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Associations between adolescent and adult socioeconomic status and risk of obesity and overweight in Danish adults
Obesity Research & Clinical Practice (2014) 8, e163—e171
ORIGINAL ARTICLE
Associations between adolescent and adult socioeconomic status and risk of obesity and overweight in Danish adults Sinead M. Boylan a,∗, Timothy P. Gill a, Helle Hare-Bruun b, Lars B. Andersen c,d, Berit L. Heitmann a,b,c a
Boden Institute for Obesity, Nutrition, Exercise and Eating Disorders, University of Sydney, NSW 2006, Australia b Research Centre for Prevention and Health, Glostrup University Hospital, DK-2600 Glostrup, Denmark c Institute of Sports Science and Clinical Biomechanics, and National Institute of Public Health, University of Southern Denmark, DK-5230 Odense M, Denmark d Department of Sports Medicine, Norwegian School of Sport Sciences, Norway Received 13 December 2012 ; received in revised form 21 March 2013; accepted 24 March 2013
KEYWORDS Obesity; Overweight; Socioeconomic
∗
Summary Background: It has been suggested that socioeconomic status (SES) may influence the risk of obesity; however it is important to consider individual changes in SES over the life-course in addition to SES at specific time-points to better understand the complex associations with obesity. We explored the relationship between lifetimespecific and life-course SES and risk of obesity and overweight in Danish adults. Methods: Data were used from the Danish Youth and Sports Study (DYSS) — a 20—22 year follow-up study of Danish teenagers born between 1964 and 1969. Baseline data gathered in 1983 and 1985 included self-reported BMI, SES and physical activity. The follow-up survey (2005) repeated these assessments in addition to an assessment of diet. Complete data on adolescent and adult SES and BMI were available for 623 participants. Results: Following adjustments, adolescent SES had no significant association with overweight/obesity in this sample, however females of low or medium adult SES were significantly more likely to be overweight/obese compared to those of high SES (low SES: OR: 2.7; 95% CI: (1.3—5.8); p = 0.008; medium SES: OR: 4.0, 95% CI (1.6—10.2); p = 0.003). Females who decreased in SES during adulthood were significantly more likely to be overweight/obese compared to those who remained of high SES (OR: 3.1; 95% CI (1.1—9.2); p = 0.04).
Corresponding author. Tel.: +61 2 9036 3006; fax: +61 2 9036 3184. E-mail address: [email protected] (S.M. Boylan).
1871-403X/$ — see front matter © 2013 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.orcp.2013.03.006
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S.M. Boylan et al. Conclusion: Effects of early life-factors may be conditional upon the environment in adulthood, particularly for the women. Further research should consider the timing of SES exposure and the mechanisms which may be responsible for the socioeconomic gradients in prevalence of obesity and overweight. © 2013 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Introduction The socio-economic gradients in health are of great public health concern [1]. The last couple of decades have seen a plethora of literature on socioeconomic differences in health outcomes such as coronary heart disease, stroke and all-cause mortality [2,3]. Obesity is believed to be one of the mechanisms responsible for such associations; therefore the socioeconomic gradients among obesity rates have been extensively examined in recent years. One of the largest bodies of evidence, a review by Sobal and Stunkard [4] found consistently inverse associations between socioeconomic status (SES) and obesity for women in developed societies, and a higher likelihood of obesity among those of higher SES in developing societies. The association between obesity and SES has been supported by more recent reviews [5,6] and there is now reasonable evidence that associations between SES and obesity may vary according to gender. Stronger associations are found among females from high income countries compared to males [4,7]. The relationship between obesity and SES may be bi-directional as obesity may influence SES, however SES may also influence the risk of obesity [8]. It has become widely recognized that social and biological factors that have developed over the lifecourse may influence adult health status and this has led to an increased interest in the life-course determinants of obesity [9—11]. Examining SES at a single time-point in life does not take into account the temporal nature of this association and may only partly explain the contributions of socioeconomic factors to health status or how these change over time [12]. In an attempt to best capture the individual cumulative and dynamic nature of SES, several models have been developed to examine life-course SES. The sensitive-periods model recognize that individuals may be more vulnerable to low SES exposure during certain time-points, leading to permanent (critical period) or modifiable (sensitive period) changes in disease risk [13]. Indeed, a recent meta-analysis found inverse associations between childhood socioeconomic status (SES) and
adult obesity, however effect sizes were typically reduced when adjustments were made for adult SES (education) [7]. Similarly, other studies which have adjusted for adulthood SES typically find reduced effect sizes. This suggests that adulthood SES is important in explaining the observed associations between childhood SES and obesity. The social-mobility model acknowledges that SES is dynamic and it incorporates the trajectory of socioeconomic mobility across one’s lifetime in determining disease risk [13]. Studies examining the effects of SES trajectory have produced inconsistent results with negative effects found among both the downwardly [9,14—16] and upwardly social mobile individuals [17]. Others did not find any association between social mobility and BMI or waist-hip ratio [18]. It may be the accumulation of multiple adverse circumstances experienced by lower SES groups throughout the life-course which lead to social inequalities in health [19]. This accumulation of risk model is the summed effect of SES that interacts to increase disease risk across a person’s life-course [13], and may therefore help understand the mechanisms by which social inequities in health develop and are maintained. However, there has been little examination of individual cumulative influences of adverse circumstances during a lifetime on social inequalities in obesity [20,21]. Irrespective of model choice, it is important to consider the range of confounders which exist in the obesity—SES relationship, particularly parental and childhood/adolescent BMI as it is well established that adolescent and parental obesity influences the development of obesity later in life [22]. Our hypothesis is that during a lifetime, SES influences the risk of adult obesity and overweight. Therefore, the aims of this current study were to explore the relationship between lifetimespecific and life-course SES and risk of adult obesity and overweight by (a) examining the relationship between adolescent and adult SES and the risk of adult obesity and overweight; (b) determining the impact of individual SES mobility on the risk of adult obesity and overweight; (c) evaluating the extent to which individual cumulative life-course SES is
Socioeconomic status and risk of obesity and overweight in Danish adults associated with the risk of adult obesity and overweight; and (d) exploring whether associations are gender-specific.
Subjects and methods Ethics statement The study is in accordance with the Helsinki Declaration and was approved by the Data Protection Agency (2005-2311-0111). All participants gave written informed consent.
Data collection This study used data from the Danish Youth and Sports Study (DYSS) which is a 20—22 year follow-up study of Danish teenagers born between 1964 and 1969. Details of this study is discussed elsewhere [23], however in brief, baseline data was gathered 1983 from students in gymnasium, vocational or trade school and in 1985 from teenagers drawn from the Danish Civil Registration system (n = 3008). The data gathered consisted of, among other measures, a general questionnaire including questions on self-reported height and weight from which body mass index (BMI) was calculated, SES and physical activity. Physical activity was based on information on leisure time physical activity, sports, and moderate and high intensity gardening. Each sport and activity was assigned a metabolic equivalent (MET) — details of this method have been published elsewhere [24]. The follow-up survey in 2005 consisted of the same questionnaire, a validated 195-item food frequency questionnaire [25] and a cheek scraping for DNA analysis. Parental BMI was calculated from heights and weights recorded by subjects at follow-up. At the follow-up survey, it was not possible to trace all participants from the baseline survey, however of the 1904 eligible persons invited to participate in the follow-up survey, 786 (41%) chose to participate. The data are available from the authors upon request.
SES assessment SES was assessed at baseline hence reducing the risk of introducing recall bias and was based on an open ended question on paternal occupation and education. If no SES could be assigned to the father, then maternal SES was used. Follow-up SES was based on the participant’s own occupation and education. SES was categorized into five categories based on both occupation and education as follows: SES
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group 1: >4 years further education (master level), white-collar workers with >50 subordinates, and self-employed with >20 employees); SES group 2: short or medium further education (up to 4 years), white collar workers with 11—50 subordinates (if not master level education), and self-employed with 6—20 employees (if not master level education); SES group 3: short education (up to 1 year), vocational education, white-collar workers with 1—10 subordinates (if not education corresponding to SES group 1 or 2), and self-employed with 0—5 employees (if not education corresponding to SES group 2); SES group 4: skilled workers, and white-collar workers with no subordinates (if not education corresponding to SES group 1, 2 or 3); SES group 5: semi-skilled and unskilled workers. SES groups were then further categorized as high (groups 1 and 2), medium (group 3) and low SES (groups 4 and 5). Life-course SES frameworks: Life-course frameworks based on those developed for the Framingham Offspring Study were developed and examined [26]. The sensitive-periods framework calculated the effect of SES measured during each period individually. The social-mobility framework examined life course trajectories. Four potential trajectories were investigated: stable low SES (low adolescent and low adulthood), decreasing SES (high adolescent and low adulthood), increasing SES (low adolescent and high adulthood) and stable high SES (high adolescent and high adulthood). An accumulation-of-risk model was created by summing scores for adolescent SES and adult SES. The cumulative SES scores ranged from 2 to 6 were categorized into high (score = 2), medium (score = 3—4) and low (scores = 5—6) cumulative SES similar to other studies [26—28].
