In this study we have investigated exposure patterns of atrazine and selected nonpersistent pesticides throughout a whole growing season among a group of corn farmers from the AHS cohort (Alavanja et al., 1996
). Atrazine exposure levels as measured by urinary atrazine mercapturate was significantly associated with the amount of atrazine applied but explained only part of the variability in metabolite levels. This information is being used to assess daily pesticide exposure throughout a year for an ongoing molecular epidemiologic study on various immune effects in corn farmers (Vermeulen et al., 2005
The goal of the exposure assessment in an epidemiologic study is to develop estimates of exposure for every agent of interest. In this study, 61 a.i. were reported. A standard panel analysis of the urine samples provided quantitative information on 14 a.i. (of which 6 overlapped with the 61 a.i. reported). The panel was developed to measure widespread exposures in nonoccupationally exposed subjects (Olsson et al., 2004
) and was selected for this study for practical reasons. The urine analysis panel covered four of the major pesticides used by the farmers including two of the most commonly used herbicides (atrazine and 2,4-D) and the most commonly used insecticide (chlorpyrifos) and provided some valuable data on background levels of other pesticides in a farming population (). Atrazine was the target pesticide in our study; therefore, urine collection scheduling was partially non-random with respect to atrazine applications. For other pesticides, the strategy was essentially random as urine collection days were selected a priori and independent of pesticide applications. Thus, except for atrazine, the application of the reported pesticides was not directly associated with urine collections. The data therefore have limited use for estimating exposure levels from application of other pesticides. However, the exposure data provided information on pesticide levels during seasons when the farmers are not applying pesticides, i.e., background levels. Based on the results of this study, it appears difficult to capture the application of more than one pesticide within a study if the sampling strategy is not targeted to specific pesticides of interest. To overcome this problem, and to limit practical and cost-related constraints associated with scheduled farm visits, self-collection of urine samples may be an alternative when several pesticides are of interest. In this study all samples collected as part of the self-collection protocol were obtained successfully and stored and documented appropriately until collected by the field-staff.
For atrazine, we found that urinary metabolite levels were directly related to recent application of the pesticide. However, a significant part of the between- and within-farmer variability in AZM levels could not be explained by differences in the total amount of a.i. applied or by any of the other recorded determinants. Differences in actual work practices, behaviors, metabolism and/or meteorological conditions might account for this. Under the assumption that unexplained variance leads to random misclassification our models can be used to predict exposure levels. However, the significant residual between-farmer variability observed in our model might indicate that the prediction error is not random and that this factor (although unexplained) needs to be accounted for in the prediction of the individual exposure levels. This can be done in our study by obtaining the empirical best linear unbiased predictors (EBLUPs) of the individual’s random effects parameter. Furthermore, there was some indication that even if atrazine was not recently applied, background levels (as assessed by the proportion of samples above the LOD) were higher among farmers than controls. However, these differences seemed to be minor as compared to differences in exposure levels during application. Therefore, it seems reasonable to estimate long-term exposure levels based on application information collected in diaries. It should, however, be noted that AZM is only one of the urinary metabolites of atrazine that can be measured in urine (Barr et al., 2007
). Results on background levels should therefore be interpreted with some caution.
For 2,4-D, we found that farmers had consistently higher urinary levels and larger proportions of samples above the LOD as compared to controls throughout the year, including when the pesticide was not recently applied. This suggests that farmers may be exposed to 2,4-D through other occupational, environmental or dietary sources that are not strictly related to the actual application of 2,4-D. This also seemed to hold true for the few farmers that did not apply 2,4-D themselves during this particular growing season. This observation might hint towards a more general environmental factor leading to 2,4-D exposure. However, we cannot exclude that applications were not reported accurately in the diaries, (e.g. applications actually performed by a contractor and not the farmer). Nonetheless, the elevated background levels of 2,4-D in farmers compared to controls indicates that the exposure to 2,4-D during non-application days needs to be investigated to develop an accurate estimate of long-term exposure to 2,4-D.
For acetochlor mercapturate, background levels were only slightly elevated among farmers as compared to controls while for TCPY, no differences in background levels were detected. For these compounds, it would appear that farmers’ exposure is more directly related to the application of these compounds as we observed for atrazine.
The farmers in this study experienced large day-to-day variability in exposure to atrazine because of differences in the application pattern throughout the year. This causes a challenge in epidemiologic studies because a small number of measurements may lead to imprecise estimates of long-term average exposures. Determinants of exposure were therefore identified for which information across the whole year was available. Estimation of long-term exposure based on empirical modeling of exposure using information from exposure diaries offers an advantage over using measurement data alone, as it will allow us to estimate the exposure level for each farmer for every day/week throughout the season. In a previous study among pig farmers, it was found that applying empirical models instead of the few exposure measurements available on each individual can compensate for the loss of information due to unmeasured factors affecting exposure (Preller et al., 1995
This exposure assessment study was designed to support a study of seasonal differences in immune markers related to atrazine applications and not for identifying all important determinants of atrazine exposure. As a result, the farmers were homogeneous with regard to many application characteristics. This likely limited our ability to identify other significant determinants of exposure such as glove use, washing hands, etc. which have been found to be important in other studies (Hines et al., 2001
; Stewart et al., 2001
). The model for atrazine may therefore not be suitable for predictions of urinary metabolite levels in other study populations with more diverse farming activities. Furthermore, as the sampling did not follow a strict probability sample procedure results might not necessarily reflect exposure circumstances among corn farmers in Iowa at large. However, the measured exposure levels among farmers after an application in this study are comparable to levels found in another study among Corn farmers in Iowa (Curwin et al., 2005b
In summary, the results of this study show that urinary atrazine metabolite levels were largely driven by recent application of the pesticide, and therefore the amount of pesticide applied, duration of application or acreage treated are likely to provide a valid surrogate of exposure in the molecular epidemiologic study. We observed that farmers, independent if they applied 2,4-D themselves or not have higher urinary levels of 2,4-D compared to controls throughout the active farming season even when no application of the pesticide has occurred within the last 7 days before a urine collection. Furthermore, since we were not able to identify sources of the increased background level of these farmers, these sources should be investigated in future studies of 2,4-D exposure.