Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
BACKGROUND: Few studies have considered using environmental amenities to explain social health inequalities.Nevertheless, Green spaces that promote good health may have an effect on socioeconomic health inequalities. In developed countries, there is considerable evidence that green spaces have a beneficial effect on the health of urban populations and recent studies suggest they can have a positive effect on pregnancy outcomes. To investigate the relationship between green spaces and the spatial distribution of infant mortality taking account neighborhood deprivation levels. METHODS: The study took place in Lyon metropolitan area, France. All infant deaths that occurred between 2000 and 2009 were geocoded at census block level. Each census block was assigned greenness and socioeconomic deprivation levels. The spatial--scan statistic was used to identify high risk cluster of infant mortality according to these neighborhood characteristics. RESULTS: The spatial distribution of infant mortality was not random with a high risk cluster in the south east of the Lyon metropolitan area (p<0.003). This cluster disappeared (p=0.12) after adjustment for greenness level and socioeconomic deprivation, suggesting that these factors explain part of the spatial distribution of infant mortality. These results are discussed using a conceptual framework with 3 hypothetical pathways by which green spaces may have a beneficial effect on adverse pregnancy outcomes: (i) a psychological pathway, (ii) a physiological disruption process and (iii) an environmental pathway. CONCLUSIONS: These results add some evidence to the hypothesis that there is a relationship between access to green spaces and pregnancy outcomes but further research is required to confirm this.
BMC Pregnancy and Childbirth

hal-00876083
https://hal.archives-ouvertes.fr/hal-00876083
https://hal.archives-ouvertes.fr/hal-00876083/document
DOI : 10.1186/1471-2393-13-191

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 : 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 Orodental diseases include several clinically and genetically heterogeneous disorders that can present in isolation or as part of a genetic syndrome. Due to the vast number of genes implicated in these disorders, establishing a molecular diagnosis can be challenging. We aimed to develop a targeted next-generation sequencing (NGS) assay to diagnose mutations and potentially identify novel genes mutated in this group of disorders. Methods We designed an NGS gene panel that targets 585 known and candidate genes in orodental disease. We screened a cohort of 101 unrelated patients without a molecular diagnosis referred to the Reference Centre for Oro-Dental Manifestations of Rare Diseases, Strasbourg, France, for a variety of orodental disorders including isolated and syndromic amelogenesis imperfecta (AI), isolated and syndromic selective tooth agenesis (STHAG), isolated and syndromic dentinogenesis imperfecta, isolated dentin dysplasia, otodental dysplasia and primary failure of tooth eruption. Results We discovered 21 novel pathogenic variants and identified the causative mutation in 39 unrelated patients in known genes (overall diagnostic rate: 39%). Among the largest subcohorts of patients with isolated AI (50 unrelated patients) and isolated STHAG (21 unrelated patients), we had a definitive diagnosis in 14 (27%) and 15 cases (71%), respectively. Surprisingly, COL17A1 mutations accounted for the majority of autosomal-dominant AI cases. Conclusions We have developed a novel targeted NGS assay for the efficient molecular diagnosis of a wide variety of orodental diseases. Furthermore, our panel will contribute to better understanding the contribution of these genes to orodental disease. Trial registration numbers NCT01746121 and NCT02397824
Journal of Medical Genetics

hal-01272929
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01272929
DOI : 10.1136/jmedgenet-2015-103302
PUBMED : 26502894

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
BACKGROUND: Few studies have considered using environmental amenities to explain social health inequalities.Nevertheless, Green spaces that promote good health may have an effect on socioeconomic health inequalities. In developed countries, there is considerable evidence that green spaces have a beneficial effect on the health of urban populations and recent studies suggest they can have a positive effect on pregnancy outcomes. To investigate the relationship between green spaces and the spatial distribution of infant mortality taking account neighborhood deprivation levels. METHODS: The study took place in Lyon metropolitan area, France. All infant deaths that occurred between 2000 and 2009 were geocoded at census block level. Each census block was assigned greenness and socioeconomic deprivation levels. The spatial--scan statistic was used to identify high risk cluster of infant mortality according to these neighborhood characteristics. RESULTS: The spatial distribution of infant mortality was not random with a high risk cluster in the south east of the Lyon metropolitan area (p<0.003). This cluster disappeared (p=0.12) after adjustment for greenness level and socioeconomic deprivation, suggesting that these factors explain part of the spatial distribution of infant mortality. These results are discussed using a conceptual framework with 3 hypothetical pathways by which green spaces may have a beneficial effect on adverse pregnancy outcomes: (i) a psychological pathway, (ii) a physiological disruption process and (iii) an environmental pathway. CONCLUSIONS: These results add some evidence to the hypothesis that there is a relationship between access to green spaces and pregnancy outcomes but further research is required to confirm this.
ISSN: 1471-2393

inserm-00878362
http://www.hal.inserm.fr/inserm-00878362
http://www.hal.inserm.fr/inserm-00878362/document
http://www.hal.inserm.fr/inserm-00878362/file/1471-2393-13-191.pdf
DOI : 10.1186/1471-2393-13-191