Prevalence is the proportion of individuals in a population who currently have a disease. It serves as a pre-test probability, influencing how likely a test result reflects the true disease.
- High prevalence: Positive test is more likely to be a true positive (↑ PPV)
- Low prevalence: Negative test is more likely to be a true negative (↑ NPV)
Note: Prevalence does not affect the sensitivity or specificity of a test. Those are intrinsic properties of the test.
Practical Examples
| Scenario | Population Prevalence | Test Interpretation |
|---|---|---|
| An 80-year-old patient with diabetes was tested for kidney failure | High prevalence | Positive test likely true (high PPV) |
| A 15-year-old girl was tested for myocardial infarction | Very low prevalence | Positive test likely false (low PPV); negative test likely true (high NPV) |
Explanation:
- Physicians intuitively weigh pre-test probability when interpreting test results.
- Tests are more reliable in populations where the disease is common.
- In low-prevalence populations, positive results need confirmation to rule out false positives.
Relationship with Incidence
- Incidence: Measure of new cases in a population over a specific period.
- Increasing incidence does not affect sensitivity or PPV because the test detects current disease, not onset.
Key Principles
- Prevalence ↑ → PPV ↑, NPV ↓
- Prevalence ↓ → PPV ↓, NPV ↑
- Sensitivity and specificity are intrinsic test characteristics and remain unchanged by prevalence.
- Always consider population context when interpreting test results.
Learning Objective
After studying this topic, medical students should be able to explain how disease prevalence affects positive and negative predictive values, differentiate it from incidence, and apply this understanding to interpret diagnostic tests in different patient populations effectively.
