We have developed models that identify important determinants of captan exposure among orchard applicators in the AHS. The first set of models (), which include kilogram of captan a.i. as a measure of application size, are the strongest predictive models; the second set of models (), which include application method as a measure of application size, are most comparable to the AHS pesticide exposure intensity algorithm. In addition to kilogram captan a.i. and airblast application, determinants of increased captan exposure in one or more models included formulation, repairing spray equipment, use of a spray additive, applicator age, and late tree fruit stage. Determinants associated with decreased exposure in one or more models included wearing CR gloves during mixing and applying, wearing a coverall/spray suit while mixing, and washing hands after mixing. Although use of an enclosed cab while spraying captan was identified as an exposure determinant in Dutch orchards (de Cock et al., 1998
), we did not find this effect in either univariate or multiple regression analyses, possibly because an enclosed cab was present on only 12% of the monitored days.
Formulation was an important exposure determinant for air, thigh, and forearm exposures (). On 98% of the days that captan was detected in air samples, applicators handled a wettable powder formulation while mixing. Among patch samples with detectable captan levels, the percentage of samples where applicators used a wettable powder was also high [right thigh (94%), left thigh (95%), right forearm (93%), and left forearm (93%)]. Wettable powder formulations are more likely to become airborne during mixing than liquid formulations and therefore more available for inhalation or deposition on the body. Exposure differences related to product formulation have been previously reported for pesticide urinary biomarkers (Arbuckle et al., 2002
; Alexander et al., 2006
; Thomas et al., 2010b
). Formulation was not a determinant of hand exposure, possibly because the hands were equally likely to contact liquid and wettable powder formulations during mixing. Whether formulation affects dermal uptake is unclear.
The above determinants were derived from models that used external metrics (air, hand, and dermal patches) as exposure measures; however, we also measured urinary THPI as a biomarker of captan exposure. If the external metrics we measured included the important routes of exposure, then exposure determinants identified for these external metrics should similarly modify THPI levels in the urine. To test this notion, we adjusted the algorithm to be consistent with our model results by changing the relative weights for airblast and hand spray in the [APPLY] variable and changing the [PPE] variable weights for wearing CR gloves and/or a coverall/suit. The correlation of the adjusted algorithm with urinary THPI improved substantially, going from highly non-significant using the original algorithm to statistically significant or nearly statistically significant for several THPI measures (). This improved correlation suggests that changes to the AHS algorithm to increase the contrast between airblast and hands pray and to increase the PPE reduction factor for wearing CR gloves and a coverall/suit should improve pesticide exposure intensity estimates for orchard applicators.
Some caveats should be noted in applying determinants identified from external measures to urinary THPI. First, dermal and urine sampling were conducted concurrently and the dermal sampling techniques we used, removal (hands) and interception (patches), could underestimate THPI levels by interfering with uptake. Second, THPI is a low abundance captan metabolite (1–2%; Krieger and Thongsinthusak, 1993
), which could limit detection of THPI at low exposures. Third, the effect of wearing a respirator could not be evaluated because the external exposure metrics were unaffected by respirator use.
Studies of workers applying pesticides to either crops, turf, or animals have reported reductions in pesticide exposure due to glove use of 82% (de Cock et al., 1998
), 96% (Stewart et al., 1999
), 71–98% (Hines et al., 2001
), 78% (Harris et al., 2002
), 62% (2,4-dichlorophenoxyacetic acid but no effect on 4-chloro-2-methylphenoxyacetic acid (Arbuckle et al., 2002
), 85% (Acquavella et al., 2004
), 27% (Alexander et al., 2006
), 81% (Alexander et al., 2007
), and 77–94% (Thomas et al., 2010b
). These exposure reduction estimates were obtained by either comparing reported GMs for glove versus no glove use or exponentiating the regression coefficient from models where glove use was a dichotomous (yes/no) independent variable and the dependent exposure variable had been log-transformed. While the exposure metric and pesticide varied across studies, these studies as well as our study suggest reductions in pesticide exposure intensity due to glove use range from ~60 to >95%. The association we observed between use of CR gloves and reduced exposure to the thighs was also observed for three herbicides in a study of custom applicators (Hines et al., 2001
We found higher between-worker variance as compared to within-worker variance ratios for our pesticide applicators (), indicating that observed differences in exposures were largely driven by individual behaviors and work practices. This contrasts with other studies of pesticide and non-pesticide agricultural exposures where variance ratios were higher for the within- as compared to the between-worker distributions (Kromhout and Heederik, 2005
). Our study participants were private farmers with fixed orchard acreage who sprayed captan on both days. From day-to-day, they tended to use the same equipment and PPE while spraying and to spray similar total acreage, conditions that would likely reduce day-to-day variability. For example, on the two sampled days, >90% of the applicators matched on formulation type, application method, glove use, and coverall/spray suit use. We also found a difference of ≤15% in ln(kilogram of captan sprayed), ln(number of acres sprayed), and ln(duration of application) for 54, 67, and 94% of the applicators, respectively, between the two sampled days. This day-to-day consistency in application size-related covariates is in contrast to agricultural commercial applicators whose day-to-day spraying activities depend on customer needs and who have higher day-to-day than between-worker variability (Hines et al., 2001
). Differences in the relative magnitude of the within- and between-worker variance ratios in agriculture (or any other work environment) are likely related to the particular characteristics of the tasks performed, the control measures used, and environmental conditions.
