Method natural frequency tree
No test is ever perfect - How reliable are HIV self-tests?
Why is it relevant to support consumer decisions on the basis of tests and algorithms?
Why is it problematic to support consumer decisions on the basis of tests and algorithms?
How to construct a natural frequency tree?
A. What do you need?
B. How do you proceed?
- Gigerenzer, G., & Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review, 102(4), 684.
- Hoffrage, U., & Gigerenzer, G. (1998). Using natural frequencies to improve diagnostic inferences. Academic Medicine, 73(5), 538–540.
- McDowell, M., & Jacobs, P. (2017). Meta-analysis of the effect of natural frequencies on Bayesian reasoning. Psychological Bulletin, 143(12), 1273–1312.
- Zhu, L., & Gigerenzer, G. (2006). Children can solve Bayesian problems: The role of representation in mental computation. Cognition, 98(3), 287–308.
No test is ever perfect – How reliable are HIV self-test kits?
If you want to be tested for HIV, you might feel uncomfortable going to a counselling centre for sexual health and have a consultation. Ordering an HIV test over the Internet or buying it from a drugstore or pharmacy is more anonymous. Since 2018, these tests have been available for purchase over the counter, starting at around 20 euros. But can you trust the test result? How can you assess the reliability of such quick tests? Scientists assume that only 1 out of 13 people with a positive HIV self-test result is actually infected - 12 would thus be wrongly alarmed. With our frequency tree you can see for yourself how reliable such an HIV self-test is.
How to read this figure?
Among 100,000 women and men, 17 are infected with HIV without knowing it.
According to the manufacturer, these 17 are always detected due to the sensitivity of the test. At the same time, however, 200 men and women who have no HIV infection nevertheless receive a critical test result, which should be examined further.
This means that 17 of all those who have a critical result (17 + 217) actually have HIV. Simplified: 17 out of 217 critical results. This corresponds to a positive predictive value of 7.8% (=17/217).
The probability that the HIV self-test is correct when it says "You probably have HIV" is 8%. Please decide for yourself whether buying such a test without any particular reason can be beneficial for you.
Please note that the probability is even lower if you do not belong to a risk group (e.g. drug addiction with syringe use).
All statistics on HIV self-testing are currently based on manufacturers' studies. Future studies must first show what the actual benefit-to-harm ratio of these tests is. The possibility of new risk behaviour cannot be ruled out.
Source of the proposed prevalence value: an der Heiden M et al. (2017). Epidemiologisches Bulletin, 47, 531-545.
Please note that this value can be significantly higher for certain risk groups.
Source of the proposed sensitivity and specificity values: indication from the test autotest VIH (manufacturer's conflict of interests).