Türkiye’de Obezitenin Sosyoekonomik Belirleyicileri

Year 2019, , 57 - 70, 25.10.2019

Egemen İpek

https://doi.org/10.18092/ulikidince.536601

Cited By: 10

Abstract

Bu çalışmada, Vücut Kitle İndeksi
(VKİ) yardımıyla belirlenen bireyin aşırı kilo veya obezete durumunu etkileyebilecek
sosyoekonomik faktörlerin genelleştirilmiş sıralı logit (gologit) metodolojisi
uygulanarak Türkiye için araştırılması amaçlanmıştır. Analizde, Türkiye
İstatistik Kurumu (TÜİK) tarafından oluşturulan 2014 ve 2016 yıllarına ait Türkiye
Sağlık Araştırması (TSA) veri setlerinden yararlanılmıştır. Gologit sonuçları, kadınların
erkeklere, evlilerin ise bekarlara oranla obez olma risklerinin 2 kat daha
fazla olduğunu göstermektedir. Aşırı kilo ve obeziteye yakalanma olasılıkları ile
bireyin eğitim seviyesi ve fiziksel aktivite durumu arasında güçlü negatif
ilişki bulunmuştur. Özellikle haftalık en az 10 dakikalık yürüyüş yapılan gün
sayısı arttıkça bireylerin üçüncü sınıf obeziteye yakalanma ihtimali yarı
yarıya azaltmaktadır. Bireylere ait ekonomik durum değişkenlerinden olan hane
geliri arttıkça kişilerin aşırı kilolu veya obez olma ihtimalleri de
artmaktadır. Gelişmiş ülke deneyimlerinin aksine gelişmekte olan ülkeler ve
Türkiye özelinde yapılan çalışmalara paralel olarak, kişinin gelir seviyesi
arttıkça toplam enerji ve toplam yağ alımının artacağı bu nedenle aşırı kilo ve
obezite riskinin artacağı tahmin edilmiştir.

Keywords

Obezite, Demografi, Gelir, Fiziksel Aktivite, VKİ, Türkiye

Socioeconomics Determinants of Obesity in Turkey

Abstract

The aim of this study is to investigate the
socioeconomic factors which may affect on individual’s overweight or obesity
status determined by the Body Mass Index (BMI) by applying the generalized
ordered logit (gologit) methodology for Turkey. In the analysis, Turkey
Health Interview Survey (TSA) data set generated by Turkey Statistical
Institute (TURKSTAT) is used for the year 2014 and 2016. Gologit results show
that women are twice as likely to be obese compared to men and similarly,
married couples are twice as likely to be obese compared to singles. A strong
negative correlation is found between the possibility of being overweight and
obesity and the level of education and physical activity of the individual. In
particular, as the number of days with a minimum of 10 minutes of walk per week
increases, the likelihood of the third-grade obesity is reduced by half.
Moreover, as household income, which is one of the economic status variables of
individuals, increases, the probability of individuals becoming overweight or
obese is increasing. In this study, unlike the developed countries experiences
but in parallel with studies on developing countries and Turkey, it is
estimated that as the person's income level increases the total energy and
total fat intake will increase and thus the risk of becoming overweight or
obese will increase.

