The Lasting Impact of Childhood Circumstance on Obesity in ... · obesity was rarely a problem in...

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The Lasting Impact of Childhood Circumstance on Obesity in Korea:

Results from the Korean National Health and Nutrition Examination

Survey 2007-2011

Yeonjin Lee

Introduction

In 2005, World Health Organization (WHO) published an annual report that sparked

considerable debate on global obesity epidemic that is posing drastic public health threats

worldwide (add reference to the report). In non-western societies such as those in East Asia

obesity was rarely a problem in the past, but the prevalence of obesity and chronic diseases

associated with obesity have increased rapidly with recent industrialization (Popkin 2002; Popkin

& Gordon-Larsen, 2004). Therefore an emerging challenge for researchers and public health

specialists is to gain a better understanding of factors that have contributed to the recent rise in

obesity worldwide and how environmental changes may play a role.

Many researchers have documented a relationship between socioeconomic status (SES)

and obesity. Accessibility to high-quality foods, autonomy to manage one’s social life, knowledge,

and maintenance of healthy behaviors such as physical activities influence individuals’ weight

patterns over the life course (Levi et al. 2010; Joshu et al. 2008). Obesity may be more sensitive

to early life conditions (i.e. physical and social environment) than other health epidemics as many

of its risk factors may date to childhood experiences. Questions remain concerning the

cumulative or relative impact of early life socioeconomic circumstances and the influence of

intergenerational social mobility on adult obesity especially among the societies in process of fast

transition.

Thus, examining socioeconomic inequalities in early life in the trajectory of obesity may

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reveal mechanisms underlying socioeconomic gradients in obesity in later life. The current study

examines whether respondents’ childhood socioeconomic conditions are associated with obesity

with and without controls for adult SES and health behaviors in South Korea (Korea hereafter)

as a representative of newly developed countries.

Mechanisms Underlying Early life Condition and Obesity in Later Life

Early life conditions may influence adult obesity through biological (i.e. metabolic

syndrome and increased number of fat cells), psychological and behavioral mechanisms (self-

regulation of weight, eating disorders and physical activities) operating across the life course.

Furthermore, childhood circumstances generally determined by parental socioeconomic

condition are correlated with adult SES that may influence various health outcomes (i.e. children

from higher socioeconomic families are more likely to achieve higher educational attainment and

better health outcomes that are negatively associated with obesity in developed countries). Over

the past few decades, pseudo-experimental and clinical studies have examined associations

between utero and early life conditions, metabolic syndrome and obesity in later life (Gluckman

& Hanson 2008). Deprivation during fetal and prenatal periods is thought to affect early neuro-

endocrine development and cause long-term changes in the levels of major hormone systems

such as an increase in adrenal glucocorticoid secretion. These factors are highly correlated with

the predisposition to diabetes, metabolic syndrome and obesity when followed by nutritional

abundance (Reilly et al. 2005). For instance, according to the studies based on cohorts born

during the Dutch famine (1944–1945), malnutrition during early gestation was associated with

higher obesity prevalence in later life among women, but not among men (Ravelli et al. 1999;

Stein et al. 2007). Luo et al. (2006) investigated adult obesity among the Chinese who were born

during the Great Famine (1959–1962) and found that the association between early life

deprivation and adult metabolic syndrome was statistically significant for women.

Parents may influence children’s obesity risk in later life via shared obesogenic

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circumstances such as sedentary lifestyles, eating disorders, and poor emotional regulation along

with genetic transmission (Reilly et al. 2005; Newby, 2007). Parental SES is a key factor in

childhood for determining family resources and culture such as choice of foods, value of exercise,

eating patterns and habits (Ball & Mishra, 2005). Previous studies from developed countries

show that children from the socioeconomically vulnerable households are more likely to face

psychological challenges sometimes exhibiting as obesogenic behaviors such as binge eating and

drinking (Repetti et al. 2002; Lynch et al., 1997; Power et al., 2005). Some studies further show

that mothers’ educational attainment is highly correlated with children’s food choices such as

fruits and vegetables, which may continue to influence food choices in adulthood (Lytle et al.

2003; Kremers et al. 2003; Neumark et al. 2003). Other studies report that consumption of fast

food among adolescents increases when the frequency with which families eat together at home

decreases (Gillman et al. 2000; Neumark et al. 2003).

Intergenerational transmission of SES may be another important contributor for the

association between early life conditions and obesity risk in later life through its relationship with

adult SES (Case, Fertig, & Paxson, 2005; Huang & Elo, 2009). In industrialized societies low-

SES adults are at a greater risk of being obese and having chronic diseases than high-SES adults

(Case, Fertig, & Paxson, 2005). Thus, adult SES may serve as an important connection linking

childhood disadvantage represented by parental SES to adult health outcomes (Hayward &

Gorman 2004).

Social Gradients in Obesity across Gender

Findings from previous studies address that the biological impact of early-life condition

on metabolic system, which is intimately related with obesity in later life varies by gender (Luo,

2006). Other studies suggest that since men and women have different psychological and

behavioral responses to socioeconomic status (e.g. girls with high SES family background is

more likely to be educated to care about weight compared to boys) it is necessary to investigate

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whether early life socioeconomic risk factors which may determine obesity is gender-specific. For

example, Robinson et al. (2009) examined health behaviors among U.S. black adolescents in

2007 and found that the relationship between childhood SES and the likelihood of adopting

obesity-promoting behaviors was gender-specific. Others have argued that early life conditions

may have long lasting gender-specific impacts on appetite regulation, eating behaviors, and body

weight gain patterns (Case & Menendez, 2009).

Men and women may have different socioeconomic gradient in obesity and the

association can vary by countries. According to the studies from developed countries, the

association between SES and obesity tends to be negative both for men and women. Data from

United State, Britain, Sweden, and Finland show that both men and women with less education

were more likely to be obese than were those with more education (Flegal et al. 1989). Wardle

and Griffith studied the effect of occupational status on obesity in Britain (2001). A negative

association between professional job status and obesity prevalence has been well documented in

women while not clearly observed for men (Ibid). The result suggests that gender difference in

sensitivity to body image may be the reason for gender differences in SES gradient in BMI.

