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4 results on '"Epidémiologie et écologie des maladies animales ( Epidémiologie )"'

1. Estimating denominators: satellite-based population estimates at a fine spatial resolution in a European urban area

Author

Jean-François Viel, Annelise Tran, Laboratoire Chrono-environnement ( LCE ), Université Bourgogne Franche-Comté ( UBFC ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Franche-Comté ( UFC ), Epidémiologie et écologie des maladies animales ( Epidémiologie ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), Laboratoire Chrono-environnement - CNRS - UBFC (UMR 6249) (LCE), Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Animal et gestion intégrée des risques (UPR AGIRs), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)

Subjects
Urban Population, Epidemiology, 01 natural sciences, 010104 statistics & probability, 0302 clinical medicine, MESH: Censuses, MESH : Censuses, Dynamique des populations, MESH: Spacecraft, 030212 general & internal medicine, Spacecraft, Image resolution, education.field_of_study, geography.geographical_feature_category, U10 - Informatique, mathématiques et statistiques, Granulome, [ SDV.SPEE ] Life Sciences [q-bio]/Santé publique et épidémiologie, Censuses, Census, Épidémiologie, MESH: Urban Population, Population model, S50 - Santé humaine, MESH : Urban Population, France, Cartography, Modèle mathématique, Télédétection, MESH: Population Density, Population, Urban area, 03 medical and health sciences, Population estimate, Population urbaine, Humans, MESH : Population Density, 0101 mathematics, education, MESH : France, Estimation, Population Density, geography, MESH: Humans, MESH : Spacecraft, MESH : Humans, MESH: France, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Satellite, Landsat
Abstract

International audience; BACKGROUND: There is a need for alternative approaches to obtain population denominators when census information is unavailable, unreliable, or not available at the appropriate spatial resolution. The aim of this study is to develop an exportable population model, based on a single satellite-derived indicator, for estimating fine-scale population data and characterizing high-incidence areas in an urbanized area. METHODS: A Landsat 7 enhanced thematic mapper plus image was processed to generate population density indices at the block and block-group levels, using both an unsupervised pixel-based and a supervised classification. Spatial disaggregation was used to calculate population estimates, distributing the total population of the city of Besanon (France) into census areas by means of their respective population density indices. Accuracy assessment was performed through comparisons with census counts. RESULTS: At the block-group level, the simplest model produced relatively accurate and reliable population estimates within the range of observed counts. A strong agreement was found between observed and estimated incidence rates for non-Hodgkin lymphoma (intraclass correlation coefficient [ICC] = 0.73), but not for female breast cancer (ICC = 0.40). Withdrawing the sprawled block groups improved the agreements considerably (ICC = 0.84 and 0.71, respectively). CONCLUSIONS: This apportioning procedure offers a way to obtain estimated population sizes (or at least densities) for areas with no accurate census, but does not substitute for censuses where good census data exist. Because it is rapid, relatively cheap, and computationally easy, it should be of special interest to epidemiologists, environmental scientists, and public health decision makers.

Published
2009
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2. Elevated expression of osteopontin may be related to adipose tissue macrophage accumulation and liver steatosis in morbid obesity

