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Prevalence of obesity, food choices and socio-economic status: a cross-sectional study in the north-west of Iran

Published online by Cambridge University Press:  01 December 2006

Saeed Dastgiri*
Affiliation:
School of Nutrition and Public Health, National Public Health Management Centre (NPMC), Tabriz University of Medical Sciences, Tabriz, Iran
Reza Mahdavi
Affiliation:
School of Nutrition and Public Health, National Public Health Management Centre (NPMC), Tabriz University of Medical Sciences, Tabriz, Iran
Helda TuTunchi
Affiliation:
School of Nutrition and Public Health, National Public Health Management Centre (NPMC), Tabriz University of Medical Sciences, Tabriz, Iran
Elnaz Faramarzi
Affiliation:
School of Nutrition and Public Health, National Public Health Management Centre (NPMC), Tabriz University of Medical Sciences, Tabriz, Iran
*
*Corresponding author: Email [email protected]
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Abstract

Aim

To document the epidemiological features and influencing factors of obesity in the north-west of Iran, to provide baseline information for setting up a regional population-based centre to control and prevent obesity-related disorders in the area.

Methods

In this cross-sectional study, a total of 300 subjects were selected/studied in Tabriz, one of the major cities in Iran. Data on basic characteristics, anthropometric measurements, dietary assessment and physical activity were collected. Obesity was defined as body mass index ≥ 30 kg m− 2 for both women and men.

Results

Total prevalence of obesity in the area was 22.4% (95% confidence interval (CI): 18.0–27.6). The prevalence of obesity was 24% (95% CI: 18.5–31.4) for women and 18% (95% CI: 12.5–25.6) for men. For both women and men obesity prevalence showed a positive association with age (P < 0.001), while there was a negative correlation of obesity with education and income (P < 0.001). Fruit consumption decreased the risk of obesity in both women and men (odds ratio (OR) = 0.60, 95% CI: 0.49–0.71 vs. OR = 0.62, 95% CI: 0.51–0.74, respectively). The same significant pattern was observed for the consumption of green vegetables (OR = 0.71, 95% CI: 0.57–0.63 vs. OR = 0.86, 95% CI: 0.77–0.98 for women and men, respectively), legumes (OR = 0.70, 95% CI: 0.59–0.84 vs. OR = 0.78, 95% CI: 0.66–0.91 for women and men, respectively) and dairy products (OR = 0.73, 95% CI: 0.61–0.91 vs. OR = 0.77, 95% CI: 0.63–0.93 for women and men, respectively).

Conclusions

Our study showed that educational attainment, higher income and consumption of certain food groups (i.e. vegetables, fruits, legumes and dairy products) may decrease the risk of obesity. Our findings also indicate the crucial necessity of establishing a population-based centre for obesity in the area. The essential information is now achieved to propose to local health authorities to act accordingly. However, more population-based investigations on dietary choices are needed to develop effective preventive strategies to control overweight and obesity disorders in different regions.

Type
Research Article
Copyright
Copyright © The Authors 2006

Obesity has been described by the World Health Organization as an ‘escalating epidemic’ as great as that of smoking1. Obesity has now become a global epidemic and its prevalence continues to increase in both developed and developing countries1Reference Wang, Monteiro and Popkin3. The total prevalence of overweight and obesity is estimated at between 50 and 65% of the general population in some developed countries46. An increasing trend in overweight and obesity prevalence has also been reported from other developing countries in recent yearsReference Bell, Ge and Popkin7.

Hereditary, environmental, metabolic and behavioural factors may all have a role in the development and progression of obesityReference Rippe, Crossley and Ringer8. Obesity is associated with common causes of morbidity and mortality in the population, including diabetesReference Chan, Rimm, Colditz, Stampfer and Willett9, coronary heart disease, hypertension and dyslipidaemiaReference MacMahon, Cutler, Brittain and Higgins10, Reference Denke, Sempos and Grundy11, some types of cancer1 and mental health problemsReference Stunkard and Wadden12.

The aim of the present study was to document the epidemiological features and influencing factors of obesity in the north-west of Iran, to provide baseline information for setting up a regional population-based centre to control and prevent obesity-related disorders in the area.

