Éditeur(s) : HAL CCSD Wiley
Résumé : International audience
Over the last decades, the prevalence of childhood respiratory conditions has dramatically increased worldwide. Considering the time spent in enclosed spaces, indoor air pollutants are of major interest to explain part of this increase. This study aimed to measure the concentrations of pollutants known or suspected to affect respiratory health that are present in dwellings in order to assess children's exposure. Measurements were taken in 150 homes with at least one child, in Brittany (western France), to assess the concentrations of 18 volatile organic compounds (among which four aldehydes and four trihalomethanes) and nine semi-volatile organic compounds (seven phthalates and two synthetic musks). In addition to descriptive statistics, a principal component analysis (PCA) was used to investigate grouping of contaminants. Formaldehyde was highly present and above 30 μg/m(3) in 40% of the homes. Diethyl phthalate, diisobutyl phthalate, and dimethylphthalate were quantified in all dwellings, as well as Galaxolide and Tonalide. For each chemical family, the groups appearing in the PCA could be interpreted in term of sources. The high prevalence and the levels of these compounds, with known or suspected respiratory toxicity, should question regulatory agencies to trigger prevention and mitigation actions.
ISSN: 0905-6947

hal-01162378
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01162378
DOI : 10.1111/ina.12225
PUBMED : 26010323

Éditeur(s) : HAL CCSD Springer Verlag
Résumé : International audience
In France, reducing social health inequalities has become an explicit goal of health policies over the past few years, one of its objectives is specifically the reduction of the perinatal mortality rate. This study investigates the association between infant mortality and social deprivation categories at a small area level in the Lille metropolitan area, in the north of France, to identify census blocks where public authorities should prioritize appropriate preventive actions. We used census data to establish a neighbourhood deprivation index whose multiple dimensions encompass socioeconomic characteristics. Infant mortality data were obtained from the Lille metropolitan area municipalities to estimate a death rate at the census tract level. We used Bayesian hierarchical models in order to reduce the extra variability when computing relative risks (RR) and to assess the associations between infant mortality and deprivation. Between 2000 and 2009, 668 cases of infant death occurred in the Lille metropolitan area (4.2 per 1,000 live births). The socioeconomic status is associated with infant mortality, with a clear gradient of risk from the most privileged census blocks to the most deprived ones (RR = 2.62, 95 % confidence interval [1.87; 3.70]). The latter have 24.6 % of families who were single parents and 29.9 % of unemployed people in the labor force versus 8.5 % and 7.7 % in the former. Our study reveals socio-spatial disparities in infant mortality in the Lille metropolitan area and highlights the census blocks most affected by the inequalities. Fine spatial analysis may help inform the design of preventive policies tailored to the characteristics of the local communities.
ISSN: 1092-7875

hal-00876255
https://hal.archives-ouvertes.fr/hal-00876255
DOI : 10.1007/s10995-013-1251-8
PUBMED : 23479336

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 Public Library of Science
Résumé : International audience
BACKGROUND: While a great number of papers have been published on the short-term effects of air pollution on mortality, few have tried to assess whether this association varies according to the neighbourhood socioeconomic level and long-term ambient air concentrations measured at the place of residence. We explored the effect modification of 1) socioeconomic status, 2) long-term NO2 ambient air concentrations, and 3) both combined, on the association between short-term exposure to NO2 and all-cause mortality in Paris (France).METHODS: A time-stratified case-crossover analysis was performed to evaluate the effect of short-term NO2 variations on mortality, based on 79,107 deaths having occurred among subjects aged over 35 years, from 2004 to 2009, in the city of Paris. Simple and double interactions were statistically tested in order to analyse effect modification by neighbourhood characteristics on the association between mortality and short-term NO2 exposure. The data was estimated at the census block scale (n=866).RESULTS: The mean of the NO2 concentrations during the five days prior to deaths were associated with an increased risk of all-cause mortality: overall Excess Risk (ER) was 0.94% (95%CI=[0.08;1.80]. A higher risk was revealed for subjects living in the most deprived census blocks in comparison with higher socioeconomic level areas (ER=3.14% (95%CI=[1.41-4.90], p extless0.001). Among these deprived census blocks, excess risk was even higher where long-term average NO2 concentrations were above 55.8 μg/m3 (the top tercile of distribution): ER=4.84% (95%CI=[1.56;8.24], p for interaction=0.02).CONCLUSION: Our results show that people living in census blocks characterized by low socioeconomic status are more vulnerable to air pollution episodes. There is also an indication that people living in these disadvantaged census blocks might experience even higher risk following short-term air pollution episodes, when they are also chronically exposed to higher NO2 levels
ISSN: 1932-6203

hal-01187406
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01187406
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01187406/document
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01187406/file/journal.pone.0131463.PDF
DOI : 10.1371/journal.pone.0131463

