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<title lang=en>A statistical procedure to create a neighborhood socioeconomic index for health inequalities analysis.</title>
<creator>Lalloué, Benoît</creator>
<creator>Monnez, Jean-Marie</creator>
<creator>Padilla, Cindy</creator>
<creator>Kihal, Wahida</creator>
<creator>Le Meur, Nolwenn</creator>
<creator>Zmirou-Navier, Denis</creator>
<creator>Deguen, Séverine</creator>
<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>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>Modélisation Conceptuelle des Connaissances Biomédicales ; Université de Rennes 1 (UR1) - 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>This work and the Equit'Area project are supported by the French National Research Agency (ANR, contract-2010-PRSP-002-01) and the EHESP School of Public Health. This research was also jointly supported by the Direction Générale de la Santé (DGS), the Caisse Nationale d'Assurance Maladie des Travailleurs Salariés (CNAMTS), the Régime Social des Indépendants (RSI), the Caisse Nationale de Solidarité pour l'Autonomie (CNSA), the Mission Recherche de la Direction de la Recherche, des Etudes, de l'Evaluation et des Statistiques (MiRe-DREES) and l'Institut national de prévention et de promotion de la santé (Inpes), under the research call launched by the French Institute of Public Health Research (IReSP) in 2010.</contributor>
<description>International audience</description>
<source>ISSN: 1475-9276</source>
<source>International Journal for Equity in Health</source>
<publisher>BioMed Central</publisher>
<identifier>inserm-00809310</identifier>
<identifier>http://www.hal.inserm.fr/inserm-00809310</identifier>
<identifier>http://www.hal.inserm.fr/inserm-00809310/document</identifier>
<identifier>http://www.hal.inserm.fr/inserm-00809310/file/1475-9276-12-21.pdf</identifier>
<source>http://www.hal.inserm.fr/inserm-00809310</source>
<source>International Journal for Equity in Health, BioMed Central, 2013, 12 (1), pp.21. 〈10.1186/1475-9276-12-21〉</source>
<identifier>DOI : 10.1186/1475-9276-12-21</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1186/1475-9276-12-21</relation>
<identifier>PUBMED : 23537275</identifier>
<relation>info:eu-repo/semantics/altIdentifier/pmid/23537275</relation>
<language>en</language>
<subject lang=it>Small-area analysis</subject>
<subject lang=it>Hierarchical classification</subject>
<subject lang=it>Socioeconomic status</subject>
<subject lang=it>Multidimensional index</subject>
<subject lang=it>Principal component analysis</subject>
<subject>[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>INTRODUCTION: In order to study social health inequalities, contextual (or ecologic) data may constitute an appropriate alternative to individual socioeconomic characteristics. Indices can be used to summarize the multiple dimensions of the neighborhood socioeconomic status. This work proposes a statistical procedure to create a neighborhood socioeconomic index. METHODS: The study setting is composed of three French urban areas. Socioeconomic data at the census block scale come from the 1999 census. Successive principal components analyses are used to select variables and create the index. Both metropolitan area-specific and global indices are tested and compared. Socioeconomic categories are drawn with hierarchical clustering as a reference to determine "optimal" thresholds able to create categories along a one-dimensional index. RESULTS: Among the twenty variables finally selected in the index, 15 are common to the three metropolitan areas. The index explains at least 57% of the variance of these variables in each metropolitan area, with a contribution of more than 80% of the 15 common variables. CONCLUSIONS: The proposed procedure is statistically justified and robust. It can be applied to multiple geographical areas or socioeconomic variables and provides meaningful information to public health bodies. We highlight the importance of the classification method. We propose an R package in order to use this procedure.</description>
<date>2013-03-28</date>
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