Determinants of overweight and obesity in Thai adolescent girls.
Obesity during childhood and adolescence is a serious public health problem as it increases the risk of chronic diseases throughout life. Obesity has recently been recognized as a public health threat within low and middle income nations such that research from Thailand has documented an increase in obesity in the past 10 years. While there are several descriptive studies reporting the presence of obesity among adults and children, few have examined relationships between social and dietary factors and obesity in this country. Thus the objective of this study is to examine predictors of overweight/ obesity among Thai adolescent girls.
This cross-sectional exploratory study uses logistic regression to examine predictors of overweight/obesity among Thai adolescent girls. Anthropometric, dietary, economic, demographic, and activity data were collected from 342 adolescent girls ages 9 to 18 years living in suburban Bangkok, Thailand in 2004 and in 2005.
The most important predictors of overweight/obese girls included early menarche (p < 0.01), birth order (p < 0.01), sports after school (p < 0.05), and moderate consumption of potato chips (P < 0.05). For instance, girls who were at most 10 years old at menarche were 4.24 (95% CI = 1.42, 12.72) times more likely to be overweight/obese than girls who were 12 years old.
The predictive nature of early menarche has been well documented in studies around the world. However, the other major predictors may be specific to this population in Thailand, and thus may assist in better understanding social determinants of obesity.
Keywords: Obesity, Overweight, Thailand, Adolescents
Obesity in children (Research)
Obesity in adolescence (Research)
Pawloski, Lisa R.
|Publication:||Name: Archives: The International Journal of Medicine Publisher: Renaissance Medical Publishing Audience: Academic Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2010 Renaissance Medical Publishing ISSN: 1791-4000|
|Issue:||Date: April-June, 2010 Source Volume: 3 Source Issue: 2|
|Topic:||Event Code: 310 Science & research|
|Product:||Product Code: 8000120 Public Health Care; 9005200 Health Programs-Total Govt; 9105200 Health Programs NAICS Code: 62 Health Care and Social Assistance; 923 Administration of Human Resource Programs; 92312 Administration of Public Health Programs|
As undernutrition continues to be a problem in low and middle income countries, a new problem of overnutrition has been documented in many parts of the world. Also referred to as the "nutrition transition", this epidemiological trend is defined as changes in the nutritional status and dietary intake among populations that are caused by economic, environmental, demographic, and cultural shifts. (1)
While the nutrition transition was first recognized in South America, it has also been documented in Southeast Asia, and more specifically in Thailand. Most researchers have attributed this to the improved economic and demographic trends over the last three decades in Thailand. (2) Litimaskul reported a rise in the prevalence of adult obesity in Thailand to increase from 5% in 1986 to 17.9% in 1999. (3) Researchers have also reported greater intakes in fat, animal proteins, and processed foods, contributing to increased caloric consumption, thereby increasing the risk for overweight and obesity. (4) Langendijk et al. found the prevalence of obesity among Thai children to be 10.8% in the Northeastern region. (5) They also found obesity to be greater among boys when compared to girls. Further, Sakamoto et al. found a high prevalence of obesity among pre-school children in the Central Region of Thailand and showed a marked correlation between socioeconomic status and obesity in children, such that there was greater obesity among wealthier and better-educated parents. (6)
While several studies have reported the nutritional state of children and adults in Thailand, very few studies have specifically examined adolescent girls living in this country. Most recently, Pawloski et al. published a descriptive study showing a greater prevalence of obesity among primary school girls compared to secondary school girls. (7) This finding suggests younger adolescents may be more greatly affected by obesity than their older counterparts. This trend was thought to be influenced by the nutritional transition occurring in Thailand and it is possible that younger adolescents are more likely to be influenced by a Western diet. This study further examines the data collected by Pawloski et al. by exploring determinants of overweight and obesity among Thai adolescent girls. (7) Thus the purpose of this study is to examine determinants of obesity including dietary intake, activity patterns, household structure and socioeconomic status in Thai adolescent girls.
