İbrahim GÜN
(Sağlık Yönetimi Bölümü, İstanbul Üniversitesi-Cerrahpaşa, Sağlık Bilimleri Fakültesi, İstanbul, Türkiye)
Faruk YILMAZ
(Sağlık Yönetimi Bölümü, İstanbul Üniversitesi-Cerrahpaşa, Sağlık Bilimleri Fakültesi, İstanbul, Türkiye)
İlhan Kerem ŞENEL
(Sağlık Yönetimi Bölümü, İstanbul Üniversitesi-Cerrahpaşa, Sağlık Bilimleri Fakültesi, İstanbul, Türkiye)
Yıl: 2021Cilt: 8Sayı: 2ISSN: 2148-7588 / 2687-4644Sayfa Aralığı: 147 - 152İngilizce

23 0
Efficiency Analysis of Health Systems in World Bank Countries
ABSTRACTObjective: Performance analysis is vital in the health sector owing to health expenditures, increased quality demands, and competition. In thisstudy, we aimed to evaluate the relative efficiencies of different countries that use similar health status indicators.Material and Methods: A K-means clustering algorithm with five different variables was used to ensure homogeneity among the countries selected for comparison. The resulting clusters were analyzed using an input-oriented data envelopment analysis with four inputs and three outputvariables for evaluating the relative efficiencies of countries within each cluster. Accordingly, input variables, such as current health expenditureper capita (current US$), hospital beds (per 1000 people), physicians (per 1,000 individuals), and nurses and midwives (per 1,000 people); andoutput variables, such as life expectancy at birth, maternal survival rate (per 100,000 live births), and infant survival rate (per 1,000 live births)were determined, and efficiency analysis was performed.Results: The countries were first clustered into three homogenous groups using a k-means clustering algorithm. For 177 countries whose datawere accessible (out of 189 countries), the first, second, and third clusters comprised of 74, 55, and 48 countries, respectively. Then, scale efficiency,pure technical efficiency, and technical efficiency scores were obtained by data envelopment analysis. In the first cluster, 31 countries (41.89%)were categorized as pure technical efficient, whereas in the second and third clusters, 20 (37.03%) and 23 (47.92%) countries were categorized aspure technical efficient, respectively.Conclusion: Cross-country studies are crucial for countries for the assessment of comparative positions and for improvement of their healthstatus accordingly. Policymakers can compare the relative efficiency of their countries with other countries that possess similar health resources.Accordingly, they can set achievable targets by referring data of efficient countries
DergiAraştırma MakalesiErişime Açık
  • 1. Yang CC. Measuring health indicators and allocating health resources: a DEA-based approach. Health Care Manag Sc. 2017;20(3):365-378. [Crossref]
  • 2. Bhat VN. Institutional arrangements and efficiency of health care delivery systems. Eur J Health Econ. 2005;6(3):215-222. [Crossref]
  • 3. Cetin VR, Bahce S. Measuring the efficiency of health systems of OECD countries by data envelopment analysis. Appl Econ. 2016;48(37):3497-3507. [Crossref]
  • 4. Anderson G, Hussey PS. Comparing health system performance in OECD countries. Health Affair. 2001;20(3):219-232. [Crossref]
  • 5. Puig-Junoy J. Measuring health production performance in the OECD. Appl Econ Lett. 1998;5(4):255-259. [Crossref]
  • 6. Alptekin N, Yeşilaydın G. Classifying OECD countries according to health indicators using fuzzy clustering analysis. J Bus Res. 2015;7(4):137-155. [Crossref]
  • 7. Oderkirk J, Ronchi E, Klazinga N. International comparisons of health system performance among OECD countries: Opportunities and data privacy protection challenges. Health Policy. 2013;112(1-2):9-18. [Crossref]
  • 8. Carinci F, Van Gool K, Mainz J, et al. Towards actionable international comparisons of health system performance: expert revision of the OECD framework and quality indicators. Int J Qual Health C. 2015;27(2):137-146.
