Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models

Raso, G. and Schur, N. and Utzinger, J. and Koudou, B. G. and Tchicaya, E. S. and Rohner, F. and N'Goran, E. K. and Silué, K. D. and Matthys, B. and Assi, S. and Tanner, M. and Vounatsou, P.. (2012) Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models. Malaria journal, Vol. 11 , 160.

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Official URL: http://edoc.unibas.ch/dok/A6094274

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ABSTRACT: BACKGROUND: In Cote d'Ivoire, an estimated 767,000 disability-adjusted life years are due to malaria, placing the country at position number 14 with regard to the global burden of malaria. Risk maps are important to guide control interventions, and hence, the aim of this study was to predict the geographical distribution of malaria infection risk in children aged >16 years in Cote d'Ivoire at high spatial resolution. METHODS: Using different data sources, a systematic review was carried out to compile and georeference survey data on Plasmodium spp. infection prevalence in Cote d'Ivoire, focusing on children aged >16 years. The period from 1988 to 2007 was covered. A suite of Bayesian geo-statistical logistic regression models was fitted to analyse malaria risk. Non-spatial models with and without exchangeable random effect parameters were compared to stationary and non-stationary spatial models. Non-stationarity was modelled assuming that the underlying spatial process is a mixture of separate stationary processes in each ecological zone. The best fitting model based on the deviance information criterion was used to predict Plasmodium spp. infection risk for entire Cote d'Ivoire, including uncertainty. RESULTS: Overall, 235 data points at 170 unique survey locations with malaria prevalence data for individuals aged >16 years were extracted. Most data points (n = 182, 77.4%) were collected between 2000 and 2007. A Bayesian non-stationary regression model showed the best fit with annualized rainfall and maximum land surface temperature identified as significant environmental covariates. This model was used to predict malaria infection risk at nonsampled locations. High-risk areas were mainly found in the north-central and western area, while relatively low-risk areas were located in the north at the country border, in the northeast, in the south-east around Abidjan, and in the central-west between two high prevalence areas. CONCLUSION: The malaria ris map at high spatial resolution gives an important overview of the geographical distribution of the disease in Cote d'Ivoire. It is a useful tool for the national malaria control programme and can be utilized for spatial targeting of control interventions and rational resource allocation
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Biostatistics
UniBasel Contributors:Utzinger, Jürg and Raso, Giovanna and Vounatsou, Penelope and Tanner, Marcel
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:BioMed Central
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:19 Jul 2013 07:44
Deposited On:19 Jul 2013 07:43

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