A diagnostic test for diabetes has a sensitivity of 88% and a specificity of 92%. Which statement correctly interprets the specificity value?

Explanation

## Definition of Specificity **Key Point:** Specificity is the ability of a test to correctly identify those WITHOUT the disease. It is the proportion of true negatives among all people who do NOT have the disease. ### Formula $$\text{Specificity} = \frac{TN}{TN + FP}$$ Where: - TN = True Negatives (correctly identified non-diseased individuals) - FP = False Positives (non-diseased individuals incorrectly flagged as positive) ### Interpretation A specificity of 92% means that if 100 people WITHOUT diabetes are tested, the test will correctly identify 92 of them as negative. In other words, 92% of people WITHOUT the disease will test negative. **High-Yield:** Specificity answers the question: "If someone does NOT have the disease, what is the probability the test will be NEGATIVE?" It is a **non-disease-centric** measure. ### Clinical Pearl Specificity is crucial for **confirmatory tests** and **ruling in disease** (SpPin: high Specificity = Positive result rules IN disease). A test with high specificity has few false positives, making it reliable for confirming disease. ### Comparison Table | Measure | Formula | Asks | Used For | | --- | --- | --- | --- | | **Sensitivity** | TP/(TP+FN) | If diseased, probability of +ve test? | Screening; ruling OUT | | **Specificity** | TN/(TN+FP) | If non-diseased, probability of −ve test? | Confirmation; ruling IN | | **PPV** | TP/(TP+FP) | If +ve test, probability of disease? | Clinical decision-making | | **NPV** | TN/(TN+FN) | If −ve test, probability of no disease? | Clinical decision-making |