True prevalence (pre-test) is defined as...

• A. The proportion of non-diseased individuals in the population.
• B. The actual level of disease present in the population; p(D+) = (a+b)/n.
• C. The observed proportion of positive tests in a sample.
• D. The probability a positive test is a true disease.

Answer: (B)

The question tests the distinction between true disease prevalence and what you actually observe from a diagnostic test. True prevalence is the actual proportion of individuals who truly have the disease in the population, regardless of test results. In a 2x2 table, the number of diseased people is the sum of those who test positive and those who test negative but are truly diseased, so p(D+) = (a + c) / n.

The quantity (a + b) / n represents the observed proportion of positive test results in the sample, i.e., the apparent prevalence. It reflects what you see when you look at test outcomes, not the underlying true disease rate.

The other options map to different concepts: the proportion of non-diseased is p(D−) = (b + d) / n, and the probability that a positive test actually indicates disease is the positive predictive value p(D+ | T+).

So true prevalence is the actual disease proportion in the population, not the fraction of positive tests.