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      Arterial Blood Pressure and Long-Term Exposure to Traffic-Related Air Pollution: An Analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE)

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      Environmental Health Perspectives

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          Background: Long-term exposure to air pollution has been hypothesized to elevate arterial blood pressure (BP). The existing evidence is scarce and country specific.

          Objectives: We investigated the cross-sectional association of long-term traffic-related air pollution with BP and prevalent hypertension in European populations.

          Methods: We analyzed 15 population-based cohorts, participating in the European Study of Cohorts for Air Pollution Effects (ESCAPE). We modeled residential exposure to particulate matter and nitrogen oxides with land use regression using a uniform protocol. We assessed traffic exposure with traffic indicator variables. We analyzed systolic and diastolic BP in participants medicated and nonmedicated with BP-lowering medication (BPLM) separately, adjusting for personal and area-level risk factors and environmental noise. Prevalent hypertension was defined as ≥ 140 mmHg systolic BP, or ≥ 90 mmHg diastolic BP, or intake of BPLM. We combined cohort-specific results using random-effects meta-analysis.

          Results: In the main meta-analysis of 113,926 participants, traffic load on major roads within 100 m of the residence was associated with increased systolic and diastolic BP in nonmedicated participants [0.35 mmHg (95% CI: 0.02, 0.68) and 0.22 mmHg (95% CI: 0.04, 0.40) per 4,000,000 vehicles × m/day, respectively]. The estimated odds ratio (OR) for prevalent hypertension was 1.05 (95% CI: 0.99, 1.11) per 4,000,000 vehicles × m/day. Modeled air pollutants and BP were not clearly associated.

          Conclusions: In this first comprehensive meta-analysis of European population-based cohorts, we observed a weak positive association of high residential traffic exposure with BP in nonmedicated participants, and an elevated OR for prevalent hypertension. The relationship of modeled air pollutants with BP was inconsistent.

          Citation: Fuks KB, Weinmayr G, Foraster M, Dratva J, Hampel R, Houthuijs D, Oftedal B, Oudin A, Panasevich S, Penell J, Sommar JN, Sørensen M, Tittanen P, Wolf K, Xun WW, Aguilera I, Basagaña X, Beelen R, Bots ML, Brunekreef B, Bueno-de-Mesquita HB, Caracciolo B, Cirach M, de Faire U, de Nazelle A, Eeftens M, Elosua R, Erbel R, Forsberg B, Fratiglioni L, Gaspoz JM, Hilding A, Jula A, Korek M, Krämer U, Künzli N, Lanki T, Leander K, Magnusson PK, Marrugat J, Nieuwenhuijsen MJ, Östenson CG, Pedersen NL, Pershagen G, Phuleria HC, Probst-Hensch NM, Raaschou-Nielsen O, Schaffner E, Schikowski T, Schindler C, Schwarze PE, Søgaard AJ, Sugiri D, Swart WJ, Tsai MY, Turunen AW, Vineis P, Peters A, Hoffmann B. 2014. Arterial blood pressure and long-term exposure to traffic-related air pollution: an analysis in the European Study of Cohorts for Air Pollution Effects (ESCAPE). Environ Health Perspect 122:896–905;  http://dx.doi.org/10.1289/ehp.1307725

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          Most cited references44

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          World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects.

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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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              Meta-analysis in clinical trials


                Author and article information

                Environ Health Perspect
                Environ. Health Perspect
                Environmental Health Perspectives
                16 May 2014
                September 2014
                : 122
                : 9
                : 896-905
                [1 ]IUF-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany
                [2 ]Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany
                [3 ]Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
                [4 ]CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
                [5 ]Universitat Pompeu Fabra, Barcelona, Spain
                [6 ]Swiss Tropical and Public Health Institute, Basel, Switzerland
                [7 ]University of Basel, Basel, Switzerland
                [8 ]Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
                [9 ]National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
                [10 ]Division of Environmental Medicine, Norwegian Institute of Public Health, Oslo, Norway
                [11 ]Division of Occupational and Environmental Medicine, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
                [12 ]Division of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway
                [13 ]Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
                [14 ]Danish Cancer Society Research Center, Copenhagen, Denmark
                [15 ]Department of Environmental Health, National Institute for Health and Welfare, Kuopio, Finland
                [16 ]Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom
                [17 ]Department of Epidemiology and Public Health, University College London, London, United Kingdom
                [18 ]Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
                [19 ]Julius Center for Primary Care and Health Sciences, University Medical Center Utrecht, Utrecht, the Netherlands
                [20 ]School of Public Health, Imperial College London, London, United Kingdom
                [21 ]Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden
                [22 ]IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
                [23 ]Centre for Environmental Policy, Imperial College London, United Kingdom
                [24 ]West German Heart Centre, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
                [25 ]Stockholm Gerontology Research Center, Stockholm, Sweden
                [26 ]Department of Community Medicine, Primary Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland
                [27 ]Faculty of Medicine, University of Geneva, Geneva, Switzerland
                [28 ]Endocrine and Diabetes Unit, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
                [29 ]Department of Chronic Disease Prevention, National Institute for Health and Welfare, Turku, Finland
                [30 ]Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
                [31 ]Department of Research in Inflammatory and Cardiovascular Disorders (RICAD), IMIM-Hospital del Mar, Barcelona, Spain
                [32 ]Medical School, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
                Author notes
                Address correspondence to K.B. Fuks, IUF-Leibniz Research Institute for Environmental Medicine, Auf’m Hennekamp 50, 40225 Düsseldorf, Germany. Telephone: 49 211 3389 342. E-mail: kateryna.fuks@ 123456iuf-duesseldorf.de

                Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, “Reproduced with permission from Environmental Health Perspectives”); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.

                : 01 October 2013
                : 15 May 2014
                : 16 May 2014
                : 01 September 2014

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