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      Coffee consumption and risk of endometrial cancer: a dose-response meta-analysis of prospective cohort studies

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          Abstract

          This is a dose-response (DR) meta-analysis to evaluate the association of coffee consumption on endometrial cancer (EC) risk. A total 1,534,039 participants from 13 published articles were added in this meta-analysis. The RR of total coffee consumption and EC were 0.80 (95% CI: 0.74–0.86). A stronger association between coffee intake and EC incidence was found in patients who were never treated with hormones, 0.60 (95% CI: 0.50–0.72), and subjects with a BMI ≥25 kg/m 2, 0.57 (95% CI: 0.46–0.71). The overall RRs for caffeinated and decaffeinated coffee were 0.66 (95% CI: 0.52–0.84) and 0.77 (95% CI: 0.63–0.94), respectively. A linear DR relationship was seen in coffee, caffeinated coffee, decaffeinated coffee and caffeine intake. The EC risk decreased by 5% for every 1 cup per day of coffee intake, 7% for every 1 cup per day of caffeinated coffee intake, 4% for every 1 cup per day of decaffeinated intake of coffee, and 4% for every 100 mg of caffeine intake per day. In conclusion, coffee and intake of caffeine might significantly reduce the incidence of EC, and these effects may be modified by BMI and history of hormone therapy.

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          Most cited references 51

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          Global cancer statistics.

          The global burden of cancer continues to increase largely because of the aging and growth of the world population alongside an increasing adoption of cancer-causing behaviors, particularly smoking, in economically developing countries. Based on the GLOBOCAN 2008 estimates, about 12.7 million cancer cases and 7.6 million cancer deaths are estimated to have occurred in 2008; of these, 56% of the cases and 64% of the deaths occurred in the economically developing world. Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death among females, accounting for 23% of the total cancer cases and 14% of the cancer deaths. Lung cancer is the leading cancer site in males, comprising 17% of the total new cancer cases and 23% of the total cancer deaths. Breast cancer is now also the leading cause of cancer death among females in economically developing countries, a shift from the previous decade during which the most common cause of cancer death was cervical cancer. Further, the mortality burden for lung cancer among females in developing countries is as high as the burden for cervical cancer, with each accounting for 11% of the total female cancer deaths. Although overall cancer incidence rates in the developing world are half those seen in the developed world in both sexes, the overall cancer mortality rates are generally similar. Cancer survival tends to be poorer in developing countries, most likely because of a combination of a late stage at diagnosis and limited access to timely and standard treatment. A substantial proportion of the worldwide burden of cancer could be prevented through the application of existing cancer control knowledge and by implementing programs for tobacco control, vaccination (for liver and cervical cancers), and early detection and treatment, as well as public health campaigns promoting physical activity and a healthier dietary intake. Clinicians, public health professionals, and policy makers can play an active role in accelerating the application of such interventions globally.
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            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.
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              Bias in meta-analysis detected by a simple, graphical test

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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                25 August 2015
                2015
                : 5
                Affiliations
                [1 ]Department of Science and Education, First People’s Hospital of Changde City , Changde 415003, Hunan, People’s Republic of China
                [2 ]Changsha Center for Disease Control and Prevention , Changsha 410001, Hunan, People’s Republic of China
                [3 ]Department of Oncology, First People’s Hospital of Changde City , Changde 415003, Hunan, People’s Republic of China
                [4 ]Department of Neurology, Second Affiliated Hospital of Nanchang University , Nanchang 330006, Jiangxi, People’s Republic of China
                [5 ]Department of Oncology, Affiliated Hospital of Zunyi Medical University , Address: NO.149, Dalian Road, Zunyi City, Guizhou Province, China, 653000
                Author notes
                Article
                srep13410
                10.1038/srep13410
                4548216
                26302813
                Copyright © 2015, Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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