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Afrika Ülkeleri Sağlık Sistemlerinin Etkinlik Durumlarının Veri Zarflama ve Tobit Analizleriyle Değerlendirilmesi

Year 2023, Volume: 11 Issue: Afrika, 204 - 224, 30.09.2023
https://doi.org/10.18506/anemon.1290327

Abstract

Çalışmanın amacı Afrika Birliği ülkelerinin sağlık sistemlerinin etkinlik düzeylerinin incelenmesidir. Bu amaçla 2004 ve 2010 yılları verileri ile Veri Zarflama Analizinin ölçeğe göre sabit getiri (CCR) ve ölçeğe göre değişken getiri (BCC) yöntemleri kullanılmıştır. Etkin olan ülkelerin arasında hangisinin daha etkin olduğunun belirlenmesi için Süper Etkinlik analizi, etkin olmayan ülkeler için potansiyel iyileştirme önerileri geliştirmiştir. En sonda etkinliğe etki eden faktörlerin belirlenmesi için Tobit analizi yapılmıştır. Girdi değişkenleri; doktor sayısı, hemşire sayısı, yatak sayısı’dir. Çıktı değişkenleri ise; doğumda beklenen yaşam süresi, 5 yaş altı ölüm oranı, tüberküloz oranı, kalp damar, kanser veya diyabet hastalıkları sebebi ile ölüm oranı’dır. Çalışmanın sonucunda 48 Afrika ülkesinin sağlık sistemlerinin 2004 yılında CCR yöntemi ile yapılan analizde 9’u, BCC yöntemi ile yapılan analizde ise 21'i, 2010 yılında ise CCR yöntemi ile yapılan analizde 7’si, BCC yöntemi ile yapılan analizde ise 20'si etkin bulunmuştur. Süper etkinlik analizi sonucunda 2004 yılında Senegal ve Kenya, 2010 yılında ise Mali ve Tanzanya en yüksek etkinlik skoruna sahip olmuştur. En az etkinlik skoru almış ülkeler 2004 yılında Güney Afrika Cumhuriyeti, 2010 yılında ise Gabon ve Güney Afrika Cumhuriyeti olmuştur. Tobit regresyon analizi sonuçlarına göre 1000 kişiye düşen hemşire sayısı değişkeni ulusal sağlık sistemlerinin verimsizliğini etkilemede istatistiksel olarak anlamlı bulunmuştur.

