Whereas this review found 15 relatively recent studies yielding information on cost and scale, it is more surprising that given the emphasis on scaling-up interventions since 2001, there is still little empirical cost data collected alongside programming as it expands.
Given the dearth of information, and the importance of this type of data in budgeting and financing considerations, our key recommendation is to develop routine cost-monitoring systems alongside interventions as programming expands. An important aspect of cost monitoring with respect to scale is documenting fixed versus variable costs , using consistent definitions of what is contained in fixed costs. Given possible changes in how services may be delivered, it is also important to document service delivery alongside measuring costs. Analyses should focus on monitoring costs through time and across sites, rather than on cost analyses for single years and one location. As data become more available, it is also important to exploit econometric methods to measure the extent of scale economies or inefficiencies, which will assist in the planning and financing processes.
In the absence of hard data, modelling will continue to remain a key method by which to assist financing and budgeting. This review of costs, however, suggests that linear projections that assume constant average and marginal costs will result in both over and underestimates of resource requirements for scaling up. The direction and extent of bias depends on the level of coverage. In terms of the marginal cost, a linear projection approach will only lead to an underestimation of the ‘true’ cost at relatively high levels of coverage. A linear unit-cost projection will probably underestimate the average cost at either very low or high levels of coverage.
At a national level, the cost relationships may mirror the average cost/marginal cost analysis as shown in Fig. 4. Output is measured in terms of the country population or the proportion of people reached (expressed as a percentage) and the maximum output occurs when there is 100% coverage. Increasing coverage may increase or decrease the average cost per person reached, depending on the level of coverage. Without actual data, it is not clear where exactly the maximum coverage occurs in relation to the cost curves. It could occur at any of the points A, B or C (Fig. 3a), suggesting that there could be very different costs for different countries at 100% coverage. Points A, B and C reflect that scaling up is completed as 100% of the population is covered. Second, if it is assumed for illustrative purposes that all countries have the same cost functions, then the average cost and marginal costs of reaching 100% coverage could vary, simply because of the differences in population size. The average cost per person reached may thus be lower in country B than in country C, even though both have full national coverage. This implies that costs may need to differ between small and large countries in modelling resource requirements.
Another issue in scaling up coverage at the national level is capacity and infrastructure. Within a project or activity, if the limits of the capacity to scale up are reached, then additional fixed inputs are necessary (e.g. physical buildings). Scaling up interventions to a national level raises the question about the infrastructure constraints. It may be impossible to increase coverage beyond a particular level without substantial increases in infrastructure, as shown in Fig. 4b. Therefore, in practice, there may be a large jump or discontinuity in the average cost curve over a particular range of coverage. Tools are currently being developed to model non-linear cost functions better for transportation and supervision costs, as well as fixed costs for health centres , and planning is underway to develop new resource requirements modelling for HIV/AIDS interventions to achieve the millennium development goals . Tools and models are, however, limited by the data they contain, and so collecting cost data in relationship to scale must remain a key priority.
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