Evaluation of Physical Activity, Dietary Habits, and Income as Determinants of Health in European Continental Countries: A Data Envelopment Analysis Approach

Yıl 2025, Cilt: 13 Sayı: 2, 117 - 124, 30.05.2025

Fulden Sari , Fevzi Akbulut , Zeynep Arıbaş

https://doi.org/10.30720/ered.1524818

Öz

Purpose: The study aimed to evaluate the effectiveness of physical activity, dietary habits, and income indicators in European Continental countries using the data envelopment analysis method. Material and Methods: The study analysed data from 29 of 32 European continental countries. Input variables included physical activity, walking, dietary habits, and gross national income per capita, while output variables included depressive symptoms and obesity. Spearman analysis was performed to determine the correlation between these variables. Mahalanobis distance values were calculated to identify outlier countries. Finally, Data Envelopment Analysis was performed using the R Studio package. Results: According to the Charnes Cooper Rhodes (CCR) model, the average efficiency of the countries was found to be 0.90, while it was 0.91 according to the Banker Charnes Cooper (BCC) model. Based on the efficiency scores, seven countries were identified as efficient in the CCR model, and nine in the BCC model. Among the efficient countries, Serbia and Romania had the highest efficiency scores of 1.37 in the super-efficiency analysis. Discussion: The study results indicate that if six countries increased their input variables, they could achieve higher outputs, whereas 14 countries might achieve lower outputs if they were to increase their inputs.

Anahtar Kelimeler

Health , Efficiency , Depression , Obesity , Dietary Habit

Etik Beyan

Ethics committee approval and consent forms were not required for this study since the data used were obtained from publicly available sources, namely Eurostat and the World Bank. Moreover, the study did not involve any clinical processes or directly include participants, either living or deceased.

Destekleyen Kurum

There is no funder to report for this study.

Teşekkür

None

Avrupa Kıtası Ülkelerinde Sağlığın Belirleyicileri Olarak Fiziksel Aktivite, Beslenme Alışkanlıkları ve Gelirin Değerlendirilmesi: Veri Zarflama Analizi Yaklaşımı

Öz

Amaç: Bu çalışmanın amacı, veri zarflama analizi kullanılarak Avrupa kıtasındaki ülkelerde fiziksel aktivite, beslenme alışkanlıkları ve gelir göstergelerinin etkinliğini araştırmaktır. Gereç ve Yöntem: Çalışmada, 32 Avrupa kıtası ülkesinden 29'unun verileri analiz edildi. Girdi değişkenleri arasında fiziksel aktivite, yürüme, beslenme alışkanlıkları ve kişi başına düşen gayrisafi milli gelir yer alırken, çıktı değişkenleri ise psikolojik semptomlar ve obeziteydi. Bu değişkenler arasındaki korelasyonu belirlemek için Spearman analizi yapıldı. Aykırı ülkeleri belirlemek için Mahalanobis mesafe değerleri hesaplandı. Son olarak, R Studio paketi kullanılarak Veri Zarflama Analizi yapıldı. Sonuçlar: Charnes Cooper Rhodes (CCR) modeline göre ülkelerin ortalama etkinliği 0.90, Banker Charnes Cooper (BCC) modeline göre ise 0.91 olarak bulunmuştur. Etkinlik puanlarına göre, CCR modelinde yedi ülke, BCC modelinde ise dokuz ülke etkin olarak belirlenmiştir. Etkin ülkeler arasında Sırbistan ve Romanya, süper etkinlik analizinde 1.37 ile en yüksek etkinlik puanlarına sahip olmuştur. Tartışma: Bu çalışma sonuçları, altı ülkenin girdi değişkenlerini artırmaları durumunda daha yüksek çıktılar elde edebileceğini, oysa 14 ülkenin girdilerini artırmaları durumunda daha düşük çıktılar elde edebileceğini göstermektedir.

