the lasting impact of childhood circumstance on obesity in ... · obesity was rarely a problem in...
Post on 25-Aug-2020
4 Views
Preview:
TRANSCRIPT
-
1
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
-
2
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
-
3
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
-
4
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
-
5
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
-
6
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
-
7
(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
-
8
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
-
9
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
-
10
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
-
11
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
-
12
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:
-
13
(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
-
14
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
-
15
SES (higher parental educational attainment) are more likely to be obese (RRRs for oldest cohort
= 1.24; second oldest cohort = 1.23, p
-
16
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
-
17
lowest educational attainment = 1.91, p
-
18
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
-
19
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
-
20
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
-
21
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
-
22
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
-
23
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.
-
24
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
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)
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
Table 2-2. Relative Risk Ratios from Multinomial Logit Models: Underweight, overweight and obesity versus normal weight among male born from 1964 to 1973 (N=2,564)
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 1.10 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)
Adult socio-demographic conditions
Educational attainment
(College and abovea)
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)
Occupation (Manual workera)
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)
Household income (Highest incomea)
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)
Current residence(Metropolitan areaa)
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
Table 2-2. Relative Risk Ratios from Multinomial Logit Models: Underweight, overweight and obesity versus normal weight among male born from 1964 to 1973 (N=2,564)
Health behaviors
Alcohol consumption (Lighta)
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
Table 2-3. Relative Risk Ratios from Multinomial Logit Models: Underweight, overweight and obesity versus normal weight among male born from 1974 to 1983 (N=2,118)
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 1.12 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)
Adult socio-demographic conditions
Educational attainment
(College and abovea)
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)
Occupation (Manual workera)
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)
Household income (Highest incomea)
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)
Current residence(Metropolitan areaa)
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
Table 2-3. Relative Risk Ratios from Multinomial Logit Models: Underweight, overweight and obesity versus normal weight among male born from 1974 to 1983 (N=2,118)
Health behaviors
Alcohol consumption (Lighta)
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)
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 1.09 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)
Adult socio-demographic conditions
Educational attainment
(College and abovea)
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)
Occupation (Manual workera)
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)
Not in the labor market 1.35 0.97 0.92 1.29 0.96 0.91 1.27 0.97 0.91
(0.51) (0.12) (0.10) (0.49) (0.12) (0.10) (0.48) (0.12) (0.10)
Household income (Highest incomea)
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)
Current residence(Metropolitan areaa)
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
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)
Health behaviors
Alcohol consumption (Lighta)
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
Table 3-2. Relative Risk Ratios from Multinomial Logit Models: Underweight, overweight and obesity versus normal weight among female born from 1964 to 1973 (N=3,304)
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 1.08 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)
Adult socio-demographic conditions
Educational attainment
(College and abovea)
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)
Occupation (Manual workera)
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)
Not in the labor market 0.80 0.93 0.84 0.78 0.93 0.85 0.80 0.93 0.86
(0.22) (0.14) (0.12) (0.22) (0.14) (0.12) (0.23) (0.14) (0.12)
Household income (Highest incomea)
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)
Current residence(Metropolitan areaa)
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
Table 3-2. Relative Risk Ratios from Multinomial Logit Models: Underweight, overweight and obesity versus normal weight among female born from 1964 to 1973 (N=3,304)
Health behaviors
Alcohol consumption (Lighta)
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)
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 1.15** 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)
Adult socio-demographic conditions
Educational attainment
(College and abovea)
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)
Occupation (Manual workera)
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)
Household income (Highest incomea)
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)
Current residence(Metropolitan areaa)
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
Alcohol consumption (Lighta)
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
References
1. Allison, P.D. (2000), “Multiple Imputation for Missing Data: A Cautionary Tale,” Sociological
Methods and Research,28, 301–309.
2. Aikins, Ama de-Graft. Ghana's neglected chronic disease epidemic: a developmental challenge.
Ghana medical journal, 2007, 41.4: 154.
3. Barker DJ, Winter PD, Osmond C, Margetts B, Simmonds SJ. Weight in infancy and death
from ischaemic heart disease. Lancet. 1989. 2:577–580.
