Question

Although the concepts of sensitivity and specificity are usually applied to diagnostic laboratory or screening tests, they are equally valid for other tests that indicate the presence or absence of disease. These may be tests designed to elicit certain physical signs of disease by examination (e.g. hypertension, cervical cancer, neurological defects, and tendency to hemorrhage) or questions aimed at uncovering symptoms.

Suppose that a short psychometric questionnaire that was devised to detect undiagnosed cases of personality disorder was found, after being tested in different mental health clinics using the questionnaire on people with previously diagnosed personality disorder, to have a sensitivity of 25% and a specificity of 99%.

Imagine you are a public health official in charge of deciding whether or not your clinics will use this test to diagnose personality disorder. Discuss why you would (or would not) recommend the use of a test that gave these results (specific to both this actual test, and the type of population, i.e., prevalence of disease)? Hint: Be sure to discuss results relating to sensitivity, specificity, and PPV. [4 points]

Answer 1

#. Sensitivity measures how often a test correctly generates a positive result for people who have the condition that’s being tested for (also known as the “true positive” rate). A test that’s highly sensitive will flag almost everyone who has the disease and not generate many false-negative results. (Example: a test with 90% sensitivity will correctly return a positive result for 90% of people who have the disease, but will return a negative result — a false-negative — for 10% of the people who have the disease and should have tested positive.)

Specificity measures a test’s ability to correctly generate a negative result for people who don’t have the condition that’s being tested for (also known as the “true negative” rate). A high-specificity test will correctly rule out almost everyone who doesn’t have the disease and won’t generate many false-positive results. (Example: a test with 90% specificity will correctly return a negative result for 90% of people who don’t have the disease, but will return a positive result — a false-positive — for 10% of the people who don’t have the disease and should have tested negative.)

#. The ideal test is one that has both high sensitivity and high specificity, but the value of a test depends on the situation

Generally speaking, “a test with a sensitivity and specificity of around 90% would be considered to have good diagnostic performance and a test with 100% sensitivity and specificity is the ideal test.