A new screening test is evaluated in a population of 1,000 individuals. Of these, 100 individuals are known to have the disease. The test correctly identifies 90 of the diseased individuals, while 50 non-diseased individuals are false positives. Based on the data below, what is the Positive Predictive Value (PPV) of this screening test?  Screening Test Diseased Non-Diseased  Test Positive 90 50  Test Negative 10 850  Total 100 900

1. A

   A. 90%

2. B

   B. 98%

3. C

   C. 64%

4. D

   D. 47%

Appeared in: INI-CET EXAM -2026

Explanation

• Positive Predictive Value (PPV) is the probability that a person with a positive test result truly has the disease.
• The formula for PPV is: True Positives / (True Positives + False Positives).
• From the provided data, True Positives (TP) are the 90 diseased individuals who tested positive.
• False Positives (FP) are the 50 non-diseased individuals who also tested positive.
• The calculation is PPV = 90 / (90 + 50) = 90 / 140.
• This results in approximately 0.643, or 64.3%, which rounds to 64%.

Why Other Options Were Wrong

• Option A: The value 90% represents the test's Sensitivity, not its PPV.
• Option B: The value 98% is very close to the Negative Predictive Value (NPV), not the PPV.
• Option D: This value does not correspond to any of the standard performance metrics (Sensitivity, Specificity, PPV, or NPV) for this screening test.

Related Visual

Clinical Relevance

• Nursing practice connection: Knowing Calculation and interpretation of Positive Predictive Value (PPV) for a screening test helps nurses interpret findings accurately and avoid errors in routine assessment, medication administration, and patient teaching.
• PPV is crucial for clinicians and patients to understand the real-world meaning of a positive test result, guiding decisions on further diagnostic testing or treatment.
• A low PPV, even with a highly sensitive test, can lead to over-diagnosis, unnecessary anxiety, and costly follow-up procedures for patients who are actually healthy.
• What if? - If the disease was much rarer in the population (e.g., only 10 diseased people out of 1000), the PPV would drop significantly to 10 / (10 + 50) = 16.7%, even if the test's sensitivity and specificity remained the same. This highlights that PPV is highly dependent on disease prevalence.

How to Approach the Question

• First, identify that the question asks for the 'Positive Predictive Value (PPV)'.
• Recall the definition and formula for PPV: PPV = True Positives / (Total Positives). Total Positives is the sum of True Positives and False Positives.
• Carefully extract the required values from the provided 2x2 table.
• Identify 'True Positives' (TP) as the number of diseased individuals with a positive test, which is 90.
• Identify 'False Positives' (FP) as the number of non-diseased individuals with a positive test, which is 50.
• Substitute these values into the formula: PPV = 90 / (90 + 50).

Concept Tested & Keywords

• Concept Tested: Calculation and interpretation of Positive Predictive Value (PPV) for a screening test.
• Stem keywords: screening test, Positive Predictive Value, PPV, True Positives, False Positives
• Lead-in keywords: what is
• Negative lead-in flag: false

Question ID

QsjUdTikN2GiTwb6UHeqIq

Copy ID

Reference Book

E6 Parks TextBook of Preventive & Social Medicine part 1 — Subpart A (pp 26-201 of 604) p. 141-143

E6 Medicine Harrison 22e Part 1 p. 555-557