Malaria risk in Nigeria : Bayesian geostatistical modelling of 2010 malaria indicator survey data

Adigun, Abbas B. and Gajere, Efron N. and Oresanya, Olusola and Vounatsou, Penelope. (2015) Malaria risk in Nigeria : Bayesian geostatistical modelling of 2010 malaria indicator survey data. Malaria journal, Vol. 14 , 156.

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Official URL: http://edoc.unibas.ch/dok/A6381860

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In 2010, the National Malaria Control Programme with the support of Roll Back Malaria partners implemented a nationally representative Malaria Indicator Survey (MIS), which assembled malaria burden and control intervention related data. The MIS data were analysed to produce a contemporary smooth map of malaria risk and evaluate the control interventions effects on parasitaemia risk after controlling for environmental/climatic, demographic and socioeconomic characteristics.; A Bayesian geostatistical logistic regression model was fitted on the observed parasitological prevalence data. Important environmental/climatic risk factors of parasitaemia were identified by applying Bayesian variable selection within geostatistical model. The best model was employed to predict the disease risk over a grid of 4 km(2) resolution. Validation was carried out to assess model predictive performance. Various measures of control intervention coverage were derived to estimate the effects of interventions on parasitaemia risk after adjusting for environmental, socioeconomic and demographic factors.; Normalized difference vegetation index and rainfall were identified as important environmental/climatic predictors of malaria risk. The population adjusted risk estimates ranges from 6.46% in Lagos state to 43.33% in Borno. Interventions appear to not have important effect on malaria risk. The odds of parasitaemia appears to be on downward trend with improved socioeconomic status and living in rural areas increases the odds of testing positive to malaria parasites. Older children also have elevated risk of malaria infection.; The produced maps and estimates of parasitaemic children give an important synoptic view of current parasite prevalence in the country. Control activities will find it a useful tool in identifying priority areas for intervention.
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Biostatistics > Bayesian Modelling and Analysis (Vounatsou)
UniBasel Contributors:Vounatsou, Penelope
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:BioMed Central
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:31 Dec 2015 10:57
Deposited On:05 Jun 2015 08:52

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