Journal of General Internal Medicine

, Volume 26, Issue 4, pp 373–378

Does Prevalence Matter to Physicians in Estimating Post-test Probability of Disease? A Randomized Trial

  • Thomas Agoritsas
  • Delphine S. Courvoisier
  • Christophe Combescure
  • Marie Deom
  • Thomas V. Perneger
Original Research



The probability of a disease following a diagnostic test depends on the sensitivity and specificity of the test, but also on the prevalence of the disease in the population of interest (or pre-test probability). How physicians use this information is not well known.


To assess whether physicians correctly estimate post-test probability according to various levels of prevalence and explore this skill across respondent groups.


Randomized trial.


Population-based sample of 1,361 physicians of all clinical specialties.


We described a scenario of a highly accurate screening test (sensitivity 99% and specificity 99%) in which we randomly manipulated the prevalence of the disease (1%, 2%, 10%, 25%, 95%, or no information).


We asked physicians to estimate the probability of disease following a positive test (categorized as <60%, 60–79%, 80–94%, 95–99.9%, and >99.9%). Each answer was correct for a different version of the scenario, and no answer was possible in the “no information” scenario. We estimated the proportion of physicians proficient in assessing post-test probability as the proportion of correct answers beyond the distribution of answers attributable to guessing.


Most respondents in each of the six groups (67%–82%) selected a post-test probability of 95–99.9%, regardless of the prevalence of disease and even when no information on prevalence was provided. This answer was correct only for a prevalence of 25%. We estimated that 9.1% (95% CI 6.0–14.0) of respondents knew how to assess correctly the post-test probability. This proportion did not vary with clinical experience or practice setting.


Most physicians do not take into account the prevalence of disease when interpreting a positive test result. This may cause unnecessary testing and diagnostic errors.


Bayes’ theorem predictive value of tests prevalence sensitivity and specificity diagnosis risk assessment probability evidence-based medicine 

Supplementary material

11606_2010_1540_MOESM1_ESM.doc (28 kb)
ESM 1(DOC 28 kb)


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Copyright information

© Society of General Internal Medicine 2010

Authors and Affiliations

  • Thomas Agoritsas
    • 1
  • Delphine S. Courvoisier
    • 1
  • Christophe Combescure
    • 1
  • Marie Deom
    • 1
  • Thomas V. Perneger
    • 1
  1. 1.Division of Clinical EpidemiologyUniversity Hospitals of GenevaGeneva 14Switzerland

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