Evidence map›Paper›PMID 30601243›Full record

ArticleEpidemiology (Cambridge, Mass.)2019

Modeling the Complex Exposure History of Smoking in Predicting Bladder Cancer: A Pooled Analysis of 15 Case-Control Studies.

Frits H M van Osch, Jelle Vlaanderen, Sylvia H J Jochems, Cristina Bosetti, Jerry Polesel, Stefano Porru, Angela Carta, Klaus Golka, Xuejuan Jiang, Mariana C Stern and 15 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
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  4. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Frits H M van OschFrom the Department of Complex Genetics, Nutrition and Translational Research in Metabolism (School NUTRIM), Maastricht University, Maastricht, The Netherlands.
Jelle VlaanderenInstitute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.
Sylvia H J JochemsFrom the Department of Complex Genetics, Nutrition and Translational Research in Metabolism (School NUTRIM), Maastricht University, Maastricht, The Netherlands.
Cristina BosettiUnit of Cancer Epidemiology, Department of Oncology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri Via Giuseppe La Masa, Milan, Italy.
Jerry PoleselUnit of Cancer Epidemiology, CRO Aviano National Cancer Institute, Aviano (PN), Italy.
Stefano PorruDepartment of Diagnostics and Public Health, Section of Occupational Health, University of Verona, Italy.
Angela CartaUniversity Research Center "Integrated Models for Prevention and Protection in Environmental and Occupational Health" MISTRAL, University of Brescia, Italy.
Klaus GolkaLeibniz Research Centre for Working Environment and Human Factors, Sektion Lebenswissenschaften Dortmund, Germany.
Xuejuan JiangDepartment of Preventive Medicine, University of Southern California, Los Angeles, CA.
Mariana C SternDepartment of Preventive Medicine, University of Southern California, Los Angeles, CA.
Wei-De ZhongDepartment of Urology, Guangzhou First People's Hospital, the Second Affiliated Hospital of South China University of Technology, Guangzhou, China.
Eliane KellenLeuven University Centre for Cancer Prevention (LUCK), Leuven, Belgium.
Hermann PohlabelnLeibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany.
Li TangDepartment of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY.
James MarshallDepartment of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY.
Gunnar SteineckDepartment of Oncology & Pathology, Division of Clinical Cancer Epidemiology, Karolinska Hospital, Stockholm, Sweden.
Margaret R KaragasDepartment of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH.
Kenneth C JohnsonDepartment of Epidemiology and Community Medicine, University of Ottawa, ON, Canada.
Zuo-Feng ZhangDepartments of Epidemiology, UCLA Center for Environmental Genomics, Fielding School of Public Health, University of California, Los Angeles (UCLA), Los Angeles, CA.
Jack A TaylorEpidemiology Branch, and Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC.
Carlo La VecchiaDepartment of Clinical Medicine and Community Health - Università degli Studi di Milano, Milan, Italy.
Richard T BryanInstitute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.
Frederik J van SchootenDepartment of Pharmacology and Toxicology, NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands.
Anke WesseliusFrom the Department of Complex Genetics, Nutrition and Translational Research in Metabolism (School NUTRIM), Maastricht University, Maastricht, The Netherlands.
Maurice P ZeegersFrom the Department of Complex Genetics, Nutrition and Translational Research in Metabolism (School NUTRIM), Maastricht University, Maastricht, The Netherlands.

Funding

USC/NORRIS COMPREHENSIVE CANCER CENTER (CORE) SUPPORTP30CA014089 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI MYLES G COCKBURN · 1985 to 2026
$181.4M
Environmental Exposure and DNA DamageZIAES049032 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI TAYLOR, JACK A · 2009 to 2022
$13.4M
Genetic Susceptibility And The Environment In Cancer RiskZIAES049033 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI TAYLOR, JACK A · 2009 to 2022
$7.7M
NCI NIH HHS P30 CA014089
6 · The paper itself

Abstract

backgroundFew 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.

methodsWe 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.

resultsThe 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.

conclusionsThis 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.

Indexed as

Models, BiologicalCase-Control StudiesFemaleHumansMaleRisk FactorsSmokingTime FactorsUrinary Bladder Neoplasms

Identifiers

PMID30601243
PMCPMC9023003

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.