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Correlates of body mass index among primary school children in Ho Chi Minh City, Vietnam
Public Health 181 (2020) 65e72
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Original Research
Correlates of body mass index among primary school children in Ho Chi Minh City, Vietnam N.K. Pham a, A. Sepehri b, *, T.M. Le c, V.T. Tran a a
School of Economics, University of Economics Ho Chi Minh City, Ho Chi Minh City, Viet Nam Department of Economics, University of Manitoba, Winnipeg, Manitoba R3T 2N2, Canada c Development Economics, University of Economics, Ho Chi Minh City, Viet Nam b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 July 2019 Received in revised form 3 December 2019 Accepted 9 December 2019
Objectives: To document the prevalence of overweight and obesity and examine associated risk factors. Study design: A cross-sectional survey was conducted in 16 primary public schools in eight districts of Ho Chi Minh City in 2016. A multistage clustering sampling method was used to collect a sample of 1806 pupils attending the first, second, and third grades (7e9 years). Methods: Age- and sex-adjusted body mass index (BMI) status was defined using International Obesity Taskforce cut-offs. Ordered probit regression models were used to assess the association between child BMI and its socio-economic and demographic risk factors. The model was estimated separately for boys and girls to assess the extent to which the socio-economic gradients in BMI vary by gender. Results: The prevalence of obesity among boys was twice the rate for girls (24.7 vs 12.3%). The prevalence of overweight and obesity were also higher among pupils attending schools located in urban districts than in semi-rural districts. Gender, household wealth, the frequency of having breakfast at home, parental body weight, and school location were strong predictors of child BMI status. The protective effect of having breakfast more frequently at home against the risk of overweight/obesity was more pronounced in girls than in boys. Father's body weight and child BMI were more strongly associated with boys from poorer households than boys from wealthier households, while the differences were not significant for girls. Conclusions: The high prevalence of childhood overweight and obesity indicates an urgent need for more gender-specific, effective intervention, and prevention programs. © 2019 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
Keywords: Child obesity Risk factors Primary school students Ho Chi Minh City
Introduction During the past three decades, the prevalence of childhood overweight and obesity has risen greatly worldwide, with marked variations across countries in the levels and trends.1 Although the prevalence of childhood overweight and obesity in developing countries remains far below those in developed countries, it is growing more rapidly in developing countries than in developed countries.1 Childhood obesity is emerging as a public health issue in many parts of Asia,2 particularly in large urban centers, where rapid socio-economic development combined with integration
* Corresponding author. Tel.: þ(204) 474 6241; fax: þ(204) 474 7681. E-mail addresses: [email protected] (N.K. Pham), Ardeshir.Sepehri@ umanitoba.ca (A. Sepehri), [email protected] (T.M. Le), [email protected] (V.T. Tran).
into the global economy, and the expansion of fast-food chains have contributed to changes in diets and activity patterns of both adults and children.1,3 Childhood obesity is linked to serious short- and long-term adverse health complications extending to adulthood. These adverse medical and psychosocial outcomes are extensive, including both medical comorbidities, such as asthma, high blood pressure, high cholesterol, type 2 diabetes, and sleep apnea, as well as psychological, social, and behavioral consequences resulting from body image-related problems, such as poor self-esteem, social isolation and discrimination, and depression.4e6 The implications of these adverse health effects for quality of life, productivity, and health care costs are staggering, particularly in countries where the health care system is already overburdened.7 The rapid socio-economic development combined with increasing economic and cultural integration into the world
https://doi.org/10.1016/j.puhe.2019.12.007 0033-3506/© 2019 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
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economy during the past three decades has led to a transition in the nutritional status of both children and adults in Vietnam.8 The rapid increase in the prevalence of overweight and obesity in children is emerging as a public health issue, particularly in large urban centers, such as Ho Chi Minh City. Prevalence of stunting and wasting among primary and high school children in Ho Chi Minh City declined from 24.2% in 2002e2004 to 14.3% in 2009 while the prevalence of overweight (obesity) increased from 11.6% (4%) to 21.9% (7.3%) over the same period of time.9 In recent years, a small but growing number of studies have documented the prevalence of overweight and obesity among Vietnam's school children and adolescents and examined its associated risk factors.10e14 However, much of this literature has focused on a selected set of bivariate association between overweight/obesity and sociodemographic factors. Moreover, it remains unclear, both in the context of bivariate and multivariate analysis, the extent to which the reported associations between risk factors and children's body mass index (BMI) status are mediated by gender and household wealth, the two most potent risk factors. The primary objective of this study is to fill this gap by providing a systematic analysis of child-, household- and community-specific factors associated with BMIs among primary school students (7e9 years) in Ho Chi Minh City. More specifically, using an ordered probit model, we assess the association between a wide range of child-, household-, and school-specific factors and children's BMI status. Knowledge of BMI status among primary school students, its associated risk factors, and gender-specific differences in these risk factors are essential for designing any effective interventions and assessing their effectiveness. Methods Study design and setting The data for this study were drawn from a cross-sectional study of 1806 pupils (967 girls and 839 boys) attending the first, second, and third grade (7e9 years) of 16 primary public schools from eight districts of Ho Chi Minh City between February and May 2016. A multistage clustering sampling method was used to select districts, primary schools, and three classes within each school. In the first stage, districts were selected by stratified random sampling with two strata and with probability proportional to the number of primary students. The first stratum encompasses nineteen urban districts, of which six were sampled; the second stratum includes five rural districts, of which two were chosen. In the second stage, two public schools within a sampled district were chosen by simple random sampling. We had sixteen sampled schools. In the third stage, classes within a sampled school were chosen by stratified random sampling: two classes within each grade from first to third were drawn equally. Finally, we had 96 sampled classes. All students from each randomly selected class were then invited to participate in the study. The study gained ethics approval from the Human Research Ethics Committee of the University of Economics Ho Chi Minh City and the Department of Education and Training of Ho Chi Minh City. Anthropometric measurements Body weight was measured to the nearest 0.10 kg using Omron HN289 digital personal scale while the subjects were wearing a light school uniform and no shoes. Height was measured to the nearest 0.1 cm using a Stature Meter tape. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared (kg/ m2), and age- and sex-specific cut-offs as proposed by the International Obesity Taskforce were used to define BMI status.
Questionnaires Self-administered questionnaires were used to collect information from parents, with the questionnaire covering height and weight information and a wide range of demographic and socioeconomic information. A total of 3483 pupils were approached, of whom 3196 (92%) agreed to participate in the study, and 3111 subjects completed and returned surveys. Of those who returned surveys, less than one third (34%) missed anthropometrical data on parents, and 9.5% missed data on children's eating and physical inactivity behaviors. Once we matched children's BMI status with other variables used for this study, the sample size was reduced to 1806 respondents. Children with missing data on parental BMI status were not significantly different in their BMI z-scores and socio-economic and demographic profiles than those included in the study. Study variables The outcome was a four-point ordinal indicator: (1) underweight; (2) healthy weight; (3) overweight; and (4) obese. Consistent with the previous literature, the main covariates of interest included a wide range of observed child-, household- and community-level variables related to socio-economic, demographic, activity-level, and eating profiles of a child. Childspecific variables included age, gender, having a sibling, the frequency of having breakfast at home, the frequency of having dinner at home, and time spent watching TV and/or playing video games. Age is presented by a continuous variable and gender by a dummy variable that takes the value of one when the child is male, and zero if female. Having a sibling is represented by a dummy that takes the value of one when the child has a sibling, and zero otherwise. Time spent on watching TV and playing video games are aggregated to avoid the high correlation between these two activities, and it is grouped into three groups: less than 2 h per weekday, 2e3 h per weekday, and 4 and more hours per weekday. Household-level variables included household wealth, mother's education, and parental body weight. Household wealth quintiles were calculated through principal component analysis of household ownership of a wide range of assets, including, radio, color television, CD/DVD player, telephone, air conditioner, computer, refrigerator, gas stove, microwave oven, washing machine, bicycle, motorbike and automobile, as well as the type of dwelling and having a servant or not.15 Due to the high correlation between mother's education and father's education, we chose to include only the mother's education. Parental BMIs were calculated using the self-reported weights and heights and were presented by a binary variable that takes the value of one if overweight or obese (overweight or obese classified as BMI 25 kg/m2). The frequency of having breakfast at home was presented by three dummy variables: none, less frequently (1e4 times per week), frequently (5e6 times per week), and always. Since the differences in estimated coefficients on having breakfast frequently and always at home were not statistically significant, we chose to aggregate ‘frequently’ with ‘always’ into one category. The frequency of having dinner at home was presented by a dummy variable that takes the value of one when the child had dinner frequently at home (5 times per week), and zero if the child had dinner less frequently at home (<5 times per week). The community-level variable is the school location and is represented by urban district and semi-rural district (the reference category). To assess the extent to which the association between children's BMI and sociodemographic factors varies across household wealth quintiles, we used a set of interaction terms involving these sociodemographic factors and each household wealth quintile.
