G7 Ülkelerinin Sağlık Performanslarının Analizi: LPI-HI Tabanlı DNMA Yöntemi ile Bir Uygulama

Öz

Amaç: Bu çalışmanın amacı, G7 ülkelerinin sağlık performansını analiz etmektir. Gereç ve Yöntem: Bu çalışmada, G7 ülkelerinin sağlık performansları, söz konusu ülkelerin en güncel 2023 Legatum Refah Endeksi-Sağlık Göstergesi (LPI-HI) verileri ile LPI-HI tabanlı DNMA çok kriterli karar verme (ÇKKV) yöntemi kullanılarak ölçülmüştür. Bulgular: LPI-HI tabanlı DNMA yöntemine göre belirlenen ülkelerin sağlık performansı sıralamaları Fransa, Birleşik Krallık, Kanada, İtalya, ABD, Almanya ve Japonya olarak tespit edilmiştir. Ayrıca, yalnızca Fransa ve Birleşik Krallık'ın ortalama performans değerini aşan sağlık performanslarına sahip olduğu gözlemlenmiştir. LPI-HI tabanlı DNMA MCDM yönteminin duyarlılık analizine göre duyarlı olduğu, karşılaştırmalı analizine göre güvenilir olduğu, simülasyon analizine göre ise sağlam ve istikrarlı olduğu tespit edilmiştir. Sonuç: Kanada, İtalya, ABD, Almanya ve Japonya'nın ortalama değerin altında kalan sağlık performanslarına sahip oldukları ve bu ülkelerin sağlık performanslarını iyileştirerek küresel ekonomiye daha önemli katkılarda bulunmaları gerektiği düşünülmektedir. Metodolojik olarak, duyarlılık, karşılaştırmalı ve simülasyon analizi sonuçları, G7 ülkelerinin sağlık performanslarının 2023 LPI-HI kriterleri kullanılarak LPI-HI tabanlı DNMA yöntemi ile ölçülebileceğini göstermektedir.

Anahtar Kelimeler

• sağlık performansı
• G7
• LPI-HI tabanlı DNMA

An Analysis of the Health Performance of G7 Countries: An Application Using the LPI-HI-Based DNMA Method

Öz

Aim: The purpose of this study is to analyze the health performance of G7 countries. Materials and Methods: In the study, the health performances of G7 countries were measured using the most recent and up-to-date 2023 Legatum Prosperity Index-Health Indicator (LPI-HI) data and the LPI-HI-based DNMA multi-criteria decision-making method (MCDM). Results: The health performance rankings of the countries, based on the LPI-HI-based DNMA method, have been identified as France, UK, Canada, Italy, USA, Germany, and Japan. It was also observed that only France and the UK have health performances exceeding the average performance value. Additionally, according to the sensitivity analysis of the LPI-HI based DNMA MCDM method, it was found to be sensitive; according to the comparative analysis, it was credible and reliable; and according to the simulation analysis, it was robust and stable. Conclusion: It is believed that Canada, Italy, the USA, Germany, and Japan, which have health performances below the average value, need to improve their health performance to contribute more significantly to the global economy. Methodologically, the sensitivity, comparative, and simulation analysis results indicate that the health performances of G7 countries can be measured using the 2023 LPI-HI criteria with the LPI-HI-based DNMA method.

Anahtar Kelimeler

• Health performance
• G7
• LPI-HI based DNMA

Kaynakça

1. Bergeron BP. Performance management in healthcare from key performance indicators. New York: Productivity Press;2018.
2. Wismar M, Maier CB, Glino AI, Dussault G, Figueras J, et al. Health Professional mobility and health systems. United Kingdom: World Health Organization;2011.
3. Greener I. Comparing Health Systems. Bristol: Policy Press;2023.
4. Holtz C. Global health care: Issues and policies: Issues and policies. Burlington: Jones & Bartlett Learning;2020.
5. Moullin M, Delivering excellence in health and social care: Quality, excellence and performance measurement. Berkshire:Open University Press;2002.
6. Boz, C, Önder E. OECD ülkelerinin sağlık sistemi performanslarinin değerlendirilmesi. Sosyal Güvence Dergisi, 2017;6(11):24-61.
7. Verhoeven M, Gunnarsson V, Carcillo S. Education and health in G7 countries: achieving better outcomes with less spending. Washington: International Monetary Fund Press;2007.
8. Ezoe S, Hashimoto J. Health outcomes of the G7 Hiroshima summit: Breaking the cycle of panic and neglect and achieving UHC. Lancet, 2023;401:2091-2093. DOI: 10.1016/S0140-6736(23)01230-8.

