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<datestamp>2017-12-21</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Studying relationships between environment and malaria incidence in Camopi (French Guiana) through the objective selection of buffer-based landscape characterisations.</title>
<creator>Stefani, Aurélia</creator>
<creator>Roux, Emmanuel</creator>
<creator>Fotsing, Jean-Marie</creator>
<creator>Carme, Bernard</creator>
<contributor>Expertise et spatialisation des connaissances en environnement (ESPACE)</contributor>
<contributor>Epidémiologie des parasitoses et mycoses tropicales ; Université des Antilles et de la Guyane (UAG) - Institut National de la Santé et de la Recherche Médicale (INSERM)</contributor>
<contributor>Laboratoire Hospitalo-Universitaire de Parasitologie Mycologie ; Hôpital général de Cayenne</contributor>
<contributor>Centre d'investigation clinique Antilles-Guyane ; Institut National de la Santé et de la Recherche Médicale (INSERM) - CH Cayenne</contributor>
<contributor>The study was supported by the Centre National d'Etudes Spatiales (CNES) and the Fonds Social Européen (FSE)</contributor>
<description>International audience</description>
<source>ISSN: 1476-072X</source>
<source>International Journal of Health Geographics</source>
<publisher>BioMed Central</publisher>
<identifier>inserm-00674075</identifier>
<identifier>http://www.hal.inserm.fr/inserm-00674075</identifier>
<identifier>http://www.hal.inserm.fr/inserm-00674075/document</identifier>
<identifier>http://www.hal.inserm.fr/inserm-00674075/file/1476-072X-10-65.pdf</identifier>
<source>http://www.hal.inserm.fr/inserm-00674075</source>
<source>International Journal of Health Geographics, BioMed Central, 2011, 10 (1), pp.65. 〈10.1186/1476-072X-10-65〉</source>
<identifier>DOI : 10.1186/1476-072X-10-65</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1186/1476-072X-10-65</relation>
<identifier>PUBMED : 22151738</identifier>
<relation>info:eu-repo/semantics/altIdentifier/pmid/22151738</relation>
<language>en</language>
<subject lang=en>Akaike information criterion</subject>
<subject lang=en>Malaria</subject>
<subject lang=en>Plasmodium falciparum</subject>
<subject lang=en>Plasmodium vivax</subject>
<subject lang=en>environmental risk factors</subject>
<subject lang=en>landscape modelling</subject>
<subject lang=en>remote sensing</subject>
<subject lang=en>buffer</subject>
<subject lang=en>model selection</subject>
<subject lang=en>Akaike information criterion.</subject>
<subject>[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>UNLABELLED: ABSTRACT: BACKGROUND: Malaria remains a major health problem in French Guiana, with a mean of 3800 cases each year. A previous study in Camopi, an Amerindian village on the Oyapock River, highlighted the major contribution of environmental features to the incidence of malaria attacks. We propose a method for the objective selection of the best multivariate peridomestic landscape characterisation that maximises the chances of identifying relationships between environmental features and malaria incidence, statistically significant and meaningful from an epidemiological point of view. METHODS: A land-cover map, the hydrological network and the geolocalised inhabited houses were used to characterise the peridomestic landscape in eleven discoid buffers with radii of 50, 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1000 metres. Buffer-based landscape characterisations were first compared in terms of their capacity to discriminate between sites within the geographic space and of their effective multidimensionality in variable space. The Akaike information criterion (AIC) was then used to select the landscape model best explaining the incidences of P. vivax and P. falciparum malaria. Finally, we calculated Pearson correlation coefficients for the relationships between environmental variables and malaria incidence, by species, for the more relevant buffers. RESULTS: The optimal buffers for environmental characterisation had radii of 100 m around houses for P. vivax and 400 m around houses for P. falciparum. The incidence of P. falciparum malaria seemed to be more strongly linked to environmental features than that of P. vivax malaria, within these buffers. The incidence of P. falciparum malaria in children was strongly correlated with proportions of bare soil (r = -0.69), land under high vegetation (r = 0.68) and primary forest (r = 0.54), landscape division (r = 0.48) and the number of inhabited houses (r = -0.60). The incidence of P. vivax malaria was associated only with landscape division (r = 0.49). CONCLUSIONS: The proposed methodology provides a simple and general framework for objective characterisation of the landscape to account for field observations. The use of this method enabled us to identify different optimal observation horizons around houses, depending on the Plasmodium species considered, and to demonstrate significant correlations between environmental features and the incidence of malaria.</description>
<date>2011</date>
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