This article has Open Peer Review reports available.
Association between economic status and body mass index among adolescents: a community-based cross-sectional study in Japan
- Akiko Mizuta1,
- Takeo Fujiwara2Email authorView ORCID ID profile and
- Toshiyuki Ojima3
https://doi.org/10.1186/s40608-016-0127-z
© The Author(s). 2016
Received: 14 September 2015
Accepted: 29 October 2016
Published: 10 November 2016
Abstract
Background
Childhood overweight and obesity is a growing health challenge in Japan and might be associated with childhood poverty.
We aimed to investigate the association between low economic status and body mass index (BMI) and to reveal the mediators of this association among junior high school students in Japan.
Methods
Junior high school students (N = 2968) from two cities in Shizuoka, Japan, were surveyed. Questionnaires assessed subjective economic status, weight, and height. Economic status was categorized into low and non-low, and BMI z-scores were calculated using the WHO Growth Reference. Multivariate regression analyses were conducted to determine the association between economic status and BMI z-scores, adjusted for covariates and stratified by gender.
Results
Among girls, low economic status was significantly positively associated with BMI z-scores in the crude model (coefficient: 0.35; p = 0.001). In a model adjusted for breakfast skipping, the coefficient of economic status decreased by 28.57 % but remained significant (coefficient: 0.25; p = 0.017). In the final model adjusted for other possible covariates, low economic status remained significantly positively associated with BMI z-score (coefficient: 0.22; p = 0.044). The same association was not found for boys.
Conclusions
Low economic status was positively associated with higher BMI among girls in junior high school in Japan, but this was not true for boys. Childhood poverty might be associated with overweight and obesity among adolescent girls in Japan. Health policies at junior high schools to discourage breakfast skipping might be effective for countering the association between childhood poverty and overweight in adolescent girls.
Keywords
Background
Childhood poverty is a risk factor for disease in adulthood and can have a significant impact on long-term health. In Japan, the relative child poverty rate was 15.7 % in 2010, meaning that one in six children grows up in poverty across the country [1]. Previous studies have reported on the association between socioeconomic status and childhood overweight and obesity [2, 3], and the relationships of childhood overweight with cardiovascular risk factors [4], cardiovascular disease [5], asthma [6], depression [7], and premature mortality [8, 9]. Moreover, childhood overweight and obesity have an adverse effect on socioeconomic status in young adulthood, such as lower educational attainment and income [10].
The rates of overweight and obesity among children are increasing globally, and the Asia-Pacific region is no exception [11–13]. The prevalence of obesity among Japanese schoolchildren in the 7th, 8th, and 9th grades was 9.13 %, 8.04 %, and 7.55 %, respectively, in 2015 [14]. Although these rates are relatively low compared with those in other developed countries [15], they have increased since 1977 (7th grade, 6.64 %; 8th grade, 5.63 %; and 9th grade. 4.91 %) [14]. In Japan, risk of childhood obesity might be related to irregular intake of breakfast [16, 17]. Although these risk factors are likely to be associated with childhood poverty [18], few studies have investigated the association between socioeconomic status and childhood overweight and obesity in Japan.
The aim of this study was to clarify the relationship between economic status and body mass index (BMI) among junior high school students in Japan.
Methods
Participants
Students were recruited from all three grades (7th, 8th, and 9th) of all eight public junior high schools located in Kikugawa City and Kosai City, both of which are suburban areas within Shizuoka Prefecture, located in central Japan (N = 2968). The 7th, 8th, and 9th grades in Japan are generally for students aged 13, 14, and 15 years, respectively. In the Japanese education system, junior high school is a lower-secondary school that comes after elementary school and before senior high school. The survey was conducted between December 2012 and January 2013. Homeroom teachers guided students in filling out the surveys during class time, and completed questionnaires were collected in sealed envelopes. Completion of the anonymous self-assessment questionnaire was considered consent to participate in the study. Completed questionnaires were then collected and placed in sealed envelopes individually. Questionnaire of students who rejected this survey were also collected remains blank, in the same way as other students. (Additional file 1).
Ethics statement
Written explanations of the study were provided to students, parents, and guardians, as well as homeroom teachers and principals. Those who did not wish to participate in the study could decline to respond. Written informed consent was assumed by voluntary response of the anonymous questionnaire. In Japan, according to ethical guidelines for epidemiological research, written informed consent is not necessary for observational research that does not collect human biological specimens, such as blood or urine.
