BACKGROUND: We analyzed 1,632 measurements of airborne wood dust reported to OSHA's Integrated Management Information System in the period 1979 to 1997. METHODS: The relationships between wood dust concentrations and various factors documented in the OSHA database were examined in a multiple regression model. RESULTS: Exposures ranged from less than 0.03 to 604 mg/m3, with an arithmetic mean of 7.93 and a geometric mean of 1.86. Reported exposure levels decreased substantially over time (e.g., unadjusted geometric mean in 1979 = 4.59 mg/m3; in 1997 = 0.14 mg/m3). High exposure jobs included sanders in the transportation equipment industry (unadjusted geometric mean = 17.5 mg/m3), press operators in the wood products industry (12.3 mg/m3), lathe operators in the furniture industry (7.46 mg/m3), and sanders in the wood cabinet industry (5.83 mg/m3). CONCLUSIONS: In the multiple regression model, year, state, job, and industry were found to be predictors of exposure. Year and state were likely surrogates for other factors which directly influence exposure, but which were not included in the IMIS database, such as the use of engineering control measures.
BACKGROUND: We analyzed 1,632 measurements of airborne wood dust reported to OSHA's Integrated Management Information System in the period 1979 to 1997. METHODS: The relationships between wood dust concentrations and various factors documented in the OSHA database were examined in a multiple regression model. RESULTS: Exposures ranged from less than 0.03 to 604 mg/m3, with an arithmetic mean of 7.93 and a geometric mean of 1.86. Reported exposure levels decreased substantially over time (e.g., unadjusted geometric mean in 1979 = 4.59 mg/m3; in 1997 = 0.14 mg/m3). High exposure jobs included sanders in the transportation equipment industry (unadjusted geometric mean = 17.5 mg/m3), press operators in the wood products industry (12.3 mg/m3), lathe operators in the furniture industry (7.46 mg/m3), and sanders in the wood cabinet industry (5.83 mg/m3). CONCLUSIONS: In the multiple regression model, year, state, job, and industry were found to be predictors of exposure. Year and state were likely surrogates for other factors which directly influence exposure, but which were not included in the IMIS database, such as the use of engineering control measures.
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