References

  • Babalola, T.K. & Moodley, I. (2020) Assessing the Efficiency of Health-care Facilities in Sub-Saharan Africa: A Systematic Review. Health Services Research and Managerial Epidemiology. 2020;7. http://dx.doi.org/10.1177/2333392820919604
  • Banker, R.D. and Thrall, R.M. (1992). Estimation of returns to scale using data envelopment analysis. European Journal of Operational Research 62(1), 74-84.
  • Banker, R.D., Charnes, A. & Cooper, W.W. (1984). Some yöntems for estimating and scale inefficiencies in data envelopment analysis. Management Science, 30, (9), 1078-1092.
  • Bierens, H. J. (2004). The Tobit Model. http://php.scripts.psu.edu/users/h/x/hxb11/EasyRegTours/TOBIT_Tourfiles/TOBIT.PDF
  • Bollou, F., Ngwenyama, O., & Morawczynski, O. (2006). The impact of investments in ICT, health and education on development: a DEA analysis of five African countries from 1993-1999. In 14th European Conference on Information Systems (ECIS 2006), Göteborg, Sweden, 12-14 June (pp. Paper-35). IT University of Göteborg.
  • Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. North-Holland Publishing Company European Journal of Operational Research, 2, 429-444.
  • Cooper, W., Lawrence, W., Seiford, M. and Zhu, J. (2011). Handbook on Data Envelopment Analysis. Second Edition, London: Springer.
  • Çeçen, Z., ve Akbulut, F. (2023). Düşük Gelir Grubunda Yer Alan Ülkelerin Sağlık Göstergelerinin VZA Yöntemiyle İncelenmesi. Gümüşhane Üniversitesi Sosyal Bilimler Dergisi, 14(1), 241-254.
  • Diallo, O. (2016) On the Determinants of the Efficiency and Effectiveness of Sub-Saharan African Health Systems: Beyond Sector-Specific Conditions and Factors. Available at SSRN, 2857198, http://dx.doi.org/10.2139/ssrn.2857198
  • Dünya Bankası (2021). Open Data, https://data.worldbank.org/
  • Erdem, E., ve Çelik, B. (2019). İnsani gelişme ve ekonomik büyüme ilişkisi: bazı Afrika ülkeleri üzerine bir uygulama. Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(17), 13-36.
  • Färe, R., Grosskopf, S., & Lovell, C. (1983). The Structure of Technical Efficiency, Scandinavian Journal of Economics (85), 181-90.
  • Farrel, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290.
  • Henningsen, A. (2015). Estimating Censored Regression Models in R using the censReg Package. https://cran.r-project.org/web/packages/censReg/vignettes/censReg.pdf, (18 Ağustos 2018)
  • Ibrahim, M. D., Daneshvar, S., Hocaoğlu, M. B., & Oluseye, O. W. G. (2019). An estimation of the efficiency and productivity of healthcare systems in sub-Saharan Africa: health-centred millennium development goal-based evidence. Social Indicators Research, 143, 371-389.
  • Kirigia, J. M., Asbu, E. Z., Greene, W., & Emrouznejad, A. (2007). Technical efficiency, efficiency change, technical progress and productivity growth in the national health systems of continental African countries. Eastern Africa Social Science Research Review, 23(2), 19-40.
  • Kirigia, J. M., Asbu, E. Z., Kirigia, D. G., Onwujekwe, O., Fonta, W. M., & Ichoku, H. E. (2011). Technical efficiency of human resources for health in Africa. European Journal of Business and Management, 3(4), 321-345.
  • Koopmans, T. (1951). An analysis of production as an efficient combination of activities, In: Koopmans TC, editor. Activity analysis of production and allocation, New York: Wiley
  • Lari, M. S., & Sefiddashti, S. E. (2021). Measuring the efficiency of health systems: a case of mental health in Middle East and North Africa countries. Iranian journal of public health, 50(5), 1017 -1027.
  • Meddeb, R. (2019). Efficiency of MENA region’s health systems: using DEA approach. International Journal of Innovative Science and Research Technology, 4(7), 1083-1088.
  • Novignon, J., & Nonvignon, J. (2015). Fiscal space for health in sub-Saharan African countries: an efficiency approach. African Journal of Health Economics, 4, 1-11.
  • Seiford, L. M. & Thrall, R. M. (1990). Recent developments in DEA: The mathematical programming approach to frontier analysis, Journal of Econometrics, 46 (1–2 October-November), 7-38.
  • Seiford, L. M. & Zhu. J. (1999). Infeasibility of Super-Efficiency Data Envelopment Analysis Yöntems, INFOR, 37(2), 174-187.
  • Selamzade, F. (2020). An Empirical Analysis of the Relationship Between Health Expenditures and Health Status Indicators in the Countries of the African Union, ÖZMEN M. (Ed.). Economics Studies, Ankara: Akademisyen Kitabevi
  • Selamzade, F., ve Yeşilyurt, Ö. (2021). Evaluation of Health Indicators of OECD Countries By Stochastic Frontier Analysis. Verimlilik Dergisi, (4), 35–49.
  • Sosa-Rubí, S.G., Bautista-Arredondo, S., Chivardi-Moreno, C. et al.. (2021). Efficiency, quality, and management practices in health facilities providing outpatient HIV services in Kenya, Nigeria, Rwanda, South Africa and Zambia. Health Care Manag Sci 24, 41–54. https://doi.org/10.1007/s10729-020-09541-1
  • Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica. 26 (1): 24–36. doi:10.2307/1907382
  • Top, M., Konca, M., & Sapaz, B. (2020). Technical efficiency of healthcare systems in African countries: An application based on data envelopment analysis. Health Policy and Technology, 9(1), 62-68.
  • Torun, N. (2020). Sağlık Hizmetlerinde Etkinlik Ölçümü. Ankara: Gazi Kitabevi
  • WHO Africa, (WHO Regional Office for Africa) (2022). Atlas of African Health Statistics 2022: Health situation analysis of the WHO African Region., https://www.afro.who.int/news/child-dies-every-minute-malaria-africa
  • WHO, (World Health Organization) (2015). A child dies every minute from malaria in Africa, https://www.afro.who.int/news/child-dies-every-minute-malaria-africa
  • WHO, (World Health Organization) (2023a). HIV/AIDS,WHO,Regional Office for Africa, https://www.afro.who.int/health-topics/hivaids
  • Yeşilyurt, Ö. ve Salamov, F. (2017). Türk Devletleri sağlık sistemlerinde etkinliğin ve etkinliğe etki eden faktörlerin süper etkinlik ve tobit modelleriyle değerlendirilmesi, Balkan ve Yakın Doğu Sosyal Bilimler Dergisi, 3(2), 128-138.
  • Yetim, B., İlgün, G. ve Konca, M. (2022) Dünya bankası üyesi ülkelerin sağlık sistemlerinin teknik etkinlik düzeylerinin ölçümü: veri zarflama analizine dayalı bir uygulama. Hacettepe Sağlık İdaresi Dergisi, 25(3), 549-564.
  • Yüksel, O. (2022 a). Türkiye’de bebek ölüm hızının bölgelerarası değerlendirilmesi. Munzur Üniversitesi Sosyal Bilimler Dergisi, 11 (2), 117-131.
  • Yüksel, O. (2022 b). Türkiye’deki bazı sağlık göstergelerinin stokastik sınır analizi yöntemi ile değerlendirilmesi. Uluslararası Sağlık Yönetimi ve Stratejileri Araştırma Dergisi, 8 (3), 362-375

