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Prioritization is Crucial for the Success of PrEP, Model Confirms

A mathematical model developed by researchers at Imperial College in London, based on what would happen if pre-exposure prophylaxis (PrEP) was introduced into a high-prevalence region in Kenya, shows that PrEP could be a "runaway success" or a "runaway failure," depending on a number of factors, according to a report in the March 27 edition of AIDS. These factors include adherence, whether new longer-lasting HIV drugs are used, the cost of drugs, and the overall efficiency of distribution.

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But the model shows -- as other cost-effectiveness models have done-- that by far the most influential determinant of PrEP's effectiveness is whether it is targeted accurately toward those at highest risk of HIV. A program with a fixed budget that was targeted poorly would be prohibitively expensive in terms of the money spent to prevent 1 infection, and would actually prevent few infections because a high proportion of people would be taking PrEP who would not have acquired HIV anyway.

The Model

The model described by Íde Cremin and Timothy Hallett assumes that a PrEP program is introduced into the Nyanza province of Kenya, which has an HIV prevalence (as of 2009) of nearly 14%, or about 370,000 people out of an adult population of 2.65 million.

The model looks at what would happen if a PrEP program with a fixed annual budget of US$20 million was introduced in 2015 and scaled up to maximum size by 2020. Its base case assumes a fixed cost per person per year for PrEP of $250 (with treatment for those acquiring HIV costing more or less the same) and that users will spend an average of 5 years on PrEP. It stratifies the population by gender, male circumcision status (Nyanza hosts a large voluntary medical male circumcision program), and low-, medium-, or high-risk behavior.

The model assumes PrEP with full adherence is 90% efficacious, but also assumes that adherence is imperfect; it stratifies the population into half with adequate adherence (60% of doses taken) and half with poor adherence (only 20% of doses).

This base-case model finds that 24,603 HIV infections would be prevented between 2015 and 2025, with a steady 3400 infections prevented per year by 2020. This would cost roughly $6000 per HIV infection averted. Although this cost is not calculated, this represents maybe 15-20 years' worth of HIV treatment, had the person acquired HIV.

What If Things Change?

The researchers then posit what would happen if certain positive or negative developments happen over time as the PrEP project matures:

• What if the program becomes more efficient at targeting those at highest risk, so the proportion of people taking PrEP who are at highest risk rises over 5 years from 50% to 100%? Conversely, what if it shrinks to zero (i.e., no prioritization)?
• What if adherence improves, to 90% in people with "good" adherence and to 70% in people with "poor" adherence? Conversely, what would happen if it declines to 30% and zero respectively?
• What if the cost per unit of PrEP declines to $125 or, conversely, increases to $500?
• What if the use of longer-lasting drugs improves PrEP efficacy in people with good adherence by 64% more than baseline efficacy, and even by 44% in people with poor adherence?
• What if economies of scale increase the number of people the program can put on PrEP per year over time? Or what if, conversely, decreased efficiency (maybe due to the cost of reaching ever harder-to-reach populations) reduces that figure?

The model finds that good prioritization would have by far the biggest influence on the success or failure of PrEP. Good prioritization would reduce the cost per infection averted from $6000 to $2060 and increase the annual number of infections averted to over 9000. Conversely, lack of prioritization would increase the cost per infection averted to $36,360 and reduce the annual number of infections to a couple hundred at best.

The other changes posited would have less of an impact. Improved adherence, as above, would decrease the cost per infection averted to $4000 and increase the number of infections averted to 7300 a year, while poor adherence would increase the unit cost to $9000 and decrease infections prevented to 1400 a year. Economies of scale could decrease the cost to $4700 and increase infections averted to 7300, while increased inefficiency would increase the cost to $7200 and decrease infections averted to 2150.

The model finds that the introduction of longer-lasting drugs or formulations would not, in this model, make much of a difference: perhaps only 400 or so more infections prevented a year and $200 less in unit cost.

Comments and Conclusions

The researchers comment on the importance of prioritization and suggest the introduction of a "risk score" as was used in the recent Partners PrEP Demonstration Project, but also praise the success of community-based projects in India, such as Avahan, where peer-led outreach, community mobilization, concentration on HIV "hotspots" and good data collection have optimized the prioritization of those at most need of HIV prevention measures. However, they also point out the difficulty of prioritizing "highly mobile and marginalized" populations, and say that if stigma were to arise against people taking PrEP, this would be an additional barrier. 

A recent report from Avahan shows that the program’s efficiency may have started to decline after its funding was handed over to the Indian government, showing the importance of skilled management and "bottom-up" priorities informed by community members.

Implementation science, the authors say, will have a crucial role in ensuring that PrEP becomes a "runaway success" rather than a "runaway failure."

6/10/15

Reference

I Cremin and TB Hallett TB. Estimating the range of potential epidemiological impact of pre-exposure prophylaxis: run-away success or run-away failure? AIDS 29(6):733-738. March 27, 2015.