- Email: [email protected]
Gender-specific relationships between socioeconomic disadvantage and obesity in elementary school students
Preventive Medicine 81 (2015) 138–141
Contents lists available at ScienceDirect
Preventive Medicine journal homepage: www.elsevier.com/locate/ypmed
Brief Original Report
Gender-specific relationships between socioeconomic disadvantage and obesity in elementary school students Whitney E. Zahnd a,⁎, Valerie Rogers b, Tracey Smith c, Susan J. Ryherd a, Albert Botchway a, David E. Steward d a
Center for Clinical Research, Southern Illinois University School of Medicine, 201 E. Madison, Springfield, IL 62794-9664, United States Springfield Public Schools-District 186, 900 W. Edwards, Springfield, IL 62704, United States Department of Family and Community Medicine, Southern Illinois University School of Medicine, 913 N. Rutledge, Springfield, IL 62794-9671, United States d Office of Community Health and Service, Southern Illinois University School of Medicine, 201 E. Madison, Springfield, IL 62794-9604, United States b c
a r t i c l e
i n f o
Available online 5 September 2015 Keywords: Pediatric obesity Poverty Schools
a b s t r a c t Objective. To assess the gender-specific effect of socioeconomic disadvantage on obesity in elementary school students. Methods. We evaluated body mass index (BMI) data from 2,648 first- and fourth-grade students (1,377 male and 1,271 female students) in eight elementary schools in Springfield, Illinois, between 2012 and 2014. Other factors considered in analysis were grade level, year of data collection, school, race/ethnicity, gender, and socioeconomic disadvantage (SD). Students were considered SD if they were eligible for free/reduced price lunch, a school-based poverty measure. We performed Fisher's exact test or chi-square analysis to assess differences in gender and obesity prevalence by the other factors and gender-stratified logistic regression analysis to determine if SD contributed to increased odds of obesity. Results. A higher proportion of SD female students (20.8%) were obese compared to their non-SD peers (15.2%) (p = 0.01). Unadjusted and adjusted logistic regression analysis indicated no difference in obesity in SD and non-SD male students. However, in both unadjusted and adjusted analyses, SD female students had higher odds of obesity than their peers. Even after controlling for grade level, school, year of data collection, and race/ethnicity, SD female students had 49% higher odds of obesity than their non-SD classmates (odds ratio:1.49; 95% confidence interval: 1.09–2.04). Conclusions. Obesity was elevated in SD female students, even after controlling for factors such as race/ ethnicity, but such an association was not seen in male students. Further study is warranted to determine the cause of this disparity, and interventions should be developed to target SD female students. © 2015 Elsevier Inc. All rights reserved.
Introduction Obesity prevalence among American children is now at 17%, more than triple the rate of a generation ago (Ogden et al., 2014). Rates of obesity in Illinois children are particularly high, ranking in the top quartile of states for obesity in low-income preschoolers and adolescents (Trust for America's Health & Robert Wood Johnson Foundation). Obesity in children and adolescents is defined as ≥ 95th percentile of Center for Disease Control and Prevention (CDC) 2000 growth rate charts. The BMI-for-age percentile growth categories and related percentiles are the most commonly used metric for childhood size and growth patterns (Barlow & the Expert Committee, 2007). Obese children are more likely to remain obese into adolescence and adulthood and have a heightened risk of chronic conditions, such as cardiovascular
⁎ Corresponding author at: 201 E. Madison Room 235, PO Box 19664, Springfield, IL 62794-9664, United States. E-mail address: [email protected] (W.E. Zahnd).
http://dx.doi.org/10.1016/j.ypmed.2015.08.021 0091-7435/© 2015 Elsevier Inc. All rights reserved.
