Which statement about predictive values and prevalence is true?

• A. PPV decreases with higher prevalence
• B. PPV increases with lower prevalence
• C. Predictive values do not depend on prevalence
• D. PPV increases with higher prevalence

Answer: (D)

Predictive values depend on how common the disease is in the population being tested. The positive predictive value is the probability that a person with a positive test actually has the disease. With a fixed test sensitivity and specificity, increasing disease prevalence raises the proportion of true positives among all positives, so the PPV goes up. This relationship is shown by the formula PPV = (Se × Prev) / [(Se × Prev) + (1 − Sp) × (1 − Prev)]. As prevalence increases, the numerator grows relative to the denominator, increasing PPV. Therefore, the statement that PPV increases with higher prevalence is true. The other options are incorrect because they propose the opposite relationship or claim predictive values don’t depend on prevalence. In practice, higher-prevalence settings yield higher PPVs for the same test.