Methods

In this cross-sectional study, a total of 330 eligible individuals were selected using simple random sampling. Although the subjects were routinely followed and encouraged to take part in different stages of this study, the participation rate was 90%. Three hundred subjects including 132 males and 168 females (18+ years) were eventually recruited. The setting of the study was Asadabadi region of Tabriz, one of the major cities located in the north-west of Iran. The area has previously been described in detail as reliably representative of the general Iranian population in terms of socio-economic status, lifestyle, general health situation and population structure13. Data collected included age, gender, weight, height, education, income, physical activity and food intake via a face-to-face interview with participants of the study.

Anthropometric measurements

Height and weight were measured by nutritionists. Body weight was measured using a calibrated beam scales and recorded to the nearest 0.5 kg. The subjects were measured barefoot and wearing light clothing. Height was measured using a mounted tape and recorded to the nearest 0.5 cm. Body mass index (BMI) was then calculated as weight in kilograms divided by the square of height in metres. As recommended by the Iranian Ministry of Health for the whole country, obesity was defined as BMI ≥ 30 kg m− 2 for both women and men in this studyReference Laquatra, Mahan and Escot-Stump14.

Dietary assessment and physical activity

Nutritional status and habits were assessed using a validated food-frequency questionnaire. Subjects were asked how often they ate the following groups of foods: fruit, green vegetables, bread, starchy foods (rice, potatoes and pasta), legumes (beans, peas and lentils), meat, fish (fish and tuna fish) and dairy products (milk, yoghurt and cheese).

Physical activity levels were determined using a questionnaire from the first National Health and Nutrition Examination Survey (NHANES I study)Reference Gillum, Mussolino and Ingram15, which was validated in the local language through a pilot study.

Statistical analysis

Prevalence rates, 95% confidence intervals (95% CI) and descriptive statistics were calculated to document the epidemiological features of obesity in the region. Logistic regression was used to assess the role of influencing factors on obesity.

Results

Table 1 shows the basic characteristics of the study subjects. The study sample comprised 168 women (56%) and 132 men (44%), with mean age ( ± standard deviation) of 34.6 ± 12.6 and 39.9 ± 16.1 years, respectively. Mean BMI was significantly lower in men than in women (25.2 ± 4.9 vs. 27.1 ± 5.3 kg m− 2P < 0.001). Total prevalence of obesity in the area was 22.4% (95% CI: 18.0–27.6), with 24% (95% CI: 18.5–31.4) of women and 18% (95% CI: 12.5–25.6) of men being obese.

Table 1 Basic characteristics of the study subjects

For both women and men obesity prevalence showed a positive association with age (P for trend < 0.001), while there was a negative correlation of obesity with education and income (P for trend < 0.001). The risk of obesity was low for those having more than 12 years of education (odds ratio (OR) = 0.41, 95% CI: 0.31–0.63 vs. OR = 0.62, 95% CI: 0.36–0.71 for women and men, respectively) and income of more than $US 220 per month (OR = 0.58, 95% CI: 0.41–0.67 vs. OR = 0.32, 95% CI: 0.24–0.53 for women and men, respectively) (Table 2).

Table 2 Odds ratios for obesity by socio-economic status (adjusted for physical activity)

n – number of individuals with obesity; OR – odds ratio; CI – confidence interval.

Fruit consumption decreased the risk of obesity in both women and men (OR = 0.60, 95% CI: 0.49–0.71 vs. OR = 0.62, 95% CI: 0.51–0.74, respectively). The same significant pattern was observed for the consumption of green vegetables (OR = 0.71, 95% CI: 0.57–0.63 vs. OR = 0.86, 95% CI: 0.77–0.98 for women and men, respectively), legumes (OR = 0.70, 95% CI: 0.59–0.84 vs. OR = 0.78, 95% CI: 0.66–0.91 for women and men, respectively) and dairy products (OR = 0.73, 95% CI: 0.61–0.91 vs. OR = 0.77, 95% CI: 0.63–0.93 for women and men, respectively) (Table 3).

Table 3 Odds ratios for obesity by food choice (adjusted for physical activity)

n – number of individuals with obesity; OR – odds ratio; CI – confidence interval.

Discussion

The present investigation was a cross-sectional study designed to describe some of the epidemiological features of overweight and obesity in Tabriz, one of the major cities in Iran located in the north-west of the country. The population structure and health indices of the area have been studied previously and indicate that it can reliably be considered as representative of the general population13. In this study we investigated the prevalence of obesity and its association with eating habits and socio-economic status, to provide baseline information (pilot data) to set up a regional population-based centre to control and prevent obesity-related disorders in the area. The total prevalence of obesity in the region was 22%. Women were at more risk of obesity than men. Despite the fact that the overweight and obesity prevalence was lower than corresponding figures from developed countries, the overall prevalence in our study falls within the range previously reported from some developing countries and for the world as a whole1Reference Bell, Ge and Popkin7.

