Ranking OECD Countries By Using COVID-19 And Health Policy Variables With Fuzzy AHP And Multimoora Methods

Öz

Aim: The aim of this study is to compare performance rankings of OECD countries with the MULTIMOORA method by using the health policy responses and various variables related to COVID-19. Material and Method: Study includes cross-sectional data of 37 OECD countries. Data regarding eleven variables about the current population health status in these countries are collected from open reliable sources. CO- VID-19 health policy responses of the countries recorded in the OECD database were scored and weighted scores computed by using Fuzzy AHP method. The performance rankings of OECD countries were determined by the MULTIMOORA method with use of all variables. Results: Among the policy responses highest ranking score was computed for “mobilizing and protecting health workers” and the lowest score for “increasing access to mental health services” (0,172 and 0,042 points respectively). According to the results of performance rankings by MULTIMOORA, New Zealand was located in the first and Belgium in the last place while Turkey has been the eighteenth. Conclusion: Study findings can be concluded as the COVID-19 performance of the Eastern countries is better than the Western developed countries with the exceptions of Iceland and Czechia. However, it is difficult to draw justifying conclusions about the success or failure of a country in managing the pandemic due to the complex interactions between various variables and the cross-sectional nature of the study data.

Anahtar Kelimeler

• Fuzzy AHP
• COVID-19
• Health Policy
• OECD Countries
• SARS-CoV-2

OECD Ülkelerinin COVID-19 ve Sağlık Politikaları Değişkenlerine göre Bulanık AHP ve MULTIMOORA Yöntemi İle Değerlendirilmesi

Öz

Amaç: Çalışmanın amacı, OECD ülkelerinin COVID-19 için izlediği sağlık politikaları ve diğer ilişkili değişkenlere göre performansının MULTIMOORA yöntemi ile analizidir. Materyal ve Metod: Çalışmada 37 OECD ülkesinin verileri kesitsel olarak incelenmiştir. Ülkelerinin COVID-19 dönemi performansını değerlendirilmesi için 11 değişken belirlenmiştir. Bu dönemde, ülkelerin belirlediği dokuz sağlık politikasının Bulanık AHP yöntemi ile ağırlıkları hesaplanarak ülkeler için sağlık politikası skoru belirlenmiştir. Ardından, MULTIMOORA ile ülkelerin COVID-19 ilişkili performansları analiz edilmiştir. Bulgular: Ülkelerin dokuz COVID-19 ilişkili sağlık politikaları sıralandığında, 0,172 ile en yüksek puan “Sağlık çalışanlarını harekete geçirmek ve korumak”, 0,042 en düşük puan ile “Akıl sağlığı hizmetlerine erişimi arttırmak” bulunuştur. MULTIMOORA ile yapılan COVID-19 pandemi yönetimi analizine göre ise, Yeni Zelanda en yüksek, Belçika en düşük, Türkiye ise on sekizinci sırada performansa sahip ülkeler olarak bulunmuştur. Sonuç: Çalışma bulgularına göre, İzlanda ve Çek Cumhuriyeti hariç, doğu bölgesinde yer alan ülkelerin performansının batı bölgesinde yer alan gelişmiş ülkelerden daha iyi olduğu anlaşılmaktadır. Ancak araştırmanın kesitsel yönteminin yanı sıra, değişkenlerin kendi arasındaki etkileşimleri nedeniyle, ülkelerin pandemiyi yönetmedeki başarı veya başarı- sızlığı hakkında iddialı sonuçlar çıkarmak doğru değildir.

Anahtar Kelimeler

• Analitik Hiyerarşi Süreci
• COVID-19
• MULTIMOORA
• OECD Ülkeleri
• Sağlık Polikaları

Kaynakça

1. 1. Li Q, Guan X, Wu P, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med. 2020;382(13):1199-1207. doi:10.1056/NEJMoa2001316
2. 2. WHO. “The world is not prepared for a pandemic,.” WHO. Published 2020. https://www.who.int/news/item/01-10-2020-the-best-time-to-prevent-the-next-pandemic-is-now-countries-join-voices-for-better-emergency-preparedness
3. 3. Yamey G, Schäferhoff M, Aars OK, et al. Financing of international collective action for epidemic and pandemic preparedness. Lancet Glob Heal. 2017;5(8):e742-e744. doi:10.1016/S2214-109X(17)30203-6
4. 4. Organisation for Economic Co-operation and Development. Strengthening health systems during a pandemic: The role of development finance. Tackling Coronavirus (COVID-19). 2020;(June):1-24.
5. 5. Kilbourne ED. Influenza pandemics of the 20th century. Emerg Infect Dis. 2006;12(1):9-14. doi:10.3201/eid1201.051254
6. 6. Farmer P. Social Inequalities and Emerging Infectious Diseases. Emerg Infect Dis. 1996;2(4):259-269. doi:10.3201/eid0204.960402
7. 7. Bavel JJV, Baicker K, Boggio PS, et al. Using social and behavioural science to support COVID-19 pandemic response. Nat Hum Behav. 2020;4(5):460-471. doi:10.1038/s41562-020-0884-z
8. 8. Bandayrel K, Lapinsky S, Christian M. Information technology systems for critical care triage and medical response during an influenza pandemic: a review of current systems. Disaster Med Public Health Prep. 2013;7(3):287-291. doi:10.1001/dmp.2011.45

