Parasitology



Spatial risk prediction and mapping of Schistosoma mansoni infections among schoolchildren living in western Côte d'Ivoire


G. RASO a1a2, B. MATTHYS a1a2, E. K. N'GORAN a2a3, M. TANNER a1, P. VOUNATSOU a1 and J. UTZINGER a1c1
a1 Department of Public Health and Epidemiology, Swiss Tropical Institute, P.O. Box, CH-4002 Basel, Switzerland
a2 Centre Suisse de Recherches Scientifiques, 01 BP 1303, Abidjan 01, Côte d'Ivoire
a3 UFR Biosciences, Université d'Abidjan-Cocody, 22 BP 770, Abidjan 22, Côte d'Ivoire

Article author query
raso g   [PubMed][Google Scholar] 
matthys b   [PubMed][Google Scholar] 
ngoran ek   [PubMed][Google Scholar] 
tanner m   [PubMed][Google Scholar] 
vounatsou p   [PubMed][Google Scholar] 
utzinger j   [PubMed][Google Scholar] 

Abstract

The objectives of this study were (1) to examine risk factors for Schistosoma mansoni infection among schoolchildren living in western Côte d'Ivoire, and (2) to carry forward spatial risk prediction and mapping at non-sampled locations. First, demographic and socio-economic data were obtained from 3818 children, aged 6–16 years, from 55 schools. Second, a single stool sample was examined from each child by the Kato-Katz technique to assess infection status of S. mansoni and its intensity. Third, remotely sensed environmental data were derived from satellite imagery and digitized ground maps. With these databases a comprehensive geographical information system was established. Bayesian variogram models were applied for spatial risk modelling and prediction. The infection prevalence of S. mansoni was 38·9%, ranging from 0% to 89·3% among schools. Results showed that age, sex, the richest wealth quintile, elevation and rainfall explained the geographical variation of the school prevalences of S. mansoni infection. The goodness of fit of different spatial models revealed that age, sex and socio-economic status had a stronger influence on infection prevalence than environmental covariates. The generated risk map can be used by decision-makers for the design and implementation of schistosomiasis control in this setting. If successfully validated elsewhere, this approach can guide control programmes quite generally.

(Received November 24 2004)
(Revised January 16 2005)
(Accepted January 16 2005)


Key Words: Bayesian geostatistics; Côte d'Ivoire; geographical information system; kriging; prediction; remote sensing; risk mapping; Schistosoma mansoni; spatial analysis.

Correspondence:
c1 Department of Public Health and Epidemiology, Swiss Tropical Institute, P.O. Box, CH-4002 Basel, Switzerland. Tel: +41 61 284 8129. Fax: +41 61 284 8105. E-mail: juerg.utzinger@unibas.ch


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