Past studies of diesel exhaust have generally lacked the historically relevant data necessary to identify many of these important exposure trends (Stayner et al. 1998) and have often lacked the work records necessary to identify job-related exposure differences and patterns over time (Bunn et al. 2004; Hesterberg et al. 2006; Valberg and Watson 2000). In our previous study of diesel exposure in U.S. rail- road workers (Woskie et al. 1988a, 1988b) we applied a single national average of total sus- pended particulates to control for the contribu- tion of background levels. In an earlier study of the U.S. trucking industry, Steenland et al. (1998) adjusted job-related diesel exposures historically using information on vehicle miles traveled, fuel consumption, and estimated emission factors of diesel engines. However, the relationship between occupational exposures and these characteristics was untested in the original exposure model proposed by Steenland et al. (1998), so there is no way to justify the use of these factors as temporal control fac- tors. A follow-up study that was conducted by Bailey et al. (2003) applied simulation methods to better incorporate the impact of these tem- poral controls and evaluate model uncertainty. A more recent assessment of diesel expo- sure in U.S. miners used CO levels to identify trends in work-related EC exposure conditions over time because of the absence of histori- cal EC data for this cohort (Vermeulen et al. 2010a). However, the correlation between CO and EC in the study locations where the miners worked was only moderate (Vermeulen et al. 2010b), and the use of CO as a surrogate for EC may lead to exposure misclassification bias. In the present study, historical EC data were available from the Zaebst et al. (1991) industrial hygiene survey of the trucking industry, which we used to explicitly adjust for changing EC exposure levels over time in our cohort. Also, the COH data we used to control for the indirect effect of changing background EC conditions over time at the terminals provides a much stronger surrogate marker of EC than does CO (Cass et al. 1983; Vermeulen et al. 2010b). Limitations. The retrospective assessment of exposures in this cohort required many assumptions that introduced uncertainty in the exposure estimates across workers and terminal locations in the cohort. The use of COH data from a single state (New Jersey) to estimate background exposure trends for the rest of the United States is problematic, because the temporal trend may vary across different locations. However, we captured spa- tial differences across terminal locations within the exposure modeling structure, and therefore the major limiting assumption of using the New Jersey COH data is that these spatial dif- ferences remain constant over time. Another limitation is that the present analysis addresses the historical extrapolation of worker exposures only back to 1971, which is the earliest year for which reliable exposure estimates could be constructed. Although this extrapolation period does not cover the entire period of exposure relevant to the epidemio- logic cohort, the missing years before 1971 represent a small percentage of the person years (8%) in the epidemiologic cohort. We are currently exploring options for assigning exposure levels for periods before 1971. In addition, exposure estimates for spe- cific workers depend in part on the accuracy of fuel-use timelines provided by the truck- ing companies. We have attempted to limit exposure misclassification by validating the exposure model for the current period as well as controlling for changes over time using 1970 1980 1990 2000 1970 1980 1990 2000 Year Year Figure 2. Annual EC trends in mechanics by temperature profile. For display purposes, the y-axis is scaled to exclude extreme outliers, which are defined as values > 75th percentile multiplied by the interquartile range. The boxes indicate 25th, 50th (median), and 75th percentiles. Extreme outliers are excluded for display pur- poses, and the length of the whiskers are bounded from above by 1.5 times the interquartile range. Environmental Health Perspectives • volume 119 | number 7 | July 2011 150 3) m (/µg100 n o i t a r t n e c n co 50 C E 0 Cold ( < 10°C) Warm ( > 10°C) Historical exposure to diesel in the trucking industry exposure data from Zaebst et al. (1991). In the upcoming epidemiologic analyses of this cohort, we are working to construct a range of exposure values to incorporate uncertainty around the point estimates presented here. At present, there is no established method for incorporating exposure uncertainty into a Cox proportional hazards model. Conclusion Our historical exposure model represents a sig- nificant improvement over exposure estimates used in other occupational studies of diesel and vehicular exhaust. Although the benefits of sta- tistical modeling as a retrospective assessment tool have been noted in the past (Seixas and Checkoway 1995), the use of SEMs described here provides a novel approach that represents both a more realistic and replicable framework for estimating cumulative exposure to EC in our national cohort. Overall, our assessment should reduce exposure misclassification by accounting for variation among terminals and U.S. regions, as well as changes over time. Ref Rencs Ahrens W, Stewart P. 2003. Retrospective exposure assess- ment. In: Exposure Assessment in Occupational and Environmental Epidemiology (Nieuwenhuijsen MJ, ed). London:Oxford University Press, 103–118. Bailey CR, Somers JH, Steenland K. 2003. Exposures to diesel exhaust in the International Brotherhood of Teamsters, 1950–1990. 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