Anahtar Kelimeler

Sağlık , Etkinlik , Depresyon , Obezite , Beslenme Alışkanlıkları

Kaynakça

• Adna, N., Mohd-Yusoff, N. S., Syafiqah, N., Rosly, A., Ahmad-Marzuki, N. S. I., & Balqis-Wan, N. A. (2022). Measuring The Performance of Malaysian Universities Using Charnes, Cooper and Rhodes (CCR) and Slack-Based Measure (SBM) Models. JQMA, 18(1):1-11.
• Asandului, L., Roman, M., & Fatulescu, P. (2014). The efficiency of healthcare systems in Europe: a data envelopment analysis approach. Procedia Econ Financ, 10:261-268. doi: 10.1016/S2212-5671(14)00301-3.
• Banker, R. D., & Thrall, R. M. (1992). Estimation of returns to scale using data envelopment analysis. Eur J Oper Res, 62(1):74-84. doi: 10.1016/0377-2217(92)90178-C.
• Boutari, C., & Mantzoros, C. S. (2022). A 2022 update on the epidemiology of obesity and a call to action: as its twin COVID-19 pandemic appears to be receding, the obesity and dysmetabolism pandemic continues to rage on. Metabolism, 133:155217. doi: 10.1016/j.metabol.2022.155217.
• Bowlin, W. F. (2011). Measuring performance: an introduction to data envelopment analysis (DEA). The Journal of Cost Analysis, 15(2):3-27. https://doi.org/10.1080/08823871.1998.10462318.
• Bull, F. C., Al-Ansari, S. S., Biddle, S., Borodulin, K., Buman, M. P., Cardon G., et al. (2020). World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med, 54(24):1451-1462. https://doi.org/10.1136/bjsports-2020-102955.
• Chan, Y. (2003). Biostatistics 104: correlational analysis. Singapore Med J, 44(12):614-619.
• Charnes, A., Cooper, W.W, Lewin, A. Y., & Seiford, L. M. (1997). Data envelopment analysis theory, methodology and applications. J Oper Res Soc, 48(3):332-333. https://doi.org/10.1057/palgrave.jors.2600342.
• Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. Eur J Oper Res, 2(6):429-444. https://doi.org/10.1016/0377-2217(78)90138-8.
• Conti, S., Perdixi, E., Bernini, S., Nithiya, J., Severgnini, M., & Prinelli, F. (2024). Adherence to Mediterranean diet is inversely associated with depressive symptoms in older women: findings from the NutBrain Study. Br J Nutr, 131(11):1892-1901. https://doi.org/10.1017/S0007114524000461.
• Friedman, L., & Sinuany-Stern, Z. (1998). Combining ranking scales and selecting variables in the DEA context: the case of industrial branches. Comput Oper Res, 25(9):781-791. https://doi.org/10.1016/S0305-0548(97)00102-0.
• Lee, C. C. (2023). Operating efficiency of accounting firms based on different perspectives of human resource structures. In Asia Pacific Management Review, 28(3)253-266.
• Li, C., Ning, G., Wang, L., & Chen, F. (2022). More income, less depression? Revisiting the nonlinear and heterogeneous relationship between income and mental health. Front Psychol, 14:13:1016286. doi: 10.3389/fpsyg.2022.1016286.
• Milligan, M. N., Duemling, K., Radovanovic, N., Alkozah, M., & Riblet, N. (2023). Impacts of nutrition counseling on depression and obesity: a scoping review. Obes Rev, 24(9):e13594. doi: 10.1111/obr.13594.
• Mirmirani, S., & Lippmann, M. (2004). Health care system efficiency analysis of G12 countries. IBER, 3(5).
• Oppert, J. M., Ciangura, C., & Bellicha, A. (2023). Physical activity and exercise for weight loss and maintenance in people living with obesity. Rev Endocr Metab Disord, 24(5):937-949. https://doi.org/10.1007/s11154-023-09805-5.
• Peykani, P., Saen, R. F., Seyed-Esmaeili, F. S., & Gheidar-Kheljani, J. (2021). Window data envelopment analysis approach: a review and bibliometric analysis. Expert Systems, 38(7):e12721. https://doi.org/10.1111/exsy.12721.
• Pfisterer, J., Rousch, C., Wohlfarth, D., Bachert, P., Jekauc, D., & Wunsch, K. (2022). Effectiveness of Physical-Activity-Based Interventions Targeting Overweight and Obesity among University Students—A Systematic Review. Int J Environ Res Public Helath, 19(15):9427. https://doi.org/10.3390/ijerph19159427.
• Rauf, T. (2023). Mental Health Effects of Income over the Adult Life Course. Socius, 9:1-15. doi: 10.1177/23780231231186072.
• Retzlaff-Roberts, D., Chang, C. F., & Rubin, R. M. (2004). Technical efficiency in the use of health care resources: a comparison of OECD countries. Health policy, 69(1):55-72. doi: 10.1016/j.healthpol.2003.12.002.
• Sari, F., Bayram, S., Oskay, D., & Tufan, A. (2024). Comparison of exercise capacity, physical activity level and peripheral muscle strength in systemic lupus erythematosus patients with healthy individuals. Akt Rheumatol, 49:264-270. doi: 10.1055/a-2106-7129.
• Sherman, H. D. (1982). Data envelopment analysis as a new managerial audit methodology: test and evaluation.
• Singh, B., Olds, T., Curtis, R., Dumuid, D., Virgara, R., Watson, A., et al. (2023). Effectiveness of physical activity interventions for improving depression, anxiety and distress: an overview of systematic reviews. Br J Sports Med, 57:1203-1209. https://doi.org/10.1136/bjsports-2022-106195.
• Tapias, F. S., Oyamada-Otani, V. H., Correa-Vasques, D. A., Santos-Otani, T. Z., & Riyoiti-Uchida, R. (2021). Costs associated with depression and obesity among cardiovascular patients: medical expenditure panel survey analysis. BMC Health Serv Res, 21:433. https://doi.org/10.1186/s12913-021-06428-x.
• Tsaples, G., & Papathanasiou, J. (2020). Data envelopment analysis and the concept of sustainability: a review and analysis of the literature. Renew Sustain Energy Rev, 138:110664. https://doi.org/10.1016/j. rser.2020.110664.
• Vassiloglou, M., & Giokas, D. (1990). A study of the relative efficiency of bank branches: an application of data envelopment analysis. J Oper Res Soc, 41(7):591-597. https://doi.org/10.1057/jors.1990.83.
• Zare, H., Meyerson, N. S., Nwankwo-Adania, C., & Thorpe, R. J. (2022). How Income and Income Inequality Drive Depressive Symptoms in U.S. Adults, Does Sex Matter: 2005–2016. Int J Environ Res Public Health, 19(10):6227. doi: 10.3390/ijerph19106227.
• Zielinska, M., Tuszczki, E., Michonska, I., & Deren, K. (2022). The mediterranean diet and the western diet in adolescent depression-current reports. Nutrients, 14(20):4390. doi: 10.3390/nu14204390.