Background: In a cohort study of lumber mill workers' exposure to noise and incidence of heart disease, initial noise estimates were likely overestimated because they did not account for reductions afforded by the use of hearing protection. As such information was seldom available for individual workers, modeling was necessary to predict hearing protection use and derive adjusted noise measures.
Objective: To develop a multilevel model of the likelihood of use of hearing protection devices (HPDs) for British Columbia (Canada) lumber mill workers.
Method: The study population included 13,147 workers in 14 sawmills for whom we had information on HPD use. Subjects self-reported their use of hearing protectors during routine hearing tests over their work history period. Separate multilevel logistic regression models with increasing complexity were developed for a subcohort of workers with complete information (n = 1493) and for a subcohort comprised subjects with hearing tests coinciding with their jobs (n = 10 203). The models included random intercepts for worker and for sawmill.
Results: HPD use was associated in both subcohorts with factors such as noise exposure and age. We also showed that specific jobs (such as sawfiling) and departments (planer, in particular) were strongly associated with the use of HPDs. The model illustrates the quantitative importance of including a hierarchical structure which allows for explaining potential sources of outcome variability.
Conclusions: We developed a hierarchical model to predict hearing protection use to enable correction of exposure assessments for use in retrospective epidemiological studies. We showed that this was feasible even in the absence of complete determinant information.