The Importance of Disease Prevalence in HIV Testing

Owned by Alex
Last updated: Mar 28, 2024
3 min read

Author

  • Darrell E. Ward

Publisher

  • AmFAR AIDS

Category

  • HIV Tests

Topic

  • HIV Test Accuracy

Article Type

  • Column

Publish Year

  • -

Meta Description

  • HIV testing's positive predictive value is influenced by disease prevalence. High prevalence yields more true positives, while low prevalence increases false positives.

Summary

  • This content discusses the importance of disease prevalence in HIV testing. It explains that the accuracy of a positive HIV test result, known as positive predictive value, is influenced by the prevalence of the disease in the population being tested. The content provides examples to illustrate how the prevalence of HIV infection affects the likelihood of a positive test result being true. It emphasizes that testing everyone for HIV may lead to a high number of false positive results, especially in populations with low prevalence of the disease.

Meta Tag

  • HIV Testing

  • Disease Prevalence

  • Positive Predictive Value

  • True Positives

  • False Positives

  • Specificity

  • Population

  • Infection

  • Injection Drug Users

  • Blood Donors

  • Public Health

  • AmFAR AIDS Handbook

Featured Image

 

Featured Image Alt Tag

  • Keyword of the image

The AmFAR AIDS Handbook
©1999 by American Foundation for AIDS Research and Darrell E. Ward

As state and provincial health authorities mandate HIV tests as part of routine prenatal testing for all pregnant women they violate the basic principal of positive predictive value. With no gold standard by which to confirm positive test results, this can only lead to a public health fiasco. Huge numbers of false positive test results are certain to occur. AmFAR explains:

THE IMPORTANCE OF DISEASE PREVALENCE IN HIV TESTING

Why not test everyone for HIV infection? Because all screening tests have a property known as positive predictive value -- the probability that a positive test result is truly positive. Positive predictive value is greatly influenced by the prevalence of the disease or infection in the population being tested. Why this is so is shown by calculating the positive predictive value for the same test when applied to a population in which HIV infection is highly prevalent as compared to population in which the prevalence is low.

Consider the example of a high-prevalence population -- injection drug users in a major city in which half the drug users are infected (i.e., the prevalence of HIV in the population is 50%). For convenience, the size of the population is said to be 100,000. That means 50,000 people will be infected and 50,000 uninfected. The text used in the example will have a specificity of 99.9% -- it will yield 1 false positive per 1,000 people tested, or 50 false positives per 50,000 people tested. The positive predictive value is calculated as follows:

 

Positive predictive value

=

      True positives      
True positives + False positives

49,950
50,000

= 99.9%

 

This result means that a positive test result has a 99.9% chance of being a true positive. But the probability changes when the same calculation is applied to a population with a low prevalence of infection. Take, for example, self selected and previously tested blood donors from the general population. Here the prevalence of infection might be 1 case per 100,000 people.

Thus, for every 100,000 people, 1 person is infected and 99.999 are uninfected. If the test has a specificity of 99.9%, it means 0.1% -- about 100 of the test results will be false positives. Now calculate the positive predictive value:

 

Positive predictive value

=

      True positives      
True positives + False positives

 1 
101

= 1.0%

 

This means that a positive test result in this population has only a 1% chance of being a true positive!