HIV/AIDS is one of the leading global causes of morbidity and mortality . Despite substantial successes, high rates of new infections and AIDS deaths persist. Structural factors – including poverty and limited livelihood options, stigma and discrimination, gender inequality and violence, among others – help drive and sustain the epidemic, as well as undermine the effectiveness of proven HIV interventions . There is, therefore, renewed interest in interventions that seek to address such factors [2,3] through an expanding range of structural interventions, both to modify the broader socioeconomic environment that shapes HIV risk and to enhance the uptake and effectiveness of core HIV prevention and treatment services [4–6]. Although evidence of the effectiveness of these interventions is limited, a few rigorous studies have demonstrated the potential of enhanced microfinance or cash transfer schemes to reduce HIV-related risk factors and ultimately HIV infections, while simultaneously improving other development indicators [7,8].
The changing HIV funding landscape makes the argument for investments in structural interventions all the more compelling. After a decade of unprecedented investments, external HIV financing is flat-lining, while domestic resources are increasingly expected to sustain and scale up national responses [9–12]. This shift is framed as an opportunity for more ‘shared global responsibility’ and a more prioritized investment approach [13,14]. No longer insulated by earmarked external funding , HIV programmes in resource-limited settings will increasingly compete with other health and development priorities for resources. In this context, structural interventions with multiple outcomes could become more attractive. Rather than displacing financing to other sectors, HIV funds that support structural approaches could leverage such resources, catalyzing synergistic investments across health and development sectors, as promoted by the HIV investment framework and several other policy agendas and academic works [3,13,16–22].
Despite the potential of structural interventions, there is a risk that they will not be prioritized within HIV programme resources, given the perception that they are beyond the remit of the HIV ‘sector’ [23–25]. This concern is compounded by the fact that, conventionally, investment decisions for HIV are expected to be informed by cost-effectiveness analyses (CEAs) [26–29]. These compare the costs of HIV programmes with their direct HIV outcomes only, such as HIV infections averted or life years saved. CEA typically does not factor in the multitude of health cofactors or comorbidities that are known to influence the complexity of HIV transmission, nor does it incorporate complementarities between interventions and positive spillovers . The alternative and more comprehensive approach is cost–benefit analysis (CBA), embedded in welfare economics, which is concerned with whether social benefits generated by an intervention outweigh its costs. Both social benefits and costs are measured in monetary units. This approach is very rarely used in HIV priority-setting discussions [31,32]. However, the use of cost-effectiveness rather than CBAs could potentially result in suboptimal investment choices for interventions with multiple forms of benefit . In economic terms, this would represent a ‘welfare loss’. Current budgeting arrangements further promote this silo approach, as they rarely encourage sectors to explicitly factor in the costs and benefits of their resource allocation decisions to other sectors [34–38].
Given the multiple interactions between HIV outcomes and broader health and development interventions, allocating HIV resources on the basis of HIV cost-effectiveness alone may not be optimal. To investigate this hypothesis, we examine the consequences of alternative approaches to resource allocation, based on a case study. Our analysis seeks more specifically to explore the extent to which the current approach – using HIV-focused cost-effectiveness decision rules – could lead to suboptimal HIV financing decisions; and whether there may be different ways in which the HIV sector could cofinance structural interventions.
Materials and methods
We used data from the Zomba cash transfer trial in Malawi, which provides proof-of-concept that altering socioeconomic contexts can be an effective strategy to prevent HIV. The cash transfer was provided between 2008 and 2009 to 1225 girls (ages 13–22 years) and their households, with payments being conditional upon school attendance for a subsample of 506 girls. At 18 months follow-up, HIV prevalence among schoolgirls in the intervention and control groups was compared, suggesting an adjusted reduction in prevalent HIV of 64% among those who were already in school at baseline [adjusted odds ratio (aOR) 0.36, 95% confidence interval (CI) 0.14–0.91] . In addition, significant reductions in prevalent Herpes simplex virus type two (HSV-2), school drop-out, teen pregnancy and depression were observed, as well as improvements in school enrolment, attendance and English test scores [7,39–41].
