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A new trajectory approach for investigating the association between an environmental or occupational exposure over lifetime and the risk of chronic disease: Application to smoking, asbestos, and lung cancer

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  • Emilie Lévêque
  • Aude Lacourt
  • Viviane Philipps
  • Danièle Luce
  • Pascal Guénel
  • Isabelle Stücker
  • Cécile Proust-Lima
  • Karen Leffondré

Abstract

Quantifying the association between lifetime exposures and the risk of developing a chronic disease is a recurrent challenge in epidemiology. Individual exposure trajectories are often heterogeneous and studying their associations with the risk of disease is not straightforward. We propose to use a latent class mixed model (LCMM) to identify profiles (latent classes) of exposure trajectories and estimate their association with the risk of disease. The methodology is applied to study the association between lifetime trajectories of smoking or occupational exposure to asbestos and the risk of lung cancer in males of the ICARE population-based case-control study. Asbestos exposure was assessed using a job exposure matrix. The classes of exposure trajectories were identified using two separate LCMM for smoking and asbestos, and the association between the identified classes and the risk of lung cancer was estimated in a second stage using weighted logistic regression and all subjects. A total of 2026/2610 cases/controls had complete information on both smoking and asbestos exposure, including 1938/1837 cases/controls ever smokers, and 1417/1520 cases/controls ever exposed to asbestos. The LCMM identified four latent classes of smoking trajectories which had different risks of lung cancer, all much stronger than never smokers. The most frequent class had moderate constant intensity over lifetime while the three others had either long-term, distant or recent high intensity. The latter had the strongest risk of lung cancer. We identified five classes of asbestos exposure trajectories which all had higher risk of lung cancer compared to men never occupationally exposed to asbestos, whatever the dose and the timing of exposure. The proposed approach opens new perspectives for the analyses of dose-time-response relationships between protracted exposures and the risk of developing a chronic disease, by providing a complete picture of exposure history in terms of intensity, duration, and timing of exposure.

Suggested Citation

  • Emilie Lévêque & Aude Lacourt & Viviane Philipps & Danièle Luce & Pascal Guénel & Isabelle Stücker & Cécile Proust-Lima & Karen Leffondré, 2020. "A new trajectory approach for investigating the association between an environmental or occupational exposure over lifetime and the risk of chronic disease: Application to smoking, asbestos, and lung ," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-14, August.
    • Handle: RePEc:plo:pone00:0236736
    DOI: 10.1371/journal.pone.0236736
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    1. Michael Hauptmann & Jürgen Wellmann & Jay H. Lubin & Philip S. Rosenberg & Lothar Kreienbrock, 2000. "Analysis of Exposure-Time-Response Relationships Using a Spline Weight Function," Biometrics, The International Biometric Society, vol. 56(4), pages 1105-1108, December.
    2. Cécile Proust & Hélène Jacqmin-Gadda & Jeremy M. G. Taylor & Julien Ganiayre & Daniel Commenges, 2006. "A Nonlinear Model with Latent Process for Cognitive Evolution Using Multivariate Longitudinal Data," Biometrics, The International Biometric Society, vol. 62(4), pages 1014-1024, December.
    3. Weden, M.M. & Miles, J.N.V., 2012. "Intergenerational relationships between the smoking patterns of a population-representative sample of US mothers and the smoking trajectories of their children," American Journal of Public Health, American Public Health Association, vol. 102(4), pages 723-731.
    4. Proust-Lima, Cécile & Philipps, Viviane & Liquet, Benoit, 2017. "Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i02).
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