Author

Adeline Bertola, Keith N. Frayn, Yannick Le Marchand-Brustel, Joan Tordjman, Moncef Dahman, S. McQuaid, Albert Tran, Philippe Gual, Karine Clément, Vanessa Deveaux, Jean Gugenheim, Pierre-Michel Huet, Rodolphe Anty, Déborah Rousseau, Sophie Lotersztajn, Abdelilah Wakkach, Stéphanie Bonnafous, Centre méditérannéen de médecine moléculaire (C3M), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Mondor de Recherche Biomédicale (IMRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-IFR10, Signalisation moléculaire et obésité, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-IFR50-Institut National de la Santé et de la Recherche Médicale (INSERM), Transduction du Signal : Signalisation Calcique et Phosphorylation, Université Joseph Fourier - Grenoble 1 (UJF)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Génétique, physiopathologie et ingénierie du tissu osseux (GéPITOS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Centre de Recherche des Cordeliers (CRC (UMR_S 872)), Université Paris Descartes - Paris 5 (UPD5)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), University of Oxford [Oxford], Environnement et Grandes Cultures (EGC), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Service de Chirurgie Digestive et Transplantation Hépatique, Hôpital Archet II, Digestive Center, Centre Hospitalier Universitaire de Nice (CHU Nice), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-IFR50-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Centre méditérannéen de médecine moléculaire ( C3M ), Université Nice Sophia Antipolis ( UNS ), Université Côte d'Azur ( UCA ) -Université Côte d'Azur ( UCA ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Institut Mondor de Recherche Biomédicale ( IMRB ), Institut National de la Santé et de la Recherche Médicale ( INSERM ) -IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 ( UPEC UP12 ), Université Côte d'Azur ( UCA ) -Université Côte d'Azur ( UCA ) -IFR50-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Génétique, physiopathologie et ingénierie du tissu osseux ( GéPITOS ), Université Côte d'Azur ( UCA ) -Université Côte d'Azur ( UCA ) -Centre National de la Recherche Scientifique ( CNRS ), Centre de Recherche des Cordeliers ( CRC (UMR_S 872) ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Oxford Centre for Diabetes, Endocrinology and Metabolism ( OCDEM ), Environnement et Grandes Cultures ( EGC ), AgroParisTech-Institut National de la Recherche Agronomique ( INRA ), Epidémiologie et écologie des maladies animales ( Epidémiologie ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), CHU Nice, Université Nice Sophia Antipolis (1965 - 2019) (UNS), University of Oxford, and Service de Chirurgie Digestive / Centre de Transplantation Hépatique [CHU Nice]

Subjects
Male, MESH : RNA, Messenger, Endocrinology, Diabetes and Metabolism, Mice, 0302 clinical medicine, MESH : Oleic Acid, MESH: Animals, Osteopontin, MESH : Immunoblotting, MESH : Macrophages, Liver injury, 0303 health sciences, Tumor, MESH: Middle Aged, MESH: Immunoblotting, MESH : Reverse Transcriptase Polymerase Chain Reaction, Obesity, Morbid, Hyaluronan Receptors, Adipose Tissue, Tumor necrosis factor alpha, MESH: Adipose Tissue, medicine.medical_specialty, MESH: Gene Expression, MESH: Antigens, CD44, MESH : Adipose Tissue, 03 medical and health sciences, MESH: Weight Loss, Humans, MESH : Middle Aged, RNA, Messenger, [ SDV.BBM ] Life Sciences [q-bio]/Biochemistry, Molecular Biology, MESH: Humans, Tumor Necrosis Factor-alpha, Macrophages, MESH : Humans, MESH: Oleic Acid, MESH: Adult, medicine.disease, Mice, Inbred C57BL, MESH : Gene Expression, Endocrinology, MESH: Tumor Necrosis Factor-alpha, MESH: Female, MESH: Liver, MESH : Fatty Liver, MESH: Osteopontin, Messenger, Adipose tissue, Gene Expression, Inbred C57BL, MESH: Reverse Transcriptase Polymerase Chain Reaction, MESH : Antigens, CD44, MESH : Female, MESH : Osteopontin, CD44, Morbid, Receptor, MESH: Fatty Liver, MESH : Tumor Necrosis Factor-alpha, biology, Reverse Transcriptase Polymerase Chain Reaction, Fatty liver, MESH : Adult, Middle Aged, Liver, Female, Obesity Studies, Adult, MESH : Obesity, Morbid, MESH: Cell Line, Tumor, MESH : Male, Immunoblotting, 030209 endocrinology & metabolism, MESH : Mice, Inbred C57BL, Cell Line, Insulin resistance, stomatognathic system, MESH: Mice, Inbred C57BL, Cell Line, Tumor, Internal medicine, MESH : Mice, Weight Loss, Internal Medicine, medicine, Animals, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Obesity, Antigens, MESH: Mice, 030304 developmental biology, MESH: RNA, Messenger, MESH : Cell Line, Tumor, MESH : Liver, MESH: Macrophages, MESH: Obesity, Morbid, MESH: Male, Fatty Liver, biology.protein, RNA, MESH : Animals, MESH : Weight Loss, Steatosis, Oleic Acid
Abstract

