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NAS Report and Risk Assessment |
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The determination of appropriate pesticide residue levels on food items is a challenging task. In practice, residue levels have been estimated in a variety of ways, incorporating different assumptions leading to different levels of uncertainty. Approaches taken in the estimation of residue levels range from those that may be highly theoretical and assume that all residues are present at a predetermined level to more complex, data-intensive approaches based on actual measurements of residue levels at the time the food is ready to be consumed. A variety of intermediate techniques incorporating data on pesticide use and actual field residue levels may also be used (Winter, 1992a).
Some of the methods for assessing pesticide residue levels are shown in Figure 2. (Figure 2 not available electronically) Approaches requiring the lowest cost often yield the greatest amount of data but provide the greatest overestimation of residue levels. As refinements are made in the process to accommodate additional data, the accuracy of the residue estimates is improved, but the data is less available and the cost to produce additional data rises significantly.
The most common method to predict pesticide residue levels relies uses theassumption that residues are always present for all pesticides on all food items at the maximum allowable level, known as the tolerance. This theoretical approach provides enormously exaggerated residue estimates since it fails to take into account a variety of important factors such as extent of actual pesticide use, application practices, and post-harvest effects upon residue levels (Archibald and Winter, 1990). The tolerance values themselves are artificially high since tolerances are enforcement tools rather than safety standards and are set to exceed the maximum residue levels expected during legal application of a pesticide (Winter, 1992b).
The accuracy of the residue level estimates may be improved by making refinements for the actual extent of pesticide use. While this data is sometimes available, it is often difficult to obtain. As many as 41 individual states have pesticide-use reporting plans, although most are flawed by major limitations such as difficulties in keeping the data up-to-date or selective reporting of specific pesticides and/or crops to which the pesticides are applied.
A further refinement involves the substitution of actual field residue data for theoretical residue values. Most often, this refinement uses results from state and federal regulatory pesticide monitoring programs designed to enforce tolerances. Results over the past several years have indicated that the majority of foods analyzed contained no detectable residues, that residues rarely exceeded the tolerance levels, and that most residues, when detected, appeared at only a small fraction of the tolerance level (Winter, 1992a). The monitoring programs, however, are not routinely capable of detection of all possible pesticides that could appear as residues, and the sample sizes for specific pesticide/commodity combinations are not always large enough for results to be applied to the general food supply.
While field residue data provides a significant improvement over theoretical estimates of residue levels, it may not accurately represent the residue levels that consumers are exposed to at the time the foods are eaten. A variety of factors, such as processing, handling, transportation, cooking, peeling, and washing have been shown to have dramatic effects upon the ultimate level of residue reaching the consumer. In most cases, these effects reduce the residue levels significantly, although residue levels are occasionally increased by processing, which may also produce pesticide breakdown products of potential toxicological concern. When available, correction factors accounting for post-harvest effects may be used to convert field residue data into more accurate representations of residue levels at the time of consumption. Often, however, such data is not available.
The most accurate method to assess pesticide residue levels is through the analysis of food at the time of consumption. Studies performed in such a manner are often called "market-basket" studies. One such study, performed annually by the U.S. Food and Drug Administration, is known as the Total Diet Study (Pennington and Gunderson, 1987). In this study, foods are collected from retail outlets four times each year, once from each of the four geographical areas of the country. Each collection consists of the purchase of identical foods from markets in three cities in each geographical area, and the subsamples from each city are combined to form a market basket sample for analysis. A total of 234 different food items are selected for each market basket and all of the foods are prepared by institutional kitchens using standard recipes and normal washing, peeling, mixing, and cooking procedures into table-ready forms prior to analysis. While such an approach yields superior results, it is very costly and limited in sample size and pesticide coverage. Due to these factors, concerns have been raised as to the applicability of extrapolation of the results generated from the Total Diet Study to the entire U.S. food supply. At the same time, the Total Diet Study represents the most comprehensive market basket survey in existence.
The determination of the amount of consumption of particular food items by the population at large or by particular population subgroups is also important in the assessment of dietary pesticide exposure. Multiplication of food consumption estimates by estimated pesticide residue levels leads to an estimate of pesticide exposure that can be combined with results from the toxicology assessment to estimate risks.