Evaluation of Efficiency Status of Health Systems of African Countries by Data Envelopment and Tobit Analysis

Year 2023, Volume: 11 Issue: Afrika, 204 - 224, 30.09.2023
https://doi.org/10.18506/anemon.1290327

Abstract

The aim of the study is to examine the efficiency levels of the healthcare systems in the African Union countries. For this purpose, constant returns to scale (CCR) and variable returns to scale (BCC) methods of Data Envelopment Analysis were used with the data from 2004 and 2010. Super Efficiency analysis was developed to determine which one was more efficient among the efficient countries and potential improvement suggestions for the inefficient countries. Finally, Tobit analysis was performed to determine the factors affecting efficiency. Input variables are the number of doctors, number of nurses, and number of beds. Output variables are life expectancy at birth, under-five mortality rate, tuberculosis incidence rate, and mortality rate due to cardiovascular, cancer, or diabetes diseases. As a result of the study, it was found that from 48 African countries, the healthcare systems of 9 were efficient in the analysis with the CCR method and 21 in the analysis with the CCR method in 2004, and 7 were efficient in the analysis with the CCR method and 20 in the analysis with the CCR method in 2010. As a result of the super efficiency analysis, Senegal and Kenya in 2004 and Mali and Tanzania in 2010 had the highest efficiency scores. The countries with the lowest efficiency scores were the Republic of South Africa in 2004 and Gabon and the Republic of South Africa in 2010. According to the results of Tobit regression analysis, the variable of the number of nurses per 1000 people was found to be statistically significant in affecting the unproductivity of national healthcare systems.

