Studies of targeted prevention activities among sex workers also demonstrated scale effects [43–45,47,51], with 38–88% of cost variation attributed to scale [43,44,47]. Unlike VCT, the extent of the scale effect is not unambiguously clear for these interventions; two studies suggest that significant economies of scale are possible over large project sizes (n = 6379) [43,47], but two other analyses suggest that diseconomies of scale start to occur at project sizes of 1500–2000, similar to Fig. 1a [44,51]. Closer examination of these studies suggests that these results may be explained by the underlying cost estimates: the study with significant economies of scale also found a relatively low proportion of fixed costs (5.2–8.3%), in contrast to other studies that found much higher levels of fixed costs (11–40%).
Econometric analysis, using modelled costs of scaling up coverage by 25% in sub-Saharan Africa, suggested that both prevention and treatment interventions would be operating at points of scale inefficiency or diseconomies of scale, assuming no changes to fixed inputs. As expected, diseconomies of scale are much more acute for treatment-related interventions .
This review unambiguously indicates that the scale and volume of activities are key drivers in determining costs, consistent with standard economic theory . What is less clear is the actual shape of the average and marginal cost curves, which will provide better information on the optimal size of interventions that will fully exploit economies of scale. It might also be the case that as interventions are scaled up, the way the services are delivered (e.g. the production function) might change, for example, two studies pointed to reductions in time spent with clients [43,49]. On the basis of the evidence from Table 2, Fig. 3 shows the relative ranking of HIV/AIDS interventions and likely economies (or diseconomies) of scale effects. Interventions with a small proportion of fixed costs such as VCT, or the capacity to increase to high levels of coverage (information, education and communication) are likely to demonstrate downward sloping average costs for large scales of activity, so the larger the programme the more optimal the size. On the other side of the spectrum, interventions most closely related to health facilities (STI, prevention of mother-to-child transmission) are likely to face diseconomies of scale and the U-shaped average cost suggesting that there is an optimal size for the programme. Econometric methods can help to identify these optimal sizes.
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.
1. United Nations. The millennium development goals report 2006
. New York: United Nations Department of Economic and Social Affairs; 2006.
2. Bertozzi S, Padian NS, Wegbreit J, DeMaria LM, Feldman B, Gayle H, et al
. HIV/AIDS prevention and treatment.
In: Jamison DT, Breman JG, Measham AR, Alleyne G, Claeson M, Evans DB, et al
., editors. Disease control priorities in developing countries
, 2nd ed. New York: Oxford University Press; 2006.
3. Creese A, Floyd K, Alban A, Guinness L. Cost-effectiveness of HIV/AIDS interventions in Africa: a systematic review of the evidence. Lancet 2002; 359:1635–1643.
4. Walker D. Cost and cost-effectiveness of HIV/AIDS prevention strategies in developing countries: is there an evidence base? Health Policy Plan 2003; 18:4–17.
5. Kumaranayake L, Watts C. Economic costs of HIV/AIDS prevention activities in sub-Saharan Africa. AIDS 2000; 14(Suppl. 3):S239–S252.
6. Schwartlander B, Stover J, Walker N, Bollinger L, Gutierrez JP, McGreevey W, et al
. Resource needs for HIV/AIDS. Science 2001; 292:2434–2436.
7. UNAIDS. Resource needs for an expanded response to AIDS in low and middle income countries
. Geneva: UNAIDS; 2005.
8. Stover J, Bertozzi S, Gutierrez JP, Walker N, Stanecki KA, Greener R, et al
. The global impact of scaling up HIV/AIDS prevention programs in low- and middle-income countries. Science 2006; 311:1474–1476.
9. Gutierrez JP, Johns B, Adam T, Bertozzi SM, Edejer TT, Greener R, et al
. Achieving the WHO/UNAIDS antiretroviral treatment 3 by 5 goal: what will it cost? Lancet 2004; 364:63–64.
10. Vassall A, Compernolle P. Estimating the resource needs of scaling-up HIV/AIDS and tuberculosis interventions in sub-Saharan Africa: a systematic review for national policy makers and planners. Health Policy 2006; 79:1–15.
11. Creese A, Parker D. Cost analysis in primary health care: a training manual for programme managers. Geneva: WHO; 1994.
12. Varian H. Microeconomic analysis. New York: Norton and Company; 1998.
13. Vitaliano DF. On the estimation of hospital cost functions. J Health Econ 1987; 6:305–318.
14. Wouters A. The cost and efficiency of public and private health care facilities in Ogun State, Nigeria. Health Econ 1993; 2:31–42.