4. Ball K., Mishra G., Whose socioeconomic status influences a women's obesity risk, Int. J.
Epidemiol. ; 2005, 35 (1): 131-138
5. Bell, A. Colin; GE, Keyou; POPKIN, Barry M. The road to obesity or the path to prevention:
motorized transportation and obesity in China. Obesity Research, 2002, 10.4: 277-283.
6. Becker, Anne E., et al. "Eating behaviours and attitudes following prolonged exposure to
television among ethnic Fijian adolescent girls." The British Journal of Psychiatry 180.6.
2002: 509-514.
7. Becker, Anne E. "Television, disordered eating, and young women in Fiji: Negotiating body
image and identity during rapid social change." Culture, Medicine and Psychiatry 28.4 2004:
533-559.
8. Borooah V.K., Gender bias among children in India in their diet and immunisation against
disease. Social Science & Medicine, 58, 2004; 1719–1731
9. Caballero, B. Obesity in Developing Countries: Biological and Ecological Factors. Journal of
Nutrition 2001; 131:866S–70S.
10. Carpentar K.M., Hasin D.S., Allison D.B., Faith M.S., Relationships between obesity and
DSM-IV major depressive disorder, suicide ideation, and suicide attemps Am J Public
Health. 2000 February; 90(2): 251–257.
11. Case A, Fertig A, Paxson C. The lasting impact of childhood health and circumstance Journal
of Health Economics 2005;24(2):365–389.
12. Case A., Menendez A., Sex differences in obesity rates in poor countries: Evidence from
South Africa Economics and Human Biology. 2009; 271–282
13. Chen, D. Meltzer. Beefing up with the Chans: evidence for the effects of relative incomand
income inequality on health from the China Health and Nutrition Survey. Social
Science & Medicine, 66. 2008. ; 2206–221
-
41
14. Deurenberg-Yap, M, Chew, SK, Lin, V, Tan, BY, van Staveren, WA & Deurenberg, P.
Relationships between indices of obesity and its co-morbidities among Chinese,
Malays and Indians in Singapore. Int. J. Obes. Relat. Metab. Disord. 2001; 1554–1562
15. Doyal. L. Sex, gender, and health: the need for a new approach. British Medical Journal,
323, 2001; 1061–1063
16. Flegal K, Margaret C, Ogden, Curtin, Prevalence and Trends in Obesity Among US Adults,
1999-2008; JAMA. 2010;303(3):235-241.
17. Flegal, K. M., Carroll, M. D., Kuczmarski, R. J., Johnson, C. L. Overweight and obesity in the
United States: prevalence and trends, 1960-1994. Int J Obes Relat Metab Disord.
1998; 22: 39–47.
18. Gong, Peng, et al. "Urbanisation and health in China." The Lancet 379.9818 (2012): 843-852.
19. Ghosh S., A. Kilaru, S. Ganapathy., Nutrition education and infant growth in rural Indian
infants: narrowing the gender gap? Journal of the Indian Medical Association, 100 (2002),
pp. 483–484 486–488, 490
20. Gillman MW, Rifas-Shiman SL, Frazier AL, et al. Family dinner and diet quality among older
21. Gluckman PD, Hanson M.A, Developmental and epigenetic pathways to obesity: an
evolutionary-developmental perspective International Journal of Obesity .2008; S62–S71
22. Hayward, Mark D.; GORMAN, Bridget K. The long arm of childhood: The influence of
early-life social conditions on men’s mortality. Demography, 2004, 41.1: 87-107.
23. Heraclides A., D. Witte, E.J. Brunner. The association between father’s social class and adult
obesity is not explained by educational attainment and an unhealthy lifestyle in
adulthood. European Journal of Epidemiology, 23, 2008, 573–579
24. Huang C, Elo I. Mortality of the oldest old Chinese: The role of early-life nutritional status,
socio-economic conditions, and sibling sex-composition. Population Studies 2009;
63(1) children and adolescents. Arch Fam Med. 2000;9:235– 40.
25. Hubbard V.S., Defining overweight and obesity: what are the issues? Am J Clin Nutr. 2000;
vol 5 Issue 7
26. Jang, Mi Heui, and Gyungjoo Lee. "Body Image Dissatisfaction as a Mediator of the
Association between BMI, Self-esteem and Mental Health in Early Adolescents: A
Multiple-group Path Analysis across Gender." Journal of Korean Academy of
Nursing 43.2., 2013: 165-175.