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Statistical analysis Since the dependent variable was a four-category ordinal outcome, an ordered probit model was used to examine the association between child BMI status and risk factors.16 Underlying the ordered probit model is the assumption that the relationship between each pair of outcome categories is the same (the parallel regression assumption).16 This assumption was tested using an approximate likelihood ratio test, and the test result showed that the parallel regression assumption cannot' be rejected (Х 2 ¼ 38.46, P ¼ 0.200). For assessing the potential gender differences in the socio-economic gradients in child BMI status, the regression model was estimated separately for both females and males. Stata version 14.1 (Stata Corp., College Station, TX) was used for all data analysis. Since the data uses a three-stage stratified cluster sampling methodology, the clustering of responses by the primary sampling unit (district) raises the possibility of intradistrict correlation. For controlling cluster sampling, the cluster-robust standard errors were computed using appropriate ‘svy’ commands. We also applied appropriate sampling weights to produce unbiased population estimates. Results Descriptive socio-economic and demographic characteristics of the sample population are provided in Table 1. The overall prevalence of underweight, overweight, and obesity was 5.1, 30, and 18.2%, respectively (Table 2). The prevalence of overweight and obesity was higher in boys than in girls, higher among children whose parents were overweight or obese, and higher among pupils attending schools located in urban districts than in semi-rural districts. The prevalence of overweight and obesity was also greater in children from wealthy households, although the difference was not significant for obesity. However, both parental body weight and the location of the school gradient in children's BMI status mask a wide variation in these gradients across household wealth quintiles (Figs. 1 and 2). Having an overweight or obese father significantly increased the prevalence of obesity among children from the bottom two wealth quintiles by 91.9% (13.6 vs 26.1%; P < 0.001) and by 38.8% (23.6 vs 17%; P ¼ 0.033) for the children from the top wealth quintiles. The wealth gradient in the association between parental body weight and the prevalence of overweight was less pronounced: having an overweight or obese father increased the prevalence of overweight by 23.3% (33.9 vs 27.5%; P ¼ 0.102) among children from the bottom two wealth quintiles and by 3.2% (34.2 vs 33%; P ¼ 0.730) among children from the top two wealth quintiles. Similarly, attending a school located in an urban district increased significantly the prevalence of overweight by 49.8% (31.9 vs 21.3%; P < 0.001) and obesity by 42.6% (18.4 vs 12.9%; P ¼ 0.018) among children from the bottom two wealth quintiles. By contrast, among children from the top wealth quintiles, school location was not significantly associated with the prevalence of overweight (34 vs 26.1%; P ¼ 0.159) and obesity (19.2 vs 21.2%; P ¼ 0.53). Estimated coefficients of the ordered probit model are presented in the first column of Table 3 and the marginal effects for each outcome category in columns 2e4. Estimated marginal effects show the change in likelihood of being underweight, healthy weight, overweight, or obese when the value of a covariate increases by one unit. The results indicate that child BMI status was, as expected, positively associated with a child's gender, household wealth, mother's education, parental body weight, and school location, and negatively with child's age, having any siblings, and the frequency of having breakfast at home. Boys were 13% less likely to be at a
67
Table 1 Sociodemographic characteristics of the primary school children, Ho Chi Minh City, 2016 (unweighted). Variable
n (%)
Body mass index Underweight 141 Healthy weight 1287 Overweight 828 Obese 508 Sex Male 1822 Female 1437 Age 7 years 1071 8 years 851 9 years 837 Siblings None 653 At least one 2106 Time spent watching TV or playing video games (times per weekday) <2 614 2e3 747 4 1279 Wealth quintiles (principal components of asset index) Quintile 1 (poorest) 491 Quintile 2 558 Quintile 3 579 Quintile 4 574 Quintile 5 (richest) 557 Mother's education High school or less 1444 Postsecondary 982 Mother's body mass index Healthy weight 2046 Overweight or obese 171 Father's body mass index Healthy weight 1554 Overweight or obese 569 Frequency of having breakfast at home (times per week) None 227 1e3 997 4 1345 Frequency of having dinner at home (times per week) 4 2359 5 251 School location Urban district 2029 Semi-rural district 730
(5.1) (46.7) (30.0) (18.2) (47.9) (52.1) (38.8) (30.4) (30.3) (23.7) (76.3) (23.3) (28.3) (48.4) (17.8) (20.2) (21.0) (20.8) (20.2) (59.5) (40.5) (92.3) (7.7) (73.2) (26.8) (8.8) (38.8) (52.4) (90.4) (9.6) (73.5) (26.5)
healthy weight and 5% and 11% more likely to be overweight and obese, respectively, than girls. Living in a poor or near-poor household increased the likelihood of being underweight by 5% and reduced the likelihood of being overweight and obese by 8% and 10%, respectively. The results for the underweight children should be interpreted with care considering the small number of observations (n ¼ 141). Having an overweight or obese father increased child BMI more among children from poorer households than among those from wealthier households, with living in a poor household, increasing the likelihood of being obese by as much as 21%. School location was a strong predictor of BMI only for children from poor and near-poor households, with children from urban districts being less likely to be at a healthy weight (10e14%) and more likely to be overweight (3e4%) and obese (9e12%) than their counterparts from semi-rural districts. The reported results in Table 3 may mask gender differences in the association between children's BMI status and the sociodemographic risk factors. To assess the potential gender differences in the socio-economic gradients in child BMI status, the regression model was estimated separately for both females and males. Table 4 provides the results separately for girls and boys due to space limitations, the coefficients of covariates with
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Table 2 Prevalence of underweight, overweight, and obesity among primary school children by gender, wealth quintile, parental body weight, and school location, Ho Chi Minh City, 2016 (unweighted). Characteristics
Underweight
All Males Females Parental body weight Mother's BMI status Healthy weight Overweight or obese Father's BMI status Healthy weight Overweight or obese Household wealth Quintiles 1, 2, 3 Quintiles 4, 5 School location Urban district Semi-rural district
Overweight
Obese
Prevalence (%) (95% CI)
P-valuea
Prevalence (%) (95% CI)
P-valuea
Prevalence (%) (95% CI)
P-valuea
5.1 (4.3, 5.9) 2.9 (2.0, 3.8) 7.2 (5.8, 8.5)
0.000
30.0 (28.3, 31.7) 32.2 (29.6, 34.7) 28.0 (25.7, 30.4)
0.019
18.2 (16.8, 19.7) 24.7 (22.3, 27.0) 12.3 (10.6, 14.0)
0.000
0.039
0.000
0.535 5.2 (4.2, 6.1) 4.1 (1.1, 7.1)
29.9 (27.8, 31.8) 37.4 (30.1, 44.8)
17.2 (15.5, 18.8) 28.1 (21.3, 34.9)
0.041 5.7 (4.6, 6.9) 3.5 (2.0, 5.0)
0.09
0.000
29.7 (27.4, 32.0) 33.6 (29.7, 37.5)
15.6 (13.8, 17.4) 24.6 (21.1, 28.2)
0.000 6.6 (5.4, 7.8) 3.0 (2.0, 4.0)
0.006
0.166
28.0 (25.8, 30.2) 32.9 (30.1, 35.6)
17.4 (15.5, 19.2) 19.5 (17.1, 21.8)
0.000 3.9 (3.1, 4.8) 8.3 (6.3, 10.4)
0.000
0.033
32.3 (30.3, 34.4) 23.6 (20.5, 26.6)
19.2 (17.5, 20.9) 15.6 (13.0, 18.3)
BMI, body mass index; CI, confidence intervals. a Chi-square test to compare the prevalence of underweight, overweight and obesity across sociodemographic characteristics.
40 Overweight
Obese
35
30
(%)
25
20
15
10
5
0 Bottom two wealth quintiles
Top two wealth quintiles
Bottom two wealth quintiles
Healthy weight
Top two wealth quintiles
Overweight/obese Father's body weight
Fig. 1. Prevalence of child overweight/obesity by wealth quintiles and father's body weight.
significant gender differences are shown for only two of the four outcome categories (overweight and obese). There was a strong negative association between BMI status and the frequency of having breakfast at home among girls: having breakfast at home four or more times per week reduced the likelihood of being overweight and obese by 10% and 12%, respectively. By contrast,
there was no statistically significant association between BMI status and the frequency of having breakfast at home among boys. The positive association between a child's BMI status and father's body weight was stronger among boys from poor and near-poor households than among boys from rich households, and the differences were significant at the 5% level. Among boys from poor
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69
40 Overweight
Obese
35
30
(%)
25
20
15
10
5
0 Bottom two wealth quintiles
Top two wealth quintiles
Semi-rural
Bottom two wealth quintiles
Top two wealth quintiles
Urban
Fig. 2. Prevalence of child overweight/obesity by school location and wealth quintiles.