Keywords

Obesity, Demography, Income, Physical Activity, BMI, Turkey

References

• Al-Mahroos, F. and Al-Roomi, K. (2001). Obesity Among Adult Bahraini Population: Impact of Physical Activity and Educational Level. Annals of Saudi Medicine, 21(3-4), 183-187.
• Ball, K. and Crawford, D. (2006). Socio-economic Factors in Obesity: a Case of Slim Chance in a Fat World?. Asia Pacific Journal of Clinical Nutrition, 15(Supplement), 15-20.
• Ball, K., Mishra, G. and Crawford, D. (2002). Which Aspects of Socioeconomic Status are Related to Obesity Among Men and Women?. International Journal of Obesity, 26(4), 559.
• Bereket, A. and Atay, Z., (2012). Current Status of Childhood Obesity and Its Associated Morbidities in Turkey. J. Clin. Res. Pediatr. Endocrinol., 4, 1-7.
• Borah, B. and Anirban, Basu (2013). Highlighting Differences Between Conditional And Unconditional Quantile Regression Approaches Through an Application to Assess Medication Adherence. Health Economics, 22(9), 1052-1070.
• Dastgiri S., Mahdavi R., Tutunchi H., Faramarzi E. (2009). Prevalence of Obesity, Food Choices and Socio-economic Status: a Crosssectional Study in the North-West of Iran. Public Health Nutr; 9, 996-1000.
• Dinsa, G. D., Goryakin, Y., Fumagalli, E., Suhrcke, M. (2012). Obesity and Socioeconomic Status in Developing Countries: a Systematic Review. Obesity Reviews, 13(11), 1067-1079.
• Drewnowski, A. and Specter, S.E. (2004) Poverty and Obesity: the Role of Energy Density and Energy Costs. Am J Clin Nutr, 79: 6-16.
• Erem, C. (2015). Prevalence of Overweight and Obesity in Turkey. IJC Metabolic & Endocrine, 8, 38-41.
• Erem, C., Arslan, C., Hacıhasanoğlu, A., Değer, O., Topbaş, M., Ukinç, K., Ersöz, H.Ö., Telatar, M., (2004). Prevalence of Obesity and Associated Risk Factors in a Turkish Population (Trabzon city, Turkey). Obes. Res. 12, 1117-1127.
• Firpo, S., Nicole, F., Thomas, L. (2009). Unconditional Quantile Regressions. Econometrica, 77(3), 953-973.
• Fu, V. (1998). sg88: Estimating Generalized Ordered Logit Models. Stata Technical Bulletin 44: 27-30. In Stata Technical Bulletin Reprints, 8, 160-164. College Station, TX: Stata Press.
• Grøholt, E. K., Stigum, H. and Nordhagen, R. (2008). Overweight and Obesity Among Adolescents in Norway: Cultural and Socio-Economic Differences. Journal of Public Health, 30(3), 258-265.
• Hou, X., Jia, W., Bao, Y. (2008). Risk Factors for Overweight and Obesity, and Changes in Body Mass Index of Chinese Adults in Shanghai. BMC Public Health, 8, 389.
• Ivanova, L., Dimitrov, P., Dellava, J., Hoffman, D. (2008). Prevalence of Obesity and Overweight Among Urban Adults in Bulgaria. Public Health Nutr, 11, 1407-1410.
• Kain, J., Vio, F. and Albala, C. (2003). Obesity Trends and Determinant Factors in Latin America. Cadernos de Saúde Pública, 19, 77-86.
• Karaoğlan, D., and Tansel, A. (2019). Determinants of Body Mass Index in Turkey: a Quantile Regression Analysis From a Middle Income Country. Boğaziçi Journal,32(2), 1-15.
• Kilpi, F., Webber, L., Musaigner, A., Aitsi-Selmi, A., Marsh, T., Rtveladze, K., Brown, M. (2014). Alarming Predictions for Obesity and Non-communicable Diseases in the Middle East. Public Health Nutrition, 17(5), 1078-1086.
• Koenker, R. and Bassett, G. (1978). Quantile Regressions. Econometrica, 46(1), 33-50
• Kuntz, B., and Lampert, T. (2010). Socioeconomic Factors and Obesity. Deutsches Ärzteblatt International, 107(30), 517.
• Kuskowska-Wolk, A. and Bergström, R. (1993). Trends in Body Mass Index and Prevalence of Obesity in Swedish Women 1980-89. Journal of Epidemiology & Community Health, 47(3), 195-199.
• Long, J. S. and Freese, J. (2014). Regression Models for Categorical Dependent Variables Using Stata (3rd Ed.). College Station, TX: Stata Press.
• Lu, N., Samuels, M.E., Huang, K.C. (2002). Dietary Behavior in Relation to Socioeconomic Characteristics and Self-perceived Health Status. J Health Care Poor Underserved,13, 241-57.
• Maclean, J. C., Douglas, W., Joachim, M. (2014). An Application of Unconditional Quantile Regression to Cigarette Taxes. Journal of Policy Analysis and Management, 33(1), 188-210.
