Which statement best describes the relationship between prevalence and the predictive value of a test?

• A. Prevalence has no relation to predictive value of a test
• B. Prevalence is inversely related to predictive value of a test
• C. Prevalence has relation with predictive value of a test
• D. Prevalence equals predictive value

Answer: (C)

The main concept is that predictive values depend on how common the disease is in the population being tested. Positive predictive value is the probability that a person actually has the disease given a positive result, and negative predictive value is the probability that a person does not have the disease given a negative result. These values are not fixed properties of the test; they change with prevalence (pretest probability).

As prevalence increases, the positive predictive value tends to rise because a larger share of positive results correspond to true disease. Conversely, the negative predictive value tends to fall with higher prevalence because more of the negative results may be false negatives. When prevalence is low, the opposite happens: PPV falls and NPV rises. This demonstrates that prevalence has a relationship with predictive values.

An extreme or blanket statement that there is no relation is incorrect, and saying the predictive value equals prevalence is also incorrect. The relationship is about how predictive values shift with prevalence, not about equality.