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Journal of Public Health Advance Access published online on April 8, 2008
Journal of Public Health, doi:10.1093/pubmed/fdn024
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© The Author 2008, Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved

Sociodemographic and Smoking Associated with Obesity in Adult Women in Iran: Results from the National Health Survey


Enayatollah Bakhshi, PhD Student1
Mohammad Reza Eshraghian, PhD1
Kazem Mohammad, PhD1
Abbas Rahimi Foroushani, PhD1
Hojat Zeraati, PhD1
Akbar Fotouhi, PhD1
Fraidon Siassi, PhD2
Behjat Seifi, PhD Student3

1 Department of Biostatistics, School of Public Health and Institute of Public Health Research, Tehran University/Medical Sciences, Iran
2 Department of Nutrition, School of Public Health and Institute of Public Health Research, Tehran University/Medical Sciences, Iran
3 Department of Physiology, Medicine School, Tehran University/Medical Sciences, Iran


Address correspondence to Mohammad Reza Eshraghian, E-mail: eshraghian{at}yahoo.com

Background There is no study that had a sample size sufficient to study the association between sociodemographic and smoking with obesity in Iran. The goal was to investigate these associations in the Iranian women.

Methods Multivariate statistical techniques included 14 176 women between 20 and 69 years of age. Height and weight were measured rather than self-reported.

Results In Iranian adult women, obesity ORS for the moderate and high education were 0.78 and 0.41, respectively, compared with basic level. Using low economy index as the reference, Obesity ORS for the urban women were 1.29, 1.25 and 1.28 for the lower-middle, upper-middle and high groups, respectively. Obesity ORS for the rural women were 1.71, 1.71 and 2.02 for the lower-middle, upper-middle and high groups, respectively. Obesity OR was 0.48 for active workforce compared with inactive group. Obesity OR was 0.70 for smokers women compared with nonsmokers. Using non-married as the reference group, Obesity ORS were 1.23 and 2.34 for married urban and rural women, respectively.

Conclusions Our results on the associations between age, smoking, education level, workforce and obesity are consistent with most studies, but between economic level and obesity are consistent with some study in developing countries.

Keywords: obesity, sociodemographic factors, body mass index, odds ratio


   Introduction
TOP
 Introduction
Material and methods
 Measurements
 Results
 Discussion
 Acknowledgments
 References
 
Obesity is one of the most common health problem in modern societies and is assuming epidemic proportions in both developed and developing countries.1–4 In the United States, obesity was not considered as an issue of interest in the mid-1980s, but it had become more common: and in 2003–04, ~32.2% of the US adult population were obese.5 The prevalence of obesity and overweight varies considerably between countries and between regions within countries. For example, the overall prevalence of obesity varies from 7% in France to 33% in Brazil, and the trends in developed as well as developing countries suggest that rates of obesity are increasing.6 Almost one third of adult Canadians are at increased risk of disability, disease and premature death due to obesity.7 Obesity is relatively common in Europe, especially in southern and eastern countries, and studies from repeated surveys suggest that the prevalence of obesity has been increasing last years.8

Excess body weight has been reported to be a risk factor for cardiovascular diseases, diabetes, some cancers and other diseases.9–16 Each year, an estimated 3 00 000 US adults die of causes related to obesity.17

Numerous studies have investigated the relationship between sociodemographic factors and obesity. A significant association has found between weight gain and aging.18–21

In affluent societies, lower socioeconomics status is associated with a higher prevalence of obesity among women, but more weakly and less consistently so among men.22–25

Trung et al.26 studied trends in weight gain across sociodemographic groups. They found that the lowest-income group gained as much on average as the highest-income group, but lowest-income heavier individuals gained weight faster than highest-income heavier individuals. Sirlio-Lahteenkorva et al.27 found a statistically significant association between income level and BMI within educational or occupational classes among women but not among men.

A positive association between obesity and socioeconomic status has been found among both men and women in developing countries.28,29

Although the association of overweight with smoking, alcohol consumption, dietary habits and physical activity has been analysed in many studies, the findings are not consistent. Wilsgaard et al.30 studied the association between lifestyle factors and BMI change over time.

