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True malaria prevalence in children under five: Bayesian estimation using data of malaria household surveys from

Elvire Mfueni1, Brecht Devleesschauwer2, Angel Rosas-Aguirre1

  • 1Institute of Health and Society, Université Catholique de Louvain, Brussels, Belgium.

Malaria Journal
|February 7, 2018
PubMed

Insights

Accurate malaria prevalence estimation in children under five is crucial for control efforts. Bayesian modeling in the DRC, Uganda, and Kenya revealed significant variations, highlighting the need for precise diagnostic methods.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Infectious Diseases

Background:

  • Malaria remains a leading cause of childhood mortality in sub-Saharan Africa.
  • Accurate malaria prevalence data are essential for effective control and elimination strategies.
  • Sub-Saharan Africa faces a significant burden of childhood malaria deaths.

Purpose of the Study:

  • To estimate the true prevalence of malaria in children under five in the Democratic Republic of the Congo, Uganda, and Kenya.
  • To utilize a novel Bayesian modeling framework integrating national survey data with diagnostic test performance.
  • To compare malaria prevalence across three key sub-Saharan African countries.

Main Methods:

  • Employed a Bayesian modeling framework to estimate true malaria prevalence.
  • Combined data from Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS) (n=13,573 children).
  • Accounted for conditional dependence and uncertainty in the sensitivity and specificity of rapid diagnostic tests (RDTs) and light microscopy.

Main Results:

  • Estimated true malaria prevalence: 20% (DRC), 22% (Uganda), and 1% (Kenya).
  • RDTs demonstrated satisfactory sensitivity and specificity; light microscopy showed variable sensitivity but satisfactory specificity.
  • Including fever history as a diagnostic indicator did not improve model estimates, indicating its poor performance.

Conclusions:

  • Bayesian models offer a robust approach for estimating malaria burden in the absence of a gold standard test.
  • Integrating multiple diagnostic test results and expert-derived performance data enhances prevalence estimation.
  • Accurate diagnostic performance data is critical for reliable malaria burden assessment.
Abstract

Keywords:
Bayesian data analysisMalariaSub-Saharan AfricaTrue prevalence

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