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Determinants of dust exposure in sawmills : a comparison of fixed-effects and mixed-effects predictive statistical models Friesen, Melissa Charmaine
Abstract
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.
Item Metadata
Title |
Determinants of dust exposure in sawmills : a comparison of fixed-effects and mixed-effects predictive statistical models
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2001
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Description |
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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Extent |
3978065 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2009-08-05
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0090072
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2001-11
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.