SavedAdd your journal

Journals / Acta Infologica / 2026 / Volume: 5 Issue: 1

Evaluation of Healthcare Economics of OECD Countries: Multi-Objective Statistical Optimization Model

OECD Ülkelerine ait Sağlık Ekonomilerinin Değerlendirilmesi: Çok Amaçlı İstatistiksel Optimizasyon Modeli

Abdulkadir Atalan(Gaziantep İslam Bilim ve Teknoloji Üniversitesi)0000-0003-0924-3685
Cem Çağrı Dönmez(MARMARA ÜNİVERSİTESİ)0000-0003-3289-7134
Pages
—
Publication Date
2021-06-29
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The fundamental reason for this situation represents the healthcare sector’s perception as a business in those countries

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Healthcare systems comprise the most crucial factors affecting countries economically.
📊
Key Findings & Data:Within the feasible values obtained by using a multi-objective optimization model (MOOM) because of statistical analysis, the HE of OECD countries was minimized, and an improvement of 29.13% was achieved.
💡
Conclusion & Impact:The fundamental reason for this situation represents the healthcare sector’s perception as a business in those countries
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:OECD countries · Healthcare Expenditure · Healthcare Expenditure Per Capita

Abstract

Healthcare systems comprise the most crucial factors affecting countries economically. In this study, the infrastructure and economic structure of healthcare systems belonging to the Organization for Economic Co-operation and Development (OECD) countries are discussed to evaluate the healthcare expenditures (HE) and healthcare expenditures per capita (pcHE) of OECD members. We have identified factors related to healthcare economics and analyzed the significance ratios of these factors to calculate the feasible values of the factors affecting the healthcare systems in the OECD members by statistical optimization analysis. Within the feasible values obtained by using a multi-objective optimization model (MOOM) because of statistical analysis, the HE of OECD countries was minimized, and an improvement of 29.13% was achieved. The second objective function aimed to maximize the pcHE that was estimated to be at least $5,282.37 for the OECD members. Consequently, in countries that do not have a social healthcare system, it is perceived that HE amounts are excessive. The fundamental reason for this situation represents the healthcare sector’s perception as a business in those countries

Özet

Sağlık sistemleri, ülke ekonomilerini etkileyen en önemli faktörlerden birdir. Bu çalışmada, OECD üyelerinin sağlık harcamalarını (SH) ve kişi başına düşen sağlık harcamalarını (kbSH) değerlendirmek için Ekonomik İşbirliği ve Kalkınma Teşkilatı (OECD) ülkelerine ait sağlık sistemlerinin altyapısı ve ekonomik yapısı tartışılmıştır. İstatistiksel optimizasyon analizi ile OECD üyelerindeki sağlık sistemlerini etkileyen faktörlerin uygulanabilir değerlerini hesaplamak için sağlık ekonomisi ile ilgili faktörleri belirlenerek, bu faktörlerin önemlilik oranlarını analiz ettik. İstatistiksel analiz aracılığıyla çok amaçlı optimizasyon modeli (MOOM) kullanılarak elde edilen uygulanabilir değerler içerisinde OECD ülkelerinin SH’si minimize edilmiş ve % 29,13 oranında iyileşme sağlanmıştır. İkinci amaç fonksiyonu ise OECD üyeleri için kbSH’yi maksimize etmek amacıyla en az kbSH değeri 5.282,37 dolar olarak tahmin edilmiştir. Sonuç olarak sosyal sağlık sistemi olmayan ülkelerde SH miktarlarının aşırı olduğu algılanmaktadır. Bu durumun temel nedeni, sağlık sektörünün o ülkelerdeki bir işletme olarak algılanmaktadır.

