In the Interests of Time: Improving HIV Allocative Efficiency Modelling Via Optimal Time-Varying Allocations

Introduction

International investment in the response to HIV and AIDS has plateaued and its future level is uncertain. With many countries committed to ending the epidemic, it is essential to allocate available resources efficiently over different response periods to maximize impact. The objective of this study is to propose a technique to determine the optimal allocation of funds over time across a set of HIV programmes to achieve desirable health outcomes.

Methods

We developed a technique to determine the optimal time-varying allocation of funds (1) when the future annual HIV budget is pre-defined and (2) when the total budget over a period is pre-defined, but the year-on-year budget is to be optimally determined. We use this methodology with Optima, an HIV transmission model that uses non-linear relationships between programme spending and associated programmatic outcomes to quantify the expected epidemiological impact of spending. We apply these methods to data collected from Zambia to determine the optimal distribution of resources to fund the right programmes, for the right people, at the right time.

Results and discussion

Considering realistic implementation and ethical constraints, we estimate that the optimal time-varying redistribution of the 2014 Zambian HIV budget between 2015 and 2025 will lead to a 7.6% (7.3% to 7.8%) decrease in cumulative new HIV infections compared with a baseline scenario where programme allocations remain at 2014 levels. This compares to a 5.1% (4.6% to 5.6%) reduction in new infections using an optimal allocation with constant programme spending that recommends unrealistic programmatic changes. Contrasting priorities for programme funding arise when assessing outcomes for a five-year funding period over 5-, 10- and 20-year time horizons.

Conclusions

Countries increasingly face the need to do more with the resources available. The methodology presented here can aid decision-makers in planning as to when to expand or contract programmes and to which coverage levels to maximize impact.

Below:  The percentage of infections averted between 2015 and 2025 for each of the scenarios shown in Figure 1 compared with a baseline of maintaining 2014 spending. The uncertainty bars were determined by repeating the optimization process 40 times using an ensemble of 40 projections within the uncertainty bounds of the model calibration with an ensemble of 40 cost-outcome curves within their respective uncertainty bounds (see the Supplementary file for figures illustrating the uncertainty in model calibration and the cost-outcome curves).

Below:  Annual spending on VMMC programmes and the associated change in prevalence of circumcised men. In both optimized scenarios (green and blue curves), implementation constraints (where programme scale-up/down is restricted to a maximum of 30% per year) and ethical constraints (where ART and PMTCT funding cannot be decreased) are applied. In the scenario represented by the green curve, total annual spending is fixed at 2014 levels. In this case, a large initial scale-up of the VMMC programme is not attainable because of the limited availability of unreserved funding and restrictions on programme scale-up/down. Thus, the optimal solution does not prioritize this programme. In the scenario represented by the blue curve, total annual spending is optimally determined such that total spending across the 2015 to 2025 period is the same as in all other scenarios. In this case, total annual spending is initially increased to allow for the initial rapid scale-up of the VMMC programme. Although VMMC spending is later rapidly scaled down, the proportion of circumcised men in this scenario remains considerably higher than in other scenarios.

Full article at:   http://goo.gl/PdjqPJ

By:  Andrew J Shattock,§,1 Cliff C Kerr,1,2,3 Robyn M Stuart,1,2,4 Emiko Masaki,5 Nicole Fraser,5 Clemens Benedikt,5Marelize Gorgens,5 David P Wilson,1,2 and Richard T Gray1

¹The Kirby Institute, University of New South Wales, Sydney, Australia

²The Burnet Institute, Melbourne, Australia

³School of Physics, University of Sydney, Sydney, Australia

⁴Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark

⁵The World Bank Group, Washington DC, USA

^§Corresponding author: Andrew J Shattock, The Kirby Institute, University of New South Wales, Level 6, Wallace Wurth Building, Kensington, Sydney, NSW 2052, Australia. Tel: +61 (0)2 9385 0900. (Email: ua.ude.wsnu.ybrik@kcottahsa)

More at:  https://twitter.com/hiv insight

And: http://twitter.com/Prison Health