| Summary: | Personal dust measurements (n=1516) collected over the period 1981-1997 for both research
and compliance purposes were used to construct two statistical models to predict historical dust
exposures for a cohort of 14 B.C. sawmills and 28,000 workers. Two multiple linear regression
models were built: (1) a fixed-effects model, with potential exposure determinants designated as
fixed effects; and (2) a mixed-effects model, with job title designated as a random effect and all
other variables as fixed effects. The two predictive models were validated against personal dust
exposures (n=213) from a large interior mill that was not part of the sawmill cohort.
The two models explained 36% of the dataset's variability. The predicted values were strongly
correlated with observed values for both models (fixed-effects model: Pearson r=0.617; mixedeffects
model: Pearson r = 0.619). The fixed-effects model predicted 56 of 58 jobs within ± 0.2
mg/m3 of the job geometric means. The mixed-effects model predicted 54 jobs within ± 0.2
mg/m . Multiple linear regressions revealed that the most important determinants of wood dust
exposure were process group, coastal mill location(-), number of employees(+), and annual
production levels per sawmill size(-).
The two models underestimated the validation mill's geometric mean exposure level by 0.5
mg/m³ . On average, outdoor jobs were underestimated by 0.8 mg/m³ and indoor jobs by 0.3
mg/m³ . Precisions within the datasets were poor, with GSD's of bias of 2.36 for both models in
the modeling dataset and 2.50 and 2.45 for the fixed-effects and mixed-effects models,
respectively, in the validation dataset. The predicted values from the two models were nearly
perfectly correlated for both the model building dataset (Pearson r=0.975) and the validation
dataset (Pearson r=0.985).
The mixed-effects model provided no improvement in predictive ability over the fixed-effects
model. Several jobs in the validation mill were predicted within the range of normal day-to-day
variability, but a few jobs were significantly underestimated, suggesting that the models are only
generalizable to mills of similar size, level of technology, and building/yard conditions.
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