Obesity is related to socio-economic status. Some studies have shown a positive relationship between socio-economic status and obesityReference Sobal and Stunkard16. However, this has been challenged in some other investigations reporting mainly from developing countries, which indicated an inverse correlation of obesity with socio-economic statusReference Monteiro, Moura, Conde and Popkin17Reference Jeffery, Forster, Folsom, Luepker, Jacobs and Blackburn21.

Socio-economic status is a complex construct, characterised by income, education, occupation and residence status. Our study showed an inverse relationship between socio-economic status and obesity in both males and females, similar to some previous resultsReference Lynch, Kaplan and Salonen22Reference Dennis, Pajak, Pardo, Davis, Williams and Piotrowski24. Education may play a role in obesity through shaping the knowledge of individuals about diet, physical activity and their consequences on health statusReference Ball, Mishra and Crawford25, Reference Sobal, Brownell and Fairburn26. The negative association of education with obesity may therefore be related to a low level of knowledge among individuals about intake of balanced meals in daily lifeReference Astrup, Grunwald, Melanson, Saris and Hill27, Reference Bray and Popkin28. In the present study we found a statistically significant inverse correlation between income and obesity in both genders. This too is consistent with the findings of other studiesReference Ball, Mishra and Crawford25, Reference Vande Griff and Yoked29Reference Caballero31. The role of income on obesity may be explained by the fact that a higher income may provide access to necessary resources for the prevention of obesity. Sufficient income will also clearly have a preventive role on obesity disorders as people with high income purchase healthier foods (i.e. vegetables and fruits), have more leisure time for physical activity, and have easy access to health-care facilities in order to control weight gain.

In our study, the risk of obesity increased with age. A steady pattern of weight gain across the life span until older adulthood is usually expected, and obesity may increase until the age of 50–60 years in both gendersReference Sobal, Brownell and Fairburn26. Previous studies have reported that women show generally higher obesity prevalence than men, especially after 50 years of ageReference Seidell, Kopelman and Stock32.

We found that the occurrence of obesity is related to specific food choices of the study population. Consumption of fruits, green vegetables and legumes (i.e. beans, peas and lentils) were all associated with a lower risk of obesity for both women and men. Fruits, green vegetables and legumes are important sources of dietary fibre and are low-glycaemic-index foods. These food choices have been investigated as sources that may contribute to prevent weight gainReference Liu, Willett, Manson, Hu, Rosner and Colditz33, Reference Birketvedt, Aaseth, Florholmen and Ryttig34. Similarly, it has recently been reported that the consumption of fruits, vegetables and legumes also has a role in weight management probably because their consumption decreases energy density, promotes satiety and decreases energy intakeReference Howarth, Saltzman and Roberts35.

This study showed that the risk of obesity decreased with the consumption of dairy products (milk, yoghurt and cheese). This might be related to the high calcium content of dairy products. Some other studies have found that dietary calcium intake, especially from dairy products, can have a protective effect on overweight and obesityReference Davies, Heaney, Recker, Lappe, BargerLux and Rafferty36Reference Skinner, Bounds, Carruth and Ziegler39.

Conclusions

The present study showed that educational attainment, higher income and the consumption of certain food groups including fruits, vegetables, legumes and dairy products significantly decreased the risk of obesity. Our findings also indicate the crucial necessity to establish a population-based centre for obesity in the area. The essential information is now achieved to propose to local health authorities to act accordingly. However, more population-based investigations on dietary choices are needed to develop effective preventive strategies to control overweight and obesity disorders in different regions.

Acknowledgements

We thank the staff at Asadabadi Medical Centre of Tabriz University of Medical Sciences for their assistance in this project. We also thank Research Deputy and Food & Nutrition Security Office of the University for funding this project.