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

Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
Many epidemiological studies examining long-term health effects of exposure to air pollutants have characterized exposure by the outdoor air concentrations at sites that may be distant to subjects' residences at different points in time. The temporal and spatial mobility of subjects and the spatial scale of exposure assessment could thus lead to misclassification in the cumulative exposure estimation. This paper attempts to fill the gap regarding cumulative exposure assessment to air pollution at a fine spatial scale in epidemiological studies investigating long-term health effects. We propose a conceptual framework showing how major difficulties in cumulative long-term exposure assessment could be surmounted. We then illustrate this conceptual model on the case of exposure to NO₂ following two steps: (i) retrospective reconstitution of NO₂ concentrations at a fine spatial scale; and (ii) a novel approach to assigning the time-relevant exposure estimates at the census block level, using all available data on residential mobility throughout a 10- to 20-year period prior to that for which the health events are to be detected. Our conceptual framework is both flexible and convenient for the needs of different epidemiological study designs
ISSN: 1660-4601

hal-01296781
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01296781
DOI : 10.3390/ijerph13030319
PUBMED : 26999170

Éditeur(s) : HAL CCSD
Résumé : International audience
OBJECTIVES: To detect new hazards ("signals"), occupational health monitoring systems mostly rest on the description of exposures in the jobs held and on reports by medical doctors; these are subject to declarative bias. Our study aims to assess whether job-exposure matrices (JEMs) could be useful tools for signal detection by improving exposure reporting. METHODS: Using the French national occupational disease surveillance and prevention network (RNV3P) data from 2001 to 2011, we explored the associations between disease and exposure prevalence for 3 well-known pathology/exposure couples and for one debatable couple. We compared the associations measured when using physicians' reports or applying the JEMs, respectively, for these selected diseases and across non-selected RNV3P population or for cases with musculoskeletal disorders, used as two reference groups; the ratio of exposure prevalences according to the two sources of information were computed for each disease category. RESULTS: Our population contained 58,188 subjects referred with pathologies related to work. Mean age at diagnosis was 45.8 years (95% CI 45.7; 45.9), and 57.2% were men. For experts, exposure ratios increase with knowledge on exposure causality. As expected, JEMs retrieved more exposed cases than experts (exposure ratios between 12 and 194), except for the couple silica/silicosis, but not for the MSD control group (ratio between 0.2 and 0.8). CONCLUSIONS: JEMs enhanced the number of exposures possibly linked with some conditions, compared to experts' assessment, relative to the whole database or to a reference group; they are less likely to suffer from declarative bias than reports by occupational health professionals.
International Archives of Occupational and Environmental Health

hal-01560279
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01560279
DOI : 10.1007/s00420-017-1215-1
PUBMED : 28299449

Éditeur(s) : HAL CCSD
Résumé : International audience
Epidemiology
Séoul, South Korea

hal-01118635
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01118635
DOI : 10.1097/01.ede.0000391989.10496.21

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
Semi-Volatile Organic Compounds (SVOCs) are commonly present in dwellings and several are suspected of having effects on male reproductive function mediated by an endocrine disruption mode of action. To improve knowledge of the health impact of these compounds, cumulative toxicity indicators are needed. This work derives Benchmark Doses (BMD) and Relative Potency Factors (RPF) for SVOCs acting on the male reproductive system through the same mode of action. We included SVOCs fulfilling the following conditions: detection frequency (>10%) in French dwellings, availability of data on the mechanism/mode of action for male reproductive toxicity, and availability of comparable dose-response relationships. Of 58 SVOCs selected, 18 induce a decrease in serum testosterone levels. Six have sufficient and comparable data to derive BMDs based on 10 or 50% of the response. The SVOCs inducing the largest decrease in serum testosterone concentration are: for 10%, bisphenol A (BMD10= 7.72E-07 mg/kg bw/d; RPF10= 7033679); for 50%, benzo[a]pyrene (BMD50= 0.030 mg/kg bw/d; RPF50= 1630), and the one inducing the smallest one is benzyl butyl phthalate (RPF10 and RPF50= 0.095). This approach encompasses contaminants from diverse chemical families acting through similar modes of action, and makes possible a cumulative risk assessment in indoor environments. The main limitation remains the lack of comparable toxicological data.
ISSN: 0273-2300

hal-01237085
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01237085
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01237085/document
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01237085/file/Multiple%20exposures%20to%20indoor%20contaminants_accepted.pdf
DOI : 10.1016/j.yrtph.2015.11.017

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
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.
ISSN: 1475-9276

inserm-00809310
http://www.hal.inserm.fr/inserm-00809310
http://www.hal.inserm.fr/inserm-00809310/document
http://www.hal.inserm.fr/inserm-00809310/file/1475-9276-12-21.pdf
DOI : 10.1186/1475-9276-12-21

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
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.
International Journal for Equity in Health

hal-00854028
https://hal.archives-ouvertes.fr/hal-00854028
https://hal.archives-ouvertes.fr/hal-00854028/document
DOI : 10.1186/1475-9276-12-21