OECD Ülkelerinin Sağlık ve COVID-19 Göstergelerine Yönelik Bir Değerlendirme

Year 2023, Volume: 17 Issue: 3 - Turkish Journal of Family Medicine and Primary Care, 338 - 349, 20.09.2023

Mustafa Filiz

https://doi.org/10.21763/tjfmpc.1251649

Abstract

Amaç: Bu çalışmada amaç, sağlık göstergelerinin, COVID-19 göstergelerine etkisinde OECD ülkelerinin kümele dağılımının ve başarı sıralamasının ortaya konmasıdır.
Materyal-Metot: Araştırmanın evreni 38 adet OECD ülkesi olup, örneklemi ise kullanılan değişkenlere yönelik erişim sağlanan 30 adet ülke oluşturmaktadır. Veriler OECD ve Dünya Bankasının resmi sitelerinden elde edilmiştir. Çalışmada bir ülkenin sağlık göstergelerini temsil ettiği kabul edilen beş adet değişken ve COVID-19 ile ilgili olarak dört adet değişken kullanılmıştır. Verilerin analizinde kümeleme analizi ve TOPSIS yöntemi kullanılmıştır.
Bulgular: Covid-19 göstergeleri açısından en başarılı kümenin Avusturalya ve Yeni Zelanda’nın bulunduğu küme olduğu görülmüştür. Covid-19 göstergeleri açısından en başarısız kümenin ise İsrail ve Çek Cumhuriyeti’nin bulunduğu küme olmuştur. Ülkelerin kümelenmesinde Covid-19 ölümleri ve Covid-19 vaka sayılarının etkili olduğu görülmüştür. TOPSIS analizi bulgularına göre sağlık ve Covid-19 göstergeleri ortalamasında en başarılı ülkenim İtalya olduğu ve en başarısız ülkenin ise İspanya olduğu saptanmıştır.
Sonuç: Sonuç olarak Covid-19 göstergeleri ve sağlık göstergelerine göre ülkeler kümelendiğinde sağlık arz gücü güçlü ülkelerin Covid-19 göstergelerinin de iyi olacağı anlamı taşımadığı görülmüştür. Diğer yandan sağlık arz gücü düşük ülkelerinde Covid-19 göstergelerinin de kötü olacağı anlamına gelmemektedir.

Keywords

Birinci Basamak, Sağlık Göstergeleri, TOPSIS, Kümeleme, COVID-19

Project Number

NONE

An Assessment on Health And COVID-19 Indicators of OECD Countries

Abstract

Objective: The aim of this study is to reveal the cluster distribution and success ranking of OECD countries in the impact of health indicators on COVID-19 indicators.
Material-Method: The universe of the research is 38 OECD countries, and the sample consists of 30 countries that have access to the variables used. Data were obtained from the official websites of OECD and World Bank. In the study, five variables that are considered to represent a country's health indicators and four variables related to COVID-19 were used. Cluster analysis and TOPSIS method were used in the analysis of the data.
Results: It has been seen that the most successful cluster in terms of COVID-19 indicators is the cluster in which Australia and New Zealand are located. In terms of COVID-19 indicators, the most unsuccessful cluster was Israel and the Czech Republic. According to the TOPSIS analysis findings, it was determined that the most successful country in the average of health and COVID-19 indicators was Italy and the most unsuccessful country was Spain.
Conclusion: As a result, when countries are clustered according to COVID-19 indicators and health indicators, it has been seen that countries with strong health supply power do not mean that the COVID-19 indicators will be good. On the other hand, it does not mean that COVID-19 indicators will be bad in countries with low health supply power.

