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      Low radon exposures and lung cancer risk: joint analysis of the Czech, French, and Beaverlodge cohorts of uranium miners

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          Abstract

          It is well established that high radon exposures increase the risk of lung cancer mortality. The effects of low occupational exposures and the factors that confound and modify this risk are not clear and are needed to inform current radiation protection of miners. The risk of lung cancer mortality at low radon exposures (< 100 working-level months) was assessed in the joint cohort analysis of Czech, French, and Canadian uranium miners, employed in 1953 or later. Statistical analysis was based on linear Poisson regression modeling with grouped cohort survival data. Two sensitivity analyses were used to assess potential confounding from tobacco smoking. A statistically significant linear relationship between radon exposure and lung cancer mortality was found. The excess relative risk per working-level month was 0.022 (95% confidence intervals: 0.013–0.034), based on 408 lung cancer deaths and 394,236 person-years of risk. Time since exposure was a statistically significant modifier; risk decreased with increasing time since exposure. A tendency for a decrease in risk with increasing attained age was observed, but this was not statistically significant. Exposure rate was not found to be a modifier of the excess relative risk. The potential confounding effect of tobacco smoking was estimated to be small and did not substantially change the radon–lung cancer mortality risk estimates. This joint cohort analysis provides strong evidence for an increased risk of lung cancer mortality from low occupational radon exposures. The results suggest that radiation protection measures continue to be important among current uranium miners.

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          Tobacco smoking and cancer: a meta-analysis.

          We conducted a systematic meta-analysis of observational studies on cigarette smoking and cancer from 1961 to 2003. The aim was to quantify the risk for 13 cancer sites, recognized to be related to tobacco smoking by the International Agency for Research on Cancer (IARC), and to analyze the risk variation for each site in a systematic manner. We extracted data from 254 reports published between 1961 and 2003 (177 case-control studies, 75 cohorts and 2 nested case-control studies) included in the 2004 IARC Monograph on Tobacco Smoke and Involuntary Smoking. The analyses were carried out on 216 studies with reported estimates for 'current' and/or 'former' smokers. We performed sensitivity analysis, and looked for publication and other types of bias. Lung (RR = 8.96; 95% CI: 6.73-12.11), laryngeal (RR = 6.98; 95% CI: 3.14-15.52) and pharyngeal (RR = 6.76; 95% CI: 2.86-15.98) cancers presented the highest relative risks (RRs) for current smokers, followed by upper digestive tract (RR = 3.57; 95% CI: 2.63-4.84) and oral (RR = 3.43; 95% CI: 2.37-4.94) cancers. As expected, pooled RRs for respiratory cancers were greater than the pooled estimates for other sites. The analysis of heterogeneity showed that study type, gender and adjustment for confounding factors significantly influence the RRs estimates and the reliability of the studies. Copyright 2007 Wiley-Liss, Inc.
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            Methodological issues regarding confounding and exposure misclassification in epidemiological studies of occupational exposures.

            Confounding and exposure misclassification are issues that concern epidemiologists because of their potential to bias results of studies and complicate interpretations. In occupational epidemiology both are routinely raised to argue that an observed result is either a false positive or a false negative finding. Although it is important to consider the potential for limitations of epidemiologic investigations, judgment regarding their importance should be based on their actual likelihood of occurrence. This paper is based on our experience in epidemiologic analyses and a brief review of the literature regarding confounding and exposure misclassification. Examples of substantial confounding are rare in occupational epidemiology. In fact, even for studies of occupational exposures and lung cancer, tobacco-adjusted relative risks rarely differ appreciably from the unadjusted estimates. This is surprising because it seems the perfect situation for confounding to occur. Yet, despite the lack of evidence that confounding is a common problem, nearly every epidemiologic paper includes a lengthy discussion on uncontrolled or residual confounding. On the other hand, exposure misclassification probably occurs in all studies. The only question is, how much? The direction and magnitude of nondifferential exposure misclassification (the type most likely to occur in cohort studies) on estimates of relative risks can be largely predicted given knowledge on the degree of misclassification, that is, relatively small amounts of misclassification can bias relative risks substantially towards the null. The literature, however, is full of discussions implying that misclassification of exposure is an explanation for a positive finding. These comments are not to suggest that all potential limitations for epidemiologic studies should not be considered and evaluated. We do believe, however, that the likelihood of occurrence and the direction and magnitude of the effect should be more carefully and realistically considered when making judgments about study design or data interpretation. (c) 2007 Wiley-Liss, Inc.
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              Monte Carlo sensitivity analysis and Bayesian analysis of smoking as an unmeasured confounder in a study of silica and lung cancer.

