Here are some of the references I used for todays’ Inside Health on HIV testing.
One thing I didn’t get a chance to talk about was how prevalence – the number of cases within a community – influences the false positive rate in the people being tested.
The bottom line is that false positives in a low prevalence community are more common than in a high prevalence community.
So, for example, one 4th generation HIV test has a false positive rate of 3 per 1000. So for every 1000 HIV negative people, 3 will test as positive.
If you are running the test in a low prevalence area, where the rate – as in the UK – is 0.1%, 1 in 1000, this means that in 1000 people, 4 will test positive – 3 will be false positives, and one a true positive. So if you are in a low prevalence community and test positive, you only have a 1 in 4 chance of being a true positive.
If you run the test in a high prevalence area, say for example the rate of HIV infection in men who have sex with men in London is usually given at 1 in 10, it’s quite different. The false positive rate of the test is still 3 in 1000, but there will be 100 true positives in this group. So there will be 103 positive tests altogether. The chance of a false positive test is just under 3%, at 3/103. So 100 positive tests in this group will be true positives, 3 will be false positives.
This sort of knowledge I think very important. Here are some of the references I used.