Measurement
Weight and height were self-reported in the questionnaire in kilograms and centimeters respectively, and recorded to the first decimal place. Previous studies in Japan have shown that self-reported child weight and height is highly accurate [19]. BMI (kg/m2) was calculated as weight (kg) over height squared (m2). To adjust for child age and gender, BMI z-scores were calculated based on WHO standards [20].
Because it would be difficult to obtain exact figures of students’ annual household income, we assessed economic status using a subjective measurement of economic status as follows: “How do you rate your household living-conditions as financial situation?” This item was scored on a 5-point Likert scale: (1) “difficult,” (2) “somewhat difficult,” (3) “normal,” (4) “somewhat comfortable,” and (5) “comfortable.” This subjective measurement of economic status was used in the 2010 Comprehensive Survey of Living Conditions conducted by the Japanese Ministry of Health, Labour and Welfare [21] and validated by comparing the question with annual household income reported in another study [22]. To focus on childhood poverty, we divided economic status into two categories: “low” (difficult: boys 4.28 %, girls 3.35 %) and “non-low” (somewhat difficult: boys 14.88 %, girls 19.06 %; normal: boys 63.56 %, girls 60.72 %; somewhat comfortable: boys 12.92 %, girls 10.33 %; and comfortable: boys 4.36 %, girls 6.53 %).
Covariates
The questionnaire included questions about sociodemographic factors such as gender, date of birth, grade in school, lifestyle topics including eating, activity, sleeping patterns and smoking status, self-rated health status, family members’ smoking status and, family structure. Breakfast skipping and the number of meals from convenience stores and fast-food outlets (convenience stores and fast-food outlets) were rated if the frequency of skipping breakfast and eating meals from convenience stores or fast-food outlets were ≥ 1 time per week. We also assessed participation in club activities at school (club activities at school) (“physical,” “non-physical,” “both physical and non-physical,” or “no participation”), and participation in physical and non-physical activities outside of school (activities outside of school) with a yes/no. In the Japanese education system, public junior high schools provide non-compulsory club activities that are either physical (e.g., baseball club and soccer club) or non-physical (e.g., watching movies, brass band, and chorus). For sleeping patterns, we assessed the number of times students got up and went to bed each night. Sleeping patterns were then divided by two, using a less than first quintile cut-off (less than 6.5 h per night). Self-rated health status was scored on a 5-point Likert scale of (1) “excellent,” (2) “good,” (3) “fair,” (4) “somewhat poor,” and (5) “poor,” which was then reorganized into two categories of “good” (excellent, good, and fair) and “not good” (somewhat poor, poor). We used a self-rated question for health status obtained from the 2010 Comprehensive Survey of Living Conditions [21], which was correlated with the Birleson depression self-rating scale (DSRS) (boys: Spearman’s r = 0.43, p < 0.001; girls: Spearman’s r = 0.48, p < 0.001). Smoking status was assessed using the question “Have you ever smoked before (even if only once)?” with a yes/no response. As for family members who lived together, we divided students into family structure of “living with both of their biological parents” and “those living in a single-parent family or stepfamily. Finally, family members’ smoking status (father, mother, and other) was rated and classified into two categories of “yes” (≥1 person) and “no” (none).
Statistical analysis
We examined the association between economic status and BMI z-scores using linear regression analysis. Univariate regression analysis was conducted in Model 1, and school, grade, and family structure were added to Model 2 as confounding variables. In Models 3, 4, 5, 6, and 7, we added the following possible mediators: family members’ smoking status (Model 3), breakfast skipping (Model 4), activities outside of school (Model 5), sleeping patterns (Model 6), and self-rated health status (Model 7), owing to estimate change of coefficient. Finally, multivariate regression analysis was performed for economic status using explanatory variables and an objective variable of BMI z-scores, and was adjusted for all confounders and all possible mediators (Model 8). We used STATA version 13.0 (STATA Corp LP., College Station, TX, USA) for statistical analysis.