Women especially in professional occupations tend to be more sensitive to weight related stigma

and pressure to preserve a specific body shape (Sobal & Stunkard 1989). Women having weight

problems are more likely to face social discrimination and more burdens of psychological

diseases attributable to overweight and obesity (e.g. depression) and hard to obtain higher social

status compared to men (Carpenter et al. 2000).

Obesity in Newly Industrialized Societies

Despite of global concern about obesity, much of the prior literature evaluating the

association between SES over the life course and obesity has been limited to high-income

countries, although the findings from these countries may not be applicable to low- and middle-

income countries (Monasta et al. 2010). Many newly developed societies experienced

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considerable “nutrition and epidemiology transitions” followed by rapid economic development.

This makes people grown up during social transitions have faced complicated socioeconomic

profiles in health consequences (Swinburn et al. 2011). For example, under-nutrition during early

childhood followed by over-nutrition under the certain environmental exposures may emphasize

a link between early life disadvantage and the risk of obesity and chronic diseases in later life

independent of own adult SES (Barker et al. 1989). A study by Case and Menendez (2009) on

BMI patterns of 975 individuals residing in Cape Town in South Africa in 2004- 2005, found

that South African women who spent their childhood in poverty and hunger were significantly

more likely to be obese as adults than other women after adjusting for their current social status.

As for the populations in newly developed countries, comparisons of impact of early life

condition across cohorts may help study the mechanisms through which environment and

historical conditions affect social gradients in obesity in later life (Bell, Ge, & Popkin, 2001).

During the period in process of economic growth children born to higher SES families were

selectively exposed to risk of over nutrition such as diets high in fat and sugar (Westernized diet)

and thus faced greater risks of gaining weight than children born to lower SES families; a

direction of the association between SES and obesity tended to shift from positive to negative as

countries’ level of socioeconomic development increased (Kapoor, & Anand, 2002; Kim,

Symons, & Popkin, 2004; McLaren, 2007). A study from Brazil by the end of 1980s obesity had

been positively associated with income and the relationship between income and obesity has

begun to reverse after industrialization especially among women (Monteiro et al. 2000). In Accra,

Ghana, centre in West Africa that recently urbanized, a burden related with obesity was more

likely to be found in places of high SES. In areas where people with lower SES reside in, both

high prevalence of infectious and non-communicable disease burdens were observed (de-Graft

Aikins, 2007). Studies on Chinese population showed that macro migration to urban areas in

China is significantly related with increased risk of obesity and chronic diseases as people

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changed their traditional lifestyles and diets into westernized one and passed them on to

descendents (Gong et al. 2012). Ng and colleagues (2009) evaluated the impact of community-

level urbanization on average weekly physical activity among Chinese young men and women

and found that for both gender, physical activity per a week declined as the level of urbanization

increased (Ng, Norton & Popkin 2009). At the same time, industrialization has introduced young

generations to cultural values from western societies, including ideal body images favoring

thinness which in pre-industrialized countries was viewed as a sign of poverty and unhealthiness

(Chen & Meltzer 2008). For example, adolescents in Fiji where people traditionally preferred

chubbiness to thinness as an aesthetic ideal developed an interest in diet as a means of emulating

television characters after several years of exposure to western culture (Becker 2004; Becker et al.

2002). The findings suggest the importance of identifying groups that are most susceptible to the

obesity in newly developed countries concerned with changes in a group’s demographic and SES

characteristics over time. Therefore, obesity epidemic in newly developed societies can be

hypothesized to be dynamic within an individual’s life course and across generations as

socioeconomic, environmental, and historical contexts change together with individuals’

behaviors over time (Kuh et al. 2003).

Little is known about gender differences in the relationship between social

circumstances and obesity over the life trajectory in low- and middle-income countries. Previous

studies argue that the biological and behavioral responses to the early life disadvantage

potentially impacting on obesity in later life may vary by gender (Sobal & Stunkard 1989; Luo

2006). Luo et al. (2006) investigate adult obesity among the Chinese who were born during the

Great Famine (1959–1962) and found that the association between childhood deprivation and

adult metabolic syndrome was statistically significant but only for the women. According to Lee

(2011) due to the strong gender role segregation in Korean society it is usual that boys and girls

are educated separately in schools and home with different topics from their junior high school

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(e.g. nutrition, home economics only for girls vs. carpentry only for boys) while the difference

has reduced for the recent cohorts. Thus with the same or less level of educational attainment,

women can have more knowledge on metabolism and nutritional balance and more likely to

adopt behaviors which protect against obesity than men.

Gender specific cultural values such as weight perception may pattern the differences in

obesity across SES varying by gender and the association may be distinct from that of western

societies (Sobal & Stunkard 1989; Luo 2006). Case and Menendez (2009), for example, found

that in South Africa, a cultural preference for a larger body size is more common among women

than men, and that women of higher SES are considerably more likely to be obese than lower

SES women, a pattern that is not observed among men. A preference for a larger body size

among women than men is also observed in Thailand where there is also a higher prevalence of

obesity among women with higher education and semi-professional occupation than other

women, whereas this pattern is not found among men (McraLen, 2007). However, as

industrialization proceeds apace under the influence of western countries, recent women

generations have started to favor thinness and stigmatize ‘being fat’ (Becker, 2004; Becker et al.

2002). Thus, the risk of obesity may be related to socio-cultural mechanisms different upon the

birth cohorts while resulting in gender-specific obesity gradients across SES in this society.

Korean Setting

Although Korea is not the most obese nation in the world, obesity in Korea has been an

increasing public health concern during the last decade. (Kim et al. 2004). According to the

Korean National Health Examination and Nutrition Survey (KNHANES) from 1998 to 2011,

there was a nearly 50% increase in the prevalence of overweight and obesity among Korean men

in a recent 10-year period, whereas there was no significant increase among women (Korean

Centers for Disease Control and Prevention). Considering that a population level genetic shift is

not likely to happen within a decade, the large increase in obesity prevalence, especially among

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Korean men, might be attributable to environmental and individual behavioral changes.

