Getting it right when budgets are tight: Using optimal expansion pathways to prioritize responses to concentrated and mixed HIV epidemics

Technical efficiency of national HIV/AIDS spending in 78 countries between 2010 and 2018: A data envelopment analysis

HIV/AIDS remains a leading global cause of disease burden, especially in low- and middle-income countries (LMICs). In 2020, more than 80% of all people living with HIV (PLHIV) lived in LMICs. While progress has been made in extending coverage of HIV/AIDS services, only 66% of all PLHIV were virally suppressed at the end of 2020. In addition to more resources, the efficiency of spending is key to accelerating progress towards global 2030 targets for HIV/AIDs, including viral load suppression. This study aims to estimate the efficiency of HIV/AIDS spending across 78 countries. We employed a data envelopment analysis (DEA) and a truncated regression to estimate the technical efficiency of 78 countries, mostly low- and middle-income, in delivering HIV/AIDS services from 2010 to 2018. Publicly available data informed the model. We considered national HIV/AIDS spending as the DEA input, and prevention of mother to child transmission (PMTCT) and antiretroviral treatment (ART) as outputs. The model was adjusted by independent variables to account for country characteristics and investigate associations with technical efficiency. On average, there has been substantial improvement in technical efficiency over time. Spending was converted into outputs almost twice as efficiently in 2018 (81.8%; 95% CI = 77.64, 85.99) compared with 2010 (47.5%; 95% CI = 43.4, 51.6). Average technical efficiency was 66.9% between 2010 and 2018, in other words 33.1% more outputs could have been produced relative to existing levels for the same amount of spending. There is also some variation between WHO/UNAIDS regions. European and Eastern and Southern Africa regions converted spending into outputs most efficiently between 2010 and 2018. Rule of Law, Gross National Income, Human Development Index, HIV prevalence and out-of-pocket expenditures were all significantly associated with efficiency scores. The technical efficiency of HIV investments has improved over time. However, there remains scope to substantially increase HIV/AIDS spending efficiency and improve progress towards 2030 global targets for HIV/AIDS. Given that many of the most efficient countries did not meet 2020 global HIV targets, our study supports the WHO call for additional investment in HIV/AIDS prevention and control to meet the 2030 HIV/AIDS and eradication of the AIDS epidemic.

Optima TB: A tool to help optimally allocate tuberculosis spending

Approximately 85% of tuberculosis (TB) related deaths occur in low- and middle-income countries where health resources are scarce. Effective priority setting is required to maximise the impact of limited budgets. The Optima TB tool has been developed to support analytical capacity and inform evidence-based priority setting processes for TB health benefits package design. This paper outlines the Optima TB framework and how it was applied in Belarus, an upper-middle income country in Eastern Europe with a relatively high burden of TB. Optima TB is a population-based disease transmission model, with programmatic cost functions and an optimisation algorithm. Modelled populations include age-differentiated general populations and higher-risk populations such as people living with HIV. Populations and prospective interventions are defined in consultation with local stakeholders. In partnership with the latter, demographic, epidemiological, programmatic, as well as cost and spending data for these populations and interventions are then collated. An optimisation analysis of TB spending was conducted in Belarus, using program objectives and constraints defined in collaboration with local stakeholders, which included experts, decision makers, funders and organisations involved in service delivery, support and technical assistance. These analyses show that it is possible to improve health impact by redistributing current TB spending in Belarus. Specifically, shifting funding from inpatient- to outpatient-focused care models, and from mass screening to active case finding strategies, could reduce TB prevalence and mortality by up to 45% and 50%, respectively, by 2035. In addition, an optimised allocation of TB spending could lead to a reduction in drug-resistant TB infections by 40% over this period. This would support progress towards national TB targets without additional financial resources. The case study in Belarus demonstrates how reallocations of spending across existing and new interventions could have a substantial impact on TB outcomes. This highlights the potential for Optima TB and similar modelling tools to support evidence-based priority setting.

