True versus apparent malaria infection prevalence: the contribution of a Bayesian approach

N Speybroeck, N Praet, F Claes, N Van Hong, K Torres, S Mao, P Van den Eede, T Thi Thinh, D Gamboa, T Sochantha, ND Thang, M Coosemans, P Büscher, U D'Alessandro, D Berkvens, A Erhart

    Research output: Contribution to journalA1: Web of Science-article

    Abstract

    AIMS: To present a new approach for estimating the 'true prevalence' of malaria and apply it to datasets from Peru, Vietnam, and Cambodia. METHODS: Bayesian models were developed for estimating both the malaria prevalence using different diagnostic tests (microscopy, PCR & ELISA), without the need of a gold standard, and the tests' characteristics. Several sources of information, i.e. data, expert opinions and other sources of knowledge can be integrated into the model. This approach resulting in an optimal and harmonized estimate of malaria infection prevalence, with no conflict between the different sources of information, was tested on data from Peru, Vietnam and Cambodia. RESULTS: Malaria sero-prevalence was relatively low in all sites, with ELISA showing the highest estimates. The sensitivity of microscopy and ELISA were statistically lower in Vietnam than in the other sites. Similarly, the specificities of microscopy, ELISA and PCR were significantly lower in Vietnam than in the other sites. In Vietnam and Peru, microscopy was closer to the 'true' estimate than the other 2 tests while as expected ELISA, with its lower specificity, usually overestimated the prevalence. CONCLUSIONS: Bayesian methods are useful for analyzing prevalence results when no gold standard diagnostic test is available. Though some results are expected, e.g. PCR more sensitive than microscopy, a standardized and context-independent quantification of the diagnostic tests' characteristics (sensitivity and specificity) and the underlying malaria prevalence may be useful for comparing different sites. Indeed, the use of a single diagnostic technique could strongly bias the prevalence estimation. This limitation can be circumvented by using a Bayesian framework taking into account the imperfect characteristics of the currently available diagnostic tests. As discussed in the paper, this approach may further support global malaria burden estimation initiatives.
    Original languageEnglish
    JournalPLoS ONE
    Volume6
    Issue number2
    Pages (from-to)e16705
    Number of pages7
    ISSN1932-6203
    DOIs
    Publication statusPublished - 2011

    Keywords

    • B780-tropical-medicine
    • Protozoal diseases
    • Malaria
    • Plasmodium falciparum
    • Vectors
    • Mosquitoes
    • Anopheles
    • Epidemiological modeling
    • Prevalence
    • Bayes theorem
    • Estimation
    • Diagnostics
    • Microscopy
    • Polymerase chain reaction
    • PCR
    • ELISA
    • Infection rates
    • Sensitivity
    • Specificity
    • Laboratory techniques and procedures
    • Peru
    • America-Latin
    • Vietnam
    • Cambodia
    • Asia-Southeast

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