Peer-reviewedCC BYInternational journal of epidemiology
Data-driven exploration of occupations and lung cancer risks using regularized regression models.
35 affiliations
- 1Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.
- 2Risk Analysis for Prevention, Innovation & Development, Unit Health and Work, Netherlands Organization for Applied Scientific Research TNO, Utrecht, The Netherlands.
- 3The Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France.
- 4Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.
- 5Faculty of Mathematics and Computer Science, Institute of Statistics, University of Bremen, Bremen, Germany.
- 6Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Univ Rennes, Inserm, EHESP, Pointe-à-Pitre, France.
- 7Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University (IPA), Bochum, Germany.
- 8Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, United States.
- 9Epidemiology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
- 10Occupational Cancer Research Centre, Ontario Health, Toronto, ON, Canada.
- 11Department of health Risk Assessment, Regional Authority of Public Health, Banská Bystrica, Slovakia.
- 12Faculty of Health, Catholic University, Ružomberok, Slovakia.
- 13Health Research Institute of Asturias, University of Oviedo, ISPA and CIBERESP, Oviedo, Spain.
- 14Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, United Kingdom.
- 15Department of Epidemiology, ASL Roma E, Rome, Italy.
- 16Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czechia.
- 17Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
- 18Faculty of Health Sciences, Palacky University, Olomouc, Czechia.
- 19Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen, Essen, Germany.
- 20Institute and Clinic for Occupational, Social and Environmental Medicine, LMU University Hospital, LMU Munich, Munich, Germany.
- 21Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL), Munich, Germany.
- 22Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
- 23Department of Cancer Epidemiology and Prevention, Epidemiology Unit, M. Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland.
- 24Environmental Health Department, National Institute of Public Health, Bucharest, Romania.
- 25Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
- 26Department of Medical Sciences, Cancer Epidemiology Unit, University of Turin, Turin, Italy.
- 27Public Health Laboratory Department, National Public Health Center, Budapest, Hungary.
- 28Team Exposome and Heredity, Center for Research in Epidemiology and Population Health (CESP), U1018 Inserm, University Paris-Saclay, University Paris Cité, Villejuif, France.
- 29First Faculty of Medicine, Institute of Hygiene and Epidemiology, Charles University, Prague, Czechia.
- 30Department of Social and Preventive Medicine, University of Montreal, Montreal, QC, Canada.
- 31Department of Environmental Epidemiology, The Nofer Institute of Occupational Medicine, Lodz, Poland.
- 32Institut für Medizinische Informatik Biometrie Epidemiologie, Ludwig Maximilians University, Munich, Germany.
- 33Department of Cancer Epidemiology and Prevention, N.N. Blokhin National Research Centre of oncology, Moscow, Russia.
- 34Environment and Health over the Lifecourse, ISGlobal, Barcelona, Spain.
- 35Department of Biology, Boston College, MA, United States.
Abstract
Background
Occupational epidemiological analyses typically focus on a priori hypotheses relating disease to specific exposures or occupations. We used regularized regression models to agnostically identify occupations associated with lung cancer while accounting for known lung carcinogens.Methods
We pooled data from 14 European and Canadian lung cancer case-control studies. The main analysis included 13 587 cases and 16 410 controls for men and 3199 cases and 4408 controls for women. Associations between ever-employment in various occupations and lung cancer risks were evaluated using two selection approaches: a least absolute shrinkage and selection operator regression and a two-stage Bayesian hierarchical horseshoe model accounting for age, study, smoking, and occupational exposure to known lung carcinogens. Unconditional logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for selected occupations.Results
For men, occupations consistently identified as positively associated with lung cancer included miners (OR = 1.28, 95% CI: 1.06-1.54) and bricklayers (OR = 1.30, 95% CI: 1.12-1.52), while secondary education teachers (OR = 0.37, 95% CI: 0.22-0.60), production managers (OR = 0.66, 95% CI: 0.54-0.81), and government executive officials (OR = 0.72, 95% CI: 0.62-0.83) were negatively associated. For women, secondary language and literature teachers (OR = 0.39, 95% CI: 0.20-0.72) and government executive officials (OR = 0.63, 95% CI: 0.48-0.81) showed negative associations.Conclusion
Two agnostic, data-driven models identified several occupations associated with both increased and decreased lung cancer risks. These associations persisted after accounting for known exposures and covariates, highlighting the need to investigate unidentified occupational factors.
