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.
Malar J. 2018 Feb 5;17(1):65. doi: 10.1186/s12936-018-2211-y.
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.
疟疾是撒哈拉以南非洲地区儿童死亡的主要原因之一。可靠的疟疾患病率估计对于指导和监测控制和消除疟疾的进展非常重要。本研究的目的是使用贝叶斯建模框架，结合国家家庭调查中的疟疾数据以及这些调查中使用的疟疾诊断方法的敏感性和特异性的外部信息，来估计刚果民主共和国、乌干达和肯尼亚五岁以下儿童的真实疟疾患病率，这些信息来自于快速诊断检测和显微镜检查。
本研究使用了来自刚果民主共和国（2013-2014 年的 DHS）、乌干达（2014-2015 年的 MIS）和肯尼亚（2015 年的 MIS）的人口与健康调查（DHS）和疟疾指标调查（MIS）的数据，这些数据中包含了 13573 名儿童的使用快速诊断检测和/或显微镜检查的感染状况信息。使用贝叶斯模型来估计真实的疟疾患病率，该模型考虑了两种诊断方法之间的条件依赖性，以及从专家意见中获得的它们敏感性和特异性的不确定性。
在刚果民主共和国，估计的真实疟疾患病率为 20%（95%置信区间[UI]为 17%-23%），在乌干达为 22%（95% UI 为 9-32%），在肯尼亚为 1%（95% UI 为 0-3%）。根据模型估计，快速诊断检测具有令人满意的敏感性和特异性，而显微镜检查具有可变的敏感性，但特异性令人满意。将过去 14 天内有发热史的报告添加到模型中作为第三种诊断方法，不会影响模型估计，这突出了该指标作为疟疾诊断的不佳表现。
在没有金标准测试的情况下，贝叶斯模型可以通过使用来自几种测试的个体结果和关于这些测试性能的专家意见，来协助对疟疾负担进行最佳估计。
