Interaction between Parental Education and Household Wealth on Children’s Obesity Risk

Abstract

Parents’ education and household wealth cannot be presumed to operate independently of each other. However, in traditional studies on the impact of social inequality on obesity, education and financial wealth tend to be viewed as separable processes. The present study examines the interaction of parents’ education and household wealth in relation to childhood obesity. Anthropometric measurement and questionnaire surveys were carried out on 3670 children (aged 9–12 years) and their parents from 26 elementary schools in northeast China. Results showed that the interaction term was significant for household wealth and father’s education (p < 0.01), while no significant interaction between household wealth and mother’s education was found. In a separate analysis, the interaction was statistically significant among girls for obesity risk based on BMI (p = 0.02), and among urban children for both obesity risk based on BMI (p = 0.01) and abdominal obesity risk based on WHR (p = 0.03). Specifically, when household wealth increased from the first quintile to the fifth quintile, OR for father’s education decreased from higher than 1 (OR = 1.95; 95% CI: 1.12–3.38) to non-significant for girl’s obesity risk, from non-significant to lower than 1 for urban children’s obesity risk (OR = 0.52; 95% CI: 0.32–0.86 for the fourth quintile; OR = 0.37; 95% CI: 0.19–0.73 for the fifth quintile) and from higher than 1 (OR = 1.61; 95% CI: 1.04–2.05) to non-significant for urban children’s abdominal obesity risk. These findings indicate that father’s education level interacts with household wealth to influence obesity among girls and urban children in northeast China.

Full text

International Journal of
Environmental Research
and Public Health
Article
Interaction between Parental Education
and Household Wealth on Children’s Obesity Risk
Yang Liu, Yanan Ma, Nan Jiang, Shenzhi Song, Qian Fan and Deliang Wen *
Institute of Health Science, China Medical University, No. 77 Puhe Road, Shenyang North New Area,
Shenyang 110122, China; yliu0568@cmu.edu.cn (Y.L.); ynma@cmu.edu.cn (Y.M.); ndjiang@live.ca (N.J.);
song_shenzhi@163.com (S.S.); 18040209895@163.com (Q.F.)
*Correspondence: dlwen@cmu.edu.cn; Tel./Fax: +86-024-3193-9003
Received: 12 June 2018; Accepted: 10 August 2018; Published: 15 August 2018


Abstract:
Parents’ education and household wealth cannot be presumed to operate independently of
each other. However, in traditional studies on the impact of social inequality on obesity, education and
financial wealth tend to be viewed as separable processes. The present study examines the interaction
of parents’ education and household wealth in relation to childhood obesity. Anthropometric
measurement and questionnaire surveys were carried out on 3670 children (aged 9–12 years) and
their parents from 26 elementary schools in northeast China. Results showed that the interaction
term was significant for household wealth and father’s education (p< 0.01), while no significant
interaction between household wealth and mother’s education was found. In a separate analysis,
the interaction was statistically significant among girls for obesity risk based on BMI (p= 0.02),
and among urban children for both obesity risk based on BMI (p= 0.01) and abdominal obesity risk
based on WHR
(p= 0.03)
. Specifically, when household wealth increased from the first quintile to the
fifth quintile, OR for father’s education decreased from higher than 1 (OR = 1.95; 95% CI: 1.12–3.38) to
non-significant for girl’s obesity risk, from non-significant to lower than 1 for urban children’s obesity
risk (OR = 0.52; 95% CI: 0.32–0.86 for the fourth quintile; OR = 0.37; 95% CI: 0.19–0.73 for the
fifth quintile) and from higher than 1 (OR = 1.61; 95% CI: 1.04–2.05) to non-significant for urban
children’s abdominal obesity risk. These findings indicate that father’s education level interacts with
household wealth to influence obesity among girls and urban children in northeast China.
Keywords: childhood obesity; parents’ education; household wealth; health inequalities
1. Introduction
In the last 40 years, obesity has become one of the most serious nutritional concerns for children
and adolescents, affecting countries rich and poor, with the global number of obese children and
adolescents rising more than tenfold, from 11 million in 1975 to 124 million in 2016 [
1
,
2
]. The obesity
epidemic in younger age groups brings about a large increase in the incidence of various morbidities
and shortened life expectancy [
3
]. Socioeconomic status (SES) and economic insecurity were
hypothesized to be one of the key determinants of obesity prevalence and other chronic diseases [
4
].
Tackling social distribution of obesity risk in early life is a main challenge in childhood obesity
prevention and has also been recommended as an effort to tackle inequalities in mean BMI and obesity
status across all ages [5].
Household wealth and parents’ education were the most reported economic and social dimensions
of SES that could influence children’s obesity risk [
6
–
8
]. Household wealth influences the material
environment to which children are exposed. Low income may be related to food access dilemmas
resulting from resource constraints and adverse food environments [
9
,
10
]. Parent education levels
affect parents’ ability to process health information, which leads to improved health-related decisions
Int. J. Environ. Res. Public Health 2018,15, 1754; doi:10.3390/ijerph15081754 www.mdpi.com/journal/ijerph