Estimating impact and costs
Although we did not aim to conduct a detailed economic evaluation of this intervention, we nevertheless needed to estimate its costs and impacts to illustrate the different financing approaches. The incremental impact for key indicators was calculated from the posttrial difference between intervention and control groups, multiplied by the number of girls in the impacted (sub-)group. We then translated the units of health outcomes into disability-adjusted life years (DALYs) averted, using standard formulae for HIV and estimates from the literature for the other health outcomes (see Technical appendix, http://links.lww.com/QAD/A421). Baird et al.  estimated that in a nontrial setting and at scale, the intervention could be implemented at an incremental financial provider cost of US$90 per beneficiary (B. Ozler, personal communication). All costs were adjusted to 2009US$.
Different approaches to deciding whether to finance the intervention
We compared the application of three approaches for deciding whether to finance a structural intervention to keep adolescent girls in school. In the first approach, HIV and non-HIV budget holders conduct a joint cross-sectoral CBA and fund the intervention if the benefits outweigh the costs. This should lead to the most efficient allocation across sectors. In the second silo approach, each budget holder considers the cost-effectiveness of the intervention in terms of their own objectives and funds the intervention on the basis of their sector-specific thresholds of what is cost-effective or not. In the third cofinancing approach, budget holders use CEA to determine how much they would be willing to contribute towards the intervention, assuming that other sectors cover the remaining implementation costs.
Cross-sectoral cost–benefit analysis
In the CBA approach, the decision rule is simple: investing in an intervention with a net benefit is efficient. In addition to HIV benefits, education and other health benefits are included and converted into monetary units. To estimate these, we used long-term benefit-to-cost ratios found in the literature on conditional cash transfers for school girls in developing countries . These estimate a range from 3.5 to 26 of benefits to costs achieved through increased future earnings and reduced future child mortality . HIV benefits were monetized by valuing HIV DALYs at US$1000 (in line with the other health benefits)  and discounted lifetime costs of antiretroviral therapy (ART) from South Africa  were partly adjusted to estimate cost savings from each HIV infection averted.
The silo approach requires the use of incremental cost-effectiveness ratios (ICERs). These are estimated when an intervention costs and achieves more than the status quo, as the ratio of the additional costs to the additional benefit. The decision rule is then based on comparing this ICER to a standard cost-effectiveness threshold, or willingness to pay (WTP), per HIV outcome . The WHO has set this WTP threshold at a cost per DALY averted below gross domestic product (GDP) per capita [46–48]. This is primarily derived from a human capital approach, whereby a year of life is valued as an individual's economic productivity . Although there are a range of other approaches , the WHO benchmark was taken as it is commonly used in economic evaluations of HIV interventions [29,50,51].
For the case study, the maximum contribution each (sub-) sector would be willing to make towards the intervention was calculated as the impact per sector multiplied by its WTP threshold for that outcome unit. For example, for health outcomes, the maximum contribution was the number of DALYs averted multiplied by GDP per capita (WHO threshold). As we did not find established cost-effectiveness thresholds in education [52–54], the highest ICER per education outcome found in previous economic evaluations in sub-Saharan Africa  was used as the threshold.
As illustrated in Fig. 1, the HIV budget holder would fully fund intervention A, which averts a quantity Q A of HIV-related DALYs at a total cost of C A, and therefore has an ICER below the threshold (R T). Conversely, the ICER of intervention B is above the threshold, meaning that, from an HIV perspective, B would not be considered for investment.
