Designing an optimal HIV programme for South Africa: Does the optimal package change when diminishing returns are considered?

Abstract

BackgroundSouth Africa has a large domestically funded HIV programme with highly saturated coverage levels for most prevention and treatment interventions. To further optimise its allocative efficiency, we designed a novel optimisation method and examined whether the optimal package of interventions changes when interaction and non-linear scale-up effects are incorporated into cost-effectiveness analysis.MethodsThe conventional league table method in cost-effectiveness analysis relies on the assumption of independence between interventions. We added methodology that allowed the simultaneous consideration of a large number of HIV interventions and their potentially diminishing marginal returns to scale. We analysed the incremental cost effectiveness ratio (ICER) of 16 HIV interventions based on a well-calibrated epidemiological model that accounted for interaction and non-linear scale-up effects, a custom cost model, and an optimisation routine that iteratively added the most cost-effective intervention onto a rolling baseline before evaluating all remaining options. We compared our results with those based on a league table.ResultsThe rank order of interventions did not differ substantially between the two methods- in each, increasing condom availability and male medical circumcision were found to be most cost-effective, followed by anti-retroviral therapy at current guidelines. However, interventions were less cost-effective throughout when evaluated under the optimisation method, indicating substantial diminishing marginal returns, with ICERs being on average 437% higher under our optimisation routine.ConclusionsConventional league tables may exaggerate the cost-effectiveness of interventions when programmes are implemented at scale. Accounting for interaction and non-linear scale-up effects provides more realistic estimates in highly saturated real-world settings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-017-4023-3) contains supplementary material, which is available to authorized users.

The impact, cost and cost‐effectiveness of oral pre‐exposure prophylaxis in sub‐Saharan Africa: a scoping review of modelling contributions and way forward

“…These studies span from 2007 to 2017 with 42 studies modelling oral PrEP in sub‐Saharan Africa and two studies with non‐specific settings but of relevance for generalized HIV epidemics. Applications of country‐specific modelling of oral PrEP occurred predominantly for South Africa and Kenya , followed by Botswana , Zambia , and one application each for Zimbabwe , Mozambique , Nigeria , and Uganda . Select country‐specific results were additionally presented in two studies .…”

Modelling impact and cost‐effectiveness of oral pre‐exposure prophylaxis in 13 low‐resource countries

“…Several studies have demonstrated that oral PrEP is only cost‐effective when provided to individuals at very high risk of HIV infection . Others have ranked PrEP for cost‐effectiveness along with other HIV interventions within countries . Verguet, et al examined the impact and cost‐effectiveness of PrEP for the general population across 42 sub‐Saharan African countries, finding that it was only cost‐effective for this population in countries with high HIV prevalence and low male circumcision prevalence.…”

Revealed willingness-to-pay versus standard cost-effectiveness thresholds: Evidence from the South African HIV Investment Case

“…Cost effectiveness was expressed as incremental cost across the entire HIV programme per life year saved over 20 years (2016–2035) [ 13 ]. We estimated the epidemiological impact in terms of life years saved relative to the West level 26 life table, a life table representing population survival at very low levels of HIV prevalence [ 14 ].…”

“…The optimization method has been described in detail elsewhere [ 13 ]. It used a rolling baseline that started with the most cost-effective intervention (lowest ICER) and then sequentially added interventions in order of incremental cost-effectiveness, with the baseline re-estimated after the addition of each new intervention.…”

“…It used a rolling baseline that started with the most cost-effective intervention (lowest ICER) and then sequentially added interventions in order of incremental cost-effectiveness, with the baseline re-estimated after the addition of each new intervention. In contrast to an analysis based on standard league table methodology, our approach allowed us to take into account the diminishing marginal returns of each added intervention, thereby decreasing each additional intervention’s cost effectiveness [ 13 ]. By considering South Africa’s entire HIV response and the dynamics of the epidemic, the model took into account the impact of HIV prevention services on reducing the need for treatment, and the impact of treatment on reducing transmission [ 13 ], which reduces the need for future prevention and treatment.…”