Statistical methods Statistics were conducted using SPSS v19.0. The effect of adolescent and adult SES, social mobility and accumulation on adult overweight (BMI: 25—30 kg/m2 ) and obesity (BMI: >30 kg/m2 ) were assessed by logistic regression. Models examining the relationship between adolescent SES, adulthood SES and adulthood BMI mutually adjusted for adolescent or adult SES. In addition, all models were adjusted for adolescent BMI (kg/m2 ), parental BMI (categorical predictor: no parents overweight/obese, 1 parent overweight/obese, 2 parents overweight/obese), adult smoking (current smoker/non-smoker), energy intake (KJ) and physical activity. Although the log-likelihood ratio of the unadjusted model (170.0) did not differ much from the model adjusting for smoking energy intake,
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and physical activity alone (169.7), these covariates were included in the models as they have been suggested to be partly responsible for the association between SES and BMI [29—32]. Mis-reporters of energy intake (n = 76) were excluded based on a method described elsewhere [33,34]. Birth weight (grams) did not significantly contribute to the models nor was it significantly associated with adulthood BMI (Spearman’s correlation coefficient: r = 0.03, p = 0.61) therefore it was not included in the logistic regression models.
Results Sample characteristics Following exclusion of mis-reporters and those with incomplete data on adult BMI, adolescent and adulthood SES, the final sample consisted of 623 participants (269 males and 354 females), the characteristics of which are presented in Table 1. The mean (SD) age of the sample at baseline was 16.9 (1.0) years and at follow-up was 37.7 (1.4) years. The mean (SD) daily energy intake was 10.5 (2.6) MJ. The subjects were inactive with low mean (SD) MET totals 0.27 (0.23) evident and the majority of them were current non-smokers (79%). More adult males (39%) than females (20%) were classified as overweight, however the prevalence of obesity was similar for females (7%) and males (7%). The prevalence of combined obesity and overweight across SES categories are presented in Table 2. Approximately one-third of the sample was categorized with a low adolescent SES and almost 40% of the sample increased in SES since that time period.
Table 1
Socio-economic status, adulthood obesity and overweight Among females, adolescent, adult, cumulative SES and social mobility were linearly associated with overweight/obesity (Table 2). There were a significantly higher percentage of overweight/obese females from low adult SES compared to those of high adult SES. The small sample size made it difficult to reliably examine the association between SES and obesity, however, the data indicated there were more obese participants with a low adolescent SES (8% males; 9% females) than with a high adolescent SES (4% males; 3% females) and a significantly (p = 0.002) higher percentage of obese females with a low adult SES (12.5%) than a high adult SES (3%). In addition, there were a significantly (p = 0.01) higher proportion of obese females from a low stable SES (10.9%) compared to those from a stable high SES (2%) and a significantly (p = 0.001) higher proportion of obese females with a low cumulative SES (14%) than a high cumulative SES (2%). Table 2 also shows the unadjusted and adjusted relationships between adolescent and adult SES, and combined adult overweight and obesity. After adjusting for adult energy intake, physical activity, smoking, adolescent and parental BMI and adult or adolescent SES, adolescent SES had no significant association with overweight/obesity in this sample, however females of low or medium adult SES were significantly more likely to be overweight/obese compared to those of high adult SES. Following adjustment for the aforementioned covariates, females who decreased in SES were significantly more likely to be overweight/obese compared to those who remained of high SES.
Characteristics of study sample (n = 623).
n Mean age (years) (SD) Baseline Follow-up Follow-up BMI (%) <18.5 18.5—25 25—30 30—35 >35 Smoker (%) Yes No Mean daily energy intake (MJ) (SD) Mean MET total (SD)
Males
Females
Total
269
354
623
16.9 (1.0) 37.6 (1.4)
17.0 (1.0) 37.7 (1.4)
16.9 (1.0) 37.7 (1.4)
0 54 39 6 1
3 70 20 4.5 2.5
2 63 28 5 2
20 80 11.7 (2.6) 0.29 (0.23)
21 79 9.5 (2.2) 0.25 (0.23)
21 79 10.5 (2.6) 0.27 (0.23)
Associations between SES and adult obesity/overweight. Males (n = 269) na
Females (n = 354)
Obese or overweight %
Unadjusted OR (95% CI)
Adjusted OR (95% CI)
n
Obese or overweight %
Unadjusted OR (95% CI)
Adjusted OR (95% CI)
Adolescent SES Low Medium High Trend p-value
86 83 100
49 48 42 0.3
1.3 (0.7—2.3) 1.3 (0.7—2.3) 1.0 ref
0.9 (0.4—1.9) 0.8 (0.4—1.7) 1.0 ref
137 101 116
33 26 21 0.03
1.9 (1.1—3.3) 1.3 (0.7—2.5) 1.0 ref
0.7 (0.3—1.5) 0.9 (0.4—2.0) 1.0 ref
Adult SES Low Medium High Trend p-value
63 49 157
52 55 41 0.07
1.6 (0.9—2.9) 1.8 (0.9—3.4) 1.0 ref
1.5 (0.7—3.3) 1.3 (0.5—3.3) 1.0 ref
104 56 194
40 34 18 <0.001
2.9 (1.7—5.0) 2.3 (1.2—4.5) 1.0 ref
2.7 (1.3—5.8) 4.0 (1.6—10.0) 1.0 ref
Social mobility Stable low SES Decreased SES Increased SES Stable high SES Trend p-value
25 47 103 77
56 45 41 44 0.4
1.6 1.0 0.9 1.0
1.0 0.9 0.7 1.0
(0.3—2.9) (0.4—2.4) (0.3—1.4) ref
64 56 131 87
41 36 24 14 <0.001
4.3 (1.9—9.4) 3.5 (1.5—7.9) 1.9 (0.9—4.0) 1.0 ref
2.8 (0.9—8.3) 3.1 (1.1—9.2) 1.4 (0.5—3.6) 1.0 ref
Cumulative SES Low Medium High Trend p-value
72 120 77
54 42.5 44 0.2
1.5 (0.8—2.8) 0.9 (0.5—1.7) 1.0 ref
1.1 (0.5—2.4) 0.7 (0.3—1.5) 1.0 ref
115 152 87
36.5 27 14 0.001
3.6 (1.7—7.4) 2.3 (1.1—4.7) 1.0 ref
2.3 (0.9—6.0) 2.1 (0.9—5.2) 1.0 ref
(0.6—4.0) (0.5—2.1) (0.5—1.6) ref
Socioeconomic status and risk of obesity and overweight in Danish adults
Table 2
Significance level of p < 0.05 for values in bold text. a Social mobility analyses includes different sample size as some subjects remained at a middle SES level from adolescent to adulthood and therefore were not included in the social mobility analyses.
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e168 Cumulative SES had no significant associations with adult overweight/obesity after adjustment for the same variables. As mentioned earlier, the small sample size made it difficult to reliably examine the relationship between SES and obesity, however after adjusting for the aforementioned variables, females of a medium adult SES were significantly more likely to be obese compared to those of a high SES (OR: 7.0; 95% CI (1.4—34.4); p = 0.02).
Discussion Socioeconomic status, adulthood obesity and overweight This current longitudinal survey found a significant inverse relationship between both adolescent and adulthood SES, and adult overweight/obesity among females, but not males. However, following adjustments for covariates including SES in either adolescence or adulthood, only the relationship between adult SES and overweight/obesity remained significant. These findings may indicate that the effects of adolescent-factors may still be conditional upon the environment in adulthood [35]. Evidence suggests that the inverse gradients of SES evident with risk for smoking, sedentary behavior and obesogenic diet may partly explain the association between adulthood SES and BMI [29—32,36]. Smoking, physical activity and energy intake were adjusted for in this current study, however model development indicated that adolescent and parental BMI had a stronger role to play than these covariates in the current relationship between adult SES and risk of overweight and obesity. Previous studies have examined the association between SES and obesity using only one [35,37], or at most, two [15,18,20] of the following models — sensitive-periods model, social mobility model or the accumulation of risk model. Although several studies have previously assessed the influence of social mobility on the risk of adulthood obesity and overweight, to our knowledge this is the first study to examine the association between adult overweight and obesity and SES at specific time points in addition to examining the relationship between the risk of adult overweight and obesity, social mobility and accumulation over 25 years from adolescence to adulthood.
