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

Malaria Journal

Research

Penelope Vounatsou¹  Abbas B Adigun²  Efron N Gajere³  Olusola Oresanya⁴ 

[1] Department of Public Health and Epidemiology, Swiss Tropical and Public Health Institute, P.O. Box 4002, Basel, Switzerland;University of Basel, Petersplatz 1, 4051, Basel, Switzerland;Department of Public Health and Epidemiology, Swiss Tropical and Public Health Institute, P.O. Box 4002, Basel, Switzerland;University of Basel, Petersplatz 1, 4051, Basel, Switzerland;National Centre for Remote Sensing, Jos, Nigeria;National Centre for Remote Sensing, Jos, Nigeria;National Malaria Control Programme, Abuja, Nigeria;

关键词: Bayesian variable selection;    Bayesian inference;    Markov Chain Monte Carlo;    Parasitaemia;    Malaria risk;    Control intervention;   

DOI  :  10.1186/s12936-015-0683-6

 received in 2014-10-30, accepted in 2015-04-06,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundIn 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.MethodsA 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 km2 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.ResultsNormalized 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.ConclusionsThe 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.

【 授权许可】

Unknown   
© Adigun et al.; licensee BioMed Central. 2015. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.