Screening for HIV
As of the 23rd May 2022 this website is archived and will receive no further updates.
understandinguncertainty.org was produced by the Winton programme for the public understanding of risk based in the Statistical Laboratory in the University of Cambridge. The aim was to help improve the way that uncertainty and risk are discussed in society, and show how probability and statistics can be both useful and entertaining.
Many of the animations were produced using Flash and will no longer work.
Our first screening example featured a fictitious terrorist-detecting device that was not very accurate. However, as this real example shows, even when the tests are incredibly accurate we still get some surprising results if the underlying condition is rare.
Common HIV blood tests are very accurate -- estimates vary, but it is estimated that using current techniques (ELISA and Western blot) around 99.8% of people with HIV test positive, and 99.99% of people without the virus test negative. In the UK, the prevalence of HIV in adults with no risk factors is around 1 in 10000. Thus, out of 10000 people, we expect 1 to have the virus (and they will almost certainly test positive), and one false positive. We can see this in the animation below. Thus, out of two people who test positive,we expect one to have HIV -- in other words, the probability of having HIV given a positive test result.
You can experiment with the interactive graphic to see how altering the properties of the test and the prevalence of the disease affects the test.
However, among intravenous drug users, HIV rates are much higher -- in the UK, around 1.5%. What does our test tell us here? Out of 10,000 drug users who are screened, we expect around 150 to have HIV, and we expect all of them to test positive (to the nearest whole person); we also expect around 1 false positive. Thus, given a positive test result for an IV drug-user, the probability that they have HIV is around 150/151. This example is also featured in the animation.
Attachment | Size |
---|---|
BayesTheoremHIV.xml | 613 bytes |
- Log in to post comments
Comments
Anonymous (not verified)
Sun, 24/10/2010 - 9:35am
Permalink
Typo ?
gmp26
Mon, 25/10/2010 - 1:27pm
Permalink
Typo ?
Corrected now - many thanks!
Anonymous (not verified)
Sun, 12/12/2010 - 8:12pm
Permalink
How would double testing affect results?