Éditeur(s) : HAL CCSD Nature Publishing Group
Résumé : International audience
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.
ISSN: 1559-0631

hal-01069587
https://hal.archives-ouvertes.fr/hal-01069587
https://hal.archives-ouvertes.fr/hal-01069587/document
https://hal.archives-ouvertes.fr/hal-01069587/file/Article_multiexpo_20140708_c.pdf
DOI : 10.1038/jes.2014.66

Éditeur(s) : HAL CCSD
Résumé : The purpose of this thesis is to improve the knowledge about and apply data mining techniques and some Bayesian model in the field of social and environmental health inequalities. On the neighborhood scale on the Paris, Marseille, Lyon and Lille metropolitan areas, the health event studied is infant mortality. We try to explain its risk with socio-economic data retrieved from the national census and environmental exposures such as air pollution, noise, proximity to traffic, green spaces and industries. The thesis is composed of two parts. The data mining part details the development of a procedure of creation of multidimensional socio-economic indices and of an R package that implements it, followed by the creation of a cumulative exposure index. In this part, data mining techniques are used to synthesize information and provide composite indicators amenable for direct usage by stakeholders or in the framework of epidemiological studies. The second part is about Bayesian models. It explains the "BYM" model. This model allows to take into account the spatial dimension of the data when estimating mortality risks. In both cases, the methods are exposed and several results of their usage in the above-mentioned context are presented. We also show the value of the socio-economic index procedure, as well as the existence of social inequalities of infant mortality in the studied metropolitan areas.
Cette thèse a pour but d'améliorer les connaissances concernant les techniques d'analyse de données et certains modèles bayésiens dans le domaine de l'étude des inégalités sociales et environnementales de santé. À l'échelle géographique de l'IRIS sur les agglomérations de Paris, Marseille, Lyon et Lille, l'événement sanitaire étudié est la mortalité infantile dont on cherchera à expliquer le risque avec des données socio-économiques issues du recensement et des expositions environnementales comme la pollution de l'air, les niveaux de bruit et la proximité aux industries polluantes, au trafic automobile ou aux espaces verts. Deux volets principaux composent cette thèse. Le volet analyse de données détaille la mise au point d'une procédure de création d'indices socio-économiques multidimensionnels et la conception d'un package R l'implémentant, puis la création d'un indice de multi-expositions environnementales. Pour cela, on utilise des techniques d'analyse de données pour synthétiser l'information et fournir des indicateurs composites utilisables directement par les décideurs publics ou dans le cadre d'études épidémiologiques. Le second volet concerne les modèles bayésiens et explique le modèle " BYM ". Celui-ci permet de prendre en compte les aspects spatiaux des données et est mis en œuvre pour estimer le risque de mortalité infantile. Dans les deux cas, les méthodes sont présentées et différents résultats de leur utilisation dans le contexte ci-dessus exposés. On montre notamment l'intérêt de la procédure de création d'indices socio-économiques et de multi-expositions, ainsi que l'existence d'inégalités sociales de mortalité infantile dans les agglomérations étudiées.
https://tel.archives-ouvertes.fr/tel-00943004

tel-00943004
https://tel.archives-ouvertes.fr/tel-00943004
https://tel.archives-ouvertes.fr/tel-00943004/document
https://tel.archives-ouvertes.fr/tel-00943004/file/Manuscrit_LallouA_final.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
To study social health inequalities, contextual data may constitute an appropriate alternative to individual socioeconomic characteristics, often hard to retrieve. Indices can be used to summarize the multiple dimensions of the neighborhood socioeconomic status. The main objectives of this work are to develop a statistical procedure to create a neighborhood socioeconomic index and to investigate the influence of the clustering method of the deprivation index on the measure of health inequalities. Our study setting is composed of three major French metropolitan areas: Lille, Lyon and Marseille. The statistical unit is the French census block. The methodology uses several successive principal components analyses to select variables and create the index. Deprivation categories are drawn with hierarchical clustering on principal components. Twenty socioeconomic variables are selected in the neighborhood deprivation index. An optimal clustering obtained by HC is in three classes and the clusters are ordered along the first axis of the PCA.
SFDS - 44èmes journées de Statistique - 2012
Bruxelles, Belgium