• Marins, V.M., Almeida, R.M., Pereira, R.A., Sichieri, R. (2007) The Association Between Socioeconomic Indicators and Cardiovascular Disease Risk Factors in Rio de Janeiro, Brazil. J Biosoc Sci, 39, 221-229.
• McLaren, L. (2007). Socioeconomic Status and Obesity. Epidemiologic Reviews,29, 29-48
• Molarius, A. (2003) The Contribution of Lifestyle Factors to Socioeconomic Differences in Obesity in Men and Women-a Population-based Study in Sweden. Eur J Epidemiol,18, 227-34.
• Monteiro, C., Wolney, A., Conde, L., Popkin, B. (2001). Independent Effects of Income and Education on the Risk of Obesity in the Brazilian Adult Population. Journal of Nutrition, 131, 881-886.
• Musaiger AO (2011) Overweight and Obesity in the Eastern Mediterranean: Prevalence and Possible Causes. J Obes, 407237.
• Yusuf, S., Hawken, S., Ounpuu, S., (2005). Obesity and the Risk of Myocardial Infarction in 27 000 Participants from 52 Countries: a Case–control Study. Lancet, 66, 1640–1649.
• Nguyen, M.D., Beresford, S.A., Drewnowski, A. (2007). Trends in Overweight by Socio-economic Status in Vietnam: 1992 to 2002. Public Health Nutr, 10, 115-121.
• Paeratakul, S., Lovejoy, J. C., Ryan, D. H., Bray, G. A. (2002). The relation of Gender, Race And Socioeconomic Status to Obesity and Obesity Comorbidities in a Sample of US Adults. International Journal of Obesity, 26(9), 1205.
• Proper, K. I., Cerin, E., Brown, W. J. and Owen, N. (2007). Sitting Time and Socio-economic Differences in Overweight and Obesity. International Journal of Obesity, 31(1), 169.
• Sobal, J. and Stunkard, A.J. (1989). Socioeconomic Status and Obesity: A Review of the Literature. Psychol Bull, 105, 260-275.
• Stamatakis, E. (2003). Physical activity. In: Sporston K, Primatesta P, eds. The Health Survey for England 2003, Cardiovascular Disease. London: The Stationery Office, 2004.
• Stamatakis, E., Primatesta, P., Chinn, S., Rona, R. and Falascheti, E. (2005). Overweight and Obesity Trends from 1974 to 2003 in English Children: What is the Role of Socioeconomic Factors?. Archives of Disease in Childhood, 90(10), 999-1004.
• Subramanian, S. V. and Smith, G. D. (2006). Patterns, Distribution, and Determinants of Under-and Overnutrition: a Population-based Study of Women in India. The American Journal of Clinical Nutrition, 84(3), 633-640.
• Wamala, S. P., Wolk, A., Orth-Gomér, K. (1997). Determinants of Obesity in Relation to Socioeconomic Status Among Middle-aged Swedish Women. Preventive Medicine, 26(5), 734-744.
• Wang, Y. and Beydoun, M. A. (2007). The Obesity Epidemic in the United States-Gender, Age, Socioeconomic, Racial/Ethnic, And Geographic Characteristics: a Systematic Review and Meta-regression Analysis. Epidemiologic Reviews, 29(1), 6-28.
• Wardle, J. and Griffith, J. (2001) Socioeconomic Status and Weight Control Practices in British Adults. J Epidemiol Community Health, 55, 185-90.
• Wardle, J., Waller, J., Jarvis M.J. (2002). Sex Differences in the Association of Socioeconomic Status with Obesity. Am J Public Health; 92, 1299-304.
• Williams, R. (2006). Generalized Ordered Logit/partial Proportional Odds Models for Ordinal Dependent Variables. Stata Journal, 6, 58-82.
• Williams, R. (2016). Understanding and Interpreting Generalized Ordered Logit Models. The Journal of Mathematical Sociology, 40(1), 7-20.
• Winkleby, M.A., Kraemer, H.C., Ahn, D.K. (1998). Ethnic and Socioeconomic Differences in Cardiovascular Disease Risk Factors: Findings for Women from the Third National Health and Nutrition Examination Survey, 1988-1994. JAMA, 280: 356-62.
• World Health Organization (WHO) (2018). Better Food and Nutrition in Europe: a Progress Report Monitoring Policy Implementation in the WHO European Region. WHO Regional Office for Europe, Denmark.
• Yoon, Y.S., Sang, W.O., Hye, S. P. (2006). Socioeconomic Status in Relation to Obesity and Abdominal Obesity in Korean Adults: A Focus on Sex Differences. Obesity 14(5), 909-919.
• Yumuk, V.D. (2005). Prevalence of Obesity in Turkey, Obes. Rev., 6, 9-10.