Int. J. Environ. Res. Public Health 2018,15, 1754 2 of 12
in parenting practice and which also affects their motivation to adopt a healthy lifestyle as role models
for their children [
11
,
12
]. Children of more educated parents were reported to be more likely to eat
breakfast and consume fewer calories from snacks and sweetened beverages [9].
The majority of previous studies viewed the impact of income and education on obesity risk as
separable processes. However, education and income cannot be presumed to operate independently
of each other considering their high correlation [
13
,
14
]. Additionally, both income and education
have been reported to relate to obesity in nonlinear ways [
7
,
15
,
16
]. With income and education levels
varying, the social distribution of obesity risk also varies. This necessitates research on how social
inequalities may be more important in different settings and the importance of different social factors
on different people groups [13].
According to the nutrition transition model [
17
], countries follow an economic and nutritional
progression from Stage 1 of collecting food and Stage 2 of famine to Stage 3 of receding famine,
followed closely by Stage 4 of nutrition-related non-communicable disease, and finally Stage 5 of
the development or introduction of behavioral change, including diet modification and increased
recreational activity. China is a classic example of a country moving towards nutrition transition
to Stage 4, illustrating nutrition transition in the developing world [
18
]. Accompanied by the
nation’s rapid economic growth and significant social and cultural change within the last three
decades, the prevalence of overweight and obese children in China increased by 35.22 (boys) and
25.21 (girls) percentage points (from 1.91% for boys and 1.53% for girls in 1985 to 37.13% for boys
and 26.74% for girls in 2014) based on the International Obesity Task Force criteria for children aged
7–12 years [
19
]. Learning from the tremendous change in China may help with designing nutrition
policies and interventions to reduce health hazards during the nutrition transition.
Therefore, this study explored the social distribution of childhood obesity risk in China and
investigated the interaction between parents’ education and household wealth on children’s obesity
risk. We hypothesize in China’s family sets that higher education in parents would be negatively
correlated with obesity in their children, but this higher education in parents would not have
a significant role until household wealth reached a higher level.
2. Methods
2.1. Study Populations and Sampling
From 17 May to 23 June 2017, data was collected from fourth-, fifth-, and sixth-grade students
and their parents from 26 elementary schools in Shenyang, China. This age range was sampled
due to it being a time of striking behavioral change related to the social environment. Shenyang is
the largest city in northeast China by urban population and consists of 13 administrative districts,
including 10municipal districts of Shenyang proper, 1 count-level city, and 2 counties. According to
the 2010 census, Shenyang’s total population had surpassed 8.1 million, with the urban population
comprising 6.3 million of the total.
Two schools from each of the 13 administrative districts of Shenyang city were randomly selected,
from which one class from each of the fourth-, fifth-, and sixth-grade divisions of each school was
selected to be included in the study. All students from the selected classes and their parents were
included as the sample of the present study with their consent, and all participants had the option to
withdraw from the study at any point.
2.2. Data Collection
Anthropometric measurement—including height, weight, waist circumference, and hip circumference—
was carried out on physical examination day at each school by trained investigators using techniques
prescribed by Lohman et al. [
20
]. Household questionnaires on family demographics and each household’s
wealth index were handed out to each student three days before physical examination, answered by one
or both parents, and collected at physical examination. Incomplete questionnaires or those with missing

Int. J. Environ. Res. Public Health 2018,15, 1754 3 of 12
data were filtered by the school teachers, school physician, or research personnel (SZS) and returned to
parents for completion of any inadvertently missed portions. Written informed consents were obtained
from all participating households before anthropometric measurement. The study was approved by the
China Medical University Ethics Committee (71774173).
2.3. Measurements and Variable Definitions
Weight was measured using a portable Tanita DC-430MA dual frequency body composition
monitor (TANITA Corporation, Tokyo, Japan). Standing height was measured without shoes,
by a Seca 213 portable stadiometer (Hamburg, Germany). BMI was calculated as weight (kg) divided
by height (m) squared. Waist circumference (WC) (cm) was measured at the midpoint between the
level of the xiphoid process and the top of the iliac crest, and hip circumference (HC) (cm) at the widest
point around the buttocks. Waist–hip ratio (WHR) was calculated as WC divided by HC. Obesity was
defined according to cut-off BMI recommended by Working Group on Obesity in China (WGOC),
as P95, based on data collection by the Chinese National Survey on Students Constitution and Health
on primary and secondary school students ages 7 through 18 (Table 1) [
21
]. Abdominal obesity was
defined by previously published WHR references based on Chinese children and adolescents living in
Beijing (WHR ≥P97) (Table 2) [22].
Table 1. Body mass index reference norm for screening obesity in Chinese children and adolescents [21].
Age (year) Boys Girls
9~ 21.4 21.0
10~ 22.5 22.1
11~ 23.6 23.3
12~ 24.7 24.5
Table 2.
Waist–hip ratio reference norm for screening obesity in Chinese children and adolescents [
22
].
Age (year) Boys Girls
9~ 0.88 0.92
10~ 0.85 0.90
11~ 0.83 0.88
12~ 0.82 0.87
In China, all citizens must attend school for at least nine years, known as the nine-year compulsory
education, which is free for citizens and is funded by the government. The compulsory education
includes six years of primary education, starting at age six or seven, and three years of junior
secondary education (junior middle school) for ages 12 to 15. Beyond compulsory education,
Chinese citizens could choose to pursue higher education via qualification examinations at each stage,
which could include three years of senior middle school education, any additional vocational training
or apprenticeship, and any university education in a post-secondary degree program. Tuition for
higher education is no longer subsidized by the government. Hence, in the present study, parental
education consisting of the father’s education and the mother ’s education and was divided into two
levels: (1) no or compulsory basic education and (2) higher education.
The household wealth index was based on the following indicators: household income, food
costs as a proportion of annual income, ratio of income to expenditure, self-reported evaluation of
household income compared to the local average, income growth in the last three years, satisfaction
of household income, number of private cars, number of computers, if the child has his/her own
room, and number of family trips per year. The index was generated through a principal components
analysis using the Filmer and Pritchett method to calculate factor loadings and derive a score for each
household [
14
,
23
–
25
]. Household wealth levels were divided into quintiles: first = lower than or equal