Given that Korea has experienced both a rapid industrialization during the last few

decades and a large increase in obesity in recent years, the Korean case offers a useful

opportunity to explore how social transition may shape the way in which childhood and adult

socioeconomic conditions are associated with obesity in later life (Caballero, 2001; Popkin, 1994).

Koreans, who were children during early phases of industrialization (e.g. Koreans born 1954-

1963), were subsequently exposed to very different nutritional and socioeconomic environments

in adulthood: under-nutrition in childhood and over-nutrition in adulthood. Koreans born

around the mid 1960s spent their childhood and adolescence during a period when Korea was

undergoing rapid industrialization and democratization. During this period new cultural values

from western societies and benefits from the advanced technology began to be widespread.

More recently, Korea is beginning to focus attention and raise awareness of the risk of obesity

and its association with chronic diseases and these efforts have led more recent generations to

adopt lifestyles that help maintain healthy weight, especially among professional women who

experience the most social pressure for weight control. Along with industrialization, ideal body

image and awareness of weight control that have been prevalent in western countries have also

increasingly permeated Korean society (Joh et al. 2013). Obesity might have been a symbol of

“affluence” and “health” in the past, but as social awareness of maintaining proper body weight

is heightened and Western culture is actively introduced, fatness has become stigmatized

especially among women (Joh et al. 2013). On the other hand, Korean men prefer a larger body

size, which is a symbol of masculinity, dependableness and wealth. There is rarely discrimination

against overweight and obese men. These different gender-specific cultural norms make Korean

women with higher SES more conscious of their body weight than higher SES men. The roles of

other behavioral determinants of weight such as drinking are also potentially important in its

social patterning. Korean society values collectivism and thus, drinking is considered to be an

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extension of work especially for male professional workers. This alerts men in higher

occupational status to be exposed to risk of obesity. Given the rapid social changes and unique

cultural norms in Korean society, the association between childhood and adult SES and obesity

prevalence may differ by birth cohort and gender. However, it is not known well whether

different socioeconomic risk factors dominate at different life stages, or if the impacts of these

are similar in other western societies. The primary aim of this study is to determine the extent to

which the socioeconomic patterning of obesity stratified by birth cohort and gender, with the

greatest emphasis on residual impact of early life SES. I hypothesized that the social patterning

of obesity during early life time would be stronger at older cohort. The secondary aim was to

examine variations by gender in the extent to which the social patterning of obesity is explained

by measures of childhood precursors (parental SES), adult SES, and modifiable behavioral risk

factor (drinking).

Data

The data for this study came from the Korean National Health and Nutrition

Examination Survey (KNHANES). Korean Centers for Disease Control and Prevention has

conducted periodical data collection since 1998 to assess health and nutritional status of the

civilian, non-institutionalized population in Korea (KCDC 2000). The KNHANES is a

nationally representative survey in Korea, a combination of a health interview, nutritional survey,

and health examinations with comprehensive information on biochemical and anthropometric

measurements, health status, health behaviors and socio-demographic characteristics. Therefore,

the data offer special benefits for studies that need accurate measures of a variety of health

outcomes along with information on socioeconomic backgrounds of individuals.

Standardized health examinations are directly carried out in a specially equipped mobile

examination center. Similar to the U.S. NHANES, the KNHANES is a repetitive cross-sectional

survey of Korean individuals chosen in a multistage clustered sampling frame based on

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geographical unit, sex and age, which were provided by the 2005 National Census Registry, the

latest 5-year national household census in Korea. Since the KNHANES examines independent

sets of randomly selected individuals, it rarely happens to select the same person repeatedly in

the following year (Lim et al. 2011).

The current study utilizes five of the most recent waves of KNHANES from 2007 to

2011 because key variables measuring parental SES are not available in previous years. I restrict

the sample to individuals ages over 25-57. By age 25 most people have completed their highest

level of education. I also exclude individuals who belong to cohorts born before the end of the

Korean War (1950-1953). The number of 18,217 pooled 2007-2011 KNHANES sample consists

of 6,995 males and 9,392 females. From this sample, I excluded individuals who were missing

information on BMI (N=1,242) and on core SES explanatory variables (N=431). Women who

are pregnant at the time of examination were also excluded (N=147). Missing data in parental

education and drinking variables are imputed by using multiple imputation procedure with the

Markov chain Monte Carlo algorithm. Imputed values are estimated based on the individual

information on socio-demographic characteristics and replace missing values (Allison, 2000).

Measuring Overweight and Obesity

Current study uses body mass index (BMI = weight in kg/ (height in meters)2) as an

indicator of overweight and obesity. Height and weight were measured in light indoor clothing

without shoes to the nearest 0.1 kg and 0.1 cm respectively. BMI is categorized based on the

criteria recommended by the World Health Organization (WHO) for the study of obesity among

Asians as follows: underweight (BMI

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lowering BMI cutoff points for Asians because compared to Caucasians, Asians have greater

abdominal fat distribution and higher prevalence of obesity-related diseases (type-2 diabetes and

cardiovascular diseases) at lower levels of BMI 25 kg/m2 (Hubbard 2000; Snijder et al. 2005;

Villareal et al. 2005). In addition, populations with short stature may be considered “obese” at

lower levels of BMI because those with small body structure have higher proportion of body fat

at each level of BMI (Villareal et al. 2005). The Korean Society for the Study of Obesity and

Korean medical institutions also use the lower BMI cutoff value (obesity≥25kg/m2) to define

obesity. In Singapore, many studies defined overweight based on BMI of ≥23 kg/m2 and obesity

based on BMI > 27 kg/m2. In Hong Kong, working aged individuals whose BMI is greater than

24 kg/m2 are considered to be obese (Deurenberg-Yap et al. 2001; Ko et al. 1999). Even though

there is some controversy about what BMI cutoff points should be used to define overweight or

obesity among all Asians, a systematic review of obesity-related health problems among Asian

populations reveals that the BMI correlated with the prevalence of obesity-related complications

are between 22 kg/m2 to 31 kg/m2 (Serena Low et al. 2009). Therefore, in this study we use the

BMI cutoff points to define overweight and obesity recommended for use in Asian populations

by the WHO (WHO 2000). Furthermore, through subsequent sensitivity tests with alternative

measures: continuous standardized score of BMI and relative values of BMI based on quintiles,

we will evaluate whether and to what extent the BMI cutoff points affect our conclusions.