Comparison with other studies The 2000—2002 Danish National Dietary Survey [38] reported a similar prevalence of overweight to this current study but a slightly higher prevalence
S.M. Boylan et al. of obesity. In addition other Danish studies have found inverse associations between adulthood SES, BMI and obesity [38—40] and inverse associations between childhood and adolescent SES and childhood and adolescent overweight [41,42]. Social disadvantage in childhood has also been shown to be a risk factor for obesity in adulthood [43,44], however recent research shows that this relationship weakens when adjustments are made for attained adult SES [7], as in this current study. The relationship between SES and BMI is complex and factors mediating the relationship have been extensively examined in the last few decades. One of the most consistent findings from previous research shows stronger relationships between SES and adulthood BMI among women compared to men and this study also replicated this finding. There is evidence to suggest that females may be more vulnerable to social pressures compared to males and these pressures may be greater among females with high SES than low SES [4,45]. The strong associations found among females may also be due to the fact that females of higher SES diet more than those of low SES as females with high SES may have more resources to facilitate their preferred diet or lifestyle compared to those of low SES [8]. Life-course SES and risk of adulthood obesity and overweight: Studies suggest that childhood SES is predictive of adulthood SES [46], however this may not always be the case as this current study showed that this sample was socially mobile. Almost half a century ago, Goldblatt et al. [47] found that obesity prevalence was almost twice as high among women who decreased in SES (22%) as it was among those who rose in social class (12%) [47]. In the last couple of decades researchers have once again become interested in the effects of social trajectory on weight, with some focusing on examining the mechanisms [48]. Some research has suggested that those who are obese in adolescence experience significant social disadvantage in adulthood [48]. Overall however, the results from recent studies examining the relationship between socioeconomic mobility and weight have been inconsistent [9,14—17]. This current study which assessed the trajectory of socioeconomic mobility from adolescence through to adulthood using the social-mobility model [13], found that females who decreased in SES were significantly more likely to be overweight/obese compared to those who remained at a high SES. Others suggest that socioeconomic exposures may accumulate over time and lead to poor health outcomes [20,49,50]. This current study used an accumulation of risk model to examine the association between cumulative SES and
Socioeconomic status and risk of obesity and overweight in Danish adults overweight/obesity, however, the results were not significant. The lack of a significant relationship may be partly explained by the cumulative disadvantage theory [21] which emphasizes how early exposure is critical to how cohorts differentiate over time — yet this current study did not find parental SES influenced risk of adulthood obesity or overweight. It is possible that the lack of an association between cumulative SES and adult obesity in this current study may be due to the more complex assessment of SES during adulthood compared to the assessment during adolescence. Still, these findings highlight the complexity of the relationship between SES and BMI, and also that the relationship may have changed in more recent times. Unfortunately there has been little examination of the cumulative influences of adverse circumstances on social inequalities in obesity [20,21,50]. While it is plausible that life-course circumstances may influence weight, more longitudinal studies are required to gain a more meaningful insight.
Strengths and limitations To the author’s knowledge this is the first study to assess the influence of SES at specific timepoints, social mobility and cumulative SES on risk of adulthood obesity and overweight. This current study is strengthened by its longitudinal design and prospective nature (following individuals from adolescence for more than 20 years into adulthood) — features which are unfortunately generally lacking in this field of research. Previous studies have simply identified relationships between SES and obesity [5,6]; however as mentioned earlier, this relationship may be bi-directional [6]. The longitudinal nature of this current study meant that the direction of this relationship could be examined. Although SES gradients in obesity prevalence are well-documented, it is unclear which mechanism(s) are responsible for this relationship. This current study considered some likely candidates — energy intake, physical activity and smoking; however, our results suggest that other factors may also be important to consider as adolescent SES, and adolescent and parental BMI had stronger influences on the relationship between adult SES and obesity and overweight. The study also has a number of limitations. We assessed parental SES based on an open ended question on parent’s occupation and education, while adult SES was based on a more detailed assessment. In addition, paternal, rather than maternal SES was primarily used in this current analyses while there is some evidence from other literature to suggest that maternal characteristics are more closely
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associated with the health behaviors of children and hence their risk of obesity [51,52]. The relationship between SES and adolescent obesity was not examined, however it has been suggested that this relationship is weakening [53]. Finally, recall bias may have been introduced through the use of self-reported data e.g. self-reported weight and height may lead to underestimations of obesity or overweight prevalence rates as subjects may underreport their body weight [54—56] and over-report body height [54—56]. While other studies have validated self-reported height and weight [57—59], it cannot be overlooked that under-reporting may have attenuated results in this current study.
Conclusions Compared to other developed countries, Denmark has a relatively low level of overweight and obesity but, obesity rates have been rising over the past 50 years [60,61] and it is important to understand the social gradient of obesity and overweight [38—42]. Our results suggest that the relationship between SES and BMI is complex in nature, and may have changed in more recent times. Furthermore, the fact that both adolescent and adult SES were related to overweight/obesity in adulthood, but that only the relationship between adult SES and overweight/obesity persists when considering previous SES, indicates that effects of early life-factors are conditional upon the environment in adulthood, particularly for the women. Further research is warranted on the timing of SES exposure and the mechanisms which may be responsible for the socioeconomic gradients in prevalence of obesity and overweight.
Funding This work was supported by the Danish Medical research Council (grant number 271-05-0277); and Ministry for Health (grant number 2003-0200-13).
Conflicts of interest None declared.
References [1] Commission on Social Determinants of Health. Closing the gap in a generation: health equity through action on the social determinants of health: final report of the
e170
[2]
[3]
[4] [5] [6] [7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
commission on social determinants of health. Geneva: World Health Organisation; 2008. Galobardes B, Lynch JW, Davey SG. Childhood socioeconomic circumstances and cause-specific mortality in adulthood: systematic review and interpretation. Epidemiol Rev 2004;26:7—21. Galobardes B, Lynch JW, Smith GD. Is the association between childhood socioeconomic circumstances and cause-specific mortality established? Update of a systematic review. J Epidemiol Community Health 2008;62:387—90. Sobal J, Stunkard AJ. Socioeconomic status and obesity: a review of the literature. Psychol Bull 1989;105:260—75. Ball K, Crawford D. Socioeconomic status and weight change in adults: a review. Soc Sci Med 2005;60:1987—2010. McLaren L. Socioeconomic status and obesity. Epidemiol Rev 2007;29:29—48. Senese LC, Almeida ND, Fath AK, Smith BT, Loucks EB. Associations between childhood socioeconomic position and adulthood obesity. Epidemiol Rev 2009;31:21—51. Stunkard AJ. Socioeconomic status and obesity. In: Chadwick DJ, Cardew G, editors. Ciba foundation symposium 201 — the origins and consequences of obesity. 1st ed. Chicester, UK: John Wiley & Sons; 1996. p. 174—93. Heraclides A, Brunner E. Social mobility and social accumulation across the life course in relation to adult overweight and obesity: the Whitehall II study. J Epidemiol Community Health 2010;64:714—9. Johnson DB, Gerstein DE, Evans AE, Woodward-Lopez G. Preventing obesity: a life cycle perspective. J Am Diet Assoc 2006;106:97—102. Kuh DJH, Ben-Shlomo Y, editors. A lifecourse approach to chronic disease epidemiology. 2nd ed. Oxford, UK: Oxford University Press; 2004. Davey-Smith G, Lynch J. Life course approaches to socioeconomic differentials in health. In: Kuh D, Ben-Shlomo Y, editors. A life course approach to chronic disease epidemiology. 2nd ed. Oxford, UK: Oxford University Press; 2004. p. 77—115. Kuh D, Ben-Shlomo Y, Lynch J, Hallqvist J, Power C. Life course epidemiology. J Epidemiol Community Health 2003;57:778—83. Gigante DP, Minten GC, Horta BL, Barros FC, Victora CG. Nutritional evaluation follow-up of the 1982 birth cohort, Pelotas, Southern Brazil. Rev Saude Publica 2008;42:60—9. Hart C, McConnachie A, Upton M, Watt G. Risk factors in the Midspan family study by social class in childhood and adulthood. Int J Epidemiol 2008;37:604—14. Langenberg C, Hardy R, Kuh D, Brunner E, Wadsworth M. Central and total obesity in middle aged men and women in relation to lifetime socioeconomic status: evidence from a national birth cohort. J Epidemiol Community Health 2003;57:816—22. James SA, Fowler-Brown A, Raghunathan TE, Van HJ. Life-course socioeconomic position and obesity in African American Women: the Pitt County Study. Am J Public Health 2006;96:554—60. Kavikondala S, Schooling CM, Jiang CQ, Zhang WS, Cheng KK, Lam TH, et al. Pathways to obesity in a developing population: The Guangzhou Biobank Cohort Study. Int J Epidemiol 2009;38:72—82. Kuh D, Ben-Shlomo Y, editors. A life course approach to chronic disease epidemiology: tracing the origins of ill health from early to adult life. 1st ed. Oxford, UK: Oxford University Press; 1997. Baltrus PT, Lynch JW, Everson-Rose S, Raghunathan TE, Kaplan GA. Race/ethnicity, life-course socioeconomic
S.M. Boylan et al.
