Technical efficiency of health production in Africa: A stochastic frontier analysis

Abstract

Background: Inefficiency is widespread in health systems all over the world. The World Health Organization (WHO) estimates that 20%-40% of the global health spending is wasted. In African countries, inefficiency of this magnitude will seriously hamper progress towards achieving universal health coverage and other health system goals. It is thus, significant to assess the efficiency of health systems over time in order to set the ground for identifying the contextual factors leading to inefficiency and design appropriate efficiency-enhancing measures.
Methods: Using panel data for the years 2000, 2005, 2010, and 2015, the study employs a time-variant stochastic frontier production function to assess efficiency. The input measure used is current expenditure per capita in purchasing power parity (Int$) terms and the measure of output is health-adjusted life expectancy (HALE). Moreover, mean years of schooling, GDP per capita in Int$, and out-of-pocket payment as a share of current expenditure on health were used as technical inefficiency effect variables. Data were analyzed using Frontier Version 4.1.
Results: The mean technical efficiency scores were 79.3% in 2000, 81% in 2005, 85.6% in 2010 and 88.3% in 2015. Over the four periods of time, Cabo Verde registered the highest technical efficiency scores, while Eswatini and Sierra Leone had the lowest. The minimum technical efficiency scores were 58.7% (in 2000), 59.1% (2005), 67.4% (2010) and 71.8% (2015). These indicate that despite improvements, there is a significant degree of technical inefficiency. Most of the countries among those in the bottom 10% efficiency scores are countries in Southern Africa, which in 2015 had a very high prevalence of HIV among adults, compared to the top 10%, which had prevalence rates of less than 0.1%.
The mean efficiency score increased progressively over time – a nine percentage point increase between 2000 and 2015. The elasticity of current health expenditure was positive (0.06) and statistically significant. All the technical inefficiency variables had no statistically significant effect.
Conclusions: Over the period of time covered in this study, there was some improvement in the average technical efficiency scores. However, there was also marked inefficiency in many countries, which is likely to hamper their progress towards universal health coverage and other health system goals. In a context where health spending is too low to provide needed services, it is imperative to address the causes of technical inefficiency and produce more health for the money. Furthermore, low-performing health systems should learn from their relatively high-performing peers.

Keywords

Technical efficiency / Stochastic frontier analysis / Health systems / Africa

References

[1] World Health Organization. The world health report: health systems financing:the path to universal coverage. Geneva: World Health Organization; 2010.

[2] Kirigia, Joses M, Eyob Z Asbu, et al. Technical Efficiency, efficiency Change, technical progress and productivity growth in the national health systems of continental African countries. Eastern Africa Social Science Research Review. 2007; 2: 19-40. https://doi.org/10.1353/eas.2007.0008

[3] United Nations.Transforming our world: the 2030 agenda for sustainable development a/RES/70/1. New York: United Nations; 2015.

[4] Masri MD, Asbu EZ. Productivity change of national health systems in the WHO Eastern Mediterranean region: application of DEAbased Malmquist productivity index. Glob Health Res Policy. 2018;3: 22. PMid:30083615. https://doi.org/10.1186/s41256-018-0077-8

[5] World Health Organization,United Nations Children’s Fund. Global Conference on Primary Health Care:Declaration of Astana. Astana, Kazakhstan; 25-26 October 2018. Available from: https://www.who.int/docs/default-source/primary-health/declaration/gcphc-declaration.pdf (accessed 12 August 2018).

[6] World Health Organization. World Health Statistics 2020:monitoring health for the SDGs, sustainable development goals. Geneva: World Health Organization; 2020.

[7] World Health Organization.World Health Statistics 2008. Geneva: World Health Organization; 2008.

[8] World Health Organization. GHO | By category | Index of service coverage-Data by WHO region (Global Health Observatory database. Accessed 08 March 2022).

[9] Cylus J, Pearson M. Cross-national efficiency comparisons of health systems, sub-sectors and disease areas. In: CylusJ, PapanicolasI, SmithPC, Brussels, editors. Health system efficiency:How to make measurement matter for policy and management. Belgium: World Health Organization for European Observatory on Health Systems and Policies; 2016.

