Abstract
AimsThe main focus of this study is to illustrate the importance of the statistical analysis in the evaluation of the accuracy of malaria diagnostic tests, without admitting a reference test, exploring a dataset (3317) collected in São Tomé and Príncipe.MethodsBayesian Latent Class Models (without and with constraints) are used to estimate the malaria infection prevalence, together with sensitivities, specificities, and predictive values of three diagnostic tests (RDT, Microscopy and PCR), in four subpopulations simultaneously based on a stratified analysis by age groups (, 5 years old) and fever status (febrile, afebrile).ResultsIn the afebrile individuals with at least five years old, the posterior mean of the malaria infection prevalence is 3.2% with a highest posterior density interval of [2.3–4.1]. The other three subpopulations (febrile 5 years, afebrile or febrile children less than 5 years) present a higher prevalence around 10.3% [8.8–11.7]. In afebrile children under-five years old, the sensitivity of microscopy is 50.5% [37.7–63.2]. In children under-five, the estimated sensitivities/specificities of RDT are 95.4% [90.3–99.5]/93.8% [91.6–96.0] – afebrile – and 94.1% [87.5–99.4]/97.5% [95.5–99.3] – febrile. In individuals with at least five years old are 96.0% [91.5–99.7]/98.7% [98.1–99.2] – afebrile – and 97.9% [95.3–99.8]/97.7% [96.6–98.6] – febrile. The PCR yields the most reliable results in four subpopulations.ConclusionsThe utility of this RDT in the field seems to be relevant. However, in all subpopulations, data provide enough evidence to suggest caution with the positive predictive values of the RDT. Microscopy has poor sensitivity compared to the other tests, particularly, in the afebrile children less than 5 years. This type of findings reveals the danger of statistical analysis based on microscopy as a reference test. Bayesian Latent Class Models provide a powerful tool to evaluate malaria diagnostic tests, taking into account different groups of interest.
Highlights
Malaria is caused by Plasmodium parasites that infect humans through the bites of an infected female mosquito of the genus Anopheles
Our goal is to explore the accuracy of three diagnostic tests for malaria, using Bayesian Latent Class Models (BLCM), considering their performances in four populations based on the combination of age groups and fever status
Except for the specificity of microscopy, we consider the same prior distribution for each parameters across the four subpopulations, even though M5 only admits that the specificity of microscopy and the specificity of polymerase chain reaction (PCR) are equal across subpopulations
Summary
Malaria is caused by Plasmodium parasites that infect humans through the bites of an infected female mosquito of the genus Anopheles. Plasmodium falciparum, P. vivax, P. ovale and P. malariae are the main species of malaria parasites. The first two species cause the most infections worldwide [1]. This report estimated that the number of cases of malaria changed from 233 million in 2000 to 225 million in 2009. The number of deaths due to malaria is estimated to have decreased from 985 000 in 2000 to 781 000 in 2009. As pointed out by Wongsrichanalai et al [3], the discrepancy found in worldwide malaria statistics (values range from 300 to 500 millions cases a year) emphasizes the importance of correctly diagnosing malaria to better understand its true extent
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