According to the World Health Organization, this global epidemic (obesity) is replacing more traditional public health concerns, such as undernutrition and infections diseases, as one of the most significant contributors to ill-health.3

The author's aim in this study was to investigate the association of sociodemographic and smoking with obesity by using cross-sectional data from the 1999–2000 National Health Survey in Iran.


   Material and methods
TOP
 Introduction
 Material and methods
Measurements
 Results
 Discussion
 Acknowledgments
 References
 
Data set examined
The National Health Surrey in Iran is a survey designed to gain comprehensive knowledge and information about health care problems and difficulties throughout in Iran, 1999–2000. Sampling was conducted on the basis of cluster method, each cluster comprising of eight households. The choice of eight households for the cluster size was based on one-day performance capacity of the data collection group: Four persons (two physicians, one interviewer, one lab technician). The statistical framework was based on the household lists available with every Health Department in the provinces, usually updated annually. Where household lists were available, selecting the cluster was made systematically. Data from the National Health Survey were considered in this investigation. In this study, 14 176 women, 8957 urban and 5219 rural aged 20–69 years were investigated. We excluded pregnant women from the analyses.


   Measurements
TOP
 Introduction
 Material and methods
 Measurements
Results
 Discussion
 Acknowledgments
 References
 
Height and weight were measured rather than self-reported. BMI (body mass index), our dependent variable, was calculated as weight in kilograms divided by height in meters squared. The influence of BMI was examined by using the four World Health Organization categories: (i) underweight (BMI <18.5 kg m–2), (ii) normal weight (BMI 18.5–24.9 kg m–2), (iii) overweight (BMI 25–29.9 kg m–2) and (iv) obese (BMI ≥30 kg m–2).3

Education was defined as the total years of education. The respondents were categorized into three groups: person with basic (0–8 years), moderate (9–12 years) or high (>12 years) education.

The respondents were grouped according to their place of residence as living in cities (urban) or villages (rural). To make the marital status variable, it was dichotomized into legally married and non-married groups. Information about the respondent's age was based on their self-reported birth year, and it was stratified into five 10-year age group (20–29, 30–39, 40–49, 50–59 and 60–69 years).

The consumption of alcohol is prohibited in Iran. Therefore we had no information on alcohol consumption.

There weren't information on household income and physical activity, but economic index is surrogate for household income and we used workforce factor. Due to ethical considerations, we didn't ask respondents about their income, because they were afraid of paying their taxes.

Active workforce was defined as the part of the female population that belongs to the currently employed (as employees) or self employed category as opposed to inactive workforce (being a housewife/ houseworker, pensioner, student or unemployed).

Economic index was defined as square meter of living place divided by number of household. Participants were classified by their economy index status into four classes: (i) low(economic index ≤Quartile 1), (ii) lower-middle(Quartile 1 <economic index ≤Quartile 2), (iii) upper-middle(Quartile 2 <economic index ≤Quartile 3) and (iv) high (economic index> Quartile 3).

Smoking status dichotomized into smoker (those who smoke every day and have smoked at least 100 cigarettes in their lives) versus nonsmoker (others).

We used logistic regression to estimate the probability of a report obesity as a function of age, education, economic index, workforce, place of residence, smoking and marital status. The results are presented as ORs and their 95% CIs. Age interactions and area interactions were tested for all covariates.

The Hosmer and Lemeshow test was used in this model to evaluate the significance of improved port with introduction of additional variables. All analyses were carried out by using the SPSS software Package, version 11.5.


   Results
TOP
 Introduction
 Material and methods
 Measurements
 Results
Discussion
 Acknowledgments
 References
 
The prevalence of obesity was higher among urban women (21.0%) than rural women (11.5%), and the prevalence of underweight was higher among rural (8.3%) than urban women (4.8%). About 40% of urban women and 55% of rural women had normal weight.

Table 1 shows that the mean BMI increases with age among both urban and rural women before 60 and 50 years of age, respectively, then it decreases. The mean BMI of urban women was 26.02 kg m–2 (95% CI: 25.92–26.12). The rural women had a mean BMI 24.14 kg m–2 (95% CI: 24.02–24.26).