and near-poor households, having an overweight or obese father increased the likelihood of being obese by 24%. Similarly, the positive association between a child's BMI status and father's body weight was stronger among girls from poorer households than girls from wealthier households, but the differences were not statistically significant. Discussion The overall prevalence of overweight and obesity was 30 and 18.2%, respectively, with the prevalence of obesity among boys being twice the rate for girls (24.7 vs 12.3%). Gender, household wealth, the frequency of having breakfast at home, parental body weight, and school location were strong predictors of child BMI status. The prevalence of overweight and obesity was also higher among pupils attending schools located in urban districts than in semi-rural districts. School location was a strong predictor of BMI only for children from less wealthy households, with children from urban districts being less likely to be at a healthy weight (9.8%) and more likely to be overweight and obese (4% and 8%) than their counterparts from semi-rural districts. Father's body weight and child BMI were also more strongly associated with boys from poorer households than in boys from wealthier households, while the differences were not significant for girls. These findings indicate the complex pathways linking children's BMI and household wealth. The finding that a child's BMI status was positively associated with the male gender is similar to the results of other studies in Vietnam10e12 and elsewhere in the region,6,17e19 where the prevalence of overweight and obesity among children and adolescents
has been found to be greater in males than in females. These gender differences in BMI status might reflect culture-bound conventions and roles governing body image and dietary practices.6 With a large body size for boys being valued as a sign of strength and physical dominance, parents and grandparents may encourage boys to eat more to gain weight and height.18,20,21 The evidence for the association between childhood overweight and obesity and the number of siblings is mixed, some documenting a negative association whereby children with siblings being more likely to have a lower BMI than those with no siblings,22,23 while others report no significant association between child BMI and the number of siblings.24,25 Additional children may serve as a stimulus for child-to-child interactions and increase the amount of time spent on physical activities.26,27 Having siblings may also dilute household resources, including parental time and attention with mixed consequences for child BMI. While resource dilution may lead to less food per child, particularly in poorer households, time dilution may encourage parents to adopt convenient routines such as longer time allowed for viewing TV and playing video games, which could lead to a higher BMI.28 The finding that children from wealthier households were more likely to be heavier than those from less wealthy households is consistent with studies in Vietnam and elsewhere in developing countries.2,10,12,19 Greater financial resources allow wealthier households to purchase ample foods, modern recreational amenities, such as computers and TVs, and provide their children with more generous pocket money that may be used to purchase energy-dense snacks. Our finding that the association between children's BMI and urban-rural location of school was only
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Table 3 Ordered probit regression results for the BMI status of primary school children, Ho Chi Minh City. Independent variable
Coeff. (95% CI)
Gender Female (ref) 1.00 Male 0.40 (0.29, 0.51)*** Age 0.06 (0.12, 0.00)** Siblings None (ref) 1.00 At least one 0.13 (0.26, 0.01)** Having breakfast at home None (ref) 1.00 1e3 times/week 0.23 (0.44, 0.01)** 4 times/week 0.29 (0.50, 0.08)*** Having dinner at home 4 times/week (ref) 1.00 >4 times/week 0.10 (0.31, 0.10) TV viewing/video game playing on weekdays (h/week day) <2 (ref) 1.00 23 0.03 (0.18, 0.12) 4 0.13 (0.00, 0.27)* Household wealth Quintile 1 0.44 (0.77, 0.12)*** Quintile 2 0.45 (0.77, 0.13)*** Quintile 3 0.27 (0.60, 0.05)* Quintile 4 0.01 (0.17, 0.20) Quintiles 5 (ref) 1.00 Mother's education High school or less (ref) 1.00 Postsecondary 0.11 (0.01, 0.23)* Mother's BMI statusa Normal weight (ref) Overweight/obese 0.46 (0.27, 0.65)*** Interaction: Father's BMI status wealth quintile Healthy weight quintile 1 (ref) 1.00 Overweight/obese quintile 1 0.65 (0.32, 0.99)*** Healthy weight quintile 2 (ref) 1.00 Overweight/obese quintile 2 0.29 (0.00, 0.59)* Healthy weight quintile 3 (ref) 1.00 Overweight/obese quintile 3 0.25 (0.02, 0.53)* Healthy weight quintile 4 (ref) 1.00 Overweight/obese quintile 4 0.19 (0.07, 0.44) Healthy weight quintile 5 (ref) 1.00 Overweight/obese quintile 5 0.19 (0.05, 0.43) Interaction: School location wealth quintile Rural/semi-rural * quintile 1 (ref) 1.00 Urban quintile 1 0.30 (0.00, 0.61)* Rural/semi-rural * quintile 2 (ref) 1.00 Urban quintile 2 0.42 (0.15, 0.69)*** Rural/semi-rural quintile 3 (ref) 1.00 Urban quintile 3 0.20 (0.07, 0.46) Rural/semi-rural quintiles 4, 5b (ref) 1.00 b Urban quintiles 4, 5 0.01 (0.22, 0.21)
Marginal effects Underweight
Healthy weight
Overweight
Obese
Coeff. (95% CI)
Coeff. (95% CI)
Coeff. (95% CI)
Coeff. (95% CI)
1.00 1.00 1.00 0.03 (0.04, 0.02)*** 0.13 (0.16, 0.09)*** 0.05 (0.04, 0.07)*** 0.01 (0.00, 0.01)** 0.02 (0.00, 0.04)** 0.01 (0.02, 0.00)**
1.00 0.11 (0.08, 0.13)*** 0.02 (0.03, 0.00)**
1.00 0.01 (0.00, 0.02)**
1.00 0.04 (0.00, 0.08)**
1.00 0.02 (0.03, 0.00)**
1.00 0.04 (0.07, 0.00)**
1.00 0.02 (0.00, 0.04)* 0.02 (0.01, 0.04)***
1.00 0.07 (0.01, 0.14)** 0.09 (0.03, 0.16)***
1.00 1.00 0.03 (0.06, 0.00)** 0.06 (0.11, 0.00)** 0.04 (0.07, 0.01)*** 0.08 (0.14, 0.01)***
1.00 0.01 (0.01, 0.02)
1.00 0.03 (0.03, 0.10)
1.00 0.01 (0.04, 0.01)
1.00 0.3 (0.09, 0.03)
1.00 0.00 (0.01, 0.02) 0.01 (0.02, 0.00)*
1.00 0.01 (0.04, 0.06) 0.04 (0.09, 0.00)*
1.00 0.00 (0.03, 0.02) 0.02 (0.00, 0.04)*
1.00 0.01 (0.05, 0.03) 0.04 (0.00, 0.07)*
0.05 0.05 0.03 0.00 1.00
0.13 0.13 0.08 0.00 1.00
0.08 0.08 0.04 0.00 1.00
0.10 010 0.07 0.00 1.00
(0.01, 0.10)** (0.01, 0.06)** (0.00, 0.06) (0.02, 0.01)
1.00 0.01 (0.02, 0.00)*
(0.05, 0.20)*** (0.05, 0.21)*** (0.01, 0.18)* (0.06, 0.06)
1.00 0.04 (0.07, 0.00)*
(0.14, (0.14, (0.10, (0.02,
0.02)** 0.01)** 0.01) 0.03)
1.00 0.02 (0.00, 0.03)*
(0.16, (0.16, (0.14, (0.05,
0.04)*** 0.04)*** 0.01)* 0.05)
1.00 0.03 (0.00, 0.06)*
1.00 1.00 1.00 0.03 (0.04, 0.02)*** 0.15 (0.21, 0.09)*** 0.04 (0.03, 0.05)***
1.00 0.21 (0.08, 0.34)***
1.00 0.03 1.00 0.02 1.00 0.01 1.00 0.01 1.00 0.01
1.00 (0.04, 0.02)*** 0.21 1.00 (0.03, 0.00)** 0.10 1.00 (0.03, 0.00)* 0.08 1.00 (0.03, 0.00)* 0.06 1.00 (0.03, 0.00)* 0.06
(0.14, 0.02)
1.00 0.21 1.00 0.09 1.00 0.07 1.00 0.05 1.00 0.05
1.00 0.02 1.00 0.03 1.00 0.01 1.00 0.00
1.00 0.10 1.00 (0.04, 0.01)*** 0.14 1.00 (0.03, 0.00) 0.06 1.00 (0.02, 0.02) 0.00
1.00 0.03 1.00 (0.23, 0.05)*** 0.04 1.00 (0.15, 0.02) 0.02 1.00 (0.07, 0.07) 0.00
(0.04, 0.00)**
(0.31, 0.11)*** (0.20, 0.00)* (0.17, 0.01)* (0.14, 0.02)
(0.20, 0.00)*
1.00 0.03 1.00 0.03 1.00 0.03 1.00 0.02 1.00 0.02
(0.01, 0.05)*** (0.01, 0.05)*** (0.01, 0.05)*** (0.01, 0.05)* (0.00, 0.04)*
(0.01, 0.05)*** (0.02, 0.05)*** (0.00, 0.05)* (0.03, 0.03)
1.00 0.09 1.00 0.12 1.00 0.05 1.00 0.00
(0.09, 0.34)*** (0.01, 0.18)* (0.01, 0.16)* (0.02, 0.13) (0.02, 0.12)
(0.01, 0.19)* (0.03, 0.21)*** (0.02, 0.13) (0.06, 0.05)
CI, confidence interval; Coeff., coefficients; BMI, body mass index; marginal effects, the change in likelihood of being either underweight, healthy weight, overweight, or obese when the value of a covariate increases by one unit; *P < 0.10, **P < 0.05, ***P < 0.01. a Given the small number of observations on overweight and obese mothers, only father's BMI status was interacted with wealth quintiles. b Given the small number of observations on semi-rural households in the richest wealth quintile (62), the richest top two quintiles were aggregated into one single category.
