Formaldehyde Exposure in U.S. Industries from OSHA Air Sampling Data

Abstract

National occupational exposure databanks have been cited as sources of exposure data for exposure surveillance and exposure assessment for occupational epidemiology. Formaldehyde exposure data recorded in the U.S Integrated Management Information System (IMIS) between 1979 and 2001 were collected to elaborate a multi-industry retrospective picture of formaldehyde exposures and to identify exposure determinants. Due to the database design, only detected personal measurement results (n = 5228) were analyzed with linear mixed-effect models, which explained 29% of the total variance. Short-term measurement results were higher than time-weighted average (TWA) data and decreased 18% per year until 1987 (TWA data 5% per year) and 5% per year (TWA data 4% per year) after that. Exposure varied across industries with maximal estimated TWA geometric means (GM) for 2001 in the reconstituted wood products, structural wood members, and wood dimension and flooring industries (GM = 0.20 mg/m(3). Highest short-term GMs estimated for 2001 were in the funeral service and crematory and reconstituted wood products industries (GM = 0.35 mg/m(3). Exposure levels in IMIS were marginally higher during nonprogrammed inspections compared with programmed inspections. An increasing exterior temperature tended to cause a decrease in exposure levels for cold temperatures (-5% per 5 degrees C for T < 15 degrees C) but caused an increase in exposure levels for warm temperatures (+15% per 5 degrees C for T >15 degrees C). Concentrations measured during the same inspection were correlated and varied differently across industries and sample type (TWA, short term). Sensitivity analyses using TOBIT regression suggested that the average bias caused by excluding non-detects is approximately 30%, being potentially higher for short-term data if many non-detects were actually short-term measurements. Although limited by availability of relevant exposure determinants and potential selection biases in IMIS, these results provide useful insight on formaldehyde occupational exposure in the United States in the last two decades. The authors recommend that more information on exposure determinants be recorded in IMIS.

Maternal Occupation and Term Low Birth Weight in a Predominantly Latina Population in Los Angeles, California

“…As with the other high risk occupations above, in our study, the majority (93%) of women holding these production jobs - mainly factory line work and jobs in the textile industry - were first-generation immigrant Latinas. Exposures here include chemicals from processing and dyeing of materials such as formaldehyde (37), organic dusts, musculosceletal stresses, and noise (38), all having the potential to impair fetal development (39, 40). To date, a very limited number of studies of pregnant women in these occupations considered fetal growth.…”

Characterization of lead in US workplaces using data from OSHA's integrated management information system

“…These variables have been described previously [Froines et al, 1989;Gomez, 1997]. Specific chemical and physical agents have been analyzed previously using IMIS including silica [Froines et al, 1986;Stewart and Rice, 1990;Linch et al, 1998;Yassin et al, 2005], noise [Middendorf, 2004], formaldehyde [Lavoue et al, 2008], beryllium [Henneberger et al, 2004], and wood dusts [Teschke et al, 1999] among others. Airborne lead samples have been analyzed previously for the years 1979-1985[Froines et al, 1990 and the years 1979-1997 [Okun et al, 2004].…”

Statistical modeling of occupational chlorinated solvent exposures for case–control studies using a literature-based database

“…Teschke et al (1999b) used IMIS data and a multiple regression model including terms for measurement year, state, industry group, and job group to estimate wood dust exposure levels for a population-based case-control study without having detailed questionnaire information about wood dust exposure. Lavoué et al (2008) used IMIS data to model formaldehyde concentrations as a function of inspection type, sample type, season, industry, year, number of workers, state, and mean outside temperature. However, databases such as these (i.e.…”