Keywords

Primary Care, Health Indicators, TOPSIS, Clustering, COVID-19

Supporting Institution

NONE

Project Number

NONE

Thanks

NONE

References

• 1. WHO.. World Health Organization, Coronavirus disease (COVID-19) advice for the public, Recieved from: https://www.cdc.gov/coronavirus/2019-ncov/need-extraprecautions/people-at-higher-risk.html. Access Date: 21.08.2022, 2020.
• 2. OECD. The territorial impact of COVID-19: Managing the crisis across levels of government. http://www.oecd.org/coronavirus/policy-responses/the-territorial-impactof-COVID-19-managing-the-crisis-across-levels-of-government-d3e314e1/, Access Date: 21.09.2022, 2020, 2p.
• 3. Tengilimoğlu, D., Işık, O., & Akbolat, M. Sağlık İşletmeleri Yönetimi. 8. baskı, Ankara: Nobel Yayıncılık, 2012, 136p.
• 4. Gedikli, E., Demir Uslu, Y., Yiğit, P., & Yılmaz, E. Türkiye'de COVİD-19 pandemisinin yönetimi ve joinpoint regresyon yöntemiyle analizi. Turkiye Klinikleri J Health 2021;6(4): 911-20.
• 5. Kringos, D., Carinci, F., Barbazza, E., Bos, V., Gilmore, K., & Groene, O. Managing COVID-19 within and across health systems: why we need performance intelligence to coordinate a global response. Heal Res Policy Syst 2020;18(80): 1-8.
• 6. Borgert, M., Binnekade, J., Paulus, F., Goossens, A., Vroom, M., & Dongelmans, D. Timely İndividual Audit and Feedback Significantly İmproves Transfusion Bundle Complince-A Comparative Study. Int J Qual Health Care 2016;28(5): 601-607.
• 7. Tanne, J.H., Hayasaki, E., Zastrow, M., Pulla, P., Smith, P., & Rada, A.G. COVID-19: How Doctors and Healthcare Systems Are Tackling Coronavirus Worldwide. BMJ 2020;368:m1090.
• 8. International Telecommunication Union. Economic Impact of COVID-19 on digital infrastructure” Report of an Economic Experts Roundtable organized by ITU, Geneva, 2020, 1p.
• 9. OECD. Coronavirus (COVID-19): Cities policy responses (as of 27 March 2020), https://read.oecd-ilibrary.org/view/?ref=126_126769-yen45847kf&title=CoronavirusCOVID-19-Cities-Policy-Responses. Access Date: 21.12.2022, 2020.
• 10. World Health Organization. WHO Director-General’s opening remarks at the media briefing on COVID-19–11 March 2020 Geneva, Switzerland: World Health Organization; https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-atthe-media-briefing-on COVID-19-11-march-2020.Access Date: 21.12.2022, 2020.
• 11. Abonyı, J., & Feil, B. Cluster Analysis For Data Mining and System Identification, 1. Edition, Berlin: Birkhauser Verlag AG, 2007, 204p.
• 12. Everitt, B.S., Landau, S., & Leese, M. Cluster Analysis Arnold. London: A member of the Hodder Headline Group, 2001; 141p.
• 13. Khafaie, M.A., & Rahim, F. Cross-Country Comparison of Case Fatality Rates of COVID-19/SARSCOV-2. Osong Public Health and Research Perspectives 2020;11(2): 74-80
• 14. Cordes, J., & Castro, M.C. Spatial analysis of COVID-19 clusters and contextual factors in new york city. Spatial and Spatio-Temporal Epidemiology, 2020; 34, 100355.
• 15. Verelst, F., Kuylen, E., & Beutels, P. Indications for Healthcare Surge Capacity in European Countries Facing an Exponential Increase in Coronavirus Disease Cases. Eurosurveillance, 25(13), 2000323.
• 16. Demircioğlu, M. & Eşiyok, S. COVID–19 salgını ile mücadelede kümeleme analizi ile ülkelerin sınıflandırılması. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi 2020;Bahar (Covid19-Özel Ek): 369-389 .
• 17. Kartal, E., Balaban, M. E. & Bayraktar, B. Küresel COVID-19 Salgınının Dünyada ve Türkiye’de Değişen Durumu ve Kümeleme Analizi. Journal of Istanbul Faculty of Medicine 2020; 84(1): 9-19.
• 18. Abdullah, D., Susilo, S., Ahmar, A.S., Rusli, R. & Hidayat, R. The Application of K-Means Clustering For Province Clustering in Indonesia of The Risk of The COVID-19 Pandemic Based on COVID-19 Data. Quality & Quantity 2021;56: 1283-1291.
• 19. Cheng-Ru, W., Chin-Tsaı, L., & Hsuan, P. Financial Service of Wealth Management Banking: Balanced Scorecard Approach. Journal of Social Sciences 2008; 4(4): 255-263.
• 20. Zeleny, M. MCDM: In search of new paradigms Yong, Shi; Shouyang, Wang; Gang, Kou; Jyrki, Wallenius (Ed.). New State of MCDM in the 21st Century: Selected Papers of the 20th International Conference on Multiple Criteria Decision Making 2009 içinde (3-12). First Edition. Berlin: Springer-Verlag, 2021.
• 21. Tzeng, G.H., & Huang, J.J. Multiple Attribute decision making: methods and applications. First Edition. New York: CRC Press, 2011, 123p.
• 22. Mohammed, M. A. et al., 2020. Benchmarking Methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS methods. in IEEE, 2020; 8: 99115-99131.
• 23. Majumder, P., Biswas, P., Majumder, S. Application of new TOPSIS approach to ıdentify the most significant risk factor and continuous monitoring of death of COVID-19. Electron J. Gen. Med, 2020; 17(6): em234.
• 24. Hezer, S., Gelmez, E., & Özceylan, E. Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 regional safety assessment. Journal of Infection and Public Health, 2021; 14(6): 775-786.
• 25. Alkan, N., & Kahraman, C. Evaluation of government strategies against COVİD-19 pandemic using qrung orthopair fuzzy TOPSIS method, appl. Soft Comput, 2021; 110: 107653.
• 26. Hezam, I.M., Nayeem, M.K., Foul, A. & Alrasheedi, A.F. COVID-19 Vaccine: A neutrosophic MCDM approach for determining the priority groups. Results Phys, 2021;20: 103654.
• 27. Tokalaş, S. Kamu Sağlık hizmetlerinin satın alınması, Yüksek Lisans Tezi, Marmara Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul, 2006, 77p.
• 28. WHO. World Health Organization, “Risk assessment,” 2020. Accessed: May 27, 2020. [Online]. Available: https://www. who.int/publications-detail/risk. Access Date: 17.08.2022, 2020.
• 29. OECD. OECD Data. http://stats.oecd.org/.Access Date: 17.08.2022, 2019.
• 30. World Bank.World Bank Data. http://data.worldbank.org/indicator Access Date: 17.08.2022, 2019.
• 31. OECD. Health at a Glance 2019: OECD Indicators. OECD Publishing. Paris, 2021, 209-232 p.
• 32. Chen, S.J. & Hwang, C.L. Fuzzy multiple attribute decision making methods and applications. Springer-Verlag, Berlin, 1992.
• 33. Hwang, C.L., & Yoon, K. Multiple attributes decision making methods and applications, Springer, Berlin Heidelberg, 1981.
• 34. Zhu, N., Zhang, D., & Wang, W. China novel coronavirus ınvestigating and research team. A novel coronavirus from patients with pneumonia in China. Engl J Med, 2020; 82(8): 727-733.
• 35. Spellbring, M. Nursing role in health promotion. Nurs Clin North Am. 1991; 26(4): 805-814.
• 36. Stark, M.A., Manning-Walsh, J. & Vliem, S. Caring for self while learning to care for others: A challenge for nursing students. Journal of Nursing Education. 2005; 44(6): 266-270.