What does specificity refer to in medical testing?

Specificity in Medical Testing

Specificity in medical testing refers to the proportion of individuals without the disease who are correctly identified as negative by the test. It is calculated as the number of true negatives divided by the sum of true negatives and false positives 1.

Definition and Calculation

• Specificity is mathematically expressed as:
  • Specificity = True Negatives / (True Negatives + False Positives) 1
• It measures how well a test correctly identifies people who do not have the condition being tested for 2
• A test with high specificity produces few false positive results 2

Clinical Significance

• High specificity is crucial when:

  • Confirming a diagnosis that will lead to invasive or risky treatment
  • Ruling in a condition (when a positive test result is obtained)
  • Minimizing false positive results that could lead to unnecessary anxiety, referrals, expenses, or treatments 2

• For example, when testing for amyloid pathology in Alzheimer's disease, high specificity is needed to ensure individuals without amyloid pathology are correctly identified, preventing unnecessary treatment with amyloid-lowering drugs 2

Relationship with Sensitivity

• There is an inverse relationship between sensitivity and specificity 1, 3
• When sensitivity increases, specificity typically decreases, and vice versa
• This trade-off is visualized using Receiver Operating Characteristic (ROC) curves, which plot sensitivity against (1-specificity) for different diagnostic thresholds 2, 1

Factors Affecting Specificity

1. Diagnostic threshold selection:

   • Lowering the threshold increases sensitivity but decreases specificity
   • Raising the threshold increases specificity but decreases sensitivity 2

2. Disease prevalence:

   • Higher disease prevalence is associated with lower estimated specificity 4
   • This challenges the traditional view that specificity is an intrinsic test characteristic independent of prevalence

3. Test implementation factors:

   • Laboratory methods and conditions
   • Specimen handling
   • Operator training and experience 2

Specificity vs. Predictive Values

• Specificity is an intrinsic characteristic of the test itself 1
• Unlike specificity, negative predictive value (NPV) varies with disease prevalence 3, 5
• NPV represents the proportion of subjects with a negative test result who truly do not have the disease 3
• In populations with low disease prevalence, even tests with moderate specificity can have high NPV 5

Clinical Application Example

When interpreting specificity in breast cancer screening:

• A screening mammography with digital breast tomosynthesis has a certain specificity that determines the rate of false positive results 2
• False positives lead to additional imaging that doesn't result in cancer diagnosis within 12 months 2
• The specificity of the screening method directly impacts the number of false positive results per 1,000 high-risk women screened 2

Practical Considerations

• When selecting tests for ruling out a disease, prioritize high sensitivity
• When selecting tests for confirming a diagnosis, prioritize high specificity
• Consider using the diagnostic odds ratio as a single indicator of test performance when comparing competing tests 6
• For borderline cases, consider using two cut-offs to define three categories (positive, intermediate, negative) to increase overall accuracy 2

Understanding specificity is essential for proper test selection and interpretation in clinical practice, ultimately impacting patient outcomes through appropriate diagnostic and treatment decisions.