disease, diabetes, and cancer (The Surgeon General's Vision for a Healthy & Fit Nation). Many studies have indicated that racial, ethnic, socioeconomic status (SES), and gender factors can individually contribute to an increased likelihood of obesity in children (Ogden et al., 2014; Singh et al., 2010a). Although some U.S. subgroups show improved childhood obesity trends, minority and low SES populations continue to struggle with obesity disparities. Overweight and obesity rates tend to be higher for minority children across SES parameters (Shih et al., 2013). Studies have shown that Hispanic and African American children were more likely to be obese than their white or Asian peers (Rossen, 2014; Singh et al., 2010a). Other recent studies have found obesity disparities among African American girls specifically. Wang et al. reported that severe obesity occurrence increased among U.S. youth, with higher prevalence among non-Hispanic black girls and Hispanic boys (Wang et al., 2011). Wang also found that non-Hispanic black girls aged 12– 19 years showed the highest prevalence of severe obesity. Furthermore, children from low-income and/or low education households or who live in neighborhoods with high economic deprivation had an increased risk
W.E. Zahnd et al. / Preventive Medicine 81 (2015) 138–141
of obesity compared to children from higher income households or neighborhoods (Shih et al., 2013; Singh et al., 2010b). The children of parents with only a high school diploma compared to those with a college degree, as well as children living in poverty compared to children in families with incomes N 400% of the poverty level, showed higher odds of being obese or overweight. (Singh et al., 2010b) However, the combined relationship of obesity with gender and SES is complex and less well-understood. While the literature describing the association between lower socioeconomic status, race/ethnicity, and obesity is extensive, there is a relative dearth in the literature exploring these associations stratified by gender. Evidence of association between gender, race/ethnicity, and SES could point to potential school-based and public health interventions to significantly reduce existing U.S. childhood obesity disparities. Studies examining gender-stratified effects of poverty on obesity have been performed primarily in young children (aged 2–5 years) and adolescents or have studied the effect of childhood poverty on adult obesity (Clarke et al., 2009; Gordon-Lausen et al., 2003; Hernandez & Pressler, 2014; Suglia et al., 2013). A study by Suglia and colleagues found that cumulative social risks, such as poverty-related factors (e.g. housing and food insecurity), in girls under the age of five increased their obesity risk (Suglia et al., 2013). Gordon-Larsen and colleagues reported disparities in obesity, race, and SES combined, but the only clear association was found between low SES and obesity in adolescent girls (Gordon-Lausen et al., 2003). A study by Clarke and colleagues found increased obesity in adult women associated with childhood poverty (Clarke et al., 2009). Our study is unique in that it evaluates the gender-specific association between socioeconomic disadvantage and obesity in elementary school students—an understudied population, but a key population for preventive interventions. The objective of this study was to examine the association of socioeconomic disadvantage and obesity in elementary school male and female students in Springfield, Illinois. Methods We performed a cross-sectional, gender-stratified analysis of aggregated data collected on 2,648 first- and fourth-grade students from eight schools in Springfield, Illinois Public School District 186 (SPS) in 2012, 2013 and 2014. These data were collected as part of the efforts of the Springfield Collaborative for Active Child Health, an academic-community partnership comprised of SPS, the Springfield Urban League, the Illinois Department of Public Health, the Southern Illinois University School of Medicine and other informal community partners. The collaborative is active in eight of SPS's elementary schools. Its aim is to prevent and reduce childhood obesity and promote physical activity and proper nutrition through collaboration, education, and evaluation. School nurses performed height and weight measurements during the fall of each respective year. Body mass index (BMI) was calculated using child height, weight, gender, and age at date of measurement. CDC criterion was used to define “obese” as a gender-specific, BMI-for-age percentile greater than or equal to the 95th percentile based upon the 2000 CDC growth charts (Barlow & the Expert Committee, 2007). Socioeconomic disadvantage (SD) was determined by eligibility for free or reduced rate lunch, a measure of poverty previously used in research exploring the association between socioeconomic status and obesity (Li & Hooker, 2010). Eligibility for free or reduced rate lunch is based upon a student's family's income. Students whose family income is ≤130% of the poverty level are eligible for “free lunch,” and students whose family income is between 130% and 185% of the poverty level are eligible for the “reduced lunch” rate (United States Department of Agriculture). Other factors included in our study analysis were school, gender, grade level, year of data collection, and race/ethnicity. SPS students' race/ethnicity was categorized as white, African American, Hispanic, Asian, American Indian, Native Hawaiian, or multi-racial. There were few students in the Asian, American Indian, and Native Hawaiian groups. We collapsed those race/ethnicity groups into one group: “other” race/ethnicity. Therefore, in our analysis, there were five race/ethnicity categories: white, African American, Hispanic, Multi-Racial, and “other”. Our study was approved by our institutional review board (IRB), the Springfield Committee on Research Involving Human Subjects.
