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

Atalan Abdulkadir; Dönmez Cem

doi:https://dx.doi.org/10.26650/acin.836372

Research Article

DOI :10.26650/acin.836372 IUP :10.26650/acin.836372 Full Text (PDF)

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

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.

Keywords: OECD Countries, Healthcare Expenditure, Healthcare Expenditure Per Capita, Multi-Objective Optimization Model, Statistical Analysis

DOI :10.26650/acin.836372 IUP :10.26650/acin.836372 Full Text (PDF)

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

Abdulkadir Atalan, Cem Çağrı Dönmez

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.

Keywords: OECD Ülkeleri, Sağlık Harcamaları, Kişi Başına Düşen Sağlık Harcamaları, Çok Amaçlı Optimizasyon Modeli, İstatistiksel Analiz

References

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. google scholar
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 google scholar
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. google scholar
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 google scholar
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 google scholar
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 google scholar
Atalan, A., Cinar, Z., & Cinar, M. (2020). A TRENDLINE ANALYSIS FOR HEALTHCARE EXPENDITURE PER CAPITA OF OECD MEMBERS. Sigma Journal OfEngineering And Natural Sciences, 10(3), 23-35. google scholar
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 google scholar
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 google scholar
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 google scholar
C., W. M., & B., S. W. (1977). Foundations of Cost-Effectiveness Analysis for Health and Medical Practices. New England Journal ofMedicine, 296(13), 716-721. https://doi.org/10.1056/NEJM197703312961304 google scholar
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 google scholar
Deb, K., & Kalyanmoy, D. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc. google scholar
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 google scholar
Gerdtham, U. G., S0gaard, J., Andersson, F., & Jönsson, B. (1992). An econometric analysis of health care expenditure: A cross-section study of the google scholar
OECD countries. Journal ofHealth Economics, 11(1), 63-84. https://doi.org/https://doi.org/10.1016/0167-6296(92)90025-V google scholar
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 google scholar
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. google scholar
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 google scholar
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 google scholar
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 google scholar
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 google scholar
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 google scholar
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 google scholar
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 google scholar
Ngatchou, P., Zarei, A., & El-Sharkawi, A. (2005). Pareto Multi Objective Optimization. Proceedings ofthe 13th International Conference on, Intelligent Systems Application to Power Systems, 84-91. https://doi.org/10.1109/ISAP.2005.1599245 google scholar
OECD. (2016a). OECD Data Health Expenditure (Percent of GDP). https://doi.org/http://stats.oecd.org/Index.aspx?DataSetCode=SHA google scholar
OECD. (2016b). OECD Data Health Expenditure Per Capita. https://doi.org/https://data.oecd.org/healthres/health-spending.htm google scholar
OECD. (2017a). Life expectancy at birth 2015. https://doi.org/https://data.oecd.org/healthstat/life-expectancy-at-birth.htm google scholar
OECD. (2017b). OECD Data Health Care Resources 2015. https://doi.org/http://stats.oecd.org/Index.aspx?DataSetCode=HEALTH_REAC google scholar
OECD. (2017c). Pharmaceutical spending Total, % of health spending, 2015. https://doi.org/https://data.oecd.org/healthres/pharmaceutical-spending.htm google scholar
OECD. (2017d). The Organisation for Economic Co-operation and Development: Members and partners. http://www.oecd.org/about/membersandpartners/#d. en.194378 google scholar
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 google scholar
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 google scholar
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 google scholar
WBG. (2017). The World Bank, GDP Per Capita, (current international $) 2016. https://doi.org/https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD google scholar

Citations

Copy and paste a formatted citation or use one of the options to export in your chosen format

EXPORT

APA

Atalan, A., & Dönmez, C.Ç. (2021). Evaluation of Healthcare Economics of OECD Countries: Multi-Objective Statistical Optimization Model. Acta Infologica, 5(1), 197-206. https://doi.org/10.26650/acin.836372

AMA

Atalan A, Dönmez C Ç. Evaluation of Healthcare Economics of OECD Countries: Multi-Objective Statistical Optimization Model. Acta Infologica. 2021;5(1):197-206. https://doi.org/10.26650/acin.836372

ABNT

Atalan, A.; Dönmez, C.Ç. Evaluation of Healthcare Economics of OECD Countries: Multi-Objective Statistical Optimization Model. Acta Infologica, [Publisher Location], v. 5, n. 1, p. 197-206, 2021.

Chicago: Author-Date Style

Atalan, Abdulkadir, and Cem Çağrı Dönmez. 2021. “Evaluation of Healthcare Economics of OECD Countries: Multi-Objective Statistical Optimization Model.” Acta Infologica 5, no. 1: 197-206. https://doi.org/10.26650/acin.836372

Chicago: Humanities Style

Atalan, Abdulkadir, and Cem Çağrı Dönmez. “Evaluation of Healthcare Economics of OECD Countries: Multi-Objective Statistical Optimization Model.” Acta Infologica 5, no. 1 (Nov. 2024): 197-206. https://doi.org/10.26650/acin.836372

Harvard: Australian Style

Atalan, A & Dönmez, CÇ 2021, 'Evaluation of Healthcare Economics of OECD Countries: Multi-Objective Statistical Optimization Model', Acta Infologica, vol. 5, no. 1, pp. 197-206, viewed 22 Nov. 2024, https://doi.org/10.26650/acin.836372

Harvard: Author-Date Style

Atalan, A. and Dönmez, C.Ç. (2021) ‘Evaluation of Healthcare Economics of OECD Countries: Multi-Objective Statistical Optimization Model’, Acta Infologica, 5(1), pp. 197-206. https://doi.org/10.26650/acin.836372 (22 Nov. 2024).

MLA

Atalan, Abdulkadir, and Cem Çağrı Dönmez. “Evaluation of Healthcare Economics of OECD Countries: Multi-Objective Statistical Optimization Model.” Acta Infologica, vol. 5, no. 1, 2021, pp. 197-206. [Database Container], https://doi.org/10.26650/acin.836372

Vancouver

Atalan A, Dönmez CÇ. Evaluation of Healthcare Economics of OECD Countries: Multi-Objective Statistical Optimization Model. Acta Infologica [Internet]. 22 Nov. 2024 [cited 22 Nov. 2024];5(1):197-206. Available from: https://doi.org/10.26650/acin.836372 doi: 10.26650/acin.836372

ISNAD

Atalan, Abdulkadir - Dönmez, CemÇağrı. “Evaluation of Healthcare Economics of OECD Countries: Multi-Objective Statistical Optimization Model”. Acta Infologica 5/1 (Nov. 2024): 197-206. https://doi.org/10.26650/acin.836372

Volume 5, Issue 12021, P. 197-206

TIMELINE

Submitted	05.12.2020
Accepted	11.03.2021
Published Online	27.07.2021

LICENCE

Attribution-NonCommercial (CC BY-NC)

This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.

Acta Infologica