9. 9. Chew C, Eysenbach G. Pandemics in the age of Twitter: Content analysis of tweets during the 2009 H1N1 outbreak. PLoS One. 2010;5(11):1-13. doi:10.1371/journal.pone.0014118
10. 10. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497-506.
11. 11. Timeline of WHO’s response to COVID-19. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/interactive-timeline
12. 12. Walker PGT, Whittaker C, Watson OJ, et al. The impact of COVID-19 and strategies for mitigation and suppression in low- And middle-income countries. Science (80- ). 2020;369(6502):413-422. doi:10.1126/science.abc0035
13. 13. Kayı İ, Sakarya S. Policy Analysis of Suppression and Mitigation Strategies in the Management of an Outbreak Through the Example of COVID-19 Pandemic. Infect Dis Clin Microbiol. 2020;2(1):30-41. doi:10.36519/idcm.2020.0009
14. 14. Hsiang S, Allen D, Annan-Phan S, et al. The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature. 2020;584(7820):262-267. doi:10.1038/s41586-020-2404-8
15. 15. Data OW in. Coronavirus Pandemic (COVID-19) - Statistics and Research - Our World in Data. https://ourworldindata.org/coronavirus
16. 16. COVID-19 Corona Tracker. Published 2020. https://www.coronatracker.com/
17. 17. Worldometer. Coronavirus Cases. Worldometer. doi:10.1101/2020.01.23.20018549V2
18. 18. OECD. OECD Statistics. https://stats.oecd.org/
19. 19. OECD iLibrary | Population coverage for health care.
20. 20. Tackling the coronavirus (COVID-19) crisis together: OECD policy contributions for co‑ordinated action.
21. 21. Saaty TL. The analytic hierarchy process: planning. Prior Setting Resour Alloc MacGraw-Hill, New York Int B Co. Published online 1980.
22. 22. Yang CC, Chen BS. Key quality performance evaluation using fuzzy AHP. J Chinese Inst Ind Eng. 2004;21(6):543-550. doi:10.1080/10170660409509433
23. 23. van Laarhoven PJM, Pedrycz W. A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst. 1983;11(1-3):229-241. doi:10.1016/S0165-0114(83)80082-7
24. 24. Buckley JJ. Fuzzy hierarchical analysis. Fuzzy Sets Syst. 1985;17(3):233-247. doi:10.1016/0165-0114(85)90090-9
25. 25. Chang DY. Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res. 1996;95(3):649-655. doi:10.1016/0377-2217(95)00300-2
26. 26. Vahidnia MH, Alesheikh AA, Alimohammadi A. Hospital site selection using fuzzy AHP and its derivatives. J Environ Manage. 2009;90(10):3048-3056. doi:10.1016/j.jenvman.2009.04.010
27. 27. Singh A, Prasher A. Measuring healthcare service quality from patients’ perspective: using Fuzzy AHP application. Total Qual Manag Bus Excell. 2019;30(3-4):284-300. doi:10.1080/14783363.2017.1302794
28. 28. Willem Karel Brauers, Kazimieras Zavadskas E. The MOORA method and its application to privatization in a transition economy. Control Cybern. 2006;35(2):445-469.
29. 29. Brauers WKM, Zavadskas EK. Project management by multimoora as an instrument for transition economies. Technol Econ Dev Econ. 2010;16(1):5-24. doi:10.3846/tede.2010.01
30. 30. Brauers WKM, Zavadskas EK. Multimoora optimization used to decide on a bank loan to buy property. Technol Econ Dev Econ. 2011;17(1):174-188. doi:10.3846/13928619.2011.560632
31. 31. Aytaç Adalı E, Tuş Işık A. The multi-objective decision making methods based on MULTIMOORA and MOOSRA for the laptop selection problem. J Ind Eng Int. 2017;13(2):229-237. doi:10.1007/s40092-016-0175-5
32. 32. Weekly operational update on COVID-19 - 23 October 2020.
33. 33. How to build a better health system: 8 expert essays | World Economic Forum.
34. 34. Tan-Torres Edejer T, Hanssen O, Mirelman A, et al. Projected health-care resource needs for an effective response to COVID-19 in 73 low-income and middle-income countries: a modelling study. Lancet Glob Heal. 2020;8(11):e1372-e1379. doi:10.1016/S2214-109X(20)30383-1
35. 35. COVID-19 Virus Pandemic - Worldometer.
36. 36. Redlener BI, Sachs JD, Hansen S, Hupert N. AND COUNTING – IN THE U . S .”. Published online 2020:0-12.