significant for children from poorer households may reflect an increased affinity toward a Western type of lifestyle among the wealthy in general and the fascination of urban children and adolescents with Western-style food outlets in particular.2 Similarly, the finding that having an overweight or obese father dampened the wealth gradient in children's BMI status may reflect the influence of parental patterns of dietary practice and physical activity over those of their children.29e31 The finding that the protective effect of having breakfast more frequently at home against the risk of overweight and
obesity was more pronounced in girls than in boys may reflect the culture-bound parental dietary practice and preferences for girls to be ‘thinner’ and for boys to be ‘bigger.’6,18,20 As parents and grandparents encourage boys to eat more, a bigger breakfast may not reduce food intake later in the day and could simply lead to greater daily energy intake. In fact, both acute feeding experiments and intraindividual analysis have indicated that whole day energy intake increases proportionally to breakfast calories, while other meals of the day remain largely unchanged.32,33
N.K. Pham et al. / Public Health 181 (2020) 65e72
71
Table 4 Ordered probit regression results for a selective set of covariates by gender, Ho Chi Minh City.a Independent variable
Girls (n ¼ 967)
Boys (n ¼ 839)
Marginal effects
Coeff. (95% CI)
Coeff. (95% CI)
Overweight
Having breakfast at home None 9 ref 1 1e3 times/week 0.48 4 times/week 0.57 Interaction: Father's BMI status Healthy weight quintiles 1, 1 2b (ref) 0.31 Overweight/ obese quintiles 1, 2b Healthy weight quintile 3 1 (ref) Overweight/obese quintile 0.17 3 Healthy weight quintile 4 1 (ref) Overweight/obese quintile 0.25 4 Healthy weight quintile 5 1 (ref) Overweight/obese quintile 0.25 5
Obese
Girls
Boys
Girls
Boys
Coeff. (95% CI)
Coeff. (95% CI)
Coeff. (95% CI)
Coeff. (95% CI)
1 1 1 1 1 (0.76, 0.19)*** 0.03 (0.28, 0.34) 0.09 (0.14, 0.03)*** 0.00 (0.02, 0.03) 0.10 (0.15, 0.04)*** 0.01 (0.09, 0.11) (0.85, 0.29)*** 0.00 (0.30, 0.30) 0.10 (0.15, 0.05)*** 0.00 (0.02, 0.02) 0.12 (0.19, 0.06)*** 0.00 (0.09, 0.09) 1 (0.00, 0.61)*
(0.09, 0.59)
0.11 (0.27, 0.49)
1
0.01 (0.05, 0.03) 1
0.03 (0.03, 0.09) 1
0.09 (0.29, 0.47) 1
(0.06, 0.56)
1
0.36 (0.03, 0.75)* 1
1 0.05 (0.01, 0.09)**
0.66 (0.32, 1.00)*** 1
(0.21, 0.55)
1
1
1
1
0.04 (0.05, 0.14)
0.01 (0.01, 0.03)
0.12 (0.02, 0.27)* 1
0.06 (0.03, 0.15) 1
0.01 (0.01, 0.03)
0.24 (0.11, 0.37)*** 1
1
1 0.04 (0.00, 0.08)*
0.07 (0.01, 0.16)*
0.01 (0.00, 0.02)
0.04 (0.01, 0.08)*
1
0.03 (0.09, 0.15) 1
0.06 (0.20, 0.14)
0.03 (0.08, 0.16)
CI, confidence interval; Coeff., coefficients; BMI, body mass index; marginal effects, the change in likelihood of being either underweight, healthy weight, overweight, or obese when the value of a covariate increases by one unit; *P < 0.10, **P < 0.05, ***P < 0.01. a Due to space limitation, only coefficients of covariates with significant gender differences are shown in Table 4. Other covariates correspond to those listed in Table 3. b Given the small number of observations on children with overweight/obese father in the poorest wealth quintile, the bottom two wealth quintiles were aggregated into one single category.
For assessing the robustness of results, the multivariate regression models were reestimated, while excluding parental BMI status for which data were available only for a subset of the sample. The results, available on request, are very similar. Some caveats are in order. First, like any cross-section study, our findings may be subject to omitted variable bias resulting from unobserved individual-, household- and community-specific characteristics. Furthermore, it is not possible to demonstrate a cause-and-effect relationship between child BMI status and its covariates. Second, the self-reported anthropometrical data on parents are likely to be subject to misreporting. However, the measurement error due to misreporting is likely to be smaller when BMI categories are used, as is the case in this study.34 Third, although the study sample of 1806 was likely to be representative of all grade one to three pupils attending public schools in Ho Chi Minh City (n ¼ 318,842), the findings cannot be generalized to other parts of Vietnam, particularly, the less prosperous regions. Despite these limitations, our results document a high prevalence rate of overweight and obesity among primary school children and shed light on gender and wealth differences in the association between child BMI status and its commonly identified risk factors. The emergence of childhood obesity as a public health issue has indicated an urgent need for more gender-specific, effective intervention, and prevention programs before it becomes more severe later in life. Author statements Acknowledgements The authors would like to thank the anonymous referees for their insightful comments and suggestions.
Ethical approval The study gained ethics approval from the Human Research Ethics Committee of the University of Economics Ho Chi Minh City and from the Department of Education and Training of Ho Chi Minh City. Funding This work was supported by the Rockefeller Foundation [#THS 348, 2011]. Competing interests The authors declare that they have no conflict of interest. Author contributions Nam Khanh Pham and Ardeshir Sepehri conceptualized and designed the study and drafted the initial manuscript. Tue Minh Le and Van Thu Tran conducted field research and collected the data. All authors participated in data analysis and interpretations and approved the final version of the manuscript. References 1. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980e2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014;384:766e81. 2. Gupta N, Goel K, Shah P, Misra A. Childhood obesity in developing countries: epidemiology, determinants, and prevention. Endocr Rev 2012;33:48e70.
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3. Hawke C. Uneven dietary development: linking the policies and processes of globalization with the nutrition transition, obesity and diet-related chronic diseases. Glob Health 2006;2:4. 4. Dietz WH. Health consequences of obesity in youth: childhood predictors of adult disease. Pediatre 1998;101:518e25. n L. Childhood obesity: a review of increased risk for physical and 5. Pulgaro psychological co-morbidities. Clin Ther 2013;35(1):A18e32. 6. Sweeting H. Gendered dimensions of obesity in childhood and adolescence. Nutr J 2008;7:1. 7. Popkin B, Adair L, Ng S. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev 2012;70(1):3e21. 8. Tuan NT, Tuong PD, Popkin BM. Body mass index (BMI) dynamics in Vietnam. Eur J Clin Nutr 2008;62(1):78e86. 9. Hanh TTM, Hoa VQ, Diep DTN. Increased trend of overweight and obesity in preschool and school children in Ho Chi Minh City period 2000-2010 and associated factors. J Food Nutr Sci 2013;9:69e76. 10. Hong TK, Dibley MJ, Sibbritt D, Binh PN, Trang NH, Hanh TT. Overweight and obesity are rapidly emerging among adolescents in Ho Chi Minh City, Vietnam, 2002e2004. Int J Pediatr Obes 2007;2:194e201. 11. Nguyen CK, Cao TY, Dang TD. Prevalence of overweight and obesity among primary school children in inner communes of Buon Ma Thuot in 2004 and associated factors. J Food Nutr 2006;2:22e6. 12. Nguyen PN, Hong TK, Hoang T, Nguyen DT, Robert A. High prevalence of overweight among adolescents in Ho Chi Minh City. BMC Public Health 2013;13:141. 13. Tang KH, Nguyen T, Dibley M, Sibbritt D, Phan B, Tran H. Factors associated with adolescent overweight/obesity in Ho Chi Minh City. Int J Pediat Obes 2010;5:396e403. 2010. 14. Tang HK, Dibley MJ, Sibbritt D, Tran HM. Gender and socio-economic differences in BMI of secondary high school students in Ho Chi Minh City. Asia Pac J Clin Nutr 2007;16(1):74e83. 15. Filmer D, Pritchett L. The effect of household wealth on educational attainment: evidence from 35 countries. Popul Dev Rev 1999;25(1):85e120. 16. Long S, Freese J. Regression models for categorical dependent variables using stata. 3rd edn. College Station, Texas: Stata Press Publication, Stata Corp LP; 2014. 17. Chu NF. Prevalence and trends of obesity among school children in Taiwanethe Taipei children heart study. Int J Obes Relat Metab Disord 2001;25:170e6. 18. Langendijk G, Wellings S, van Wyk M, Thompson S, McComb J, Chusilp K. The prevalence of childhood obesity in primary school children in urban Khon Kaen, Northeast Thailand. Asia Pac J Clin Nutr 2003;12(1):66e72.
