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Types of homes and ways of life : a territorial analysis of the environmental determinants that factor into the proliferation of malaria vectors in the rural region of Allada in Benin
- Source :
- Rural and Remote Health, Rural and Remote Health, James Cook University, 2015, 15 (1), Rural and Remote Health, James Cook University, 2015, 15 (1), 〈http://www.rrh.org.au/articles/subviewnew.asp?ArticleID=2696〉, ResearcherID, Rural and Remote Health, James Cook University, 2013, Europe PubMed Central
- Publication Year :
- 2015
- Publisher :
- HAL CCSD, 2015.
-
Abstract
- International audience; 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.
- Subjects :
- Rural Population
[SDE] Environmental Sciences
Emergency Medical Services
Health (social science)
Anopheles gambiae
Medicine (miscellaneous)
Pilot Projects
Standard of living
[SHS]Humanities and Social Sciences
[ SDE ] Environmental Sciences
0302 clinical medicine
Risk Factors
Photography
Benin
030212 general & internal medicine
ComputingMilieux_MISCELLANEOUS
Qualitative Research
Analysis of covariance
biology
Geography
Habitat
[SDE]Environmental Sciences
symbols
Income
territorial analysis
Seasons
[SHS] Humanities and Social Sciences
Environmental Health
Determinants of Health
Correlation coefficient
030231 tropical medicine
malaria
Researcher
Infectious Disease
infectious disease vectors
03 medical and health sciences
symbols.namesake
Tropical Health
Anopheles
parasitic diseases
[ SHS ] Humanities and Social Sciences
medicine
Animals
Humans
housing
Public Health, Environmental and Occupational Health
medicine.disease
biology.organism_classification
Pearson product-moment correlation coefficient
Socioeconomic Factors
Africa
Housing
Rural area
Malaria
Demography
Subjects
Details
- Language :
- English
- ISSN :
- 14456354
- Database :
- OpenAIRE
- Journal :
- Rural and Remote Health, Rural and Remote Health, James Cook University, 2015, 15 (1), Rural and Remote Health, James Cook University, 2015, 15 (1), 〈http://www.rrh.org.au/articles/subviewnew.asp?ArticleID=2696〉, ResearcherID, Rural and Remote Health, James Cook University, 2013, Europe PubMed Central
- Accession number :
- edsair.doi.dedup.....636629a1cb157320fd788899e9eb32b8