15. Preya C, Pink G. Scale and scope efficiencies through hospital consolidations. J Health Econ 2006; 25:1049–1068.
16. Weaver M, Deolalikar A. Economies of scale and scope in Vietnamese hospitals. Soc Sci Med 2004; 59:199–208.
17. Rowley JT, Anderson RM. Modeling the impact and cost-effectiveness of HIV prevention efforts. AIDS 1994; 8:539–548.
18. Watts C, Kumaranayake L. Thinking big: scaling-up HIV-1 interventions in sub-Saharan Africa. Lancet 1999; 354:1492.
19. Johns B, Tan-Torres TT, WHO–CHOICE. Costs of scaling up health interventions: a systematic review. Health Policy Plan 2005; 20:1–13.
20. Over M. The effect of scale on cost projections for a primary health care program in a developing country. Soc Sci Med 1986; 22:351–360.
21. Broomberg J, Soderlund N, Mills A. Economic analysis at the global level: a resource requirement model for HIV prevention in developing countries. Health Policy 1996; 38:45–65.
22. Wilkinson D, Floyd K, Gilks CF. National and provincial estimated costs and cost effectiveness of a programme to reduce mother-to-child HIV transmission in South Africa. S Afr Med J 2000; 90:794–798.
23. Bertozzi S, Gutierrez JP, Opuni M, Walker N, Schwartlander B. Estimating resource needs for HIV/AIDS health care services in low-income and middle-income countries. Health Policy 2004; 69:189–200.
24. Kumaranayake L, Kurowski C, Conteh L. Analysis of the costs of scaling-up priority health interventions in low and selected middle-income countries.
In: Macroeconomics and health: investing in health for economic development
. Geneva: World Health Organization Commission on Macroeconomics and Health; 2001.
25. Opuni M, Bertozzi S, Bollinger L, Gutierrez JP, Massiah E, McGreevey W, et al
. Resource requirements to fight HIV/AIDS in Latin America and the Caribbean. AIDS 2002; 16(Suppl. 3):S58–S65.
26. Marseille E, Kahn JG, Mmiro F, Guay L, Musoke P, Fowler MG, et al
. Cost-effectiveness of single-dose nevirapine regimen for mothers and babies to decrease vertical HIV-1 transmission in Sub-Saharan Africa. Lancet 1999; 354:803–809.
27. Rely K, Bertozzi SM, Avila-Figueroa C, Guijarro MT. Cost-effectiveness of strategies to reduce mother-to-child HIV transmission in Mexico, a low-prevalence setting. Health Policy Plan 2003; 18:290–298.
28. Pitter C, Kahn JG, Marseille E, Lule JR, McFarland DA, Ekwaru JP, et al
. Cost-effectiveness of cotrimoxazole prophylaxis among persons with HIV in Uganda. J Acquir Immune Defic Syndr 2007; 44:336–343.
29. Forsythe S, Arthur G, Ngatia G, Mutemi R, Odhiambo J, Gilks C. Assessing the cost and willingness to pay for voluntary HIV counselling and testing in Kenya. Health Policy Plan 2002; 17:187–195.
30. Uys L, Hensher M. The cost of home-based terminal care for people with AIDS in South Africa. S Afr Med J 2002; 92:624–628.
31. Deghaye N, Pawinski RA, Desmond C. Financial and economic costs of scaling up the provision of HAART to HIV-infected health care workers in KwaZulu-Natal. S Afr Med J 2006; 96:140–143.
32. Boulle A, Kenyon C, Skordis J, Wood R. Exploring the costs of a limited public sector antiretroviral treatment programme in South Africa. S Afr Med J 2002; 92:811–817.
33. Kumaranayake L, Vickerman P, Walker D, Samoshkin S, Romantzov V, Emelyanova Z, et al
. The cost-effectiveness of HIV preventive measures among injecting drug users in Svetlogorsk, Belarus. Addiction 2004; 99:1565–1576.
34. Terris-Prestholt F, Watson-Jones D, Mugeye K, Kumaranayake L, Ndeki L, Weiss H, et al
. Is antenatal syphilis screening still cost-effective in Sub-Saharan Africa? Sex Transm Infect 2003; 79:375–381.
35. Vickerman P, Terris-Prestholt F, Delany S, Kumaranayake L, Rees H, Watts C. Are targeted HIV prevention activities cost-effective in high prevalence settings? Results from a sexually transmitted infection treatment project for sex workers in Johannesburg, South Africa. Sex Transm Dis 2006; 33(10 Suppl.):S122–S132.
36. Terris-Prestholt F, Kumaranayake L, Obasi A, Cleophas-Mazige B, Makhkha M, Todd J, et al
. From trial intervention to scale-up: Costs of an sdolescent sexual health program in Mwanza, Tanzania. Sex Transm Dis 2006; 33(10 Suppl.):S133–S139.