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BibTeX
@article{ge2026datadriven,
title = {{Data-driven exploration of occupations and lung cancer risks using regularized regression models.}},
author = {Calvin Ge and Jia Nie and Lützen Portengen and Ann Olsson and Joachim Schüz and Wolfgang Ahrens and Christine Barul and Thomas Behrens and Thomas Brüning and Neil E Caporaso and Dario Consonni and Paul A Demers and Eleonora Fabianova and Guillermo Fernández-Tardón and John K Field and Francesco Forastiere and Lenka Foretova and Per Gustavsson and Vladimir Janout and Karl-Heinz Jöckel and Stefan Karrasch and Benjamin Kendzia and Hans Kromhout and Maria Teresa Landi and Jolanta Lissowska and Dana Mates and John R McLaughlin and Franco Merletti and Enrica Migliore and Tamás Pándics and Loredana Radoi and Lorenzo Richiardi and Miriam Schejbalova and Jack Siemiatycki and Beata Świątkowska and Heinz-Erich Wichmann and David Zaridze and Kurt Straif and Susan Peters and Roel Vermeulen},
journal = {International journal of epidemiology},
year = {2026},
month = oct,
doi = {10.1093/ije/dyag183},
url = {https://europepmc.org/article/MED/42909464},
note = {CC BY},
}
RIS
TY - JOUR
TI - Data-driven exploration of occupations and lung cancer risks using regularized regression models.
AU - Calvin Ge
AU - Jia Nie
AU - Lützen Portengen
AU - Ann Olsson
AU - Joachim Schüz
AU - Wolfgang Ahrens
AU - Christine Barul
AU - Thomas Behrens
AU - Thomas Brüning
AU - Neil E Caporaso
AU - Dario Consonni
AU - Paul A Demers
AU - Eleonora Fabianova
AU - Guillermo Fernández-Tardón
AU - John K Field
AU - Francesco Forastiere
AU - Lenka Foretova
AU - Per Gustavsson
AU - Vladimir Janout
AU - Karl-Heinz Jöckel
AU - Stefan Karrasch
AU - Benjamin Kendzia
AU - Hans Kromhout
AU - Maria Teresa Landi
AU - Jolanta Lissowska
AU - Dana Mates
AU - John R McLaughlin
AU - Franco Merletti
AU - Enrica Migliore
AU - Tamás Pándics
AU - Loredana Radoi
AU - Lorenzo Richiardi
AU - Miriam Schejbalova
AU - Jack Siemiatycki
AU - Beata Świątkowska
AU - Heinz-Erich Wichmann
AU - David Zaridze
AU - Kurt Straif
AU - Susan Peters
AU - Roel Vermeulen
PY - 2026
DA - 2026/10/01
JO - International journal of epidemiology
DO - 10.1093/ije/dyag183
UR - https://europepmc.org/article/MED/42909464
AB - Background Occupational epidemiological analyses typically focus on a priori hypotheses relating disease to specific exposures or occupations. We used regularized regression models to agnostically identify occupations associated with lung cancer while accounting for known lung carcinogens.Methods We pooled data from 14 European and Canadian lung cancer case-control studies. The main analysis included 13 587 cases and 16 410 controls for men and 3199 cases and 4408 controls for women. Associations between ever-employment in various occupations and lung cancer risks were evaluated using two selection approaches: a least absolute shrinkage and selection operator regression and a two-stage Bayesian hierarchical horseshoe model accounting for age, study, smoking, and occupational exposure to known lung carcinogens. Unconditional logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for selected occupations.Results For men, occupations consistently identified as positively associated with lung cancer included miners (OR = 1.28, 95% CI: 1.06-1.54) and bricklayers (OR = 1.30, 95% CI: 1.12-1.52), while secondary education teachers (OR = 0.37, 95% CI: 0.22-0.60), production managers (OR = 0.66, 95% CI: 0.54-0.81), and government executive officials (OR = 0.72, 95% CI: 0.62-0.83) were negatively associated. For women, secondary language and literature teachers (OR = 0.39, 95% CI: 0.20-0.72) and government executive officials (OR = 0.63, 95% CI: 0.48-0.81) showed negative associations.Conclusion Two agnostic, data-driven models identified several occupations associated with both increased and decreased lung cancer risks. These associations persisted after accounting for known exposures and covariates, highlighting the need to investigate unidentified occupational factors.
SN - 0300-5771
SN - 1464-3685
ER -