Socioeconomic inequalities in metabolic syndrome and its components in a sample of Iranian Kurdish adults
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
OBJECTIVES: The worldwide incidence of metabolic syndrome (MetS) has increased in recent decades. In this study, we investigated the socioeconomic inequalities associated with MetS and its components in a sample of the Iranian Kurdish population. METHODS: We used data from 3,996 participants, aged 35 years to 70 years, from the baseline phase of the Dehgolan Prospective Cohort Study (February 2018 to March 2019). The concentration index and concentration curve were used to measure inequality and the Blinder-Oaxaca decomposition method was used to examine the contribution of various determinants to the observed socioeconomic inequality in MetS and its components. RESULTS: The prevalence of MetS was 34.44% (95% confidence interval [CI], 32.97 to 35.93). The prevalence of MetS was 26.18% for those in the highest socioeconomic status (SES), compared with 40.51% for participants in the lowest SES. There was a significant negative concentration index for MetS (C=-0.13; 95% CI, -0.16 to -0.09), indicating a concentration of MetS among participants with a lower SES. The most prevalent component was abdominal obesity (59.14%) with a significant negative concentration index (C=-0.21; 95% CI, -0.25 to -0.18). According to decomposition analysis, age, gender, and education were the highest contributing factors to inequality in MetS and its components. CONCLUSIONS: This study showed socioeconomic inequality in MetS. People with a low SES were more likely to have MetS. Therefore, policymakers and health managers need to develop appropriate strategies to reduce these inequalities in MetS across age groups, genders, and education levels, especially among women and the elderly.
Abstract
OBJECTIVES: The worldwide incidence of metabolic syndrome (MetS) has increased in recent decades. In this study, we investigated the socioeconomic inequalities associated with MetS and its components in a sample of the Iranian Kurdish population. METHODS: We used data from 3,996 participants, aged 35 years to 70 years, from the baseline phase of the Dehgolan Prospective Cohort Study (February 2018 to March 2019). The concentration index and concentration curve were used to measure inequality and the Blinder-Oaxaca decomposition method was used to examine the contribution of various determinants to the observed socioeconomic inequality in MetS and its components. RESULTS: The prevalence of MetS was 34.44% (95% confidence interval [CI], 32.97 to 35.93). The prevalence of MetS was 26.18% for those in the highest socioeconomic status (SES), compared with 40.51% for participants in the lowest SES. There was a significant negative concentration index for MetS (C=-0.13; 95% CI, -0.16 to -0.09), indicating a concentration of MetS among participants with a lower SES. The most prevalent component was abdominal obesity (59.14%) with a significant negative concentration index (C=-0.21; 95% CI, -0.25 to -0.18). According to decomposition analysis, age, gender, and education were the highest contributing factors to inequality in MetS and its components. CONCLUSIONS: This study showed socioeconomic inequality in MetS. People with a low SES were more likely to have MetS. Therefore, policymakers and health managers need to develop appropriate strategies to reduce these inequalities in MetS across age groups, genders, and education levels, especially among women and the elderly.
참고문헌 (50)
- Huang PL, "A comprehensive definition for metabolic syndrome", Dis Model Mech, 2, pp.231~237 (2009)
- Saklayen MG, "The global epidemic of the metabolic syndrome", Curr Hypertens Rep, 20, pp.12~ (2018)
- Noubiap JJ, "Geographic distribution of metabolic syndrome and its components in the general adult population : a meta-analysis of global data from 28 million individuals", Diabetes Res Clin Pract, 188, pp.109924~ (2022)
- Kalan Farmanfarma K, "Prevalence of metabolic syndrome in Iran : a meta-analysis of 69 studies", Diabetes Metab Syndr, 13, pp.792~799 (2019)
- Tabatabaei-Malazy O, "A nationwide study of metabolic syndrome prevalence in Iran; a comparative analysis of six definitions", PLoS One, 16, pp.e0241926~ (2021)
- Nugent R, "Investing in non-communicable disease prevention and management to advance the Sustainable Development Goals", Lancet, 391, pp.2029~2035 (2018)
- Sattar N, "Can metabolic syndrome usefully predict cardiovascular disease and diabetes? Outcome data from two prospective studies", Lancet, 371, pp.1927~1935 (2008)
- "Handbook on health inequality monitoring with a special focus on low- and middle-income countries" (2013)[URL]
- Wang D, "Association between socioeconomic status and health behaviour change before and after non-communicable disease diagnoses : a multicohort study", Lancet Public Health, 7, pp.e670~e682 (2022)
- Loucks EB, "Socioeconomic disparities in metabolic syndrome differ by gender : evidence from NHANES III", Ann Epidemiol, 17, pp.19~26 (2007)
- Abbate M, "Socioeconomic inequalities in metabolic syndrome by age and gender in a Spanish working population", Int J Environ Res Public Health, 18, pp.10333~ (2021)
- Hoveling LA, "Understanding socioeconomic differences in incident metabolic syndrome among adults: what is the mediating role of health behaviours?", Prev Med, 148, pp.106537~ (2021)
- Liu J, "Trends in metabolic syndrome among US youth, from 1999 to 2018", JAMA Pediatr, 176, pp.1043~1045 (2022)
