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<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd> <responseDate>2018-01-15T18:24:17Z</responseDate> <request identifier=oai:HAL:hal-01296781v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request> <GetRecord> <record> <header> <identifier>oai:HAL:hal-01296781v1</identifier> <datestamp>2018-01-04</datestamp> <setSpec>type:ART</setSpec> <setSpec>subject:sdv</setSpec> <setSpec>collection:UNIV-RENNES1</setSpec> <setSpec>collection:UNIV-AG</setSpec> <setSpec>collection:IRSET</setSpec> <setSpec>collection:IFR140</setSpec> <setSpec>collection:BIOSIT</setSpec> <setSpec>collection:UR1-UFR-SVE</setSpec> <setSpec>collection:STATS-UR1</setSpec> <setSpec>collection:UR1-HAL</setSpec> <setSpec>collection:EHESP</setSpec> <setSpec>collection:USPC</setSpec> <setSpec>collection:UR1-SDV</setSpec> <setSpec>collection:UNIV-ANGERS</setSpec> </header> <metadata><dc> <publisher>HAL CCSD</publisher> <title lang=en>A Conceptual Framework for the Assessment of Cumulative Exposure to Air Pollution at a Fine Spatial Scale</title> <creator>Wahida, Kihal-Talantikite</creator> <creator>Padilla, Cindy M.</creator> <creator>Denis, Zmirou-Navier</creator> <creator>Olivier, Blanchard</creator> <creator>Géraldine, Le Nir</creator> <creator>Philippe, Quenel</creator> <creator>Séverine, Deguen</creator> <contributor>Institut de recherche, santé, environnement et travail [Rennes] (Irset) ; Université d'Angers (UA) - Université des Antilles et de la Guyane (UAG) - Université de Rennes 1 (UR1) - École des Hautes Études en Santé Publique [EHESP] (EHESP) - Institut National de la Santé et de la Recherche Médicale (INSERM) - Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )</contributor> <description>International audience</description> <source>ISSN: 1660-4601</source> <source>EISSN: 1660-4601</source> <source>International Journal of Environmental Research and Public Health</source> <publisher>MDPI</publisher> <identifier>hal-01296781</identifier> <identifier>https://hal-univ-rennes1.archives-ouvertes.fr/hal-01296781</identifier> <source>https://hal-univ-rennes1.archives-ouvertes.fr/hal-01296781</source> <source>International Journal of Environmental Research and Public Health, MDPI, 2016, 13 (3), pp.319. 〈10.3390/ijerph13030319〉</source> <identifier>DOI : 10.3390/ijerph13030319</identifier> <relation>info:eu-repo/semantics/altIdentifier/doi/10.3390/ijerph13030319</relation> <identifier>PUBMED : 26999170</identifier> <relation>info:eu-repo/semantics/altIdentifier/pmid/26999170</relation> <language>en</language> <subject lang=en> Long-term</subject> <subject lang=en> residential mobility</subject> <subject lang=en>Air Pollution</subject> <subject lang=en> cumulative exposure assessment</subject> <subject lang=en> fine spatial scale</subject> <subject>[SDV] Life Sciences [q-bio]</subject> <type>info:eu-repo/semantics/article</type> <type>Journal articles</type> <description lang=en>Many epidemiological studies examining long-term health effects of exposure to air pollutants have characterized exposure by the outdoor air concentrations at sites that may be distant to subjects' residences at different points in time. The temporal and spatial mobility of subjects and the spatial scale of exposure assessment could thus lead to misclassification in the cumulative exposure estimation. This paper attempts to fill the gap regarding cumulative exposure assessment to air pollution at a fine spatial scale in epidemiological studies investigating long-term health effects. We propose a conceptual framework showing how major difficulties in cumulative long-term exposure assessment could be surmounted. We then illustrate this conceptual model on the case of exposure to NO₂ following two steps: (i) retrospective reconstitution of NO₂ concentrations at a fine spatial scale; and (ii) a novel approach to assigning the time-relevant exposure estimates at the census block level, using all available data on residential mobility throughout a 10- to 20-year period prior to that for which the health events are to be detected. Our conceptual framework is both flexible and convenient for the needs of different epidemiological study designs</description> <date>2016</date> </dc> </metadata> </record> </GetRecord> </OAI-PMH>