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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>2018-01-15T18:33:13Z</responseDate> <request identifier=oai:HAL:hal-01069587v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request> <GetRecord> <record> <header> <identifier>oai:HAL:hal-01069587v1</identifier> <datestamp>2018-01-11</datestamp> <setSpec>type:ART</setSpec> <setSpec>subject:math</setSpec> <setSpec>subject:stat</setSpec> <setSpec>subject:sdv</setSpec> <setSpec>collection:UNIV-AG</setSpec> <setSpec>collection:UNIV-RENNES1</setSpec> <setSpec>collection:CNRS</setSpec> <setSpec>collection:INRIA</setSpec> <setSpec>collection:IECN</setSpec> <setSpec>collection:INSMI</setSpec> <setSpec>collection:SANTE_PUB_INSERM</setSpec> <setSpec>collection:IFR140</setSpec> <setSpec>collection:INRIA-LORRAINE</setSpec> <setSpec>collection:INRIA-NANCY-GRAND-EST</setSpec> <setSpec>collection:IRSET</setSpec> <setSpec>collection:IRSET-ERD</setSpec> <setSpec>collection:BIOSIT</setSpec> <setSpec>collection:UNIV-LORRAINE</setSpec> <setSpec>collection:INRIA_TEST</setSpec> <setSpec>collection:UR1-UFR-SVE</setSpec> <setSpec>collection:INRIA2</setSpec> <setSpec>collection:EHESP</setSpec> <setSpec>collection:UR1-HAL</setSpec> <setSpec>collection:USPC</setSpec> <setSpec>collection:UR1-SDV</setSpec> <setSpec>collection:IRSET-9</setSpec> <setSpec>collection:UNIV-ANGERS</setSpec> <setSpec>collection:NANCY-2014</setSpec> <setSpec>collection:MOS</setSpec> <setSpec>collection:FNCLCC</setSpec> </header> <metadata><dc> <publisher>HAL CCSD</publisher> <title lang=fr>Data analysis techniques: a tool for cumulative exposure assessment.</title> <creator>Lalloué, Benoît</creator> <creator>Monnez, Jean-Marie</creator> <creator>Padilla, Cindy</creator> <creator>Kihal, Wahida</creator> <creator>Zmirou-Navier, Denis</creator> <creator>Deguen, Séverine</creator> <contributor>EA Management des Organisations de Santé (EA MOS) ; École des Hautes Études en Santé Publique [EHESP] (EHESP) - PRES Sorbonne Paris Cité</contributor> <contributor>École des Hautes Études en Santé Publique [EHESP] (EHESP)</contributor> <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> <contributor>Biology, genetics and statistics (BIGS) ; Inria Nancy - Grand Est ; Institut National de Recherche en Informatique et en Automatique (Inria) - Institut National de Recherche en Informatique et en Automatique (Inria) - Institut Élie Cartan de Lorraine (IECL) ; Université de Lorraine (UL) - Centre National de la Recherche Scientifique (CNRS) - Université de Lorraine (UL) - Centre National de la Recherche Scientifique (CNRS)</contributor> <contributor>Probabilités et statistiques ; Institut Élie Cartan de Lorraine (IECL) ; Université de Lorraine (UL) - Centre National de la Recherche Scientifique (CNRS) - Université de Lorraine (UL) - Centre National de la Recherche Scientifique (CNRS)</contributor> <contributor>Faculté de Médecine [Nancy] ; Université de Lorraine (UL)</contributor> <contributor>Cette recherche a été financée conjointement par la Direction Générale de la Santé (DGS), la Caisse Nationale d'Assurance Maladie des Travailleurs Salariés (CNAMTS), le Régime Social des Indépendants (RSI), la Caisse Nationale de Solidarité pour l'Autonomie (CNSA), la Mission Recherche de la Direction de la Recherche, des Etudes, de l'Evaluation et des Statistiques (MiRe-DREES) et l'Institut national de prévention et de promotion de la santé (Inpes), via un appel à projet de recherche lancé par l'Institut de Recherche en Santé Publique (IReSP) en 2010.</contributor> <description>International audience</description> <source>ISSN: 1559-0631</source> <source>EISSN: 1559-064X</source> <source>Journal of Exposure Science and Environmental Epidemiology</source> <publisher>Nature Publishing Group</publisher> <identifier>hal-01069587</identifier> <identifier>https://hal.archives-ouvertes.fr/hal-01069587</identifier> <identifier>https://hal.archives-ouvertes.fr/hal-01069587/document</identifier> <identifier>https://hal.archives-ouvertes.fr/hal-01069587/file/Article_multiexpo_20140708_c.pdf</identifier> <source>https://hal.archives-ouvertes.fr/hal-01069587</source> <source>Journal of Exposure Science and Environmental Epidemiology, Nature Publishing Group, 2015, 25, pp.222-230. 〈10.1038/jes.2014.66〉</source> <identifier>DOI : 10.1038/jes.2014.66</identifier> <relation>info:eu-repo/semantics/altIdentifier/doi/10.1038/jes.2014.66</relation> <identifier>PUBMED : 25248936</identifier> <relation>info:eu-repo/semantics/altIdentifier/pmid/25248936</relation> <language>en</language> <subject lang=en>Cumulative exposure</subject> <subject lang=en>Hierarchical Clustering</subject> <subject lang=en>Multiple Factor Analysis</subject> <subject lang=en>Environmental index</subject> <subject>[MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]</subject> <subject>[STAT.TH] Statistics [stat]/Statistics Theory [stat.TH]</subject> <subject>[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie</subject> <subject>[MATH.MATH-GM] Mathematics [math]/General Mathematics [math.GM]</subject> <type>info:eu-repo/semantics/article</type> <type>Journal articles</type> <description lang=en>Everyone is subject to environmental exposures from various sources, with negative health impacts (air, water and soil contamination, noise, etc.or with positive effects (e.g. green space). Studies considering such complex environmental settings in a global manner are rare. We propose to use statistical factor and cluster analyses to create a composite exposure index with a data-driven approach, in view to assess the environmental burden experienced by populations. We illustrate this approach in a large French metropolitan area. The study was carried out in the Great Lyon area (France, 1.2 M inhabitants) at the census Block Group (BG) scale. We used as environmental indicators ambient air NO2 annual concentrations, noise levels and proximity to green spaces, to industrial plants, to polluted sites and to road traffic. They were synthesized using Multiple Factor Analysis (MFA), a data-driven technique without a priori modeling, followed by a Hierarchical Clustering to create BG classes. The first components of the MFA explained, respectively, 30, 14, 11 and 9% of the total variance. Clustering in five classes group: (1) a particular type of large BGs without population; (2) BGs of green residential areas, with less negative exposures than average; (3) BGs of residential areas near midtown; (4) BGs close to industries; and (5) midtown urban BGs, with higher negative exposures than average and less green spaces. Other numbers of classes were tested in order to assess a variety of clustering. We present an approach using statistical factor and cluster analyses techniques, which seem overlooked to assess cumulative exposure in complex environmental settings. Although it cannot be applied directly for risk or health effect assessment, the resulting index can help to identify hot spots of cumulative exposure, to prioritize urban policies or to compare the environmental burden across study areas in an epidemiological framework.Journal of Exposure Science and Environmental Epidemiology advance online publication, 24 September 2014; doi:10.1038/jes.2014.66.</description> <contributor>Equit'Area</contributor> <date>2015</date> <contributor>ANR-2010-PRSP-002-01, ANR-2010-PRSP-002-01</contributor> </dc> </metadata> </record> </GetRecord> </OAI-PMH>