Which statement best characterizes the relationship between prevalence and predictive values?

Which statement best characterizes the relationship between prevalence and predictive values?

• A. PPV decreases as prevalence increases and NPV increases as prevalence decreases.
• B. PPV remains constant with changing prevalence.
• C. NPV remains constant with changing prevalence.
• D. PPV increases with higher prevalence; NPV increases with lower prevalence.

Answer: (D)

Predictive values depend on how common the disease is in the group being tested. Positive predictive value (the chance a positive result is a true disease) rises as disease prevalence increases, because there are more true cases among those tested. Negative predictive value (the chance a negative result is truly disease-free) rises as disease prevalence decreases, because most people tested don’t have the disease. So in higher-prevalence settings, PPV goes up; in lower-prevalence settings, NPV goes up. Sensitivity and specificity are intrinsic to the test, but predictive values shift with prevalence. This is why the best statement is that PPV increases with higher prevalence and NPV increases with lower prevalence.

Predictive values depend on how common the disease is in the group being tested. Positive predictive value (the chance a positive result is a true disease) rises as disease prevalence increases, because there are more true cases among those tested. Negative predictive value (the chance a negative result is truly disease-free) rises as disease prevalence decreases, because most people tested don’t have the disease. So in higher-prevalence settings, PPV goes up; in lower-prevalence settings, NPV goes up. Sensitivity and specificity are intrinsic to the test, but predictive values shift with prevalence. This is why the best statement is that PPV increases with higher prevalence and NPV increases with lower prevalence.