Explanatory Variables

Among the demographic characteristics, I control for age in all analyses. I also control

for current marital status (married, separated/divorced/widowed, never married). Survey years

are controlled for.

Because the survey does not have a direct measure of the respondents’ socioeconomic

circumstances and nutritional status in childhood, I use parental educational attainment when the

respondents were 14 years old as a measure of childhood SES. Respondents were asked: What is

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your father’s highest degree? And what is your mother’s highest degree? Education level of the

father and the mother are combined into one variable named ‘parental education’ given their

high correlation. Higher educational attainment of either mother or father is used to address

respondents’ parental SES. Parental education is grouped into quartiles based on their birth

cohorts.

I also include several indicators of current SES: educational attainment, household

income, occupation, and place of residence. Education is coded as follows: less than elementary

school (12 years). Household income is derived from the income of all household members and

divided into tertiles per a month:

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(8) frequency of inability to remember after drinking, (9) injury because of drinking, and (10) any

suggestion to cut down by family members, friends, or doctors. Scores for the each item were

summed up into a continuous variable and categorized into three groups based on the cut-offs as

follows: 0–7, 8–14, and ≥15. These cut-off points were based on previous studies about

detecting hazardous drinkers (Flegal et al. 2010). Each survey year from the five waves (from

2007 to 2011) also controlled in all models. Table 1 provides descriptive statistics for all variables

used in the analysis by gender.

Methods and Model Description

To estimate the associations between childhood and adult SES and underweight,

overweight, and obesity, I estimate multinomial logistic regression models. I assessed differential

obesity–social status associations for those individuals born from 1954 to 1963 (the initial stage

of economic growth), those born in 1964-1973 and those born after 1974 by testing for an age–

SES interaction and considering historical changes in Korean context and undertook the main

analyses split by birth cohort. All models are estimated separately for men and women as well

and the results are presented as relative risk ratios (RRRs) with standard errors (S.E.). All

equations are weighted to represent the national sample of Korea.

Model 1 I control for parental education (indicators of early-life conditions), age at the

time of interview, and survey years. In Model 2, I introduce indicators of adult socio-

demographic variables to see whether introducing current SES changes the association between

childhood SES and risk of obesity. In Model 3, I add health behavior to model 2 and examine

whether health behaviors are mediators of the impact of SES on overweight and obesity. In

model 4, I interact parental educational attainment with current SES to evaluate whether current

SES modifies the impact of childhood SES on the risk of obesity among men and women. I use

Wald statistics and their p-values for testing for whether the interaction is significant. The 0.1

significance level is used because the aim of the paper is to study the associations between

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several potential predictors of obesity, and all included characteristics have a theoretical

connection with the dependent variable. However, I mostly report and discuss associations

significant at the p

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SES (higher parental educational attainment) are more likely to be obese (RRRs for oldest cohort

= 1.24; second oldest cohort = 1.23, p

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individuals with lowest level of educational attainment become less likely to be obese than

individuals with highest degree with marginal statistical power to explain (RRR= 0.58, p

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lowest educational attainment = 1.91, p

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considerably change the association between childhood and adult SES and weight just as found

in older cohorts.

I introduce interaction terms between childhood SES and current SES factors to the

model adjusted for childhood- and adult- SES and related covariates to investigate whether

childhood SES modifies the association of current socioeconomic conditions with weight after

controlling for other related covariates. Three interaction terms are included into the final model

in preliminary test: parental education*current education, parental education*household income,

parental education*occupation. Statistical significance from the multivariate Wald test is found in

the interaction effect between parental education and current occupational status among men

with respect to improvement of model fit (p = 0.03). The result shows that higher level of

parental educational attainment is associated with reduced prevalence of obesity especially

among professional males and females of the latest cohort (Figure 1 & 2). For example, although

professional male workers are likely to be exposed to higher risk factors to be obese compared to

manual worker counterpart, if he has been raised by highly educated parents, the risks of being

obese seem to be reduced by around 20% (p

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association between childhood SES and obesity among women but it has a limited impact on

men across the birth cohorts. The explanatory role of current SES in the association between

childhood SES and obesity seems apparently more important for those who spent their early

childhood in the process of industrialization (born from 1964 to 1973) than other cohorts,

particularly among women with the greatest emphasis on those of education and income. For

earlier cohorts of men, positive social gradients in obesity were observed and the gradients began

to shift into negative direction for the youngest cohort. In a society with high level of gender

inequality like in Korea, strong and negative association between adulthood SES and obesity may

indicate that women are more likely to be vulnerable to socioeconomic disadvantages given that

they have less economic power to control their own resources. They may be also more sensitive

to the effects of socioeconomic disadvantages which usually closely related with risk factors of

being obese (Doyal, 2001; Rogers, 2006).

The findings of gender difference in SES gradients in obesity over the life trajectory

partially concur with earlier studies (Doyal, 2001; Rogers, 2006; Wells et al., 2012). Past studies

have found stronger negative relationships between early-life SES and adult obesity risk in

women than men (Heraclides et al., 2008; Khlat et al., 2009; Langenberg et al., 2003; Case &

Menendez, 2009), addressing greater sensitivity of females to such early life disadvantage; girls in

poverty tend to be more stunted (Borooah, 2004; Ghosh et al., 2002; Moestue, 2009). The result

from comparison of models of the youngest cohort supports the argument that parental SES in

early life is negatively related with overweight only for women after controlling for other

covariates. However, across birth cohorts, a residual impact of childhood SES on weight later in

life was much stronger for men than women in Korea. Strong and positive relationship between

childhood SES and obesity found only among men can be explained as follows. Given that

Korea was agrarian society before the industrialization started during the 1960s, dairy products

and foods containing high animal fat and low fiber were allowable only for the wealthiest people

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and thus “chubbiness” of children is a sign of prosperity. Before the end of 1980s, having a car and

using elevator are unusual for middle-class Korean families and only children with highest SES

could enjoy the benefit of technology. Promotion of knowledge of metabolism and nutritional

balance to educate mother to reduce obesity risk among children was not initiated until a few

years ago (Lim, 2011). Therefore, many Koreans from higher SES origin adopted and maintain

dietary habits and behaviors conducive to the development of obesity during their early life

which may influence BMI trajectory over their life-course. The shift in economic structure from

light industry to electronic industry expanded mass media market which transplanted the western

culture into Korea. Introduced culture idealized westernized features such as tallness and

slimness and generated new body awareness in post-industrialized Korean society (after 1970s).

