Which statement about predictive values depends on disease prevalence?

• A. Sensitivity.
• B. Predictive values are independent of prevalence.
• C. Positive predictive value and negative predictive values depend on true prevalence.
• D. Specificity is the same as predictive value.

Answer: (C)

Predictive values are post-test probabilities that reflect how likely a test result is to be correct in the context of how common the disease is in the population being tested. Positive predictive value is the proportion of those with a positive result who truly have the disease, while negative predictive value is the proportion of those with a negative result who truly do not have the disease. Because these values depend on how many people actually have the disease in the group being tested, they change with disease prevalence. When prevalence is higher, a larger share of positive results are true positives, so the positive predictive value increases; when prevalence is lower, many positives are false positives, lowering the positive predictive value. Conversely, higher prevalence raises the chance that a negative result could be a false negative, reducing the negative predictive value, and lower prevalence tends to increase the negative predictive value. Sensitivity and specificity, on the other hand, are intrinsic to the test and do not depend on prevalence.