Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models
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...
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ftdoajarticles:oai:doaj.org/article:39440c066b44439a9b5384136db55730 2023-05-15T15:14:56+02:00 Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models 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 2012-05-01T00:00:00Z https://doi.org/10.1186/1475-2875-11-160 https://doaj.org/article/39440c066b44439a9b5384136db55730 EN eng BMC http://www.malariajournal.com/content/11/1/160 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-11-160 1475-2875 https://doaj.org/article/39440c066b44439a9b5384136db55730 Malaria Journal, Vol 11, Iss 1, p 160 (2012) Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2012 ftdoajarticles https://doi.org/10.1186/1475-2875-11-160 2022-12-30T22:27:55Z 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 ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 11 1 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
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Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 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 Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
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 ... |
format |
Article in Journal/Newspaper |
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 |
author_facet |
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 |
author_sort |
Raso Giovanna |
title |
Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models |
title_short |
Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models |
title_full |
Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models |
title_fullStr |
Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models |
title_full_unstemmed |
Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models |
title_sort |
mapping malaria risk among children in côte d’ivoire using bayesian geo-statistical models |
publisher |
BMC |
publishDate |
2012 |
url |
https://doi.org/10.1186/1475-2875-11-160 https://doaj.org/article/39440c066b44439a9b5384136db55730 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 11, Iss 1, p 160 (2012) |
op_relation |
http://www.malariajournal.com/content/11/1/160 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-11-160 1475-2875 https://doaj.org/article/39440c066b44439a9b5384136db55730 |
op_doi |
https://doi.org/10.1186/1475-2875-11-160 |
container_title |
Malaria Journal |
container_volume |
11 |
container_issue |
1 |
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1766345322649354240 |