Int. J. Environ. Res. Public Health 2018,15, 1754 4 of 12
to 20% of the study sample, second = 21–40%, third = 41–60%, fourth = 61–80%, and fifth = higher
than 80% of the study sample [
24
]. Sensitivity analysis was performed to classify the lowest 40% of
households into “poor”, the highest 20% as “rich” and the rest as the “middle” group [24].
According to the residential registration system of China, citizens belonged to two residential
groups: “urban” and “rural”. This data was collected as an item in the questionnaire.
2.4. Statistical Analysis
STATA 14.0 survey commands (svy) were used to analyze the data and account for the complex
design effect, taking into account the effect of clustering and unequal weights as appropriate when
computing frequencies, proportions, variance, standard errors, and confidence intervals.
Firstly, prevalence of obesity by subgroup was computed using a chi-squared test to compare the
differences between sex, residence registration area, household wealth level, and parents’ education level.
Secondly, according to the hypothesis proposed by the present study, father’s education level and
mother’s education level were focal independent variables, and household wealth was the moderator
variable. Interaction terms between parents’ education level (no or compulsory basic education/higher
education, reference category = no or compulsory basic education) and household wealth quintiles in
continuous forms were fitted into logistic regression models. The interaction terms were examined for
significance using the Wald test (the null hypothesis being that the interaction terms in the regression
model were equal to zero) and the likelihood ratio (LR) test (examining whether the data was better
fitted using a model with or without the interaction).
Next, considering the characteristics of obesity and Chinese society, analyses were performed
separately for boys and for girls and then for urban residences and for rural residences. Firstly, boys and
girls differ in body composition, patterns of weight gain, hormone biology, and susceptibility to certain
social, ethnic, genetic, and environmental factors. In addition, social economic status was reported
to influence boys’ and girls’ obesity statuses differently. Secondly, as a developing country, China is
characterized by the urban–rural dual structure. Large disparities exist between urban and rural
areas, including but not limited to the urban and rural income gap, different social security systems,
and unequal distribution of public resources.
Finally, ORs and the confidence interval for the local independent variables (father’s education
level and mother’s education level) were calculated at different values of the moderator by
transforming the continuous moderator variable (household wealth quintile in its continuous form)
and then rerunning the regression analysis.
All analyses were performed twice, first using obesity defined by BMI and then using abdominal
obesity defined by WHR as dependent variables, in sequence. All analyses were carried out using the
software STATA 14.0 (Stata Corp., College Station, TX, USA). Statistical significance was defined by
ap-value of <0.05.
3. Results
The coverage attained by the anthropometric examination was 3670 (95% of the total number of
enrolled students from selected grades 4, 5, and 6 of the 26 schools). As shown in Table 3, the average
age of children in the present study was 10.8
±
1.0 years. Forty-nine percent of the children were
girls and 44.8% of the children were from urban families. The overall prevalence of obesity was
17.0%, defined by BMI, and the overall prevalence of abdominal obesity was 8.1%, defined by WHR.
Table 3also shows distribution of parents’ education in each household wealth quintile. As shown
in Table 4, boys had a statistically higher obesity prevalence compared to girls (p< 0.001). There was
no significant difference in childhood obesity prevalence between different household wealth levels,
father’s education levels, mother’s education levels, or residence registration area. There was no
significant difference in childhood abdominal obesity prevalence between different sexes, household
wealth levels, father’s education levels or mother ’s education levels or residence registration area.

Int. J. Environ. Res. Public Health 2018,15, 1754 5 of 12
Table 3. Demographic characteristics of the 3670 children enrolled in the study.
Characteristic All
Participants, N 3670
Age, years 10.8 ±1.0
Sex, n (%)girls 1799 (49.0)
Residence area, n (%)urban 1645 (44.8)
Obesity defined by BMI, n (%) 623 (17.0)
Abdominal obesity defined by WHR, n (%) 297 (8.1)
Household wealth quintile
Fifthquintile wealth (richest) 712 (19.4)
Fourthquintile wealth 584 (15.9)
Thirdquintile wealth 891 (24.3)
Second quintile wealth 757 (20.6)
First quintile wealth (poorest) 726 (19.8)
Parental education
Father’s education, n (%)higher 1821 (49.6)
Mother’s education, n (%)higher 1802 (49.1)
Higher parental education in each household wealth quintile, n (%)
Fifthquintile wealth (richest)
Fathers with higher education 486 (68.3)
Mothers with higher education 494 (69.4)
Fourthquintile wealth
Fathers with higher education 318 (54.5)
Mothers with higher education 330 (56.5)
Thirdquintile wealth
Fathers with higher education 506 (56.8)
Mothers with higher education 491 (55.1)
Second quintile wealth
Fathers with higher education 286 (37.8)
Mothers with higher education 287 (37.9)
First quintile wealth (poorest)
Fathers with higher education 225 (31.0)
Mothers with higher education 200 (27.6)
Table 4. Prevalence of childhood obesity by subgroups.
Obesity Abdominal Obesity
N(%) p1N(%) p1
Sex
Boys 381 (20.4) <0.001 147 (7.9) 0.57
Girls 242 (13.5) 150 (8.3)
Residence area
Urban 292 (17.8) 0.38 137 (8.3) 0.21
Rural 331 (16.4) 160 (7.9)
Household wealth level
Fifth quintile wealth 123 (17.3)
0.53
41 (5.8)
0.15
Fourth quintile wealth 111 (19.0) 54 (9.3)
Third quintile wealth 148 (16.6) 67 (7.5)
Second quintile wealth 126 (16.6) 66 (8.7)
First quintile wealth 115 (15.8) 69 (9.5)
Father’s education
None/basic 317 (17.1) 0.74 154 (8.3) 0.71
Higher 306 (16.8) 143 (7.9)
Mother’s education
None/basic 300 (16.1) 0.22 161 (8.6) 0.37
Higher 323 (17.0) 136 (7.6)
1Chi-squared test was used.

