Define sensitivity, specificity, positive predictive value, and negative predictive value, and explain how prevalence affects them.

• A. Sensitivity is true positive rate; specificity is true negative rate; PPV/NPV depend on disease prevalence; higher prevalence raises PPV and lowers NPV.
• B. Sensitivity is the false positive rate; specificity is the false negative rate; PPV/NPV depend on disease prevalence.
• C. PPV equals sensitivity; NPV equals specificity.
• D. Prevalence does not affect predictive values.

Answer: (A)

Understanding how a diagnostic test performs and how disease prevalence changes what we expect from a positive or negative result is tested here.

Sensitivity is the true positive rate: among those who actually have the disease, how many does the test correctly identify as positive? Specificity is the true negative rate: among those who do not have the disease, how many does the test correctly identify as negative?

Positive predictive value (PPV) is the probability that someone truly has the disease given that their test result is positive. Negative predictive value (NPV) is the probability that someone truly does not have the disease given that their test result is negative. Both PPV and NPV depend on how common the disease is in the tested population (the prevalence), in addition to the test’s sensitivity and specificity.

As prevalence increases, PPV increases (more of the positives are true positives) and NPV decreases (more of the negatives become false negatives relative to the larger pool of diseased individuals). When prevalence is very low, PPV tends to be lower and NPV tends to be higher.

The statement captures these ideas: sensitivity is the true positive rate; specificity is the true negative rate; PPV/NPV depend on disease prevalence, and higher prevalence raises PPV while lowering NPV. The other choices mix up definitions and ignore how prevalence affects predictive values.