Apparent prevalence (or post-test) is defined as...

• A. The true prevalence adjusted for test characteristics.
• B. The proportion of individuals testing positive: p(T+) = (a+c)/n.
• C. The actual level of disease present in the population.
• D. The probability that a negative test indicates absence of disease.

Answer: (B)

Apparent prevalence is the observed proportion of positive test results in the population being studied. It reflects what you see from the test data, without adjusting for imperfect test performance.

In the usual two-by-two setup, the total number of people who test positive is the sum of those who really have the disease and test positive plus those who do not have the disease but test positive. Dividing that count by the total number tested gives the apparent prevalence: p(T+) = (a + c) / n.

This differs from the true prevalence, which is the actual fraction of the population that has the disease regardless of test results. Apparent prevalence can overestimate or underestimate true prevalence depending on the test’s sensitivity and specificity. It is not the same as the post-test estimate of true prevalence after adjusting for test characteristics, nor is it the negative predictive value, which is the probability that a negative result correctly indicates absence of disease.