27.Joh, H-K., et al. "Gender and Socioeconomic Status in Relation to Weight Perception and
Weight Control Behavior in Korean Adults." Obesity facts 6.1, 2013: 17-27.
-
42
28. Joshu, C.E., et al. Personal, neighbourhood and urban factors associated with obesity in the
United States. Journal of Epidemiology and Community Health, 2008; 62, 202-208.
29. Kim S., Symons M., Popkin BM, Contrasting Socioeoconomic Profiles related to healthier
lifestyles in China and the U.S., Am. J. Epidemiol. 2004; 159 (2):184-191.
30. Kim D.J. The Epidemiology of Diabetes in Korea, Diabetes Metab J. 2011;35(4):303-308
31. Kim Y.H., Suh Y.K., Choi H. BMI and Metabolic disorders in South Korean Adults. Obesity
Research. 2004 pages 445–453.
32. Ko, GTC, Chan, JC, Cockram, CS & Woo, J. Prediction of hypertension, diabetes,
dyslipidaemia or albuminuria using simple anthropometric indexes in Hong Kong
Chinese. Int. J. Obes. Relat. Metab. Disord., 1999, 1136–1142.
33. Ko, Ki Dong, et al. "Obesity Explains Gender Differences in the Association Between
Education Level and Metabolic Syndrome in South Korea The Results From the
Korean National Health and Nutrition Examination Survey 2010." 2013; Asia-Pacific
Journal of Public Health.
34. Kapoor, S. K.; ANAND, K. Nutritional transition: a public health challenge in developing
countries. Journal of epidemiology and community health, 2002, 56.11: 804-805.
35. Khlat, M., F. Jusot, I. Ville. Social origins, early hardship and obesity: a strong association in
women, but not in men? Social Science & Medicine, 68 (2009), 1692–1699.
36. Kim, Soowon; SYMONS, Michael; POPKIN, Barry M. Contrasting socioeconomic profiles
related to healthier lifestyles in China and the United States. American journal of
epidemiology, 2004, 159.2: 184-191.
37. Kremers SP, Brug J, de Vries H, Engels RC. Parenting style and adolescent fruit
consumption. Appetite. 2003;41:43–50.
38. Kuh, Diana, et al. Life course epidemiology. Journal of epidemiology and community health, 2003,
57.10: 778.
39. Langenberg C., R. Hardy, D. Kuh, E. Brunner, M. Wadsworth. Central and total obesity in
middle aged men and women in relation to lifetime socioeconomic status: evidence
from a national birth cohort. Journal of Epidemiology and Community Health, 57 (2003),
816–822
40. Levi, J., et al. Future as in fat: How obesity threatens America’s future: 2010: Trust for
America’s health.
-
43
41. Lee J.H., et al. Original Articles : Role of Hyperinsulinemia and Glucose Intolerance in the
Pathogenesis of Nonalcoholic Fatty Liver in Patients with Normal Body Weight.
the Korean Journal of Internal Medicine
42. Lim S, Shin H, Song JH et al. Increasing prevalence of metabolic syndrome in Korea: the
Korean National Health and Nutrition Examination Survey for 1998–2007.
Diabetes Care, 2011;34:1323–1328
43. Luo Z.H., Mu R., Zhang X.B., Famine and overweight in China, Review of Agriculture Economics,
2006; 296-304
44. Lynch JW, Kaplan GA, Salonen JT. Why do poor people behave poorly? Variation in adult
health behaviours and psychosocial characteristics by stages of the socioeconomic
lifecourse. Soc Sci Med. 1997;44:809–819
45. Lytle, L. A., Varnell, S., Murray, D. M., Story, M., Perry, C., Birnbaum, A. S., & Kubik, M. Y.
Predicting adolescents’ intake of fruits and vegetables. Journal of Nutrition Education
and Behavior,2003,35, 170–175.