References

  • Babalola, T.K. & Moodley, I. (2020) Assessing the Efficiency of Health-care Facilities in Sub-Saharan Africa: A Systematic Review. Health Services Research and Managerial Epidemiology. 2020;7. http://dx.doi.org/10.1177/2333392820919604
  • Banker, R.D. and Thrall, R.M. (1992). Estimation of returns to scale using data envelopment analysis. European Journal of Operational Research 62(1), 74-84.
  • Banker, R.D., Charnes, A. & Cooper, W.W. (1984). Some yöntems for estimating and scale inefficiencies in data envelopment analysis. Management Science, 30, (9), 1078-1092.
  • Bierens, H. J. (2004). The Tobit Model. http://php.scripts.psu.edu/users/h/x/hxb11/EasyRegTours/TOBIT_Tourfiles/TOBIT.PDF
  • Bollou, F., Ngwenyama, O., & Morawczynski, O. (2006). The impact of investments in ICT, health and education on development: a DEA analysis of five African countries from 1993-1999. In 14th European Conference on Information Systems (ECIS 2006), Göteborg, Sweden, 12-14 June (pp. Paper-35). IT University of Göteborg.
  • Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. North-Holland Publishing Company European Journal of Operational Research, 2, 429-444.
  • Cooper, W., Lawrence, W., Seiford, M. and Zhu, J. (2011). Handbook on Data Envelopment Analysis. Second Edition, London: Springer.
  • Çeçen, Z., ve Akbulut, F. (2023). Düşük Gelir Grubunda Yer Alan Ülkelerin Sağlık Göstergelerinin VZA Yöntemiyle İncelenmesi. Gümüşhane Üniversitesi Sosyal Bilimler Dergisi, 14(1), 241-254.
  • Diallo, O. (2016) On the Determinants of the Efficiency and Effectiveness of Sub-Saharan African Health Systems: Beyond Sector-Specific Conditions and Factors. Available at SSRN, 2857198, http://dx.doi.org/10.2139/ssrn.2857198
  • Dünya Bankası (2021). Open Data, https://data.worldbank.org/
  • Erdem, E., ve Çelik, B. (2019). İnsani gelişme ve ekonomik büyüme ilişkisi: bazı Afrika ülkeleri üzerine bir uygulama. Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(17), 13-36.
  • Färe, R., Grosskopf, S., & Lovell, C. (1983). The Structure of Technical Efficiency, Scandinavian Journal of Economics (85), 181-90.
  • Farrel, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290.
  • Henningsen, A. (2015). Estimating Censored Regression Models in R using the censReg Package. https://cran.r-project.org/web/packages/censReg/vignettes/censReg.pdf, (18 Ağustos 2018)
  • Ibrahim, M. D., Daneshvar, S., Hocaoğlu, M. B., & Oluseye, O. W. G. (2019). An estimation of the efficiency and productivity of healthcare systems in sub-Saharan Africa: health-centred millennium development goal-based evidence. Social Indicators Research, 143, 371-389.
  • Kirigia, J. M., Asbu, E. Z., Greene, W., & Emrouznejad, A. (2007). Technical efficiency, efficiency change, technical progress and productivity growth in the national health systems of continental African countries. Eastern Africa Social Science Research Review, 23(2), 19-40.
  • Kirigia, J. M., Asbu, E. Z., Kirigia, D. G., Onwujekwe, O., Fonta, W. M., & Ichoku, H. E. (2011). Technical efficiency of human resources for health in Africa. European Journal of Business and Management, 3(4), 321-345.
  • Koopmans, T. (1951). An analysis of production as an efficient combination of activities, In: Koopmans TC, editor. Activity analysis of production and allocation, New York: Wiley
  • Lari, M. S., & Sefiddashti, S. E. (2021). Measuring the efficiency of health systems: a case of mental health in Middle East and North Africa countries. Iranian journal of public health, 50(5), 1017 -1027.
  • Meddeb, R. (2019). Efficiency of MENA region’s health systems: using DEA approach. International Journal of Innovative Science and Research Technology, 4(7), 1083-1088.
