GHTM

Global Health and Tropical Medicine

  • GHTM
    • Vision
    • Mission
    • Governance
    • Scientific Advisory Board
  • News
    • Outreach
    • Events
      • GHTM Sessions
      • Workshops
    • Articles
    • Jobs
  • Research
    • Cross-cutting issues
      • Global Pathogen Dispersion and Population Mobility
      • Drug Discovery and Drug Resistance
      • Diagnostics
      • Public Health Information
      • Fair Research Partnerships
    • Research Groups
      • PPS – Population health, policies and services
      • THOP – TB, HIV and opportunistic diseases and pathogens
      • VBD – Vector borne diseases and pathogens
      • IHC – Individual health care
    • Research in numbers
      • 2020
      • 2019
      • 2018
      • 2017
    • Projects
      • Ongoing Projects
    • Members
      • Population health, policies and services
        • PPS PhD members
        • PPS non PhD members
      • TB, HIV and opportunistic diseases and pathogens
        • THOP PhD members
        • THOP non PhD members
      • Vector-borne diseases and pathogens
        • VBD PhD members
        • VBD non PhD members
      • Individual Health Care
        • IHC PhD members
        • IHC non PhD members
      • Technical / administrative support
  • Publications
  • Education
    • Master Theses
    • PhD Theses
  • Services
Home / Publicações / Bayesian Latent Class Models in malaria diagnosis

Bayesian Latent Class Models in malaria diagnosis

  • Autores: de Oliveira MR, Do Rosário V, Gonçalves L, Lee PW, Shaio MF, Subtil A
  • Journal: PLoS One
  • Link: http://www.ncbi.nlm.nih.gov/pubmed/22844405

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.

Share this:

  • Click to share on Facebook (Opens in new window)
  • Click to share on Twitter (Opens in new window)
  • Click to share on LinkedIn (Opens in new window)
  • Click to share on Pinterest (Opens in new window)
  • Click to share on WhatsApp (Opens in new window)
  • Click to print (Opens in new window)

Events

NOVA Sciencepreneur program: registration is open

  The Sciencepreneur ® program is aimed at NOVA scientists who are seeking to create value … [Read More...]

IHMT selected for the pilot phase of the Research Data Repository Service of the FCT

  In order to promote good practices in Open Science with regard to research data and … [Read More...]

Paulo Ferrinho interviewed for the new e-magazine of European and Developing Countries Clinical Trials Partnership (EDCTP)

Paulo Ferrinho, professor and Diretor of Public Global Health Departament at the Instituto de … [Read More...]

How can we improve the environmental performance of our laboratories?

  Every day in NOVA's laboratories research is carried out with the consumption of numerous … [Read More...]

Call for PhD Studentships

The Institute of Hygiene and Tropical Medicine (IHMT), Universidade Nova de Lisboa (NOVA), through … [Read More...]

IHMT | GHTM – APPLICATIONS ARE OPEN!

IHMT | GHTM - Applications are open for three research vacancies:   One position - PhD … [Read More...]

About GHTM

GHTM is a R&D Center that brings together researchers from IHMT with a track record in Tropical Medicine and International/Global Health. It aims at strengthening Portugal's role as a leading partner in the development and implementation of a global health research agenda. Our evidence-based interventions contribute to the promotion of equity in health and to improve the health of populations.

Contacts

Rua da Junqueira, 100
1349-008 Lisboa
Portugal
+351 213 652 600
+351 213 632 105

  • Facebook
  • YouTube

Subscribe Newsletter

  • How to get to GHTM/IHMT
  • GHTM Sessions
  • Research Groups
  • Cross-cutting issues
© Copyright 2023 IHMT-UNL Todos os Direitos Reservados.
  • Universidade Nova de Lisboa
  • Fundação para a Ciência e a Tecnologia

    Project UID/Multi/04413/2013