The new body awareness has become the social beauty norm in the highly competitive job and

marriage markets especially for women (Jang, 2013). Women who fail to fit the socially defined

body standards are easily marginalized and discriminated against major competitions. The recent

awareness spread out among the young women that “slim body” gives an advantage over the life

spheres encourages Korean women to eagerly manipulate their bodies toward the desired ideal

by investing their money and time for dieting, exercising and/or medical (or pseudo medical)

treatment. The result found among the youngest female cohort that higher SES women are more

likely to be underweight give an empirical evidence support the trend. On the other hand,

western culture makes people prefer bigger body of male to smaller one as they consider big

body a symbol of masculinity, power and wealth. Social attitudes are more lenient and relatively

neutral for obese men (Ibid). In Korea, the extent of social pressure and stigmatization against

gaining weight is disproportionately concentrated on women and this tendency is consistent with

previous reports in other developed countries in that there is a clear social gradient in overweight

and obesity among women (Waldle & Griffith, 2001). For Korean men, heavy drinking habits

are likely to be the most important factor increasing BMI. Heavy drinking can cause obesity by

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increasing calorie intakes from alcohol as well as by hindering healthy weight control behaviors

such as exercising. Traditionally in Korea, men (especially who are in professional job) tend to

place high value on sociality and consider drinking gathering as an extension of formal work. The

finding is in the same direction with previous studies showing that heavy male drinkers were less

active in controlling their weight, even though their weight perceptions were not different from

those of non- or moderate drinkers (Ko et al., 2013). Given the high prevalence of and rising

trends in heavy drinking among Korean men (KHNES 2012), result of current study should be

seriously considered to develop effective public intervention.

Individuals belong to the youngest cohort, whose parents are highly educated, seem to

have less likelihood to be obese for both men and women. When medical knowledge spread out

after industrialization children grown up under highly educated parents easily obtain knowledge

concerned with maintaining healthy weight and integrate the knowledge into their life styles such

as foods with less sugar and physical activities on a regular base. In other words, knowledge and

life habits passed by parents seem to have strong effect on protecting adult obesity especially for

the men and women achieved higher adult SES via professional job. For example men in

professional jobs can use their knowledge to offset negative impact of traditional working culture

including frequent social gathering with heavy drinks and severe stress. In this sense, the result is

consistent with the previous findings of Ball and Mishra (2005) that mother’s socioeconomic

status might be more influential on weight control patterns of people with high SES. People are

more likely to respond to or model their dieting or weight loss attempts on the suggestions or

behaviors of their care givers.

Conclusion

Using data from South Korean KHNES (2007-2011), the study is able to identify the

relationship between childhood SES and current SES and obesity prevalence by gender and birth

cohorts. I find that men with higher SES origin and with greater nutritional catch up during the

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social transition are more likely to be at significantly greater risk of obesity and the gradient

decreases as the society develops. On the other hand, higher SES is related with less likelihood of

being overweight and obesity among women regardless of their childhood SES. Women having

more resources seem to use these resources to get and maintain slim body fit to meet social

expectation while men with more resources do not seem to have this tendency. This might be

intimately related with the Korean socioeconomic shift and the culture of gender norms and

body images.

With interesting findings the study has several limitations. Cross-sectional data do not

provide information on the direction of causality between SES and obesity. Although an

apparent cross-sectional relationship between obesity and SES has been found, it is possibly also

affected by a reverse causation: obesity leads to downward SES. In this case, the relationship

between SES and adulthood obesity may be confounded by obesity in childhood or adolescent,

which I was not able to control for in our study. Due to the limitation in data availability, I can

only use maternal and paternal education as indicators of childhood SES. However, many

previous studies argue that education is a proper and consistent measurement of general SES

(Ball & Mishra, 2005). Given the fact that Korea has experienced educational expansion, the

future steps should be needed to consider variations by cohorts and periods with respect to the

effect of education of both parents and respondents on obesity prevalence.

Understanding the differences that men and women face in their risks of obesity is a

necessary and important first step for developing effective policy and public health intervention.

The result shows that men with higher SES are highly likely to be obese but not women in Korea.

In this case, campaign that spread information on the necessity of regular exercise, fat and

nutrition content of food toward general target would not that be efficient to reduce obesity risk

that current Korean society is facing. One way to address the high prevalence among men in

higher SES is to better educate employers about the relevant risks related with obesity and

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encourage them to change occupational culture (i.e. do not force drinking etc.). In that way,

men’s perceptions of healthy BMI may change. There may be an upper bound on the extent to

which such campaigns will be successful, however, if a man’s ability to regulate his appetite is

compromised by the eating and exercise behaviors he already adopted and has maintained from

his early life. As current results emphasize potential role of parents in reducing obesity later in

life, comprehensive evaluation about the socioeconomic conditions over the life-course

trajectory is necessary to understand obesity risk the society is facing and develop effective health

intervention.

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Table1. Sample Characteristics by Gender Ages 25-57, Cohort 1954-1989, 2007–2011 Korean National Health and Nutrition Examination Survey (KNHANES), (N=16,371)

Men Women p-value

Characteristics (N= 6,985) (N= 9,386)

Weight status N % N %

Underweight (0

25

Table1. Sample Characteristics by Gender Ages 25-57, Cohort 1954-1989, 2007–2011 Korean National Health and Nutrition Examination Survey (KNHANES), (N=16,371)

Region of current residence

Metropolitan area

3,193

45.7

4,406

46.9

0.12

Outside metropolitan area 3,792 54.3 4,980 53.1

Health Behaviors Alcohol consumption

Light 2,698 38.7 7,834 83.5 0.00

Middle 2,148 30.7 1,131 12.1

Heavy 2,139 30.5 421 4.5

1. Parental education coded based on the percentage by respondents’ generation given the large change in Korean educational system.