[10] Fried HO, Lovell CAK, Schmidt SS. Efficiency and productivity. In FriedHO, LovellCAK, SchmidtSS ( The measurement of productive efficiency and productivity growth,Eds.). 2008. New York: Oxford University Press; https://doi.org/10.1093/acprof:oso/9780195183528.001.0001

[11] Seiford LM, Thrall RM. Recent Developments in DEA: the mathematical programming approach to frontier analysis. Journal of Econometrics. 1990; 46: 7-38. https://doi.org/10.1016/0304-4076(90)90045-U

[12] Coelli T, Rao DSP, Battese G. An introduction to efficiency and productivity analysis. Boston: Academic Publishers; 1998. https://doi.org/10.1007/978-1-4615-5493-6

[13] Aigner D, Lovell CAK, Schmidt P. Formulation and estimation of stochastic frontier function models. Journal of Econometrics. 1977; 6:21-37. https://doi.org/10.1016/0304-4076(77)90052-5

[14] Meeusen W, van den Broeck J. Technical efficiency and dimension of the firm: Some results on the use of frontier production functions. Empirical Economics. 1977; 2: 109-122. https://doi.org/10.1007/BF01767476

[15] Coelli TJ, Rao DSP, O’Donnell CJ, et al. An Introduction to Efficiency and Productivity Analysis. 2nd ed.ed. New York: Springer; 2005.174 p.

[16] Battese GA, Coelli TJ. A Model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics. 1995; 20: 325-332. https://doi.org/10.1007/BF01205442

[17] Greene W. Distinguishing between heterogeneity and inefficiency: stochastic frontier analysis of the World Health Organization’s panel data on national health care systems. Health Econ. 2004; 13(10):959-80. PMid:15455464. https://doi.org/10.1002/hec.938

[18] Jacobs R, Smith PC, Street A. Measuring efficiency in health care: analytic techniques and health policy. Cambridge: Cambridge University Press; 2006. https://doi.org/10.1017/CBO9780511617492

[19] Sun D, Ahn H, Lievens T, et al. Evaluation of the performance of national health systems in 2004-2011: an analysis of 173 countries. PLoS One. 2017; 12(3): e0173346. PMid:28282397. https://doi.org/10.1371/journal.pone.0173346

[20] Masri MD, Asbu EZ. Productivity change of national health systems in the WHO Eastern Mediterranean region: application of DEAbased Malmquist productivity index. Glob Health Res Policy. 2018;3: 22. PMid:30083615. https://doi.org/10.1186/s41256-018-0077-8

[21] Ibrahim MD, Daneshvar S. Efficiency Analysis of Healthcare System in Lebanon Using Modified Data Envelopment Analysis. Journal of Healthcare Engineering. 2018; 2018. PMid:30057729. https://doi.org/10.1155/2018/2060138

[22] World Health Organization, The Global Health Observatory. Healthy life expectancy (HALE) at birth (who.int) (Accessed 01 March 2022).

[23] Labbe JA. Health-Adjusted Life Expectancy:Concepts and Estimates.In: PreedyV.R., Eds.) Watson R.R. (Handbook of Disease Burdens and Quality of Life Measures. New York: Springer; 2010.https://doi.org/10.1007/978-0-387-78665-0_23

[24] Constantin O. Health Care Efficiency Across Countries: A Stochastic Frontier Analysis. Applied Econometrics and International Development. 2011; 11(1): 5-14.

[25] United Nations Development Program.Human Development Reports 1990-2020. Human Development Reports 1990-2020 | Human Development Reports (undp. org) (accessed 10 September 2021).

[26] Coelli TJ.A Guide to FRONTIER Version 4.1: A Computer Program for Stochastic Frontier Production and cost Function Estimation. CEPA Working Paper No. 7/96, Department of Econometrics, University of New England, Armidale. 1996. Available from: http://www.uq.edu.au/economics/cepa/frontier.php

[27] World Health Organization.Global Health Observatory. Prevalence of HIV among adults aged 15 to 49. Estimates by country. GHO | By category | Prevalence of HIV among adults aged 15 to 49-Estimates by country (who.int).