A new rapid test for cervical cancer screening has a sensitivity of 85% and specificity of 90% applied to a population with a prevalence of 1%. The Positive Predictive Value (PPV) is approximately:
- A 7.9% ✓
- B 49.7%
- C 85%
- D 99%
Correct answer: A. 7.9%
Explanation
Using Bayes' theorem: in 10,000 people with 1% prevalence, there are 100 true positives and 9,900 negatives. True positives (TP) = 100 × 0.85 = 85; False positives (FP) = 9900 × 0.10 = 990. PPV = TP / (TP + FP) = 85 / (85 + 990) = 85/1075 ≈ 7.9%. This illustrates how in low-prevalence populations, even a test with high specificity yields a low PPV—a critical concept for deciding where screening is appropriate. False positive burden in this scenario is nearly 12 times higher than true positives.
Reference: Park's Textbook of Preventive and Social Medicine, 27th ed.
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