19. Lee H, Duffey KJ, Kim C, Popkin BM. The relationship between family and child weight status by household structure in South Korea: 2007e2010. Nutr Diabetes 2013;3:e73. 20. Cui Z, Huxley R, Wu Y, Dibley MJ. Temporal trends in overweight and obesity of children and adolescents from nine Provinces in China from 1991e2006. Int J Pediatr Obes 2010;5:365e74. 21. Gulati S, Misra A, Colles SL, Kondal D, Gupta N, Goel K, et al. Dietary intakes and familial correlates of overweight/obesity: a four-cities in India. Ann Nutr Metab 2013;62(4):279e90. 22. Chen AY, Escarce JJ. Family structure and childhood obesity, early childhood longitudinal studyekindergarten cohort. Prev Chronic Dis 2010;7(3):A50. 23. Ochiai H, Shirasawa T, Ohtsu T, Nishimbura R, Morimoto A, Obuchi R, et al. Number of siblings, birth order, and childhood overweight: a population-based cross-sectional study in Japan. BMC Public Health 2012;12:766. 24. Kromeyer-Hauschild K, Zellner K, Jaeger U, Hoyer H. Prevalence of overweight and obesity among school children in Jena (Germany). Int J Obes Relat Metab Disord 1999;23:1143e50. 25. De Vito E, La Torre G, Langiano E, Berardi D, Ricciardi G. Overweight and obesity among secondary school children in central Italy. Eur J Epidemiol 1999;15:649e54. 26. Blake J. Family size and the quality of children. Demography 1981;18:421e42. 27. Downey DB. Number of siblings and intellectual development. The resource dilution explanation. Am Psychol 2001;56:497e504. 28. Lazarou C, Spteriades ES. Children's physical activity, TV watching and obesity in Cyprus: the CYKIDS study. Eur J Public Health 2010;20(1):70e7. 29. Benton D. Role of parents in the determination of the food preferences of children and the development of obesity. Int J Obes Relat Metab Disord 2004;28: 858e69. 30. Bogaert N, Steinbeck KS, Baur LA, Brock K, Bermingham MA. Food, activity and family - environmental vs biochemical predictors of weight gain in children. Eur J Clin Nutr 2003;57:1242e9. 31. Jacobson KC, Rowe DC. Genetic and shared environmental influences on adolescent BMI: interactions with race and sex. Behav Genet 1998;28(4): 265e78. 32. Cotton JR, Burley VJ, Blundell JE. Fat and satiety: no additional intensification of satiety following a fat supplemented breakfast. Int J Obes 1992;16(Suppl. 1):11. 33. Schusdziarra V, Hausmann M, Wittke S, Mittermeier J, Kellner M, Naumann A, et al. Impact of breakfast on daily energy intake-an analysis of absolute versus relative breakfast calories. Nutr J 2011;10:5. 34. Greene W, Harris M, Hollingsworth B, Maitra P. A latent class model for obesity. Econ Lett 2014;123:1e5.
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Original Research
Correlates of body mass index among primary school children in Ho Chi Minh City, Vietnam N.K. Pham a, A. Sepehri b, *, T.M. Le c, V.T. Tran a a
School of Economics, University of Economics Ho Chi Minh City, Ho Chi Minh City, Viet Nam Department of Economics, University of Manitoba, Winnipeg, Manitoba R3T 2N2, Canada c Development Economics, University of Economics, Ho Chi Minh City, Viet Nam b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 July 2019 Received in revised form 3 December 2019 Accepted 9 December 2019
Objectives: To document the prevalence of overweight and obesity and examine associated risk factors. Study design: A cross-sectional survey was conducted in 16 primary public schools in eight districts of Ho Chi Minh City in 2016. A multistage clustering sampling method was used to collect a sample of 1806 pupils attending the first, second, and third grades (7e9 years). Methods: Age- and sex-adjusted body mass index (BMI) status was defined using International Obesity Taskforce cut-offs. Ordered probit regression models were used to assess the association between child BMI and its socio-economic and demographic risk factors. The model was estimated separately for boys and girls to assess the extent to which the socio-economic gradients in BMI vary by gender. Results: The prevalence of obesity among boys was twice the rate for girls (24.7 vs 12.3%). The prevalence of overweight and obesity were also higher among pupils attending schools located in urban districts than in semi-rural districts. Gender, household wealth, the frequency of having breakfast at home, parental body weight, and school location were strong predictors of child BMI status. The protective effect of having breakfast more frequently at home against the risk of overweight/obesity was more pronounced in girls than in boys. Father's body weight and child BMI were more strongly associated with boys from poorer households than boys from wealthier households, while the differences were not significant for girls. Conclusions: The high prevalence of childhood overweight and obesity indicates an urgent need for more gender-specific, effective intervention, and prevention programs. © 2019 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
Keywords: Child obesity Risk factors Primary school students Ho Chi Minh City
Introduction During the past three decades, the prevalence of childhood overweight and obesity has risen greatly worldwide, with marked variations across countries in the levels and trends.1 Although the prevalence of childhood overweight and obesity in developing countries remains far below those in developed countries, it is growing more rapidly in developing countries than in developed countries.1 Childhood obesity is emerging as a public health issue in many parts of Asia,2 particularly in large urban centers, where rapid socio-economic development combined with integration
* Corresponding author. Tel.: þ(204) 474 6241; fax: þ(204) 474 7681. E-mail addresses: [email protected] (N.K. Pham), Ardeshir.Sepehri@ umanitoba.ca (A. Sepehri), [email protected] (T.M. Le), [email protected] (V.T. Tran).
into the global economy, and the expansion of fast-food chains have contributed to changes in diets and activity patterns of both adults and children.1,3 Childhood obesity is linked to serious short- and long-term adverse health complications extending to adulthood. These adverse medical and psychosocial outcomes are extensive, including both medical comorbidities, such as asthma, high blood pressure, high cholesterol, type 2 diabetes, and sleep apnea, as well as psychological, social, and behavioral consequences resulting from body image-related problems, such as poor self-esteem, social isolation and discrimination, and depression.4e6 The implications of these adverse health effects for quality of life, productivity, and health care costs are staggering, particularly in countries where the health care system is already overburdened.7 The rapid socio-economic development combined with increasing economic and cultural integration into the world
https://doi.org/10.1016/j.puhe.2019.12.007 0033-3506/© 2019 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
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N.K. Pham et al. / Public Health 181 (2020) 65e72
economy during the past three decades has led to a transition in the nutritional status of both children and adults in Vietnam.8 The rapid increase in the prevalence of overweight and obesity in children is emerging as a public health issue, particularly in large urban centers, such as Ho Chi Minh City. Prevalence of stunting and wasting among primary and high school children in Ho Chi Minh City declined from 24.2% in 2002e2004 to 14.3% in 2009 while the prevalence of overweight (obesity) increased from 11.6% (4%) to 21.9% (7.3%) over the same period of time.9 In recent years, a small but growing number of studies have documented the prevalence of overweight and obesity among Vietnam's school children and adolescents and examined its associated risk factors.10e14 However, much of this literature has focused on a selected set of bivariate association between overweight/obesity and sociodemographic factors. Moreover, it remains unclear, both in the context of bivariate and multivariate analysis, the extent to which the reported associations between risk factors and children's body mass index (BMI) status are mediated by gender and household wealth, the two most potent risk factors. The primary objective of this study is to fill this gap by providing a systematic analysis of child-, household- and community-specific factors associated with BMIs among primary school students (7e9 years) in Ho Chi Minh City. More specifically, using an ordered probit model, we assess the association between a wide range of child-, household-, and school-specific factors and children's BMI status. Knowledge of BMI status among primary school students, its associated risk factors, and gender-specific differences in these risk factors are essential for designing any effective interventions and assessing their effectiveness. Methods Study design and setting The data for this study were drawn from a cross-sectional study of 1806 pupils (967 girls and 839 boys) attending the first, second, and third grade (7e9 years) of 16 primary public schools from eight districts of Ho Chi Minh City between February and May 2016. A multistage clustering sampling method was used to select districts, primary schools, and three classes within each school. In the first stage, districts were selected by stratified random sampling with two strata and with probability proportional to the number of primary students. The first stratum encompasses nineteen urban districts, of which six were sampled; the second stratum includes five rural districts, of which two were chosen. In the second stage, two public schools within a sampled district were chosen by simple random sampling. We had sixteen sampled schools. In the third stage, classes within a sampled school were chosen by stratified random sampling: two classes within each grade from first to third were drawn equally. Finally, we had 96 sampled classes. All students from each randomly selected class were then invited to participate in the study. The study gained ethics approval from the Human Research Ethics Committee of the University of Economics Ho Chi Minh City and the Department of Education and Training of Ho Chi Minh City. Anthropometric measurements Body weight was measured to the nearest 0.10 kg using Omron HN289 digital personal scale while the subjects were wearing a light school uniform and no shoes. Height was measured to the nearest 0.1 cm using a Stature Meter tape. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared (kg/ m2), and age- and sex-specific cut-offs as proposed by the International Obesity Taskforce were used to define BMI status.