References

1World Health Organization (WHO). Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation on Obesity. Geneva: WHO, 1998Google Scholar
2US Department of Health and Human Services, Public Health Service. The Surgeon General's Call to Action to Prevent and Decrease Overweight and Obesity. Rockville, MD: Office of the Surgeon General, 2001Google Scholar
3Wang, Y, Monteiro, C, Popkin, BM. Trends of obesity and underweight in older children and adolescents in the United States, Brazil, China, and Russia. American Journal of Clinical Nutrition 2002; 75: 971–7CrossRefGoogle ScholarPubMed
4Australian Institute of Health and Welfare (AIHW). A Growing Problem: Trends and Patterns in Overweight and Obesity among Adults in Australia, 1980 to 2001. Bulletin & AIHW Cat. No. AUS 39. Canberra: AIHW, 2003Google Scholar
5Flegal, KM, Carroll, MD, Ogden, CL, Johnson, CL. Prevalence and trends in obesity among US adults, 1999–2000. Journal of the American Medical Association 2002; 288: 1723–7CrossRefGoogle ScholarPubMed
6The Comptroller and Auditor General. Tackling Obesity in England. London: National Audit Office, 2001Google Scholar
7Bell, A, Ge, K, Popkin, B. Weight gain and its predictors in Chinese adults. International Journal of Obesity and Related Metabolic Disorders 2001; 25: 1079–86CrossRefGoogle ScholarPubMed
8Rippe, JM, Crossley, S, Ringer, R. The obesity epidemic: a mandate for a multidisciplinary approach. Journal of the American Dietetic Association 1998; 27: 95155Google Scholar
9Chan, JM, Rimm, EB, Colditz, GA, Stampfer, MJ, Willett, WC. Obesity, fat distribution and weight gain as risk factors for clinical diabetes in men. Diabetes Care 1994; 17: 961–9CrossRefGoogle ScholarPubMed
10MacMahon, S, Cutler, J, Brittain, E, Higgins, M. Obesity and hypertension: epidemiological and clinical issues. European Heart Journal 1987; 8: 5770CrossRefGoogle ScholarPubMed
11Denke, MA, Sempos, CT, Grundy, SM. Excess body weight an unrecognized contributor to high blood cholesterol levels in white American women. Archives of Internal Medicine 1994; 154: 401–10CrossRefGoogle Scholar
12Stunkard, AJ, Wadden, TA. Psychological aspects of severe obesity. American Journal of Clinical Nutrition 1992; 55(Suppl. 2): 524S–32CrossRefGoogle ScholarPubMed
13Asadabadi Population and Health Survey. Tabriz Medical Journal 2003; (Suppl. 59): 1S–25Google Scholar
14Laquatra, I. Nutrition for weight management. In: Mahan, K, Escot-Stump, S, eds. Krause's Food, Nutrition and Diet Therapy 11th ed.Philadelphia, PA: WB Saunders, 2004; 565Google Scholar
15Gillum, RF, Mussolino, ME, Ingram, DD. Physical activity and stroke incidence in women and men. The NHANES I Epidemiologic Follow-up Study. American Journal of Epidemiology 1996; 143: 860–9CrossRefGoogle ScholarPubMed
16Sobal, J, Stunkard, AJ. Socio-economic status and obesity: a review of the literature. Psychological Bulletin 1989; 105: 260–75CrossRefGoogle Scholar
17Monteiro, CA, Moura, EC, Conde, WL, Popkin, BM. Socio-economic status and obesity in adult populations of developing countries. A review. Bulletin of the World Health Organization 2004; 82: 940–6Google ScholarPubMed
18McMurray, RG, Harrell, JS, Deng, S, Bradley, CB, Cox, LM, Bangdiwala, SI. The influence of physical activity, socio-economic status and ethnicity on the weight status of adolescents. Obesity Research 2000; 8: 130–9CrossRefGoogle Scholar
19Reidpath, DD, Burns, C, Garrard, J, Mahoney, M, Townsend, M. An ecological study of the relationship between social and environmental determinants of obesity. Health and Place 2002; 8: 141–5CrossRefGoogle ScholarPubMed
20Martikainen, PT, Marmot, MG. Socio-economic differences in weight gain and determinants and consequences of coronary risk factors. American Journal of Clinical Nutrition 1999; 69: 719–26CrossRefGoogle Scholar
21Jeffery, RW, Forster, L, Folsom, AR, Luepker, RV, Jacobs, DR, Blackburn, H. The relationship between social status and body mass index in the Minnesota Heart Health Program. International Journal of Obesity 1989; 13: 5967Google ScholarPubMed