9. Paviotti I, Greco E, Maremonti F. Advancing the global health agenda of G7 the challenge of malaria elimination. Rome: Istituto Affari Internazionali (IAI);2024.
10. Dwight Richard. Searching for real maintenance performance measures. Journal of Quality in Maintenance Engineering, 1999;5(3):258-275. DOI: 10.1108/13552519910282728.
11. Hauber R. Performance measurement in der forschung und entwicklung. Wiesbaden: Gabler Verlag;2002.
12. Grüning M. Performance-measurement-systeme: Messung und steuerung von unternehmensleistung. Wiesbaden: Gabler Edition Wissenschaft;2002.
13. Andersen B, Fagerhaug T, Performance measurement explained: Designing and implementing your state-of-the-art system. Milwaukee: Asq Pr; 2001
14. Sonnentag S, Frese M. Performance concepts and performance theory. In Sonnentag S. New Jersey: John Wiley & Sons;2002.p.3-25. DOI: 10.1002/0470013419.ch1
15. Wang J, Dean TJ, Bos E, Preker A, Peabody J. Measuring country performance on health: Selected indicators for 115 countries. Chicago: World Bank Publication;1999.
16. Loevinsohn B. Performance based contracting for health services in developing countries: A toolkit. Chicago: World Bank Publication;2008.
17. Gittell J H. High performance healthcare: Using the power of relationships to achieve quality, efficiency and resilience. New York: McGraw Hill;2009.
18. Emanuel E J. Which country has the world's best health care? New York: PublicAffairs; 2020.
19. Papanicolas I, Rajan D, Karanikolos M. Assessing health systems performance for UHC: Rationale and approach. In Papanicolas I. Rajan D, Karanikolos M, Soucat A, Figueras J, Geneve: World Health Organization; 2022,p.1-7
20. Smith PC, Mossialos E, Papanicolas I. Performance Measurement forhealth system ımprovement: Experiences, challenges and prospects. Copenhagen: WHO Regional Office for Europe;2008.
21. Bell JA, Nuzzo JB, Global health security index: Advancing collective action and accountability amid global crisis. Washington: Nuclear Threat Initiative;2021
22. WHO. Monitoring health and health system performance in the Eastern Mediterranean Region: Core indicators and indicators on the health-related Sustainable Development Goals 2019. Cairo: WHO Regional Office for the Eastern Mediterranean;2020.
23. PAHO, WHO. Health indicators: Conceptual and operational considerations. Washington: WHO Regional Office for Americans;2019.
24. OECD. Health at a glance 2023: OECD indicators. Paris: OECD Publishing;2023. DOI: 10.1787/7a7afb35-en
25. BASF-2023. Health Performance Index (HPI). BASF Web Site. May 12, 2023. Available at:https://www.basf.com/global/en/who-we-are/sustainability/we-value-people-and-treat-them-with-respect/employees/occupational-medicine-and-health. Accesses on July 25, 2024.
26. Legatum Institute. The 2023 legatum prosperity index. London:The Legatum Institute Foundation;2023.
27. WHO. Innovative technologies that address global health concerns. Geneva: World Health Organization Press;2010.
28. Cucciniello M, Nasi G. Evaluation of the Impacts of innovation in the health care sector: A comparative Analysis. Public Management Review, 2014;16:90-116.
29. Moreira MRA, Gherman M, Sousa PSA. Does innovation influence the performance of healthcare organizations? Innovation Organization & Management, 2017;19(3):335-352.
30. Nassani AA, Javed A, Rosak-Szyrocka J, Pilar L, Yousaf Z, Haffar M. Major Determinants of ınnovation performance in the context of healthcare sector. Int. J. Environ. Res. Public Health, 2023;20:1-14. DOI: 10.3390/ijerph20065007.
31. Dimble V, Menon N. Health policy, health outcomes, and economic growth. International Growth Centre, 2017;Policy note:35408:1-14.
32. Bloom DE, Kuhn M, Prettner K. Health and economic growth. Bonn: The IZA Institute of Labor Economics;2018
33. Ridhwan MM, Nijkamp P, Ismail A. Irsyad LM. The effect of health on economic growth: ameta-regression analysis. Empirical Economics, 2022;63:3211–3251. DOI: 10.1007/s00181-022-02226-4.
34. Islam M M, Mondal MNI, Khoj H. Effects of health factors on GDP growth: Empirical evidence from Saudi Arabia. Sustainability, 2023;15:1-22. DOI: 10.3390/su15118732.
35. Fumagalli E, Pintor AMP, Suhrcke M. The impact of health on economic growth: A narrative literature review. Health Policy, 2024;143:1-15. DOI: 10.1016/j.healthpol.2024.105039.
36. Previtali F, Picco E, Gragnano A, Miglioretti M. The relationship between work, health and job performance for a sustainable working life: A case study on older manual employees in an Italian steel factory. Int. J. Environ. Res. Public Health, 2022;19:1-14. DOI: 10.3390/ijerph192114586.
37. Chang R. The Impact of employees’ health and well-being on job. Journal of Education, Humanities and Social Sciences, 2024;29: 372-378.