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  		Table 1 Mean body mass index (BMI) by place of residence and age group in a random sample of 14 176 women in Iran, 1999–2000

 
Table 2 shows that the obesity was more prevalent among urban woman aged 50–60 years and rural women aged 40–50 years. Obesity was less common among more highly educated women. The obese were over-represented among inactive workforce, whereas thinness was common among active workforce. The prevalence of obesity was higher among married and nonsmoker women.


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  		Table 2 Prevalence of obesitya according to place of residence and other sociodemographic and smoking in a random sample of 14 176 women in Iran, 1999–2000

 
Table 3 shows that age was directly associated with obesity before 50 years of age. Using 20–30 years as the reference group, obesity odds ratios for age groups 30–40, 40–50, 50–60 and 60–69 years were 2.02 (95% CI: 1.78–2.30), 2.73 (95% CI: 2.38–3.13), 2.58 (95% CI: 2.19–3.03) and 1.62 (95% CI: 1.35–1.95), respectively.


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  		Table 3 Adjusteda odds ratios for the likelihood of being obeseb, by sociodemographic and smoking among random sample of 14 176 Iranian women in the logistic regression analysis, 1999–2000

 
Overall, subjects with lower education had more obesity. Using basic education as the reference group, obesity odds ratios for the moderate and high groups were 0.78 (9% CI: 0.69–0.89) and 0.41 (95% CI: 0.28–0.62), respectively.

We found a statistically significant association between economic index level and obesity (except among urban women in lower-middle level). Using low as the reference group, obesity odds ratios for the urban women were 1.29 (95% CI: 0.86–1.91), 1.25 (95% CI: 1.07–1.45) and 1.28 (95% CI: 1.10–1.50) for the lower-middle, upper-middle and high groups, respectively. Obesity odds ratios for the rural women were 1.71 (95% CI: 1.32–2.23), 1.71 (95% CI: 1.33–2.19) and 2.02 (95% CI: 1.58–2.58) for the lower-middle, upper-middle and high groups, respectively. Our test of interaction by area showed that the association between economy index (except lower-middle level (p = 0.06) and obesity was different among rural and urban women. In other words, among subjects with upper-middle level, the OR of 1.25 for urban was significantly different from the OR of 1.71 for rural women and among subjects with high level, the OR of 1.28 for urban was significantly different from the OR of 2.02 for rural women. Among subjects with upper-middle level, the OR of 1.29 for urban women was not significantly different from the OR of 1.71 for rural women.

An inverse association observed between workforce level and obesity among women. Obesity odds ratio was 0.48 (95% CI: 0.39–0.61) for active women.

An inverse association observed between smoking status and obesity. Obesity odds ratio was 0.70 (95% CI: 0.50–0.99) for smokers women.

An association observed between marital status and obesity. Obesity odds ratios were 1.23 (95% CI: 1.07–1.41) and 2.34 (95% CI: 1.77–3.08) for married urban and rural women, respectively. Furthermore, a significant area by marital status interaction difference was observed. In other words, the OR of 1.23 for urban women was significantly different from the OR of 2.34 for rural women.


   Discussion
TOP
 Introduction
 Material and methods
 Measurements
 Results
 Discussion
Acknowledgments
 References
 
In this cross-sectional study, we assessed association between sociodemographic, smoking and obesity in 14 176 women aged 20–69 years in Iran.

Among subjects aged 20–30 years, approximately one out of nine urban women and one out of 17 rural women were obese. Among subjects aged 30–40 years, one out of four urban women and one out of seven rural women were obese. Among subjects 40–50 years, approximately two seventh of urban women and approximately two eleventh of rural women were obese. Overall, among subjects aged 20–50 years, prevalence of obesity increased with increasing age. Among urban women aged 50–60 years, prevalence of obesity increased too, but among rural women, it decreased compared with previous age group. In this age group, almost more than one thirds of urban women and less than one seventh of rural women were obese. In 60–69 age group, in about two ninth of urban women and less than one ninth of rural women were obese. In all age group, urban women had upper prevalence of obesity compared with rural women, especially in 20–30 age group. It is interesting to note that the associations between age and obesity are very similar in rural and urban areas, even though obesity prevalence differs by region. Our results are consistent with most studies.5,18–20