Results
Participant characteristics
Characteristics of Participants
Boys (n = 1169) | Girls (n = 1133) | |||
|---|---|---|---|---|
n or mean | % or SD | n or mean | % or SD | |
Family characteristics | ||||
Economic status | ||||
Non-low (comfortable, somewhat comfortable, normal, somewhat difficult) | 1119 | (95.72) | 1095 | (96.65) |
Low (difficult) | 50 | (4.28) | 38 | (3.35) |
Family structure | ||||
Both biological parents | 964 | (82.46) | 928 | (81.91) |
Single parent or stepfamily | 205 | (17.54) | 205 | (18.09) |
Family members’ smoking status (mother, father, or others) | ||||
None | 565 | (48.33) | 527 | (46.51) |
Yes (≥1 person) | 604 | (51.67) | 606 | (53.49) |
Child characteristics | ||||
BMI | 18.92 | (2.63) | 19.19 | (2.57) |
BMI z-score (WHO standard) | −0.17 | (0.68) | −0.18 | (0.63) |
< −2 SD | 53 | (4.53) | 36 | (3.18) |
≥ −2 SD and < −1 SD | 215 | (18.39) | 220 | (19.42) |
≥ −1 SD and < +1 SD | 793 | (67.84) | 797 | (70.34) |
≥ +1 SD and < +2 SD | 94 | (8.04) | 67 | (5.91) |
≥ +2 SD | 14 | (1.20) | 13 | (1.15) |
Grade in school | ||||
7th | 396 | (33.88) | 387 | (34.16) |
8th | 382 | (32.68) | 361 | (31.86) |
9th | 391 | (33.45) | 385 | (33.98) |
Breakfast skipping | ||||
No (eat breakfast daily) | 1032 | (88.28) | 963 | (85.00) |
Yes (skip breakfast ≥ 1 time per week) | 137 | (11.72) | 170 | (15.00) |
Convenience stores or fast-food outlets | ||||
< 1 time per week | 959 | (82.04) | 961 | (84.82) |
≥ 1 time per week | 210 | (17.96) | 172 | (15.18) |
Club activities at school | ||||
Physical | 850 | (72.71) | 575 | (50.75) |
Non-physical | 77 | (6.59) | 322 | (28.42) |
No participation | 242 | (20.70) | 236 | (20.83) |
Activities outside of school | ||||
Yes | 398 | (34.05) | 466 | (41.13) |
No | 771 | (65.95) | 667 | (58.87) |
Sleeping patterns | ||||
≥ 6 h 30 min per day (Q2–Q5) | 955 | (81.69) | 826 | (72.90) |
< 6 h 30 min per day (Q1) | 214 | (18.31) | 307 | (27.10) |
Self-rated health status | ||||
Good (excellent, good, fair) | 1084 | (92.73) | 1033 | (91.17) |
Not good (somewhat poor or poor) | 85 | (7.27) | 100 | (8.83) |
Smoking status | ||||
No | 1,126 | (96.32) | 1107 | (97.71) |
Yes | 43 | (3.68) | 26 | (2.29) |
Economic status and covariates
Association Between Economic Status and Possible Mediators
Boys | Girls | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
Non-Low | Low | p | Non-Low | Low | p | |||||
n | % | n | % | n | % | n | % | |||
Family characteristics | ||||||||||
Family structure | ||||||||||
Both biological parents | 926 | (96.06) | 38 | (3.94) | 0.219 | 910 | (98.06) | 18 | (1.94) | <0.001 |
Single parent or stepfamily | 193 | (94.15) | 12 | (5.85) | 185 | (90.24) | 20 | (9.76) | ||
Family members’ smoking status (mother, father, or others) | ||||||||||
None | 542 | (95.93) | 23 | (4.07) | 0.736 | 514 | (97.53) | 13 | (2.47) | 0.122 |
Yes (≥1 person) | 577 | (95.53) | 27 | (4.47) | 581 | (95.87) | 25 | (4.13) | ||
Child characteristics | ||||||||||
Breakfast skipping | ||||||||||
No (eat breakfast daily) | 990 | (95.93) | 42 | (4.07) | 0.336 | 944 | (98.03) | 19 | (1.97) | <0.001 |
Yes (skip breakfast ≥ 1 time per week) | 129 | (94.16) | 8 | (5.84) | 151 | (88.82) | 19 | (11.18) | ||
Convenience stores or fast-food outlets | ||||||||||
< 1 time per week | 916 | (95.52) | 43 | (4.48) | 0.455 | 929 | (96.67) | 32 | (3.33) | 0.915 |
≥ 1 time per week | 203 | (96.67) | 7 | (3.33) | 166 | (96.51) | 6 | (3.49) | ||
Club activities at school | ||||||||||