OBJECTIVE—Osteopontin (OPN) plays an important role in the development of insulin resistance and liver complications in dietary murine models. We aimed to determine the expression pattern of OPN and its receptor CD44 in obese patients and mice according to insulin resistance and liver steatosis. RESEARCH DESIGN AND METHODS—OPN and CD44 expressions were studied in 52 morbidly obese patients and in mice. Cellular studies were performed in HepG2 cells. RESULTS—Hepatic OPN and CD44 expressions were strongly correlated with liver steatosis and insulin resistance in obese patients and mice. This increased OPN expression could be due to the accumulation of triglycerides, since fat loading in HepG2 promotes OPN expression. In contrast, OPN expression in adipose tissue (AT) was enhanced independently of insulin resistance and hepatic steatosis in obese patients. The elevated OPN expression in AT was paralleled with the AT macrophage infiltration, and both phenomena were reversed after weight loss. The circulating OPN level was slightly elevated in obese patients and was not related to liver steatosis. Further, AT did not appear to secrete OPN. In contrast, bariatric surgery–induced weight loss induced a strong increase in circulating OPN. CONCLUSIONS—The modestly elevated circulating OPN levels in morbidly obese patients were not related to liver steatosis and did not appear to result from adipose tissue secretion. In subcutaneous AT, expression of OPN was directly related to macrophage accumulation independently from liver complications. In contrast, hepatic OPN and CD44 expressions were related to insulin resistance and steatosis, suggesting their local implication in the progression of liver injury.

Published
2009
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3. Use of high spatial resolution satellite imagery to characterize landscapes at risk for bluetongue

Author

Fabienne Biteau-Coroller, Guillaume Gerbier, Hélène Guis, Annelise Tran, Stéphane De La Rocque, Thierry Baldet, François Roger, Jean-François Viel, Frédéric Mauny, Bruno Barragué, Epidémiologie et écologie des maladies animales (Cirad-EMVT-UPR 16 Epidémiologie), Département Elevage et médecine vétérinaire (Cirad-EMVT), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Laboratoire Chrono-environnement - CNRS - UBFC (UMR 6249) (LCE), Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Epidémiologie et écologie des maladies animales ( Epidémiologie ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), Laboratoire Chrono-environnement ( LCE ), and Université Bourgogne Franche-Comté ( UBFC ) -Université de Franche-Comté ( UFC ) -Centre National de la Recherche Scientifique ( CNRS )

Subjects
Male, Mediterranean climate, Geographic information system, MESH: Satellite Communications, MESH: Logistic Models, Mediterranean Basin, Disease Outbreaks, 0403 veterinary science, remote sensing, 0302 clinical medicine, MESH : Insect Vectors, Satellite imagery, MESH: Animals, bluetongue, geographic information systems, [ SDV.SPEE ] Life Sciences [q-bio]/Santé publique et épidémiologie, Vegetation, Culicoides, Épidémiologie, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, Fièvre catarrhale du mouton, 03 medical and health sciences, MESH : Disease Outbreaks, MESH: Bluetongue, MESH : Geographic Information Systems, Paysage, Sheep, landscape ecology, General Veterinary, [SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, 15. Life on land, MESH: ROC Curve, Insect Vectors, MESH : Satellite Communications, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, Logistic Models, MESH: Female, MESH : Logistic Models, L73 - Maladies des animaux, Ceratopogonidae, Risk Factors, MESH: Risk Factors, [SDV.BC.IC]Life Sciences [q-bio]/Cellular Biology/Cell Behavior [q-bio.CB], MESH : Female, MESH: Disease Outbreaks, biology, [SDV.BA]Life Sciences [q-bio]/Animal biology, Facteur du milieu, 04 agricultural and veterinary sciences, Satellite Communications, MESH : Risk Factors, MESH: Ceratopogonidae, MESH : Bluetongue, MESH: Sentinel Surveillance, [SDV.IMM]Life Sciences [q-bio]/Immunology, Female, epidemiology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], France, Cartography, MESH : Sentinel Surveillance, Écologie, Télédétection, 040301 veterinary sciences, MESH : Male, 030231 tropical medicine, MESH: Sheep, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, MESH: Insect Vectors, MESH : Sheep, Latitude, Animals, MESH : France, MESH: Geographic Information Systems, MESH : Ceratopogonidae, business.industry, Végétation, Zone méditerranéenne, biology.organism_classification, MESH: Male, MESH: France, ROC Curve, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, MESH : Animals, U30 - Méthodes de recherche, Landscape ecology, business, Sentinel Surveillance, MESH : ROC Curve
Abstract