We found a statistically significant inverse association between educational level and obesity for women. Women with higher education were leaner than those with lower education. It is also interesting to note that the associations between education level and obesity are very similar in rural and urban areas, even though obesity prevalence differs by region. Our findings on consistent educational variation of obesity in women are similar to those of other studies.21–25

In most studies, nonsmokers have a significantly higher average BMI than smokers.31–35 Our results on the association between smoking and obesity are basically in line with these studies. We observed an inverse association between smoking and obesity. Likewise, the inverse association seen between smoking and obesity should not be used to counteract the efforts undertaken against this habit.

In our study, there was a significant association between marriage and obesity. Iranian non-married women were less likely to be obese than their married counterparts. This association was stronger in rural than in urban women. This difference in obesity is related to lifestyle and other characteristic. For example, the age of marriage in rural women is less than in urban women.

Studies from several countries on marital status and weight show mixed results. In a recent US study BMI did not predict the likelihood of either being married or divorced.36 Most studies, however, report that marriage is associated with higher relative weight or weight gain36,37 although this may not be true in all population groups.

The consistency of the results obtained with those observed in other studies carried out in countries with very different sociodemographic factors than Iran suggests that these variations in BMI and obesity are independent of external factors.

Although a low income level may promote weight gain,38 an inverse relation between weight and socioeconomic status exists in affluent societies, especially in women,39 and is not uniform, the literature generally describes a positive association between weight loss attempts and socioeconomics status.40–46 It seems an unlikely explanation for our finding. We observed a statistically significant association between upper-middle and high levels of economic index and obesity. These associations were stronger in rural than in urban women. The association between economic index and obesity is a complex one; it is probably bidirectional and confounded by other factors such as heredity.47 The differences in weight between economic index groups may also reflect differences in other risk factors such as dietary habits. Some studies, however, have suggested that differences in health behavior only partly explain the association between economic index and relative weight.38,46 The differences between economic index groups in obesity may also be affected by social and cultural norms that vary by population and economic index level. It is not straightforward matter to compare those results with ours, because of the different study designs, time span, different region and method of analysis. Our results are consistent with some study in developing countries.48

Our finding showed that lower educational level and high economic index are related to increases in obesity. This seems contradictory but we do not think so: (i) the system of higher education is different in Iran. There students take an exit exam in their last year of high school. The people with the highest scores attend the best universities in the country. Other students can go to other kinds of colleges or get jobs. At most of universities, education is free of charge; (ii) economy, business, social affair, etc. are controlled by low educated people (Bazarry). For example, selling handmade Persian carpets, handcrafts, etc. are brisk business in Iran. We can't exactly say that high educated in welfare compared with low educated.

In our study active workforce was associated with a lower prevalence of obesity in Iranian women. In other words, women in the inactive workforce were more likely to be obese than their active counterparts. It is possible that there are differences in sedentary lifestyle and food habits. Obesity may also be more acceptable among unemployed persons. It is possible that there is more discrimination against the obese, or obese women may end up in lower status jobs through stronger selective processes in Iran.

Our study had several strengths. It was performed in a nationally representative sample of the Iranian women. To our knowledge, ours is the first study that had a sample size sufficient to study demographic and health behavior factors and obesity in adult women. This study included women aged 20–75 years and our findings may be generalized to other people. Height and weight were actually measured rather than self-reported. It is well known that self-reports underestimate the prevalence of obesity.49,50 In general, BMI is underestimated in all socioeconomic groups, and the existing studies have filed to find any clear socioeconomic pattern in this bias.49 Some studies have found more underreporting among higher socioeconomic groups,51 whereas others have found more bias in lower socioeconomic groups.49 Studies reporting reduced income levels as a result of deviant body weight are so far confined to young overweight women.52–54 Moreover, these studies use self-reported income data and education or occupation have been used as controlled background variables only. Self-reported income is subject to nonresponse and reporting bias. There are some limitations of our study. This study is a cross-sectional study, which means that we cannot draw definitive conclusions concerning the direction of causality. However, this should be confirmed by further longitudinal studies. Unfortunately, income and physical activity were not used in our investigation. It is a limitation that in this study marital status could be categorized into legally married and non-married only. Non-married people are also a very heterogeneous group and should be more closely examined in further studies.

