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      Public support for smoke-free policies in outdoor areas and (semi-)private places: a systematic review and meta-analysis

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          Summary

          Background

          Smoke-free policies are essential to protect people against tobacco smoke exposure. To successfully implement smoke-free policies that go beyond enclosed public places and workplaces, public support is important. We undertook a comprehensive systematic review of levels and determinants of public support for indoor (semi-)private and outdoor smoke-free policies.

          Methods

          In this systematic review and meta-analysis, six electronic databases were searched for studies (published between 1 January 2004 and 19 January 2022) reporting support for (semi-)private and outdoor smoke-free policies in representative samples of at least 400 respondents aged 16 years and above. Two reviewers independently extracted data and assessed risk of bias of individual reports using the Mixed Methods Appraisal Tool. The primary outcome was proportion support for smoke-free policies, grouped according to location covered. Three-level meta-analyses, subgroup analyses and meta-regression were performed.

          Findings

          14,749 records were screened, of which 107 were included; 42 had low risk of bias and 65 were at moderate risk. 99 studies were included in the meta-analyses, reporting 326 measures of support from 896,016 individuals across 33 different countries. Support was pooled for indoor private areas (e.g., private cars, homes: 73%, 95% confidence interval (CI): 66–79), indoor semi-private areas (e.g., multi-unit housing: 70%, 95% CI: 48–86), outdoor hospitality areas (e.g., café and restaurant terraces: 50%, 95% CI: 43–56), outdoor non-hospitality areas (e.g., school grounds, playgrounds, parks, beaches: 69%, 95% CI: 64–73), outdoor semi-private areas (e.g., shared gardens: 67%, 95% CI: 53–79) and outdoor private areas (e.g., private balconies: 41%, 95% CI: 18–69). Subcategories showed highest support for smoke-free cars with children (86%, 95% CI: 81–89), playgrounds (80%, 95% CI: 74–86) and school grounds (76%, 95% CI: 69–83). Non-smokers and ex-smokers were more in favour of smoke-free policies compared to smokers. Support generally increased over time, and following implementation of each smoke-free policy.

          Interpretation

          Our findings suggested that public support for novel smoke-free policies is high, especially in places frequented by children. Governments should be reassured about public support for implementation of novel smoke-free policies.

          Funding

          doi 10.13039/100002129, Dutch Heart Foundation; , doi 10.13039/501100014780, Lung Foundation Netherlands; , doi 10.13039/501100004622, Dutch Cancer Society; , doi 10.13039/501100003092, Dutch Diabetes Research Foundation; and doi 10.13039/501100012028, Netherlands Thrombosis Foundation; .

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          Most cited references139

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          Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

          Summary Background Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. Methods GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk–outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk–outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk–outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51–12·1) deaths (19·2% [16·9–21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12–9·31) deaths (15·4% [14·6–16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253–350) DALYs (11·6% [10·3–13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0–9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10–24 years, alcohol use for those aged 25–49 years, and high systolic blood pressure for those aged 50–74 years and 75 years and older. Interpretation Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. Funding Bill & Melinda Gates Foundation.
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            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.
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              The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers

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                Author and article information

                Contributors
                Journal
                eClinicalMedicine
                EClinicalMedicine
                eClinicalMedicine
                Elsevier
                2589-5370
                09 May 2023
                May 2023
                09 May 2023
                : 59
                : 101982
                Affiliations
                [a ]Department of Public Health, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, Netherlands
                [b ]Edinburgh Medical School, The University of Edinburgh, Edinburgh, UK
                [c ]Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, UK
                [d ]Division of Neonatology, Department of Neonatal and Paediatric Intensive Care, Erasmus MC Sophia Children’s Hospital, University Medical Centre Rotterdam, Rotterdam, Netherlands
                [e ]Department of Obstetrics and Gynaecology, Erasmus MC Sophia Children’s Hospital, University Medical Centre Rotterdam, Rotterdam, Netherlands
                Author notes
                []Corresponding author. University Medical Centre Rotterdam, Rotterdam 3000 CB, Netherlands. j.been@ 123456erasmusmc.nl
                Article
                S2589-5370(23)00159-1 101982
                10.1016/j.eclinm.2023.101982
                10225670
                37256097
                7d3ca5ab-8b5a-4a90-bc4e-4a419f47380a
                © 2023 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 2 November 2022
                : 10 April 2023
                : 12 April 2023
                Categories
                Articles

                tobacco smoke pollution,tobacco smoking,child,public opinion,surveys and questionnaires,systematic review,meta-analysis,policy making,smoke-free policy

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