Which statement best defines sensitivity of a diagnostic test?

• A. The proportion of diseased individuals who test positive.
• B. The proportion of non-diseased individuals who test negative.
• C. The probability that a positive test truly indicates disease.
• D. The probability that a negative test truly indicates absence of disease.

Answer: (A)

Sensitivity measures how good a test is at identifying disease when it is actually present. It is the proportion of individuals with the disease who test positive, calculated as true positives divided by all those who truly have the disease. Therefore, the statement describing the proportion of diseased individuals who test positive matches this concept exactly.

In practice, a test with high sensitivity is helpful for ruling out disease when the result is negative (a negative result in a highly sensitive test often means the disease is unlikely, a idea summarized by “SNOUT”). It also means it will miss relatively few diseased individuals (low false negatives).

The other ideas refer to different concepts: the proportion of non-diseased people who test negative is specificity, not sensitivity; the probability that a positive result actually indicates disease is the positive predictive value; and the probability that a negative result indicates absence of disease is the negative predictive value.