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Spatially explicit Schistosoma infection risk in eastern Africa using Bayesian geostatistical modelling

Schur, Nadine and Hürlimann, Eveline and Stensgaard, Anna-Sofie and Chimfwembe, Kingford and Mushinge, Gabriel and Simoonga, Christopher and Kabatereine, Narcis B. and Kristensen, Thomas K. and Utzinger, Jürg and Vounatsou, Penelope. (2013) Spatially explicit Schistosoma infection risk in eastern Africa using Bayesian geostatistical modelling. Acta tropica : Zeitschrift für Tropenwissenschaften und Tropenmedizin, 128 (2). pp. 365-377.

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Official URL: http://edoc.unibas.ch/dok/A6194572

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Abstract

Schistosomiasis remains one of the most prevalent parasitic diseases in the tropics and subtropics, but current statistics are outdated due to demographic and ecological transformations and ongoing control efforts. Reliable risk estimates are important to plan and evaluate interventions in a spatially explicit and cost-effective manner. We analysed a large ensemble of georeferenced survey data derived from an open-access neglected tropical diseases database to create smooth empirical prevalence maps for Schistosoma mansoni and Schistosoma haematobium for a total of 13 countries of eastern Africa. Bayesian geostatistical models based on climatic and other environmental data were used to account for potential spatial clustering in spatially structured exposures. Geostatistical variable selection was employed to reduce the set of covariates. Alignment factors were implemented to combine surveys on different age-groups and to acquire separate estimates for individuals aged ≤20 years and entire communities. Prevalence estimates were combined with population statistics to obtain country-specific numbers of Schistosoma infections. We estimate that 122 million individuals in eastern Africa are currently infected with either S. mansoni, or S. haematobium, or both species concurrently. Country-specific population-adjusted prevalence estimates range between 12.9% (Uganda) and 34.5% (Mozambique) for S. mansoni and between 11.9% (Djibouti) and 40.9% (Mozambique) for S. haematobium. Our models revealed that infection risk in Burundi, Eritrea, Ethiopia, Kenya, Rwanda, Somalia and Sudan might be considerably higher than previously reported, while in Mozambique and Tanzania, the risk might be lower than current estimates suggest. Our empirical, large-scale, high-resolution infection risk estimates for S. mansoni and S. haematobium in eastern Africa can guide future control interventions and provide a benchmark for subsequent monitoring and evaluation activities.
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Biostatistics > Bayesian Modelling and Analysis (Vounatsou)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Eco System Health Sciences > Health Impact Assessment (Utzinger)
UniBasel Contributors:Utzinger, Jürg and Vounatsou, Penelope
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:Elsevier Science Publ.
ISSN:0001-706X
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:25 Oct 2017 05:19
Deposited On:18 Jul 2014 09:10

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