22Lynch, JW, Kaplan, GA, Salonen, JT. Why do poor people behave poorly? Variation in adults' health behaviors and psychosocial characteristics by stages of the socio-economic life course. Social Science & Medicine 1997; 44: 809–19CrossRefGoogle Scholar
23Berkman, LF, Breslow, L. Health and Ways of Living: The Almeda County Study. New York: Oxford University Press, 1983Google Scholar
24Dennis, BH, Pajak, A, Pardo, B, Davis, CE, Williams, OD, Piotrowski, W. Weight gain and its correlates in Poland between 1983 and 1993. International Journal of Obesity and Related Metabolic Disorders 2000; 24: 1507–13CrossRefGoogle ScholarPubMed
25Ball, K, Mishra, G, Crawford, D. Which aspects of socio-economic status are related to obesity among men and women? International Journal of Obesity and Related Metabolic Disorders 2002; 26: 559–65CrossRefGoogle ScholarPubMed
26Sobal, J. Social influences on body weight. In: Brownell, KD, Fairburn, CG, eds. Eating Disorders and Obesity. London: The Guildford Press, 1995; 73–7Google Scholar
27Astrup, A, Grunwald, GK, Melanson, EL, Saris, WH, Hill, JO. The role of low fat diets in body weight control: a meta-analysis of ad libitum dietary intervention studies. International Journal of Obesity and Related Metabolic Disorders 2000; 24: 1545–52CrossRefGoogle ScholarPubMed
28Bray, GA, Popkin, BM. Dietary fat intake does affect obesity? American Journal of Clinical Nutrition 1998; 68: 1157–73CrossRefGoogle ScholarPubMed
29Vande Griff, D, Yoked, T. Obesity rates, income, and suburban sprawl: an analysis of US states. Health and Place 2004; 10: 221–9CrossRefGoogle Scholar
30Nestle, M, Wing, R, Brich, L, Disogra, L, Drewnowski, A, Middleton, S. Behavioral and social influences on food choice. Nutrition Reviews 1998; 56: 169–88Google ScholarPubMed
31Caballero, B. A nutrition paradox – underweight and obesity in developing countries. New England Journal of Medicine 2005; 352: 1514–6CrossRefGoogle ScholarPubMed
32Seidell, JC. Epidemiology, definition and classification of obesity. In: Kopelman, PG, Stock, MJ, eds. Clinical Obesity. Cambridge: Blackwell, 1998; 117Google Scholar
33Liu, S, Willett, WC, Manson, JE, Hu, FB, Rosner, B, Colditz, G. Relation between changes in intakes of dietary fiber and grain products and changes in weight and development of obesity among middle-aged women. American Journal of Clinical Nutrition 2003; 87: 920–7CrossRefGoogle Scholar
34Birketvedt, GS, Aaseth, J, Florholmen, JR, Ryttig, K. Long term effect of fiber supplement and reduced energy intake on body weight and blood lipids in overweight subjects. Acta Medica (Hradec Kralove) 2000; 43: 129–32CrossRefGoogle ScholarPubMed
35Howarth, NC, Saltzman, E, Roberts, SB. Dietary fiber and weight regulation. Nutrition Reviews 2001; 59: 129–39CrossRefGoogle ScholarPubMed
36Davies, KM, Heaney, RP, Recker, RR, Lappe, JM, BargerLux, J, Rafferty, K, et al. . Calcium intake and body weight. Journal of Clinical Endocrinology and Metabolism 2000; 85: 4635–8Google ScholarPubMed
37Zemel, MB, Shi, H, Greer, B, Dirienzo, D, Zemel, PC. Regulation of adiposity by dietary calcium. FASEB Journal 2000; 14: 1132–8CrossRefGoogle ScholarPubMed
38Carruth, BR, Skinners, JD. The role of dietary calcium and other nutrients in moderating body fat in pre school children. International Journal of Obesity and Related Metabolic Disorders 2001; 25: 559–66CrossRefGoogle Scholar
39Skinner, JD, Bounds, W, Carruth, BR, Ziegler, P. Longitudinal calcium intake is negatively related to children's body fat indexes. Journal of the American Dietetic Association 2003; 103: 1626–31CrossRefGoogle ScholarPubMed
Figure 0

Table 1 Basic characteristics of the study subjects

Figure 1

Table 2 Odds ratios for obesity by socio-economic status (adjusted for physical activity)

Figure 2

Table 3 Odds ratios for obesity by food choice (adjusted for physical activity)