If a patient tests positive for a disease, which of the following represents the probability of her actually having the disease?

Correct Answer: C. Positive predictive value

Positive Predictive Value (PPV) is the probability that a person who tests positive actually has the disease. This is the clinically relevant question: "If my test is positive, what is the chance I truly have the disease?" PPV is calculated as: TP/(TP+FP), where TP = true positives and FP = false positives. In other words, it answers the posterior probability of disease given a positive test result. This is fundamentally different from sensitivity, which asks "if the disease is present, will the test detect it?" PPV depends not only on test characteristics (sensitivity and specificity) but also on the prevalence of the disease in the population being tested. In high-prevalence populations (e.g., TB screening in a chest clinic in India), PPV is higher; in low-prevalence populations (e.g., screening asymptomatic individuals), PPV is lower even with the same test. This is why the same diagnostic test has different clinical utility in different settings. When a patient receives a positive test result, they naturally want to know: "Do I actually have this disease?" The answer is the PPV. This is the clinician's perspective and the patient's perspective—making it the most clinically actionable measure.

Why the other options are wrong

A. Negative predictive value — NPV is the probability of NOT having the disease given a NEGATIVE test result (TN/(TN+FN)). The question explicitly states the patient tests POSITIVE, so NPV is irrelevant here. NPV answers the opposite clinical scenario: 'If my test is negative, am I truly disease-free?' This is a common trap—students confuse the direction of the test result. B. Sensitivity — Sensitivity (TP/(TP+FN)) is the probability of testing positive GIVEN that the disease is present—it answers 'How good is the test at detecting disease?' This is a test property, not a patient probability. The question asks about the patient's probability of having disease given a positive test, which is the inverse conditional probability. Sensitivity is fixed for a test; PPV varies by prevalence. D. Specificity — Specificity (TN/(TN+FP)) is the probability of testing negative GIVEN that disease is absent—it measures the test's ability to correctly identify non-diseased individuals. Like sensitivity, it is a test characteristic, not a patient probability. A positive test result tells us nothing directly about specificity; we need PPV to know the likelihood of actual disease in this positive patient.

High-Yield Facts

Mnemonics

PPV = Patient Probability (Positive test) When patient gets a POSITIVE result, they ask 'Do I have it?' → Answer is PPV. When test is NEGATIVE, they ask 'Am I free?' → Answer is NPV. Sensitivity/Specificity are about the TEST, not the patient. SNOUT & SPIN (for remembering what each measures) SNout = SENsitivity rules OUT disease (high sensitivity = good at ruling out). SPin = SPecificity rules IN disease (high specificity = good at ruling in). But once you have a positive test, you need PPV to know the actual probability.

NBE Trap

NBE often pairs sensitivity/specificity with the clinical question of "does the patient have disease?" to trap students who confuse test properties with patient probabilities. The discriminator is recognizing that only PPV directly answers the post-test probability question.

Clinical Pearl

In Indian clinical practice, a patient presenting with a positive rapid TB test (e.g., GeneXpert MTB/RIF) in a chest clinic has a very high PPV (_{95%), but the same positive test in an asymptomatic contact has lower PPV (}60–70%) due to lower disease prevalence in that population. This is why clinicians always interpret test results in clinical context, not in isolation.

_Reference: Park's Textbook of Preventive and Social Medicine, Ch. 10 (Epidemiology); Harrison's Principles of Internal Medicine, Ch. 3 (Clinical Decision Making)_