International audience; The recent and rapid spread in the Mediterranean Basin of bluetongue, a viral disease of ruminants transmitted by some species of Culicoides (biting midges), highlights the necessity of determining the conditions of its emergence. This study uses high spatial resolution satellite imagery and methods from landscape ecology science to identify environmental parameters related to bluetongue occurrence in Corsica, a French Mediterranean island where the disease occurred for the first time in 2000. A set of environmental variables recorded in the neighborhood of 80 sheep farms were related to case occurrence through a logistic regression model computed within three subsequent buffer distances of 0.5, 1 and 2 km. The results reveal the role of landscape metrics, particularly those characterizing land-use units such as prairies and woodlands, as well as farm type, latitude and sunshine to explain the presence of bluetongue. Internal and external validation both indicate that the best results are obtained with the 1 km buffer size model (area under Receiver Operating Characteristic curve = 0.9 for internal validation and 0.81 for external validation). The results show that high spatial resolution remote sensing (i.e. 10 m pixels) and landscape ecology approaches contribute to improving the understanding of bluetongue epidemiology.

Published
2007
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4. A multiple fine-scale satellite-derived landscape approach: example of bluetongue modelling in Corsica

Author

Guis , Hélène, Tran , Annelise, Mauny , Frédéric, Baldet , Thierry, Barragué , Bruno, Gerbier , Guillaume, Viel , Jean-François, Roger , François, De La Rocque , Stéphane, Epidémiologie et écologie des maladies animales ( Epidémiologie ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), Laboratoire Chrono-environnement ( LCE ), Université Bourgogne Franche-Comté ( UBFC ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Franche-Comté ( UFC ), Laboratoire de mécanique des solides ( LMS ), École polytechnique ( X ) -MINES ParisTech - École nationale supérieure des mines de Paris-Centre National de la Recherche Scientifique ( CNRS ), Epidémiologie et écologie des maladies animales (Cirad-EMVT-UPR 16 Epidémiologie), Département Elevage et médecine vétérinaire (Cirad-EMVT), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Laboratoire Chrono-environnement - CNRS - UBFC (UMR 6249) (LCE), Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Paysage, Distribution spatiale, [ SDV.SPEE ] Life Sciences [q-bio]/Santé publique et épidémiologie, L73 - Maladies des animaux, Épidémiologie, Système d'information géographique, Écologie animale, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Fièvre catarrhale du mouton, ComputingMilieux_MISCELLANEOUS
Abstract

Landscape ecology is seldom used in epidemiology. The aim of this study is to assess the possible improvements that can be derived from the use of landscape approaches on several scales when exploring local differences in disease distribution, using bluetongue (BT) in Corsica as an example. The environment of BT-free and BT-infected sheep farms is described on a fine scale, using high resolution satellite images and a digital elevation model. Land-coverage is characterised by classifying the satellite image. Landscape metrics are calculated to quantify the number, diversity, length of edge and connectance of vegetation patches. The environment is described for three sizes of buffers around the farms. The models are tested with and without landscape metrics to see if such metrics improve the models. Internal and external validation of the models is performed and the relative impact of scale versus variables on the discriminatory ability of the models is explored. Results show that for all scales and irrespective of the number of parameters included, models with landscape metrics perform better than those without. The 1-km buffer model combines both the best scale of application and the best set of variables. It has a good discriminating ability and good sensitivity and specificity.

Published
2007

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