Claxton et al.  propose an alternative decision approach to overcome the challenge posed by interventions with cross-sectoral costs and impacts, wherein CBA is not feasible, coined the ‘compensation test’. If other sectors can compensate the implementing sector for its surplus (or net) cost (i.e. the cost over and above the value of the benefits to the implementer), then the intervention should be funded. Our approach is slightly different in that we propose to use CEA-based thresholds and to have actual, not hypothetical, compensation through a cofinancing mechanism if the sum of each sector's WTP for its specific benefits is greater than the intervention's cost, but no single sector is willing to pay the full implementation cost. Whereas CBA approaches tend to capture multiple long-term economic benefits, the CEA-based approaches often relate more to immediate intervention outcomes , which is likely to be closer to financing decisions in practice. Similar to intervention B in Fig. 1, this would amount to the HIV sector financing up to C T, as long as other sectors contribute C B–C T to enable implementation.
We then compare the silo and cofinancing approaches by estimating the welfare loss in relation to the allocation obtained under the optimal cross-sectoral CBA approach. This was estimated as the net benefit foregone by not implementing an efficient intervention. The assumption is that the alternative use of resources for each sector is to do nothing.
Approaches to determine the HIV share
In principle, the share the HIV sector could be willing to pay would be equivalent to its threshold (maximum WTP). However, in cases in which benefits substantially outweigh costs, it may be possible to invest less HIV funds. In this case, the HIV sector could establish its minimum WTP as the residual amount that other sectors would not cover, that is total costs minus the sum of other sectors’ WTP, as long as this is below C T.
Another approach would be for the HIV sector to pay its ‘fair share’ of the costs, based on the share of HIV benefits (and treatment cost savings) in total benefits estimated by the cross-sectoral CBA . We estimate shares with these two approaches.
Given that the case study is merely illustrative of various financing approaches, we explore how our results are dependent on changes in intervention cost, monetary valuation of an HIV DALY, WTP thresholds applied and the use of the intervention's weighted effect on the HIV/sexually transmitted infection (STI) and reproductive health indicators.
Further details on all the parameters used and a technical description of methods can be found in the Technical appendix, http://links.lww.com/QAD/A421.
As summarized in Table 1, we estimate that the intervention averted 208 DALYs by averting six HIV infections, 19 HSV-2 infections, 10 teen pregnancies and 46 depression cases. In terms of education objectives, the intervention led to 193 baseline drop-outs reenrolling in school, 77 additional years of school attendance and 24 drop-outs averted. Educational attainment was also improved among baseline school girls (conditional arm).
The 18-month intervention targeting 1225 beneficiaries cost an estimated US$110 250 (see Table 2). Discounted treatment costs saved from the prevented HIV infections are about US$35 966. We estimate a benefit–cost ratio of 6.4. In the cross-sectoral CBA approach, this intervention would therefore be financed, generating a long-term net benefit of US$404 088. In all the sensitivity analyses, the intervention would be funded.
Table 3 presents the WTP estimates from converting the short-term trial outcomes into total WTP per subsector. We find that the HIV sector would be willing to pay US$31 732 for this intervention, whereas the other health subsectors would contribute US$66 621 and the education sector US$62 393.
With the silo approach, where sectors budget in isolation without considering other sectors’ benefits, none of the (sub-)sectors would be willing to fund the intervention. The welfare loss (or discounted net benefit forgone) is US$404 088. However, with the cofinancing approach, the sum of each subsector's maximum contributions would be greater than the full implementation cost. The intervention would therefore be funded, generating the long-term net benefit and no welfare loss.
The sensitivity analyses in Table 3 clearly show that financing outcomes for the silo and cofinancing approaches are very sensitive to the total intervention cost and the WTP thresholds per sector. In the higher cost scenario, the intervention would no longer be attractive, even with cofinancing. If the health threshold was increased to WHO's upper bound (three times GDP/capita), the non-HIV health sector would fund the intervention. The likelihood that the intervention would be financed will also be greatly influenced by the education sector's WTP. By assuming the lowest ICER in the education literature as the opportunity cost of the investment, such a scheme would not be considered, even with cofinancing. Using the weighted intervention effect, the HIV budget holder would be willing to cover up to 68% of the costs.
