When new tests come out, they’re evaluated for how well they work. As tests are evaluated, you’ll often read the words sensitivity, specificy and predictive positive value. Here is a pointer about what those words mean:
Sensitivity refers to the “true positive rate.” It measures how frequently the test is positive when the person being tested actually has the disease. For example, when a test has 80% sensitivity, the test detects 80% of patients with the disease (true positives). However, 20% of patients with the disease are not detected (false negatives) by the test.
Specificity refers to the “true negative rate.” It measures how frequently the test is negative when the person being tested doesn’t have the disease. For example, when a test has 80% specificity, the test correctly reports 80% of patients without the disease as test negative (true negatives). However, 20% of patients without the disease are incorrectly identified as testing positive (false positives) by the test.
Positive Predictive Value is the measure of how likely it is that a positive test is a true positive rather than a false positive. This depends on how many people in the population being tested have had the disease. When there are very few people in the population that have had the disease, then there’s a higher chance that a positive test is a false positive. When there are many people in a population that have had the disease, then there’s a higher chance that a positive test is a true positive.