- Wu HF, "Age, gender, and socioeconomic gradients in metabolic syndrome : biomarker evidence from a large sample in Taiwan, 2005-2013", Ann Epidemiol, 27, pp.315~322 (2017)
- Blanquet M, "Socio-economics status and metabolic syndrome : a meta-analysis", Diabetes Metab Syndr, 13, pp.1805~1812 (2019)
- Najafi F, "Socioeconomic-related inequalities in overweight and obesity : findings from the PERSIAN cohort study", BMC Public Health, 20, pp.214~ (2020)
- Rahimi E, "A detailed explanation and graphical representation of the Blinder-Oaxaca decomposition method with its application in health inequalities", Emerg Themes Epidemiol, 18, pp.12~ (2021)
- Yang C, "Trends and influence factors in the prevalence, intervention, and control of metabolic syndrome among US adults, 1999-2018", BMC Geriatr, 22, pp.979~ (2022)
- Piko P, "Changes in the prevalence of metabolic syndrome and its components as well as in relevant preventive medication between 2006 and 2018 in the Northeast Hungarian population", J Pers Med, 11, pp.52~ (2021)
- Laidmäe VI, "Health and illness are social phenomena", J Soc Health Diabetes, 2, pp.115~117 (2014)
- Link BG, "McKeown and the idea that social conditions are fundamental causes of disease", Am J Public Health, 92, pp.730~732 (2002)
- Moradpour Farhad, "The Dehgolan Prospective Cohort Study (DehPCS) on non-communicable diseases in a Kurdish community in the west of Iran", Epidemiology and Health, 43, pp.1~9 (2021), 한국역학회[DOI]
- Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults, "Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III)", JAMA, 285, pp.2486~2497 (2001)
- Grundy SM, "Diagnosis and management of the metabolic syndrome : an American Heart Association", National Heart, Lung, and Blood Institute Scientific Statement, 2005, pp.112, 2735~2752
- Sepanlou S, "Prevalence, awareness, treatment, and control of hypertension based on ACC", AHA versus JNC7 guidelines in the PERSIAN cohort study, 2022, pp.12, 4057~
- Filmer D, "Estimating wealth effects without expenditure data--or tears : an application to educational enrollments in states of India", Demography, 38, pp.115~132 (2001)
- "Analyzing health equity using household survey data: a guide to techniques and their implementation" (2008)[URL]
- Wagstaff A, "The bounds of the concentration index when the variable of interest is binary, with an application to immunization inequality", Health Econ, 14, pp.429~432 (2005)
- Koolman X, "On the interpretation of a concentration index of inequality", Health Econ, 13, pp.649~656 (2004)
- Regidor E, "Measures of health inequalities : part 2", J Epidemiol Community Health, 58, pp.900~903 (2004)
- Jones FL, "Decomposing differences between groups : a cautionary note on measuring discrimination", Sociol Methods Res, 12, pp.323~343 (1984)
- "oaxaca: Blinder-Oaxaca decomposition" (2014)[URL]
- Brunner EJ, "Social inequality in coronary risk : central obesity and the metabolic syndrome. Evidence from the Whitehall II study", Diabetologia, 40, pp.1341~1349 (1997)
- Mielke GI, "Socioeconomic correlates of sedentary behavior in adolescents : systematic review and meta-analysis", Sports Med, 47, pp.61~75 (2017)
- Cockerham WC, "Theoretical approaches to research on the social determinants of obesity", Am J Prev Med, 63(1 Suppl 1), pp.S8~S17 (2022)
- Shafiee G, "Socioeconomic inequality in cardio-metabolic risk factors in a nationally representative sample of Iranian adolescents using an Oaxaca-Blinder decomposition method : the CASPIAN-III study", J Diabetes Metab Disord, 18, pp.145~153 (2019)
- Moslem Soofi, "Measurement and Decomposition of Socioeconomic Inequality in Metabolic Syndrome: A Cross-sectional Analysis of the RaNCD Cohort Study in the West of Iran", 예방의학회지, 56(1), pp.50~58 (2023), 대한예방의학회
- Hajian-Tilaki K, "Metabolic syndrome and its associated risk factors in Iranian adults : a systematic review", Caspian J Intern Med, 6, pp.51~61 (2015)
- Mirhosseini NZ, "Prevalence of the metabolic syndrome and its influencing factors among adolescent girls in Mashhad, Iran", Asia Pac J Clin Nutr, 18, pp.131~136 (2009)
- Delavar MA, "Prevalence of metabolic syndrome among middle aged women in Babol, Iran", Southeast Asian J Trop Med Public Health, 40, pp.612~628 (2009)
- Colombet Z, "Socioeconomic inequalities in metabolic syndrome in the French West Indies", BMC Public Health, 19, pp.1620~ (2019)
- De Long NE, "Early-life chemical exposures and risk of metabolic syndrome", Diabetes Metab Syndr Obes, 10, pp.101~109 (2017)
- Kelishadi R, "Dyslipidemia-from prevention to treatment", pp.73~78 (2012), IntechOpen
- Mullie P, "Dietary patterns and socioeconomic position", Eur J Clin Nutr, 64, pp.231~238 (2010)
- Perel P, "Household wealth and the metabolic syndrome in the Whitehall II study", Diabetes Care, 29, pp.2694~2700 (2006)
- Allen L, "Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-income countries : a systematic review", Lancet Glob Health, 5, pp.e277~e289 (2017)
- Fateh M, "Socioeconomic inequality in hypertension in Iran", J Hypertens, 32, pp.1782~1788 (2014)
- Peykari N, "Socioeconomic inequalities and diabetes : a systematic review from Iran", J Diabetes Metab Disord, 14, pp.8~ (2015)
- Baum F, "Why behavioural health promotion endures despite its failure to reduce health inequities", Sociol Health Illn, 36, pp.213~225 (2014)
- NCD Countdown 2030 collaborators, "NCD Countdown 2030 : pathways to achieving Sustainable Development Goal target 3. 4", Lancet, 396, pp.918~934 (2020)
