Positive predictive value of screening tests PPV versus

Positive predictive value of screening tests

PPV versus sensitivity: different denominators! Disease + Disease - Total Test + a b a+b Test - c d c+d Total a+c (total disease) b+d (total healthy) a+b+c+d Sensitivity = those who screen positive/ total number with disease [a/(a+c)] PPV= those who are true positives/total positive screens [a/(a+b)]

How prevalence affects PPV This is for the sceptics- the mathematical proof follows on the next few slides… • Start with a population of 1000 people • Assume that you have a screening test with sensitivity= 90% and specificity= 90% • How will PPV be affected if the disease prevalence is 10% in one case and 1% in another?

Disease prevalence 1% Disease + Test Total 9 1 10 Disease - Total 99 891 990 108 892 1000 • Prevalence of 1% means 10 disease cases in a population of 1000 • Sensitivity of 90% means 9 cases are detected on screen • Specificity of 90% means that 891 out of 990 healthy people are correctly identified as true negatives • PPV= 9/108= 0. 08= 8% or for every 100 people who screen positive, only 8 truly have the disease

Disease prevalence 10% Test + Test Total Disease + Disease - Total 90 10 100 90 810 900 180 820 1000 • Prevalence of 10% means 100 disease cases in a population of 1000 • Sensitivity of 90% means 90 cases are detected on screen • Specificity of 90% means that 810 out of 900 healthy people are correctly identified as true negatives • PPV= 90/180= 0. 5= 50% or for every 100 people who screen positive, 50 will actually have the disease

Ergo… Given a fixed sensitivity and specificity for a screening test, the positive predictive value increases with increasing disease prevalence. Q. E. D.