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Mapping Environmental Inequalities Relevant for Health for Informing Urban Planning Interventions—A Case Study in the City of Dortmund, Germany

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      Spatial differences in urban environmental conditions contribute to health inequalities within cities. The purpose of the paper is to map environmental inequalities relevant for health in the City of Dortmund, Germany, in order to identify needs for planning interventions. We develop suitable indicators for mapping socioeconomically-driven environmental inequalities at the neighborhood level based on published scientific evidence and inputs from local stakeholders. Relationships between socioeconomic and environmental indicators at the level of 170 neighborhoods were analyzed continuously with Spearman rank correlation coefficients and categorically applying chi-squared tests. Reclassified socioeconomic and environmental indicators were then mapped at the neighborhood level in order to determine multiple environmental burdens and hotspots of environmental inequalities related to health. Results show that the majority of environmental indicators correlate significantly, leading to multiple environmental burdens in specific neighborhoods. Some of these neighborhoods also have significantly larger proportions of inhabitants of a lower socioeconomic position indicating hotspots of environmental inequalities. Suitable planning interventions mainly comprise transport planning and green space management. In the conclusions, we discuss how the analysis can be used to improve state of the art planning instruments, such as clean air action planning or noise reduction planning towards the consideration of the vulnerability of the population.

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          Bringing context back into epidemiology: variables and fallacies in multilevel analysis.

          A large portion of current epidemiologic research is based on methodologic individualism: the notion that the distribution of health and disease in populations can be explained exclusively in terms of the characteristics of individuals. The present paper discusses the need to include group- or macro-level variables in epidemiologic studies, thus incorporating multiple levels of determination in the study of health outcomes. These types of analyses, which have been called contextual or multi-level analyses, challenge epidemiologists to develop theoretical models of disease causation that extend across levels and explain how group-level and individual-level variables interact in shaping health and disease. They also raise a series of methodological issues, including the need to select the appropriate contextual unit and contextual variables, to correctly specify the individual-level model, and, in some cases, to account for residual correlation between individuals within contexts. Despite its complexities, multilevel analysis holds potential for reemphasizing the role of macro-level variables in shaping health and disease in populations.

            Author and article information

            [1 ]Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 6, Enschede 7500 AE, The Netherlands
            [2 ]Department of Social Epidemiology, Institute for Public Health and Nursing Research, University of Bremen, Grazer Str. 4, Bremen 28359, Germany; steffen.schuele@ (S.A.S.); gabriele.bolte@ (G.B.)
            [3 ]Department of Community Health, Hochschule für Gesundheit, Gesundheitscampus 6–8, Bochum 44801, Germany; heike.koeckler@
            Author notes
            [* ]Correspondence: j.flacke@ ; Tel.: +31-53-4874-381
            Role: Academic Editor
            Int J Environ Res Public Health
            Int J Environ Res Public Health
            International Journal of Environmental Research and Public Health
            13 July 2016
            July 2016
            : 13
            : 7
            (Academic Editor)
            © 2016 by the authors; licensee MDPI, Basel, Switzerland.

            This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (



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