A lognormal distribution-based exposure assessment method for unbalanced data

We present a generalization of existing statistical methodology for assessing occupational exposures while explicitly accounting for between- and within-worker sources of variability. The approach relies upon an intuitively reasonable model for shift-long exposures, and requires repeated exposure measurements on at least some members of a random sample of workers from a job group. We make the methodology more readily applicable by providing the necessary details for its use when the exposure data are unbalanced (that is, when there are varying numbers of measurements per worker). The hypothesis testing strategy focuses on the probability that an arbitrary worker in a job group experiences a long-term mean exposure above the occupational exposure limit (OEL). We also provide a statistical approach to aid in the determination of an appropriate intervention strategy in the event that exposure levels are deemed unacceptable for a group of workers. We discuss important practical considerations associated with the methodology, and we provide several examples using unbalanced sets of shift-long exposure data taken on workers in various sectors of the nickel-producing industry. We conclude that the statistical methods discussed afford sizable practical advantages, while maintaining similar overall performance to that of existing methods appropriate for balanced data only.