Investigation of cause-specific mortality rates of European Union member and candidate countries by World Health Organization global health estimate categories

Objectives: It is aimed to examine the position of our country and the European Union member and candidate countries in terms of mortality rates according to the motality causes defined in global health estimate categories determined by the World Health Organization and to reveal the similarities or differences. Methods: According to the World Health Organization global health estimate categories given in the Global Burden of Disease 2019 study of World Health Organization, age-standardized mortality rates per 100,000 population were obtained for a total of 31 European Union member and candidate countries, and the muldimensional scaling analysis performed groups of the countries according to their dimensions obtained from multidimensional scale were determined and among these groups comparisons have been made. Results: As a result of applying multidimensional scaling analysis, it was seen that countries can be represented in two-dimensional space according to the variables of interest. Conclusions: It has been observed that our country differs from countries with cardiovascular diseases in the first dimension from the World Health Organization categories, while in the second dimension, infectious and parasitic diseases differ from countries with high standardized mortality rates.

Keywords:

Multidimensional scaling, World Health Organization Global health estimate categories, Euclidean distance,

PDF

___

1. WHO methods and data sources for country-level causes of death 2000-2019. (Global Health Estimates Technical Paper WHO/DDI/DNA/GHE/2020.2). Geneva, World Health Organization; 2020.
2. Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020.
3. Yavuz Emik K, Önal AE. [The epidemiological study of death in Turkey between 2009-2016 and the importance of the death statement system]. J Ist Faculty Med 2019;82):149-55. [Article in Turkish]
4. Akturan S, Gümüş B, Özer Ö, Balandız H, Erenler AK. [Death Rates and Causes of Death in Turkey Between 2009 and 2016 Based on TUIK Data]. Konuralp Tıp Dergisi 2019;11:9-16. [Article in Turkish]
5. Türkiye İstatistik Kurumu. Available at: www.tuik.gov.tr. Accessed May 13, 2021.
6. World Health Organization. WHO Mortality Database. Available at: http://www.who.int/healthinfo/statistics/mortality_rawdata/en/. Accessed May 13, 2021.
7. Sungur İ, Çöl M. [Situation of Mortality Related Important Indicators in the World and in Turkey]. Ankara Üniversitesi Tıp Fakültesi Mecmuası 2016;69:1-6. [Article in Turkish]
8. Türkiye İstatistik Kurumu Ölüm ve Ölüm Nedeni İstatistikleri, 2019. Available at: https://data.tuik.gov.tr. Accessed May 13, 2021.
9. Nasraoui O, Ben N'Cir C-E (eds). Clustering Methods for Big Data Analytics. 1st edition. Springer Nature Switzerland, 2019.
10. Anderberg MR. Cluster Analysis for Applications. Academic Press: New York, 1973.
11. Alpar R.; Çok Değişkenli İstatistiksel Yöntemler, Detay Yayıncılık, Ankara, 2011.
12. Horozoğlu Ö, Hallaç Y, Sığırlı D. [Examination of Turkey’s and European Union member states’ health indicators with multidimensionel scaling]. Uludağ Üniversitesi Tıp Fakültesi Dergisi 2017;43:49-52. [Article in Turkish]
13. Özdamar K. Paket Programlarla İstatistiksel Veri Analizi 2, Kaan Kitabevi, Eskişehir, 1999.
14. Lilien GL, Rangaswamy A. Marketing Engineering: Computer Assited Marketing Analysis and Planning, Prentice Hall, New Jersey, 2003.
15. Rouzier R, Perou CM, Symmans WF, Ibrahim N, Cristofanilli M, Anderson K, et al. Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin Cancer Res 2005;11:5678-85.
16. Mackay DB, Easley RF, Zinnes YL. A single ideal point model for market structure analysis. J Marketing Res 1995;32:433-43.
17. Kalaycı Ş. SPSS Uygulamalı Çok Değişkenli İstatistik Teknikleri, 4. Baskı, Asil Yayın Dağıtım, 2009.
18. Lorr M. Cluster Analysis for Social Scientists, Jossey-Bass Publishers, London, 1983.
19. Tatlıdil H. Uygulamalı Çok Değişkenli İstatistiksel Analiz, Cem Web Ofset, Ankara, 1996.
20. Kruskal JB. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrica 1964;29:1-27.
21. Mead A. Review of the development of multidimensional scaling methods. Statistician 1992;41:27-39.
22. Johnson RA, Wichern DW. Applied Multivariate Statistical Analysis, Prentice Hall, Upper Saddle River, New Jersey, USA, 1999.
23. Wilkermaier F. An introduction to MDS. Sound Quality Research Unit, 2003.
24. Everitt B, Dunn G. Applied Multivariate Data Analysis, Oxford Uni. Press, New York, 1992:101.
25. MacQueen JB. Some Methods For Classification and Analysis of Multivariate Observations. Proc. of the Fifth Berkeley Symp. on Mathematical Statistics and Probability. USA: Berkeley; 1967:281-97.
26. Nakip M. Pazarlama Araştırmaları: Teknikler ve (SPSS Destekli) Uygulamalar. Seçkin Yayıncılık, Ankara, 2003.
27. Çelik Ş. [Classification of provinces in Turkey in terms of livestock using multidimensional scaling analysis]. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Dergisi 2015;31:159-64. [Article in Turkish]
28. Hair JF, Anderson RE, Tatham RL, eds. Multivariate Data Analysis With Readings. 2nd ed., Mcmillan Book Company: London, 2006.

ISSN: 2149-3189
Yayın Aralığı: Yılda 6 Sayı
Başlangıç: 2015
Yayıncı: Prusa Medikal Yayıncılık Limited Şirketi

Arşiv