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Malar J. 2018 Feb 5;17(1):65. doi: 10.1186/s12936-018-2211-y.

True malaria prevalence in children under five: Bayesian estimation using data of malaria household surveys from three sub-Saharan countries.

Author information

Institute of Health and Society, Université Catholique de Louvain, Brussels, Belgium.
Department of Public Health and Surveillance, Scientific Institute of Public Health (WIV-ISP), Brussels, Belgium.
School of Economic, Political and Policy Sciences, The University of Texas, Dallas, TX, USA.
Kenya Medical Research Institute, Kisumu, Kenya.
Population & Health Theme, Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya.
Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.
Centre Hospitalier Monkole, Kinshasa, Democratic Republic of the Congo.
Department of Biology, University of Cincinnati, Cincinnati, OH, USA.



Malaria is one of the major causes of childhood death in sub-Saharan countries. A reliable estimation of malaria prevalence is important to guide and monitor progress toward control and elimination. The aim of the study was to estimate the true prevalence of malaria in children under five in the Democratic Republic of the Congo, Uganda and Kenya, using a Bayesian modelling framework that combined in a novel way malaria data from national household surveys with external information about the sensitivity and specificity of the malaria diagnostic methods used in those surveys-i.e., rapid diagnostic tests and light microscopy.


Data were used from the Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS) conducted in the Democratic Republic of the Congo (DHS 2013-2014), Uganda (MIS 2014-2015) and Kenya (MIS 2015), where information on infection status using rapid diagnostic tests and/or light microscopy was available for 13,573 children. True prevalence was estimated using a Bayesian model that accounted for the conditional dependence between the two diagnostic methods, and the uncertainty of their sensitivities and specificities obtained from expert opinion.


The estimated true malaria prevalence was 20% (95% uncertainty interval [UI] 17%-23%) in the Democratic Republic of the Congo, 22% (95% UI 9-32%) in Uganda and 1% (95% UI 0-3%) in Kenya. According to the model estimations, rapid diagnostic tests had a satisfactory sensitivity and specificity, and light microscopy had a variable sensitivity, but a satisfactory specificity. Adding reported history of fever in the previous 14 days as a third diagnostic method to the model did not affect model estimates, highlighting the poor performance of this indicator as a malaria diagnostic.


In the absence of a gold standard test, Bayesian models can assist in the optimal estimation of the malaria burden, using individual results from several tests and expert opinion about the performance of those tests.


Bayesian data analysis; Malaria; Sub-Saharan Africa; True prevalence

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