In the U.S., food consumption estimates are most commonly derived from the results of U.S. Department of Agriculture (USDA) Nationwide Food Consumption Surveys. These surveys involve tens of thousands of individuals in the 48 continental states and Hawaii, Alaska, and Puerto Rico. A stratified probability sampling process is used and individuals are asked to describe the types and amounts of foods they consumed, both at home and away from home, for a defined three-day period. Results enable summarizations of consumption patterns for 10 food groups and 43 food subgroups, each of 10 sex-age categories, four income levels, three urbanization categories, two racial groups, and each of the four seasons.
The adequacy of this approach has been called into question. A major limitation appears to be the sample size for certain subpopulation groups such as nursing infants, which was considered from the latest (1987-88) survey to be too small to enable statistical credibility. In addition, the accuracy of surveys of this type is limited by recall bias and recall errors. The 1987-88 survey, in fact, was severely criticized by the U.S. General Accounting Office, which concluded that the data should not be used unless the greatest caution is employed due to the survey's low response rate (GAO, 1991).
As a result of the problems with the 1987-88 survey, much food consumption data used for risk assessment purposes still derives from the 1977-78 survey. Comparisons from 1987-88 and 1977-78 results indicate that fruit and vegetable consumption has increased, as has wine consumption among adults, wheras the consumption of beef and distilled spirits has decreased (Chaisson, 1990).
Differences in food consumption patterns among children are particularly dramatic. The 1987-88 survey indicated that children consumed 2.7 times as much apple juice than they did in 1977-78 and that their consumption of chicken and turkey has also increased. In contrast, the consumption of beef, pork, milk, eggs, and cooked fruits and vegetables by children decreased over the 10-year period (Chaisson, 1990).
In addition to problems resulting from difficulties in acquiring accurate food consumption estimates from national surveys, individual variation in food consumption by members of the same population subgroup is also difficult to account for in the risk assessment process. This is particularly important in determining acute exposure to pesticides in the diet, since short-term consumption of particular foods may differ dramatically from average consumption.
To compensate for some of the inaccuracies in food consumption estimation, it is often suggested that average levels of consumption be replaced by consumption estimates of those consuming the greatest amounts of individual food items. This approach may overcome the possibility of underestimating the exposures to some members of the population but may also lead to unrealistic exposure estimates for the general population since it does not allow for compensation in the diet to reduce consumption of other foods. It is likely that this issue will be considered in the upcoming NRC report.
For non-carcinogenic risks, the exposure estimates are compared with the ADI (or RfD) values obtained during the toxicological assessment. In cases where the estimated exposure is at levels below the ADI, the exposure is considered to be too low for any toxicity to occur, and the risk is deemed to be insignificant or negligible.
For the protection of public health, it is hoped that exposures to pesticides in the diet will always be below the ADI values. It is quite likely, however, that some exposures may exceed the ADI, particularly when conservative assumptions concerning uncertainty factors, residue levels, and food consumption estimates are used. In cases where exposure exceeds ADI levels, one needs to realize that while the NOEL levels are toxicity thresholds, the ADI values are not derived from the NOELs are not. The finding that exposure may exceed the ADI value may suggest that regulatory changes are necessary or that more realistic data on exposure to the pesticide is needed. Exposures in excess of the ADI, however, should not be interpreted as exposures that do indeed cause harm since substantial substantial margins of safety may already be incorporated into the risk assessment practice.
To calculate potential cancer risks, the exposure estimates are multiplied by the cancer potency factor (Q1*). This yields a theoretical cancer risk with its units of excess cancers per population exposed. Often, cancer risks are reported in terms of "excess cancers per million persons exposed over a lifetime," or "cancers per million" (cancers x 10-6). The figure of one cancer per million (1 x 10-6) is often considered to represent a "negligible" cancer risk, since it pales in comparison to the actual lifetime risk of cancer in the U.S. (approximately 250,000 x 10-6, or one in four).
Caution must be exercised to avoid misrepresentation of cancer risk estimates. Since significant uncertainty is factored into the process of carcinogen risk assessment through the use of conservative mathematical models and statistical corrections, the calculated cancer risks represent the upper bound level of risk; the actual risks may be significantly lower or even zero (Winter, 1992a). Incorporation of a "body count" analysis, in which the risk estimates are converted into "actual" cases of human cancer, is therefore inappropriate. It is critical to remember that risk assessment is a tool on which to base regulatory decisions and set priorities and should not be considered as a tool that predicts actual health effects.
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