MATERIALS AND METHODS
The data, including anthropometric, age at menarche, wealth, demographic, dietary, and activity information were collected in Rangsit, Thailand, a peri-urban community 40 km north of Bangkok. A total of 500 girls were invited to participate in the study. Data were collected from a convenience sample of 410 adolescent girls ages nine to eighteen years who agreed to participate and signed informed consent forms, thus a response rate of 82%. However, only 344 girls had complete data for the variables of age, weight and height which in this study were used in the calculation of the dependent variable, body mass index (BMI).
Participants were recruited from two local public primary and secondary schools. Random sampling was not possible, as the school was not large enough to allow for it. Further, school officials required all eligible girls to be able to participate. Informed consents were sent home to 500 girls a week before data collection took place. Only students who had informed consent forms signed and returned were eligible to participate. Human subjects' approval was made by the George Mason University Human Subjects Review Board and the Thammasat University Faculty of Medicine Human Subjects Review Board. Participants and their parents were given assent and consent forms translated into Thai.
Height and weight measurements were taken by the researcher in a clinical setting and followed the methods described by Lohman, Roche, and Martorell. Weight was collected using a digital body fat scale (Tanita). (8) Height measurements were done using a field portable anthropometer (GPM Seritex Brand).
To determine overweight and obese girls, comparison data were chosen to serve as a reference rather than a standard. Because no large reference sample was available from Thailand with which to make a comparison, these data were compared with international cut-off points for BMI-for-age developed by Cole et al. (9)
Due to the relatively small number of overweight and obese adolescents in this sample, overweight and obese individuals were collapsed into one category called overweight/obese. Thus, the dependent variable is dichotomous, comprised of normal weight and overweight/obese adolescents.
Age at menarche
To determine age at menarche, a retrospective method was used such that girls were asked if they had begun to menstruate and at what age it began.
Dietary behaviour and activity questionnaires
To determine dietary behaviour, a dietary behaviour instrument was developed by Thai and U.S. researchers, specific to this population. The behaviour instrument was modelled after a dietary instrument which was developed by the researchers in previous studies in Nicaragua and the U.S. (10,11) and content validity was established by Thai researchers at Thammasat University as well as researchers from George Mason University. The questionnaires were translated into Thai and back-translated to ensure the meaning of the instrument. Questions concerned general dietary behaviours included a food frequency component in which participants were asked how many servings per week they consumed specific types of foods, i.e. chips, vegetables, sodas, fruits, etc. These dietary variables were categorized into serving amounts (i.e., none, one, two, etc) based on the individual distributions of servings for both normal weight and overweight/obese girls. To better understand activity patterns, girls were asked about how often they exercised and participated in sports during and after school. They were also asked about how many minutes they spent doing sedentary activities such as television viewing and computer usage. In the data analysis, the variables of television viewing and computer usage were combined to create a "screen time" variable that indicates hours spent in these sedentary activities.
Sociodemographic questions assessed the participants' birthdates, birth order, and household income. Birthdates were verified by each school using student records. Income was recorded as the amount of Thai Baht earned per month. The classification of the income variable into three categories ([less than or equal to] 10,000, 11,000-24,000 and [greater than or equal to] 25,000) was determined based on the distribution of the individual incomes and using measures such as quartiles to identify their economic status relative to the median.
Descriptive statistics were conducted for both groups. The chi-square test was used to assess differences between the normal and overweight/obese adolescents for all predictor variables. We performed stepwise logistic regression analysis to assess the impact of various predictor variables on the risk of overweight/obesity among these adolescent girls. Furthermore, decision tree analysis using the CART software 5.012 was carried out to confirm the logistic regression findings.
