ASSESSING DISEASE PREVALENCE FROM INACCURATE TEST-RESULTS - TEACHING AN OLD DOG NEW TRICKS

Estimates of disease prevalence are needed for the interpretation of t est results as well as for public health decisions. Assessing prevalen ce may be difficult if a definitive test is unavailable, impractical, or expensive. A formula derived from Bayes' theorem can calculate the prevalence of disease in a population by incorporating test results wi th a knowledge of the sensitivity and specificity of a test. This pape r reviews this formula and provides examples evaluating the prevalence of HIV disease, the usefulness of ventilation-perfusion scans in diag nosing pulmonary embolism, and settings where screening tests should n ot be applied. These examples demonstrate that precise yet inexpensive estimates of disease prevalence are possible by enhancing the usefuln ess of an inaccurate test.