🔖

Cite This Article

Atalan, A., Dönmez, C. Ç. (2021). Evaluation of Healthcare Economics of OECD Countries: Multi-Objective Statistical Optimization Model. Acta Infologica, Volume: 5 Issue: 1. https://ajindex.com/articles/1350027

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (35)

  1. Ajami, S., Ketabi, S., & MahmoodAbadi, H. B. (2013). Reducing Waiting Time in Emergency Department at Ayatollah-Kashani Hospital Using Simulation. Journal of Health Administration, 16(51), 84–94.
  2. Albouy, V., Davezies, L., & Debrand, T. (2010). Health expenditure models: A comparison using panel data. Economic Modelling, 27(4), 791–803. https://doi.org/https://doi.org/10.1016/j.econmod.2010.02.006
  3. Arah, O. A., Westert, G. P., Hurst, J., & Klazinga, N. S. (2006). A conceptual framework for the OECD Health Care Quality Indicators Project. International Journal for Quality in Health Care, 18(supply 1), 5–13.
  4. Atalan, A. (2018). Türkiye Sağlık Ekonomisi için İstatistiksel Çok Amaçlı Optimizasyon Modelinin Uygulanması. İşletme Ekonomi ve Yönetim Araştırmaları Dergisi, 1(1), 34–51. http://dergipark.gov.tr/download/article-file/414076
  5. Atalan, A. (2019). THE IMPACTS OF HEALTHCARE RESOURCES ON SERVICES OF EMERGENCY DEPARTMENT: DISCRETE EVENT SIMULATION WITH BOX-BEHNKEN DESIGN. PONTE International Scientific Researchs Journal, 75(6), 12–23. https://doi.org/10.21506/j.ponte.2019.6.10
  6. Atalan, A. (2020). Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi, 9(1), 8–16. https://doi.org/10.37989/gumussagbil.538111
  7. Atalan, A., Cinar, Z., & Cinar, M. (2020). A TRENDLINE ANALYSIS FOR HEALTHCARE EXPENDITURE PER CAPITA OF OECD MEMBERS. Sigma Journal Of Engineering And Natural Sciences, 10(3), 23–35.
  8. Atalan, A., & Donmez, C. (2019). Employment of Emergency Advanced Nurses of Turkey: A Discrete-Event Simulation Application. Processes, 7(1), 48. https://doi.org/10.3390/pr7010048
  9. Baltagi, B. H., & Moscone, F. (2010). Health care expenditure and income in the OECD reconsidered: Evidence from panel data. Economic Modelling, 27(4), 804–811. https://doi.org/https://doi.org/10.1016/j.econmod.2009.12.001
  10. Bichay, N. (2020). Health insurance as a state institution: The effect of single-payer insurance on expenditures in OECD countries. Social Science & Medicine, 113454. https://doi.org/10.1016/j.socscimed.2020.113454
  11. C., W. M., & B., S. W. (1977). Foundations of Cost-Effectiveness Analysis for Health and Medical Practices. New England Journal of Medicine, 296(13), 716–721. https://doi.org/10.1056/NEJM197703312961304
  12. Carides, G. W., Heyse, J. F., & Iglewicz, B. (2000). A regression-based method for estimating mean treatment cost in the presence of right-censoring. Biostatistics, 1(3), 299–313. https://doi.org/10.1093/biostatistics/1.3.299
  13. Deb, K., & Kalyanmoy, D. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc.
  14. Dukhanin, V., Searle, A., Zwerling, A., Dowdy, D. W., Taylor, H. A., & Merritt, M. W. (2018). Integrating social justice concerns into economic evaluation for healthcare and public health: A systematic review. Social Science and Medicine, 198, 27–35. https://doi.org/10.1016/j.socscimed.2017.12.012
  15. Gerdtham, U. G., Søgaard, J., Andersson, F., & Jönsson, B. (1992). An econometric analysis of health care expenditure: A cross-section study of the OECD countries. Journal of Health Economics, 11(1), 63–84. https://doi.org/https://doi.org/10.1016/0167-6296(92)90025-V
  16. Gregori, D., Petrinco, M., Bo, S., Desideri, A., Merletti, F., & Pagano, E. (2011). Regression models for analyzing costs and their determinants in health care: an introductory review. International Journal for Quality in Health Care, 23(3), 331–341. https://doi.org/10.1093/intqhc/mzr010
