In a surveillance case definition, how do sensitivity and positive predictive value differ?

• A. Sensitivity measures the frequency of false positives.
• B. PPV measures the proportion of true disease cases among all those tested.
• C. Sensitivity is unrelated to the detection of true cases.
• D. Sensitivity is the ability to identify true cases; positive predictive value is the probability that a reported case truly has the disease.

Answer: (D)

The main idea here is that sensitivity and positive predictive value describe two different aspects of how well a surveillance case definition works. Sensitivity is about catching true disease cases. It answers: of all people who actually have the disease, what fraction does the system identify as cases? A high sensitivity means few true cases are missed (few false negatives).

Positive predictive value, on the other hand, is about the accuracy of the cases that are reported. It answers: given that the system labeled someone a case, what is the probability that they truly have the disease? This depends on how many false positives there are and on how common the disease is in the population.

To make this tangible: if 100 people truly have the disease and the case definition flags 90 of them, sensitivity is 90%. If the system reports 150 cases in total and only 90 are real cases, PPV is 90/150, or 60%. This shows how sensitivity and PPV measure different things—one is about identifying actual cases, the other about the correctness of the reported cases.

Note briefly why the other ideas don’t fit: sensitivity isn’t about the frequency of false positives (that would relate to PPV and specificity), and PPV isn’t the proportion of true cases among all those tested (that describes prevalence or the probability of disease among positives, not among all tested).