Questionnaires Self-administered questionnaires were used to collect information from parents, with the questionnaire covering height and weight information and a wide range of demographic and socioeconomic information. A total of 3483 pupils were approached, of whom 3196 (92%) agreed to participate in the study, and 3111 subjects completed and returned surveys. Of those who returned surveys, less than one third (34%) missed anthropometrical data on parents, and 9.5% missed data on children's eating and physical inactivity behaviors. Once we matched children's BMI status with other variables used for this study, the sample size was reduced to 1806 respondents. Children with missing data on parental BMI status were not significantly different in their BMI z-scores and socio-economic and demographic profiles than those included in the study. Study variables The outcome was a four-point ordinal indicator: (1) underweight; (2) healthy weight; (3) overweight; and (4) obese. Consistent with the previous literature, the main covariates of interest included a wide range of observed child-, household- and community-level variables related to socio-economic, demographic, activity-level, and eating profiles of a child. Childspecific variables included age, gender, having a sibling, the frequency of having breakfast at home, the frequency of having dinner at home, and time spent watching TV and/or playing video games. Age is presented by a continuous variable and gender by a dummy variable that takes the value of one when the child is male, and zero if female. Having a sibling is represented by a dummy that takes the value of one when the child has a sibling, and zero otherwise. Time spent on watching TV and playing video games are aggregated to avoid the high correlation between these two activities, and it is grouped into three groups: less than 2 h per weekday, 2e3 h per weekday, and 4 and more hours per weekday. Household-level variables included household wealth, mother's education, and parental body weight. Household wealth quintiles were calculated through principal component analysis of household ownership of a wide range of assets, including, radio, color television, CD/DVD player, telephone, air conditioner, computer, refrigerator, gas stove, microwave oven, washing machine, bicycle, motorbike and automobile, as well as the type of dwelling and having a servant or not.15 Due to the high correlation between mother's education and father's education, we chose to include only the mother's education. Parental BMIs were calculated using the self-reported weights and heights and were presented by a binary variable that takes the value of one if overweight or obese (overweight or obese classified as BMI 25 kg/m2). The frequency of having breakfast at home was presented by three dummy variables: none, less frequently (1e4 times per week), frequently (5e6 times per week), and always. Since the differences in estimated coefficients on having breakfast frequently and always at home were not statistically significant, we chose to aggregate ‘frequently’ with ‘always’ into one category. The frequency of having dinner at home was presented by a dummy variable that takes the value of one when the child had dinner frequently at home (5 times per week), and zero if the child had dinner less frequently at home (<5 times per week). The community-level variable is the school location and is represented by urban district and semi-rural district (the reference category). To assess the extent to which the association between children's BMI and sociodemographic factors varies across household wealth quintiles, we used a set of interaction terms involving these sociodemographic factors and each household wealth quintile.
N.K. Pham et al. / Public Health 181 (2020) 65e72
Statistical analysis Since the dependent variable was a four-category ordinal outcome, an ordered probit model was used to examine the association between child BMI status and risk factors.16 Underlying the ordered probit model is the assumption that the relationship between each pair of outcome categories is the same (the parallel regression assumption).16 This assumption was tested using an approximate likelihood ratio test, and the test result showed that the parallel regression assumption cannot' be rejected (Х 2 ¼ 38.46, P ¼ 0.200). For assessing the potential gender differences in the socio-economic gradients in child BMI status, the regression model was estimated separately for both females and males. Stata version 14.1 (Stata Corp., College Station, TX) was used for all data analysis. Since the data uses a three-stage stratified cluster sampling methodology, the clustering of responses by the primary sampling unit (district) raises the possibility of intradistrict correlation. For controlling cluster sampling, the cluster-robust standard errors were computed using appropriate ‘svy’ commands. We also applied appropriate sampling weights to produce unbiased population estimates. Results Descriptive socio-economic and demographic characteristics of the sample population are provided in Table 1. The overall prevalence of underweight, overweight, and obesity was 5.1, 30, and 18.2%, respectively (Table 2). The prevalence of overweight and obesity was higher in boys than in girls, higher among children whose parents were overweight or obese, and higher among pupils attending schools located in urban districts than in semi-rural districts. The prevalence of overweight and obesity was also greater in children from wealthy households, although the difference was not significant for obesity. However, both parental body weight and the location of the school gradient in children's BMI status mask a wide variation in these gradients across household wealth quintiles (Figs. 1 and 2). Having an overweight or obese father significantly increased the prevalence of obesity among children from the bottom two wealth quintiles by 91.9% (13.6 vs 26.1%; P < 0.001) and by 38.8% (23.6 vs 17%; P ¼ 0.033) for the children from the top wealth quintiles. The wealth gradient in the association between parental body weight and the prevalence of overweight was less pronounced: having an overweight or obese father increased the prevalence of overweight by 23.3% (33.9 vs 27.5%; P ¼ 0.102) among children from the bottom two wealth quintiles and by 3.2% (34.2 vs 33%; P ¼ 0.730) among children from the top two wealth quintiles. Similarly, attending a school located in an urban district increased significantly the prevalence of overweight by 49.8% (31.9 vs 21.3%; P < 0.001) and obesity by 42.6% (18.4 vs 12.9%; P ¼ 0.018) among children from the bottom two wealth quintiles. By contrast, among children from the top wealth quintiles, school location was not significantly associated with the prevalence of overweight (34 vs 26.1%; P ¼ 0.159) and obesity (19.2 vs 21.2%; P ¼ 0.53). Estimated coefficients of the ordered probit model are presented in the first column of Table 3 and the marginal effects for each outcome category in columns 2e4. Estimated marginal effects show the change in likelihood of being underweight, healthy weight, overweight, or obese when the value of a covariate increases by one unit. The results indicate that child BMI status was, as expected, positively associated with a child's gender, household wealth, mother's education, parental body weight, and school location, and negatively with child's age, having any siblings, and the frequency of having breakfast at home. Boys were 13% less likely to be at a
67
Table 1 Sociodemographic characteristics of the primary school children, Ho Chi Minh City, 2016 (unweighted). Variable
n (%)
Body mass index Underweight 141 Healthy weight 1287 Overweight 828 Obese 508 Sex Male 1822 Female 1437 Age 7 years 1071 8 years 851 9 years 837 Siblings None 653 At least one 2106 Time spent watching TV or playing video games (times per weekday) <2 614 2e3 747 4 1279 Wealth quintiles (principal components of asset index) Quintile 1 (poorest) 491 Quintile 2 558 Quintile 3 579 Quintile 4 574 Quintile 5 (richest) 557 Mother's education High school or less 1444 Postsecondary 982 Mother's body mass index Healthy weight 2046 Overweight or obese 171 Father's body mass index Healthy weight 1554 Overweight or obese 569 Frequency of having breakfast at home (times per week) None 227 1e3 997 4 1345 Frequency of having dinner at home (times per week) 4 2359 5 251 School location Urban district 2029 Semi-rural district 730
(5.1) (46.7) (30.0) (18.2) (47.9) (52.1) (38.8) (30.4) (30.3) (23.7) (76.3) (23.3) (28.3) (48.4) (17.8) (20.2) (21.0) (20.8) (20.2) (59.5) (40.5) (92.3) (7.7) (73.2) (26.8) (8.8) (38.8) (52.4) (90.4) (9.6) (73.5) (26.5)
healthy weight and 5% and 11% more likely to be overweight and obese, respectively, than girls. Living in a poor or near-poor household increased the likelihood of being underweight by 5% and reduced the likelihood of being overweight and obese by 8% and 10%, respectively. The results for the underweight children should be interpreted with care considering the small number of observations (n ¼ 141). Having an overweight or obese father increased child BMI more among children from poorer households than among those from wealthier households, with living in a poor household, increasing the likelihood of being obese by as much as 21%. School location was a strong predictor of BMI only for children from poor and near-poor households, with children from urban districts being less likely to be at a healthy weight (10e14%) and more likely to be overweight (3e4%) and obese (9e12%) than their counterparts from semi-rural districts. The reported results in Table 3 may mask gender differences in the association between children's BMI status and the sociodemographic risk factors. To assess the potential gender differences in the socio-economic gradients in child BMI status, the regression model was estimated separately for both females and males. Table 4 provides the results separately for girls and boys due to space limitations, the coefficients of covariates with
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Table 2 Prevalence of underweight, overweight, and obesity among primary school children by gender, wealth quintile, parental body weight, and school location, Ho Chi Minh City, 2016 (unweighted). Characteristics
Underweight
All Males Females Parental body weight Mother's BMI status Healthy weight Overweight or obese Father's BMI status Healthy weight Overweight or obese Household wealth Quintiles 1, 2, 3 Quintiles 4, 5 School location Urban district Semi-rural district
Overweight
Obese
Prevalence (%) (95% CI)
P-valuea
Prevalence (%) (95% CI)
P-valuea
Prevalence (%) (95% CI)
P-valuea
5.1 (4.3, 5.9) 2.9 (2.0, 3.8) 7.2 (5.8, 8.5)
0.000
30.0 (28.3, 31.7) 32.2 (29.6, 34.7) 28.0 (25.7, 30.4)
0.019
18.2 (16.8, 19.7) 24.7 (22.3, 27.0) 12.3 (10.6, 14.0)
0.000
0.039
0.000
0.535 5.2 (4.2, 6.1) 4.1 (1.1, 7.1)
29.9 (27.8, 31.8) 37.4 (30.1, 44.8)
17.2 (15.5, 18.8) 28.1 (21.3, 34.9)
0.041 5.7 (4.6, 6.9) 3.5 (2.0, 5.0)
0.09
0.000
29.7 (27.4, 32.0) 33.6 (29.7, 37.5)
15.6 (13.8, 17.4) 24.6 (21.1, 28.2)
0.000 6.6 (5.4, 7.8) 3.0 (2.0, 4.0)
0.006
0.166
28.0 (25.8, 30.2) 32.9 (30.1, 35.6)
17.4 (15.5, 19.2) 19.5 (17.1, 21.8)
0.000 3.9 (3.1, 4.8) 8.3 (6.3, 10.4)
0.000
0.033
32.3 (30.3, 34.4) 23.6 (20.5, 26.6)
19.2 (17.5, 20.9) 15.6 (13.0, 18.3)
BMI, body mass index; CI, confidence intervals. a Chi-square test to compare the prevalence of underweight, overweight and obesity across sociodemographic characteristics.
