Last year, a landmark clinical trial on the use of antiretroviral therapy (ART) to not only treat HIV but also prevent further transmission revealed that early initiation of ART reduced the rate of new infections among heterosexual couples by 96 percent. That study, HPTN 052, spurred numerous questions among healthcare researchers and policy makers. Should all patients with HIV start ART sooner after their diagnosis than has been the case thus far? If not everyone, what populations should be prioritized for this approach? What are the costs associated with earlier initiation of ART? Can patients who might not benefit clinically from earlier treatment be given ART strictly as a means of preventing new infections? Considering the sprawling range of HIV, how can these questions be examined quickly and accurately?
These and other complex questions are tackled in a new collection of articles recently published by PLoS Medicine. Entitled “Investigating the Impact of Treatment on New HIV Infections, the collection wades through the extraordinarily complex issues at play to provide comprehensive, nuanced insights toward converting the results of HPTN 052 into improved policies where HIV care is concerned. Using mathematical modeling to explore cost, potential efficacy, and other factors, this collection serves as a springboard for policy changes that could dramatically reduce the future spread of HIV, the virus that causes AIDS.
Current HIV treatment guidelines by the World Health Organization and other outlets call for ART to begin when CD4 cell counts reach at or under 350 cells per microliter. Typically, the number of CD4 cells — part of the infection-fighting immune system — decrease as the disease progresses. HPTN 052 found a benefit to starting treatment earlier, when CD4 counts are above 350 cells per microliter. Specifically, among heterosexual couples where one partner is infected and the other isn’t (so-called serodiscordant), earlier treatment reduced the rate at which HIV spread to the uninfected partner.
But moving toward earlier ART as a blanket policy isn’t so easy, and isn’t necessarily the next logical step. As Bärninghausen and colleagues write in their contribution to the PLoS collection, “HIV Treatment as Prevention: Issues in Economic Evaluation,” earlier ART can have negative consequences. If patients do not adhere to treatment, the disease can become resistant later on, making recovery less likely. The drugs have side effects that often require medical attention, adding to the physical burden earlier than may benefit the patient, even though it may halt viral spread. An individual’s economic productivity may also be curbed by ART therapy. And, the notion of giving drugs to someone infected with HIV as a way to prevent new infections, even when it might not benefit the person taking the medication, raises ethical concerns.
Yet the potential for treatment as prevention to stop the spread of HIV cannot be denied, as HPTN 052 made clear. In addition to a decrease in the number of HIV cases, earlier ART means patients won’t have to wait until their health begins to deteriorate before entering care. Economically, patients who start ART sooner rather than later may avoid that phase of deteriorating health and therefore keep working and active.
As the PLoS collection makes clear, the central question seems to be how best to implement the approach. In their article, “HIV Treatment as Prevention: Optimising the Impact of Expanded HIV Treatment Programmes,” Delva and colleagues provide a detailed analysis of how to prioritize the various sub-populations of those infected with HIV for treatment-as-prevention interventions. As they write, prioritizing patients according to CD4 cell count — initiating treatment at 350–500 cells/microliter — may not prevent new infections; patients with lower cell counts tend to have more highly transmissible disease. The cost of such a measure is significant because it would add 20% of the HIV-infected patients to care. Despite its questionable value, however, this approach may be the most acceptable because treatment need and access has historically been determined by CD4 count. The following figure, from Delva and colleagues, shows HIV transmission and mortality by CD4 count.
Prioritizing patients according to viral load might actually make more sense. Evidence from serodiscordant couples shows that infectiousness increases with increasing viral load (ART reduces viral load, which is why those on therapy are less contagious), though not drastically. There is also persuasive data showing that people with a high viral load progress rapidly to AIDS, pointing to a severe need for urgent treatment. But without more solid evidence on its clinical benefit (as opposed to its epidemiological benefit related to the surrounding uninfected population), stratifying by viral load is unlikely to gain traction.
Delva et al also evaluated the usefulness of prioritizing pregnant women, those with active tuberculosis disease, those in a serodiscordant long-term relationship, female sex workers, men who have sex with men, and people who inject drugs. Stable, serodiscordant relationships seem an obvious place to start because of its clear ability to reduce the spread of HIV. Yet data suggest that countries with the highest HIV rates tend to have the lowest rates of such couples, and finding those individuals may be difficult. Plus, prioritizing people in a serodiscordant relationship over those in a concordant relationship (both infected) may meet resistance. Theoretically, prioritizing female sex workers for early ART could have the greatest epidemiological benefit. The approach is also feasible, with previous intervention efforts having met with success. But treatment retention tends to be low among this subgroup, and prioritizing this group would be controversial.
Here is where mathematical modeling comes in (as was covered in the first post here on this collection). Several papers in the PLoS Medicine collection examine ART treatment as prevention using various models, an approach that enables a faster and cheaper analysis than would a clinical trial. According to Eaton et al, who compared 12 different models in their contribution to the collection, although the models vary in structure, complexity, and parameters, “all suggest that ART, at high levels of access and with high adherence, has the potential to substantially reduce new HIV infections.” The models suggest that an ambitious scale-up of earlier ART could have a dramatic short-term epidemiological impact. But when it comes to the long-term benefit, the results are more varied. In the following figure, (B) shows the reduction in HIV incidence by the year 2020 when access is expanded to 80% of the HIV-positive population with 85% retention of care.
If there is one constant note rung throughout this collection, it is that determining the best approach for HIV treatment as prevention is extremely difficult. But the papers also make clear that some change in policy is warranted, that expanding access to ART will, somehow, reduce the spread of HIV.
At least, that is the vision. Kumi Smith and colleagues add their analysis of ecological observations to the mix. With this type of study, observational data are used to shine a light on the link between exposure and outcome at the level of populations, rather than individuals. As the authors write, “[a]lthough we expect an impact of ART at the population level, the magnitude of the effect may not be as great as some hope…” The barriers to treatment as prevention may also diminish its potential, says David Wilson in his paper, “HIV Treatment as Prevention: Natural Experiments Highlight Limits of Antiretroviral Treatment as HIV Prevention.” Below, Wilson shows steps required in order to reduce onward transmission from someone infected with HIV:
The task at hand may be to find the middle ground between the potential benefit shown in many mathematical models and the difficult reality chronicled in natural experiments and ecological analyses. As The HIV Modelling Consortium Treatment as Prevention Editorial Writing Group (including many contributors to this collection) states in an introduction to the collection, “[t]he question of how to best use the tools that have been shown to reduce HIV transmission will likely dominate the field of HIV prevention for the foreseeable future.”