Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Spatially-explicit risk profiling of Plasmodium falciparum infections at a small scale: A geostatistical modelling approach
Malaria Journal, Volume 7, Article 111, Year 2008
Notification
URL copied to clipboard!
Description
Background. There is a renewed political will and financial support to eradicate malaria. Spatially-explicit risk profiling will play an important role in this endeavour. Patterns of Plasmodium falciparum infection prevalence were examined among schoolchildren in a highly malaria-endemic area. Methods. A questionnaire was administered and finger prick blood samples collected from 3,962 children, aged six to 16 years, attending 55 schools in a rural part of western Côte d'Ivoire. Information was gathered from the questionnaire on children's socioeconomic status and the use of bed nets for the prevention of malaria. Blood samples were processed with standardized, quality-controlled methods for diagnosis of Plasmodium spp. infections. Environmental data were obtained from satellite images and digitized maps. Bayesian variogram models for spatially-explicit risk modelling of P. falciparum infection prevalence were employed, assuming for stationary and non-stationary spatial processes. Findings. The overall prevalence of P. falciparum infection was 64.9%, ranging between 34.0% and 91.9% at the unit of the school. Risk factors for a P. falciparum infection included age, socioeconomic status, not sleeping under a bed net, distance to health care facilities and a number of environmental features (i.e. normalized difference vegetation index, rainfall and distance to rivers). After taking into account spatial correlation only age remained significant. Non-stationary models performed better than stationary models. Conclusion. Spatial risk profiling of P. falciparum prevalence data provides a useful tool for targeting malaria control intervention, and hence will play a role in the quest of local elimination and ultimate eradication of the disease. © 2008 Silué et al; licensee BioMed Central Ltd.
Authors & Co-Authors
Silué, Kigbafori Dieudonné
Cote D'ivoire, Abidjan
Université de Cocody-abidjan
Cote D'ivoire, Abidjan
Centre Suisse de Recherches Scientifiques Abidjan
Raso, Giovanna
Australia, Brisbane
The University of Queensland
Australia, Brisbane
Qimr Berghofer Medical Research Institute
Yapi, Ahoua
Cote D'ivoire, Abidjan
Université de Cocody-abidjan
Vounatsou, P.
Switzerland, Allschwil
Swiss Tropical and Public Health Institute Swiss Tph
Tanner, Marcel
Switzerland, Allschwil
Swiss Tropical and Public Health Institute Swiss Tph
N'Goran, Eliézer Kouakou
Cote D'ivoire, Abidjan
Université de Cocody-abidjan
Cote D'ivoire, Abidjan
Centre Suisse de Recherches Scientifiques Abidjan
Utzinger, Jürg
Switzerland, Allschwil
Swiss Tropical and Public Health Institute Swiss Tph
Statistics
Citations: 64
Authors: 7
Affiliations: 5
Identifiers
Doi:
10.1186/1475-2875-7-111
e-ISSN:
14752875
Research Areas
Infectious Diseases
Maternal And Child Health
Study Design
Randomised Control Trial
Cross Sectional Study
Study Locations
Ivory Coast