Int. J. Environ. Res. Public Health 2018,15, 1754 6 of 12
As shown in Table 5, the interaction terms between household wealth and father’s education was
significant for children’ obesity risk (p< 0.01 for interaction term). The LR test for whether the model
was better fit with or without the interaction terms also provided strong evidence of the interaction
effect of household wealth and father’s education on children’ obesity risk (p< 0.05).
Table 5.
Interaction between parents’ education and household wealth quintiles on obesity and
abdominal obesity risk.
nOR 195% CI p-Value
Obesity
Fathers with higher education 0.76 0.55, 1.04 0.08
Mothers with higher education 1.26 0.87, 1.81 0.20
Household wealth quintiles 3670 1.10 0.98, 1.23 0.10
Fathers with higher education ×Household wealth quintiles 0.77 0.66, 0.91 <0.01
Mothers with higher education ×Household wealth quintiles 1.12 0.94, 1.33 0.20
Constant 1.04 0.44, 2.47 0.92
Abdominal obesity
Fathers with higher education 1.08 0.80, 1.44 0.59
Mothers with higher education 0.88 0.55, 1.40 0.56
Household wealth 3670 0.91 0.74, 1.13 0.38
Fathers with higher education ×Household wealth quintiles 0.85 0.67, 1.08 0.17
Mothers with higher education ×Household wealth quintiles 1.16 0.80, 1.66 0.41
Constant 0.02 0.00, 0.12 <0.01
1Controlled for age (years), sex, residence area (urban/rural) and school (which school the children belonged to).
Table 6showed the separate analysis by sex. Interaction terms between household wealth and
father’s education was significant for girls’ obesity risk (p< 0.05 for interaction terms) but not for that
of boys.
Table 7showed the separate analysis by residence area (urban/rural). The interaction terms
between household wealth and father’s education were significant for children’s obesity risk (p< 0.05
for interaction term) and abdominal obesity risk (p< 0.05 for interaction term) in urban areas but not
in rural areas.
Figure 1demonstrated details of the interaction between household wealth and father’s education
on children’s obesity by showing OR (95% CI) for father’s education within different household
wealth quintiles. When household wealth increased from the first quintile to the fifth quintile, OR for
father’s education decreased from higher than 1 (OR = 1.95; 95% CI: 1.12–3.38) to non-significant for
girl’s obesity risk (Figure 1A), from non-significant to lower than 1 for urban children’s obesity risk
(OR = 0.52; 95% CI: 0.32–0.86 for the fourth quintile; OR = 0.37; 95% CI: 0.19–0.73 for the fifth quintile)
(Figure 1C) and from higher than 1 (OR = 1.61; 95% CI: 1.04–2.05) to non-significant for urban
children’s abdominal obesity risk (Figure 1D).
Results of sensitivity analysis (classifying the lowest 40% wealth index of households into “poor”,
the highest 20% as “rich”, and the rest as the “middle” group) showed a similar interaction pattern
between household wealth and parental education (Tables S1–S3, Figures S1 and S2).

Int. J. Environ. Res. Public Health 2018,15, 1754 7 of 12
Table 6. Interaction between parent education and household wealth quintiles on obesity and abdominal obesity risk separated by sex.
Model for Boys Model for Girls
nOR 195% CI pValue nOR 195% CI p-Value
Obesity
Fathers with higher education 0.63
0.40, 1.00
0.02 0.93
0.60, 1.48
0.77
Mothers with higher education 1.36
0.94, 1.96
0.10 1.14
0.61, 2.14
0.66
Household wealth quintiles
1871
1.11
0.95, 1.30
0.17 1799 1.09
0.91, 1.31
0.30
Fathers with higher education ×Household wealth quintiles 0.85
0.68, 1.07
0.15 0.69
0.51, 0.94
0.02
Mothers with higher education
×
Household wealth quintiles
1.10
0.83, 1.34
0.63 1.16
0.87, 1.54
0.28
Constant 1.36
0.27, 6.78
0.68 0.19
0.05, 0.77
0.02
Abdominal obesity
Fathers with higher education 1.01
0.56, 1.81
0.98 1.15
0.71, 1.87
0.54
Mothers with higher education 1.02
0.56, 1.86
0.95 0.79
0.46, 1.34
0.35
Household wealth
1871
0.94
0.72, 1.24
0.65 1799 0.87
0.70, 1.09
0.22
Fathers with higher education ×Household wealth quintiles 0.97
0.72, 1.31
0.85 0.79
0.51, 1.21
0.25
Mothers with higher education
×
Household wealth quintiles
1.01
0.67, 1.53
0.95 1.25
0.82, 1.91
0.28
Constant 0.02
0.00, 0.11
<0.01 0.04
0.00, 0.31
0.01
1Controlled for age (years), residence area (urban/rural), and school (which school the children belonged to).
Table 7. Interaction between parent education and household wealth quintiles on obesity and abdominal obesity risk separated by residence area.
Model for Urban Model for Rural
nOR 195% CI p-Value nOR 195% CI p-Value
Obesity
Fathers with higher education 0.73
0.50, 1.07
0.10 0.75
0.48, 1.17
0.18
Mothers with higher education 1.35
0.87, 2.08
0.16 1.19
0.80, 1.77
0.35
Household wealth quintiles
1645
0.99
0.85, 1.15
0.85 2025 1.13
1.01, 1.26
0.04
Fathers with higher education ×Household wealth quintiles 0.73
0.59, 0.90
0.01 0.93
0.72, 1.20
0.56
Mothers with higher education
×
Household wealth quintiles
1.24
0.89, 1.72
0.18 1.13
0.91, 1.42
0.25
Constant 0.58
0.18, 1.83
0.32 1.45
0.28, 7.56
0.64
Abdominal obesity
Fathers with higher education 0.87
0.60, 1.26
0.43 1.37
0.77, 2.45
0.26
Mothers with higher education 0.98
0.66, 1.45
0.90 0.81
0.45, 1.45
0.45
Household wealth
1645
0.91
0.64, 1.29
0.58 2025 0.88
0.70, 1.10
0.23
Fathers with higher education ×Household wealth quintiles 0.74
0.57, 0.96
0.03 1.07
0.70, 1.61
0.74
Mothers with higher education
×
Household wealth quintiles
1.26
0.87, 1.85
0.20 1.09
0.70, 1.68
0.68
Constant 0.02
0.00, 0.12
<0.01 0.01
0.00, 0.14
<0.01
1Controlled by age (years), sex (boy/girl) and school (which school the children belonged to).