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36]
position, and body weight trajectories over 34 years: the Alameda County Study. Am J Public Health 2005;95:1595—601. Ferraro KF, Kelley-Moore JA. Cumulative disadvantage and health: long-term consequences of obesity? Am Sociol Rev 2003;68:707—29. Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med 1997;337:869—73. Hare-Bruun H, Togo P, Andersen LB, Heitmann BL. Adult food intake patterns are related to adult and childhood socioeconomic status. J Nutr 2011;141:928—34. Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 2000;32:S498—504. Tjonneland A, Overvad K, Haraldsdottir J, Bang S, Ewertz M, Jensen OM. Validation of a semiquantitative food frequency questionnaire developed in Denmark. Int J Epidemiol 1991;20:906—12. Smith BT, Lynch JW, Fox CS, Harper S, Abrahamowicz M, Almeida ND, et al. Life-course socioeconomic position and type 2 diabetes mellitus: the Framingham Offspring Study. Am J Epidemiol 2011;173: 438—47. Loucks EB, Lynch JW, Pilote L, Fuhrer R, Almeida ND, Richard H, et al. Life-course socioeconomic position and incidence of coronary heart disease: the Framingham Offspring Study. Am J Epidemiol 2009;169: 829—36. Loucks EB, Pilote L, Lynch JW, Richard H, Almeida ND, Benjamin EJ, et al. Life course socioeconomic position is associated with inflammatory markers: the Framingham Offspring Study. Soc Sci Med 2010;71: 187—95. Li F, Fisher KJ, Bauman A, Ory MG, Chodzko-Zajko W, Harmer P, et al. Neighborhood influences on physical activity in middle-aged and older adults: a multilevel perspective. J Aging Phys Act 2005;13:87—114. National High Blood Pressure Program Working Group on Hypertension Control in Children and Adolescents. Updated on the 1987 task force report on high blood pressure in children and adolescents: a working group report from the National High Blood Pressure Program. Pediatrics 1996;98:649—58. Pekkanen J, Tuomilehto J, Uutela A, Vartiainen E, Nissinen A. Social class, health behaviour, and mortality among men and women in eastern Finland. BMJ 1995;311: 589—93. Turrell G, Hewitt B, Patterson C, Oldenburg B, Gould T. Socioeconomic differences in food purchasing behaviour and suggested implications for diet-related health promotion. J Hum Nutr Diet 2002;15:355—64. Boylan S, Welch A, Pikhart H, Malyutina S, Pajak A, Kubinova R, et al. Dietary habits in three Central and Eastern European countries: the HAPIEE study. BMC Public Health 2009;9:439. Welch AA, Luben R, Khaw KT, Bingham SA. The CAFE computer program for nutritional analysis of the EPICNorfolk food frequency questionnaire and identification of extreme nutrient values. J Hum Nutr Diet 2005;18: 99—116. Power C, Manor O, Matthews S. Child to adult socioeconomic conditions and obesity in a national cohort. Int J Obes Relat Metab Disord 2003;27:1081—6. Boylan S, Lallukka T, Lahelma E, Pikhart H, Malyutina S, Pajak A, et al. Socio-economic circumstances and food
Socioeconomic status and risk of obesity and overweight in Danish adults
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
[45]
[46]
[47] [48]
habits in Eastern, Central and Western European populations. Public Health Nutr 2011;14:678—87. Barros AJ, Victora CG, Horta BL, Goncalves HD, Lima RC, Lynch J. Effects of socioeconomic change from birth to early adulthood on height and overweight. Int J Epidemiol 2006;35:1233—8. Groth MV, Fagt S, Stockmarr A, Matthiessen J, Biltoft-Jensen A. Dimensions of socioeconomic position related to body mass index and obesity among Danish women and men. Scand J Public Health 2009;37:418—26. Nielsen TL, Wraae K, Brixen K, Hermann AP, Andersen M, Hagen C. Prevalence of overweight, obesity and physical inactivity in 20- to 29-year-old, Danish men, Relation to sociodemography, physical dysfunction and low socioeconomic status: the Odense Androgen Study. Int J Obes (Lond) 2006;30:805—15. Sarlio-Lahteenkorva S, Lissau I, Lahelma E. The social patterning of relative body weight and obesity in Denmark and Finland. Eur J Public Health 2006;16:36—40. Due P, Damsgaard MT, Rasmussen M, Holstein BE, Wardle J, Merlo J, et al. Socioeconomic position, macroeconomic environment and overweight among adolescents in 35 countries. Int J Obes (Lond) 2009;33:1084—93. Kristensen PL, Wedderkopp N, Moller NC, Andersen LB, Bai CN, Froberg K. Tracking and prevalence of cardiovascular disease risk factors across socio-economic classes: a longitudinal substudy of the European Youth Heart Study. BMC Public Health 2006;6:20. Parsons TJ, Power C, Logan S, Summerbell CD. Childhood predictors of adult obesity: a systematic review. Int J Obes Relat Metab Disord 1999;23(Suppl. 8):S1—107. Power C, Graham H, Due P, Hallqvist J, Joung I, Kuh D, et al. The contribution of childhood and adult socioeconomic position to adult obesity and smoking behaviour: an international comparison. Int J Epidemiol 2005;34:335—44. Finkelstein EA, Ruhm CJ, Kosa KM. Economic causes and consequences of obesity. Annu Rev Public Health 2005;26:239—57. Beller E, Hout M. Intergenerational social mobility: the United States in comparative perspective. Future Child 2006;16:19—36. Goldblatt PB, Moore ME, Stunkard AJ. Social factors in obesity. J Am Med Assoc 1965;192:1039—44. Gortmaker SL, Must A, Perrin JM, Sobol AM, Dietz WH. Social and economic consequences of overweight in adolescence and young adulthood. N Engl J Med 1993;329:1008—12.
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[49] Everson SA, Maty SC, Lynch JW, Kaplan GA. Epidemiologic evidence for the relation between socioeconomic status and depression, obesity, and diabetes. J Psychosom Res 2002;53:891—5. [50] Novak M, Ahlgren C, Hammarstrom A. A life-course approach in explaining social inequity in obesity among young adult men and women. Int J Obes (Lond) 2006;30:191—200. [51] Favaro A, Santonastaso P. Effects of parents’ psychological characteristics and eating behaviour on childhood obesity and dietary compliance. J Psychosom Res 1995;39: 145—51. [52] Shrewsbury V, Wardle J. Socioeconomic status and adiposity in childhood: a systematic review of cross-sectional studies 1990—2005. Obesity (Silver Spring) 2008;16:275—84. [53] Wang Y, Zhang Q. Are American children and adolescents of low socioeconomic status at increased risk of obesity? Changes in the association between overweight and family income between 1971 and 2002. Am J Clin Nutr 2006;84:707—16. [54] Cullum A, McCarthy A, Gunnell D, Davey SG, Sterne JA, Ben-Shlomo Y. Dietary restraint and the mis-reporting of anthropometric measures by middle-aged adults. Int J Obes Relat Metab Disord 2004;28:426—33. [55] Hill A, Roberts J. Body mass index: a comparison between self-reported and measured height and weight. J Public Health Med 1998;20:206—10. [56] Nawaz H, Chan W, Abdulrahman M, Larson D, Katz DL. Self-reported weight and height: implications for obesity research. Am J Prev Med 2001;20:294—8. [57] Avila-Funes JA, Gutiérrez-Robledo LM, Ponce De Leon Rosales S. Validity of height and weight self-report in Mexican adults: results from the national health and aging study. J Nutr Health Aging 2004;8:355—61. [58] Lee DH, Shin A, Kim J, Yoo KY, Sung J. Validity of selfreported height and weight in a Korean population. J Epidemiol 2011;21:30—6. [59] Spencer EA, Appleby PN, Davey GK, Key TJ. Validity of self-reported height and weight in 4808 EPIC-Oxford participants. Public Health Nutr 2002;5:561—5. [60] Due P, Heitmann BL, Sorensen TI. Prevalence of obesity in Denmark. Obes Rev 2007;8:187—9. [61] Pearson S, Olsen LW, Hansen B, Sorensen TI. Increase in overweight and obesity amongst Copenhagen schoolchildren, 1947—2003. Ugeskr Laeger 2005;167: 158—62.
