GELİR GRUPLARINA GÖRE COVID-19 PANDEMİSİNDE SAĞLIK SİSTEM PERFORMANSI ETKİNLİĞİNİN ÖLÇÜLMESİ

Abstract

Veri Zarflama Analizi (VZA), araştırmacıların ve politika yapıcıların en iyi uygulamaları belirlemesine, performansı karşılaştırmasına ve sağlık sistemlerinde iyileştirme alanlarını belirlemesine olanak tanımaktadır. Bu çalışmanın amacı, 4 farklı gelir grubunda yer alan ülkelerin COVID-19 pandemisinde sağlık hizmetleri performans etkinliklerini belirlemek ve söz konusu ülkeler arasında karşılaştırma yapmaktadır. Çalışma kapsamında örneklem seçimi yapılmayıp ülkelerin verilerine ulaşılmaya çalışılmıştır. Veriler Dünya Bankası (DB) veri tabanından elde edilmiştir. Verileri tam olan 88 ülke, düşük gelirli, alt orta gelirli, üst orta gelirli ve yüksek gelirli şeklinde dört farklı gelir gruplarına ayrılmıştır. 2019 yılı COVID-19 öncesini, 2020 yılı COVID-19 pandemisi sırasında ülkelerin sağlık sistem performansını ölçmek amacıyla kullanılmıştır. Veriler, VZA ile analiz edilmiştir. Analiz sonucunda COVID-19 pandemisinde sağlık sistem performanslarına göre düşük gelirli ülkelerin %7’sinde (n=1), alt orta gelirli ülkelerin %12’sinde (n=2), üst orta gelirli ülkelerin %38’nde (n=9), yüksek gelirli ülkelerin %61’nde (n=20) etkinlik skorlarında azalış meydana gelmiştir. COVID 19 öncesi döneme göre en fazla azalışın yüksek gelirli ve üst orta gelirli ülkelerde görülmüştür. COVID-19 salgını sırasında ülkelerin sağlık sistemi göstergeleri iyi olsa bile, büyük şehir nüfusu, uluslararası seyahat ve önleyici tedbirlerin uygulanmasındaki zorluklar gibi faktörlerden üst-orta gelirli ve yüksek gelirli ülkelerin daha fazla etkilenmesi muhtemeldir.

Keywords

• COVID 19
• Veri Zarflama Analizi
• Sağlık Sistem Performansı
• Gelir Grupları

MEASURING HEALTH SYSTEM PERFORMANCE EFFICIENCY IN THE COVID-19 PANDEMIC BY INCOME GROUPS

Abstract

Data Envelopment Analysis (DEA) allows researchers and policymakers to determine best practices, compare performance, and identify areas for improvement in healthcare systems. The aim of this study is to determine the healthcare service performance efficiency of countries in four different income groups during the COVID-19 pandemic and make comparisons among these countries. In the scope of the study, no sampling was conducted, and efforts were made to access the data of countries. The data were obtained from the World Bank database. The 88 countries with complete data were categorized into four different income groups: low-income, lower-middle-income, upper-middle-income, and high-income. The year 2019 was used as the pre-COVID-19 period, while the year 2020 was used to measure the healthcare system performance of countries during the COVID-19 pandemic. The data were analyzed using VZA. As a result of the analysis, during the COVID-19 pandemic, a decrease in efficiency scores was observed in 7% of low-income countries (n=1), 12% of lower-middle-income countries (n=2), 38% of upper-middle-income countries (n=9), and 61% of high-income countries (n=20) based on their healthcare system performance. The highest decrease compared to the pre-COVID-19 period was observed in high-income and upper-middle-income countries. Even if countries had good healthcare system indicators during the COVID-19 pandemic, factors such as a large urban population, international travel, and challenges in implementing preventive measures are likely to have a greater impact on upper-middle-income and high-income countries

Keywords

• COVID-19
• Data Envelopment Analysis
• Health System Performance
• Income Groups

