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Modeling the Complex Exposure History of Smoking in Predicting Bladder Cancer

A Pooled Analysis of 15 Case–Control Studies

van Osch, Frits H. M.a,b; Vlaanderen, Jellec; Jochems, Sylvia H. J.a,b; Bosetti, Cristinad; Polesel, Jerrye; Porru, Stefanof,g; Carta, Angelag,h; Golka, Klausi; Jiang, Xuejuanj,k; Stern, Mariana C.j; Zhong, Wei-Del; Kellen, Elianem; Pohlabeln, Hermannn; Tang, Lio; Marshall, Jameso; Steineck, Gunnarp; Karagas, Margaret R.q; Johnson, Kenneth C.r; Zhang, Zuo-Fengs; Taylor, Jack A.t; La Vecchia, Carlou; Bryan, Richard T.b; van Schooten, Frederik J.v; Wesselius, Ankea; Zeegers, Maurice P.a,b,w

doi: 10.1097/EDE.0000000000000964

Background: Few studies have modeled smoking histories by combining smoking intensity and duration to show what profile of smoking behavior is associated with highest risk of bladder cancer. This study aims to provide insight into the association between smoking exposure history and bladder cancer risk by modeling both smoking intensity and duration in a pooled analysis.

Methods: We used data from 15 case–control studies included in the bladder cancer epidemiology and nutritional determinants study, including a total of 6,874 cases and 17,727 controls. To jointly interpret the effects of intensity and duration of smoking, we modeled excess odds ratios per pack–year by intensity continuously to estimate the risk difference between smokers with long duration/low intensity and short duration/high intensity.

Results: The pattern observed from the pooled excess odds ratios model indicated that for a fixed number of pack–years, smoking for a longer duration at lower intensity was more deleterious for bladder cancer risk than smoking more cigarettes/day for a shorter duration. We observed similar patterns within individual study samples.

Conclusions: This pooled analysis shows that long duration/low intensity smoking is associated with a greater increase in bladder cancer risk than short duration/high intensity smoking within equal pack–year categories, thus confirming studies in other smoking-related cancers and demonstrating that reducing exposure history to a single metric such as pack–years was too restrictive.

From the aDepartment of Complex Genetics, Nutrition and Translational Research in Metabolism (School NUTRIM), Maastricht University, Maastricht, The Netherlands

bInstitute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom

cInstitute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands

dUnit of Cancer Epidemiology, Department of Oncology, IRCCS – Istituto di Ricerche Farmacologiche Mario Negri Via Giuseppe La Masa, Milan, Italy

eUnit of Cancer Epidemiology, CRO Aviano National Cancer Institute, Aviano (PN), Italy

fDepartment of Diagnostics and Public Health, Section of Occupational Health, University of Verona, Italy

gUniversity Research Center “Integrated Models for Prevention and Protection in Environmental and Occupational Health” MISTRAL, University of Brescia, Italy

hDepartment of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Italy

iLeibniz Research Centre for Working Environment and Human Factors, Sektion Lebenswissenschaften Dortmund, Germany

jDepartment of Preventive Medicine, University of Southern California, Los Angeles, CA

kDepartment of Ophthalmology, University of Southern California, Los Angeles, CA

lDepartment of Urology, Guangzhou First People’s Hospital, the Second Affiliated Hospital of South China University of Technology, Guangzhou, China

mLeuven University Centre for Cancer Prevention (LUCK), Leuven, Belgium

nLeibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany

oDepartment of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY

pDepartment of Oncology & Pathology, Division of Clinical Cancer Epidemiology, Karolinska Hospital, Stockholm, Sweden

qDepartment of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH

rDepartment of Epidemiology and Community Medicine, University of Ottawa, ON, Canada

sDepartments of Epidemiology, UCLA Center for Environmental Genomics, Fielding School of Public Health, University of California, Los Angeles (UCLA), Los Angeles, CA

tEpidemiology Branch, and Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC

uDepartment of Clinical Medicine and Community Health – Università degli Studi di Milano, Milan, Italy

vDepartment of Pharmacology and Toxicology, NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands

wDepartment of Complex Genetics, Public Health and Primary Care (School CAPHRI), Maastricht University, Maastricht, The Netherlands.

Submitted August 17, 2018; accepted December 20, 2018.

The authors report no conflicts of interest.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (

Data availability: Computing code is available by request to the corresponding author. Data from this consortium is not available offsite and cannot be shared online.

Correspondence: Frits H. M. van Osch, Department of Complex Genetics, P.O. Box 616, 6200 MD Maastricht, The Netherlands. E-mail:

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