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Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models.
Malar J. 2012 May 09; 11:160.MJ

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.

Authors+Show Affiliations

Département Environnement et Santé, Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, BP 1303, Abidjan 01, Côte d'Ivoire. giovanna.raso@gmail.comNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

22571469

Citation

Raso, Giovanna, et al. "Mapping Malaria Risk Among Children in Côte d'Ivoire Using Bayesian Geo-statistical Models." Malaria Journal, vol. 11, 2012, p. 160.
Raso G, Schur N, Utzinger J, et al. Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models. Malar J. 2012;11:160.
Raso, G., Schur, N., Utzinger, J., Koudou, B. G., Tchicaya, E. S., Rohner, F., N'goran, E. K., Silué, K. D., Matthys, B., Assi, S., Tanner, M., & Vounatsou, P. (2012). Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models. Malaria Journal, 11, 160. https://doi.org/10.1186/1475-2875-11-160
Raso G, et al. Mapping Malaria Risk Among Children in Côte d'Ivoire Using Bayesian Geo-statistical Models. Malar J. 2012 May 9;11:160. PubMed PMID: 22571469.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models. AU - Raso,Giovanna, AU - Schur,Nadine, AU - Utzinger,Jürg, AU - Koudou,Benjamin G, AU - Tchicaya,Emile S, AU - Rohner,Fabian, AU - N'goran,Eliézer K, AU - Silué,Kigbafori D, AU - Matthys,Barbara, AU - Assi,Serge, AU - Tanner,Marcel, AU - Vounatsou,Penelope, Y1 - 2012/05/09/ PY - 2011/10/28/received PY - 2012/04/23/accepted PY - 2012/5/11/entrez PY - 2012/5/11/pubmed PY - 2013/3/7/medline SP - 160 EP - 160 JF - Malaria journal JO - Malar J VL - 11 N2 - 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. SN - 1475-2875 UR - https://www.unboundmedicine.com/medline/citation/22571469/Mapping_malaria_risk_among_children_in_Côte_d'Ivoire_using_Bayesian_geo_statistical_models_ L2 - https://malariajournal.biomedcentral.com/articles/10.1186/1475-2875-11-160 DB - PRIME DP - Unbound Medicine ER -