Question

*A new antibody test detects serum antibodies against virus X (sensitivity 99%, specificity 90%). When applied in a group of hospitalized patients diagnosed as having virus X infections, the test is found to have a positive predictive value of 30%. What best explains this difference between the positive predictive values?

Answer 1

The difference in positive predictive values (PPV) is due to the combination of sensitivity and specificity of the antibody test. The test has a high sensitivity of 99% and a specificity of 90%. A higher percentage of positive results are false positives, resulting in a lower proportion of true positive cases among all positive results.

Step-by-step explanation:

The difference in positive predictive values (PPV) is explained by the combination of sensitivity and specificity of the antibody test. Sensitivity measures the ability of the test to correctly identify true positive cases, while specificity measures the ability to correctly identify true negative cases. In this case, the antibody test has a high sensitivity of 99%, meaning it correctly detects the presence of virus X antibodies in 99% of infected patients. However, the specificity is lower at 90%, which means there is a higher chance of false positive results. The positive predictive value is calculated as the proportion of true positive results out of all positive results. In this scenario, the test has a positive predictive value of 30%, indicating that out of all the positive results, only 30% are true positive cases. This difference in PPV is mainly due to the lower specificity of the test. It means that a higher percentage of positive results are false positives, leading to a lower proportion of true positive cases among all positive results.