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

Raso, Giovanna, Schur, Nadine, Utzinger, Jürg, Koudou, Benjamin, Tchicaya, Emile S, Rohner, Fabian, N'goran, Eliézer K., Silué, Kigbafori D., Matthys, Barbara, Assi, Serge, Tanner, Marcel and Vounatsou, Penelope (2012) 'Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models.'. Malaria Journal, Vol 11, p. 160.

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Abstract

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.

Item Type:	Article
Additional Information:	The electronic version of this article is the complete one and can be found online at: http://www.malariajournal.com/content/11/1/160
Subjects:	WB Practice of Medicine > Medical Climatology > WB 700 Medical climatology. Geography of disease WC Communicable Diseases > Tropical and Parasitic Diseases > WC 755 Epidemiology WS Pediatrics > Diseases of Children and Adolescents > By System > WS 300 Hemic and lymphatic system
Digital Object Identifer (DOI):	https://doi.org/10.1186/1475-2875-11-160
Depositing User:	Mary Creegan
Date Deposited:	02 Apr 2013 15:21
Last Modified:	25 Jan 2022 10:00
URI:	https://archive.lstmed.ac.uk/id/eprint/3305

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