IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i21p13856-d952374.html
   My bibliography  Save this article

Work-Related Factors and Lung Cancer Survival: A Population-Based Study in Switzerland (1990–2014)

Author

Listed:
  • Nicolas Bovio

    (Center for Primary Care and Public Health (Unisanté), University of Lausanne, 1010 Lausanne, Switzerland)

  • Michel Grzebyk

    (Department of Occupational Epidemiology, National Research and Safety Institute (INRS), 54500 Vandoeuvre lès Nancy, France)

  • Patrick Arveux

    (Center for Primary Care and Public Health (Unisanté), University of Lausanne, 1010 Lausanne, Switzerland)

  • Jean-Luc Bulliard

    (Center for Primary Care and Public Health (Unisanté), University of Lausanne, 1010 Lausanne, Switzerland
    Neuchâtel and Jura Cancer Registry, 2000 Neuchâtel, Switzerland)

  • Arnaud Chiolero

    (Population Health Laboratory, University of Fribourg, 1700 Fribourg, Switzerland
    Valais Cancer Registry, Valais Health Observatory, 1950 Sion, Switzerland
    Institute of Primary Health Care (BIHAM), University of Bern, 3012 Bern, Switzerland
    School of Population and Global Health, McGill University, Montréal, QC H3A 1G1, Canada)

  • Evelyne Fournier

    (Geneva Cancer Registry, University of Geneva, 1211 Geneva, Switzerland)

  • Simon Germann

    (Center for Primary Care and Public Health (Unisanté), University of Lausanne, 1010 Lausanne, Switzerland)

  • Isabelle Konzelmann

    (Valais Cancer Registry, Valais Health Observatory, 1950 Sion, Switzerland)

  • Manuela Maspoli

    (Neuchâtel and Jura Cancer Registry, 2000 Neuchâtel, Switzerland)

  • Elisabetta Rapiti

    (Geneva Cancer Registry, University of Geneva, 1211 Geneva, Switzerland)

  • Irina Guseva Canu

    (Center for Primary Care and Public Health (Unisanté), University of Lausanne, 1010 Lausanne, Switzerland)

Abstract

While previous Swiss studies have demonstrated differences in lung cancer mortality between occupational groups, no estimates are available on the association of occupation-related factors with lung cancer survival. This study aimed at determining whether occupation or work-related factors after diagnosis affect lung cancer survival. We used cancer registry records to identify lung cancer patients diagnosed between 1990 and 2014 in western Switzerland ( n = 5773) matched with the Swiss National Cohort. The effect of occupation, the skill level required for the occupation, and the socio-professional category on 5-year lung cancer survival was assessed using non-parametric and parametric methods, controlling for histological type and tumour stage. We found that the net survival varied across skill levels and that the lowest skill level was associated with worse survival in both men and women. In the parametric models with minimal adjustment, we identified several occupational groups at higher risk of mortality compared to the reference category, particularly among men. After adjustment for histological type of lung cancer and tumour stage at diagnosis, most hazard ratios remained higher than 1, though non-statistically significant. Compared to top managers and self-employed workers, workers in paid employment without specific information on occupation were identified as the most at-risk socio-professional category in nearly all models. As this study was conducted using a relatively small sample and limited set of covariates, further studies are required, taking into account smoking habits and administrated cancer treatments. Information on return to work and working conditions before and after lung cancer diagnosis will also be highly valuable for analysing their effect on net lung cancer survival in large nationwide or international studies. Such studies are essential for informing health and social protection systems, which should guarantee appropriate work conditions for cancer survivors, beneficial for their quality of life and survival.

