Benjamin Lysaniuk1, Roman Ladsous2, Martine Tabeaud3, Gilles Cottrell4, Cédric Pennetier5, André Garcia6. 1. Pôle de Recherche pour l'Organisation et la Diffusion de l'Information Géographique, Paris, France. Benjamin.Lysaniuk@univ-paris1.fr. 2. Institut de Géographie, Université Paris, Paris, France. Ladsousroman@live.fr. 3. Institut de Géographie, Université Paris, Paris, France. Martine.Tabeaud@univ-paris1.fr. 4. Faculté des Sciences de la Santé, Centre d'Etude et de Recherche sur le Paludisme Associé à la Grossesse et à l'Enfance, Cotonou, Benin. Gilles.Cottrell@ird.fr. 5. Caractérisation et Contrôle des Populations de Vecteurs/ Maladies Infectieuses et Vecteurs Ecologie Génétique Evolution et Contrôle, Montpellier, France. Cedric.Pennetier@ird.fr. 6. Mère et Enfant Face aux Infections Tropicales, Université Paris Descartes, Paris, France. André.Garcia@ird.fr.
Abstract
INTRODUCTION: Anthropogenic factors, as well as environmental factors, can explain fine-scale spatial differences in vector densities and seasonal variations in malaria. In this pilot study, numbers of Anopheles gambiae were quantified in concessions in a rural area of southern Benin, West Africa, in order to establish whether vector number and human factors, such as habitat and living practices, are related. METHODS: The courtyard homes of 64 concessions (houses and private yards) were systematically and similarly photographed. Predefined features in the photographed items were extracted by applying an analysis grid that listed vector resting sites or potential breeding sites and also more general information about the building materials used. These data were analysed with respect to entomological data (number of mosquitoes caught per night) using the Kruskal-Wallis test, Pearson correlation coefficients, and analysis of covariance (ANCOVA). RESULTS: Three recurrent habitat/household types and living practices were identified that corresponded to different standards of living. These were related to the average number of mosquitoes captured per night: type I=0.88 anopheles/night; type II=0.85; and type III 0.55, but this was not statistically significant (Kruskal-Wallis test; p=0.41). There were no significant relationships between the number of potential breeding sites and number of mosquitoes caught (Pearson's correlation coefficient=-0.09, p=0.53). ANCOVA analysis of building materials and numbers of openings did not explain variation in the number of mosquitoes caught. CONCLUSIONS: Three dwelling types were identified by using predetermined socio-environmental characteristics but there was no association found in this study between vector number and habitat characteristics as was suspected.
INTRODUCTION: Anthropogenic factors, as well as environmental factors, can explain fine-scale spatial differences in vector densities and seasonal variations in malaria. In this pilot study, numbers of Anopheles gambiae were quantified in concessions in a rural area of southern Benin, West Africa, in order to establish whether vector number and human factors, such as habitat and living practices, are related. METHODS: The courtyard homes of 64 concessions (houses and private yards) were systematically and similarly photographed. Predefined features in the photographed items were extracted by applying an analysis grid that listed vector resting sites or potential breeding sites and also more general information about the building materials used. These data were analysed with respect to entomological data (number of mosquitoes caught per night) using the Kruskal-Wallis test, Pearson correlation coefficients, and analysis of covariance (ANCOVA). RESULTS: Three recurrent habitat/household types and living practices were identified that corresponded to different standards of living. These were related to the average number of mosquitoes captured per night: type I=0.88 anopheles/night; type II=0.85; and type III 0.55, but this was not statistically significant (Kruskal-Wallis test; p=0.41). There were no significant relationships between the number of potential breeding sites and number of mosquitoes caught (Pearson's correlation coefficient=-0.09, p=0.53). ANCOVA analysis of building materials and numbers of openings did not explain variation in the number of mosquitoes caught. CONCLUSIONS: Three dwelling types were identified by using predetermined socio-environmental characteristics but there was no association found in this study between vector number and habitat characteristics as was suspected.
Entities:
Keywords:
Africa; Determinants of Health; Environmental Health; Infectious Disease; Qualitative Research; Researcher; Tropical Health