Abstract

Background: Rectal cancer has been previously associated with exposure to metalworking fluids in a cohort mortality study of autoworkers.

Objective: To better specify the exposure–response relationship with straight metalworking fluids (mineral oils) by applying non-parametric regression methods that avoid linearity constraints and arbitrary exposure cut points and by lagging exposure to account for cancer latency, in a nested case–control analysis.

Methods: In addition to the classical Poisson regression with categorical exposure, survival models with penalised splines were used to estimate the exposure–response relationship between cumulative exposure to straight metalworking fluid and mortality from rectal cancer. Exposures to water-based metalworking fluids were treated as potential confounders, and all exposures were lagged by 5, 10, 15 and 20 years to account for cancer latency. The influence of the highest exposures was dealt with by a log transformation and outlier removal. The sensitivity of the penalised splines to alternative criteria for model selection and to the placement of knots was also examined.

Results: The hazard ratio for mortality from rectal cancer increased essentially linearly with cumulative exposure to straight metalworking fluid (with narrow confidence bands) up to a maximum of 2.2 at the 99th centile of exposure and then decreased (with wide confidence bands). Lagging exposure up to 15 years increased the initial steepness of the curve and raised the maximum hazard ratio to 3.2.

Conclusions: Non-parametric smoothing of lagged exposures has shown stronger evidence for a causal association between straight metalworking fluid and rectal cancer than was previously described using standard analytical methods. This analysis suggests an exposure–response trend that is close to linear and statistically significant over most of the exposure range and that increases further with lagged exposures. Smoothing should be regularly applied to environmental studies with quantitative exposure estimates to refine characterisation of the dose–response relationship.