A cost function for HIV prevention services: is there a 'u' - shape?

Cost Eff Resour Alloc. 2007 Nov 5;5:13. doi: 10.1186/1478-7547-5-13.


Background: Global resource needs estimation is a critical part of addressing the HIV/AIDS epidemic. To generate these estimates knowledge of costs and cost structures is required. The evidence base for costs of HIV prevention programmes is limited. Even less is known about the existence of economies scale and whether, as economic theory suggests, average costs form a 'u'-shaped curve as scale increases. Using an econometric analysis, this paper addresses this question by estimating marginal costs and economies of scale for HIV prevention programmes for vulnerable groups in Southern India with different levels of coverage.

Methods: Two hybrid translog-cost functions were estimated. First, expenditure data from 78 state-funded HIV prevention projects in Andhra Pradesh were used to explore the impact of scale, institutional history and price on costs; second, economic cost data from 16 commercial sex worker projects across Tamil Nadu and Andhra Pradesh were analysed to additionally assess the impact of the value of inputs not reported in expenditure data and location. Coefficient estimates were used to calculate marginal costs and economies of scale.

Results: The econometric model yielded a good fit (R2 = 0.46, p < 0.001 and R2 = 0.79, p < 0.001, for the expenditure and economic cost datasets, respectively). The economies of scale index was greater than 1 for both datasets and fell as coverage increased. Analysis of the expenditure data found economies of scale were not exhausted, with a 0.002% change in total cost for each extra person reached and an 11% difference in total cost between target group categories. Estimation using the economic cost data suggests a point of minimum efficient scale at around 1750-2000 people reached, a 0.03% change in total cost for each extra person reached, and 28% lower costs in Tamil Nadu than Andhra Pradesh.

Conclusion: Econometric analysis of these standardized datasets provides insights into how costs change with coverage, the impact of project location and nature of the project target group. The results demonstrate the importance of understanding the nature of the cost function when designing, budgeting and estimating resource requirements for scaling up coverage of HIV prevention projects.