In summary, the present study has suggested an association between several sociodemographic, smoking and obesity. Increased age, low educational level, high economic index, nonsmoker and being married as well as workforce inactivity were clearly associated with obesity.


   Acknowledgments
TOP
 Introduction
 Material and methods
 Measurements
 Results
 Discussion
 Acknowledgments
References
 
This study was financed by a grant from Tehran University of Medical Science & Health Services. The authors acknowledge the National Health Survey for their data, coordinated at the Department of Biostatistics, School of Public Health and Institute of Public Health Research, Tehran University of Medical Science, Iran. We are also grateful to the editor and anonymous reviewer for their suggestions which led to substantial improvements in this manuscript.


   References
TOP
 Introduction
 Material and methods
 Measurements
 Results
 Discussion
 Acknowledgments
 References
 

  1. Mokdad AH, Serdula MK, Dietz WH, et al. The spread of the obesity epidemic in the United States, 1991–1998. JAMA (1999) 282:1519–22.[Abstract/Free Full Text]
  2. Seidell JS, Rissanen AM. Time trends in the world-wide prevalence of obesity. In: Handbook of Obesity—Bray GA, Bouchard C, James WPT, eds. (1998) New York: Marcel Dekker. 79–91.
  3. World Health Organisation. Obesity: preventing and managing the global epidemic. (1998) Switzerland: World Health Organisation. Report of a WHO Consultation on obesity. Geneva 3–5 June 1997.
  4. Kuulasmaa K, Tunstall-Pedoe H, Dobson A, et al. Estimation of contribution of changes in classic risk factors to trends in coronary-event rates across the WHO MONICA Project populations. Lancet (2000) 355:675–87.[CrossRef][Web of Science][Medline]
  5. Ogden CL, Carroll MD, Curtin LR, et al. Prevalence of overweight and obesity in the United States, 1999–2004. JAMA (2006) 295(13):1549–55.[Abstract/Free Full Text]
  6. Saw SM, Rajan U. The epidemiology of obesity: a review. Ann Acad Med Singapore (1997) 26:489–93.[Medline]
  7. Birmingham CL, Mller JL, Palepu A, et al. The cost of obesity in Canada. CMAJ (1999) I60(4):483–8.
  8. Seidell JC. Obesity in Europe; Scaling an epidemic. Int J Obes Relat Metab Disord (1995) 19:1–4.[Web of Science][Medline]
  9. Kannel WB, D'Agosting RB, Cobb JL. Effect of weight on cardiovascular disease. Am J Clin Nutr (1996) 63:195–225.
  10. Murphy TK, Calle EE, Rodriguez C, et al. Body mass index and colon cancer mortality in a large prospective study. Am J Epidemiol (2000) 152:847–54.[Abstract/Free Full Text]
  11. Van den Brandt PA, Spiegelman D, Yaun SS. Pooled analysis of prospective cohort studies on height, weight and breast cancer risk. Am J Epidemiol (2000) 152:514–27.[Abstract/Free Full Text]
  12. Resnik HE, Valsania P, Halter JB, Lin X. Relations of weight gain and weight loss on subsequent diabetes risk in overweight adults. J Epidemiol Community Health (2000) 54:596–602.[Abstract/Free Full Text]
  13. Corrada MM, Kawas CH, Mozzafar F, Paganini-Hill A. Association of body mass index and weight change with all-cause mortality in the elderly. Am J Epidemiol (2005) 163:938–49.[CrossRef][Web of Science]
  14. Magnusson PKE, Rasmussen F, Lawlor DA, et al. Association of body mass index with suicide mortality: a prospective cohort study of more than one mollion men. Am J Epedimiol (2006) 163:1–8.[CrossRef]
  15. Tikkinen KAO, Auvinen A, Huhtala H, Tammela TL. Nocturia and obesity: a population-based study in Finland. Am J Epidemiol (2006) 163:1003–11.[Abstract/Free Full Text]
  16. Mishra V, Arnold F, Semenov G, et al. Epidemiology of obesity and hypertension and related risk factors in Uzbekistan. Eur J Clin Nutr (2006) 69:1355–66.
