Kazakhstan can achieve ambitious HIV targets despite expected donor withdrawal by combining improved ART procurement mechanisms with allocative and implementation efficiencies

Abstract

Background: Despite a non-decreasing HIV epidemic, international donors are soon expected to withdraw funding from Kazakhstan. Here we analyze how allocative, implementation, and technical efficiencies could strengthen the national HIV response under assumptions of future budget levels.

Methodology: We used the Optima model to project future scenarios of the HIV epidemic in Kazakhstan that varied in future antiretroviral treatment unit costs and management expenditure-two areas identified for potential cost-reductions. We determined optimal allocations across HIV programs to satisfy either national targets or ambitious targets. For each scenario, we considered two cases of future HIV financing: the 2014 national budget maintained into the future and the 2014 budget without current international investment.

Findings: Kazakhstan can achieve its national HIV targets with the current budget by (1) optimally re-allocating resources across programs and (2) either securing a 35% [30%-39%] reduction in antiretroviral treatment drug costs or reducing management costs by 44% [36%-58%] of 2014 levels. Alternatively, a combination of antiretroviral treatment and management cost-reductions could be sufficient. Furthermore, Kazakhstan can achieve ambitious targets of halving new infections and AIDS-related deaths by 2020 compared to 2014 levels by attaining a 67% reduction in antiretroviral treatment costs, a 19% [14%-27%] reduction in management costs, and allocating resources optimally.

Significance: With Kazakhstan facing impending donor withdrawal, it is important for the HIV response to achieve more with available resources. This analysis can help to guide HIV response planners in directing available funding to achieve the greatest yield from investments. The key changes recommended were considered realistic by Kazakhstan country representatives.

Fig 1. Gross domestic product per capita, ART unit costs, and key HIV/AIDS program spending data in selected EECA countries.

This figure highlights key HIV-related spending data from selected countries in the Eastern Europe and Central Asia region. Only countries for which data were available are illustrated. The pie charts represent total HIV spending in 2014, and the bar graphs represent national gross domestic product (GDP) per capita and ART unit costs. The red text within the parentheses represents the proportion of the respective national budget consumed by management costs.

Fig 2. Contour plot of thresholds to achieve national and ambitious targets with varying levels of management cost reductions and treatment cost reductions.

This figure illustrates the estimated reduction in management costs and treatment costs required to achieve i) national targets (light grey region), and ii) ambitious targets (dark grey region) should the annual budget be restricted to a) 2014 levels (Fig 2A), or b) 2014 levels without international donor funding (Fig 2B). The colored contours show the thresholds for percentage reductions in both newly acquired HIV infections and AIDS-related deaths by 2020 compared to 2014 levels. The ‘no increase’ contour is the threshold for satisfying the national targets (and is hence the border for the light grey region), whilst the ‘50% decrease’ contour satisfies the ambitious targets (and is hence the border for the dark grey region). In each simulation, the proportion of the budget dedicated to direct programs is optimally distributed across programs to minimize incidence, minimize deaths, and virtually eliminate MTCT.

Fig 3. Allocations to programs under the status-quo scenario and the realistic scenario to achieve ambitious targets.

This figure illustrates the 2014 allocation to HIV programs in Kazakhstan, alongside the optimal distribution of funds under the realistic scenario to achieve ambitious targets. This bar illustrates the ‘best-fit’ result, whilst uncertainty bounds around the program allocations are presented in Table 1 and S4 Fig.

Fig 4. Epidemiological outcomes under the status-quo scenario and realistic scenario to achieve ambitious targets in 2020 compared to 2014.

This figure illustrates the corresponding epidemiological outcomes that are estimated to arise by implementing the allocations presented in Fig 3 (status-quo scenario, and the optimized realistic scenario to achieve ambitious targets) between 2015 and 2020. In each sub chart, the first column represents the value of the indicator in 2014.