Int. J. Environ. Res. Public Health 2018,15, 1754 8 of 12
Int. J. Environ. Res. Public Health 2018, 15, x 8 of 12
(A) (B)
(C) (D)
Figure 1. OR (95% CI) for father’s education level at different values of the household wealth quintile
among girls and among urban children. (A) OR (95% CI) between father education and girls’ obesity
risk; (B) OR (95% CI) between father education and girls’ abdominal obesity risk; (C) OR (95% CI)
between father education and urban children’s obesity risk; (D) OR (95% CI) between father education
and urban children’s abdominal obesity risk.
4. Discussion
This study was one of the first to report on the interaction between parents’ education level and
household wealth in childhood obesity risk. The interaction term was significant for household
wealth and father’s education. No interaction between household wealth and mother’s education
was found. In separate analysis, the interaction was statistically significant among girls for obesity
risk based on BMI, and among urban children for both obesity risk based on BMI and abdominal
obesity risk based on WHR.
4.1. Comparison with Prior Studies and Plausible Mechanisms
The present study examined how household wealth interacted with father’s education by
showing the association between father’s education and children’s obesity risk in different household
wealth quintiles. According to the results, when household wealth increased, OR for father’s
education decreased from higher than 1 to non-significant for girl’s obesity risk, from non-significant
to lower than 1 for urban children’s obesity risk, and from higher than 1 to non-significant for urban
children’s abdominal obesity risk. Change of the association, which is from positive to non-significant
or from non-significant to negative with the increase in household wealth, is partially consistent with
our hypothesis that education would not be negatively associated with obesity until income reaches
a certain level. The change may be explained by the “Obesity Kuznets curve” and nutrition transition
[16–18]: as income rises, people consume more calories and obesity rates increase; as income
continues to rise, personal health becomes a more valued asset to decrease obesity levels. During this
Figure 1.
OR (95% CI) for father’s education level at different values of the household wealth quintile
among girls and among urban children. (
A
) OR (95% CI) between father education and girls’ obesity
risk; (
B
) OR (95% CI) between father education and girls’ abdominal obesity risk; (
C
) OR (95% CI)
between father education and urban children’s obesity risk; (
D
) OR (95% CI) between father education
and urban children’s abdominal obesity risk.
4. Discussion
This study was one of the first to report on the interaction between parents’ education level and
household wealth in childhood obesity risk. The interaction term was significant for household wealth
and father’s education. No interaction between household wealth and mother’s education was found.
In separate analysis, the interaction was statistically significant among girls for obesity risk based on BMI,
and among urban children for both obesity risk based on BMI and abdominal obesity risk based on WHR.
4.1. Comparison with Prior Studies and Plausible Mechanisms
The present study examined how household wealth interacted with father’s education by showing
the association between father’s education and children’s obesity risk in different household wealth
quintiles. According to the results, when household wealth increased, OR for father’s education
decreased from higher than 1 to non-significant for girl’s obesity risk, from non-significant to
lower than 1 for urban children’s obesity risk, and from higher than 1 to non-significant for urban
children’s abdominal obesity risk. Change of the association, which is from positive to non-significant
or from non-significant to negative with the increase in household wealth, is partially consistent
with our hypothesis that education would not be negatively associated with obesity until income
reaches a certain level. The change may be explained by the “Obesity Kuznets curve” and nutrition

