Which factor tends to decrease Negative Predictive Value?

• A. Low disease prevalence
• B. High disease prevalence
• C. High specificity
• D. High sensitivity

Answer: (B)

Negative Predictive Value is the probability that someone with a negative test truly does not have the disease. It depends on how common the disease is in the tested group, because only then do the numbers of true negatives and false negatives change.

NPV can be expressed as true negatives divided by (true negatives plus false negatives). When disease prevalence rises, more people actually have the disease, so the potential for false negatives grows (even with the same test sensitivity). This means the portion of negative results that are actually from diseased individuals increases, lowering the likelihood that a negative result truly means no disease. In other words, higher prevalence makes it more likely that a negative test misses disease, pulling the NPV downward.

High sensitivity helps reduce the number of false negatives, which would tend to increase NPV, while high specificity affects false positives and doesn’t directly drive NPV in the same way. Lower prevalence, on the other hand, makes negatives more reliably true negatives, increasing NPV.