CART, which was firmly established by Breiman et al., constitutes a nonparametric tool that makes no distributional assumptions and is not affected by outliers or collinearities. (13) It can be used to classify data that involve both continuous and categorical variables and it can effectively handle a large number of variables submitted for analyses. The accuracy of CART is comparable with linear and logistic regression. These important features of CART have made this technique popular in various fields including medicine, public health and biological sciences. (14,15)
The sample consisted of 344 girls of whom 82.5% had normal weight and 17.5% were overweight/obese. Table 1 provides descriptive information about the variables used in the analyses and adjusted odds ratio (OR) with 95% confidence intervals. Chi-square analyses revealed that a significantly higher percent of overweight/obese girls experienced menarche at a younger age in comparison to normal weight girls (p < 0.01). Furthermore, normal and overweight/obese girls differed significantly in regards to their order of birth (p < 0.01) with more overweight/obese girls being born first (61.7%). The two groups also differed according to specific aspects of their diet. For instance, 49.1% of the overweight/obese girls tended to consume one serving of potato chips per week while only 28.1% of normal-weight individuals did so. Normal-weight girls, however, had a higher consumption of at least two and three servings of chips per week than overweight/obese ones (p < 0.05). They were also significantly (p < 0.05) more likely to eat sweets on a daily basis (23.5%) compared to overweight/obese girls (11.9%).
Significant differences were also observed in certain activities outside of school. In particular, 30.0% of overweight/obese girls held a job while only 17.1% of normal-weight girls did so (p < 0.05). They were also more likely to participate in sports after school (41.7%) in comparison to 27.0% participation among the normal-weight individuals (p < 0.05). No significant differences were observed between these two groups and the other predictor variables listed in Table 1.
The logistic regression analysis indicates that girls who were at most 10 years old at menarche were 4.24 (95% CI = 1.42, 12.72) times more likely to be overweight/obese than girls who were 12 years old. On the other hand, girls who were at least 13 years old at menarche were less likely to be overweight /obese than girls who were 12 years old (OR = 0.21; 95% CI = 0.06, 0.76). Also, those adolescents born second were less likely to be overweight/obese than firstborn ones (OR = 0.37, 95% CI = 0.15, 0.90). In addition, adolescents whose families earned an income of at least 25,000 Thai Baht per month were 3.11 (95% CI = 1.22, 7.97) times more likely to be overweight/obese than those earning an income between 11,000 and 24,000 Thai Baht per month. Girls who consumed one serving of chips/week were 2.34 (95% CI = 1.01, 5.54) times more likely to be overweight/obese than girls who did not consume any. None of the other variables were found to be significant in predicting overweight/obesity in this sample.
The classification tree analysis (Figure 1) confirmed the findings of the logistic regression methodology as the same variables were selected in predicting overweight/obese adolescents. Specifically, age at menarche, servings of chips per week, and income were selected in that order as the most important variables in discriminating between the two classes. Overall, overweight/obese girls were more likely to have their period start before the age of 10 years old. Individuals with menarche occurring later in their adolescent years were also likely to be overweight/obese if they consumed one serving of potato chips per week and their family's income was [greater than or equal to] 25,000 Thai Baht per month.
