This Working Paper should not be reported as representing the views of the IMF.

Choices on discount rates have important implications for the outcomes of economic evaluations of health interventions and policies. In global health, such evaluations typically apply a discount rate of 3% for health outcomes and costs, mirroring guidance developed for high-income countries, notably the USA. The article investigates the suitability of these guidelines for global health [i.e. with a focus on low- and middle-income countries (LMICs)] and seeks to identify best practice. Our analysis builds on an overview of the academic literature on discounting in health evaluations, existing academic or government-related guidelines on discounting, a review on discount rates applied in economic evaluations in global health, and cross-country macroeconomic data. The social discount rate generally applied in global health of 3% annually is inconsistent with rates of economic growth experienced outside the most advanced economies. For low- and lower-middle-income countries, a discount rate of at least 5% is more appropriate, and one around 4% for upper-middle-income countries. Alternative approaches—e.g. motivated by the returns to alternative investments or by the cost of financing—could usefully be applied, dependent on policy context. The current practise could lead to systematic bias towards over-valuing the future costs and health benefits of interventions. For health economic evaluations in global health, guidelines on discounting need to be adapted to take account of the different economic contexts of LMICs.

No abstract

Optima is a software package for modeling HIV epidemics and interventions that we developed to address practical policy and program problems encountered by funders, governments, health planners, and program implementers. Optima's key feature is its ability to perform resource optimization to meet strategic HIV objectives, including HIV-related financial commitment projections and health economic assessments. Specifically, Optima allows users to choose a set of objectives (such as minimizing new infections, minimizing HIV-related deaths, and/or minimizing long-term financial commitments) and then determine the optimal resource allocation (and thus program coverage levels) for meeting those objectives. These optimizations are based on the following: calibrations to epidemiological data; assumptions about the costs of program implementation and the corresponding coverage levels; and the effects of these programs on clinical, behavioral, and other epidemiological outcomes. Optima is flexible for which population groups (specified by behavioral, epidemiological, and/or geographical factors) and which HIV programs are modeled, the amount of input data used, and the types of outputs generated. Here, we introduce this model and compare it with existing HIV models that have been used previously to inform decisions about HIV program funding and coverage targets. Optima has already been used in more than 20 countries, and there is increasing demand from stakeholders to have a tool that can perform evidence-based HIV epidemic analyses, revise and prioritize national strategies based on available resources, set program coverage targets, amend subnational program implementation plans, and inform the investment strategies of governments and their funding partners.

Anna Vassall and colleagues discuss the need for, and challenges facing, innovative and sustainable financing of the HIV response. Please see later in the article for the Editors' Summary

The paper addresses the impact ofHIV/AIDS on per capita output and income, with particular emphasis on the role of labor mobility between the formal and informal sectors, and the impact of the epidemic on investment decisions. The study finds that HIV / AIDS affects both the supply oflabor and the demand for labor in the formal sector. Only if there is a significant rise in the capital-labor ratio, will there be an increase in formal sector employment. However, this is associated with a decline in the rate of return to capital. To the extent that companies respond to this by reducing investment, conventional models underestimate the adverse impact on employment, per capita output, and income. The analysis of the impact ofHIV/AIDS on output is complemented by an assessment of the impact on income.

This Working Paper should not be reported as representing the views of the IMF.

Background The “greying of AIDS” – the aging of the population living with HIV who benefit from antiretroviral treatment (ART) and the emergence of age-related non-communicable diseases (NCDs) – has been well documented. The emerging health systems challenges – eg, the implications of HIV on the disease burden from NCDs on the population level, and the evolving role of HIV as a co-morbidity or co-existing disease of various NCDs – are less well understood. The paper elucidates these challenges by providing a quantitative analysis of HIV-NCD interactions for Botswana. Methods We projected the prevalence of HIV and of selected NCDs in Botswana using demographic and HIV-specific estimates building on data on the state and the dynamics of the HIV epidemic, using the Spectrum modelling software, and extrapolating on estimates of the prevalence of NCDs from the 2015 global burden of disease (GBD). Results HIV has slowed down overall population aging and thus has attenuated the growing burden of many NCDs so far, because cohorts reaching old age have been decimated by AIDS-related mortality in the 1990s and early 2000s. Aging and the rise in the prevalence of NCDs, however, will accelerate rapidly from about 2030 because of reduced attrition of cohorts living with HIV since the start of the ART scale-up in Botswana. While HIV prevalence will decline over time, the health needs of people living with HIV will become more complex. HIV prevalence among the growing populations affected by various important NCDs will not decline for decades, because of the aging of the population living with HIV and interactions between HIV, ART and NCDs. Conclusions Even though HIV prevalence is projected to decline steeply to 2030 because of reduced HIV incidence, the prevalence of HIV among people affected by many of the most important NCDs will increase or barely change. While the health care needs of people living with HIV will increase and become more complex, HIV will also emerge as a key factor complicating the management of the growing burden of NCDs. Health systems will need to prepare for the challenge of large numbers of patients living with both HIV and NCDs.