Physical | 819 | (96.35) | 31 | (3.65) | 0.065 | 562 | (97.74) | 13 | (2.26) | 0.092 |
Non-physical | 70 | (90.91) | 7 | (9.09) | 309 | (95.96) | 13 | (4.04) | ||
No participation | 230 | (95.04) | 12 | (4.96) | 224 | (94.92) | 12 | (5.08) | ||
Activities outside of school | ||||||||||
Yes | 378 | (94.97) | 20 | (5.03) | 0.364 | 456 | (97.85) | 10 | (2.15) | 0.059 |
No | 741 | (96.11) | 30 | (3.89) | 639 | (95.80) | 28 | (4.20) | ||
Sleeping patterns | ||||||||||
≥ 6 h 30 min (Q2-Q5) | 915 | (95.81) | 40 | (4.19) | 0.752 | 805 | (97.46) | 21 | (2.54) | 0.013 |
< 6 h 30 min (Q1) | 204 | (95.33) | 10 | (4.67) | 290 | (94.46) | 17 | (5.54) | ||
Self-rated health status | ||||||||||
Good (excellent, good, fair) | 1041 | (96.03) | 43 | (3.97) | 0.061 | 1008 | (97.58) | 25 | (2.42) | <0.001 |
Not good (somewhat poor or poor) | 78 | (91.76) | 7 | (8.24) | 87 | (87.00) | 13 | (13.00) | ||
Smoking status | ||||||||||
No | 1079 | (95.83) | 47 | (4.17) | 0.373 | 1072 | (96.84) | 35 | (3.16) | 0.019 |
Yes | 40 | (93.02) | 3 | (6.98) | 23 | (88.46) | 3 | (11.54) | ||
Economic status and possible mediators related to BMI z-scores
Among boys, low economic status was not associated with BMI z-scores (coefficient: −0.07, p = 0.460). However, among girls, low economic status was significantly associated with BMI z-scores (coefficient: 0.35, p = 0.001). In Model 2, this association remained significant among girls (coefficient: 0.32, p = 0.002).
Relationship between economic status and BMI Z-Scores of Boys
Model 1a | Model 2b (+ grade, school, family structure) | Model 3 (Model 2 + family members’ smoking status) | Model 4 (Model 2 + breakfast skipping) | Model 5 (Model 2 + activities outside of school) | Model 6 (Model 2 + sleeping patterns) | Model 7 (Model 2 + self-rated health status) | Model 8c (Model 2 + all mediators) | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
B | SE | p | B | SE | p | B | SE | p | B | SE | p | B | SE | p | B | SE | p | B | SE | p | B | SE | p | |
Family characteristics | ||||||||||||||||||||||||
Economic status | ||||||||||||||||||||||||
Low (ref: non-low) | −0.07 | 0.10 | 0.460 | −0.10 | 0.10 | 0.327 | −0.10 | 0.10 | 0.326 | −0.10 | 0.10 | 0.312 | −0.10 | 0.10 | 0.302 | −0.10 | 0.10 | 0.318 | −0.10 | 0.10 | 0.291 | −0.11 | 0.10 | 0.241 |
Family structure | ||||||||||||||||||||||||
Single parent or stepfamily (ref: both biological parents) | 0.15** | 0.05 | 0.005 | 0.15** | 0.05 | 0.005 | 0.15** | 0.05 | 0.005 | 0.14** | 0.05 | 0.009 | 0.15** | 0.05 | 0.004 | 0.15** | 0.05 | 0.004 | 0.15** | 0.05 | 0.005 | 0.14** | 0.05 | 0.006 |
Family members’ smoking status | ||||||||||||||||||||||||
Yes (ref: none) | 0.003 | 0.04 | 0.944 | 0.014 | 0.04 | 0.725 | 0.01 | 0.04 | 0.798 | |||||||||||||||
Child characteristics | ||||||||||||||||||||||||
Breakfast skipping | ||||||||||||||||||||||||
Yes (ref: no) | 0.13* | 0.06 | 0.029 | 0.11 | 0.06 | 0.083 | 0.08 | 0.06 | 0.199 | |||||||||||||||
Convenience stores or fast-food outlets | ||||||||||||||||||||||||
<1 time per week (ref: ≥ 1 time per week) | −0.01 | 0.05 | 0.802 | −0.05 | 0.05 | 0.343 | ||||||||||||||||||
Club activities at school | ||||||||||||||||||||||||
Non-physical (ref: physical) | 0.11 | 0.08 | 0.193 | 0.02 | 0.08 | 0.804 | ||||||||||||||||||
No participation (ref: physical) | 0.03 | 0.05 | 0.538 | 0.03 | 0.07 | 0.665 | ||||||||||||||||||
Activities outside of school | ||||||||||||||||||||||||
No (ref: yes) | −0.06 | 0.04 | 0.130 | −0.07 | 0.04 | 0.078 | −0.08 | 0.04 | 0.049 | |||||||||||||||