              Conventional confidence intervals reflect uncertainty due to random error but omit uncertainty due to biases, such as confounding, selection bias, and measurement error. Such uncertainty can be quantified, especially if the investigator has some idea of the amount of such bias. A traditional sensitivity analysis produces one or more point estimates for the exposure effect hypothetically adjusted for bias, but it does not provide a range of effect measures given the likely range of bias. Here the authors used Monte Carlo sensitivity analysis and Bayesian bias analysis to provide such a range, using data from a US silica-lung cancer study in which results were potentially confounded by smoking. After positing a distribution for the smoking habits of workers and referents, a distribution of rate ratios for the effect of smoking on lung cancer, and a model for the bias parameter, the authors derived a distribution for the silica-lung cancer rate ratios hypothetically adjusted for smoking. The original standardized mortality ratio for the silica-lung cancer relation was 1.60 (95% confidence interval: 1.31, 1.93). Monte Carlo sensitivity analysis, adjusting for possible confounding by smoking, led to an adjusted standardized mortality ratio of 1.43 (95% Monte Carlo limits: 1.15, 1.78). Bayesian results were similar (95% posterior limits: 1.13, 1.84). The authors believe that these types of analyses, which make explicit and quantify sources of uncertainty, should be more widely adopted by epidemiologists.
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                Author and article information

                Contributors
                (613) 992-8567 , rachel.lane@canada.ca , rlane030@uottawa.ca
                ladislav.tomasek@suro.cz
                lydia.zablotska@ucsf.edu
                estelle.rage@irsn.fr
                fmomoli@uottawa.ca
                jlittle@uottawa.ca
                Journal
                Int Arch Occup Environ Health
                Int Arch Occup Environ Health
                International Archives of Occupational and Environmental Health
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0340-0131
                1432-1246
                8 February 2019
                8 February 2019
                2019
                : 92
                : 5
                : 747-762
                Affiliations
                [1 ]ISNI 0000 0001 2182 2255, GRID grid.28046.38, School of Epidemiology and Public Health, , Faculty of Medicine, University of Ottawa, ; Room 101, 600 Peter Morand Crescent, Ottawa, ON K1G 5Z3 Canada
                [2 ]ISNI 0000 0001 2287 345X, GRID grid.467646.1, Directorate of Environmental and Radiation Protection and Assessment (DERPA), , Canadian Nuclear Safety Commission (CNSC), ; 280 Slater Street, Station B, P.O. Box 1046, Ottawa, ON K1P 5S9 Canada
                [3 ]ISNI 0000 0000 9236 6202, GRID grid.436407.2, National Radiation Protection Institute (SURO), ; Bartoskova 28, 140 00 Prague, Czech Republic
                [4 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Department of Epidemiology and Biostatistics, School of Medicine, , University of California, San Francisco, ; 550 16th St, San Francisco, CA 94158 USA
                [5 ]ISNI 0000 0001 1414 6236, GRID grid.418735.c, Institute for Radiological Protection and Nuclear Safety, ; B.P. 17, 92262 Fontenay-aux-Roses Cedex, France
                [6 ]ISNI 0000 0000 9402 6172, GRID grid.414148.c, Centre for Practice-Changing Research (Room L1152), Ottawa Hospital Research Institute, , Children’s Hospital of Eastern Ontario Research Institute, ; 401 Smyth Rd, Ottawa, ON K1H 8L1 Canada
                Author information
                http://orcid.org/0000-0003-2830-2644
                Article
                1411
                10.1007/s00420-019-01411-w
                6556158
                30737558
                ffad2c89-b73f-4e79-820e-9b8b032b6648
                © The Author(s) 2019

                OpenAccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 23 July 2018
                : 23 January 2019
                Categories
                Original Article
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2019

                Occupational & Environmental medicine
                radon,lung cancer,uranium mining,epidemiology,cohort,risk,smoking
                Occupational & Environmental medicine
                radon, lung cancer, uranium mining, epidemiology, cohort, risk, smoking

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