26

Table 2-1. Relative Risk Ratios from Multinomial Logit Models: Underweight, overweight and obesity versus normal weight among male born from 1954 to 1963 (N=2,303)

Under Over Obesity Under Over Obesity Under Over Obesity Under Over Obesity

VARIABLES weight weight weight weight weight weight weight weight

Early life conditions

Parental education 1.07 1.12* 1.24*** 1.04 1.03 1.16** 1.04 1.03 1.16** 0.81 1.04 1.20**

(0.20) (0.07) (0.08) (0.21) (0.07) (0.08) (0.21) (0.07) (0.08) (0.20) (0.09) (0.10)

Adult socio-demographic conditions

Educational attainment

(College and abovea)

Elementary 0.49 0.80 0.91 0.56 0.80 0.91 0.49 0.80 0.92

(0.29) (0.17) (0.18) (0.33) (0.17) (0.18) (0.30) (0.18) (0.19)

Middle school 0.34* 0.77 0.98 0.36* 0.77 0.98 0.34* 0.77 0.98

(0.20) (0.15) (0.18) (0.22) (0.15) (0.18) (0.21) (0.15) (0.18)

High school 0.53 0.92 0.97 0.56 0.92 0.97 0.58 0.92 0.96

(0.23) (0.14) (0.14) (0.24) (0.14) (0.14) (0.26) (0.14) (0.14)

Occupation (Manual workera)

Service worker 1.13 1.38* 1.79*** 1.17 1.38* 1.79*** 1.21 1.38* 1.79***

(0.63) (0.24) (0.30) (0.65) (0.24) (0.30) (0.68) (0.24) (0.30)

Professional worker 0.78 0.98 1.61*** 0.77 0.98 1.61*** 0.07* 1.02 1.96**

(0.38) (0.15) (0.23) (0.38) (0.15) (0.23) (0.10) (0.38) (0.66)

Not in the labor market 3.15*** 0.83 0.87 3.07** 0.83 0.87 3.06** 0.83 0.87

(1.36) (0.17) (0.17) (1.33) (0.17) (0.17) (1.33) (0.17) (0.17)

Household income (Highest incomea)

Low income 1.54 0.76* 1.01 1.51 0.76* 1.02 1.49 0.76* 1.02

(0.68) (0.11) (0.14) (0.67) (0.11) (0.14) (0.66) (0.11) (0.14)

Middle income 1.31 0.73** 1.10 1.34 0.73** 1.10 1.32 0.73** 1.10

(0.57) (0.10) (0.14) (0.59) (0.10) (0.14) (0.58) (0.10) (0.14)

Current residence(Metropolitan areaa)

Rural 1.26 0.86 1.03 1.26 0.86 1.03 1.22 0.86 1.03

(0.42) (0.10) (0.11) (0.42) (0.10) (0.11) (0.41) (0.10) (0.11)

27


Health behaviors

Alcohol consumption (Lighta)

Middle 0.52 1.12 1.07 0.52 1.12 1.07

(0.25) (0.16) (0.14) (0.25) (0.16) (0.14)

Heavy 0.59 0.94 0.97 0.62 0.94 0.97

(0.25) (0.13) (0.12) (0.26) (0.13) (0.12) Interaction

Parental education*Professional workers 2.51* 0.98 0.92

(1.20) (0.15) (0.12)

*** p

28





Parental education 1.10 1.13* 1.23*** 1.06 1.04 1.14* 1.08 1.04 1.14* 1.19 0.93 1.11

(0.21) (0.08) (0.08) (0.21) (0.08) (0.08) (0.22) (0.08) (0.08) (0.30) (0.10) (0.10)




Elementary 0.29 0.57 0.60 0.28 0.55 0.58* 0.29 0.53* 0.58*

(0.37) (0.22) (0.19) (0.36) (0.21) (0.19) (0.38) (0.20) (0.19)

Middle school 0.95 0.71 1.08 0.90 0.68 1.04 0.93 0.65 1.03

(0.58) (0.21) (0.25) (0.55) (0.20) (0.25) (0.56) (0.19) (0.24)

High school 0.58 1.02 0.90 0.57 1.01 0.89 0.57 1.02 0.89

(0.20) (0.13) (0.10) (0.20) (0.13) (0.10) (0.20) (0.13) (0.10)


Service worker 1.12 2.40*** 1.95*** 1.10 2.33*** 1.91*** 1.06 2.41*** 1.92***

(0.50) (0.30) (0.49) (0.39) (0.29) (0.48) (0.41) (0.30) (0.30)

Professional worker 0.67 1.26 1.16 0.68 1.23 1.14 1.45 0.65 1.00

(0.26) (0.15) (0.26) (0.18) (0.15) (1.59) (0.27) (0.36) (0.66)

Not in the labor market 0.45 1.53* 1.13 0.45 1.60* 1.19 0.44 1.64* 1.20

(0.33) (0.27) (0.33) (0.41) (0.29) (0.33) (0.42) (0.29) (0.17)


Low income 1.52 0.81 0.67*** 1.57 0.84 0.70*** 1.57 0.84 0.70***

(0.62) (0.12) (0.09) (0.64) (0.12) (0.09) (0.64) (0.12) (0.09)

Middle income 1.49 0.91 0.79* 1.57 0.93 0.81* 1.57 0.93 0.81*

(0.59) (0.09) (0.63) (0.13) (0.10) (0.63) (0.13) (0.10) (0.10)


Rural 0.77 1.06 1.15 0.78 1.06 1.14 0.79 1.06 1.14

(0.23) (0.11) (0.11) (0.23) (0.12) (0.11) (0.23) (0.12) (0.11)

29


Health behaviors


Middle 0.93 1.46*** 1.60*** 0.93 1.46*** 1.60***

(0.35) (0.19) (0.19) (0.35) (0.20) (0.19)

Heavy 1.62 1.81*** 1.66*** 1.63 1.80*** 1.66***

(0.56) (0.25) (0.20) (0.57) (0.25) (0.20) Interaction

Parental education*Professional workers 0.75 1.28 1.05

(0.30) (0.19) (0.14)

