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      Associations of daily diet-related greenhouse gas emissions with the incidence and mortality of chronic diseases: a systematic review and meta-analysis of epidemiological studies

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          Abstract

          OBJECTIVES

          Although the entire process extending from food production to dietary consumption makes a large contribution to total greenhouse gas (GHG) emissions, little and inconsistent evidence exists on the epidemiological associations of daily diet-related GHG emissions with chronic disease risk or all-cause mortality. This systematic review and meta-analysis explored the observational epidemiological relationship between daily diet-related GHG emissions and health outcomes, including the risk of chronic diseases and all-cause mortality.

          METHODS

          Original articles published in English until May 2022 were identified by searching PubMed, Ovid-Embase, Web of Science, CINAHL, and Google Scholar. The extracted data were pooled using both fixed-effects and random-effects meta-analyses and presented as hazard and risk ratios (RRs) with 95% confidence intervals (CIs).

          RESULTS

          In total, 7 cohort studies (21 study arms) were included for qualitative synthesis and meta-analysis. The GHG emissions of dietary consumption showed a significant positive association with the risk of chronic disease incidence and mortality in both fixed-effects and random-effects models (fixed: RR, 1.04; 95% CI, 1.03 to 1.05; random: RR, 1.04; 95% CI, 1.02 to 1.06). This positive association was robust regardless of how daily diet-related GHG emissions were grouped. More strongly animal- based diets showed higher GHG emissions. However, there were only a few studies on specific chronic diseases, and the subgroup analysis showed insignificant results. There was no evidence of publication bias among the studies (Egger test: p=0.79).

          CONCLUSIONS

          A higher GHG-emission diet was found to be associated with a greater risk of all-cause mortality.

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          Most cited references60

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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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            Rayyan—a web and mobile app for systematic reviews

            Background Synthesis of multiple randomized controlled trials (RCTs) in a systematic review can summarize the effects of individual outcomes and provide numerical answers about the effectiveness of interventions. Filtering of searches is time consuming, and no single method fulfills the principal requirements of speed with accuracy. Automation of systematic reviews is driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making. We developed Rayyan (http://rayyan.qcri.org), a free web and mobile app, that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability. For the beta testing phase, we used two published Cochrane reviews in which included studies had been selected manually. Their searches, with 1030 records and 273 records, were uploaded to Rayyan. Different features of Rayyan were tested using these two reviews. We also conducted a survey of Rayyan’s users and collected feedback through a built-in feature. Results Pilot testing of Rayyan focused on usability, accuracy against manual methods, and the added value of the prediction feature. The “taster” review (273 records) allowed a quick overview of Rayyan for early comments on usability. The second review (1030 records) required several iterations to identify the previously identified 11 trials. The “suggestions” and “hints,” based on the “prediction model,” appeared as testing progressed beyond five included studies. Post rollout user experiences and a reflexive response by the developers enabled real-time modifications and improvements. The survey respondents reported 40% average time savings when using Rayyan compared to others tools, with 34% of the respondents reporting more than 50% time savings. In addition, around 75% of the respondents mentioned that screening and labeling studies as well as collaborating on reviews to be the two most important features of Rayyan. As of November 2016, Rayyan users exceed 2000 from over 60 countries conducting hundreds of reviews totaling more than 1.6M citations. Feedback from users, obtained mostly through the app web site and a recent survey, has highlighted the ease in exploration of searches, the time saved, and simplicity in sharing and comparing include-exclude decisions. The strongest features of the app, identified and reported in user feedback, were its ability to help in screening and collaboration as well as the time savings it affords to users. Conclusions Rayyan is responsive and intuitive in use with significant potential to lighten the load of reviewers.
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              Meta-analysis in clinical trials

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                Author and article information

                Journal
                Epidemiol Health
                Epidemiol Health
                EPIH
                Epidemiology and Health
                Korean Society of Epidemiology
                2092-7193
                2023
                30 December 2022
                : 45
                : e2023011
                Affiliations
                [1 ]Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Korea
                [2 ]Institute for Health and Society, Hanyang University, Seoul, Korea
                [3 ]Department of Preventive Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
                Author notes
                Correspondence: Mi Kyung Kim Department of Preventive Medicine, Hanyang University College of Medicine, 222 Wangsimni-ro, Sungdong-gu, Seoul 04763, Korea E-mail: kmkkim@ 123456hanyang.ac.kr
                Author information
                http://orcid.org/0000-0003-1159-4855
                http://orcid.org/0000-0003-0154-9161
                http://orcid.org/0000-0002-4995-6543
                http://orcid.org/0000-0001-8503-2631
                Article
                epih-45-e2023011
                10.4178/epih.e2023011
                10581893
                36596731
                b30ccb96-4d7e-411a-9b53-6110dcbf5843
                © 2023, Korean Society of Epidemiology

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 October 2022
                : 30 December 2022
                Categories
                Systematic Review

                Public health
                systematic review,meta-analysis,greenhouse gases,diet,mortality
                Public health
                systematic review, meta-analysis, greenhouse gases, diet, mortality

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