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 (n=3317) collected in São Tomé and Príncipe. METHODS: Bayesian Latent Class Models (wi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3402519?pdf=render |
| _version_ | 1819238229651488768 |
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| author | Luzia Gonçalves Ana Subtil M Rosário de Oliveira Virgílio do Rosário Pei-Wen Lee Men-Fang Shaio |
| author_facet | Luzia Gonçalves Ana Subtil M Rosário de Oliveira Virgílio do Rosário Pei-Wen Lee Men-Fang Shaio |
| author_sort | Luzia Gonçalves |
| collection | DOAJ |
| 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 (n=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 (< 5, ≥ 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 ≥ 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. |
| first_indexed | 2024-12-23T13:32:55Z |
| format | Article |
| id | doaj.art-767a353d8a674e46b925cc6f33e3d44a |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-23T13:32:55Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-767a353d8a674e46b925cc6f33e3d44a2022-12-21T17:45:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0177e4063310.1371/journal.pone.0040633Bayesian Latent Class Models in malaria diagnosis.Luzia GonçalvesAna SubtilM Rosário de OliveiraVirgílio do RosárioPei-Wen LeeMen-Fang ShaioAIMS: 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 (n=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 (< 5, ≥ 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 ≥ 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.http://europepmc.org/articles/PMC3402519?pdf=render |
| spellingShingle | Luzia Gonçalves Ana Subtil M Rosário de Oliveira Virgílio do Rosário Pei-Wen Lee Men-Fang Shaio Bayesian Latent Class Models in malaria diagnosis. PLoS ONE |
| 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 |
| url | http://europepmc.org/articles/PMC3402519?pdf=render |
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