Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models - PubMed (original) (raw)
doi: 10.1186/1475-2875-11-160.
Nadine Schur, Jürg Utzinger, Benjamin G Koudou, Emile S Tchicaya, Fabian Rohner, Eliézer K N'goran, Kigbafori D Silué, Barbara Matthys, Serge Assi, Marcel Tanner, Penelope Vounatsou
Affiliations
- PMID: 22571469
- PMCID: PMC3483263
- DOI: 10.1186/1475-2875-11-160
Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models
Giovanna Raso et al. Malar J. 2012.
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.
Figures
Figure 1
Geographical distribution of Plasmodium spp. surveys locations based on compiled data on children aged <16 years between 1988 and 2007 in Côte d’Ivoire. The data were stratified in two categories: survey carried out before and since the year 2000. The extent of three major ecozones in the country, which was derived from various satellite data, is displayed in the background. Centroids of the ecozones are given as black symbols.
Figure 2
Map of Plasmodium spp. prevalence obtained from surveys on children aged <16 years carried out between the years 1988 and 2007 in Côte d’Ivoire.
Figure 3
Smoothed risk map of Plasmodium spp. infection for children aged <16 years in Côte d’Ivoire using a Bayesian non-stationary logistic regression model.
Figure 4
Map of the standard deviation of model-based predictions of Plasmodium spp. infection risk inferred from the Bayesian non-stationary logistic regression model.
References
- WHO. The World Health Report 2004: Changing history. Geneva: World Health Organization; 2004.
- WHO. World Malaria Report 2009. Geneva: World Health Organization; 2009.
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