38. Kanberoğlu Z, Günsan N. Relationship between sustainable development and health the case of Türkiye. International Journal of Contemporary Economics and Administrative Sciences, 2018;8(2):114-128.
39. Drastichová M, Filzmoser P, Gajanin R. Relationships between wellbeing and sustainable development in a group of selected developed countries. Problems of Sustainable Development, 2023;18(2):49-77. DOI: 10.35784/preko.3941.
40. Yiğit A. Performance analysis of OECD countries based on health outcomes and expenditure indicators. Journal of International Health Sciences and Management, 2019;5(9):114-123.
41. Sarıyıldız AY. Evaluation of the health performances of the regions affiliated to the the ministry of health by multi-criteria decision making techniques. Journal of Health Science Medicine, 2022;5(6):1562-1567. DOI: 10.32322/jhsm.1136265.
42. Sielska A. Comparison of healthcare performance and its determinants in European Countries using the TOPSIS approach. Warsaw Forum of Economic Sociology, 2019;2(20):71-94.
43. Bordbar N, Shojaei P, Kavosi Z, Joulaei H, Ravangard R, Bastani B. Comparison of health status indicators in Iran with the eastern mediterranean countries using multiple attribute decision-making methods. Iranian Journal of Medical Sciences 2022;47(6):566-576.
44. Torkayesh AE, Pamucar D, Ecer F, Chatterjee P. An integrated BWM-LBWA-CoCoSo framework for evaluation of healthcare sectors in Eastern Europe. Socio-Economic Planning Sciences, 2021;78:101052. DOI: 10.1016/j.seps.2021.101052.
45. Sevim F, Ugurluoglu Aldogan E, Evaluation of health systems performance of OECD countries using MOORA method. Journal of Health Management, 2024;26(1):172-183. DOI:10.1177/09720634231215131.
46. Durur Fatih, Turgut M. Evaluation of health system performance of G7 countries. V. International Cappadocia Scientific Research Congress. Cappadocia/Nevşehir: Academic Publischers, 2023:927-932.
47. McBride B, Hawkes S, Kent B. Soft power and global health: The sustainable development goals (SDGs) era health agendas of the G7, G20 and BRICS. BMC Public Health, 2019;19(185):1-14. DOI: 10.1186/s12889-019-7114-5.
48. Ministry of Foreign Affairs of Japan-2023. G7 hiroshima leaders’ communiqué-G7 Hiroshima Summit Web site. July 8, 2023. Available at:https://www.mofa.go.jp/ms/g7hs_s/page1e_000666.html. Accessed on July 15, 2024.
49. Wu X, Liao H. Comparison Analysis between DNMA Method and other MCDM Methods. ICSES Transaction on Neural and Fuzzy Computing, 2019;2(1):4-10.
50. Ecer F. Çok kriterli karar verme. Ankara: Seçkin Yayıncılık;2020
51. Nie S, Huchang LL, Wu X, Tang M, Al-Barakati A. Hesitant fuzzy linguistic dnma method with cardinal consensus reaching process for shopping mall location selection. International Journal of Strategic Property Management, 2019;23(6):420–434. DOI: /10.3846/ijspm.2019.10851
52. Ecer F, Zolfani S H. Evaluating economic freedom via a multi-criteria MEREC-DNMA model-based composite system: Case of opec countries. Technological and Economic Development of Economy, 2022;28(4):1158–1181. DOI: 10.3846/tede.2022.17152.
53. Dündar S. Performance analysis of regional development agencies by LMAW-DNMA methods. Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 2023;18(2):354–380. DOI: 10.17153/oguiibf.1234630.
54. Lukić R. Analysis of research and development performance indicators of The European Union and Serbia. European Project Management Journal, 2023;13(2):63-77. DOI: 10.56889/ryju6757
55. Mishra R, Rani P, Saha A, Hezam IM, Cavallaro F, Chakrabortty RK. An extended DNMA-based multi-criteria decision-making method and its application in the assessment of sustainable location for a lithium-ion batteries’ manufacturing plant. Heliyon,2023;9:1-24. DOI: 10.1016/j.heliyon.2023.e14244
56. Rahimi M, Kumar P, Moazzamigodarzi M, Mishra AR. Digital transformation challenges in sustainable financial service systems using novel interval valued Pythagorean fuzzy double normalization based multiple aggregation approach. Environment, Development and Sustainability, 2022:1-33. DOI: 10.1007/s10668-022-02719-3
57. Acar E, Tama Birkocak D, Özdağoğlu A, Ünal Z, Özdemir G, Abreu MJ. Prioritizing sustainable denim fabric through integrated decision-making framework. Materials, 2024;17:1-16. DOI: 10.3390/ma17133291
58. Gigovič L, Pamučar D, Bajič Z, Milićević M. The Combination of expert judgment and GIS-MAIRCA analysis for the selection of sites for ammunition depots. Sustainability, 2016;8(4):1-30. DOI: 10.3390/su8040372
59. Keshavarz-Ghorabaee M, Amiri M, Zavadskas EK. Turskis Z, Antucheviciene J. Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 2021;13:1-20.
60. Walters SJ. Quality of life outcomes in clinical trials and health-care evaluation: A practical guide to analysis and interpretation. New York: Wiley; 2009.