Sleeping patterns | ||||||||||||||||||||||||
<6 h 30 min (ref: ≥ 6 h 30 min) | 0.19*** | 0.05 | <0.001 | 0.18*** | 0.05 | <0.001 | 0.17** | 0.05 | 0.001 | |||||||||||||||
Self-rated health status | ||||||||||||||||||||||||
Not good (ref: good) | 0.09 | 0.08 | 0.217 | 0.11 | 0.08 | 0.151 | 0.08 | 0.08 | 0.326 | |||||||||||||||
Smoking status | ||||||||||||||||||||||||
Yes (ref: no) | 0.13 | 0.11 | 0.226 | 0.04 | 0.11 | 0.736 | ||||||||||||||||||
Relationship Between Economic Status and BMI Z-Scores of Girls
Model 1a | Model 2b (+ grade, school, family structure) | Model 3 (Model 2 + family members’ smoking status) | Model 4 (Model 2 + breakfast skipping) | Model 5 (Model 2 + activities outside of school) | Model 6 (Model 2 + sleeping patterns) | Model 7 (Model 2 + self-rated health status) | Model 8c (Model 2 + all mediators) | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
B | SE | p | B | SE | p | B | SE | p | B | SE | p | B | SE | p | B | SE | p | B | SE | p | B | SE | p | |
Family characteristics | ||||||||||||||||||||||||
Economic status | ||||||||||||||||||||||||
Low (ref: non-low) | 0.35** | 0.10 | 0.001 | 0.32** | 0.10 | 0.002 | 0.31** | 0.10 | 0.003 | 0.25* | 0.11 | 0.017 | 0.31** | 0.10 | 0.003 | 0.32** | 0.11 | 0.003 | 0.30** | 0.11 | 0.005 | 0.21* | 0.11 | 0.044 |
Family structure | ||||||||||||||||||||||||
Single parent or stepfamily (ref: both biological parents) | 0.14** | 0.05 | 0.005 | 0.11* | 0.05 | 0.021 | 0.11* | 0.05 | 0.021 | 0.08 | 0.05 | 0.094 | 0.11* | 0.05 | 0.026 | 0.11* | 0.05 | 0.022 | 0.11* | 0.05 | 0.023 | 0.07 | 0.05 | 0.139 |
Family members’ smoking status | ||||||||||||||||||||||||
Yes (ref: none) | 0.08* | 0.04 | 0.042 | 0.07* | 0.04 | 0.049 | 0.05 | 0.04 | 0.211 | |||||||||||||||
Child characteristics | ||||||||||||||||||||||||
Breakfast skipping | ||||||||||||||||||||||||
Yes (ref: no) | 0.26*** | 0.05 | <0.001 | 0.22*** | 0.05 | <0.001 | 0.21*** | 0.05 | <0.001 | |||||||||||||||
Convenience stores or fast-food outlets | ||||||||||||||||||||||||
<1 time per week (ref: ≥ 1 time per week) | −0.03 | 0.05 | 0.529 | −0.06 | 0.05 | 0.220 | ||||||||||||||||||
Club activities at school | ||||||||||||||||||||||||
Non-physical (ref: physical) | 0.07 | 0.04 | 0.120 | 0.03 | 0.04 | 0.448 | ||||||||||||||||||
No participation (ref: physical) | −0.04 | 0.05 | 0.379 | −0.05 | 0.07 | 0.450 | ||||||||||||||||||
Activities outside of school | ||||||||||||||||||||||||
No (ref: yes) | 0.12** | 0.04 | 0.001 | 0.10* | 0.04 | 0.010 | 0.10* | 0.04 | 0.012 | |||||||||||||||
Sleeping patterns | ||||||||||||||||||||||||
<6 h 30 min (ref: ≥ 6 h 30 min) | 0.07 | 0.04 | 0.113 | 0.04 | 0.04 | 0.394 | 0.004 | 0.04 | 0.921 | |||||||||||||||
Self-rated health status | ||||||||||||||||||||||||
Not good (ref: good) | 0.16* | 0.07 | 0.015 | 0.10 | 0.07 | 0.130 | 0.08 | 0.07 | 0.250 | |||||||||||||||
Smoking status | ||||||||||||||||||||||||
Yes (ref: no) | 0.18 | 0.12 | 0.150 | 0.11 | 0.12 | 0.389 | ||||||||||||||||||
Discussion
We found that low economic status was significantly associated with high BMI z-score for adolescent girls, independent of family structure or lifestyle factors such as physical activity. Further, we found that breakfast skipping could be a possible mediator of this association. To the best of our knowledge, this population-based study is the first to show an association between childhood poverty and obesity among adolescent Japanese girls.