  17. Allison D, Fontaine KB, Manson JE, et al. Annual deaths attributable to obesity in the United States. JAMA (1999) 282(16):1530–8.[Abstract/Free Full Text]
  18. Flegal KM, Caroll MD, Kuczmarski RJ, Johnson CL. Overweight and obesity in the United States: prevalence and trends, 1960–1994. Int J Obes (1998) 22:39–47.[CrossRef][Web of Science][Medline]
  19. Lewis CE, Jacobs DR, McCreath H, et al. Weight gain continues in the 1990s: 10 year trends in weight and overweight from the CARDIA Study. Am J Epidemiol (2000) 151(12):1172–81.[Abstract/Free Full Text]
  20. Bagust A, Roberts BL, Haycox AK, Barrow S. The additional lost of obesity to the health service and the potential for resource savings from effective interventions. Eur J Public Health (1999) 9:258–64.[Abstract/Free Full Text]
  21. Klumbien J, Petkeviciene J, Helasoja V, et al. Sociodemographic and health behaviour factors associated with obesity in adult populations in Estonia, Finland and Lithuania. Eur J Public Health (2004) 14:390–4.[Abstract/Free Full Text]
  22. Sarlio-Lahteenkorva S, Lahelma E. The association of body mass index with social and economic disadvantage in women and men. Int J Epidemiol (1999) 28:445–9.[Abstract/Free Full Text]
  23. Laurier D, Guiguet M, Chau NP, et al. Prevalence of obesity: a comparative survey in France, United Kingdom and the United States. Int J Obes (1992) 16:565–72.[Web of Science][Medline]
  24. Molarius A, Seidell JC, San S, et al. Educational level, relative body weight, and changes in their association over 10 years: an international perspective from the WHO MONICA Project. Am J Public Health (2000) 90:1260–8.[Abstract/Free Full Text]
  25. Wamala SP, Wolk A, Orth-Gomer K. Determinants of obesity in relation to socioeconomic status among middle-aged Swedish women. Prev Med (1997) 26:734–44.[CrossRef][Web of Science][Medline]
  26. Truong KD, Sturm R. Weight gain trends across sociodemographic groups in the United States. Am J Public Health (2005) 95:1602–7.[Abstract/Free Full Text]
  27. Sarlio-Lahteenkorva S, Silventoinen K, Lahelma E. Relative weight and income at different levels of socioeconomic status. Am J Public Health (2004) 94:468–72.[Abstract/Free Full Text]
  28. The INCLEN Multicentre Collaborative Group. Socioeconomic status and risk factors for cardiovascular disease: a multicentre collaborative study in the International Clinical Epidemiology Network (INCLEN). J Clin Epidemiol (1994) 47:1401–9.[CrossRef][Web of Science][Medline]
  29. Nube M, Asenso-Okyere WK, van den Boom GJ. Body mass index as indicator of standard of living in developing countries. Eur J Clin Nutr (1998) 52:136–44.[CrossRef][Web of Science][Medline]
  30. Wilsgaard T, Jacobsen BK, Arnesen E. Determining lifestyle correlates of body mass index using multilevel analyses: The Tromso study 1979–2001. Am J Epedimiol (2005) 162:1179–88.[CrossRef]
  31. Jeffery RW, Forster JL, Folsom AR, et al. The relationship between social status and body mass index in the Minnesota Heart Health Program. Int J Obes (1989) 13:59–67.[Web of Science][Medline]
  32. Kawada T. Difference of body mass index stratified by the period of smoking cessation from a cross-sectional study. Arch Med Res (2004) 35:181–4.[CrossRef][Web of Science][Medline]
  33. Laaksonen M, Rahkonen O, Prattala R. Smoking status and relative weight by educational level in Finland, 1978–1995. Prev Med (1998) 27:431–7.[CrossRef][Web of Science][Medline]
  34. Rasky E, Stronegger WJ, Freidl W. The relationship between body weight and patterns of smoking in women and men. Int J Epidemiol (1996) 25:1208–12.[Abstract/Free Full Text]
