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The objective was to construct a retrospective job-exposure matrix (JEM) for the Norwegian silicon carbide industry. More than 3300 historical total dust measurements were available (1967-2005); however, there were few measurements of other agents. Total dust measurements were therefore used as the basis for the JEM, and a novel method was developed to estimate exposure to other agents. Multiple linear regression models were developed to describe historical exposure to total dust. Exposure estimates were extrapolated backward to periods without exposure data by adjustments for process and work-hour related changes. An exposure assessment study was performed where total dust was sampled in parallel with fibers or respirable dust. The respirable dust was analyzed for the content of quartz, cristobalite, and silicon carbide. Mixed-effect models were developed to estimate the exposure to these agents from total dust exposure, plant, and job group. Exposure to asbestos and polycyclic aromatic hydrocarbons was assigned qualitatively. Multiple linear regression models of total dust described historical exposure best in the furnace department (R(2) (adj) = 0.49-0.74). Models in the other departments explained less variance (R(2) (adj) = 0.12-0.32). Exposure determinants and total dust explained a substantial proportion of the between- (70-100%) and within-worker (8.0-54%) variance in the mixed-effect models. The relative bias between available historical measurements and the estimated exposure to dust components varied between -39% (fiber) and 40% (quartz). However, corrections were not considered necessary due to limitations in the historical data. The component-specific metrices were sufficiently different from each other (r(Pearson) < 0.7), with the exception of total and respirable dust (r(Pearson) = 0.84) and total dust and cristobalite (r(Pearson) = 0.72), and will enable component-specific epidemiologic analyses in the future. Improved and less correlated estimates of exposure levels for the different components in the dust were obtained with the updated exposure assessment. Due to limitations in the measurement data, assumptions had to be made, especially in the period before 1967. [Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Occupational and Environmental Hygiene for the following free supplemental resource: a file containing tables outlining multiple linear regression models for prediction of total dust exposure in the processing departments of Norwegian SiC producing plants, evaluation of the predictive abilities of the reduced total dust models, and mixed models for pedicting exposure to fibers and respirable quartz, cristobalite, non-fibrous silicon carbide and respirable dust from total dust exposure.].

Objectives The Network on the Coordination and Harmonisation of European Occupational Cohorts (OMEGA-NET) was set up to enable optimization of the use of industrial and general population cohorts across Europe to advance aetiological research. High-quality harmonized exposure assessment is crucial to derive comparable results and to enable pooled analyses. To facilitate a harmonized research strategy, a concerted effort is needed to catalogue available occupational exposure information. We here aim to provide a first comprehensive overview of exposure assessment tools that could be used for occupational epidemiological studies. Methods An online inventory was set up to collect meta-data on exposure assessment tools. Occupational health researchers were invited via newsletters, editorials, and individual e-mails to provide details of job-exposure matrices (JEMs), exposure databases, and occupational coding systems and their associated crosswalks to translate codes between different systems, with a focus on Europe. Results Meta-data on 36 general population JEMs, 11 exposure databases, and 29 occupational coding systems from more than 10 countries have been collected up to August 2021. A wide variety of exposures were covered in the JEMs on which data were entered, with dusts and fibres (in 14 JEMs) being the most common types. Fewer JEMs covered organization of work (5) and biological factors (4). Dusts and fibres were also the most common exposures included in the databases (7 out of 11), followed by solvents and pesticides (both in 6 databases). Conclusions This inventory forms the basis for a searchable web-based database of meta-data on existing occupational exposure information, to support researchers in finding the available tools for assessing occupational exposures in their cohorts, and future efforts for harmonization of exposure assessment. This inventory remains open for further additions, to enlarge its coverage and include newly developed tools. [ABSTRACT FROM AUTHOR]