gms | German Medical Science

Introduction: The association between occupational exposure to welding fumes and lung cancer has been extensively studied. The most common exposure metric is the cumulative index of exposure (the product of intensity and duration of exposure). The use of this metric implicitly assumes that the effect of exposure is additive and that the impact of cumulative exposure on lung-cancer risk is the same regardless of dose or temporal pattern. However, it is possible that individuals with the same cumulative exposure index, but different temporal exposure patterns show different risks. Therefore, there is still a need for research to adequately capture the time-varying intensity of exposure and to identify critical time-windows during which exposure has the strongest impact on lung-cancer risk.

Methods: Latent Class Mixed Models (LCMM) to identify trajectory classification profiles (latent classes) of exposure histories were proposed to estimate associations with lung-cancer risk. LCMM simplifies heterogeneous lifetime exposure into more homogeneous classes and identifies distinct subgroups of exposed individuals, following a similar exposure pattern over lifetime. This approach could provide a more complete picture of the welding history, opening a different perspective on the dose-effect relationship between exposure and lung-cancer risk. The purpose of this study was to determine latent classes for welding-fume exposure in two German population-based case-control studies (3,498 lung-cancer cases and 3,539 control subjects) and to use these classes to estimate smoking-adjusted odds ratios (OR) with 95% confidence interval (95%CI) via multiple conditional logistic regression. Before applying the LCMM function, exposure levels for each welding activity were determined using a measurement-based job-exposure matrix with estimates from 15,473 inhalable measurements taken at welding workplaces.

Results: LCMM identified four latent classes of welding-fume exposure as the best solution according to fit and diagnostic criteria. The highest lung-cancer risks were observed for one class in which exposure to welding fumes in the past 10 years prior to the interview was highest and the average duration of welding was also quite high at 30 years (OR=1.71, 95%CI 0.92-3.15). Participants in one other class with long-term very high intensity (median up to 1,000 µg/m³ experienced for more than 20 years before the interview) showed higher risk estimates for lung cancer compared to men who were never exposed to welding fumes (OR=1.26, 95%CI 0.46-3.49). Restricting the analysis to regular welders, revealed higher exposure levels to welding fumes, especially for one class. In this class, the median of exposure was always higher than 520 µg/m³ in each working decade. The associated risk estimates for lung cancer were slightly higher risk (OR=1.39, 95%CI 1.14-1.70) compared with all other welders.

Conclusions: Trajectory classification is a good way to summarize different exposure scenarios in the study population and leads to a better understanding of lifetime variability of exposure levels. The highest relative lung-cancer risk estimates were observed for the group with recent high exposure levels to welding fumes. In summary, LCMM opens new perspectives of dose-effect relationships and could be employed to complement established methods in occupational epidemiology.

The authors declare that they have no competing interests.

The authors declare that a positive ethics committee vote has been obtained.