In this study of 31 2,4-D applicators, we constructed pharmacokinetically based exposure models that related urinary 2,4-D measurements to various work-related tasks assessed in detailed daily diaries up to 7 days before urine collection. The primary model (model A) incorporated pharmacokinetic weights based on a study by Harris et al. (2001)
that found exposures from 6 days before urine collection contributed to the measured urinary 2,4-D levels. A model that simply related urinary 2,4-D measurements to determinants from the previous day would have given incorrect results because applicators typically worked with 2,4-D on multiple days before the urine sample was collected. Given the extended excretion time of 2,4-D, a large urinary measurement, for instance, could be due to an exposure that occurred up to 5 days previously but would be incorrectly attributed to determinants from only one day previous. A practical solution to this problem would have been to exclude any urine sample with a 2,4-D application in the previous 6 days. In many circumstances, this would result in exclusion of a large fraction of study participants. In our study, this would have left only 37 observations for the analysis. Moreover, because of the pharamacokinetics of 2,4-D, the contribution of exposure from the day of application to a subsequently collected 12-h urine sample is minimal resulting in a small biological signal.
A more appropriate set of weights for the daily determinants was not suggested by our data; a model with separate ln-total time variables for the day of urine sample collection and 5 days previous did not produce a better fit compared with a model that included a single ln-total time variable constructed using the Harris et al. (2001)
weights (results not shown).
As models A and B were quite similar, it seems that the specific weights assigned to each day of exposure are not absolutely critical, although the sizes of some of the effect estimates of the exposure predictors changed substantially between the two models. However, when the numbers of days of exposure that are considered were altered, the specific determinants in the models changed. The models obtained under these weighting schemes were, however, less consistent with each other and resulted in counterintuitive results, such as lower urinary 2,4-D levels for participants reporting wet pants or shirts. It seems that the number of days considered under the Harris et al. (2001)
model provided the most plausible exposure determinants. Moreover, when these determinants were tested under the other weighting schemes, most of the determinants were found to be significant and these models had only a marginal loss of fit (as indicated by the AIC) as compared with the “optimal” models actually derived under these weighting schemes. The differences in the obtained models are probably a reflection of the pitfalls of using stepwise procedures instead of expert judgment in model building. Also, because we evaluated a large number of predictors with urinary 2,4-D levels, we expect some spurious associations due to chance. Nonetheless, these observed differences may have relatively limited consequences when using the models for predicting exposures (i.e., it is the best fit to the observed data). However, for understanding exposure determinants and for estimating the impact control measures might have, dependence of the effect estimates on the chosen weighting schemes makes these models difficult to interpret.
Arbuckle et al. (2002)
examined predictors of urinary 2,4-D levels among 126 farm applicators in the first 24 h after the first pesticide application of the season (Arbuckle et al., 2002
). The variables pesticide formulation, protective clothing, application equipment, handling practice, and personal hygiene practice were found to explain 39% of the variability in 2,4-D dose. The mean and geometric mean urinary levels among 43 applicators reporting use of 2,4-D were 27.63 and 5.63 μg/l, respectively. The mean and geometric mean levels in our study were much higher (185.9 and 56.1 μg/l, respectively). This may be due to the longer duration and more frequent application by our participants. The strong effect of month of application may suggest a build-up of internal 2,4-D levels over time; however, this could be a reflection of changing work habits with changes in the work environment (e.g., less clothing as the temperature rises).
In a study of turf applicators, Harris et al. (2002)
used two 24-h urine measurements to calculate total doses received by applicators over 6 days of application. The calculations were based on data from the earlier pharmacokinetic study (Harris et al., 2001
) and on the assumption that the ratio of the total dose to the amount of 2,4-D used did not change within the 6-day time period for an individual applicator. The total doses were subsequently linked to determinants from a questionnaire completed at the conclusion of urine sampling. Harris et al. (2002)
found that volume of pesticide applied explained 20% of the variation in 2,4-D dose among 98 professional turf applicators over a 1-week period (mean and geometric mean daily dose of 2,4-D 1399 and 420 μg, respectively). Type of spray nozzle used and the use of gloves while spraying explained an additional 43% of variation in 2,4-D dose (Harris et al., 2002
In a study of 34 farm applicators and their families with urine samples collected 1 day before through 3 days after an application, glove use, repairing equipment, and number of acres treated were found to be the most significant predictors of 2,4-D concentration among applicators (geometric mean urinary 2,4-D concentration 1, 2, and 3 days after application was 33.4, 33.3, and 16.3 μg/g creatinine, respectively) (Alexander et al., 2007
As with our study, earlier studies found that a proxy for “amount” of 2,4-D used was an important determinant of exposure. In our study, ln-transformed total time handling 2,4-D and application concentration were found to be predictors of urinary 2,4-D levels when using the Harris et al. (2001)
weights. In contrast with earlier studies, protective clothing was not found to be an important predictor in our models.
The detailed work diaries allowed for a comprehensive examination of the impact of work tasks, personal protective equipment, and personal hygiene practices over multiple days on urinary 2,4-D concentration; however, the primary model explained only 16% of
and 23% of
in urinary 2,4-D concentration. The small amount of explained variation may be in part because we modeled 12-h urinary 2,4-D concentrations rather than doses, which the pharmacokinetic weights were based on (Harris et al., 2001
). We were unable to model dose because data on the times and volumes of urine sample collection were unavailable. Unobserved behavioral or environmental factors may also account for a large portion of the unexplained variability. These factors (e.g., more detail on use of personal protective equipment and protective practices, contamination of surfaces in the home and elsewhere, meteorological conditions, quality of application equipment, skin permeability differences) may be difficult to capture in questionnaires, but studies to identify them are needed.