What is the difference between sensitivity and specificity in medical screening tests?

Understanding Sensitivity and Specificity in Screening Tests

Sensitivity is the ability of a test to correctly identify those with the disease (true positive rate), while specificity is the ability of a test to correctly identify those without the disease (true negative rate).

Key Definitions

Sensitivity

• Defined as the percentage of positive test results obtained when evaluating only specimens that are truly positive 1
• The proportion of pregnancies with a condition that have a positive test result 1
• The percentage of people with the disease who are detected by the test 1
• Mathematically calculated as: true positives ÷ (true positives + false negatives) 1

Specificity

• Defined as the percentage of negative test results reported when only truly negative specimens are evaluated 1
• The proportion of unaffected pregnancies identified by the test as being negative 1
• The percentage of people without the disease who are correctly labeled by the test as not having the disease 1
• Mathematically calculated as: true negatives ÷ (true negatives + false positives) 1

Relationship Between Sensitivity and Specificity

• These measures are inversely related - as sensitivity increases, specificity typically decreases 1
• For any single test and underlying disease, there is a trade-off between these two parameters
• The optimal cut-off for diagnosis can be obtained from the Receiver Operating Characteristics (ROC) curve, which plots sensitivity vs. 1-specificity over the entire range of test results 1

Clinical Implications

When Sensitivity is High

• A highly sensitive test is most useful when negative (rules out disease)
• Few false negatives
• Good for screening tests where missing a disease would be harmful
• Example: A test with 95% sensitivity for anencephaly would miss only 5% of cases 1

When Specificity is High

• A highly specific test is most useful when positive (rules in disease)
• Few false positives
• Good for confirmatory tests where false positives would lead to unnecessary interventions
• Example: Higher specificity reduces overreferrals in developmental screening 1

Important Related Concepts

Positive and Negative Predictive Values

• Positive Predictive Value (PPV): The likelihood that a patient with a positive test result actually has the disease 1
• Negative Predictive Value (NPV): The likelihood that a patient with a negative test result really does not have the disease 1
• Unlike sensitivity and specificity, predictive values are affected by disease prevalence 1
• In populations with low disease prevalence, even tests with high sensitivity and specificity can have low positive predictive values 1

Overall Accuracy (Concordance)

• Combines sensitivity and specificity into a single measure of the percentage of cases for which the test result agrees with the true status 1
• Can be strongly influenced by the positive-negative mix of the test case set if sensitivity and specificity rates are not similar 1

Common Pitfalls

1. Assuming sensitivity and specificity are fixed properties of a test, when they can vary based on:

   • Population case mix
   • Disease severity
   • Cut-off values used

2. Applying predictive values from one population to another with different disease prevalence

3. Using sensitivity and specificity to estimate individual patient's probability of disease, when predictive values are more appropriate for this purpose

4. Failing to consider that screening tests and diagnostic tests have different performance requirements

Practical Applications

• Screening tests typically prioritize sensitivity to minimize missed cases
• Diagnostic/confirmatory tests typically prioritize specificity to minimize false positives
• The accepted standard for developmental screening is approximately 70-80% sensitivity and 80% specificity 1
• When evaluating a test's performance, it's important to consider all four metrics (sensitivity, specificity, PPV, NPV) together rather than in isolation

By understanding these fundamental concepts, clinicians can better select and interpret screening tests to optimize patient outcomes related to morbidity, mortality, and quality of life.