  • 9. Tchouaket EN, Lamarche PA, Goulet L, Contandriopoulos AP. Health care system performance of 27 OECD countries. Int J Health Plan M. 2012;27(2):104-129. [Crossref]
  • 10. Mobley IV LR, Magnussen J. An international comparison of hospital efficiency: does institutional environment matter? Appl Econ. 1998;30(8):1089-1100. [Crossref]
  • 11. Steinmann L, Dittrich G, Karmann A, Zweifel P. Measuring and comparing the (in) efficiency of German and Swiss hospitals. Eur J Health Econ. 2004;5(3):216-226. [Crossref]
  • 12. Spinks J, Hollingsworth B. Cross-country comparisons of technical efficiency of health production: a demonstration of pitfalls. Appl Econ. 2009;41(4):417-427. [Crossref]
  • 13. Alexander CA, Busch G, Stringer K. Implementing and interpreting a data envelopment analysis model to assess the efficiency of health systems in developing countries. IMA J Manag Math. 2003;14(1):49-63. [Crossref]
  • 14. Grosskopf S, Self S, Zaim O. Estimating the efficiency of the system of healthcare financing in achieving better health. Appl Econ. 2006;38(13):1477-1488. [Crossref]
  • 15. Retzlaff-Roberts D, Chang CF, Rubin RM. Technical efficiency in the use of health care resources: a comparison of OECD countries. Health Policy. 2004;69(1):55-72. [Crossref]
  • 16. Samut PK, Cafri R. Analysis of the efficiency determinants of health systems in OECD countries by DEA and panel tobit. Soc Indic Res. 2016;129(1):113-132. [Crossref]
  • 17. Rand WM. Objective criteria for the evaluation of clustering methods. J Am Statist Assoc. 1971;66(336):846-850. [Crossref]
  • 18. Suner A, Çelikoğlu CC. Choosing a health institution with multiple correspondence analysis and cluster analysis in a population based study. İzmir J Econ. 2010;25(2):43-55.
  • 19. Rana S, Jasola S, Kumar R. A review on particle swarm optimization algorithms and their applications to data clustering. Artificial Intelligence Review. 2011;35(3):211-222. [Crossref]
  • 20. Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM Computing Surveys (CSUR). 1999;31(3):264-323. [Crossref]
  • 21. Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. Eur J Oper Res. 1978;2(6):429-444. [Crossref]
  • 22. Banker RD, Charnes A, Cooper WW. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci. 1984;30(9):1078-1092. [Crossref]
  • 23. Cook WD, Zhu J. Modeling performance measurement: applications and implementation issues in DEA: Springer Science & Business Media; 2006. [Crossref]
  • 24. Afonso A, St Aubyn M. Relative efficiency of health provision: A DEA approach with non-discretionary inputs. ISEG-UTL Economics Working Paper. 2006(33). https://papers.ssrn.com/sol3/papers. cfm?abstract_id=952629 [Crossref]
  • 25. World Bank Open Data [Internet]. World Bank. 2016 [cited 02.02.2019]. Available from: https://data.worldbank.org.
  • 26. World Bank Country and Lending Groups: World Bank; 2016 [Available from: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519.
  • 27. Boz C, Önder E. The health system performance evaluation of OECD countries. J Social Insurance. 2017;6:24-61. 28. Thornton J. Estimating a health production function for the US: some new evidence. Appl Econ. 2002;34(1):59-62. [Crossref]
  • 29. Shaw JW, Horrace WC, Vogel RJ. The determinants of life expectancy: an analysis of the OECD health data. Southern Econ J. 2005:768-783. [Crossref]
  • 30. Joumard I, André C, Nicq C, Chatal O. Health status determinants: lifestyle, environment, health care resources and efficiency. Environment, Health Care Resources and Efficiency (May 27, 2010) OECD Economics Department Working Paper. 2010(627). [Crossref]

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