  17. Iii, J. H. E., Hwang, Y., & Nagarajan, N. J. (2001). Management control and hospital cost reduction: additional evidence. Journal of Accounting and Public Polic, 20(1), 73–88.
  18. Jones, A. M., Lomas, J., & Rice, N. (2015). Healthcare Cost Regressions: Going Beyond the Mean to Estimate the Full Distribution. Health Economics, 24(9), 1192–1212. https://doi.org/10.1002/hec.3178
  19. Liu, Y., Lei, H., Zhang, D., & Wu, Z. (2018). Robust optimization for relief logistics planning under uncertainties in demand and transportation time. Applied Mathematical Modelling, 55(Supplement C), 262–280. https://doi.org/https://doi.org/10.1016/j.apm.2017.10.041
  20. Malehi, A. S., Pourmotahari, F., & Angali, K. A. (2015). Statistical models for the analysis of skewed healthcare cost data: a simulation study. Health Economics Review, 5, 11. https://doi.org/10.1186/s13561-015-0045-7
  21. Mason, K., Duggan, J., & Howley, E. (2017). Multi-objective dynamic economic emission dispatch using particle swarm optimisation variants. Neurocomputing, 270(Supplement C), 188–197. https://doi.org/https://doi.org/10.1016/j.neucom.2017.03.086
  22. McCoskey, S. K., & Selden, T. M. (1998). Health care expenditures and GDP: panel data unit root test results. Journal of Health Economics, 17(3), 369–376. https://doi.org/https://doi.org/10.1016/S0167-6296(97)00040-4
  23. Mihaylova, B., Briggs, A., O’Hagan, A., & Thompson, S. G. (2011). Review of Statistical Methods for Analysing Healthcare Resources and Costs. Health Economics, 20(8), 897–916. https://doi.org/10.1002/hec.1653
  24. Muyl, F., Dumas, L., & Herbert, V. (2004). Hybrid method for aerodynamic shape optimization in automotive industry. Computers and Fluids, 33(5), 849–858. https://doi.org/https://doi.org/10.1016/j.compfluid.2003.06.007
  25. Ngatchou, P., Zarei, A., & El-Sharkawi, A. (2005). Pareto Multi Objective Optimization. Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, 84–91. https://doi.org/10.1109/ISAP.2005.1599245
  26. OECD. (2016a). OECD Data Health Expenditure (Percent of GDP). https://doi.org/http://stats.oecd.org/Index.aspx?DataSetCode=SHA
  27. OECD. (2016b). OECD Data Health Expenditure Per Capita. https://doi.org/https://data.oecd.org/healthres/health-spending.htm
  28. OECD. (2017a). Life expectancy at birth 2015. https://doi.org/https://data.oecd.org/healthstat/life-expectancy-at-birth.htm
  29. OECD. (2017b). OECD Data Health Care Resources 2015. https://doi.org/http://stats.oecd.org/Index.aspx?DataSetCode=HEALTH_REAC
  30. OECD. (2017c). Pharmaceutical spending Total, % of health spending, 2015. https://doi.org/https://data.oecd.org/healthres/pharmaceutical-spending.htm
  31. OECD. (2017d). The Organisation for Economic Co-operation and Development: Members and partners. http://www.oecd.org/about/membersandpartners/#d. en.194378
  32. OECD. (2017e). World Health Organization’s Global Health Workforce Statistics, OECD, supplemented by country data. Physicians (per 1,000 people). https://data.worldbank.org/indicator/SH.MED.PHYS.ZS
  33. Palasca, S., & Jaba, E. (2015). Economic Crisis’ Repercussions on European Healthcare Systems. Procedia Economics and Finance, 23(Supplement C), 525–533. https://doi.org/https://doi.org/10.1016/S2212-5671(15)00568-7
  34. Steiner, M. T. A., Datta, D., Neto, P. J. S., Scarpin, C. T., & Figueira, J. R. (2015). Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil. Omega, 52(Supplement C), 53–64. https://doi.org/https://doi.org/10.1016/j.omega.2014.10.005
  35. WBG. (2017). The World Bank, GDP Per Capita, (current international $) 2016. https://doi.org/https://data.worldbank.org/indicator/NY.GDP.PCAP. PP.CD