Available online at www.sciencedirect.com
ORIGINAL ARTICLE
Associations between adolescent and adult socioeconomic status and risk of obesity and overweight in Danish adults Sinead M. Boylan a,∗, Timothy P. Gill a, Helle Hare-Bruun b, Lars B. Andersen c,d, Berit L. Heitmann a,b,c a
Boden Institute for Obesity, Nutrition, Exercise and Eating Disorders, University of Sydney, NSW 2006, Australia b Research Centre for Prevention and Health, Glostrup University Hospital, DK-2600 Glostrup, Denmark c Institute of Sports Science and Clinical Biomechanics, and National Institute of Public Health, University of Southern Denmark, DK-5230 Odense M, Denmark d Department of Sports Medicine, Norwegian School of Sport Sciences, Norway Received 13 December 2012 ; received in revised form 21 March 2013; accepted 24 March 2013
KEYWORDS Obesity; Overweight; Socioeconomic
∗
Summary Background: It has been suggested that socioeconomic status (SES) may influence the risk of obesity; however it is important to consider individual changes in SES over the life-course in addition to SES at specific time-points to better understand the complex associations with obesity. We explored the relationship between lifetimespecific and life-course SES and risk of obesity and overweight in Danish adults. Methods: Data were used from the Danish Youth and Sports Study (DYSS) — a 20—22 year follow-up study of Danish teenagers born between 1964 and 1969. Baseline data gathered in 1983 and 1985 included self-reported BMI, SES and physical activity. The follow-up survey (2005) repeated these assessments in addition to an assessment of diet. Complete data on adolescent and adult SES and BMI were available for 623 participants. Results: Following adjustments, adolescent SES had no significant association with overweight/obesity in this sample, however females of low or medium adult SES were significantly more likely to be overweight/obese compared to those of high SES (low SES: OR: 2.7; 95% CI: (1.3—5.8); p = 0.008; medium SES: OR: 4.0, 95% CI (1.6—10.2); p = 0.003). Females who decreased in SES during adulthood were significantly more likely to be overweight/obese compared to those who remained of high SES (OR: 3.1; 95% CI (1.1—9.2); p = 0.04).
Corresponding author. Tel.: +61 2 9036 3006; fax: +61 2 9036 3184. E-mail address: [email protected] (S.M. Boylan).
1871-403X/$ — see front matter © 2013 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.orcp.2013.03.006
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S.M. Boylan et al. Conclusion: Effects of early life-factors may be conditional upon the environment in adulthood, particularly for the women. Further research should consider the timing of SES exposure and the mechanisms which may be responsible for the socioeconomic gradients in prevalence of obesity and overweight. © 2013 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Introduction The socio-economic gradients in health are of great public health concern [1]. The last couple of decades have seen a plethora of literature on socioeconomic differences in health outcomes such as coronary heart disease, stroke and all-cause mortality [2,3]. Obesity is believed to be one of the mechanisms responsible for such associations; therefore the socioeconomic gradients among obesity rates have been extensively examined in recent years. One of the largest bodies of evidence, a review by Sobal and Stunkard [4] found consistently inverse associations between socioeconomic status (SES) and obesity for women in developed societies, and a higher likelihood of obesity among those of higher SES in developing societies. The association between obesity and SES has been supported by more recent reviews [5,6] and there is now reasonable evidence that associations between SES and obesity may vary according to gender. Stronger associations are found among females from high income countries compared to males [4,7]. The relationship between obesity and SES may be bi-directional as obesity may influence SES, however SES may also influence the risk of obesity [8]. It has become widely recognized that social and biological factors that have developed over the lifecourse may influence adult health status and this has led to an increased interest in the life-course determinants of obesity [9—11]. Examining SES at a single time-point in life does not take into account the temporal nature of this association and may only partly explain the contributions of socioeconomic factors to health status or how these change over time [12]. In an attempt to best capture the individual cumulative and dynamic nature of SES, several models have been developed to examine life-course SES. The sensitive-periods model recognize that individuals may be more vulnerable to low SES exposure during certain time-points, leading to permanent (critical period) or modifiable (sensitive period) changes in disease risk [13]. Indeed, a recent meta-analysis found inverse associations between childhood socioeconomic status (SES) and
adult obesity, however effect sizes were typically reduced when adjustments were made for adult SES (education) [7]. Similarly, other studies which have adjusted for adulthood SES typically find reduced effect sizes. This suggests that adulthood SES is important in explaining the observed associations between childhood SES and obesity. The social-mobility model acknowledges that SES is dynamic and it incorporates the trajectory of socioeconomic mobility across one’s lifetime in determining disease risk [13]. Studies examining the effects of SES trajectory have produced inconsistent results with negative effects found among both the downwardly [9,14—16] and upwardly social mobile individuals [17]. Others did not find any association between social mobility and BMI or waist-hip ratio [18]. It may be the accumulation of multiple adverse circumstances experienced by lower SES groups throughout the life-course which lead to social inequalities in health [19]. This accumulation of risk model is the summed effect of SES that interacts to increase disease risk across a person’s life-course [13], and may therefore help understand the mechanisms by which social inequities in health develop and are maintained. However, there has been little examination of individual cumulative influences of adverse circumstances during a lifetime on social inequalities in obesity [20,21]. Irrespective of model choice, it is important to consider the range of confounders which exist in the obesity—SES relationship, particularly parental and childhood/adolescent BMI as it is well established that adolescent and parental obesity influences the development of obesity later in life [22]. Our hypothesis is that during a lifetime, SES influences the risk of adult obesity and overweight. Therefore, the aims of this current study were to explore the relationship between lifetimespecific and life-course SES and risk of adult obesity and overweight by (a) examining the relationship between adolescent and adult SES and the risk of adult obesity and overweight; (b) determining the impact of individual SES mobility on the risk of adult obesity and overweight; (c) evaluating the extent to which individual cumulative life-course SES is
Socioeconomic status and risk of obesity and overweight in Danish adults associated with the risk of adult obesity and overweight; and (d) exploring whether associations are gender-specific.
Subjects and methods Ethics statement The study is in accordance with the Helsinki Declaration and was approved by the Data Protection Agency (2005-2311-0111). All participants gave written informed consent.
Data collection This study used data from the Danish Youth and Sports Study (DYSS) which is a 20—22 year follow-up study of Danish teenagers born between 1964 and 1969. Details of this study is discussed elsewhere [23], however in brief, baseline data was gathered 1983 from students in gymnasium, vocational or trade school and in 1985 from teenagers drawn from the Danish Civil Registration system (n = 3008). The data gathered consisted of, among other measures, a general questionnaire including questions on self-reported height and weight from which body mass index (BMI) was calculated, SES and physical activity. Physical activity was based on information on leisure time physical activity, sports, and moderate and high intensity gardening. Each sport and activity was assigned a metabolic equivalent (MET) — details of this method have been published elsewhere [24]. The follow-up survey in 2005 consisted of the same questionnaire, a validated 195-item food frequency questionnaire [25] and a cheek scraping for DNA analysis. Parental BMI was calculated from heights and weights recorded by subjects at follow-up. At the follow-up survey, it was not possible to trace all participants from the baseline survey, however of the 1904 eligible persons invited to participate in the follow-up survey, 786 (41%) chose to participate. The data are available from the authors upon request.
SES assessment SES was assessed at baseline hence reducing the risk of introducing recall bias and was based on an open ended question on paternal occupation and education. If no SES could be assigned to the father, then maternal SES was used. Follow-up SES was based on the participant’s own occupation and education. SES was categorized into five categories based on both occupation and education as follows: SES
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group 1: >4 years further education (master level), white-collar workers with >50 subordinates, and self-employed with >20 employees); SES group 2: short or medium further education (up to 4 years), white collar workers with 11—50 subordinates (if not master level education), and self-employed with 6—20 employees (if not master level education); SES group 3: short education (up to 1 year), vocational education, white-collar workers with 1—10 subordinates (if not education corresponding to SES group 1 or 2), and self-employed with 0—5 employees (if not education corresponding to SES group 2); SES group 4: skilled workers, and white-collar workers with no subordinates (if not education corresponding to SES group 1, 2 or 3); SES group 5: semi-skilled and unskilled workers. SES groups were then further categorized as high (groups 1 and 2), medium (group 3) and low SES (groups 4 and 5). Life-course SES frameworks: Life-course frameworks based on those developed for the Framingham Offspring Study were developed and examined [26]. The sensitive-periods framework calculated the effect of SES measured during each period individually. The social-mobility framework examined life course trajectories. Four potential trajectories were investigated: stable low SES (low adolescent and low adulthood), decreasing SES (high adolescent and low adulthood), increasing SES (low adolescent and high adulthood) and stable high SES (high adolescent and high adulthood). An accumulation-of-risk model was created by summing scores for adolescent SES and adult SES. The cumulative SES scores ranged from 2 to 6 were categorized into high (score = 2), medium (score = 3—4) and low (scores = 5—6) cumulative SES similar to other studies [26—28].