The results suggest that girls who are economically advantaged are more likely to be overweight/obese, such that girls from families of higher incomes, those who hold jobs, those who participate in sports, and girls who are first born are more likely to be overweight/obese. These findings from Thailand are striking because they are somewhat the opposite of what is occurring in the U.S. and other developed countries. In the U.S., those who are at greater risk of obesity include more disadvantaged populations as well as those of lower socioeconomic status. For example, Singh et al. conducted a multilevel analysis of state and regional disparities regarding childhood and adolescent obesity in the U.S. and found low socioeconomic status and geographic location to be predictors of obesity. (16) Further, O'Dea et al. revealed obesity in Australian adolescents to be higher among those of lower socioeconomic status and among disadvantaged minorities. (17) Additionally, Wardle et al. conducted a longitudinal analysis in Great Britain among adolescents and also concluded those of low socioeconomic status to be at greater risk for developing obesity. (18) These findings also support earlier work by Sobal and Stunkard showed that there is a positive correlation between obesity and socioeconomic status in developing countries while this relationship is negative in developed countries. (20) Similar results have also been reported in China depicting a greater evidence of obesity among those of high socioeconomic status. (21)
[FIGURE 1 OMITTED]
With the exception of consuming a greater quantity of chips, these data also reveal little evidence to suggest that overweight/obese girls are consuming greater quantities of snacks or sodas, have longer periods of screen time, or are eating fewer fruits and vegetables compared to girls of average weight. Again, these findings are quite different from reports on obese children and adolescents from the U.S. and other countries which suggest strong correlations between obesity and low consumption of fruits and vegetables and high consumption of sugary drinks, and screen time. For example, Shields reviewed Canadian and U.S. Health surveys to examine determinants of overweight and obesity among children and adolescents and found that those who consumed fewer fruits and vegetables were at significantly greater risk of being overweight or obese. (22) Burke et al. found similar results to Shields among Australian adolescents. (23) Furthermore, Mark and Janssen reported that increased screen time increases the risk of metabolic syndrome among Canadian adolescents. (24)
Thus, among these girls in Thailand, the findings may ultimately suggest a "privilege" factor, for which girls who are first born or of higher socioeconomic status may have a greater risk of being overweight/obese than those who are not as privileged. Consumption of chips would also suggest a "privilege" factor. Even though chips are widely distributed in Thailand, they are typically more available at larger and more western style stores. Informal interviews with parents supported these results such that parents expressed that as their wealth increased they were more apt to succumb to the child's demands of western style foods and snacks. Parents expressed that they would choose restaurants which would provide traditional Thai foods for them and more western style cuisine for their children.
In addition to demographic and dietary factors, those who had an early age of menarche were more likely to be overweight/obese. These results are supported by a variety of literature worldwide, which suggest that overweight and obesity during early growth and development may influence the timing of maturation and age at menarche. (26) Thus, these data may suggest the need to intervene significantly earlier than the onset of the pubertal growth spurt.
Limitations of this study include the relatively small sample size and that it is a convenience sample. Also, the cross-sectional nature of the data does not allow for a longitudinal assessment of participants' BMI throughout adolescence. Future studies should collect data longitudinally to determine how changes in the socioeconomic status and diet impact their weight status at different stages during adolescence. Qualitative studies that assess cultural related beliefs about weight, wealth, and diet would be helpful in understanding the dynamics of the Thai culture and its impact on weight gain among young girls.
This is one of the first studies to examine the impact of dietary and socioeconomic factors on the risk of overweight/ obesity in Thai adolescent girls. The findings reveal that a "privilege" factor, which refers to families with high incomes, increased risk of overweight/obesity in comparison to those who were not as privileged. Overall, dietary indicators suggested that overweight/obese girls did not necessarily report having poorer diets than those who were not overweight/obese. Further, girls with early age at menarche were more likely to be overweight/obese. These data represent a specific population in Thailand and have potential to assist in developing nutrition intervention programs focusing on obesity prevention in high risk communities, such as those of higher socioeconomic status.
Lisa Pawloski and Manyat Ruchiwit conceived the study and collected the data in Thailand. Manyat Ruchiwit facilitated and obtained permissions from the schools and Human Subjects Review Board in Thailand. Panagiota Kitsantas assisted in the design and conducted the statistical data analyses. All three authors contributed to the drafting and writing of the manuscript. All authors have seen and approved the final version of this paper.
Conflict of interest: None declared.
The authors would like to acknowledge the Thailand-U.S. Fulbright Foundation and the Faculty of Nursing at Thammasat University.
