Bayesian risk maps for Schistosoma mansoni and hookworm mono-Infections in a setting where both parasites co-exist

Raso, G., Vounatsou, P., McManus, D. P. and Utzinger, J. (2007) Bayesian risk maps for Schistosoma mansoni and hookworm mono-Infections in a setting where both parasites co-exist. Geospatial Health, 2 1: 85-96. doi:10.4081/gh.2007.257


Author Raso, G.
Vounatsou, P.
McManus, D. P.
Utzinger, J.
Title Bayesian risk maps for Schistosoma mansoni and hookworm mono-Infections in a setting where both parasites co-exist
Journal name Geospatial Health   Check publisher's open access policy
ISSN 1827-1987
Publication date 2007-01-01
Sub-type Article (original research)
DOI 10.4081/gh.2007.257
Open Access Status DOI
Volume 2
Issue 1
Start page 85
End page 96
Total pages 12
Editor Robert Bergquist
Place of publication Italy
Publisher Universita degli Studi di Napoli Federico II
Language eng
Subject C1
321202 Epidemiology
730101 Infectious diseases
Abstract There is growing interest in the use of Bayesian geostatistical models for predicting the spatial distribution of parasitic infections, including hookworm, Schistosoma mansoni and co-infections with both parasites. The aim of this study was to predict the spatial distribution of mono-infections with either hookworm or S. mansoni in a setting where both parasites co-exist. School-based cross-sectional parasitological and questionnaire surveys were carried out in 57 rural schools in the Man region, western Côte d'Ivoire. A single stool specimen was obtained from each schoolchild attending grades 3-5. Stool specimens were processed by the Kato-Katz technique and an ether concentration method and examined for the presence of hookworm and S. mansoni eggs. The combined results from the two diagnostic approaches were considered for the infection status of each child. Demographic data (i.e. age and sex) were obtained from readily available school registries. Each child's socio-economic status was estimated, using the questionnaire data following a household-based asset approach. Environmental data were extracted from satellite imagery. The different data sources were incorporated into a geographical information system. Finally, a Bayesian spatial multinomial regression model was constructed and the spatial patterns of S. mansoni and hookworm mono-infections were investigated using Bayesian kriging. Our approach facilitated the production of smooth risk maps for hookworm and S. mansoni mono-infections that can be utilized for targeting control interventions. We argue that in settings where S. mansoni and hookworm co-exist and control efforts are under way, there is a need for both mono- and co-infection risk maps to enhance the cost-effectiveness of control programmes.
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID 071657
Institutional Status UQ

 
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Created: Sat, 29 Mar 2008, 02:59:00 EST