The measurement of cultural and information costs is undoubtedly difficult, but it is essential that we understand how these hidden but real costs impede the attainment of efficient preventive strategies. In Fig. 4, we offer some speculative yet plausible possibilities. The figure shows hypothetical total cost curves for improvements in only one dimension, namely, targeting efficiency. As we move to the right on the horizontal axis, the target population represents a higher proportion of the total population reached by the intervention. In the figure, we posit that information costs display a pattern of rising marginal cost. For example, ascertaining the locations of the most popular gay bars and brothels in a city may entail little or no cost. However, further improvements in targeting men who have sex with men and sex workers will entail costly epidemiological or behavioural surveys. By contrast, we posit that cultural costs display a pattern of diminishing marginal cost. In a country with strong cultural and religious barriers, there may be significant political resistance to targeting interventions to men who have sex with men or to IDU. Changing the situation may entail years of educational campaigns and pilot studies, that is, significant investments before any result can be observed. As it becomes socially acceptable to intervene in such populations, however, the political and cultural barriers decline. In the figure, we have depicted cultural costs as relatively large compared with information costs at low levels of targeting efficiency. There is so little mapping of risk populations and behaviours in most countries, despite the relatively low information cost, that cultural costs are likely to be binding.
The same distinction between rising and falling marginal costs can apply to the other two dimensions: cost-effectiveness and technical efficiency. In the cost-effectiveness dimension, informational marginal costs may similarly rise because more sophisticated studies are needed to evaluate the effectiveness of additional interventions in new settings, whereas cultural marginal costs fall for reasons similar to those described in the targeting dimension in Fig. 4. In the technical efficiency dimension, by contrast, informational marginal costs may decrease because of the high initial setup costs and lower maintenance costs of performance monitoring systems. With respect to cultural costs, increased transparency and reduced corruption may also entail high initial setup costs, so that marginal costs may also decrease.
To assess the relative efficiency of alternative interventions to prevent HIV/AIDS, we have proposed a conceptual three-dimensional model that distinguishes between cost-effectiveness, targeting efficiency and technical efficiency. We have also stressed the importance of hidden but significant informational and cultural costs in moving from inefficient to efficient points within our proposed three-dimensional efficiency calculus. Our hope is that such a framework will prove useful in the allocation of existing resources for HIV prevention in the short run and the design of new prevention strategies in the long run.
Since the 1993 World Development Report, there have been repeated calls for additional information on the relative costs and benefits of the range of available prevention interventions in different settings. This point was strongly underscored in the 2006 Disease Control Priorities Project . Acquiring such information is costly and time-consuming. It will require complex, long-term studies of cost-effectiveness. Sufficient information already exists in many countries, however, to improve the reach of available interventions to target populations. As suggested by our three-dimensional framework, improvements in targeting efficiency may have payoffs even greater than the potential gains from greater knowledge of cost-effectiveness. To the extent that there are political, religious and cultural impediments to improved target, the challenge is not to acquire more information about key populations, but to learn how to surmount what we have called ‘cultural costs’. What is more, donors and national governments alike have largely ignored the technical efficiency of their implementation partners. Recent evidence, including the data in Fig. 3, suggests enormous variation in unit cost per service delivered. Our three-dimensional framework suggests that improvements in management training and monitoring at the local level could in principle have even greater payoffs.
The main limitation of this study is the absence of adequate information to make our model fully operational at the quantitative level. At the same time, our purpose is to highlight where the gaps in information lie. Given that decisions have to be made in the absence of complete information, we propose that it is better to make decisions within the framework outlined here.
Another limitation is that our theoretical discussion of cost-effectiveness refers only to a single prevention intervention, whereas in reality, prevention campaigns always consist of a mix of multiple interventions. We presented our theoretical model for a single intervention primarily for clarity of exposition. We think that the basic principles of our model can be readily generalized to the analysis of multiple interventions.
What is more, our model treats targeting efficiency as a simple proportionality factor. That is, our cost-effectiveness equation includes the simple multiplicative term ‘proportion of treated individuals in the target population’, with a value that can range from 0 to 100%. We know from the vaccination literature, however, that 100% coverage is not necessary in many cases. Reaching the last 5% of the target population can be extremely expensive yet confer relatively low benefits in terms of infections averted.
Finally, our presentation does not squarely confront the problem of minimum scale. We have implicitly assumed that a region or a country seeks to maximize the number of HIV infections prevented within a fixed prevention budget of sufficient size. We do not discuss the possibility that a country's prevention budget may fall below the level at which investments in greater cost-effectiveness are infeasible. Determining the minimal necessary size of a specific country's prevention budget is not a simple problem and requires further discussion.
The authors acknowledge helpful comments received at the following meetings: International AIDS Economics Network: Pre-Conference Meeting, Toronto, 2006; Seminario Internacional de Prioridades en Salud, The World Bank, 2007; Impact Evaluation Workshops, The World Bank, 2006–2007; XII Congreso de Investigación en Salud Pública, Cuernavaca, Mexico, 2007. The authors also wish to thank reviewers from Harvard University for their helpful comments on a previous version of the manuscript, as well as the insights of the journal referees and editorial board.
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Here, we convert all of the components of our efficiency calculus into percentage terms. First, let us assume that there is a benchmark cost-effectiveness against which all potential interventions are to be gauged. For purposes of exposition, we assume a benchmark of US$1000 per HIV infection averted, which is considerably lower than the currently attainable value of US$3900 estimated by Stover et al. . In our illustrative numerical example, the intervention had a cost-effectiveness of US$5000 per HIV infection averted. Therefore, in percentage terms, its intervention efficiency was US$1000/US$5000 = 20%. The overall efficiency of the intervention was therefore 20% × 10% × 50% = 1%. Abbreviating ‘cost-effectiveness ratio’ to CER, we get the following verbal equation:
Overall efficiency of prevention intervention = Benchmark CER as a proportion of intervention's theoretical CER × Proportion of treated individuals in the target population × Minimum unit cost as a proportion of actual unit cost
Each term in the foregoing equation is expressed in percentage terms. The overall efficiency is equal to the product of the intervention efficiency, the targeting efficiency, and the technical efficiency.
Figure 5 presents a graphical representation of this conceptual framework. Each axis measures one of the three components on a scale from 0 to 100%. The origin 0 represents zero efficiency in each dimension. The point A corresponds to 100% overall efficiency. The point B corresponds to an intervention whose cost-effectiveness ratio equals the benchmark CER, whose treated population consists entirely of individuals from the target group. The technical efficiency is nearly zero, that is, the unit cost of service far exceeds the efficient minimum unit cost. Our illustrative numerical example would be located at the point C. Whether or not the three dimensions are explicitly considered in the decision-making process, any allocation of resources for HIV prevention necessarily corresponds to some point in the three-dimensional space.
The data presented in Fig. 1, Fig. 2 and Fig. 3 suggest that many countries are not operating near the overall efficient point A in our three-dimensional model. If the data presented were representative of the global state of resource allocation for HIV prevention, then point D might not be far from reality in 2003. At that point, 50% of funds were allocated to cost-effective interventions, whereas 20% of key target populations were covered by preventive services and 60% of technical feasible outputs were being produced with the funds expended. This is of course completely speculative, but illustrates the need to consider all three aspects of efficiency when allocating and using prevention resources.