Understanding HIV Incidence
Why are HIV incidence rates important for AIDS vaccine trials?
To capture the severity of an epidemic, researchers often refer to prevalence and incidence rates. For HIV, prevalence refers to the number of individuals in a population infected with the virus at a certain time point. HIV prevalence can be determined by conducting widespread testing in a region or country and then projecting the total number of infected people.
Incidence refers to the number of people who are newly infected with HIV over time. These figures are usually reported as a percentage and represent the rate of people who are infected in a year or during another specified period of time. Incidence is more difficult to determine than prevalence, but it is also more valuable because it shows how the epidemic is progressing at the current time. This can help explain the dynamics of the epidemic, the speed at which HIV is spreading in light of current sexual or drug-use behaviors, and the effectiveness of available HIV prevention technologies. Accurate estimates of HIV incidence are also indispensable to the design of HIV prevention trials, including those testing AIDS vaccine candidates.
The "power" of incidence
Researchers are searching for a vaccine that could prevent transmission of HIV. But to test the efficacy of vaccine candidates, some volunteers must become infected through exposure in their community for researchers to know if an intervention is effective or not. Volunteers are never purposely exposed to HIV. Researchers compare the number of infections that naturally occur during the trial between a group of volunteers that received the vaccine and another that didn't.
Statisticians "power" a study to show if an intervention is effective based on the number of people they predict will become HIV-infected during the trial. This prediction is based on the HIV incidence in that population and determines, among other things, how many volunteers must be included in the trial.
If the actual incidence during the course of the trial ends up being much lower than predicted, it can profoundly affect the study. Even small differences can have an enormous impact. In a trial where statisticians assume an HIV incidence rate of 5% and a rate of only 4% is actually observed, 25% more volunteers would have to be recruited or the trial would be inconclusive. Expanding recruitment affects the length and cost of the trial. If the incidence is too low the trial could also be stopped prematurely by the data safety monitoring board (see VAX June 2007 Primer onUnderstanding Data Safety Monitoring Boards).
For these reasons it is critical to start a trial with the most accurate incidence estimates possible within the specific population where a study will occur.
Ways to measure incidence
The gold standard method for measuring HIV incidence is the prospective cohort study where researchers follow large groups of HIV uninfected individuals over long periods of time, testing them at regular intervals to see if any have become HIV infected, enabling them to determine the rate of infection. These studies are time-consuming, labor-intensive, and expensive, and add substantially to the already complex process of conducting a clinical trial. Consequently some sponsors may use previously-published incidence data to design a study. But this approach can be risky. Two Phase III microbicide trials that were based on previously-published HIV incidence data were recently stopped before investigators could determine the efficacy of the candidates because the incidence during the trials was so much lower than anticipated (see Spotlight article).
There are several other faster ways to estimate HIV incidence. One involves using mathematical models to predict incidence based on existing prevalence data. Another approach is to test large numbers of people for HIV using immunological tests that can identify people who were recently infected with HIV. These tests recognize either parts of HIV or antibodies to the virus that are detectable within a defined period very early in the course of HIV infection. One of the immunological tests or assays detects the plasma levels of p24 antigen, which is an HIV protein that reaches peak levels very soon after a person is infected. Once the immune system generates HIV-specific antibodies, generally within just a couple of months after initial infection, they bind to the p24 antigen and make it undetectable.
Another approach is to use a combination of two HIV antibody tests (ELISA assays) of differing sensitivity. If antibodies to HIV are detectable by the more sensitive test, another test that is purposely made less sensitive is used to see if antibodies are still detectable. The theory is that only individuals who have been HIV infected for a long time would have developed a strong and broad enough immune response to the virus to be detectable by the less sensitive test.
A third method for detecting recent infection is known as the BED assay, because it was originally developed based on the B, E, and D clades of HIV (see VAX July 2006 Primer on Understanding HIV Clades). The premise of this test is that as the immune system ramps up production of HIV-specific antibodies over time, these responses evolve from having a weaker to a stronger attraction or ability to bind to HIV. The BED assay involves an HIV antibody test that measures the percentage of all antibodies that are specific to HIV. This ratio is then compared with a set of predefined parameters to determine if an infection is classified as recent or not.
Unfortunately none of these methods are reliable or work universally—all of them substantially overestimate incidence in African populations and this can be dangerous when starting AIDS vaccine trials. Researchers generally agree that there is no substitute for the traditional cohort study to accurately determine HIV incidence. Several groups, including IAVI and the US Military HIV Research Program, are currently conducting incidence studies in Africa in preparation for efficacy trials of AIDS vaccine candidates.