Combining Decision Rules from Classification Tree Models and Expert Assessment to Estimate Occupational Exposure to Diesel Exhaust for a Case-Control Study.

Objectives: To efficiently and reproducibly assess occupational diesel exhaust exposure in a Spanish case-control study, we examined the utility of applying decision rules that had been extracted from expert estimates and questionnaire response patterns using classification tree (CT) models from a similar US study.

Methods: First, previously extracted CT decision rules were used to obtain initial ordinal (0-3) estimates of the probability, intensity, and frequency of occupational exposure to diesel exhaust for the 10 182 jobs reported in a Spanish case-control study of bladder cancer. Second, two experts reviewed the CT estimates for 350 jobs randomly selected from strata based on each CT rule's agreement with the expert ratings in the original study [agreement rate, from 0 (no agreement) to 1 (perfect agreement)]. Their agreement with each other and with the CT estimates was calculated using weighted kappa (κ w) and guided our choice of jobs for subsequent expert review. Third, an expert review comprised all jobs with lower confidence (low-to-moderate agreement rates or discordant assignments, n = 931) and a subset of jobs with a moderate to high CT probability rating and with moderately high agreement rates (n = 511). Logistic regression was used to examine the likelihood that an expert provided a different estimate than the CT estimate based on the CT rule agreement rates, the CT ordinal rating, and the availability of a module with diesel-related questions.

Results: Agreement between estimates made by two experts and between estimates made by each of the experts and the CT estimates was very high for jobs with estimates that were determined by rules with high CT agreement rates (κ w: 0.81-0.90). For jobs with estimates based on rules with lower agreement rates, moderate agreement was observed between the two experts (κ w: 0.42-0.67) and poor-to-moderate agreement was observed between the experts and the CT estimates (κ w: 0.09-0.57). In total, the expert review of 1442 jobs changed 156 probability estimates, 128 intensity estimates, and 614 frequency estimates. The expert was more likely to provide a different estimate when the CT rule agreement rate was <0.8, when the CT ordinal ratings were low to moderate, or when a module with diesel questions was available.

Conclusions: Our reliability assessment provided important insight into where to prioritize additional expert review; as a result, only 14% of the jobs underwent expert review, substantially reducing the exposure assessment burden. Overall, we found that we could efficiently, reproducibly, and reliably apply CT decision rules from one study to assess exposure in another study.