Chronic exposure to high levels of noise may be associated with increased risk of cardiovascular disease. We therefore undertook a quantitative retrospective exposure assessment using predictive statistical modelling to estimate historical exposures to noise among a cohort of 27 499 sawmill workers as part of an investigation of acute myocardial infarction mortality.Methods:
Noise exposure data were gathered from research, industry and regulatory sources. An exposure data matrix was defined and exposure level estimated for job title/mill/time period combinations utilising regression analysis to model determinants of noise exposure. Cumulative exposure and duration of exposure metrics were calculated for each subject. These were merged with work history data, and exposure–response associations were tested in subsequent epidemiological studies, reported elsewhere.Results:
Over 14 000 noise measurements were obtained from British Columbia sawmills. A subset, comprising 1901 full-shift dosimetry measurements from cohort mills was used in producing a predictive model (R2 = 0.51). The model was then used to estimate noise exposures for 3809 “cells” of an exposure data matrix representing 81 jobs at 14 mills over several decades. Various exposure metrics were then calculated for subjects; mean cumulative exposure was 101 dBA*year. Mean durations of employment in jobs with exposure above thresholds of 85, 90 and 95 dBA, were 9.9, 7.0 and 3.2 years, respectively.Conclusions:
The utility of predictive statistical modelling for occupational noise exposure was demonstrated. The model required input data that were relatively easily obtained, even retrospectively. Remaining issues include adequate handling of the use of hearing protectors that likely bias exposure estimation.