  • Novignon, J., & Nonvignon, J. (2015). Fiscal space for health in sub-Saharan African countries: an efficiency approach. African Journal of Health Economics, 4, 1-11.
  • Seiford, L. M. & Thrall, R. M. (1990). Recent developments in DEA: The mathematical programming approach to frontier analysis, Journal of Econometrics, 46 (1–2 October-November), 7-38.
  • Seiford, L. M. & Zhu. J. (1999). Infeasibility of Super-Efficiency Data Envelopment Analysis Yöntems, INFOR, 37(2), 174-187.
  • Selamzade, F. (2020). An Empirical Analysis of the Relationship Between Health Expenditures and Health Status Indicators in the Countries of the African Union, ÖZMEN M. (Ed.). Economics Studies, Ankara: Akademisyen Kitabevi
  • Selamzade, F., ve Yeşilyurt, Ö. (2021). Evaluation of Health Indicators of OECD Countries By Stochastic Frontier Analysis. Verimlilik Dergisi, (4), 35–49.
  • Sosa-Rubí, S.G., Bautista-Arredondo, S., Chivardi-Moreno, C. et al.. (2021). Efficiency, quality, and management practices in health facilities providing outpatient HIV services in Kenya, Nigeria, Rwanda, South Africa and Zambia. Health Care Manag Sci 24, 41–54. https://doi.org/10.1007/s10729-020-09541-1
  • Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica. 26 (1): 24–36. doi:10.2307/1907382
  • Top, M., Konca, M., & Sapaz, B. (2020). Technical efficiency of healthcare systems in African countries: An application based on data envelopment analysis. Health Policy and Technology, 9(1), 62-68.
  • Torun, N. (2020). Sağlık Hizmetlerinde Etkinlik Ölçümü. Ankara: Gazi Kitabevi
  • WHO Africa, (WHO Regional Office for Africa) (2022). Atlas of African Health Statistics 2022: Health situation analysis of the WHO African Region., https://www.afro.who.int/news/child-dies-every-minute-malaria-africa
  • WHO, (World Health Organization) (2015). A child dies every minute from malaria in Africa, https://www.afro.who.int/news/child-dies-every-minute-malaria-africa
  • WHO, (World Health Organization) (2023a). HIV/AIDS,WHO,Regional Office for Africa, https://www.afro.who.int/health-topics/hivaids
  • Yeşilyurt, Ö. ve Salamov, F. (2017). Türk Devletleri sağlık sistemlerinde etkinliğin ve etkinliğe etki eden faktörlerin süper etkinlik ve tobit modelleriyle değerlendirilmesi, Balkan ve Yakın Doğu Sosyal Bilimler Dergisi, 3(2), 128-138.
  • Yetim, B., İlgün, G. ve Konca, M. (2022) Dünya bankası üyesi ülkelerin sağlık sistemlerinin teknik etkinlik düzeylerinin ölçümü: veri zarflama analizine dayalı bir uygulama. Hacettepe Sağlık İdaresi Dergisi, 25(3), 549-564.
  • Yüksel, O. (2022 a). Türkiye’de bebek ölüm hızının bölgelerarası değerlendirilmesi. Munzur Üniversitesi Sosyal Bilimler Dergisi, 11 (2), 117-131.
  • Yüksel, O. (2022 b). Türkiye’deki bazı sağlık göstergelerinin stokastik sınır analizi yöntemi ile değerlendirilmesi. Uluslararası Sağlık Yönetimi ve Stratejileri Araştırma Dergisi, 8 (3), 362-375
There are 36 citations in total.

Details

Primary Language Turkish
Subjects Sports Science and Exercise (Other)
Journal Section Research Article
Authors

Fuad Selamzade 0000-0002-2436-8948

Özgür Yeşilyurt 0000-0001-9252-3375

Early Pub Date September 29, 2023
Publication Date September 30, 2023
Acceptance Date July 31, 2023
Published in Issue Year 2023 Volume: 11 Issue: Afrika

Cite

APA Selamzade, F., & Yeşilyurt, Ö. (2023). Afrika Ülkeleri Sağlık Sistemlerinin Etkinlik Durumlarının Veri Zarflama ve Tobit Analizleriyle Değerlendirilmesi. Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 11(Afrika), 204-224. https://doi.org/10.18506/anemon.1290327

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