40 Overweight
Obese
35
30
(%)
25
20
15
10
5
0 Bottom two wealth quintiles
Top two wealth quintiles
Bottom two wealth quintiles
Healthy weight
Top two wealth quintiles
Overweight/obese Father's body weight
Fig. 1. Prevalence of child overweight/obesity by wealth quintiles and father's body weight.
significant gender differences are shown for only two of the four outcome categories (overweight and obese). There was a strong negative association between BMI status and the frequency of having breakfast at home among girls: having breakfast at home four or more times per week reduced the likelihood of being overweight and obese by 10% and 12%, respectively. By contrast,
there was no statistically significant association between BMI status and the frequency of having breakfast at home among boys. The positive association between a child's BMI status and father's body weight was stronger among boys from poor and near-poor households than among boys from rich households, and the differences were significant at the 5% level. Among boys from poor
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40 Overweight
Obese
35
30
(%)
25
20
15
10
5
0 Bottom two wealth quintiles
Top two wealth quintiles
Semi-rural
Bottom two wealth quintiles
Top two wealth quintiles
Urban
Fig. 2. Prevalence of child overweight/obesity by school location and wealth quintiles.
and near-poor households, having an overweight or obese father increased the likelihood of being obese by 24%. Similarly, the positive association between a child's BMI status and father's body weight was stronger among girls from poorer households than girls from wealthier households, but the differences were not statistically significant. Discussion The overall prevalence of overweight and obesity was 30 and 18.2%, respectively, with the prevalence of obesity among boys being twice the rate for girls (24.7 vs 12.3%). Gender, household wealth, the frequency of having breakfast at home, parental body weight, and school location were strong predictors of child BMI status. The prevalence of overweight and obesity was also higher among pupils attending schools located in urban districts than in semi-rural districts. School location was a strong predictor of BMI only for children from less wealthy households, with children from urban districts being less likely to be at a healthy weight (9.8%) and more likely to be overweight and obese (4% and 8%) than their counterparts from semi-rural districts. Father's body weight and child BMI were also more strongly associated with boys from poorer households than in boys from wealthier households, while the differences were not significant for girls. These findings indicate the complex pathways linking children's BMI and household wealth. The finding that a child's BMI status was positively associated with the male gender is similar to the results of other studies in Vietnam10e12 and elsewhere in the region,6,17e19 where the prevalence of overweight and obesity among children and adolescents
has been found to be greater in males than in females. These gender differences in BMI status might reflect culture-bound conventions and roles governing body image and dietary practices.6 With a large body size for boys being valued as a sign of strength and physical dominance, parents and grandparents may encourage boys to eat more to gain weight and height.18,20,21 The evidence for the association between childhood overweight and obesity and the number of siblings is mixed, some documenting a negative association whereby children with siblings being more likely to have a lower BMI than those with no siblings,22,23 while others report no significant association between child BMI and the number of siblings.24,25 Additional children may serve as a stimulus for child-to-child interactions and increase the amount of time spent on physical activities.26,27 Having siblings may also dilute household resources, including parental time and attention with mixed consequences for child BMI. While resource dilution may lead to less food per child, particularly in poorer households, time dilution may encourage parents to adopt convenient routines such as longer time allowed for viewing TV and playing video games, which could lead to a higher BMI.28 The finding that children from wealthier households were more likely to be heavier than those from less wealthy households is consistent with studies in Vietnam and elsewhere in developing countries.2,10,12,19 Greater financial resources allow wealthier households to purchase ample foods, modern recreational amenities, such as computers and TVs, and provide their children with more generous pocket money that may be used to purchase energy-dense snacks. Our finding that the association between children's BMI and urban-rural location of school was only
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Table 3 Ordered probit regression results for the BMI status of primary school children, Ho Chi Minh City. Independent variable
Coeff. (95% CI)
Gender Female (ref) 1.00 Male 0.40 (0.29, 0.51)*** Age 0.06 (0.12, 0.00)** Siblings None (ref) 1.00 At least one 0.13 (0.26, 0.01)** Having breakfast at home None (ref) 1.00 1e3 times/week 0.23 (0.44, 0.01)** 4 times/week 0.29 (0.50, 0.08)*** Having dinner at home 4 times/week (ref) 1.00 >4 times/week 0.10 (0.31, 0.10) TV viewing/video game playing on weekdays (h/week day) <2 (ref) 1.00 23 0.03 (0.18, 0.12) 4 0.13 (0.00, 0.27)* Household wealth Quintile 1 0.44 (0.77, 0.12)*** Quintile 2 0.45 (0.77, 0.13)*** Quintile 3 0.27 (0.60, 0.05)* Quintile 4 0.01 (0.17, 0.20) Quintiles 5 (ref) 1.00 Mother's education High school or less (ref) 1.00 Postsecondary 0.11 (0.01, 0.23)* Mother's BMI statusa Normal weight (ref) Overweight/obese 0.46 (0.27, 0.65)*** Interaction: Father's BMI status wealth quintile Healthy weight quintile 1 (ref) 1.00 Overweight/obese quintile 1 0.65 (0.32, 0.99)*** Healthy weight quintile 2 (ref) 1.00 Overweight/obese quintile 2 0.29 (0.00, 0.59)* Healthy weight quintile 3 (ref) 1.00 Overweight/obese quintile 3 0.25 (0.02, 0.53)* Healthy weight quintile 4 (ref) 1.00 Overweight/obese quintile 4 0.19 (0.07, 0.44) Healthy weight quintile 5 (ref) 1.00 Overweight/obese quintile 5 0.19 (0.05, 0.43) Interaction: School location wealth quintile Rural/semi-rural * quintile 1 (ref) 1.00 Urban quintile 1 0.30 (0.00, 0.61)* Rural/semi-rural * quintile 2 (ref) 1.00 Urban quintile 2 0.42 (0.15, 0.69)*** Rural/semi-rural quintile 3 (ref) 1.00 Urban quintile 3 0.20 (0.07, 0.46) Rural/semi-rural quintiles 4, 5b (ref) 1.00 b Urban quintiles 4, 5 0.01 (0.22, 0.21)
Marginal effects Underweight
Healthy weight
Overweight
Obese
Coeff. (95% CI)
Coeff. (95% CI)
Coeff. (95% CI)
Coeff. (95% CI)
1.00 1.00 1.00 0.03 (0.04, 0.02)*** 0.13 (0.16, 0.09)*** 0.05 (0.04, 0.07)*** 0.01 (0.00, 0.01)** 0.02 (0.00, 0.04)** 0.01 (0.02, 0.00)**
1.00 0.11 (0.08, 0.13)*** 0.02 (0.03, 0.00)**
1.00 0.01 (0.00, 0.02)**
1.00 0.04 (0.00, 0.08)**
1.00 0.02 (0.03, 0.00)**
1.00 0.04 (0.07, 0.00)**
1.00 0.02 (0.00, 0.04)* 0.02 (0.01, 0.04)***
1.00 0.07 (0.01, 0.14)** 0.09 (0.03, 0.16)***
1.00 1.00 0.03 (0.06, 0.00)** 0.06 (0.11, 0.00)** 0.04 (0.07, 0.01)*** 0.08 (0.14, 0.01)***
1.00 0.01 (0.01, 0.02)
1.00 0.03 (0.03, 0.10)
1.00 0.01 (0.04, 0.01)
1.00 0.3 (0.09, 0.03)
1.00 0.00 (0.01, 0.02) 0.01 (0.02, 0.00)*
1.00 0.01 (0.04, 0.06) 0.04 (0.09, 0.00)*
1.00 0.00 (0.03, 0.02) 0.02 (0.00, 0.04)*
1.00 0.01 (0.05, 0.03) 0.04 (0.00, 0.07)*
0.05 0.05 0.03 0.00 1.00
0.13 0.13 0.08 0.00 1.00
0.08 0.08 0.04 0.00 1.00
0.10 010 0.07 0.00 1.00
(0.01, 0.10)** (0.01, 0.06)** (0.00, 0.06) (0.02, 0.01)
1.00 0.01 (0.02, 0.00)*
(0.05, 0.20)*** (0.05, 0.21)*** (0.01, 0.18)* (0.06, 0.06)
1.00 0.04 (0.07, 0.00)*
