Which statement correctly describes how disease prevalence affects positive predictive value (PV+)?

• A. PV+ is not influenced by prevalence.
• B. PV+ increases with higher disease prevalence.
• C. PV+ equals sensitivity.
• D. PV+ decreases with higher prevalence.

Answer: (B)

The main idea is that positive predictive value (PV+) depends on how common the disease is in the population. When disease prevalence is higher, a positive test result is more likely to be a true positive, so PV+ goes up. With the test’s sensitivity and specificity held constant, PV+ can be described by the formula PV+ = [sensitivity × prevalence] / [sensitivity × prevalence + (1 − specificity) × (1 − prevalence)]. As prevalence increases, the numerator grows and the balance of the denominator shifts in a way that raises PV+. For a concrete sense, imagine a test with 90% sensitivity and 95% specificity: at 1% prevalence, PV+ is relatively low; at 10% prevalence, PV+ becomes much higher. This reflects why a positive result is more trustworthy when disease is common.

The other statements don’t fit because PV+ is not independent of prevalence, so it isn’t constant regardless of how common the disease is. PV+ is not simply the same as sensitivity, since PV+ is the probability of disease given a positive test, whereas sensitivity is the probability of a positive test given disease. And PV+ does not decrease as prevalence rises; it increases with higher prevalence.