Gita Shafiee1, Mostafa Qorbani2,3, Ramin Heshmat1, Fatemeh Mohammadi4, Ali Sheidaei5, Mohammad Esmaeil Motlagh6, Armita Mahdavi-Gorabi3, Gelayol Ardalan3, Zeinab Ahadi3, Roya Kelishadi7. 1. 1Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran. 2. 2Non-communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran. 3. 3Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran. 4. 4Food and Nutrition Policy and Planning Research Department, National Nutrition & Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 5. 5Department of Epidemiology and Biostatistics, Shahid Beheshti University of Medical Science, Tehran, Iran. 6. 6Department of Pediatrics, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. 7. 7Department of Pediatrics, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Abstract
OBJECTIVES: The present research was conducted aiming at assessing the association of socioeconomic inequality in the prevalence of risk factors associated with cardio-metabolic disorders in a sample population of nationally representative Iranian adolescents and to identify its influencing factors. METHODS: This study was conducted as part of a national-based surveillance program performed on 5625 individuals aged 10-18 years in 27 provinces in Iran. To determine the socioeconomic status (SES) of participants, we defined a new variable by applying the principal component analysis. Doing so, the socioeconomic inequality in cardio-metabolic risk factors was examined over the tertiles of SES using concentration index (C). Then, Oaxaca-Blinder decomposition analysis was carried out in order to decide upon the roots of inequality in the health system. RESULTS: The mean (standard deviation) age of participants was 14.73 (2.41) years. The prevalence of cardio-metabolic parameters had considerable difference across SES tertiles. Elevated fasting blood glucose (FBG), elevated triglycerides (TG), abdominal obesity, elevated total cholesterol (TC), and metabolic syndrome (MetS) increased linearly by increasing SES tertiles. C index for depressed high density lipoprotein- cholesterol (HDL-C) was negative, which was suggestive of inequality in favor of high SES groups and for other cardio-metabolic parameters, it was positive, which indicate inequality was in favor of the lowest SES groups. The highest gap between the first and third tertiles of socioeconomic was for frequency of abdominal obesity; 13.18% of the lowest SES groups and 20.11% of the highest SES groups had abdominal obesity which accounts 6.93% gap in favor of the highest SES groups. The living area could be named as the main variables standing for the inequality of elevated FBS, elevated LDL-c, low HDL-c and abdominal obesity frequency between the first and the last SES group. In addition, BMI could stand as the main independent variable explaining the gap in elevated TG, elevated TC, elevated BP and MetS prevalence across the lowest and the highest SES group. CONCLUSIONS: The study revealed the considerable inequality in the prevalence of cardio-metabolic risk factors between the highest and the lowest SES groups of Iranian adolescents. Living area and BMI are the two main factors which explained inequality in prevalence of cardio metabolic risk factors between SES groups. These estimations could provide health policy markers with practical information for future complementary analyses.
OBJECTIVES: The present research was conducted aiming at assessing the association of socioeconomic inequality in the prevalence of risk factors associated with cardio-metabolic disorders in a sample population of nationally representative Iranian adolescents and to identify its influencing factors. METHODS: This study was conducted as part of a national-based surveillance program performed on 5625 individuals aged 10-18 years in 27 provinces in Iran. To determine the socioeconomic status (SES) of participants, we defined a new variable by applying the principal component analysis. Doing so, the socioeconomic inequality in cardio-metabolic risk factors was examined over the tertiles of SES using concentration index (C). Then, Oaxaca-Blinder decomposition analysis was carried out in order to decide upon the roots of inequality in the health system. RESULTS: The mean (standard deviation) age of participants was 14.73 (2.41) years. The prevalence of cardio-metabolic parameters had considerable difference across SES tertiles. Elevated fasting blood glucose (FBG), elevated triglycerides (TG), abdominal obesity, elevated total cholesterol (TC), and metabolic syndrome (MetS) increased linearly by increasing SES tertiles. C index for depressed high density lipoprotein- cholesterol (HDL-C) was negative, which was suggestive of inequality in favor of high SES groups and for other cardio-metabolic parameters, it was positive, which indicate inequality was in favor of the lowest SES groups. The highest gap between the first and third tertiles of socioeconomic was for frequency of abdominal obesity; 13.18% of the lowest SES groups and 20.11% of the highest SES groups had abdominal obesity which accounts 6.93% gap in favor of the highest SES groups. The living area could be named as the main variables standing for the inequality of elevated FBS, elevated LDL-c, low HDL-c and abdominal obesity frequency between the first and the last SES group. In addition, BMI could stand as the main independent variable explaining the gap in elevated TG, elevated TC, elevated BP and MetS prevalence across the lowest and the highest SES group. CONCLUSIONS: The study revealed the considerable inequality in the prevalence of cardio-metabolic risk factors between the highest and the lowest SES groups of Iranian adolescents. Living area and BMI are the two main factors which explained inequality in prevalence of cardio metabolic risk factors between SES groups. These estimations could provide health policy markers with practical information for future complementary analyses.
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