title: True versus apparent malaria infection prevalence: the contribution of a Bayesian approach.
abstract: OBJECTIVE
  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.
authors.affiliation: Research Institute Health and Society, Université Catholique de Louvain la Neuve, Brussels, Belgium. nspeybroeck@itg.be
authors.forename: Niko
authors.initials: N
authors.lastname: Speybroeck
authors.forename: Nicolas
authors.initials: N
authors.lastname: Praet
authors.forename: Filip
authors.initials: F
authors.lastname: Claes
authors.forename: Nguyen
authors.initials: N
authors.lastname: Van Hong
authors.forename: Kathy
authors.initials: K
authors.lastname: Torres
authors.forename: Sokny
authors.initials: S
authors.lastname: Mao
authors.forename: Peter
authors.initials: P
authors.lastname: Van den Eede
authors.forename: Ta
authors.initials: T
authors.lastname: Thi Thinh
authors.forename: Dioni
authors.initials: D
authors.lastname: Gamboa
authors.forename: Tho
authors.initials: T
authors.lastname: Sochantha
authors.forename: Ngo Duc
authors.initials: ND
authors.lastname: Thang
authors.forename: Marc
authors.initials: M
authors.lastname: Coosemans
authors.forename: Philippe
authors.initials: P
authors.lastname: Büscher
authors.forename: Umberto
authors.initials: U
authors.lastname: D'Alessandro
authors.forename: Dirk
authors.initials: D
authors.lastname: Berkvens
authors.forename: Annette
authors.initials: A
authors.lastname: Erhart
mesh_terms: D000293:Adolescent; D000328:Adult; D001499:Bayes Theorem; D002160:Cambodia / Q000453:epidemiology; D002648:Child; D002675:Child, Preschool; D003937:Diagnosis, Differential; D003955:Diagnostic Tests, Routine / Q000592:standards / Q000706:statistics & numerical data; D006801:Humans; D007223:Infant; D008288:Malaria / Q000175:diagnosis* / Q000453:epidemiology*; D008875:Middle Aged; D010568:Peru / Q000453:epidemiology; D015995:Prevalence; D012680:Sensitivity and Specificity; D014744:Vietnam / Q000453:epidemiology; D055815:Young Adult