A new rapid diagnostic test for COVID-19 is evaluated against RT-PCR (gold standard) in a hospital in Delhi. Among 500 confirmed COVID-19 patients, the rapid test correctly identified 450. Among 300 individuals without COVID-19, the test correctly identified 285 as negative. What is the specificity of this rapid test?

Explanation

## Specificity Calculation **Key Point:** Specificity measures the ability of a test to correctly identify those WITHOUT the disease. It is the proportion of true negatives among all disease-negative individuals. ### Formula $$\text{Specificity} = \frac{TN}{TN + FP}$$ Where: - TN (True Negatives) = individuals without disease correctly identified as negative = 285 - FP (False Positives) = individuals without disease incorrectly classified as positive = 300 − 285 = 15 ### Calculation $$\text{Specificity} = \frac{285}{285 + 15} = \frac{285}{300} = 0.95 = 95\%$$ **High-Yield:** Specificity is **health-focused**—it answers: "Of all people who DO NOT have the disease, how many does the test correctly exclude?" It is independent of the number of disease-positive individuals or false negatives. **Clinical Pearl:** A high-specificity test is used for **confirmation** and **ruling in** disease (SpPIn: Specificity, Positive, rule In). A positive result on a high-specificity test is highly reassuring of true disease. ### Relationship Between Sensitivity and Specificity | Metric | Formula | Interpretation | Clinical Use | |--------|---------|-----------------|---------------| | **Sensitivity** | TP ÷ (TP + FN) | Detects disease | Screening (SnNOut) | | **Specificity** | TN ÷ (TN + FP) | Excludes health | Confirmation (SpPIn) | **Mnemonic:** **SnNOut / SpPIn** - **Sn**Nsitive test, **N**egative result → rule **Out** disease - **Sp**ecific test, **P**ositive result → rule **In** disease