  35. Molarius A, Seidell JC, Sans S, et al. Smoking and relative body weight: an international perspective from the WHO MONICA Project. J Epidemiol Community Health (1997) 51:252–60.[Abstract/Free Full Text]
  36. Jeffery RW, Rick AM. Cross-sectional and longitudinal associations between body mass index and marriage-related factors. Obes Res (2002) 10:809–15.[Web of Science][Medline]
  37. Sobal J, Rauschenbach B, Frongillo EA. Marital status changes and body weight changes: a US longitudinal analysis. Soc Sci Med (2002) 56:1543–55.[Web of Science]
  38. Jeffery RW, French SA. Socioeconomic status and weight control practices among 20 to 45 year old women. Am J Public Health (1996) 86:1005–10.[Abstract/Free Full Text]
  39. Sobal J, Stunkard AJ. Socioeconomic status and obesity: a review of the literature. Psychol Bull (1989) 105:260–75.[CrossRef][Web of Science][Medline]
  40. Paxon SJ, Sculthorpe A, Gibbons K. Weight-loss strategies and beliefs in high and low socioeconomic areas of Melbourne. Aust J Public Health (1994) 18:412–7.[Web of Science][Medline]
  41. Stephenson MG, Levy AS, Sass NL, McGarvey WE. 1985 NHIS findings: nutrition knowledge and baseline data for the weight-loss objectives. Public Health Rep (1987) 102:61–7.[Web of Science][Medline]
  42. French SA, Jeffery RW. Consequences of dieting to lose weight: effects on physical activity and mental health. Health Psychol (1994) 13:195–212.[CrossRef][Web of Science][Medline]
  43. Serdula MK, Collins ME, Williamson DF, et al. Weight control practices of U.S. adolescents and adults. Ann Intern Med (1993) 119:667–71.[Abstract/Free Full Text]
  44. Biener L, Healton A. Women diets of normal weight: their motives, goals and risks. Am J Public Health (1995) 85:714–7.[Abstract/Free Full Text]
  45. French SA, Jeffery RW. Current dieting, weight loss history, and weight suppression: behavioral correlates of three dimensions of dieting. Addict Behav (1997) 22:31–44.[CrossRef][Web of Science][Medline]
  46. Jeffery RW, French SA, Forster JL, Spry VM. Socioeconomic status differences in health behaviors related to obesity: the Healthy Worker Project. Int J Obesity (1991) 15:689–96.[Web of Science][Medline]
  47. Stunkard AJ, Sorensen TIA. Obesity and socioeconomic status-a complex relation. N Engl J Med. (1993) 329:1036–37.[Free Full Text]
  48. Griffiths P, Bently M. Women of higher socio-economic status are more likely to be overweight in Karnataka, India. Eur J Clin Nutr (2005) 95:1217–20.
  49. Bostrom G, Diderichsen F. Socioeconomic differences in misclassification of height, weight and body mass index based on questionnaire data. Int J Epidemiol (1997) 26:860–6.[Abstract/Free Full Text]
  50. Kuskowska-Wolk A, Bergstrom R, Bostrom G. Relationship between questionnaire data and medical records of height, weight and body mass index. Int J Obes (1992) 16:1–9.[Web of Science][Medline]
  51. Niedhamer I, Bugel I, Bonenfant S, et al. Validity of self-reported weight and height in the French GAZEL Cohort. Int J Obese (2000) 24:1111–8.[CrossRef]
  52. Gortmaker SL, Must A, Perrin JM, et al. Social and economic consequevces of overweight in adolescence and young adulthood. N Engl J Med (1993) 329:1008–12.[Abstract/Free Full Text]
  53. Sargent JD, Blanchflower DG. Obesity and stature in adolescence and earning in young adulthood. Analysis of a British birth cohort. Arch Pediatr Adolesc Med (1994) 148:681–7.[Abstract/Free Full Text]
  54. Averett S, Korenman S. Black-white differences in social and economic consequences of obesity. Int J Obes (1999) 23:166–73.[CrossRef][Web of Science][Medline]

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