Purpose: Existing estimated lung cancer risks per unit of asbestos exposure are mainly based on, and applicable to, high exposure levels. To assess the risk at low cumulative asbestos exposure, we provide new evidence by fitting flexible meta-regression models, a notably new and more robust method.
Methods: Studies were selected if lung cancer risk per cumulative asbestos exposure in at least two exposure categories was reported. From these studies (n = 19), we extracted 104 risk estimates over a cumulative exposure range of 0.11-4,710 f-y/ml. We fitted linear and natural spline meta-regression models to these risk estimates. A natural spline allows risks to vary nonlinearly with exposure, such that estimates at low exposure are less affected by estimates in the upper exposure categories. Associated relative risks (RRs) were calculated for several low cumulative asbestos exposures.
Results: A natural spline model fitted our data best. With this model, the relative lung cancer risk for cumulative exposure levels of 4 and 40 f-y/ml was estimated between 1.013 and 1.027, and 1.13 and 1.30, respectively. After stratification by fiber type, a non-significant three- to fourfold difference in RRs between chrysotile and amphibole fibers was found for exposures below 40 f-y/ml. Fiber-type-specific risk estimates were strongly influenced by a few studies.
Conclusions: The natural spline regression model indicates that at lower asbestos exposure levels, the increase in RR of lung cancer due to asbestos exposure may be larger than expected from previous meta-analyses. Observed potency differences between different fiber types are lower than the generally held consensus. Low-exposed industrial or population-based cohorts with quantitative estimates of asbestos exposure a required to substantiate the risk estimates at low exposure levels from our new, flexible meta-regression.