Assessing occupational styrene exposure in the European and US Glass Reinforced Plastics Industry for the period...

We aimed to develop a method for assessing occupational styrene exposures for application in epidemiological studies on risks of lymphohematopoietic neoplasms and other malignant and non-malignant diseases in the European and the US glass reinforced plastics industries.

We estimated a linear mixed effects model based on individual airborne personal measurements of styrene from the glass reinforced plastics industry in Denmark, Norway, Sweden, UK, and the US. The most suitable model was chosen based on its predictive power as assessed using cross validation with different combinations of predictors; and by comparing their prediction errors.

We created a database containing 21,201 personal and area measurements but a subset of 14,440 personal measurements that spanned a period from 1962 to 2018, were used in the analysis. The selected model included fixed effects for year, sampling duration, measurement reason, product, process and random effects for country and worker. There was strong agreement between the model's predictions and actual exposure values indicating a good fit (Lin's CCC: 0.85 95% CI 0.84, 0.85). There were regional differences in exposure levels, with the UK and the US having comparable exposures that were higher than those in the Nordic countries. Higher exposures were consistent with measurements collected for inspection purposes, the lamination process, and specific products. Styrene exposure levels have decreased annually on average by 7%.

Our exposure model and the resulting exposure predictions will enable estimation of lifetime occupational exposure for individual workers in the European and the US glass reinforced plastics industry and possibly related health risks among employees. The approach facilitates understanding of the uncertainty in our prediction model and can inform analysis of the bias that application of our exposure assessment approach can produce in epidemiologic analyses of exposure-response associations. Addressing systematic sources of bias can increase confidence in the conclusions of the epidemiologic analysis.