References

1. Adang, E. M., & Borm, G. F. (2007). Is There an association between economic performance and public satisfaction in health care? The European Journal of Health Economics, 8(3), 279-285.
2. Cansever, İ. H., & Şenol, O. (2022). Gelişmiş ülkelerin sağlık sistemleri verimlilikleri ile COVID-19 performansları ilişkisi üzerine bir araştırma. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 22(2), 611-628.
3. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
4. Choi, E. M. (2021). COVID-19 vaccines for low-and middle-income countries. Transactions of the Royal Society of Tropical Medicine and Hygiene, 115(5), 447-456.
5. Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software (Vol. 2, p. 489). New York: Springer.
6. De Cos, P. H., & Moral-Benito, E. (2014). Determinants of health-system efficiency: evidence from OECD countries. Int J Health Care Finance Econ, 14, 69–93.
7. Duran, D., Bauhoff, S., Berman, P., Gaudet, T., Konan, C., Ozaltin, E., & Kruk, M. E. (2020). The role of health system context in the design and implementation of performance-based financing: evidence from Cote d’Ivoire. BMJ Global Health, 5(9), e002934.
8. Flaxman, S., Mishra, S., Gandy, A., Unwin, H. J. T., Mellan, T. A., Coupland, H., ... & Bhatt, S. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584(7820), 257-261.

9. Gannon, B. (2004). Technical efficiency of hospitals in Ireland (No. HRBWP18).
10. Hadad, S., Hadad, Y., & Simon-Tuval, T. (2013). Determinants of healthcare system’s efficiency in OECD countries. The European Journal of Health Economics, 14(2), 253-265.
11. Hollingsworth, B. (2008). The measurement of efficiency and productivity of health care delivery. Health Economics, 17(10), 1107-1128.
12. 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(1), 371-389.
13. IOM. (2022). National Migration Health Physicianb https://www.iom.int/sites/g/files/tmzbdl486/files/inline-files/national-migration-health-physician-nob-zambia-2022-correct.pdf Erişim tarihi: 02.06.2023
14. Mete, B., Şimşir, İ., & Kırılmaz, H. (2022). COVID-19 pandemisinde sağlık sistemlerinin performansı üzerine geleneksel bir derleme. Turkiye Klinikleri J Health Sci, 7(3), 919-30.
15. Mirmirani, S., Li H. C., & Ilacqua, J. A. (2008). Health care efficiency in transition economies: an application of data envelopment analysis. International Business & Economics Research Journal, 7(2), 47-56.
16. Patanavanich, R., & Glantz, S. A. (2020). Smoking is associated with COVID-19 progression: a meta-analysis. Nicotine and tobacco research, 22(9), 1653-1656.
17. Ranney, M. L., Griffeth, V., & Jha, A. K. (2020). Critical supply shortages—the need for ventilators and personal protective equipment during the COVID-19 pandemic. New England Journal of Medicine, 382(18), e41.
18. Retzlaff-Roberts, D., Chang, C. F., & Rubin, R. M. (2004). Technical efficiency in the use of health care resources: A comparison of OECD countries. Health Policy, 69, 55–72.
19. Sel, A. (2021). COVID 19 pandemisinde sağlık sistemi gelişmelerinin etkinliğinin ölçülmesi: G-20 üzerine bir inceleme. Kırklareli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(2), 181-202.
20. Selamzade, F., & Özdemir, Y. (2020). COVID-19’a karşı OECD ülkelerinin etkinliğinin VZA ile değerlendirilmesi. Electronic Turkish Studies, 15(4), 977-991.
21. Shirouyehzad, H., Jouzdani, J., & Khodadadi Karimvand, M. (2020). Fight against COVID-19: a global efficiency evaluation based on contagion control and medical treatment. Journal of Applied Research on Industrial Engineering, 7(2), 109-120.
22. Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38(3-4), 145-156.
23. Woldometers. (2023). COVID-19 Coronavirus Pandemic. https://www.worldometers.info/coronavirus/ Erişim tarihi: 02.06.2023
24. WHO. (2018). Tobacco. https://www.who.int/health-topics/tobacco Erişim tarihi: 01.06.2023
25. World Bank. (2023a). DataBank. https://data.worldbank.org/ Erişim tarihi:30.05.2023
26. World Bank. (2023b). New World Bank country classifications by income level: 2022-2023. https://blogs.worldbank.org/opendata/new-world-bank-country-classifications-income-level-2022-2023 Erişim tarihi: 01.06.2023
27. Yesilaydin, G. (2019). Examination of differences in health indicators between efficient and inefficient countries. Pak J Med Sci., 35(1), 172-176.