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Use of geographic indicators of healthcare, environment and socioeconomic factors to characterize environmental health disparities
Auteur(s) : Padilla, Cindy Kihal-Talantikite, Wahida Perez, Sandra Deguen, Severine
Auteurs secondaires : 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 ) École des Hautes Études en Santé Publique [EHESP] (EHESP) Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE) ; Aix Marseille Université (AMU) - Université d'Avignon et des Pays de Vaucluse (UAPV) - Université Côte d'Azur (UCA) - Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) City of Nice Financial support as a post-doctoral grant

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
Background: An environmental health inequality is a major public health concern in Europe. However just few studies take into account a large set of characteristics to analyze this problematic. The aim of this study was to identify and describe how socioeconomic, health accessibility and exposure factors accumulate and interact in small areas in a French urban context, to assess environmental health inequalities related to infant and neonatal mortality.Methods: Environmental indicators on deprivation index, proximity to high-traffic roads, green space, and healthcare accessibility were created using the Geographical Information System. Cases were collected from death certificates in the city hall of each municipality in the Nice metropolitan area. Using the parental addresses, cases were geocoded to their census block of residence. A classification using a Multiple Component Analysis following by a Hierarchical Clustering allow us to characterize the census blocks in terms of level of socioeconomic, environmental and accessibility to healthcare, which are very diverse definition by nature. Relation between infant and neonatal mortality rate and the three environmental patterns which categorize the census blocks after the classification was performed using a standard Poisson regression model for count data after checking the assumption of dispersion.Results: Based on geographic indicators, three environmental patterns were identified. We found environmental inequalities and social health inequalities in Nice metropolitan area. Moreover these inequalities are counterbalance by the close proximity of deprived census blocks to healthcare facilities related to mother and newborn. So therefore we demonstrate no environmental health inequalities related to infant and neonatal mortality.Conclusion: Examination of patterns of social, environmental and in relation with healthcare access is useful to identify census blocks with needs and their effects on health. Similar analyzes could be implemented and considered in other cities or related to other birth outcomes.
ISSN: 1476-069X

halshs-01366285
https://halshs.archives-ouvertes.fr/halshs-01366285
https://halshs.archives-ouvertes.fr/halshs-01366285/document
https://halshs.archives-ouvertes.fr/halshs-01366285/file/art_10.1186_s12940-016-0163-7.pdf
PUBMED : 27449640

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