Statistical methods Statistics were conducted using SPSS v19.0. The effect of adolescent and adult SES, social mobility and accumulation on adult overweight (BMI: 25—30 kg/m2 ) and obesity (BMI: >30 kg/m2 ) were assessed by logistic regression. Models examining the relationship between adolescent SES, adulthood SES and adulthood BMI mutually adjusted for adolescent or adult SES. In addition, all models were adjusted for adolescent BMI (kg/m2 ), parental BMI (categorical predictor: no parents overweight/obese, 1 parent overweight/obese, 2 parents overweight/obese), adult smoking (current smoker/non-smoker), energy intake (KJ) and physical activity. Although the log-likelihood ratio of the unadjusted model (170.0) did not differ much from the model adjusting for smoking energy intake,
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and physical activity alone (169.7), these covariates were included in the models as they have been suggested to be partly responsible for the association between SES and BMI [29—32]. Mis-reporters of energy intake (n = 76) were excluded based on a method described elsewhere [33,34]. Birth weight (grams) did not significantly contribute to the models nor was it significantly associated with adulthood BMI (Spearman’s correlation coefficient: r = 0.03, p = 0.61) therefore it was not included in the logistic regression models.
Results Sample characteristics Following exclusion of mis-reporters and those with incomplete data on adult BMI, adolescent and adulthood SES, the final sample consisted of 623 participants (269 males and 354 females), the characteristics of which are presented in Table 1. The mean (SD) age of the sample at baseline was 16.9 (1.0) years and at follow-up was 37.7 (1.4) years. The mean (SD) daily energy intake was 10.5 (2.6) MJ. The subjects were inactive with low mean (SD) MET totals 0.27 (0.23) evident and the majority of them were current non-smokers (79%). More adult males (39%) than females (20%) were classified as overweight, however the prevalence of obesity was similar for females (7%) and males (7%). The prevalence of combined obesity and overweight across SES categories are presented in Table 2. Approximately one-third of the sample was categorized with a low adolescent SES and almost 40% of the sample increased in SES since that time period.
Table 1
Socio-economic status, adulthood obesity and overweight Among females, adolescent, adult, cumulative SES and social mobility were linearly associated with overweight/obesity (Table 2). There were a significantly higher percentage of overweight/obese females from low adult SES compared to those of high adult SES. The small sample size made it difficult to reliably examine the association between SES and obesity, however, the data indicated there were more obese participants with a low adolescent SES (8% males; 9% females) than with a high adolescent SES (4% males; 3% females) and a significantly (p = 0.002) higher percentage of obese females with a low adult SES (12.5%) than a high adult SES (3%). In addition, there were a significantly (p = 0.01) higher proportion of obese females from a low stable SES (10.9%) compared to those from a stable high SES (2%) and a significantly (p = 0.001) higher proportion of obese females with a low cumulative SES (14%) than a high cumulative SES (2%). Table 2 also shows the unadjusted and adjusted relationships between adolescent and adult SES, and combined adult overweight and obesity. After adjusting for adult energy intake, physical activity, smoking, adolescent and parental BMI and adult or adolescent SES, adolescent SES had no significant association with overweight/obesity in this sample, however females of low or medium adult SES were significantly more likely to be overweight/obese compared to those of high adult SES. Following adjustment for the aforementioned covariates, females who decreased in SES were significantly more likely to be overweight/obese compared to those who remained of high SES.
Characteristics of study sample (n = 623).
n Mean age (years) (SD) Baseline Follow-up Follow-up BMI (%) <18.5 18.5—25 25—30 30—35 >35 Smoker (%) Yes No Mean daily energy intake (MJ) (SD) Mean MET total (SD)
Males
Females
Total
269
354
623
16.9 (1.0) 37.6 (1.4)
17.0 (1.0) 37.7 (1.4)
16.9 (1.0) 37.7 (1.4)
0 54 39 6 1
3 70 20 4.5 2.5
2 63 28 5 2
20 80 11.7 (2.6) 0.29 (0.23)
21 79 9.5 (2.2) 0.25 (0.23)
21 79 10.5 (2.6) 0.27 (0.23)
Associations between SES and adult obesity/overweight. Males (n = 269) na
Females (n = 354)
Obese or overweight %
Unadjusted OR (95% CI)
Adjusted OR (95% CI)
n
Obese or overweight %
Unadjusted OR (95% CI)
Adjusted OR (95% CI)
Adolescent SES Low Medium High Trend p-value
86 83 100
49 48 42 0.3
1.3 (0.7—2.3) 1.3 (0.7—2.3) 1.0 ref
0.9 (0.4—1.9) 0.8 (0.4—1.7) 1.0 ref
137 101 116
33 26 21 0.03
1.9 (1.1—3.3) 1.3 (0.7—2.5) 1.0 ref
0.7 (0.3—1.5) 0.9 (0.4—2.0) 1.0 ref
Adult SES Low Medium High Trend p-value
63 49 157
52 55 41 0.07
1.6 (0.9—2.9) 1.8 (0.9—3.4) 1.0 ref
1.5 (0.7—3.3) 1.3 (0.5—3.3) 1.0 ref
104 56 194
40 34 18 <0.001
2.9 (1.7—5.0) 2.3 (1.2—4.5) 1.0 ref
2.7 (1.3—5.8) 4.0 (1.6—10.0) 1.0 ref
Social mobility Stable low SES Decreased SES Increased SES Stable high SES Trend p-value
25 47 103 77
56 45 41 44 0.4
1.6 1.0 0.9 1.0
1.0 0.9 0.7 1.0
(0.3—2.9) (0.4—2.4) (0.3—1.4) ref
64 56 131 87
41 36 24 14 <0.001
4.3 (1.9—9.4) 3.5 (1.5—7.9) 1.9 (0.9—4.0) 1.0 ref
2.8 (0.9—8.3) 3.1 (1.1—9.2) 1.4 (0.5—3.6) 1.0 ref
Cumulative SES Low Medium High Trend p-value
72 120 77
54 42.5 44 0.2
1.5 (0.8—2.8) 0.9 (0.5—1.7) 1.0 ref
1.1 (0.5—2.4) 0.7 (0.3—1.5) 1.0 ref
115 152 87
36.5 27 14 0.001
3.6 (1.7—7.4) 2.3 (1.1—4.7) 1.0 ref
2.3 (0.9—6.0) 2.1 (0.9—5.2) 1.0 ref
(0.6—4.0) (0.5—2.1) (0.5—1.6) ref
Socioeconomic status and risk of obesity and overweight in Danish adults
Table 2
Significance level of p < 0.05 for values in bold text. a Social mobility analyses includes different sample size as some subjects remained at a middle SES level from adolescent to adulthood and therefore were not included in the social mobility analyses.
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e168 Cumulative SES had no significant associations with adult overweight/obesity after adjustment for the same variables. As mentioned earlier, the small sample size made it difficult to reliably examine the relationship between SES and obesity, however after adjusting for the aforementioned variables, females of a medium adult SES were significantly more likely to be obese compared to those of a high SES (OR: 7.0; 95% CI (1.4—34.4); p = 0.02).
Discussion Socioeconomic status, adulthood obesity and overweight This current longitudinal survey found a significant inverse relationship between both adolescent and adulthood SES, and adult overweight/obesity among females, but not males. However, following adjustments for covariates including SES in either adolescence or adulthood, only the relationship between adult SES and overweight/obesity remained significant. These findings may indicate that the effects of adolescent-factors may still be conditional upon the environment in adulthood [35]. Evidence suggests that the inverse gradients of SES evident with risk for smoking, sedentary behavior and obesogenic diet may partly explain the association between adulthood SES and BMI [29—32,36]. Smoking, physical activity and energy intake were adjusted for in this current study, however model development indicated that adolescent and parental BMI had a stronger role to play than these covariates in the current relationship between adult SES and risk of overweight and obesity. Previous studies have examined the association between SES and obesity using only one [35,37], or at most, two [15,18,20] of the following models — sensitive-periods model, social mobility model or the accumulation of risk model. Although several studies have previously assessed the influence of social mobility on the risk of adulthood obesity and overweight, to our knowledge this is the first study to examine the association between adult overweight and obesity and SES at specific time points in addition to examining the relationship between the risk of adult overweight and obesity, social mobility and accumulation over 25 years from adolescence to adulthood.
