title: Comparison of formaldehyde exposure levels in two multi-industry occupational exposure databanks using multimodel inference.
abstract: The recent increase in international multicenter epidemiologic studies of occupational hazards and the current development of risk analysis tools based on multinational exposure data provide an incentive to evaluate the extrapolation of exposure data across countries. We compared formaldehyde measurements in the French (COLCHIC) and U.S. (IMIS) occupational exposure databases for 1986-2001 using multimodel inference. In this approach, conclusions are based on a set of plausible models rather than on a single model. Modeled variables included data source, industry, year, and sample type (short-term or long-term). The model set included 72 models corresponding to testing all variables and 5 interactions and estimation of 93 coefficients. As it is impossible to classify non-detects (ND) as short-term or long-term samples, they were removed from IMIS prior to analysis. Respectively, 3143 and 2646 IMIS and COLCHIC data, spread across 28 common industries, were analyzed. The full model explained 21% of the total variability. All fixed effects and the source-industry interaction were shown as strong predictors of exposure. The temporal trend (5% yearly decrease) and difference between short-term and long-term data (short-term greater than long-term by two-fold) were similar across the two databanks. Industry-specific differences between IMIS and COLCHIC were detected only for health-related activities for which COLCHIC levels were higher (~ four-fold). Sensitivity analyses were conducted to assess the impact of excluding NDs from IMIS. They showed initial predicted industry-specific geometric means for IMIS data potentially multiplied by factors from 0.42 to 0.98 (long-term data) and from 0.11 to 1.4 (short-term data) when NDs were included with various distributions across the short-term and long-term categories. The most realistic scenario yielded 0.82 for long-term (18% overestimation) data and 1.05 (5% underestimation) for short-term data. Although tempered by a probable non-detect bias, our analysis showed that both databanks provide a similar multi-industry portrait of formaldehyde exposure despite a potential for very different occupational settings. Our results offer encouraging insight about extrapolation of exposure data across countries.
authors.affiliation: Centre de recherche du CHUM, Université de Montréal, Montréal, Quebec, Canada. Jerome.lavoue@umontreal.ca
authors.forename: J
authors.initials: J
authors.lastname: Lavoué
authors.forename: M
authors.initials: M
authors.lastname: Gérin
authors.forename: R
authors.initials: R
authors.lastname: Vincent
mesh_terms: D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D004785:Environmental Pollutants / Q000032:analysis*; D005557:Formaldehyde / Q000032:analysis*; D005602:France; D006801:Humans; D016273:Occupational Exposure / Q000032:analysis*; D014481:United States
chemical_list: D004785:Environmental Pollutants; D005557:Formaldehyde