acres-treated data are incorporated, a theoretical U.S.
average oncogenic risk can be estimated, but it will disregard the high
probability that regional populations will be exposed at far higher levels.
For example, if the acres-treated estimate for an apple fungicide is 0.1 and
this estimate is used to adjust the risk estimate, it is probable that risk to
individuals who eat the treated apples will be underestimated by an order
of magnitude. If it is assumed that all acres are treated, however, then risk
will be overestimated for a large percentage of the population.
Crop-level risk estimates adjusted by the acres-treated data underes-
timate upper-bound risk for some percentage of the U.S. population.
Individual chemical risk estimates and pesticide group risk estimates for
herbicides, insecticides, and fungicides assumed that 100 percent of the
acres were treated and will probably overestimate risk for some per-
centage of the population.
The degree of over- and underestimation will be directly related to
(though only partially controlled by) the degree that pesticide use and
foods are evenly distributed around the country. This dispersion will vary
considerably among individual pesticide-crop combinations. In all cases,
the degree of uncertainty is inversely related to the percentage of acres
treated; for example, if 100 percent of all corn acres are treated with
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194 APPENDIX B
atrazine, the uncertainty associated with the percentage-acres-treated
statistic is negligible.
Toxicological Certainty
Oncogenic potency factors (Q*) were used as primary indicators of
toxicity in this report. They were derived primarily from the linearized
multistage low-dose extrapolation procedure used by the Office of Pesti-
cide Programs, yielding upper- and lower-bound estimates of excess
tumor incidence/unit of dose statistic. In order to introduce a margin of
safety into the risk assessment process that will in part compensate for (1)
uncertainties in characterizing the oncogenic response, (2) the existence
of sensitive individuals in the population, and (3) possible synergism of
pesticides or metabolites, the upper bound of the 95 percent confidence
interval, or Q*, is used as the potency factor by the EPA and in all cases
for this study. Most potency factors used to estimate risk in this study are
averages derived from the results of several positive oncogenicity studies.
The Q*'s derived from individual studies on the same compound can vary
by an order of magnitude or more.
Interspecies extrapolation modeling is a predictive device based on the
best available evidence. Perhaps its greatest value lies in the ability to
compare relative risks associated with individual chemicals and among
clusters of possible chemical substitutes, provided the chemicals being
compared were all tested in a similar manner.
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. Consider the case in which we are 95
percent confident that each component of our risk equation is an
upper-bound estimate (i.e., 95 percent of the cases will be less than the
level cited). The resulting probability of our risk estimate is then
(0.95)T x (0.95)C x (0.95)A= (0.81)RISK.
In summary, of the four components of the risk estimate, the use of the
tolerance rather than residue levels is the factor most likely to overesti-
OCR for page 195
METHODOLOGY FOR ESTIMATING ONCOGENIC RISKS 195
mate risk since residues are commonly far below tolerance levels. The
food consumption estimates used may cause risk to be underestimated
because of differences in diet among subpopulations. When acreage-
treated adjustment is applied as a U.S. average, exposure of regional
populations can be underestimated, depending upon chemical use and
food distribution patterns. Finally, it is extremely difficult to characterize
the certainty surrounding the oncogenic potency factor (Q*) other than to
recognize that it represents the conservative upper bound of the number
of excess tumors per unit dose at the 95 percent confidence interval.
NOTES
1. Code of Federal Regulations. 1986a. Title 21, Part 193. Tolerances for pesticides in food
administered by EPA. Washington, D.C.: U.S. Government Printing Office.
2. Code of Federal Regulations. 1986b. Title 40, Part 180. Tolerances and exemptions from
tolerances for pesticide chemicals in or on raw agricultural commodities. Washington,
D.C.: U.S. Government Printing Office.
3. Research Triangle Institute (RTI). 1985. Documentation of Analysis and Statistical
Methods Used in the Tolerance Assessment System. RTI Publ. RTI/2751/04-09F.
Research Triangle Park, N.C.
4. Rizek, Robert. 1985. Nationwide Food Consumption Survey: 1977-78. Hyattsville,
Md.: U.S. Department of Agriculture.
5. White, S. B., E. Crouch, and M. Cirillo. 1983. The Construction of a Raw Agricultural
Commodity Data Base. Research Triangle Institute Project 252U-2123-7. Research
Triangle Park, N.C.
6. U. S. Department of Agriculture. 1981. Agricultural Statistics. Washington, D.C.: U.S.
Government Printing Office.
7. Magness, J. R., G. M. Marble, and C. C. Compton. 1971. Food and Feed Crops of the
United States Bulletin 828. JR-4. New Jersey Agricultural Experiment Station.
8. National Research Council. 1979. 1977 Survey of Industry on the Use of Food
Additives. 3 vols. Washington, D.C.: National Academy Press.
9. International Tariff Commission. 1984. Spices and herbs. In summary of Trade and
Tariff Information. Publication 288. From personal communication with John Reeder,
Washington, D.C.
10. Research Triangle Institute, 1985, Appendix D.
11. Research Triangle Institute, 1985.
12. Id., p. 22.
13. Id., p. 25.
14. Id., Appendix D.
15. U.S. Food and Drug Administration. 1986. Annual Report of Food Residues in
Domestic and Imported Commodities: 1985. Washington, D.C.
16. U.S. Environmental Protection Agency. 1986a. Guidelines for estimating exposures.
Federal Register 51: 185. 9-24-86.
17. U.S. Environmental Protection Agency. 1986b. Guidelines for carcinogen risk assess-
ment. Federal Register 51:185. 9-24-86; Research Triangle Institute, p. 50.
18. U.S. General Accounting Office. 1986. Pesticide Need to Enhance FDA's Ability to
Protect the Public From Illegal Residues. GAO/ACED 87-7. Washington, D.C.: U.S.
General Accounting Office.
Representative terms from entire chapter:
tolerance file