In the United States, child obesity is associated with low economic status [23], although it is also associated with high economic status [24]. A study of four European countries that used different measures of economic status found no association between parental education and childhood BMI [25]. Thus, differences in race or other possible confounding factors related to culture should be considered when interpreting our findings. Nonetheless, our study adds to the literature by finding an association between childhood poverty and overweight among adolescent girls in Japan, an association that has not been clearly found in other developed countries.
Family income is important in controlling children’s BMI because healthier food tends to be expensive and participation in physical activities can be costly [26–29]. Previous studies have shown that childhood poverty induces breakfast skipping [30], which can lead to high BMI among adolescent girls who regularly skip breakfast [31, 32]. Our result for girls was similar to a previous study’s finding of an association between overweight and breakfast skipping at least once per week [33]. Moreover, breakfast skipping in adolescence is associated not only with overweight but also with larger waist circumference and higher levels of fasting insulin, total cholesterol, and low-density lipoprotein cholesterol [34]. Breakfast skipping is also associated with smoking, drinking, and physical inactivity [35]. Education programs that emphasize eating breakfast daily and target poor families with adolescent girls might serve to prevent childhood overweight and obesity in Japan. School lunch programs in Japan could also ensure a certain level of nutrition, and provide an opportunity to educate students on the importance of a healthy diet. Thus, improvements in school lunch programs and initiatives targeting breakfast skipping in poor families might be effective interventions in Japan, as they have been in the United States [36].
For girls, the association between economic status and BMI remained significant after adjusting for family structure. This finding only among girls is consistent with that of a previous study [37]. Childhood poverty and single-parent families need to be considered as different pathways leading to increased BMI. This is an important consideration because subsidies for single parents might not be effective for preventing overweight among girls. Further study is needed to elucidate the effect of poverty and single parenthood on weight.
This study has several limitations. First, due to its cross-sectional study design, reverse causality might explain the association between overweight adolescent girls and low economic status. Second, height, weight, economic status, and health status were self-reported. Specifically, self-reported BMI is reported to be slightly lower than measured BMI in adolescent girls [19]. However, on average, self-reported weight and BMI were found to be valid representations of their measured counterparts [38]. Third, although the response rate was high, not every student responded to the questionnaire. It is possible that students with lower economic status were less likely to respond to the questionnaire, so the strength of association might have been underestimated. However, the rate of students who considered their family’s financial situation to be “difficult” (boys, 4.28 %; girls, 3.35 %) was close to the rate of low household income reported in a previous study (5.8 %) [39]. Despite these limitations, due to a large sample from two communities, a substantial percentage of students with low economic status were identified. Further longitudinal studies are needed to confirm the association.
Conclusion
This study clarified that low economic status mediated by breakfast skipping was significantly associated with higher BMI z-score in adolescent girls in Japan. Health policies at junior high schools to discourage breakfast skipping might be effective for countering the association between childhood poverty and overweight in adolescent girls.
Abbreviation
- BMI:
-
Body mass index
Declarations
Acknowledgements
The authors would like to thank all the students who participated in this study, as well as the Boards of Education of Kikugawa City and Kosai City, Shizuoka Prefecture.
Funding
This study was supported by grants from the Ministry of Education, Culture, Sports, Science and Technology, Japan (Research Project Number: 24593437, 15 K11880). The funding source had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.
Availability of data and materials
The data supporting our findings can be sent upon reasonable request.
Authors’ contributions
AM and TO designed the study and were responsible for conducting the study. AM, TO, and TF analyzed and interpreted the data. AM and TF drafted and revised the manuscript. All authors read and approved the final manuscript.
Author’s information
Mizuta A, RN, PHN, PhD, is an assistant professor at Department of Community Health Nursing, Hamamatsu University School of Medicine, Shizuoka, Japan.
Fujiwara T, MD, PhD, MPH, is a professor at Department of Global Health Promotion, Tokyo Medical and Dental University (TMDU), Tokyo, Japan.
Ojima T, MD, PhD, is a professor at Department of Community Health and Preventive Medicine, Hamamatsu University School of Medicine, Shizuoka, Japan.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
This study was conducted with approval from the Institutional Review Board at Hamamatsu University School of Medicine (No. 24–147), and in accordance with the standards of the Declaration of Helsinki. Written informed consent was assumed by voluntary return of the questionnaire.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
References
- The Organisation for Economic Co-operation and Development (OECD). StatExtracts Income Distribution and Poverty: Child poverty rates 2010. www.oecd.org/els/soc/CO2_2_ChildPoverty_Jan2014.xls. Accessed 29 Sept 2016.
