Negative predictive value depends on which factor?

• A. Sensitivity
• B. Specificity
• C. Disease prevalence
• D. Positive predictive value

Answer: (C)

Negative predictive value is the probability that someone who tests negative truly does not have the disease. This value is influenced by how common the disease is in the population being tested. When disease prevalence is low, most people don’t have the disease, so a negative result is very likely to be a true negative, giving a high NPV. When prevalence is high, a larger share of those tested actually has the disease, so a negative result is more likely to miss it, lowering the NPV.

To see it with fixed test characteristics, imagine a test with 95% sensitivity and 95% specificity. In a population with 1% disease prevalence, the NPV is extremely high (about 99.95%), because almost everyone is disease-free and the few false negatives are outweighed by true negatives. If prevalence rises to 20%, the NPV drops (about 98.7%), since more diseased individuals exist and a small number of false negatives among negatives has a bigger impact on the proportion of true negatives.

So, disease prevalence is the factor that the negative predictive value depends on.