*** p

30





Parental education 1.12 1.11* 1.14** 1.13 1.09 1.13** 1.14 1.10 1.13** 1.34 1.10 1.24***

(0.16) (0.06) (0.06) (0.17) (0.07) (0.06) (0.17) (0.07) (0.06) (0.26) (0.08) (0.09)




Elementary 0.00 1.44 1.63 0.00 1.39 1.54 0.00 1.39 1.61

(0.00) (1.75) (1.98) (0.00) (1.69) (1.88) (0.00) (1.69) (1.97)

Middle school 0.00 0.93 1.77 0.00 0.91 1.72 0.00 0.90 1.75

(0.00) (0.41) (0.66) (0.00) (0.41) (0.64) (0.00) (0.41) (0.65)

High school 1.54 0.81 1.09 1.59 0.80* 1.07 1.58 0.80* 1.08

(0.47) (0.11) (0.13) (0.49) (0.10) (0.13) (0.48) (0.10) (0.13)


Service worker 1.17 1.15 1.59*** 1.22 1.11 1.51** 1.14 1.11 1.47**

(0.53) (0.22) (0.27) (0.56) (0.21) (0.26) (0.53) (0.21) (0.26)

Professional worker 1.25 1.16 1.58*** 1.27 1.15 1.56*** 4.05* 1.16 2.70***

(0.49) (0.18) (0.23) (0.50) (0.18) (0.23) (3.35) (0.39) (0.82)

Not in the labor market 1.12 0.88 1.06 1.08 0.90 1.09 0.96 0.89 1.03

(0.47) (0.16) (0.18) (0.46) (0.17) (0.19) (0.42) (0.17) (0.18)


Low income 0.98 0.96 1.09 0.98 0.96 1.09 1.02 0.96 1.10

(0.39) (0.14) (0.15) (0.38) (0.14) (0.15) (0.40) (0.14) (0.15)

Middle income 2.06** 0.92 1.10 2.06** 0.93 1.11 2.08** 0.93 1.10

(0.71) (0.13) (0.14) (0.71) (0.13) (0.14) (0.72) (0.13) (0.14)


Rural 0.59* 1.22* 0.93 0.58* 1.22* 0.93 0.58* 1.22* 0.93

(0.17) (0.14) (0.10) (0.17) (0.14) (0.10) (0.17) (0.14) (0.10)

31


Health behaviors


Middle 0.68 1.15 1.27* 0.66 1.15 1.26*

(0.26) (0.16) (0.16) (0.26) (0.16) (0.16)

Heavy 0.66 1.29* 1.47*** 0.65 1.29* 1.47***

(0.27) (0.20) (0.20) (0.26) (0.20) (0.20) Interaction

Parental education*Professional workers 0.63 0.99 0.80**

(0.19) (0.12) (0.09)

*** p

32

Table 3-1. Relative Risk Ratios from Multinomial Logit Models: Underweight, overweight and obesity versus normal weight among female born from 1954 to 1963 (N=3,179)




Parental education 1.09 0.93 0.91** 1.07 1.00 1.12* 1.06 0.99 1.11* 1.12 0.96 1.12*

(0.17) (0.05) (0.04) (0.18) (0.06) (0.06) (0.18) (0.06) (0.06) (0.20) (0.06) (0.06)




Elementary 0.91 1.92*** 3.51*** 0.85 1.87*** 3.44*** 0.87 1.88*** 3.41***

(0.48) (0.36) (0.66) (0.46) (0.36) (0.65) (0.47) (0.36) (0.65)

Middle school 0.88 1.50** 2.51*** 0.91 1.48** 2.51*** 0.92 1.51** 2.48***

(0.42) (0.26) (0.44) (0.43) (0.26) (0.44) (0.44) (0.27) (0.44)

High school 0.69 1.35** 1.48** 0.69 1.34* 1.48** 0.68 1.38** 1.46**

(0.27) (0.20) (0.23) (0.27) (0.20) (0.23) (0.27) (0.21) (0.23)


Service worker 1.66 1.25* 1.12 1.61 1.23 1.11 1.59 1.25 1.10

(0.67) (0.16) (0.14) (0.66) (0.16) (0.14) (0.65) (0.16) (0.14)

Professional worker 0.88 1.08 1.00 0.89 1.08 0.99 3.23 0.41* 1.26

(0.51) (0.20) (0.18) (0.52) (0.20) (0.18) (4.36) (0.21) (0.60)


(0.51) (0.12) (0.10) (0.49) (0.12) (0.10) (0.48) (0.12) (0.10)


Low income 0.89 0.92 1.07 0.88 0.92 1.06 0.87 0.93 1.06

(0.30) (0.10) (0.12) (0.30) (0.10) (0.12) (0.29) (0.11) (0.12)

Middle income 0.72 0.80** 1.01 0.70 0.79* 1.00 0.70 0.80* 1.00

(0.26) (0.09) (0.11) (0.25) (0.09) (0.11) (0.25) (0.09) (0.11)


Rural 2.31*** 1.07 1.25** 2.35*** 1.07 1.25** 2.32*** 1.08 1.25**

(0.66) (0.10) (0.11) (0.67) (0.10) (0.11) (0.66) (0.10) (0.11)

33


Health behaviors


Middle 0.00 1.02 0.85 0.00 1.02 0.85

(1.03) (0.16) (0.15) (0.01) (0.16) (0.15)

Heavy 3.51** 1.39 1.61 3.50** 1.39 1.61

(1.87) (0.41) (0.46) (1.86) (0.41) (0.46) Interaction

Parental education*Professional workers 0.59 1.45** 0.91

(0.31) (0.26) (0.16)

*** p

34





Parental education 1.08 0.92 0.90* 1.08 0.97 1.06 1.09 0.98 1.06 0.97 0.96 1.00

(0.12) (0.06) (0.05) (0.12) (0.06) (0.07) (0.12) (0.06) (0.06) (0.12) (0.07) (0.07)




Elementary 2.72** 1.97** 3.03*** 3.10** 2.01** 3.02*** 2.86** 1.98** 2.92***

(1.18) (0.61) (0.88) (1.37) (0.63) (0.88) (1.26) (0.62) (0.85)