- O'Dea JA, Dibley MJ. Prevalence of obesity, overweight and thinness in Australian children and adolescents by socioeconomic status and ethnic/cultural group in 2006 and 2012. Int J Public Health. 2014;59:819–28.View ArticlePubMedGoogle Scholar
- Achat HM, Stubbs JM. Socio-economic and ethnic differences in the prevalence of overweight and obesity among school children. J Paediatr Child Health. 2014;50:E77–84.View ArticlePubMedGoogle Scholar
- Must A, Strauss RS. Risks and consequences of childhood and adolescent obesity. Int J Obes. 1999;23:S2–S11.View ArticleGoogle Scholar
- Freedman DS, Dietz WH, Srinivasan SR, Berenson GS. The relation of overweight to cardiovascular risk factors among children and adolescents: The Bogalusa Heart Study. Pediatrics. 1999;103:1175–82.View ArticlePubMedGoogle Scholar
- Gilliland FD, Berhane K, Islam T, McConnell R, Gauderman WJ, Gilliland SS, et al. Obesity and the risk of newly diagnosed asthma in school-age children. Am J Epidemiol. 2003;158:406–15.View ArticlePubMedGoogle Scholar
- Goodman E, Whitaker RC. A Prospective study of the role of depression in the development and persistence of adolescent obesity. Pediatrics. 2002;110:497–504.View ArticlePubMedGoogle Scholar
- Hoffmans MDAF, Kromhout D, de Lezenne Coulander C. The impact of Body Mass Index of 78,612 18-year old dutch men on 32-year mortality from all causes. J Clin Epidemiol. 1988;41:749–56.View ArticlePubMedGoogle Scholar
- Freedman DS, Serdula MK, Khan LK. The adult health consequences of childhood obesity. In: Chen K, Dietz WH, editors. Nestle Nutrition Workshop Series 2002. Philadelphia: Lippincott-Raven; 1999. p. 63–82.Google Scholar
- Gortmaker SL, Must A, Perrin JM, Sobol AM, Dietz WH. Social and economic consequences of overweight in adolescence and young adulthood. N Engl J Med. 1993;329:1008–12.View ArticlePubMedGoogle Scholar
- Wang Y, Lobstein T. Worldwide trends in childhood overweight and obesity. Pediatr Obes. 2006;1:11–25.View ArticleGoogle Scholar
- Wang Y, Lim H. The global childhood obesity epidemic and the association between socio-economic status and childhood obesity. Int Rev Psychiatry. 2012;24:176–88.View ArticlePubMedPubMed CentralGoogle Scholar
- Asia Pacific Cohort Studies Collaboration. The burden of overweight and obesity in the Asia–Pacific region. Obes Rev. 2007;8:191–6.View ArticleGoogle Scholar
- Ministry of Education, Culture, Sports, Science and Technology. Table No 4 Trends in incidence of child obesity 1977–2015. http://www.e-stat.go.jp/SG1/estat/List.do?bid=000001014499. Accessed 29 Sept 16. (in Japanese).
- Lobstein T, Baur L, Uauy R. Reported prevalence of child and adolescent overweight and obesity. Obes Rev. 2004;5 Suppl 1:86–97.Google Scholar
- Sekine M, Yamagami T, Saito T, Nanri S, Kawaminami K, Tokui N, et al. Characteristic lifestyles in 6-year-old children with obese parents: results of the Toyama Birth Cohort study. Environ Health Prev Med. 2001;6:104–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Sun Y, Sekine M, Kagamimori S. Lifestyle and overweight among Japanese adolescents: the Toyama Birth Cohort Study. J Epidemiol. 2009;19:303–10.View ArticlePubMedPubMed CentralGoogle Scholar
- Moore GF, Tapper K, Murphy S, Lynch R, Raisanen L, Pimm C, et al. Associations between deprivation, attitudes towards eating breakfast and breakfast eating behaviours in 9–11-year-olds. Public Health Nutr. 2007;10:582–9.View ArticlePubMedGoogle Scholar
- Yoshitake N, Okuda M, Sasaki S, Kunitsugu I, Hobara T. Validity of self-reported body mass index of Japanese children and adolescents. Pediatr Int. 2012;54:397–401.View ArticlePubMedGoogle Scholar
- WHO. Physical status, the use and interpretation of anthropometry. Geneva: World Health Organization; 1995.Google Scholar
- Statistics and Information Department Minister's Secretariat, Ministry of Health, Labour and Welfare. Comprehensive survey of living conditions 2010. Tokyo: Health, Labour and Welfare Statistics Association; 2012. (in Japanese).Google Scholar
