Which metric depends on disease prevalence when interpreting a positive test result?

• A. Sensitivity
• B. Specificity
• C. Predictive value positive
• D. Predictive value negative

Answer: (C)

Interpreting a positive test result depends on the likelihood that the person truly has the disease after you’ve seen a positive result. That likelihood is the predictive value positive. It blends how common the disease is in the population (prevalence) with how the test performs (sensitivity and specificity). When disease is rare, many positives will be false positives, so the predictive value positive is low. When disease is more common, positives are more likely to reflect true disease, so the predictive value positive goes up. The relationship can be summarized by the formula: PPV = (sensitivity × prevalence) / [(sensitivity × prevalence) + ((1 − specificity) × (1 − prevalence))], which shows how prevalence directly influences PPV. Sensitivity and specificity themselves are intrinsic to the test and do not depend on how common the disease is in the tested population.