Int. J. Environ. Res. Public Health 2018,15, 1754 9 of 12
transition [
16
–
18
]: as income rises, people consume more calories and obesity rates increase; as income
continues to rise, personal health becomes a more valued asset to decrease obesity levels. During this
process, education is believed to act as a “social vaccine” and bring the decrease in obesity level when
income reaches a certain level [
7
]. A systematic review showed that the relationship between education
and obesity was modified by the country’s economic development level and that an inverse association
between educational attainment and obesity was more common in studies of higher-income countries
and a positive association was more common in lower-income countries [
7
]. A previous ecological
study also showed that culture development would not take effect to attenuate obesity prevalence
until a country’s economy developed to a certain level [26].
The sex-dependent result where only girls are significantly affected by their fathers’ education
level and household wealth is consistent with previous findings that indicate females are more
vulnerable to socioeconomic conditions [
27
–
30
]. For example, obesity risk between different ethnic
groups in the United States was greater in females [30]; and the relationship between education level
and obesity was stronger among women, both in developed countries and in developing countries [
16
].
In addition, it was reported that girls are more influenced by parental practice than boys, and parental
control was more significant among overweight or obese girls, which was not observed among boys of
the same weight groups [27,31].
Socioeconomic status (SES) should not be explored without considering the macro social
environment that individuals are exposed to [
14
,
32
]. In the present study, parents’ education level
interacted with household wealth to correlate with obesity risk in urban children but not in rural
children. There are several likely explanations. Firstly, rural areas tend to adhere to more traditional
lifestyles. Parents with different education levels may not necessarily indicate very diverse ways in
parenting practice. Secondly, food environment and built environment tend to have less diversity in
rural areas. Different levels of household wealth may not be able to provide as diverse a material
environment for children in rural areas as compared to in urban areas due to lack of options.
The disparity of parental roles on child health was reported by several previous studies [
33
–
35
].
The present study suggests that only higher education in fathers was linked with lower obesity risk
when household wealth increased. One possible reason for this is that the education–overweight
association is believed to be described by an inverted U-shaped curve [
7
,
36
]. As the nutrition transition
progresses in a country, education acts as a “social vaccine” against increasing risk of overweight or
obesity. In developing countries like China, paternal characteristics reflect the socioeconomic status
of the entire household, and the father’s parenting often determines the extent of support from the
family as a whole for the child’s weight loss and maintenance. For mothers, however, food provision
and child nutrition practices were central to the traditional constructs of mothering [
36
], causing the
downward turning point on the inverted U-shaped education-overweight curve to not be as apparent
as that for fathers when mothers place more focus on feeding their children and less on reducing
habits of overconsumption. Additionally, the effect of higher education levels of mothers on feeding
practice is offset or reversed by a decrease in the time spent on childcare when higher education is
often linked with more time-consuming and mentally demanding careers. Decreased attention from
career-driven mothers may leave children vulnerable to obesogenic environments or to being overfed
by grandparents [
37
,
38
]. Further qualitative studies are needed to delve deeper into the difference
between the father’s role and the mother ’s role in children’s obesity risk.
4.2. Implications for Intervention
The present study suggests, especially for countries undergoing nutrition transition Stages 1 through
4, that higher education in fathers may negatively relate to children’s obesity risk with increasing wealth.
Whereas many current intervention programs target the matriarchs, further longitudinal evaluation of
the effect of father’s education on children’s obesity risk may incorporate fathers into family intervention
plans. In addition, we call for nutrition interventionists to take the family socioeconomic context into
consideration when designing family-based interventions. The effects of nutritional interventions would

Int. J. Environ. Res. Public Health 2018,15, 1754 10 of 12
not persist without considering the families’ socioeconomic context. In this way, nutrition and parenting
based interventions could be more tailored to each family’s own unique situation.
The study boasts several strengths. Its main contribution lies in the interaction between parents’ education
and household wealth on shaping the socioeconomic context of obesity risk, which was not previously
explored among children. This went beyond simple uni-dimensional analyses of SES and childhood obesity
and examined how they intersect. In addition, considering the disease feature of obesity and the social features
of China, the present study went further to test the difference of the interaction between boys and girls and
between urban areas and rural areas. The processes giving rise to illness and health are often inherently
complex, especially for conditions such as obesity. This requires going beyond uni-dimensional analysis based
on only one economic class, gender, caste, or ethnicity [
13
]. Different from traditional literature on the impact
of social diversity on health, a fundamental hypothesis in the present study is that multiple dimensions interact
with each other to shape the distribution of obesity. Secondly, the obesity status of children was measured
by both BMI as obesity and by WHR as abdominal obesity, which were used to confirm the consistency of
associations. Furthermore, household wealth was measured by a series of income-related questions and family
affluence questions, which provided a more comprehensive outcome than simply studying household income
as a single measure—as seen in most previous studies.
The present study is not without limitations. Firstly, this was a cross-sectional study where all
co-variates were measured simultaneously, so causal inferences cannot be made. A longitudinal study
is warranted to resolve the effect of father’s education and household wealth on children’s obesity
risk. However, where this may be the main concern for studies among adults, the SES is measured
by parents’ characteristics when studying children, and therefore reverse causality and health selection,
would be rare [39]
. Secondly, while investigating the interaction between parental education and household
wealth, sex of children and residence area were also taken into account and presumed to influence the
interaction differently, such as for boys versus girls and for urban areas versus rural areas. Therefore,
analyses were performed separately by sex and by residence area in two models. A more consistent method
would be to group by sex separately in the separated analyses on rural versus urban living environment.
However, the small cell size brought by increased levels of stratification (household wealth, parental
education, sex, and residence area) was a major reason to not do so. Future studies with larger sample sizes
may help to improve the interaction analysis between these various factors.
5. Conclusions
Father’s education level interacts with household wealth to influence obesity among girls and
urban children in northeast China. With increase in household wealth, OR for the effect of father’s
education on children’s obesity risk decreased. No interaction effect was found between household
wealth and mother’s education.
Supplementary Materials:
The following are available online at http://www.mdpi.com/1660-4601/15/8/1754/
s1. Table S1: Interaction between parent education and household wealth categories on obesity and abdominal
obesity risk, Table S2: Interaction between parent education and household wealth categories on obesity and
abdominal obesity risk separated by sex, Table S3: Interaction between parent education and household wealth
categories on obesity and abdominal obesity risk separated by residence area, Figure S1: OR (95%CI) for parent
education level at different values of the household wealth categories among girls, Figure S2: OR (95%CI) for
parent education level at different values of the household wealth categories among urban residences.
Author Contributions:
Conceptualization, D.W. and Y.L.; Methodology, Y.M.; Validation, N.J.; Formal Analysis,
Y.L.; Investigation, S.S.; Data Curation, Q.F.; Writing—Original Draft Preparation, Y.L.; Writing—Review and
Editing, N.J., Y.M., S.S., Q.F., and D.W.
Funding: This research was funded by the National Natural Science Foundation of China (71774173).
Acknowledgments:
The authors wish to thank Amina Aitsi-Selmi for talking with us about interaction analysis.
The authors also thank Ji Qi, Peirong Gao, Min Ye, Xin Ai, Hong Wang, Dongman Ye, Jiapeng, Jie Li, Huayu Tang,
Yingying Jiang, Fei Zhu, Mengke Zhang, Lianting Zhuang, Minzhi Zhou, Liyuanxu, Ya Liu, Yue Xiao, and Chong
He for help in data collection.
Conflicts of Interest: The authors declare no conflict of interest.