37. Forsythe S, Mangkalopakorn C, Citwarakorn A, Masvichian N. Cost of providing sexually transmitted disease services in Bangkok. AIDS 1998; 12(Suppl. 2):S73–S80.
38. Thaineua V, Sirinirund P, Tanbanjong A, Lallemant M, Soucat A, Lamboray JL. From research to practice: use of short course zidovudine to prevent mother-to-child HIV transmission in the context of routine health care in Northern Thailand. Southeast Asian J Trop Med Public Health 1998; 29:429–442.
39. Dandona L, Sisodia P, Ramesh YK, Kumar SG, Kumar AA, Rao MC, et al
. Cost and efficiency of HIV voluntary counselling and testing centres in Andhra Pradesh, India. Natl Med J India 2005; 18:26–31.
40. McConnel CE, Stanley N, du Plessis JA, Pitter CS, Abdulla F, Coovadia HM, et al
. The cost of a rapid-test VCT clinic in South Africa. S Afr Med J 2005; 95:968–971.
41. Carrara V, Terris-Prestholt F, Kumaranayake L, Mayaud P. Operational and economic evaluation of a NGO-led sexually transmitted infections intervention in north-western Cambodia. Bull WHO 2005; 83:434–442.
42. Terris-Prestholt F, Kumaranayake L, Foster S, Kamali A, Kinsman J, Basajja V, et al
. The role of community acceptance over time for costs of HIV and STI prevention interventions: analysis of the Masaka Intervention Trial, Uganda, 1996–1999. SexTransm Dis 2006; 33(10 Suppl.):S111–S116.
43. Dandona L, Sisodia P, Kumar SG, Ramesh YK, Kumar AA, Rao MC. HIV prevention programmes for female sex workers in Andhra Pradesh, India: outputs, cost and efficiency. BMC Public Health 2005; 5:98.
44. Guinness L, Kumaranayake L, Bhuvaneswari R, Sankaranarayanan G, Vannela G, Raghupathi P, et al
. Does scale matter? The costs of HIV prevention interventions for commercial sex workers in India. Bull WHO 2005; 83:747–755.
45. Chandrashekar S, Kumaranayake L, Ramesh BM, Blanchard J, Alary M. Learning effects on the costs of phased scale-up implementation of targeted HIV prevention among high risk populations in Karnataka, India
. Presented at the International AIDS Conference
. Toronto, Canada, 13–18 August 2006 [Abstract CDD1243].
46. Kumaranayake L, Chandrashekar S, Washington R, Alary M. The economics of STI provision in scaling-up HIV prevention among high risk populations in Karnataka, India
. Presented at the International AIDS Conference
. Toronto, Canaca, 13–18 August 2006 [Abstract MoPe0676].
47. Marseille E, Dandona L, Marshall N, Gaist P, Bautista-Arredondo S, Rollins B, et al
. HIV prevention costs and program scale: data from the PANACEA project in five low and middle-income countries. BMC Health Serv Res 2007; 7:108–116.
48. Kumaranayake L, Watts C. HIV/AIDS prevention and care interventions in Sub-Saharan Africa: an econometric analysis of the costs of scaling-up. S Afr J Econ 2000; 68:1012–1033.
49. Kumaranayake L. Production and cost functions for voluntary counselling and testing: what is the scale effect? A study from Malawi.
Presented at the International AIDS Economics Network Meeting
. Bangkok, Thailand, July 2004.
50. Terris-Prestholt F, Vyas S, Kumaranayake L, Mayaud P, Watts C. The costs of treating curable sexually transmitted infections in low- and middle-income countries: a systematic review. SexTransm Dis 2006; 33(10 Suppl.):S153–S166.
51. Guinness L, Kumaranayake L, Hanson K. A cost function for HIV prevention services. Is there a ‘U’ shape?
Presented at the 6th World Congress on Health Economics, International Health Economics Association
. Copenhagen, Denmark, July 2007.
52. Kumaranayake L, Watts C, Kurowski C, Conteh L. Estimating the infrastructure requirements for an expanded response to HIV/AIDS in low and middle-income countries.
Presented at the XIVth International AIDS Conference
. Barcelona, July 2002 [Abstract TuPeE5106].
53. Johns B, Baltussen R. Accounting for the cost of scaling-up health interventions. Health Econ 2004; 13:1117–1124.
54. Nordstrom A, Tan-Torres Edejer T, Evans D. What will it cost to attain the health MDGs? Bull WHO 2007; 85:246.