From an HIV perspective, we find that the HIV sector's share would be at most 29% in the cofinancing scenario (range: 12–86%). However, given the other sectors’ contributions, the HIV budget may not need to be tapped at all, as there would be no financing gap left by other sectors.
With the ‘fair share’ approach (Table 2), we estimated total long-term benefits and cost savings of US$514 338, of which 25% were HIV-related (range: 4–57%). By apportioning intervention costs using this figure, we find that the HIV share would be about US$27 773. This represents a cost per HIV DALY averted of US$297, just below WHO's threshold.
This study explored financing outcomes for a structural HIV intervention, based on different decision approaches. We find that allocatively efficient structural interventions may be less likely to be prioritized, financed and taken to scale where sectors evaluate their options in isolation. Existing approaches for assessing the value for money of interventions with multiple outcomes seek to internalize the external benefits, thereby broadening to a societal perspective [35,36,58], but are not at present extensively used in resource allocation by HIV decision-makers. A cofinancing approach, on the other hand, also minimizes welfare loss and could potentially be incorporated in a sector budgeting perspective. Decision rules based on cost-effectiveness thresholds could still support this approach as a potential method to explore the range of contributions from different sector budgets.
Our findings suggest that cofinancing would be worth considering for programmes that are relatively low cost, but for which no sector is willing or able to finance the full costs. It may also only work if WTP thresholds from each sector's perspective are clearly defined and are solely based on their own objectives. For example, if measures of HIV outcomes are used that include wider social benefits (not just welfare gains from disability), there may be the risk of double-counting benefits. Although WTP for DALYs is relatively well defined, we were not able to identify similar international WTP thresholds for the education sector, and thus may overestimate the education sector's WTP. That being said, several potential poverty reduction and gender equity benefits from such an intervention were not measured by the trial and could have offset this effect.
It should also be noted that the use of normative cost-effectiveness thresholds as decision rules in health has been questioned [29,45] and even WHO's lower threshold is perceived as being too high to serve as a useful decision rule in many low and middle-income settings. Nationally determined thresholds could overcome this.
Another concern is that these thresholds may not reflect sectors’ budget constraints. However, in this case, affordability may not be a major issue. On the basis of a previous analysis, the annual cost of this cash transfer scheme targeting all poor girls in Malawi currently in secondary school  would be about US$3.2 million. The education sector's maximum share of 57% or US$1.8 million would represent 0.8% of the national education budget in 2011/2012, while the health sector's share (US$1.9 million) would be 0.9% of the health budget . Although not necessarily required, the HIV sector's maximum share of 29% or US$928 000 would claim 1.2% of the national HIV budget . Clearly, what is important is the relative effectiveness of investing these amounts in the next best HIV, health or education programme, but the investment as such does not appear unsustainable.
Using a cofinancing approach has implications for the design of HIV interventions, because certain elements could be particularly critical to specific sector objectives and thus important for the financial viability of the intervention, even when not directly beneficial for HIV itself. For example, removing the conditionality of the cash transfer may reduce the cost of the intervention , without impacting HIV outcomes, but may affect educational outcomes, making it less attractive for the education sector and therefore less likely to be cofinanced.
Cofinancing may provide an opportunity to realize development synergies, but it will require effective cross-sectoral coordination mechanisms for planning, implementation and financing. These may entail transaction costs that could influence the cost–benefit equation. There are several possible ways to achieve this. The first-best (and more efficient) approach would be for budget allocations to structural interventions to be incorporated at a centralized Ministry of Finance/Treasury level, before budgets are allocated to sectors. This may be possible as part of joint public expenditure planning processes (e.g. Medium-Term Expenditure Frameworks), but in practice may fall through the gaps given their complexity. A second-best scenario could involve setting up a basket-funding mechanism, whereby other sectors become donors of a programme that would be implemented by a single line ministry. Some examples of such joint budgeting initiatives for health and social care can be found in high-income countries [61–63], generally targeting specific patient groups or broader health promotion efforts. In countries where donor funds are important, this could also be a mechanism by which multilateral or bilateral aid earmarked for HIV is channelled towards structural approaches. National AIDS Coordinating Authorities operating as supraministerial and cross-sectoral coordination bodies in several countries could play a key role in facilitating such processes and serving as an example for other cross-sectoral issues .