Tuberculosis (TB) remains a leading global cause of death and morbidity, and 85% of deaths occur in countries where resources for TB care and control are limited. Many countries cannot finance all TB interventions or technologies, which means difficult decisions on what to prioritise and publically finance. Modelling tools can help decision-makers set priorities based on evidence, in a systematic and transparent way. This study presents Optima TB, a tool that estimates which allocations of spending across interventions will most likely maximise specified objectives—such as minimising TB deaths, prevalence and incidence. In partnership with local decision-makers and stakeholders, Optima TB was applied in Belarus. Recommendations from the model findings include focussing investment on outpatient rather than inpatient care and actively finding people with TB (e.g. through contact tracing) rather than mass testing of the population. The recommended reallocations of spending could reduce TB prevalence and deaths by up to 45% and 50%, respectively, by 2035 for the same amount of spending. Key stakeholders were engaged throughout the analysis and findings and uncertainty around the results were clearly communicated with decision-makers. The timeliness of the results helped inform national dialogue on TB care reform, among other key policy discussions.

Optimization Models for HIV/AIDS Resource Allocation: A Systematic Review

OBJECTIVE

This study reviews optimization models for human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS) resource allocation.

METHODS

We searched 2 databases for peer-reviewed articles published from January 1985 through August 2019 that describe optimization models for resource allocation in HIV/AIDS. We included models that consider 2 or more competing HIV/AIDS interventions. We extracted data on selected characteristics and identified similarities and differences across models. We also assessed the quality of mathematical disease transmission models based on the best practices identified by a 2010 task force.

RESULTS

The final qualitative synthesis included 23 articles that used 14 unique optimization models. The articles shared several characteristics, including the use of dynamic transmission modeling to estimate health benefits and the inclusion of specific high-risk groups in the study population. The models explored similar HIV/AIDS interventions that span primary and secondary prevention and antiretroviral treatment. Most articles were focused on sub-Saharan African countries (57%) and the United States (39%). There was notable variation in the types of optimization objectives across the articles; the most common was minimizing HIV incidence or maximizing infections averted (87%). Articles that utilized mathematical modeling of HIV disease and transmission displayed variable quality.

CONCLUSIONS

This systematic review of the literature identified examples of optimization models that have been applied in different settings, many of which displayed similar features. There were similarities in objective functions across optimization models, but they did not align with global HIV/AIDS goals or targets. Future work should be applied in countries facing the largest declines in HIV/AIDS funding.

Sharing the costs of structural interventions: What can models tell us?

BACKGROUND

The global HIV response needs to both integrate with the broader health system and tackle the structural drivers of HIV. Cross-sectoral financing arrangements in which different sectors agree to co-finance structural interventions - have been put forward as promising frameworks to address these concerns. However, co-financing arrangements remain rare for HIV, and there is no consensus on how to distribute costs.

METHODS

We use case studies to investigate how structural interventions can be incorporated within three quantitative decision-making frameworks. First, we consider cost-benefit analyses (CBA) using an opioid substitution therapy (OST) program in Armenia; second, we construct a theoretical example to illustrate the lessons game theory can shed on the co-financing arrangements implied by CBA; and third we consider allocative efficiency analyses using needle-syringe programs (NSPs) in Belarus.

RESULTS

A cross-sectoral cost-benefit analysis of OST in Armenia demonstrates that the share of that should be funded by the HIV sector depends on the willingness to pay (WTP) to avert an HIV-related DALY, the long-term cost-benefit ratio, and the HIV risk reduction from OST. For reasonable parameter values, the HIV sector's share ranges between 0-48%. However, the Shapley value--a game-theoretic solution to cost attribution that ensures each sector gains as much or more as they would from acting independently--implies that the HIV sector's share may be higher. In Belarus, we find that the HIV sector should be willing to co-finance structural interventions that would increase the maximal attainable coverage of NSPs, with the contribution again depending on the WTP to avert an HIV-related DALY.

