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

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Raso Giovanna, Schur Nadine, Utzinger Jürg, Koudou Benjamin G, Tchicaya Emile S, Rohner Fabian, N’Goran Eliézer K, Silué Kigbafori D, Matthys Barbara, Assi Serge, Tanner Marcel, Vounatsou Penelope,
Project Development of spatial statistical methodology for the analysis of health demographic surveillance system (DSS) data
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Original article (peer-reviewed)

Journal Malaria Journal
Volume (Issue) 11(1)
Page(s) 160 - 160
Title of proceedings Malaria Journal
DOI 10.1186/1475-2875-11-160

Open Access

Type of Open Access Publisher (Gold Open Access)


Background In Côte 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 Côte d’Ivoire at high spatial resolution. Methods Using different data sources, a systematic review was carried out to compile and geo-reference survey data on Plasmodium spp. infection prevalence in Côte 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 Côte 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 non-sampled 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 north-east, in the south-east around Abidjan, and in the central-west between two high prevalence areas. Conclusion The malaria risk map at high spatial resolution gives an important overview of the geographical distribution of the disease in Côte 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.