hal-00734769
https://hal.archives-ouvertes.fr/hal-00734769
https://hal.archives-ouvertes.fr/hal-00734769/document
https://hal.archives-ouvertes.fr/hal-00734769/file/submission_44.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
Background: Everyone is subject to environmental exposures from various sources, with negative health impacts (air, water and soil contamination, noise...) or with positive effects (e.g. green space). Studies considering such complex environmental settings in a global manner are rare.Aims: We propose to use data mining techniques 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 on a large French metropolitan area.Methods: The study was carried out in the Great Lyon area (France, 1,260,348 inhabitants in 2007, 527 km²) at the census block group (BG) scale. Indicators on NO2 annual concentrations, noise levels, proximity to green spaces and to industrial plants, and road traffic were synthetized using Multiple Factor Analysis (MFA), which allows to explore the relations between exposures and BGs without a priori knowledge, and to synthetize indicators of different types. Hierarchical clustering was then used to create BG classes. Results: The four first components of the MFA explained respectively 23, 14, 13 and 12% of the total variance. Clustering in 3 classes group: 1) BGs far from industries, greener and with less noise and air pollution than the average; 2) BGs close to industries with less green spaces and air pollution than the average; and 3)BGs far from industries but with higher levels of noise, air pollution and traffic. Greater numbers of classes were tested in order to assess a variety of clustering.Conclusions: We present a data driven approach using data mining techniques, which seem overlooked for cumulative exposure assessment 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.
Environment and Health – Bridging South, North, East and West. Conference of ISEE, ISES and ISIAQ.
Basel, Switzerland

hal-01432884
https://hal.archives-ouvertes.fr/hal-01432884
https://hal.archives-ouvertes.fr/hal-01432884/document
https://hal.archives-ouvertes.fr/hal-01432884/file/Poster_2013_ISEE_20130711.pdf

Éditeur(s) : HAL CCSD Scientific Research Publishing
Résumé : International audience
In order to study social inequalities, indices can be used to summarize the multiple dimensions of the socioeconomic status. As a part of the Equit'Area Project, a public health program focused on social and environmental health inequalities; a statistical procedure to create (neighborhood) socioeconomic indices was developed. This procedure uses successive principal components analyses to select variables and create the index. In order to simplify the application of the procedure for non-specialists, the R package SesIndexCreatoR was created. It allows the creation of the index with all the possible options of the procedure, the classification of the resulting index in categories using several classical methods, the visualization of the results, and the generation of automatic reports.
ISSN: 2161-718X

hal-01161775
https://hal.archives-ouvertes.fr/hal-01161775
https://hal.archives-ouvertes.fr/hal-01161775/document
https://hal.archives-ouvertes.fr/hal-01161775/file/OJS_2015060814030742.pdf
DOI : 10.4236/ojs.2015.54031

Éditeur(s) : HAL CCSD
Résumé : National audience
Afin de prendre en compte les inégalités sociales de santé, il est fréquent d'utiliser des indices socio-économique qui synthétisent différents aspects du statut socio-économique (SES) à l'échelle de l'individu, du voisinage ou de la région. De nombreux indices existent déjà mais nombre d'entre eux utilisent un faible nombre de variables, des méthodes d'agrégation simples et/ou sélectionnent les variables uniquement depuis la littérature. Nous avons développé une nouvelle procédure de création d'indices SES visant à sélectionner à partir d'un grand nombre de variables celles qui composeront l'indice et à les agréger à l'aide de techniques d'analyses de données. Cette procédure est plus complexe à mettre en œuvre pour des non statisticiens que d'autres approches existantes, c'est pourquoi nous l'avons implémentée dans un package R nommé SesIndexCreatoR. Ce package vise à donner des outils aussi simples d'emploi que possible pour effectuer la procédure de création et exploiter ses résultats, tout en conservant les différentes possibilités et la flexibilité qu'elle offre.
Deuxièmes rencontres R
Lyon, France

Droits : info:eu-repo/semantics/OpenAccess
hal-01433087
https://hal.archives-ouvertes.fr/hal-01433087
https://hal.archives-ouvertes.fr/hal-01433087/document
https://hal.archives-ouvertes.fr/hal-01433087/file/SesIndexCreatoR_20130328.pdf