{
  "abstract": "Objective The association between 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 fume concentration and duration of exposure). Individuals with the same cumulative exposure but different temporal exposure patterns may 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.Material and Methods Latent Class Mixed Models (LCMM) simplify heterogeneous lifetime exposure into more homogeneous classes and identify distinct subgroups of individuals, following a similar exposure pattern. We determined 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 we used these classes to estimate smoking-adjusted OR with 95% CI via logistic regression. Before applying the LCMM function, exposure levels for each welding activity were determined using a measurement-based job-task-exposure-matrix with estimates from 15,473 personal measurements of inhalable fume 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 the class in which welding-fume exposure in the past 10 years before the interview/diagnosis was highest (median 450 µg/m3) with an average duration of welding of 30 years (OR=1.71, 95%CI 0.92-3.15). Participants in one other class with long-term high intensity (median up to 1,000 µg/m3 experienced more than 20 years before the interview/diagnosis) also showed higher lung-cancer risks compared to non-exposed men (OR=1.26, 95%CI 0.46-3.49).Conclusions The highest relative lung-cancer risks were observed after a recent high exposure to welding fumes. LCMM opens new perspectives of dose-effect relationships and could be employed to complement established methods in occupational epidemiology.",
  "authors": [
    {
      "affiliations": [
        "Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany. Leibniz-Institute for Prevention Research and Epidemiology-BIPS GmH and Institute for Statistics, University of Bremen, Epidemiological Methods and Etiological Research, Bremen, Germany. Helmholtz Centre Munich, Institute of Epidemiology I, Neuherberg, Germany. Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Germany"
      ],
      "name": "Benjamin Kendzia"
    },
    {
      "affiliations": [
        "Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany. Leibniz-Institute for Prevention Research and Epidemiology-BIPS GmH and Institute for Statistics, University of Bremen, Epidemiological Methods and Etiological Research, Bremen, Germany. Helmholtz Centre Munich, Institute of Epidemiology I, Neuherberg, Germany. Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Germany"
      ],
      "name": "Dirk Taeger"
    },
    {
      "affiliations": [
        "Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany. Leibniz-Institute for Prevention Research and Epidemiology-BIPS GmH and Institute for Statistics, University of Bremen, Epidemiological Methods and Etiological Research, Bremen, Germany. Helmholtz Centre Munich, Institute of Epidemiology I, Neuherberg, Germany. Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Germany"
      ],
      "name": "Hermann Pohlabeln"
    },
    {
      "affiliations": [
        "Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany. Leibniz-Institute for Prevention Research and Epidemiology-BIPS GmH and Institute for Statistics, University of Bremen, Epidemiological Methods and Etiological Research, Bremen, Germany. Helmholtz Centre Munich, Institute of Epidemiology I, Neuherberg, Germany. Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Germany"
      ],
      "name": "Wolfgang Ahrens"
    },
    {
      "affiliations": [
        "Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany. Leibniz-Institute for Prevention Research and Epidemiology-BIPS GmH and Institute for Statistics, University of Bremen, Epidemiological Methods and Etiological Research, Bremen, Germany. Helmholtz Centre Munich, Institute of Epidemiology I, Neuherberg, Germany. Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Germany"
      ],
      "name": "Heinz-Erich Wichmann"
    },
    {
      "affiliations": [
        "Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany. Leibniz-Institute for Prevention Research and Epidemiology-BIPS GmH and Institute for Statistics, University of Bremen, Epidemiological Methods and Etiological Research, Bremen, Germany. Helmholtz Centre Munich, Institute of Epidemiology I, Neuherberg, Germany. Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Germany"
      ],
      "name": "Karl-Heinz Jöckel"
    },
    {
      "affiliations": [
        "Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany. Leibniz-Institute for Prevention Research and Epidemiology-BIPS GmH and Institute for Statistics, University of Bremen, Epidemiological Methods and Etiological Research, Bremen, Germany. Helmholtz Centre Munich, Institute of Epidemiology I, Neuherberg, Germany. Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Germany"
      ],
      "name": "Thomas Brüning"
    },
    {
      "affiliations": [
        "Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany. Leibniz-Institute for Prevention Research and Epidemiology-BIPS GmH and Institute for Statistics, University of Bremen, Epidemiological Methods and Etiological Research, Bremen, Germany. Helmholtz Centre Munich, Institute of Epidemiology I, Neuherberg, Germany. Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Germany"
      ],
      "name": "Thomas Behrens"
    }
  ],
  "title": "8277869 Trajectories of occupational exposure to welding fumes and its impact on lung cancer risks: A latent class modelling approach",
  "uid": "dd19218b-8bbb-589e-ad50-99265fff58fb"
}