Int. J. Environ. Res. Public Health 2018,15, 1754 11 of 12
References
1.
GBD 2015 Obesity Collaborators. Health Effects of Overweight and Obesity in 195 Countries over 25 Years.
N. Engl. J. Med. 2017,377, 13–27. [CrossRef] [PubMed]
2.
Abarca-Gómez, L.; Abdeen, Z.A.; Hamid, Z.A.; Abu-Rmeileh, N.M.; Acosta-Cazares, B.; Acuin, C.; Agyemang, C.
Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: A pooled
analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet
2017,390, 16–22. [CrossRef]
3.
Olshansky, S.J.; Passaro, D.J.; Hershow, R.C.; Layden, J.; Carnes, B.A.; Brody, J.; Ludwig, D.S. A potential
decline in life expectancy in the United States in the 21st century. N. Engl. J. Med.
2005
,352, 1138–1145.
[CrossRef] [PubMed]
4.
Elgar, F.J.; Xie, A.; Pförtner, T.K.; White, J.; Pickett, K.E. Relative deprivation and risk factors for obesity in
Canadian adolescents. Soc. Sci. Med. 2016,152, 111–118. [CrossRef] [PubMed]
5.
Bann, D.; Cooper, R.; Wills, A.K.; Adams, J.; Kuh, D. Socioeconomic position across life and body composition
in early old age: Findings from a British birth cohort study. J. Epidemiol. Community Health
2014
,68, 516–523.
[CrossRef] [PubMed]
6.
Dinsa, G.D.; Goryakin, Y.; Fumagalli, E.; Suhrcke, M. Obesity and socioeconomic status in developing
countries: A systematic review. Obes. Rev. 2012,13, 1067–1079. [CrossRef] [PubMed]
7.
Cohen, A.K.; Rai, M.; Rehkopf, D.H.; Abrams, B. Educational attainment and obesity: A systematic review.
Obes. Rev. 2013,14, 989–1005. [CrossRef] [PubMed]
8.
Ulijaszek, S.J. Socio-economic status, forms of capital and obesity. J. Gastrointest. Cancer
2012
,43, 3–7.
[CrossRef] [PubMed]
9.
Kant, A.K.; Graubard, B.I. Family income and education were related with 30-year time trends in dietary
and meal behaviors of American children and adolescents. J. Nutr.
2013
,143, 690–700. [CrossRef] [PubMed]
10.
Department of Health Public Health Research Consortium; Law, C.; Power, C.; Graham, H.; Merrick, D.
Obesity and health inequalities. Obes. Rev. 2007,8(Suppl. 1), 19–22. [CrossRef] [PubMed]
11.
Wolfson, J.A.; Gollust, S.E.; Niederdeppe, J.; Barry, C.L. The role of parents in public views of strategies to
address childhood obesity in the United States. Milbank Q. 2015,93, 73–111. [CrossRef] [PubMed]
12.
Pulsifer, D.P.; Lakhtakia, A.; Narkhede, M.S. Education and Health: Evaluating Theories and Evidence.
NBER Work. Pap. 2006,10, 129–138.
13.
Sen, G.; Iyer, A.; Mukherjee, C. A methodology to Analyse the Intersections of Social Inequalities in Health.
J. Hum. Dev. Capab. 2009,10, 397–415. [CrossRef]
14.
Aitsi-Selmi, A.; Chandola, T.; Friel, S.; Nouraei, R.; Shipley, M.J.; Marmot, M.G. Interaction between Education
and Household Wealth on the Risk of Obesity in Women in Egypt. PLoS ONE
2012
,7, e39507. [CrossRef]
[PubMed]
15.
Jeon, H.; Salinas, D.; Baker, D.P. Non-linear education gradient across the nutrition transition: Mothers’ overweight
and the population education transition. Public Health Nutr. 2015,18, 3172–3182. [CrossRef] [PubMed]
16.
Grecu, A.M.; Rotthoff, K.W. Economic growth and obesity: Findings of an Obesity Kuznets curve.
Appl. Econ. Lett. 2015,22, 539–543. [CrossRef]
17.
Popkin, B.M. An overview on the nutrition transition and its health implications: The Bellagio meeting.
Public Health Nutr. 2002,5, 93–103. [PubMed]
18.
Popkin, B.M. Will China’s nutrition transition overwhelm its health care system and slow economic growth?
Health Aff. 2008,27, 1064–1076. [CrossRef] [PubMed]
19.
Zhang, Y.; Zhao, J.; Chu, Z.; Zhou, J. Increasing prevalence of childhood overweight and obesity in a coastal
province in China. Pediatr. Obes. 2016,11, e22. [CrossRef] [PubMed]
20.
Lohman, T.J.; Roache, A.F.; Martorell, R. Anthropometric Standardization Reference Manual. Med. Sci. Sports Exerc.
1991,24.
21.
Ji, C.Y. Working Group on Obesity in China. Report on childhood obesity in China (1)—Body mass index
reference for screening overweight and obesity in Chinese school-age children. Biomed. Environ. Sci.
2005
,
18, 390–400. [PubMed]
22.
Lv, R.R.; Xu, Z.; Sun, Y.; Teng, L.X.; Gao, W. Reference norms of waisthip ratio for Chinese children and
adolescents living in Beijing. Chin. J. Sch. Health 2012,33, 750–751. (In Chinese)