The higher total variability we observed for dermal as compared to air exposures is consistent with that reported in other studies (Kromhout et al., 1993
). In all our models, >50% of the total variability was not explained by the fixed effects. Partitioning this unexplained variability into within- and between-worker components is useful for understanding the degree to which factors that influence exposures between workers (e.g. work practices, equipment differences, PPE use) and factors that influence day-to-day exposures within workers (e.g. workload, amount of chemical used, changes in PPE use, meteorological conditions) underlie the unexplained variability. For example, for body and hand exposures, generally more of the unexplained variability was between workers as compared to within workers; however, the reverse was true for air exposures, suggesting a different focus is needed for identifying additional exposure determinants for dermal and air exposures.
Our results highlight several important issues for pesticide exposure assessment. First, pesticide exposure determinants can be body site and exposure route specific, e.g formulation was an important determinant of captan air, thigh, and forearm exposure, but not of hand exposure, a difference having implications for understanding exposure mechanisms and for methods to reduce exposure. This dependency of pesticide exposure determinants on site/route sampled has been previously reported in Dutch orchards (de Cock et al., 1998
) and in AHS applicators applying 2,4-D (Thomas et al., 2010b
). Second, given the importance that formulation can play in pesticide exposures together with the difficulty applicators can have recalling-specific formulations for products used in the past, including difficulty distinguishing between formulation of the purchased product and the physical state of the applied material, better methods are needed to capture product formulation in epidemiological studies. Third, application method appears to be a useful surrogate for measures of application size in specific situations; a not unreasonable notion in that methods used to apply pesticides to small acreages may not be practical for large acreages.
Finally, we note that the range of reduction afforded by the use of CR gloves reported in the literature is quite wide (27–98%). It is unclear if differences in glove composition, glove age, manner of wearing gloves, product formulation, or other factors explain this variability. In an exploratory analysis using a one-way analysis of variance (NLMIXED) with three levels of glove age (no/old/new glove) with worker as a random effect, we found that glove age was significant for hand exposure only when worn during application (P = 0.018) but not mixing, marginally significant if adjusted for kilogram of captan applied (P = 0.051), and not significant in a full multiple regression model (data not shown). Thus, future studies should examine glove performance factors in detail.
Strengths of this study include a large sample size, detailed observations of applicator activities, and repeated measurements to allow estimation of within- and between-worker distributions. We had only two repeat measurements per applicator, a number that was constrained by the frequency of seasonal captan applications across the group. Kromhout et al. (1993)
found that both the number of measurements and the number of workers had a negligible effect on the between-worker variance ratio, but a significantly greater influence on the within-worker variance ratio when the total number of measurements was >25 and the total number of workers >7, conditions that were met in our study. It is possible that if the observational period had been longer and additional measurements collected on each worker, we might have observed larger within-worker variance ratios.
Study limitations include a significant amount of left-censoring in our exposure metrics (44–54%); however, bias in parameter estimates due to left-censoring was minimized by using MLE techniques. Caution should be used in interpreting the magnitude of the regression coefficients for formulation in our models due to the small number (≤5) of air and patch samples with detectable values among applicators using a liquid formulation. Univariate analyses included a large number of comparisons and some statistically significant findings could have occurred by chance. If interested, the reader could perform a Bonferroni multiple comparison correction on a selected set of covariates. Because this was an observational study with applicators choosing their work practices and equipment, we could not randomize potential exposure determinants. The AHS participants in this study were all private farmer applicators and exposure determinants identified for this group may not be entirely applicable to commercial applicators or to other a.i.s.
In summary, the most consistent determinants of captan exposure among the AHS orchard applicators were a measure of application size (either kilogram captan a.i. applied or application method), wearing CR gloves and/or a coverall/suit, repairing spray equipment, and product formulation. Adjustment of the [APPLY] and [PPE] variable weights in AHS pesticide exposure algorithm based on our findings substantially improved the correlation between the AHS algorithm and urinary THPI levels. Since the unexplained variability was largely in the between-worker component, future efforts to identify additional determinants of AHS orchard applicator pesticide exposures should focus on behavioral and work practice factors that vary between applicators rather than factors that vary from day-to-day.