Middle school 0.17** 1.53** 2.65*** 0.20** 1.56** 2.63*** 0.19** 1.55** 2.58***

(0.12) (0.31) (0.50) (0.14) (0.32) (0.50) (0.13) (0.32) (0.50)

High school 0.69* 1.23* 1.75*** 0.73 1.23* 1.75*** 0.73 1.23* 1.76***

(0.13) (0.13) (0.20) (0.14) (0.13) (0.20) (0.14) (0.13) (0.20)


Service worker 0.88 1.13 1.00 0.89 1.13 1.00 0.90 1.13 1.01

(0.28) (0.18) (0.15) (0.28) (0.18) (0.15) (0.29) (0.18) (0.15)

Professional worker 0.58* 1.01 0.79 0.57* 1.00 0.80 0.11** 0.83 0.30***

(0.19) (0.13) (0.19) (0.17) (0.13) (0.10) (0.35) (0.14) (0.66)


(0.22) (0.14) (0.12) (0.22) (0.14) (0.12) (0.23) (0.14) (0.12)


Low income 1.17 0.87 1.30** 1.15 0.87 1.30** 1.17 0.88 1.31**

(0.25) (0.11) (0.15) (0.25) (0.11) (0.15) (0.25) (0.11) (0.16)

Middle income 0.58** 0.99 1.08 0.58** 1.00 1.08 0.58** 1.00 1.08

(0.13) (0.11) (0.12) (0.13) (0.11) (0.12) (0.13) (0.11) (0.12)


Rural 1.12 1.06 1.06 1.12 1.06 1.06 1.13 1.06 1.07

(0.19) (0.10) (0.10) (0.19) (0.10) (0.10) (0.19) (0.10) (0.10)

35


Health behaviors


Middle 0.43** 0.90 0.99 0.43** 0.90 0.99

(0.17) (0.14) (0.15) (0.17) (0.14) (0.15)

Heavy 0.77 0.84 1.15 0.77 0.84 1.16

(0.42) (0.22) (0.28) (0.43) (0.22) (0.28) Interaction

Parental education*Professional workers 1.81** 1.07 1.44**

(0.51) (0.16) (0.23)

*** p

36

Table 3-3. Relative Risk Ratios from Multinomial Logit Models: Underweight overweight and obesity versus Normal weight among female born from 1974 to 1983 (N=2,903)




Parental education 1.15** 0.85*** 0.85*** 1.11 0.90* 0.97 1.11 0.90* 0.97 1.18* 0.94 1.07

(0.07) (0.05) (0.04) (0.08) (0.05) (0.06) (0.08) (0.05) (0.06) (0.10) (0.07) (0.07)




Elementary 0.32 0.73 2.12* 0.31 0.70 2.06 0.32 0.72 2.14*

(0.39) (0.48) (0.96) (0.38) (0.47) (0.94) (0.39) (0.48) (0.97)

Middle school 0.73 0.79 1.88* 0.72 0.77 1.82* 0.75 0.80 1.91*

(0.41) (0.35) (0.62) (0.41) (0.35) (0.60) (0.43) (0.36) (0.64)

High school 1.01 1.16 1.75*** 1.00 1.15 1.71*** 1.02 1.16 1.73***

(0.14) (0.15) (0.20) (0.15) (0.15) (0.20) (0.15) (0.15) (0.21)


Service worker 1.24 0.54** 0.69 1.23 0.53** 0.67 1.21 0.52** 0.65

(0.52) (0.15) (0.19) (0.52) (0.15) (0.19) (0.51) (0.14) (0.18)

Professional worker 1.50 0.53** 0.70 1.50 0.53** 0.70 2.19 0.78 1.69

(0.58) (0.18) (0.58) (0.14) (0.18) (1.14) (0.31) (0.67) (0.82)

Not in the labor market 1.63 0.56** 1.09 1.63 0.56** 1.09 1.60 0.55** 1.06

(0.62) (0.16) (0.18) (0.46) (0.17) (0.19) (0.42) (0.17) (0.18)


Low income 0.80 1.45** 1.75*** 0.80 1.45** 1.75*** 0.80 1.45** 1.73***

(0.12) (0.22) (0.26) (0.12) (0.22) (0.26) (0.12) (0.22) (0.25)

Middle income 0.64*** 1.24 1.40** 0.64*** 1.24 1.40** 0.63*** 1.23 1.38**

(0.09) (0.18) (0.20) (0.09) (0.18) (0.20) (0.09) (0.18) (0.20)


Rural 1.26* 0.87 1.15 1.27* 0.87 1.15 1.27* 0.88 1.16

(0.16) (0.10) (0.12) (0.16) (0.10) (0.12) (0.16) (0.10) (0.12)

37

Table 3-3. Relative Risk Ratios from Multinomial Logit Models: Underweight overweight and obesity versus Normal weight among female born from 1974 to 1983 (N=2,903)

Health behaviors


Middle 0.96 0.92 1.15 0.95 0.91 1.14

(0.17) (0.15) (0.18) (0.17) (0.15) (0.17)

Heavy 1.11 1.19 1.16 1.10 1.18 1.13

(0.29) (0.26) (0.26) (0.29) (0.26) (0.25) Interaction

Parental education*Professional workers 0.86 0.85 0.69***

(0.11) (0.11) (0.09)

*** p

38

Figure 1. Moderating Effects of Professional Jobs and Parental Educational Attainment on the Predicted Value of Obesity for Men

-.4

-.3

-.2

-.1

0.1

Pre

dic

ted V

alu

e o

f O

besity

less

than

ele

men

tary

sch

ool

mid

dle

scho

ol

mor

e th

an c

ollege

high

sch

ool

Parental Education

professional worker=0 professional worker=1

39

Figure 2. Moderating Effects of Professional Jobs and Parental Educational Attainment on the Predicted Value of Obesity for Women

-2.5

-2-1

.5-1

-.5

0

Pre

dic

ted V

alu

e o

f O

besity

less

than

ele

men

tary

sch

ool

mid

dle

scho

ol

mor

e th

an c

ollege

high

sch

ool

Parental Education

professional worker=0 professional worker=1

40

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