In our analysis, we modelled the share from the HIV budget, assuming that different sectors adopt a cooperative stance. If sectors are more combative, negotiation could lead to them understating their WTP to let others cover the costs, leading to unfunded or underfunded interventions, akin to a coordination game such as ‘chicken’ in game theory. This may be exacerbated where other sectors consider that the HIV response has received a disproportionate amount of external financing . Other governance challenges are to be expected related to the feasibility of sector line ministries and sector-orientated donors agreeing to pool budgets, as this would imply a loss of control for some.
Whichever institutional approach is adopted, the cofinancing approach is likely to be data-hungry, as evidence of impact across sectors is required. Findings will also depend on which benefits are evaluated and modelled, as the fewer benefits considered, the higher the HIV share. As a starting point, interdisciplinary evaluation approaches, building on an evidence-based theory of change, could be a way to ensure that the most plausible benefits are captured. Another concern is that ICERs fail to reflect disparities in health gains between different groups and using them as decision rules therefore excludes equity objectives from the equation. This could potentially be mitigated by considering extended ICERs .
Finally, it should be noted that economic evaluation is only one input (at best) in the inherently political process of priority-setting. Several other factors influence financing decisions, such as affordability, historical budgets, equity and so on . These may be increased when decision-making covers multiple sectors. Although efforts have been made to improve decision modelling to factor in a wider range of criteria [66–68], to date, the process by which value for money data and other factors are translated into resource allocation remains a black box. In a recent prioritization exercise based on CBA studies of 17 HIV interventions in sub-Saharan Africa, the same cash transfer intervention was ranked third by African Civil Society, fifth by American students and tenth by a panel of economic Nobel laureates . This illustrates how the same economic data can lead to quite heterogeneous financing decisions, underlining the need for a transparent deliberative process, whereby value judgements are made explicit and resource allocation is a weighted reflection of societal preferences .
In the new constrained economic climate, sustainable financing for HIV responses is urgently needed. Alongside the conventional sources of public financing , cofinancing of structural interventions could potentially be an additional avenue that has not been sufficiently explored. Otherwise, structural interventions may be underfunded and their cross-sectoral benefits foregone. Cofinancing provides an opportunity to avoid the current zero-sum nature of silo approaches to budgeting, whereby HIV's gain is another sector's loss. Instead, some structural approaches have the potential to result in a ‘win-win’ situation in which multiple HIV, health and development objectives are achieved simultaneously. Embedding HIV responses into broader national priorities would further encourage domestic ownership and sustainability. The key messages are listed in Box 1.
Therefore, we suggest that HIV programmes actively seek opportunities to cofinance development efforts that have been shown to produce direct HIV benefits, with the magnitude of the benefits informing the resources invested. This would help realize the promise of development synergies, accelerate progress across the Millennium Development Goals and shape new models of governance and financing in the post-2015 era.
M.R., A.V., B.L. and C.W. contributed to the conceptualization of the study. M.R. and J.L. collected the Zomba trial data, reviewed the literature and conducted a preliminary analysis. M.R. finalized the analysis and drafted the manuscript with A.V. All authors reviewed and edited the manuscript.
The authors would like to sincerely thank Dr Berk Özler for providing additional information on the Zomba trial data on costs and impact. We also greatly appreciate the insightful suggestions received from the two anonymous reviewers. This work was conducted as part of the STRIVE research programme consortium (Tackling the Structural Drivers of HIV), funded by UKaid from the Department for International Development (DfID), as well as with funding from the Rush Foundation. The views are the authors’ alone and do not represent the organizations with which they are affiliated or those of the funders.
Conflicts of interest
The authors declare that they have no conflicts of interest.
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