Suggested Citation

  • Nicolas Bovio & Michel Grzebyk & Patrick Arveux & Jean-Luc Bulliard & Arnaud Chiolero & Evelyne Fournier & Simon Germann & Isabelle Konzelmann & Manuela Maspoli & Elisabetta Rapiti & Irina Guseva Canu, 2022. "Work-Related Factors and Lung Cancer Survival: A Population-Based Study in Switzerland (1990–2014)," IJERPH, MDPI, vol. 19(21), pages 1-16, October.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:13856-:d:952374
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/21/13856/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/21/13856/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Isabelle Clerc-Urmes & Michel Grzebyk & Guy Hedelin, 2014. "Net survival estimation with stns," Stata Journal, StataCorp LP, vol. 14(1), pages 87-102, March.
    3. Narjust Duma, 2020. "Lung-cancer researchers and clinicians must pay more attention to women," Nature, Nature, vol. 587(7834), pages 13-13, November.
    4. Maja Pohar Perme & Janez Stare & Jacques Estève, 2012. "On Estimation in Relative Survival," Biometrics, The International Biometric Society, vol. 68(1), pages 113-120, March.
    5. Nathalie Grafféo & Fabienne Castell & Aurélien Belot & Roch Giorgi, 2016. "A log-rank-type test to compare net survival distributions," Biometrics, The International Biometric Society, vol. 72(3), pages 760-769, September.
    6. Zhe-Yu Yang & Ching-Huang Lai & Ching-Liang Ho & Chung-Ching Wang, 2021. "Epidemiological Study of Return to Work and Mortality in Lung Cancer Survivors," IJERPH, MDPI, vol. 19(1), pages 1-11, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    2. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    3. Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    4. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    5. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
    6. Norah Alyabs & Sy Han Chiou, 2022. "The Missing Indicator Approach for Accelerated Failure Time Model with Covariates Subject to Limits of Detection," Stats, MDPI, vol. 5(2), pages 1-13, May.
    7. Feldkircher, Martin, 2014. "The determinants of vulnerability to the global financial crisis 2008 to 2009: Credit growth and other sources of risk," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 19-49.
    8. Eunsil Seok & Akhgar Ghassabian & Yuyan Wang & Mengling Liu, 2024. "Statistical Methods for Modeling Exposure Variables Subject to Limit of Detection," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 435-458, July.
    9. Ida Kubiszewski & Kenneth Mulder & Diane Jarvis & Robert Costanza, 2022. "Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 139-148, February.
    10. Georges Steffgen & Philipp E. Sischka & Martha Fernandez de Henestrosa, 2020. "The Quality of Work Index and the Quality of Employment Index: A Multidimensional Approach of Job Quality and Its Links to Well-Being at Work," IJERPH, MDPI, vol. 17(21), pages 1-31, October.
    11. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    12. Esef Hakan Toytok & Sungur Gürel, 2019. "Does Project Children’s University Increase Academic Self-Efficacy in 6th Graders? A Weak Experimental Design," Sustainability, MDPI, vol. 11(3), pages 1-12, February.
    13. J M van Niekerk & M C Vos & A Stein & L M A Braakman-Jansen & A F Voor in ‘t holt & J E W C van Gemert-Pijnen, 2020. "Risk factors for surgical site infections using a data-driven approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    14. Joost R. Ginkel, 2020. "Standardized Regression Coefficients and Newly Proposed Estimators for $${R}^{{2}}$$R2 in Multiply Imputed Data," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 185-205, March.
    15. Lara Jehi & Xinge Ji & Alex Milinovich & Serpil Erzurum & Amy Merlino & Steve Gordon & James B Young & Michael W Kattan, 2020. "Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
    16. Matthew Carli & Mary H. Ward & Catherine Metayer & David C. Wheeler, 2022. "Imputation of Below Detection Limit Missing Data in Chemical Mixture Analysis with Bayesian Group Index Regression," IJERPH, MDPI, vol. 19(3), pages 1-17, January.
    17. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    18. Tsai, Tsung-Han, 2016. "A Bayesian Approach to Dynamic Panel Models with Endogenous Rarely Changing Variables," Political Science Research and Methods, Cambridge University Press, vol. 4(3), pages 595-620, September.
    19. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    20. Debra Javeline & Tracy Kijewski-Correa & Angela Chesler, 2019. "Does it matter if you “believe” in climate change? Not for coastal home vulnerability," Climatic Change, Springer, vol. 155(4), pages 511-532, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:13856-:d:952374. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.