(0.14, (0.14, (0.10, (0.02,
0.02)** 0.01)** 0.01) 0.03)
1.00 0.02 (0.00, 0.03)*
(0.16, (0.16, (0.14, (0.05,
0.04)*** 0.04)*** 0.01)* 0.05)
1.00 0.03 (0.00, 0.06)*
1.00 1.00 1.00 0.03 (0.04, 0.02)*** 0.15 (0.21, 0.09)*** 0.04 (0.03, 0.05)***
1.00 0.21 (0.08, 0.34)***
1.00 0.03 1.00 0.02 1.00 0.01 1.00 0.01 1.00 0.01
1.00 (0.04, 0.02)*** 0.21 1.00 (0.03, 0.00)** 0.10 1.00 (0.03, 0.00)* 0.08 1.00 (0.03, 0.00)* 0.06 1.00 (0.03, 0.00)* 0.06
(0.14, 0.02)
1.00 0.21 1.00 0.09 1.00 0.07 1.00 0.05 1.00 0.05
1.00 0.02 1.00 0.03 1.00 0.01 1.00 0.00
1.00 0.10 1.00 (0.04, 0.01)*** 0.14 1.00 (0.03, 0.00) 0.06 1.00 (0.02, 0.02) 0.00
1.00 0.03 1.00 (0.23, 0.05)*** 0.04 1.00 (0.15, 0.02) 0.02 1.00 (0.07, 0.07) 0.00
(0.04, 0.00)**
(0.31, 0.11)*** (0.20, 0.00)* (0.17, 0.01)* (0.14, 0.02)
(0.20, 0.00)*
1.00 0.03 1.00 0.03 1.00 0.03 1.00 0.02 1.00 0.02
(0.01, 0.05)*** (0.01, 0.05)*** (0.01, 0.05)*** (0.01, 0.05)* (0.00, 0.04)*
(0.01, 0.05)*** (0.02, 0.05)*** (0.00, 0.05)* (0.03, 0.03)
1.00 0.09 1.00 0.12 1.00 0.05 1.00 0.00
(0.09, 0.34)*** (0.01, 0.18)* (0.01, 0.16)* (0.02, 0.13) (0.02, 0.12)
(0.01, 0.19)* (0.03, 0.21)*** (0.02, 0.13) (0.06, 0.05)
CI, confidence interval; Coeff., coefficients; BMI, body mass index; marginal effects, the change in likelihood of being either underweight, healthy weight, overweight, or obese when the value of a covariate increases by one unit; *P < 0.10, **P < 0.05, ***P < 0.01. a Given the small number of observations on overweight and obese mothers, only father's BMI status was interacted with wealth quintiles. b Given the small number of observations on semi-rural households in the richest wealth quintile (62), the richest top two quintiles were aggregated into one single category.
significant for children from poorer households may reflect an increased affinity toward a Western type of lifestyle among the wealthy in general and the fascination of urban children and adolescents with Western-style food outlets in particular.2 Similarly, the finding that having an overweight or obese father dampened the wealth gradient in children's BMI status may reflect the influence of parental patterns of dietary practice and physical activity over those of their children.29e31 The finding that the protective effect of having breakfast more frequently at home against the risk of overweight and
obesity was more pronounced in girls than in boys may reflect the culture-bound parental dietary practice and preferences for girls to be ‘thinner’ and for boys to be ‘bigger.’6,18,20 As parents and grandparents encourage boys to eat more, a bigger breakfast may not reduce food intake later in the day and could simply lead to greater daily energy intake. In fact, both acute feeding experiments and intraindividual analysis have indicated that whole day energy intake increases proportionally to breakfast calories, while other meals of the day remain largely unchanged.32,33
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71
Table 4 Ordered probit regression results for a selective set of covariates by gender, Ho Chi Minh City.a Independent variable
Girls (n ¼ 967)
Boys (n ¼ 839)
Marginal effects
Coeff. (95% CI)
Coeff. (95% CI)
Overweight
Having breakfast at home None 9 ref 1 1e3 times/week 0.48 4 times/week 0.57 Interaction: Father's BMI status Healthy weight quintiles 1, 1 2b (ref) 0.31 Overweight/ obese quintiles 1, 2b Healthy weight quintile 3 1 (ref) Overweight/obese quintile 0.17 3 Healthy weight quintile 4 1 (ref) Overweight/obese quintile 0.25 4 Healthy weight quintile 5 1 (ref) Overweight/obese quintile 0.25 5
Obese
Girls
Boys
Girls
Boys
Coeff. (95% CI)
Coeff. (95% CI)
Coeff. (95% CI)
Coeff. (95% CI)
1 1 1 1 1 (0.76, 0.19)*** 0.03 (0.28, 0.34) 0.09 (0.14, 0.03)*** 0.00 (0.02, 0.03) 0.10 (0.15, 0.04)*** 0.01 (0.09, 0.11) (0.85, 0.29)*** 0.00 (0.30, 0.30) 0.10 (0.15, 0.05)*** 0.00 (0.02, 0.02) 0.12 (0.19, 0.06)*** 0.00 (0.09, 0.09) 1 (0.00, 0.61)*
(0.09, 0.59)
0.11 (0.27, 0.49)
1
0.01 (0.05, 0.03) 1
0.03 (0.03, 0.09) 1
0.09 (0.29, 0.47) 1
(0.06, 0.56)
1
0.36 (0.03, 0.75)* 1
1 0.05 (0.01, 0.09)**
0.66 (0.32, 1.00)*** 1
(0.21, 0.55)
1
1
1
1
0.04 (0.05, 0.14)
0.01 (0.01, 0.03)
0.12 (0.02, 0.27)* 1
0.06 (0.03, 0.15) 1
0.01 (0.01, 0.03)
0.24 (0.11, 0.37)*** 1
1
1 0.04 (0.00, 0.08)*
0.07 (0.01, 0.16)*
0.01 (0.00, 0.02)
0.04 (0.01, 0.08)*
1
0.03 (0.09, 0.15) 1
0.06 (0.20, 0.14)
0.03 (0.08, 0.16)
CI, confidence interval; Coeff., coefficients; BMI, body mass index; marginal effects, the change in likelihood of being either underweight, healthy weight, overweight, or obese when the value of a covariate increases by one unit; *P < 0.10, **P < 0.05, ***P < 0.01. a Due to space limitation, only coefficients of covariates with significant gender differences are shown in Table 4. Other covariates correspond to those listed in Table 3. b Given the small number of observations on children with overweight/obese father in the poorest wealth quintile, the bottom two wealth quintiles were aggregated into one single category.
For assessing the robustness of results, the multivariate regression models were reestimated, while excluding parental BMI status for which data were available only for a subset of the sample. The results, available on request, are very similar. Some caveats are in order. First, like any cross-section study, our findings may be subject to omitted variable bias resulting from unobserved individual-, household- and community-specific characteristics. Furthermore, it is not possible to demonstrate a cause-and-effect relationship between child BMI status and its covariates. Second, the self-reported anthropometrical data on parents are likely to be subject to misreporting. However, the measurement error due to misreporting is likely to be smaller when BMI categories are used, as is the case in this study.34 Third, although the study sample of 1806 was likely to be representative of all grade one to three pupils attending public schools in Ho Chi Minh City (n ¼ 318,842), the findings cannot be generalized to other parts of Vietnam, particularly, the less prosperous regions. Despite these limitations, our results document a high prevalence rate of overweight and obesity among primary school children and shed light on gender and wealth differences in the association between child BMI status and its commonly identified risk factors. The emergence of childhood obesity as a public health issue has indicated an urgent need for more gender-specific, effective intervention, and prevention programs before it becomes more severe later in life. Author statements Acknowledgements The authors would like to thank the anonymous referees for their insightful comments and suggestions.
Ethical approval The study gained ethics approval from the Human Research Ethics Committee of the University of Economics Ho Chi Minh City and from the Department of Education and Training of Ho Chi Minh City. Funding This work was supported by the Rockefeller Foundation [#THS 348, 2011]. Competing interests The authors declare that they have no conflict of interest. Author contributions Nam Khanh Pham and Ardeshir Sepehri conceptualized and designed the study and drafted the initial manuscript. Tue Minh Le and Van Thu Tran conducted field research and collected the data. All authors participated in data analysis and interpretations and approved the final version of the manuscript. References 1. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980e2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014;384:766e81. 2. Gupta N, Goel K, Shah P, Misra A. Childhood obesity in developing countries: epidemiology, determinants, and prevention. Endocr Rev 2012;33:48e70.
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