- Shingo T, Yki T, Yoshihiro K. Touka Shiyakudo wo mochiita koureisetai no seikatsusuijyun no hiyouka. Gov Audit Rev. 2004;30:109–28. (in Japanese). Google Scholar
- Goodman E. The role of socioeconomic status gradients in explaining differences in US adolescents’ health. Am J Public Health. 1999;89:1522–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Haas JS, Lee LB, Kaplan CP, Sonneborn D, Phillips KA, Liang S-Y. The association of race, socioeconomic status, and health insurance status with the prevalence of overweight among children and adolescents. Am J Public Health. 2003;93:2105–10.View ArticlePubMedPubMed CentralGoogle Scholar
- Pigeot I, Barba G, Chadjigeorgiou C, De Henauw S, Kourides Y, Lissner L, et al. Prevalence and determinants of childhood overweight and obesity in European countries: pooled analysis of the existing surveys within the IDEFICS Consortium. Int J Obes. 2009;33:1103–10.View ArticleGoogle Scholar
- Caballero B. A nutrition paradox—underweight and obesity in developing countries. N Engl J Med. 2005;352:1514–16.View ArticlePubMedGoogle Scholar
- Danielzik S, Czerwinski-Mast M, Langnäse K, Dilba B, Müller M. Parental overweight, socioeconomic status and high birth weight are the major determinants of overweight and obesity in 5–7 y-old children: baseline data of the Kiel Obesity Prevention Study (KOPS). Int J Obes. 2004;28:1494–502.View ArticleGoogle Scholar
- Kleiser C, Rosario AS, Mensink GB, Prinz-Langenohl R, Kurth B-M. Potential determinants of obesity among children and adolescents in Germany: results from the cross-sectional KiGGS Study. BMC Public Health. 2009;9:46.View ArticlePubMedPubMed CentralGoogle Scholar
- Kinra S, Nelder RP, Lewendon GJ. Deprivation and childhood obesity: a cross sectional study of 20973 children in Plymouth, United Kingdom. J Epidemiol Community Health. 2000;54:456–60.Google Scholar
- Shaw ME. Adolescent breakfast skipping: an Australian study. Adolescence. 1998;33:851–61.PubMedGoogle Scholar
- Kovarova M, Vignerova J, Blaha P, Osancova K. Bodily characteristics and lifestyle of Czech children aged 7.00 to 10.99 years, incidence of childhood obesity. Cent Eur J Public Health. 2002;10:169–73.PubMedGoogle Scholar
- Cartwright M, Wardle J, Steggles N, Simon AE, Croker H, Jarvis MJ. Stress and dietary practices in adolescents. Health Psychol. 2003;22:362–9.View ArticlePubMedGoogle Scholar
- Croezen S, Visscher T, Ter Bogt N, Veling M, Haveman-Nies A. Skipping breakfast, alcohol consumption and physical inactivity as risk factors for overweight and obesity in adolescents: results of the E-MOVO project. Eur J Clin Nutr. 2009;63:405–12.View ArticlePubMedGoogle Scholar
- Smith KJ, Gall SL, McNaughton SA, Blizzard L, Dwyer T, Venn AJ. Skipping breakfast: longitudinal associations with cardiometabolic risk factors in the Childhood Determinants of Adult Health Study. Am J Clin Nutr. 2010;92:1316–25.View ArticlePubMedGoogle Scholar
- Keski-Rahkonen A, Kaprio J, Rissanen A, Virkkunen M, Rose RJ. Breakfast skipping and health-compromising behaviors in adolescents and adults. Eur J Clin Nutr. 2003;57:842–53.View ArticlePubMedGoogle Scholar
- Anzman-Frasca S, Djang HC, Halmo MM, Dolan PR, Economos CD. Estimating impacts of a breakfast in the classroom program on school outcomes. JAMA Pediatr. 2015;169:71–7.View ArticlePubMedGoogle Scholar
- Chaput J, Brunet M, Tremblay A. Relationship between short sleeping hours and childhood overweight/obesity: results from the ‘Quebec en Forme’Project. Int J Obes. 2006;30:1080–5.View ArticleGoogle Scholar
- Himes JH, Hannan P, Wall M, Neumark-Sztainer D. Factors associated with errors in self-reports of stature, weight, and body mass index in Minnesota adolescents. Ann Epidemiol. 2005;15:272–8.View ArticlePubMedGoogle Scholar
- Kachi Y, Otsuka T, Kawada T. Socioeconomic status and overweight: a population-based cross-sectional study of Japanese children and adolescents. J Epidemiol. 2015;25:463–9.Google Scholar
