## **Core Concept**
The positive predictive value (PPV) of a test is the probability that a subject with a positive test result truly has the disease. It is calculated using the formula: PPV = (Sensitivity × Prevalence) / [(Sensitivity × Prevalence) + ((1 - Specificity) × (1 - Prevalence))]. This concept is crucial in understanding the utility of diagnostic tests in clinical practice.

## **Why the Correct Answer is Right**
To calculate the PPV, we substitute the given values into the formula: Sensitivity = 99% or 0.99, Specificity = 99% or 0.99, and Prevalence = 5/1000 or 0.005.
PPV = (0.99 × 0.005) / [(0.99 × 0.005) + ((1 - 0.99) × (1 - 0.005))]
PPV = 0.00495 / [0.00495 + (0.01 × 0.995)]
PPV = 0.00495 / [0.00495 + 0.00995]
PPV = 0.00495 / 0.0149
PPV ≈ 0.332 or 33.2%
Therefore, the correct answer is the one closest to 33.2%.

## **Why Each Wrong Option is Incorrect**
- **Option A:** This option is incorrect because its value does not match our calculated PPV of approximately 33.2%.
- **Option B:** This option is incorrect for the same reason as Option A; it does not align with the calculated value.
- **Option D:** This option suggests a PPV of nearly 100%, which is not feasible given the provided sensitivity, specificity, and prevalence values.

## **Clinical Pearl / High-Yield Fact**
A key point to remember is that the PPV of a test is heavily influenced by the prevalence of the disease in the population being tested. Even with a highly sensitive and specific test like ELISA for HIV, a low disease prevalence results in a lower PPV, meaning more false positives will occur. This concept is critical in interpreting test results in different clinical settings.

## **Correct Answer: B. 33%**