Comparison with other studies The 2000—2002 Danish National Dietary Survey [38] reported a similar prevalence of overweight to this current study but a slightly higher prevalence
S.M. Boylan et al. of obesity. In addition other Danish studies have found inverse associations between adulthood SES, BMI and obesity [38—40] and inverse associations between childhood and adolescent SES and childhood and adolescent overweight [41,42]. Social disadvantage in childhood has also been shown to be a risk factor for obesity in adulthood [43,44], however recent research shows that this relationship weakens when adjustments are made for attained adult SES [7], as in this current study. The relationship between SES and BMI is complex and factors mediating the relationship have been extensively examined in the last few decades. One of the most consistent findings from previous research shows stronger relationships between SES and adulthood BMI among women compared to men and this study also replicated this finding. There is evidence to suggest that females may be more vulnerable to social pressures compared to males and these pressures may be greater among females with high SES than low SES [4,45]. The strong associations found among females may also be due to the fact that females of higher SES diet more than those of low SES as females with high SES may have more resources to facilitate their preferred diet or lifestyle compared to those of low SES [8]. Life-course SES and risk of adulthood obesity and overweight: Studies suggest that childhood SES is predictive of adulthood SES [46], however this may not always be the case as this current study showed that this sample was socially mobile. Almost half a century ago, Goldblatt et al. [47] found that obesity prevalence was almost twice as high among women who decreased in SES (22%) as it was among those who rose in social class (12%) [47]. In the last couple of decades researchers have once again become interested in the effects of social trajectory on weight, with some focusing on examining the mechanisms [48]. Some research has suggested that those who are obese in adolescence experience significant social disadvantage in adulthood [48]. Overall however, the results from recent studies examining the relationship between socioeconomic mobility and weight have been inconsistent [9,14—17]. This current study which assessed the trajectory of socioeconomic mobility from adolescence through to adulthood using the social-mobility model [13], found that females who decreased in SES were significantly more likely to be overweight/obese compared to those who remained at a high SES. Others suggest that socioeconomic exposures may accumulate over time and lead to poor health outcomes [20,49,50]. This current study used an accumulation of risk model to examine the association between cumulative SES and
Socioeconomic status and risk of obesity and overweight in Danish adults overweight/obesity, however, the results were not significant. The lack of a significant relationship may be partly explained by the cumulative disadvantage theory [21] which emphasizes how early exposure is critical to how cohorts differentiate over time — yet this current study did not find parental SES influenced risk of adulthood obesity or overweight. It is possible that the lack of an association between cumulative SES and adult obesity in this current study may be due to the more complex assessment of SES during adulthood compared to the assessment during adolescence. Still, these findings highlight the complexity of the relationship between SES and BMI, and also that the relationship may have changed in more recent times. Unfortunately there has been little examination of the cumulative influences of adverse circumstances on social inequalities in obesity [20,21,50]. While it is plausible that life-course circumstances may influence weight, more longitudinal studies are required to gain a more meaningful insight.
Strengths and limitations To the author’s knowledge this is the first study to assess the influence of SES at specific timepoints, social mobility and cumulative SES on risk of adulthood obesity and overweight. This current study is strengthened by its longitudinal design and prospective nature (following individuals from adolescence for more than 20 years into adulthood) — features which are unfortunately generally lacking in this field of research. Previous studies have simply identified relationships between SES and obesity [5,6]; however as mentioned earlier, this relationship may be bi-directional [6]. The longitudinal nature of this current study meant that the direction of this relationship could be examined. Although SES gradients in obesity prevalence are well-documented, it is unclear which mechanism(s) are responsible for this relationship. This current study considered some likely candidates — energy intake, physical activity and smoking; however, our results suggest that other factors may also be important to consider as adolescent SES, and adolescent and parental BMI had stronger influences on the relationship between adult SES and obesity and overweight. The study also has a number of limitations. We assessed parental SES based on an open ended question on parent’s occupation and education, while adult SES was based on a more detailed assessment. In addition, paternal, rather than maternal SES was primarily used in this current analyses while there is some evidence from other literature to suggest that maternal characteristics are more closely
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associated with the health behaviors of children and hence their risk of obesity [51,52]. The relationship between SES and adolescent obesity was not examined, however it has been suggested that this relationship is weakening [53]. Finally, recall bias may have been introduced through the use of self-reported data e.g. self-reported weight and height may lead to underestimations of obesity or overweight prevalence rates as subjects may underreport their body weight [54—56] and over-report body height [54—56]. While other studies have validated self-reported height and weight [57—59], it cannot be overlooked that under-reporting may have attenuated results in this current study.
Conclusions Compared to other developed countries, Denmark has a relatively low level of overweight and obesity but, obesity rates have been rising over the past 50 years [60,61] and it is important to understand the social gradient of obesity and overweight [38—42]. Our results suggest that the relationship between SES and BMI is complex in nature, and may have changed in more recent times. Furthermore, the fact that both adolescent and adult SES were related to overweight/obesity in adulthood, but that only the relationship between adult SES and overweight/obesity persists when considering previous SES, indicates that effects of early life-factors are conditional upon the environment in adulthood, particularly for the women. Further research is warranted on the timing of SES exposure and the mechanisms which may be responsible for the socioeconomic gradients in prevalence of obesity and overweight.
Funding This work was supported by the Danish Medical research Council (grant number 271-05-0277); and Ministry for Health (grant number 2003-0200-13).
Conflicts of interest None declared.
References [1] Commission on Social Determinants of Health. Closing the gap in a generation: health equity through action on the social determinants of health: final report of the
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[2]
[3]
[4] [5] [6] [7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
commission on social determinants of health. Geneva: World Health Organisation; 2008. Galobardes B, Lynch JW, Davey SG. Childhood socioeconomic circumstances and cause-specific mortality in adulthood: systematic review and interpretation. Epidemiol Rev 2004;26:7—21. Galobardes B, Lynch JW, Smith GD. Is the association between childhood socioeconomic circumstances and cause-specific mortality established? Update of a systematic review. J Epidemiol Community Health 2008;62:387—90. Sobal J, Stunkard AJ. Socioeconomic status and obesity: a review of the literature. Psychol Bull 1989;105:260—75. Ball K, Crawford D. Socioeconomic status and weight change in adults: a review. Soc Sci Med 2005;60:1987—2010. McLaren L. Socioeconomic status and obesity. Epidemiol Rev 2007;29:29—48. Senese LC, Almeida ND, Fath AK, Smith BT, Loucks EB. Associations between childhood socioeconomic position and adulthood obesity. Epidemiol Rev 2009;31:21—51. Stunkard AJ. Socioeconomic status and obesity. In: Chadwick DJ, Cardew G, editors. Ciba foundation symposium 201 — the origins and consequences of obesity. 1st ed. Chicester, UK: John Wiley & Sons; 1996. p. 174—93. Heraclides A, Brunner E. Social mobility and social accumulation across the life course in relation to adult overweight and obesity: the Whitehall II study. J Epidemiol Community Health 2010;64:714—9. Johnson DB, Gerstein DE, Evans AE, Woodward-Lopez G. Preventing obesity: a life cycle perspective. J Am Diet Assoc 2006;106:97—102. Kuh DJH, Ben-Shlomo Y, editors. A lifecourse approach to chronic disease epidemiology. 2nd ed. Oxford, UK: Oxford University Press; 2004. Davey-Smith G, Lynch J. Life course approaches to socioeconomic differentials in health. In: Kuh D, Ben-Shlomo Y, editors. A life course approach to chronic disease epidemiology. 2nd ed. Oxford, UK: Oxford University Press; 2004. p. 77—115. Kuh D, Ben-Shlomo Y, Lynch J, Hallqvist J, Power C. Life course epidemiology. J Epidemiol Community Health 2003;57:778—83. Gigante DP, Minten GC, Horta BL, Barros FC, Victora CG. Nutritional evaluation follow-up of the 1982 birth cohort, Pelotas, Southern Brazil. Rev Saude Publica 2008;42:60—9. Hart C, McConnachie A, Upton M, Watt G. Risk factors in the Midspan family study by social class in childhood and adulthood. Int J Epidemiol 2008;37:604—14. Langenberg C, Hardy R, Kuh D, Brunner E, Wadsworth M. Central and total obesity in middle aged men and women in relation to lifetime socioeconomic status: evidence from a national birth cohort. J Epidemiol Community Health 2003;57:816—22. James SA, Fowler-Brown A, Raghunathan TE, Van HJ. Life-course socioeconomic position and obesity in African American Women: the Pitt County Study. Am J Public Health 2006;96:554—60. Kavikondala S, Schooling CM, Jiang CQ, Zhang WS, Cheng KK, Lam TH, et al. Pathways to obesity in a developing population: The Guangzhou Biobank Cohort Study. Int J Epidemiol 2009;38:72—82. Kuh D, Ben-Shlomo Y, editors. A life course approach to chronic disease epidemiology: tracing the origins of ill health from early to adult life. 1st ed. Oxford, UK: Oxford University Press; 1997. Baltrus PT, Lynch JW, Everson-Rose S, Raghunathan TE, Kaplan GA. Race/ethnicity, life-course socioeconomic
S.M. Boylan et al.
