Evaluation of the health performances of the regions affiliated to the the ministry of health by multi-criteria decision making techniques

Year 2022, , 1562 - 1567, 25.10.2022

Abdurrahman Yunus Sarıyıldız

https://doi.org/10.32322/jhsm.1136265

Abstract

Aim: The aim of this study is to determine the health performances of the regions in the 2019 Health Statistics Yearbook by using multi-criteria decision making techniques.
Material and Method: The study is a cross-sectional study and the data used in the study were obtained from the Ministry of Health Statistics Yearbook 2019. The population of the study consists of 12 regions (Western Anatolia, Western Black Sea, Eastern Black Sea, Eastern Black Sea, Eastern Marmara, Aegean, Istanbul, Central Anatolia, Mediterranean, Northeastern Anatolia, Western Marmara, Southeastern Anatolia, and Central Anatolia) included in the 2019 Health Statistics Yearbook. No sample was selected, and all regions were included in the study. ENTROPY Method was used for weighting the criteria and TOPSIS Method was used for ranking the alternatives. A total of 11 criteria, including six benefit criteria (number of general practitioners per 100,000 people, number of specialists per 100,000 people, number of hospital beds per 10,000 people, number of nurses and midwives per 100,000 people, number of hemodialysis devices per million people, and number of MRI devices per million people) and 5 cost criteria (infant mortality rate, maternal mortality rate, population per family medicine unit, crude mortality rate, population per 112 emergency aid station) were evaluated. Analyses were performed in Microsoft Excel program.
Results: In the study, the three most effective criteria used to determine the health performances of the regions were respectively determined as maternal mortality rate (28.68%), population per 112 emergency aid stations (17.43%), and crude death rate (15.63%). As a result of the analyzes of the TOPSIS Method, the five regions with the best health performance among the regions are Western Anatolia (0.68), Western Black Sea (0.66), Eastern Black Sea (0.65), Eastern Marmara (0.63), and Aegean (0.56) has been identified. While the average performance score of the regions is found as 0.53, Istanbul (0.51), Middle East Anatolia (0.50), Mediterranean (0.49), Northeast Anatolia (0.46), West Marmara (0.44), Southeastern Anatolia (0.40), and Central Anatolia (0.33) regions remained below this average.
Conclusion: The most important criteria in evaluating the health performances of regions are; maternal mortality rate, population per 112 emergency aid stations, and crude death rate. The regions with the best health performance are Western Anatolia, Western Black Sea and Eastern Black Sea. In order to improve the health performance of the regions, maternal mortality rate, crude death rate and population per family physician should be reduced.

Keywords

Multi-criteria decision making techniques, ENTROPY, health performance, TOPSIS

References

• Eren H, Ömürbek N. Aggregation and analysis of performance in terms of Turkey’s health indicators. Mehmet Akif Ersoy University Journal of Social Sciences Institute 2019; 11: 421-52.
• Arslanhan S. How rising health expenditures affect key health indicators. TEPAV Evaluation Note. 2010.
• Saygın ZÖ, Kundakçı N. Evaluation of OECD countries in terms of health indicators with EDAS and ARAS methods. Alanya Academic Review 2020; 4: 911-38.
• United Nations. Global indicator framework for the Sustainable Development Goals and targets of the 2030 Agenda for Sustainable Development (Accessed September 20, 2021).
• Tekin B. Grouping of cities in terms of primary health indicators in Turkey: an application of cluster analysis. Journal of the Faculty of Economics and Administrative Sciences 2015; 5: 389-416.
• Sayılı U, Aksu Sayman Ö, Vehid S, Köksal SS, Erginöz E. Comparison of health indicators and health expenditures of Turkey and OECD countries. Online Turkish Journal of Health Sciences 2017; 2: 1-12.
• Sonğur C. Cluster analysis of organization for economic cooperation and development countries according to health indicators. Journal of Social Security 2016; 6: 197-224.
• First National Center. Understanding Health Indicators. Ottawa: National Aboriginal Health Organization, 2007.
• Ömürbek N, Altın FG, Şimşek, A, Eren H. Evaluation of the efficiencies of the cities in terms of health indicators in Turkey using ENTROPY-based data envelopment Analysis. Süleyman Demirel University Visionary J 2021; 12: 16-45.
• Akçakanat Ö, Eren H, Aksoy E, Ömürbek V. Performance evalualiton by ENTROPY and WASPAS methods at banking sector. The Journal of Faculty of Economics Administrative Scicences 2017; 22: 285-300.
• Zou ZH, Yun Y, Sun JN. Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. J Environment Sci (China) 2006; 18: 1020–3.
• Zhu Y, Tian D, Yan F. Effectiveness of entropy weight method in decision-making. Hindawi Mathematical Problems Engineering 2020. https://doi.org/10.1155/2020/3564835
• Mohammed MA, Abdulkareem KH, Al-Wisy AS, et al. Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS Methods. IEEE Access, 2020; 8: 99115-31.
• Yong D. Plant location selection based on fuzzy TOPSIS. Int J Adv Manufact Technol 2006; 28: 839-44.
• Zhang H, Gu CL, Gu LW, Zhang Y. The evaluation of tourism destination competitiveness by TOPSIS & information entropy─A case in the Yangtze River Delta of China. Tourism Management 2011; 32: 443-51.
• Şantaş F, Şantaş G. The current situation and ranking of Turkey, regions and provinces in terms of health variables. Hitit J Soc Sci 2018; 11: 2419-32.
• Öksüzkaya M. Examining the effectiveness among regions in the health sector. Gazi Üniversitesi Sosyal Bilimler Derg 2017; 4: 280-300.
• Özdemir A. Measuring of healthcare service delivery efficiency of NUTS-1 territories in Turkey using data envelopment analysis. J Health Sci Adiyaman University 2020; 6: 231-42.