Setting Performance Standards for a Cost-Effective Human Immunodeficiency Virus Cure Strategy in South Africa

Why and where an HIV cure is needed and how it might be achieved

Despite considerable global investment, only 60% of people who live with HIV currently receive antiretroviral therapy. The sustainability of current programmes remains unknown and key incidence rates are declining only modestly. Given the complexities and expenses associated with lifelong medication, developing an effective curative intervention is now a global priority. Here we review why and where a cure is needed, and how it might be achieved. We argue for expanding these efforts from resource-rich regions to sub-Saharan Africa and elsewhere: for any intervention to have an effect, region-specific biological, therapeutic and implementation issues must be addressed.

Applying the Behavioural and Social Sciences Research (BSSR) Functional Framework to HIV Cure Research

INTRODUCTION

The search for an HIV cure involves important behavioural and social processes that complement the domains of biomedicine. However, the field has yet to tap into the full potential of behavioural and social sciences research (BSSR). In this article, we apply Gaist and Stirratt's BSSR Functional Framework to the field of HIV cure research.

DISCUSSION

The BSSR Functional Framework describes four key research domains: (1) basic BSSR (understanding basic behavioural and social factors), (2) elemental BSSR (advancing behavioural and social interventions), (3) supportive BSSR (strengthening biomedically focused clinical trials), and (4) integrative BSSR (building multi-disciplinary combination approaches for real-world implementation). In revisiting and applying the BSSR Functional Framework, we clarify the importance of BSSR in HIV cure research by drawing attention to such things as: how language and communication affect the meaning of "cure" to people living with HIV (PLHIV) and broader communities; how cure affects the identity and social position of PLHIV; counselling and support interventions to address the psychosocial needs and concerns of study participants related to analytical treatment interruptions (ATIs); risk reduction in the course of ATI study participation; motivation, acceptability, and decision-making processes of potential study participants related to different cure strategies; HIV care providers' perceptions and attitudes about their patients' participation in cure research; potential social harms or adverse social events associated with cure research participation; and the scalability of a proven cure strategy in the context of further advances in HIV prevention and treatment. We also discuss the BSSR Functional Framework in the context of ATIs, which involve processes at the confluence of the BSSR domains.

CONCLUSIONS

To move HIV cure regimens through the translational research pathway, attention will need to be paid to both biomedical and socio-behavioural elements. BSSR can contribute an improved understanding of the human and social dimensions related to HIV cure research and the eventual application of HIV cure regimens. The BSSR Functional Framework provides a way to identify advances, gaps and opportunities to craft an integrated, multi-disciplinary approach at all stages of cure research to ensure the real-world applicability of any strategy that shows promise.

Maximising the global health impact of future HIV cure-related interventions through advance planning

Thinking about public health impact should inform HIV curative investigations. Should an effective HIV cure or sustained viral remission intervention emerge from ongoing investigations, implementation strategies aimed at ensuring global access will be needed if these approaches are to be impactful, and planning accordingly makes sense now. Specifically, we discuss three key access barriers to future cure-related interventions: high cost of the strategy; non-financial challenges to procurement, distribution and point-of-care delivery; and non-adherence and the need for long-term monitoring. As we argue, plans and decision-making for overcoming each of these barriers will need to be developed in advance. An evaluation of remaining barriers and likely global impact of the leading strategies under investigation should inform decisions on which strategy might receive funding priority. Among the strategies being investigated, implementation barriers for latency-reversing agents, immunotherapy and combination antiretroviral therapy (ART) may be overcome on a global scale with some effort. Overcoming implementation barriers for medically complex and high-risk interventions, such as stem cell and, to some degree, gene therapy, may be less feasible.

Implementing Generalized Additive Models to Estimate the Expected Value of Sample Information in a Microsimulation Model: Results of Three Case Studies

BACKGROUND

The expected value of sample information (EVSI) can help prioritize research but its application is hampered by computational infeasibility, especially for complex models. We investigated an approach by Strong and colleagues to estimate EVSI by applying generalized additive models (GAM) to results generated from a probabilistic sensitivity analysis (PSA).

METHODS

For 3 potential HIV prevention and treatment strategies, we estimated life expectancy and lifetime costs using the Cost-effectiveness of Preventing AIDS Complications (CEPAC) model, a complex patient-level microsimulation model of HIV progression. We fitted a GAM-a flexible regression model that estimates the functional form as part of the model fitting process-to the incremental net monetary benefits obtained from the CEPAC PSA. For each case study, we calculated the expected value of partial perfect information (EVPPI) using both the conventional nested Monte Carlo approach and the GAM approach. EVSI was calculated using the GAM approach.

RESULTS

For all 3 case studies, the GAM approach consistently gave similar estimates of EVPPI compared with the conventional approach. The EVSI behaved as expected: it increased and converged to EVPPI for larger sample sizes. For each case study, generating the PSA results for the GAM approach required 3 to 4 days on a shared cluster, after which EVPPI and EVSI across a range of sample sizes were evaluated in minutes. The conventional approach required approximately 5 weeks for the EVPPI calculation alone.

CONCLUSION

Estimating EVSI using the GAM approach with results from a PSA dramatically reduced the time required to conduct a computationally intense project, which would otherwise have been impractical. Using the GAM approach, we can efficiently provide policy makers with EVSI estimates, even for complex patient-level microsimulation models.