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Bayesian Latent Class Models in Malaria Diagnosis

AIMS: The 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 ([Image: see text]3317) collected in São Tomé and Príncipe. METHODS: Bayesian Latent C...

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Autores principales: Gonçalves, Luzia, Subtil, Ana, de Oliveira, M. Rosário, do Rosário, Virgílio, Lee, Pei-Wen, Shaio, Men-Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3402519/
https://www.ncbi.nlm.nih.gov/pubmed/22844405
http://dx.doi.org/10.1371/journal.pone.0040633
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author Gonçalves, Luzia
Subtil, Ana
de Oliveira, M. Rosário
do Rosário, Virgílio
Lee, Pei-Wen
Shaio, Men-Fang
author_facet Gonçalves, Luzia
Subtil, Ana
de Oliveira, M. Rosário
do Rosário, Virgílio
Lee, Pei-Wen
Shaio, Men-Fang
author_sort Gonçalves, Luzia
collection PubMed
description AIMS: The 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 ([Image: see text]3317) collected in São Tomé and Príncipe. METHODS: Bayesian 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 ([Image: see text], [Image: see text] 5 years old) and fever status (febrile, afebrile). RESULTS: In 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 [Image: see text] 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. CONCLUSIONS: The 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.
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spelling pubmed-34025192012-07-27 Bayesian Latent Class Models in Malaria Diagnosis Gonçalves, Luzia Subtil, Ana de Oliveira, M. Rosário do Rosário, Virgílio Lee, Pei-Wen Shaio, Men-Fang PLoS One Research Article AIMS: The 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 ([Image: see text]3317) collected in São Tomé and Príncipe. METHODS: Bayesian 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 ([Image: see text], [Image: see text] 5 years old) and fever status (febrile, afebrile). RESULTS: In 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 [Image: see text] 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. CONCLUSIONS: The 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. Public Library of Science 2012-07-23 /pmc/articles/PMC3402519/ /pubmed/22844405 http://dx.doi.org/10.1371/journal.pone.0040633 Text en Gonçalves et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Gonçalves, Luzia
Subtil, Ana
de Oliveira, M. Rosário
do Rosário, Virgílio
Lee, Pei-Wen
Shaio, Men-Fang
Bayesian Latent Class Models in Malaria Diagnosis
title Bayesian Latent Class Models in Malaria Diagnosis
title_full Bayesian Latent Class Models in Malaria Diagnosis
title_fullStr Bayesian Latent Class Models in Malaria Diagnosis
title_full_unstemmed Bayesian Latent Class Models in Malaria Diagnosis
title_short Bayesian Latent Class Models in Malaria Diagnosis
title_sort bayesian latent class models in malaria diagnosis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3402519/
https://www.ncbi.nlm.nih.gov/pubmed/22844405
http://dx.doi.org/10.1371/journal.pone.0040633
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