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36]
position, and body weight trajectories over 34 years: the Alameda County Study. Am J Public Health 2005;95:1595—601. Ferraro KF, Kelley-Moore JA. Cumulative disadvantage and health: long-term consequences of obesity? Am Sociol Rev 2003;68:707—29. Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med 1997;337:869—73. Hare-Bruun H, Togo P, Andersen LB, Heitmann BL. Adult food intake patterns are related to adult and childhood socioeconomic status. J Nutr 2011;141:928—34. Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 2000;32:S498—504. Tjonneland A, Overvad K, Haraldsdottir J, Bang S, Ewertz M, Jensen OM. Validation of a semiquantitative food frequency questionnaire developed in Denmark. Int J Epidemiol 1991;20:906—12. Smith BT, Lynch JW, Fox CS, Harper S, Abrahamowicz M, Almeida ND, et al. Life-course socioeconomic position and type 2 diabetes mellitus: the Framingham Offspring Study. Am J Epidemiol 2011;173: 438—47. Loucks EB, Lynch JW, Pilote L, Fuhrer R, Almeida ND, Richard H, et al. Life-course socioeconomic position and incidence of coronary heart disease: the Framingham Offspring Study. Am J Epidemiol 2009;169: 829—36. Loucks EB, Pilote L, Lynch JW, Richard H, Almeida ND, Benjamin EJ, et al. Life course socioeconomic position is associated with inflammatory markers: the Framingham Offspring Study. Soc Sci Med 2010;71: 187—95. Li F, Fisher KJ, Bauman A, Ory MG, Chodzko-Zajko W, Harmer P, et al. Neighborhood influences on physical activity in middle-aged and older adults: a multilevel perspective. J Aging Phys Act 2005;13:87—114. National High Blood Pressure Program Working Group on Hypertension Control in Children and Adolescents. Updated on the 1987 task force report on high blood pressure in children and adolescents: a working group report from the National High Blood Pressure Program. Pediatrics 1996;98:649—58. Pekkanen J, Tuomilehto J, Uutela A, Vartiainen E, Nissinen A. Social class, health behaviour, and mortality among men and women in eastern Finland. BMJ 1995;311: 589—93. Turrell G, Hewitt B, Patterson C, Oldenburg B, Gould T. Socioeconomic differences in food purchasing behaviour and suggested implications for diet-related health promotion. J Hum Nutr Diet 2002;15:355—64. Boylan S, Welch A, Pikhart H, Malyutina S, Pajak A, Kubinova R, et al. Dietary habits in three Central and Eastern European countries: the HAPIEE study. BMC Public Health 2009;9:439. Welch AA, Luben R, Khaw KT, Bingham SA. The CAFE computer program for nutritional analysis of the EPICNorfolk food frequency questionnaire and identification of extreme nutrient values. J Hum Nutr Diet 2005;18: 99—116. Power C, Manor O, Matthews S. Child to adult socioeconomic conditions and obesity in a national cohort. Int J Obes Relat Metab Disord 2003;27:1081—6. Boylan S, Lallukka T, Lahelma E, Pikhart H, Malyutina S, Pajak A, et al. Socio-economic circumstances and food
Socioeconomic status and risk of obesity and overweight in Danish adults
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
[45]
[46]
[47] [48]
habits in Eastern, Central and Western European populations. Public Health Nutr 2011;14:678—87. Barros AJ, Victora CG, Horta BL, Goncalves HD, Lima RC, Lynch J. Effects of socioeconomic change from birth to early adulthood on height and overweight. Int J Epidemiol 2006;35:1233—8. Groth MV, Fagt S, Stockmarr A, Matthiessen J, Biltoft-Jensen A. Dimensions of socioeconomic position related to body mass index and obesity among Danish women and men. Scand J Public Health 2009;37:418—26. Nielsen TL, Wraae K, Brixen K, Hermann AP, Andersen M, Hagen C. Prevalence of overweight, obesity and physical inactivity in 20- to 29-year-old, Danish men, Relation to sociodemography, physical dysfunction and low socioeconomic status: the Odense Androgen Study. Int J Obes (Lond) 2006;30:805—15. Sarlio-Lahteenkorva S, Lissau I, Lahelma E. The social patterning of relative body weight and obesity in Denmark and Finland. Eur J Public Health 2006;16:36—40. Due P, Damsgaard MT, Rasmussen M, Holstein BE, Wardle J, Merlo J, et al. Socioeconomic position, macroeconomic environment and overweight among adolescents in 35 countries. Int J Obes (Lond) 2009;33:1084—93. Kristensen PL, Wedderkopp N, Moller NC, Andersen LB, Bai CN, Froberg K. Tracking and prevalence of cardiovascular disease risk factors across socio-economic classes: a longitudinal substudy of the European Youth Heart Study. BMC Public Health 2006;6:20. Parsons TJ, Power C, Logan S, Summerbell CD. Childhood predictors of adult obesity: a systematic review. Int J Obes Relat Metab Disord 1999;23(Suppl. 8):S1—107. Power C, Graham H, Due P, Hallqvist J, Joung I, Kuh D, et al. The contribution of childhood and adult socioeconomic position to adult obesity and smoking behaviour: an international comparison. Int J Epidemiol 2005;34:335—44. Finkelstein EA, Ruhm CJ, Kosa KM. Economic causes and consequences of obesity. Annu Rev Public Health 2005;26:239—57. Beller E, Hout M. Intergenerational social mobility: the United States in comparative perspective. Future Child 2006;16:19—36. Goldblatt PB, Moore ME, Stunkard AJ. Social factors in obesity. J Am Med Assoc 1965;192:1039—44. Gortmaker SL, Must A, Perrin JM, Sobol AM, Dietz WH. Social and economic consequences of overweight in adolescence and young adulthood. N Engl J Med 1993;329:1008—12.
e171
[49] Everson SA, Maty SC, Lynch JW, Kaplan GA. Epidemiologic evidence for the relation between socioeconomic status and depression, obesity, and diabetes. J Psychosom Res 2002;53:891—5. [50] Novak M, Ahlgren C, Hammarstrom A. A life-course approach in explaining social inequity in obesity among young adult men and women. Int J Obes (Lond) 2006;30:191—200. [51] Favaro A, Santonastaso P. Effects of parents’ psychological characteristics and eating behaviour on childhood obesity and dietary compliance. J Psychosom Res 1995;39: 145—51. [52] Shrewsbury V, Wardle J. Socioeconomic status and adiposity in childhood: a systematic review of cross-sectional studies 1990—2005. Obesity (Silver Spring) 2008;16:275—84. [53] Wang Y, Zhang Q. Are American children and adolescents of low socioeconomic status at increased risk of obesity? Changes in the association between overweight and family income between 1971 and 2002. Am J Clin Nutr 2006;84:707—16. [54] Cullum A, McCarthy A, Gunnell D, Davey SG, Sterne JA, Ben-Shlomo Y. Dietary restraint and the mis-reporting of anthropometric measures by middle-aged adults. Int J Obes Relat Metab Disord 2004;28:426—33. [55] Hill A, Roberts J. Body mass index: a comparison between self-reported and measured height and weight. J Public Health Med 1998;20:206—10. [56] Nawaz H, Chan W, Abdulrahman M, Larson D, Katz DL. Self-reported weight and height: implications for obesity research. Am J Prev Med 2001;20:294—8. [57] Avila-Funes JA, Gutiérrez-Robledo LM, Ponce De Leon Rosales S. Validity of height and weight self-report in Mexican adults: results from the national health and aging study. J Nutr Health Aging 2004;8:355—61. [58] Lee DH, Shin A, Kim J, Yoo KY, Sung J. Validity of selfreported height and weight in a Korean population. J Epidemiol 2011;21:30—6. [59] Spencer EA, Appleby PN, Davey GK, Key TJ. Validity of self-reported height and weight in 4808 EPIC-Oxford participants. Public Health Nutr 2002;5:561—5. [60] Due P, Heitmann BL, Sorensen TI. Prevalence of obesity in Denmark. Obes Rev 2007;8:187—9. [61] Pearson S, Olsen LW, Hansen B, Sorensen TI. Increase in overweight and obesity amongst Copenhagen schoolchildren, 1947—2003. Ugeskr Laeger 2005;167: 158—62.
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