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Spatially explicit burden estimates of malaria in Tanzania : Bayesian geostatistical modeling of the malaria indicator survey data

Gosoniu, L. and Msengwa, A. and Lengeler, C. and Vounatsou, P.. (2012) Spatially explicit burden estimates of malaria in Tanzania : Bayesian geostatistical modeling of the malaria indicator survey data. PLoS ONE, Vol. 7, H. 5 , e23966.

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Official URL: http://edoc.unibas.ch/dok/A6094388

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Abstract

A national HIV/AIDS and malaria parasitological survey was carried out in Tanzania in 2007-2008. In this study the parasitological data were analyzed: i) to identify climatic/environmental, socio-economic and interventions factors associated with child malaria risk and ii) to produce a contemporary, high spatial resolution parasitaemia risk map of the country. Bayesian geostatistical models were fitted to assess the association between parasitaemia risk and its determinants. Bayesian kriging was employed to predict malaria risk at unsampled locations across Tanzania and to obtain the uncertainty associated with the predictions. Markov chain Monte Carlo (MCMC) simulation methods were employed for model fit and prediction. Parasitaemia risk estimates were linked to population data and the number of infected children at province level was calculated. Model validation indicated a high predictive ability of the geostatistical model, with 60.00% of the test locations within the 95% credible interval. The results indicate that older children are significantly more likely to test positive for malaria compared with younger children and living in urban areas and better-off households reduces the risk of infection. However, none of the environmental and climatic proxies or the intervention measures were significantly associated with the risk of parasitaemia. Low levels of malaria prevalence were estimated for Zanzibar island. The population-adjusted prevalence ranges from [Formula: see text] in Kaskazini province (Zanzibar island) to [Formula: see text] in Mtwara region. The pattern of predicted malaria risk is similar with the previous maps based on historical data, although the estimates are lower. The predicted maps could be used by decision-makers to allocate resources and target interventions in the regions with highest burden of malaria in order to reduce the disease transmission in the country
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Biostatistics
UniBasel Contributors:Vounatsou, Penelope and Lengeler, Christian
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:Public Library of Science
e-ISSN:1932-6203
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
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Last Modified:31 Aug 2018 06:39
Deposited On:16 Aug 2013 07:29

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