CONCLUSION

Many interventions known to have cross-sectoral benefits have historically been funded from HIV budgets, but this may change in the future. The question of how to distribute the costs of structural interventions is critical, and frameworks that decision-makers use to inform resource allocations will need to take this into account.

Optimal allocation of HIV resources among geographical regions

BACKGROUND

Health resources are limited, which means spending should be focused on the people, places and programs that matter most. Choosing the mix of programs to maximize a health outcome is termed allocative efficiency. Here, we extend the methodology of allocative efficiency to answer the question of how resources should be distributed among different geographic regions.

METHODS

We describe a novel geographical optimization algorithm, which has been implemented as an extension to the Optima HIV model. This algorithm identifies an optimal funding of services and programs across regions, such as multiple countries or multiple districts within a country. The algorithm consists of three steps: (1) calibrating the model to each region, (2) determining the optimal allocation for each region across a range of different budget levels, and (3) finding the budget level in each region that minimizes the outcome (such as reducing new HIV infections and/or HIV-related deaths), subject to the constraint of fixed total budget across all regions. As a case study, we applied this method to determine an illustrative allocation of HIV program funding across three representative oblasts (regions) in Ukraine (Mykolayiv, Poltava, and Zhytomyr) to minimize the number of new HIV infections.

RESULTS

Geographical optimization was found to identify solutions with better outcomes than would be possible by considering region-specific allocations alone. In the case of Ukraine, prior to optimization (i.e. with status quo spending), a total of 244,000 HIV-related disability-adjusted life years (DALYs) were estimated to occur from 2016 to 2030 across the three oblasts. With optimization within (but not between) oblasts, this was estimated to be reduced to 181,000. With geographical optimization (i.e., allowing reallocation of funds between oblasts), this was estimated to be further reduced to 173,000.

CONCLUSIONS

With the increasing availability of region- and even facility-level data, geographical optimization is likely to play an increasingly important role in health economic decision making. Although the largest gains are typically due to reallocating resources to the most effective interventions, especially treatment, further gains can be achieved by optimally reallocating resources between regions. Finally, the methods described here are not restricted to geographical optimization, and can be applied to other problems where competing resources need to be allocated with constraints, such as between diseases.

Measuring financial risk protection in health benefits packages: scoping review protocol to inform allocative efficiency studies

INTRODUCTION

To progress towards Universal Health Coverage (UHC), countries will need to define a health benefits package of services free at the point of use. Financial risk protection is a core component of UHC and should therefore be considered a key dimension of health benefits packages. Allocative efficiency modelling tools can support national analytical capacity to inform an evidence-based selection of services, but none are currently able to estimate financial risk protection. A review of existing methods used to measure financial risk protection can facilitate their inclusion in modelling tools so that the latter can become more relevant to national decision making in light of UHC.

METHODS AND ANALYSIS

This protocol proposes to conduct a scoping review of existing methods used to measure financial risk protection and assess their potential to inform the selection of services in a health benefits package. The proposed review will follow the methodological framework developed by Arksey and O'Malley and the subsequent recommendations made by Levac et al. Several databases will be systematically searched including: (1) PubMed; (2) Scopus; (3) Web of Science and (4) Google Scholar. Grey literature will also be scanned, and the bibliography of all selected studies will be hand searched. Following the selection of studies according to defined inclusion and exclusion criteria, key characteristics will be collected from the studies using a data extraction tool. Key characteristics will include the type of method used, geographical region of focus and application to specific services or packages. The extracted data will then be charted, collated, reported and summarised using descriptive statistics, a thematic analysis and graphical presentations.

ETHICS AND DISSEMINATION

The scoping review proposed in this protocol does not require ethical approval. The final results will be disseminated via publication in a peer-reviewed journal, conference presentations and shared with key stakeholders.