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Lisa R. Pawloski , Panagiota Kitsantas , Manyat Ruchiwit 
 Department of Global and Community Health, College of Health and Human Services, George Mason University, Fairfax, VA 22030, USA
 Department of Health Administration and Policy, College of Health and Human Services, George Mason University, Fairfax, VA 22030, USA
 Department of Mental Health and Psychiatric Nursing, Thammasat University, Klong Luang, Pathumthani, 12121 Thailand
Corresponding author: Lisa R. Pawloski, PhD
Department of Global and Community Health
College of Health and Human Services
George Mason University
4400 University Drive, MSN 5B7
Fairfax, VA 22030, USA
Tel.: +1 703 993 4628
Fax: +1 703 993 190E-mail: email@example.com
Table 1. Sociodemographic and Dietary Characteristics (%) among Normal and Overweight/Obese Thai Adolescents Normal Overweight/ P-value (n=284)% obese (n=60)% Age at menarche <0.001 At most 10 yrs old 4.2 21.7 11 yrs old 24.3 21.7 12 yrs old 54.2 48.3 At least 13 yrs old 17.3 8.3 Order of birth 0.004 First 54.3 61.7 Second 36.9 18.3 Third and higher 8.9 20.0 Number of siblings 0.210 None 31.9 40.0 One 45.7 33.3 At least two 22.3 26.7 Income 0.135 [less than or equal to] 10,000 39.0 43.3 11,000-24,000 36.2 23.3 [greater than or equal to] 25,000 24.8 33.3 Number of meals eaten/day 0.135 Two 16.3 16.7 At least three 83.7 83.3 Meals eaten at home 0.906 One 22.4 20.0 Two 59.1 60.0 At least three 18.5 20.0 Snacks/day 0.823 One 45.2 43.3 Two 35.9 40.0 [greater than or equal to] 3 18.9 16.7 Servings of chips/week 0.014 None 24.1 17.5 One 28.1 49.1 Two 21.6 19.3 [greater than or equal to] 3 26.3 14.0 Number of sodas/week 0.501 None 17.1 25.0 One 21.5 19.6 Two 21.8 16.1 Three 17.1 21.4 [greater than or equal to] 4 22.5 17.9 Servings of vegetables/day 0.467 None 3.9 6.9 One 18.9 13.8 Two 32.9 39.7 [greater than or equal to] 3 44.3 39.7 Number of fruit servings/day 0.235 One 57.1 47.5 Two 26.4 37.3 [greater than or equal to] 3 16.4 15.3 Sweets/everyday 0.048 Yes 23.5 11.9 No 76.5 88.1 Number of servings of milk/day 0.655 None 6.5 3.4 One 44.6 46.6 Two 33.0 37.9 [greater than or equal to] 3 15.9 12.1 Exercise 0.486 Always 46.7 40.4 Usually 18.1 24.6 Sometimes 35.1 35.1 Walk to school 0.766 No 94.3 93.3 Yes 5.7 6.7 Work 0.021 No 82.9 70.0 Yes 17.1 30.0 Sports in school 0.495 No 27.3 31.7 Yes 72.7 68.3 Sports after school 0.023 No 73.0 58.3 Yes 27.0 41.7 Screen time (TV & computer hrs/day) 0.023 <4 hours 37.5 33.9 4-5 hours 20.7 22.0 6-7 hours 25.7 25.4 [greater than or equal to] 8 hours 16.1 18.6 Table 2. Adjusted Odds Ratios (95% CI) of Sociodemographic and Dietary Characteristics among Normal and Overweight/ Obese Thai Adolescents OR (95% CI) P-value Age at menarche At most 10 yrs old 4.24 (1.42, 12.72) 0.040 11 yrs old 1.05 (0.46, 2.38) 0.615 12 yrs old 1.00 At least 13 yrs old 0.21 (0.06, 0.76) 0.050 Order of birth First 1.00 Second 0.37 (0.15, 0.90) 0.050 Third and higher 2.11 (0.82, 5.39) 0.457 Income [less than or equal to] 10,000 2.21 (0.91, 5.35) 0.094 11,000-24,000 1.00 [greater than or equal to] 25,000 3.11 (1.22, 7.97) 0.025 Servings of chips/week None 1.00 One 2.34 (1.01, 5.54) 0.050 Two 1.15 (0.41, 3.23) 0.790 [greater than or equal to] 3 0.61 (0.18, 1.96) 0.405
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