Int. J. Environ. Res. Public Health 2018,15, 1754 12 of 12
23.
Rutstein, S.; Johnson, K. The DHS Wealth Index (DHS Comparative Report No. 6); ORC Macro: Calverton,
MD, USA, 2004.
24.
Vyas, S.; Kumaranayake, L. Constructing socio-economic status indices: How to use principal components
analysis. Health Policy Plan. 2006,21, 459–468. [CrossRef] [PubMed]
25.
Krefis, A.C.; Schwarz, N.G.; Nkrumah, B.; Acquah, S.; Loag, W.; Sarpong, N.; May, J. Principal component
analysis of socioeconomic factors and their association with malaria in children from the Ashanti Region,
Ghana. Malar. J. 2010,9, 201. [CrossRef] [PubMed]
26.
Liu, Y.; Trude, A.C.B.; Ma, Y.; Ding, N.; Wensel, C.R.; Reed, B.N.; Poirier, L.; Gittelsohn, J.; Wen, D. Is Obesity
an Ineluctable Consequence of Economic Growth? An Ecological Study. (Submitted).
27.
Sweeting, H.N. Gendered dimensions of obesity in childhood and adolescence. Nutr. J.
2008
,7. [CrossRef]
[PubMed]
28.
Kimm, S.Y.; Obarzanek, E.; Barton, B.A.; Aston, C.E.; Similo, S.L.; Morrison, J.A.; McMahon, R.P. Race,
socioeconomic status, and obesity in 9-to 10-year-old girls: The NHLBI Growth and Health Study.
Ann. Epidemiol. 1996,6, 266–275. [CrossRef]
29.
Jeffery, R.W. Socioeconomic status, ethnicity and obesity in women. Ann. Epidemiol.
1996
,6, 263–265.
[CrossRef]
30.
Wang, Y.; Zhang, Q. Are American children and adolescents of low socioeconomic status at increased risk
of obesity? Changes in the association between overweight and family income between 1971 and 2002.
Am. J. Clin. Nutr. 2006,84, 707–716. [CrossRef] [PubMed]
31.
Birch, L.L.; Fisher, J.O. Development of eating behaviors among children and adolescents. Pediatrics
1998
,
101, 539–549. [PubMed]
32.
Monteiro, C.A.; Moura, E.C.; Conde, W.L.; Popkin, B.M. Socioeconomic status and obesity in adult
populations of developing countries: A review. Bull. World Health Organ. 2004,82, 940–946. [PubMed]
33.
Akinyemiju, T.F.; Zhao, X.; Sakhuja, S.; Jolly, P. Life-course socio-economic status and adult BMI in Ghana;
analysis of the WHO study on global ageing and adult health (SAGE). Int. J. Equity Health
2016
,15, 185.
[CrossRef] [PubMed]
34.
Ball, K.; Mishra, G.D. Whose socioeconomic status influences a woman’s obesity risk: Her mother’s,
her father’s, or her own? Int. J. Epidemiol. 2006,35, 131–138. [CrossRef] [PubMed]
35.
Stein, R.I.; Epstein, L.H.; Raynor, H.A.; Kilanowski, C.K.; Paluch, R.A. The influence of parenting change on
pediatric weight control. Obes. Res. 2005,13, 1749–1755. [CrossRef] [PubMed]
36.
Warin, M.; Turner, K.; Moore, V.; Davies, M. Bodies, mothers and identities: Rethinking obesity and the BMI.
Sociol. Health Illn. 2008,30, 97–111. [CrossRef] [PubMed]
37.
Ziolguest, K.M.; Dunifon, R.E.; Kalil, A. Parental Employment and Children’s Body Weight: Mothers, Others,
and Mechanisms. Soc. Sci. Med. 2013,95, 52–59. [CrossRef] [PubMed]
38.
Li, B.; Adab, P.; Cheng, K.K. The role of grandparents in childhood obesity in China-evidence from a mixed
methods study. Int. J. Behav. Nutr. Phys. Act. 2015,12, 91. [CrossRef] [PubMed]
39.
Wang, Y. Cross-national comparison of childhood obesity: The epidemic and the relationship between
obesity and socioeconomic status. Int. J. Epidemiol. 2001,30, 1129–1136. [CrossRef] [PubMed]
©
2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).