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

Published online by Cambridge University Press:  24 March 2005

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

Keywords

Bayesian geostatisticsCôte d'Ivoiregeographical information systemkrigingpredictionremote sensingrisk mappingSchistosoma mansonispatial analysis
Type
Research Article
Information
Parasitology , Volume 131 , Issue 1 , July 2005 , pp. 97 - 108
Copyright
© 2005 Cambridge University Press

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