46. McLaren L. Socioeconomic status and obesity. Epidemiol Rev2007; 29: 29–48.
47. Moestue H., Can anthropometry measure gender discrimination? An analysis using WHO
standards to assess the growth of Bangladeshi children Public Health Nutrition, 12
(2009), pp. 1085–1091
48. Monasta, L., et al. Early‐life determinants of overweight and obesity: a review of systematic
reviews. Obesity Reviews, 2010, 11.10: 695-708.
49. Newby, P. K. Are dietary intakes and eating behaviors related to childhood obesity? A
comprehensive review of the evidence. The Journal of Law, Medicine & Ethics, 2007,
35.1: 35-60.
50. Neumark-Sztainer D, Hannan PJ, Story M, Croll J, Perry C. Family meal patterns:
associations with sociodemographic characteristics and improved dietary intake
among adolescents. J Am Diet Assoc. 2003;103:317–22.
51. Ng, Shu Wen, Edward C. Norton, and Barry M. Popkin. "Why have physical activity levels
declined among Chinese adults? Findings from the 1991–2006 China health and
nutrition surveys." Social science & medicine 68.7 (2009): 1305-1314.
52. Paeratakul, S., Lovejoy, J. C., Ryan, D. H., Bray, G. A. The relation of gender, race and
socioeconomic status to obesity and obesity comorbidities in a sample of US
adults. Int J Obes Relat Metab Disord. 2002; 26: 1205–1210.
-
44
53. Popkin, Barry M. "The shift in stages of the nutrition transition in the developing world
differs from past experiences!." Public health nutrition 5.1A , 2002: 205-214.
54. Popkin, Barry M., and Penny Gordon-Larsen. "The nutrition transition: worldwide obesity
dynamics and their determinants." International journal of obesity 28, 2004: S2-S9.
55. Power C, Graham H, Due P. The contribution of childhood and adult socioeconomic
position to adult obesity and smoking behaviour: an international comparison.
Int J Epidemiol. 2005;34:335–344
56. Ravelli, Anita CJ, et al. "Obesity at the age of 50 y in men and women exposed to famine
prenatally." The American journal of clinical nutrition 70.5.,1999: 811-816.
57. Reilly, John J., et al. Early life risk factors for obesity in childhood: cohort study. Bmj, 2005,
330.7504: 1357.
58. Robinson W., Gordon, P, Kaufman J, Suchindran C, Stevens J. The female-male disparity in
obesity prevalence among black American young adults: contributions of
sociodemographic characteristics of the childhood family Am J Clin Nutr, 2009.
1204-1212.
59. Rogers. W.A. Feminism and public health ethics. Journal of Medical Ethics, 32, 2006,. 351–354
60. Serena Low, Mien Chew Chin, , Stefan Ma , Derrick Heng , Deurenberg-Yap, Rationale for
redefining Obesity in Asians Ann Acad Med Singapore 2009;38:66-74
61. Sobal J, Stunkard A. Socioeconomic status and obesity: a review of the literature. PsycholBull.
1989; 105:260-275
36. Snijder, M.B., R.M. van Dam, and J.C. Seidell. “What Aspects of Body Fat Are Particularly
Hazardous and How Do We Measure Them?” International Journal of
Epidemiology 2005; 35:82–92
62. Stein A.D., H.S. Kahn, A. Rundle, P.A. Zybert, K. van der Pal-de Bruin, L.H. Lumey
Anthropometric measures in middle age after exposure to famine during
gestation: evidence from the Dutch famine American Journal of Clinical Nutrition;
2007; 869–876
63. Swinburn, Boyd A., et al. The global obesity pandemic: shaped by global drivers and local
environments. The Lancet, 2011, 378.9793: 804-814.
64. Villareal, D., C.M. Apovian, R.F. Kushner, and S. Klein. “Obesity in Older Adults: Technical
Review and Position Statement of the American Society for Nutrition and
NAASO, The Obesity Society.” Obesity Research 2005.13:1849–63
65. Waldle J., Griffith J, Socioeconomic status and weight control practices in British
-
45
adults. J Epidemiol Community Health 2001:55:185-190.
66. Wells, Jonathan CK, et al. "Associations of economic and gender inequality with global
obesity prevalence: understanding the female excess." Social Science &
Medicine 75.3.2012.: 482-490.
top related