9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Estimating the current and future cancer burden in Canada: methodological framework of the Canadian population attributable risk of cancer (ComPARe) study

      protocol

      Read this article at

      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

          Introduction

          The Canadian Population Attributable Risk of Cancer project aims to quantify the number and proportion of cancer cases incident in Canada, now and projected to 2042, that could be prevented through changes in the prevalence of modifiable exposures associated with cancer. The broad risk factor categories of interest include tobacco, diet, energy imbalance, infectious diseases, hormonal therapies and environmental factors such as air pollution and residential radon.

          Methods and analysis

          Using a national network, we will use population-attributable risks (PAR) and potential impact fractions (PIF) to model both attributable (current) and avoidable (future) cancers. The latency periods and the temporal relationships between exposures and cancer diagnoses will be accounted for in the analyses. For PAR estimates, historical exposure prevalence data and the most recent provincial and national cancer incidence data will be used. For PIF estimates, we will model alternative or ‘counterfactual’ distributions of cancer risk factor exposures to assess how cancer incidence could be reduced under different scenarios of population exposure, projecting incidence to 2042.

          Dissemination

          The framework provided can be readily extended and applied to other populations or jurisdictions outside of Canada. An embedded knowledge translation and exchange component of this study with our Canadian Cancer Society partners will ensure that these findings are translated to cancer programmes and policies aimed at population-based cancer risk reduction strategies.

          Related collections

          Most cited references31

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

          Global burden of gastric cancer attributable to Helicobacter pylori.

          We previously estimated that 660,000 cases of cancer in the year 2008 were attributable to the bacterium Helicobacter pylori (H. pylori), corresponding to 5.2% of the 12.7 million total cancer cases that occurred worldwide. In recent years, evidence has accumulated that immunoblot (western blot) is more sensitive for detection of anti-H. pylori antibodies than ELISA, the detection method used in our previous analysis. The purpose of this short report is to update the attributable fraction (AF) estimate for H. pylori after briefly reviewing new evidence, and to reassess the global burden of cancer attributable to H. pylori. We therefore reviewed the literature for studies comparing the risk of developing non-cardia gastric cancer (NCGC) in cases and controls, using both ELISA and multiple antigen immunoblot for detection of H. pylori. The results from prospective studies were combined, and the new pooled estimates were applied to the calculation of the AF for H. pylori in NCGC, then to the burden of infection-related cancers worldwide. Using the immunoblot-based data, the worldwide AF for H. pylori in NCGC increased from 74.7% to 89.0%. This implies approximately 120,000 additional cases of NCGC attributable to H. pylori infection for a total of around 780,000 cases (6.2% instead of 5.2% of all cancers). These updated estimates reinforce the role of H. pylori as a major cause of cancer.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            The occurrence of lung cancer in man.

            M L Levin (1953)
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Age-period-cohort models for the Lexis diagram.

              Analysis of rates from disease registers are often reported inadequately because of too coarse tabulation of data and because of confusion about the mechanics of the age-period-cohort model used for analysis. Rates should be considered as observations in a Lexis diagram, and tabulation a necessary reduction of data, which should be as small as possible, and age, period and cohort should be treated as continuous variables. Reporting should include the absolute level of the rates as part of the age-effects. This paper gives a guide to analysis of rates from a Lexis diagram by the age-period-cohort model. Three aspects are considered separately: (1) tabulation of cases and person-years; (2) modelling of age, period and cohort effects; and (3) parametrization and reporting of the estimated effects. It is argued that most of the confusion in the literature comes from failure to make a clear distinction between these three aspects. A set of recommendations for the practitioner is given and a package for R that implements the recommendations is introduced. Copyright 2006 John Wiley & Sons, Ltd.
                Bookmark

                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2018
                1 August 2018
                : 8
                : 7
                : e022378
                Affiliations
                [1 ] departmentDepartments of Oncology and Community Health Sciences, Cumming School of Medicine , University of Calgary , Calgary, Alberta, Canada
                [2 ] departmentDepartment of Cancer Epidemiology and Prevention Research , CancerControl Alberta, Alberta Health Services , Calgary, Alberta, Canada
                [3 ] departmentDepartment of Health Research Methods, Evidence, and Impact , McMaster University , Hamilton, Ontario, Canada
                [4 ] departmentDepartment of Public Health Sciences , Queen’s University , Kingston, Ontario, Canada
                [5 ] departmentDepartment of Epidemiology, Biostatistics and Occupational Health , McGill University , Montreal, Quebec, Canada
                [6 ] departmentDepartment of Oncology , McGill University , Montreal, Quebec, Canada
                [7 ] departmentOccupational Cancer Research Centre , Cancer Care Ontario , Toronto, Ontario, Canada
                [8 ] departmentDepartment of Health Sciences , Carleton University , Ottawa, Ontario, Canada
                [9 ] Canadian Cancer Society , Toronto, Ontario, Canada
                [10 ] Cancer Care Ontario , Toronto, Ontario, Canada
                [11 ] College of Public Health and Human Sciences, Oregon State University , Corvallis, Oregon, USA
                Author notes
                [Correspondence to ] Dr Darren R Brenner; darren.brenner@ 123456ucalgary.ca
                Article
                bmjopen-2018-022378
                10.1136/bmjopen-2018-022378
                6074628
                30068623
                f03b34ca-f643-4791-b050-79f50d710500
                © Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 16 February 2018
                : 01 June 2018
                : 29 June 2018
                Categories
                Epidemiology
                Protocol
                1506
                1692
                Custom metadata
                unlocked

                Medicine
                epidemiology,potential impact fraction,population attributable risk,cancer,risk factors
                Medicine
                epidemiology, potential impact fraction, population attributable risk, cancer, risk factors

                Comments

                Comment on this article