49
views
0
recommends
+1 Recommend
1 collections
    7
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Quantification of the smoking-associated cancer risk with rate advancement periods: meta-analysis of individual participant data from cohorts of the CHANCES consortium

      research-article
      , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on behalf of the Consortium on Health and Ageing: Network of Cohorts in Europe and the United States (CHANCES)
      BMC Medicine
      BioMed Central
      Smoking, Cancer, Incidence, Mortality, Cohort, Meta-analysis

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Smoking is the most important individual risk factor for many cancer sites but its association with breast and prostate cancer is not entirely clear. Rate advancement periods (RAPs) may enhance communication of smoking related risk to the general population. Thus, we estimated RAPs for the association of smoking exposure (smoking status, time since smoking cessation, smoking intensity, and duration) with total and site-specific (lung, breast, colorectal, prostate, gastric, head and neck, and pancreatic) cancer incidence and mortality.

          Methods

          This is a meta-analysis of 19 population-based prospective cohort studies with individual participant data for 897,021 European and American adults. For each cohort we calculated hazard ratios (HRs) for the association of smoking exposure with cancer outcomes using Cox regression adjusted for a common set of the most important potential confounding variables. RAPs (in years) were calculated as the ratio of the logarithms of the HRs for a given smoking exposure variable and age. Meta-analyses were employed to summarize cohort-specific HRs and RAPs.

          Results

          Overall, 140,205 subjects had a first incident cancer, and 53,164 died from cancer, during an average follow-up of 12 years. Current smoking advanced the overall risk of developing and dying from cancer by eight and ten years, respectively, compared with never smokers. The greatest advancements in cancer risk and mortality were seen for lung cancer and the least for breast cancer. Smoking cessation was statistically significantly associated with delays in the risk of cancer development and mortality compared with continued smoking.

          Conclusions

          This investigation shows that smoking, even among older adults, considerably advances, and cessation delays, the risk of developing and dying from cancer. These findings may be helpful in more effectively communicating the harmful effects of smoking and the beneficial effect of smoking cessation.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12916-016-0607-5) contains supplementary material, which is available to authorized users.

          Related collections

          Most cited references41

          • Record: found
          • Abstract: found
          • Article: not found

          Quantifying heterogeneity in a meta-analysis.

          The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Meta-analysis in clinical trials

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A basic introduction to fixed-effect and random-effects models for meta-analysis.

              There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics. In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd. Copyright © 2010 John Wiley & Sons, Ltd.
                Bookmark

                Author and article information

                Contributors
                jmordonezmena@gmail.com
                +49-6221-42-1300 , h.brenner@dkfz.de
                Journal
                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central (London )
                1741-7015
                5 April 2016
                5 April 2016
                2016
                : 14
                : 62
                Affiliations
                [ ]Network Aging Research (NAR), Heidelberg University, Heidelberg, Germany
                [ ]Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120 Heidelberg, Germany
                [ ]International Agency for Research on Cancer (IARC), Lyon, France
                [ ]Department of Chronic Diseases, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
                [ ]Department of Gastroenterology and Hepatology, University Medical Centre, Utrecht, The Netherlands
                [ ]Division of Epidemiology and Biostatistics, the School of Public Health, Imperial College London, London, United Kingdom
                [ ]Department of Social & Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
                [ ]UKCRC Centre of Excellence for Public Health, Queens University of Belfast, Belfast, UK
                [ ]Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
                [ ]Department of Public Health and Clinical Medicine, Cardiology, and Heart Center, Umeå University, Umeå, Sweden
                [ ]National Institute for Health and Welfare (THL), Helsinki, Finland
                [ ]Nutritional Epidemiology Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Rockville, MD USA
                [ ]Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
                [ ]Division of Human Nutrition, Wageningen University, Wageningen, The Netherlands
                [ ]Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
                [ ]Nutritional Research, Department of Public Health and Clinical Medicine, and Arcum, Arctic Research Centre at Umeå University, Umeå, Sweden
                [ ]Diet, Genes and Environment, Danish Cancer Society Research Center, Copenhagen, Denmark
                [ ]Jagiellonian University Medical College, Faculty of Health Sciences, Krakow, Poland
                [ ]Institute of Internal and Preventive Medicine, Novosibirsk, Russia
                [ ]National Institute of Public Health, Prague, Czech Republic
                [ ]Institute of Cardiology of Lithuanian University of Health Sciences, Kaunas, Lithuania
                [ ]Department Epidemiology and Public Health, University College London, London, UK
                [ ]Hellenic Health Foundation, Athens, Greece
                [ ]University of Athens, Medical School, Department of Hygiene, Epidemiology and Medical Statistics, Athens, Greece
                [ ]Institute for Translational Epidemiology and Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
                [ ]German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
                [ ]Division of Preventive Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
                Article
                607
                10.1186/s12916-016-0607-5
                4820956
                27044418
                e0900d1f-8f99-44b0-98ee-a70d209f885a
                © Ordóñez-Mena et al. 2016

                Open AccessThis 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. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 30 January 2016
                : 18 March 2016
                Funding
                Funded by: FP7 framework program, DG-RESEARCH, European Commission
                Award ID: 242244
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Medicine
                smoking,cancer,incidence,mortality,cohort,meta-analysis
                Medicine
                smoking, cancer, incidence, mortality, cohort, meta-analysis

                Comments

                Comment on this article