How should HIV resources be allocated? Lessons learnt from applying Optima HIV in 23 countries

Introduction

With limited funds available, meeting global health targets requires countries to both mobilize and prioritize their health spending. Within this context, countries have recognized the importance of allocating funds for HIV as efficiently as possible to maximize impact. Over the past six years, the governments of 23 countries in Africa, Asia, Eastern Europe and Latin America have used the Optima HIV tool to estimate the optimal allocation of HIV resources.

Methods

Each study commenced with a request by the national government for technical assistance in conducting an HIV allocative efficiency study using Optima HIV. Each study team validated the required data, calibrated the Optima HIV epidemic model to produce HIV epidemic projections, agreed on cost functions for interventions, and used the model to calculate the optimal allocation of available funds to best address national strategic plan targets. From a review and analysis of these 23 country studies, we extract common themes around the optimal allocation of HIV funding in different epidemiological contexts.

Results and discussion

The optimal distribution of HIV resources depends on the amount of funding available and the characteristics of each country's epidemic, response and targets. Universally, the modelling results indicated that scaling up treatment coverage is an efficient use of resources. There is scope for efficiency gains by targeting the HIV response towards the populations and geographical regions where HIV incidence is highest. Across a range of countries, the model results indicate that a more efficient allocation of HIV resources could reduce cumulative new HIV infections by an average of 18% over the years to 2020 and 25% over the years to 2030, along with an approximately 25% reduction in deaths for both timelines. However, in most countries this would still not be sufficient to meet the targets of the national strategic plan, with modelling results indicating that budget increases of up to 185% would be required.

Conclusions

Greater epidemiological impact would be possible through better targeting of existing resources, but additional resources would still be required to meet targets. Allocative efficiency models have proven valuable in improving the HIV planning and budgeting process.

Cost and cost-effectiveness analysis of pre-exposure prophylaxis among men who have sex with men in two hospitals in Thailand

INTRODUCTION

In 2014, the Government of Thailand recommended pre-exposure prophylaxis (PrEP) as an additional HIV prevention programme within Thailand's National Guidelines on HIV/AIDS Treatment Prevention. However, to date implementation and uptake of PrEP programmes have been limited, and evidence on the costs and the epidemiological and economic impact is not available.

METHODS

We estimated the costs associated with PrEP provision among men having sex with men (MSM) participating in a facility-based, prospective observational cohort study: the Test, Treat and Prevent HIV Programme in Thailand. We created a suite of scenarios to estimate the cost-effectiveness of PrEP and sensitivity of the results to the model input parameters, including PrEP programme effectiveness, PrEP uptake among high-risk and low-risk MSM, baseline and future antiretroviral therapy (ART) coverage, condom use, unit cost of delivering PrEP, and the discount rate.

RESULTS

Drug costs accounted for 82.5% of the total cost of providing PrEP, followed by lab testing (8.2%) and personnel costs (7.8%). The estimated costs of providing the PrEP package in accordance with the national recommendation ranges from US$223 to US$311 per person per year. Based on our modelling results, we estimate that PrEP would be cost-effective when provided to either high-risk or all MSM. However, we found that the programme would be approximately 32% more cost-effective if offered to high-risk MSM than it would be if offered to all MSM, with an incremental cost-effectiveness ratio of US$4,836 per disability-adjusted life years (DALY) averted and US$7,089 per DALY averted respectively. Cost-effectiveness acceptability curves demonstrate that 80% of scenarios would be cost-effective when PrEP is provided solely to higher-risk MSM.

CONCLUSION

We provide the first estimates on cost and cost-effectiveness of PrEP in the Asia-Pacific region, and offer insights on how to deliver PrEP in combination with ART. While the high drug cost poses a budgeting challenge, incorporating PrEP delivery into an existing ART programme could be a cost-effective strategy to prevent HIV infections among MSM in Thailand.