Epidemiology: Practice Question

Question

Which of the following describes the ability of a test to correctly identify people who do not have the disease (i.e., the proportion of true negatives among all disease-free individuals)?

Answer Choices

  1. Specificity
  2. Sensitivity
  3. Positive predictive value
  4. Negative predictive value
Show Answer and Explanations

Specificity — Correct answer

Correct. Specificity is a measure of a tests true negative rate, in other words a measure of how good the test is at detecting negative results in people without the disease.

Specificity (the true negative rate) is a measure of a test's ability to exclude a condition correctly, in other words, specificity measures the ability of a test to correctly identify all the negative cases out of all the actual negative cases. It tells us how well the test can rule out individuals who do not have the condition. A high specificity indicates that the test is good at correctly identifying individuals who do not have the condition, reducing the likelihood of unnecessary interventions for those who are healthy. A positive result in a test with high specificity is useful for "ruling in" disease, since the test rarely gives positive results in healthy patients. High specificity is good as a confirmatory test.

Specificity = (True Negatives) / (True Negatives + False Positives) = True Negatives / Total number of healthy people in the population

Sensitivity

Incorrect. Sensitivity is a measure of a test's ability to identify a condition correctly, in other words how frequently the test is positive in people with the disease, or true positive rate.

Sensitivity (true positive rate) is a measure of a test's ability to identify a condition correctly - how frequently the test is positive in people with the disease. It measures the ability of a test or model to correctly identify all the positive cases out of all the actual positive cases. A high sensitivity indicates that the test is good at identifying individuals who truly have the condition, minimizing the chances of missing positive cases. It is most relevant in situations where failing to detect a positive case could have serious consequences. A negative result in a test with high sensitivity is be useful for "ruling out" disease because it rarely misdiagnoses cases in which patients have the disease.

Sensitivity = (True Positives) / (True Positives + False Negatives) = True Positives / Total number of sick people in the population

Positive predictive value

Incorrect. Positive predictive value measures the probability that patients with a positive test truly have the disease - how likely a positive result is to actually be positive.

True positives / (True positives + False positives)

Negative predictive value

Incorrect. Negative predictive value measures the probability that patients with a negative test truly do not have the disease - how likely a negative result is to actually be negative.

True negatives / (True negatives + False negatives)

Summary

Summary: Specificity is a measure of a test's ability to exclude a condition correctly. High specificity is good for ruling in a disease and is good as a confirmatory test. Specificity = # True Negatives / # of healthy individuals (true negatives + false positives)
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