Estimating the Efficiency of Healthcare Facilities Providing HIV/AIDS Treatment in Zambia: A Data Envelopment Approach

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 215)


Zambia and many other countries in Sub-Saharan Africa face a key challenge of sustaining high levels of coverage of AIDS treatment under prospects of dwindling global resources for HIV/AIDS treatment. Policy debate in HIV/AIDS is increasingly paying more focus to efficiency in the use of available resources. In this chapter, we apply Data Envelopment Analysis (DEA) to estimate short term technical efficiency of 34 HIV/AIDS treatment facilities in Zambia. The data consists of input variables such as human resources, medical equipment, building space, drugs, medical supplies, and other materials used in providing HIV/AIDS treatment. Two main outputs namely, numbers of ART-years (Anti-Retroviral Therapy-years) and pre-ART-years are included in the model. Results show the mean technical efficiency score to be 83 %, with great variability in efficiency scores across the facilities. Scale inefficiency is also shown to be significant. About half of the facilities were on the efficiency frontier. We also construct bootstrap confidence intervals around the efficiency scores.


Data Envelopment Analysis HIV/AIDS treatment Healthcare efficiency Zambia Managing Service Productivity 



We are grateful to the Clinton Health Access Initiative Zambia Country Office for supporting the data collection and making the data available for this analysis. We thank Mr Bona Chitah of the University of Zambia, Department of Economics, for assistance in explaining the data collection process. The views expressed in this chapter do not necessarily represent those of the funder. Any errors are our own.


  1. Coelli, T., Prasada Rao, D. S., & Battese, G. E. (1998). An introduction to efficiency and productivity analysis. Boston: Kluwer Academic.CrossRefGoogle Scholar
  2. Fare, R., Grosskopf, S., & Lovell, C. A. K. (1994). Production frontiers. Cambridge: Cambridge University Press.Google Scholar
  3. Gonzalez, X. M., & Miles, D. (2002). Statistical precision of DEA: Applications to Spanish public services. Applied Economics Letters, 9(2), 127–132.CrossRefGoogle Scholar
  4. Ichoku, H., Fonta, W. M., Onwujekwe, O. E., & Kirigia, J. M. (2011). Evaluating the technical efficiency of hospitals in South Eastern Nigeria. European Journal of Business and Management, 3(2), 24–37.Google Scholar
  5. Kirigia, J. M., Emrouznejad, A., & Sambo, L. G. (2002). Measurement of technical efficiency of public hospitals in Kenya: Using data envelopment analysis. Journal of Medical Systems, 26(1), 39–45.CrossRefGoogle Scholar
  6. Kirigia, J. M., Lambo, E., & Sambo, L. G. (2000). Are public hospitals in Kwazulu-Natal province of South Africa technically efficient? African Journal of Health Sciences, 7(3–4), 25–32.Google Scholar
  7. Kirigia, J. M., Sambo, L. G., Renner, A., Alemu, W., Seasa, S., & Bah, Y. (2011). Technical efficiency of primary health units in Kailahun and Kenema districts of Sierra Leone. International Archives of Medicine, 4, 15.CrossRefGoogle Scholar
  8. Leach-Kemon, K., Chou, D. P., Schneider, M. T., Tardif, A., Dieleman, J. L., Brooks, B. P. C., et al. (2012). The global financial crisis has led to a slowdown in growth of funding to improve health in many developing countries. Health Affairs, 31(1), 228–235.CrossRefGoogle Scholar
  9. Masiye, F. (2007). Investigating health system performance: An application of data envelopment analysis to Zambian hospitals. BMC Health Services Research, 7(1), 58.CrossRefGoogle Scholar
  10. Masiye, F., Kirigia, J. M., Emrouznejad, A., Sambo, L. G., Mounkaila, A., Chimfwembe, D., et al. (2006). Efficient management of health centres human resources in Zambia. Journal of Medical Systems, 30(6), 473–481.CrossRefGoogle Scholar
  11. Over, M. (2011). Achieving an AIDS transition: Preventing infections to sustain treatment. Washington, DC: Center for Global Development.Google Scholar
  12. Renner, A., Kirigia, J. M., Zere, A. E., Barry, S. P., Kirigia, D. G., Kamara, C., et al. (2005). Technical efficiency of peripheral health units in Pujehun district of Sierra Leone: A DEA application. BMC Health Services Research, 5, 77.CrossRefGoogle Scholar
  13. Simar, L., & Wilson, P. W. (1998a). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44, 49–61.CrossRefGoogle Scholar
  14. Simar, L., & Wilson, P. W. (1998b). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61.CrossRefGoogle Scholar
  15. Simar, L., & Wilson, P. W. (2000a). A general methodology for bootstrapping nonparametric frontier models. Journal of Applied Statistics, 27(6), 779–802.CrossRefGoogle Scholar
  16. Simar, L., & Wilson, P. W. (2000b). Statistical inference in nonparametric frontier models: The state of the art. Journal of Productivity Analysis, 13, 49–78.CrossRefGoogle Scholar
  17. Wilson, P. W. (2006). FEAR: A software package for frontier efficiency analysis with R. Accessed February 2013, from
  18. Zhu, J. (2009). Quantitative models for performance evaluation and benchmarking: Data envelopment analysis with spreadsheets (2nd ed.). Boston: Springer Science.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of ZambiaLusakaZambia
  2. 2.Aston Business SchoolAston UniversityBirminghamUK

Personalised recommendations