A screening test for cervical cancer has a sensitivity of 80% and specificity of 90%. In a population with a disease prevalence of 1%, the Positive Predictive Value (PPV) of the test is approximately:

• A 80%
• B 90%
• C 99%
• D 7.5% ✓

Explanation

PPV = TP / (TP + FP). In a population of 10,000 with 1% prevalence: True positives = 0.80 × 100 = 80; False positives = 0.10 × 9,900 = 990. PPV = 80 / (80 + 990) = 80/1070 ≈ 7.5%. This illustrates the fundamental problem with screening in low-prevalence populations: even a highly specific test produces far more false positives than true positives, reducing PPV dramatically. This is why screening should be targeted at higher-risk populations (e.g., older women or those with HPV infection for cervical cancer), where prevalence is higher and PPV increases. Bayes' theorem underpins this calculation.

Reference: Park's Textbook of Preventive and Social Medicine, 27th ed.

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Written and medically reviewed by the StethoPrep medical team.