139
Statistical analyses We performed Fisher's exact test or chi-square test of independence to assess differences in gender and obesity proportions by school, year of data collection, grade, SD, and race/ethnicity. We tested a multilevel model to account for clustering effects due to having students “clustered” in schools. The resulting likelihood ratio test indicated that the random effect was non-significant, and the intraclass correlation value was very low (b 0.01), indicating that multilevel models were not necessary to test our data (Hayes, 2006). We ultimately performed gender-stratified unadjusted and adjusted logistic regression to assess the effect of SD on obesity. Analyses were performed in SPSS 22 (IBM Corporation). Adjusted models controlled for school, year of data collection, grade level, and race/ethnicity.
Results There were no differences in the year of data collection, socioeconomic status, or race/ethnicity by gender (Table 1). However, the proportion of male and female students differed by grade (p = 0.02). The prevalence of obesity significantly varied by school, grade level, and socioeconomic status, but not by year of data collection or race. Obesity prevalence differed by grade, as 16.3% of first-grade students and 20.3% of fourth-grade students were obese (p = 0.01) (Table 1). A higher proportion of SD students (19.6%) were obese compared to non-SD students (16.0%) (p = 0.02). There was no difference in obesity prevalence between SD and non-SD male students (18.4% and 16.7%, respectively; p = 0.46). However, obesity was more prevalent in SD female students compared to their non-SD peers (20.8% and 15.2%, respectively; p = 0.01). Unadjusted logistic regression indicated no difference in non-SD male students compared to SD male students (Table 2). This remained after controlling for school, year of data collection, grade level, and race/ethnicity. In female students, unadjusted logistic regression yielded increased odds of obesity in SD female students (odds ratio [OR] = 1.47; 95% confidence interval [CI] = 1.09–2.00). This association remained after controlling for year of data collection, school, grade level, and race/ethnicity (OR = 1.49; 95% CI = 1.09–2.04). Discussion We analyzed BMI data from 2,648 1st- and 4th-grade students over a 3-year time period. These analyses indicated gender difference by grade, but not by any other factors. Obesity prevalence differed by school and by socioeconomic status, as a higher percentage of SD students were obese compared to non-SD students overall and among female students specifically. Logistic regression analysis indicated that there were no differences in likelihood of obesity in SD and non-SD male students, even after controlling for relevant factors. However, analysis of female students indicated that SD female students had a higher chance of being obese compared to their non-SD peers. The increased likelihood of obesity was maintained in adjusted analysis. After controlling for race/ethnicity, grade level, year of data collection, and school, female students who were socioeconomically disadvantaged had 49% higher odds of being obese compared to their non-SD peers. Our findings corroborate other studies suggesting a gender-specific link between lower socioeconomic status and obesity. Using data from the nationally representative 2007 National Survey of Children's Health, a study by Singh and colleagues, found that adolescent girls who lived in neighborhoods with poorer socioeconomic conditions were two to four times more likely to be overweight or obese than girls from wealthier neighborhoods (Singh et al., 2010a). Another study by Suglia and colleagues used data from the Fragile Families and Child Wellbeing Study, a study that surveyed families of preschool children in twenty U.S. cities, and found that there was a greater risk of obesity in five year old girls with greater cumulative social risks (Suglia et al., 2013). Suglia suggests that unmeasured factors associated with social stress
140
W.E. Zahnd et al. / Preventive Medicine 81 (2015) 138–141
Table 1 Student demographics by gender and obesity status of elementary school students in Springfield, Illinois, 2012–2014. All students (n = 2,648) Schoola A B C D E F G H Year of data collectiona 2012 2013 2014 Gradeb,c 1st grade 4th grade Socioeconomic status SD Non-SD Race/Ethnicitya White African American Hispanic Multi-Racial Other
Male students (n = 1,378)
Female students (n = 1,271)
313 (11.8%) 288 (10.9%) 256 (9.7%) 325 (12.3%) 442 (16.7%) 241 (9.1%) 456 (17.2%) 327 (12.3%)
173 (55.3%) 151 (52.4%) 127 (49.6%) 157 (48.3%) 249 (56.3%) 111 (46.1%) 244 (53.5%) 165 (50.5%)
140 (44.7%) 137 (47.6%) 129 (50.4%) 168 (51.7%) 193 (43.7%) 130 (53.9%) 212 (46.5%) 162 (49.5%)
902 (34.1%) 780 (29.5%) 966 (36.5%)
459 (50.9%) 417 (53.5%) 501 (51.9%)
443 (49.1%) 363 (46.5%) 465 (48.1%)
1328 (50.2%) 1318 (49.8%)
662 (49.8%) 714 (54.2%)
666 (50.2%) 604 (45.8%)
1703 (64.3%) 945 (35.7%)
887 (64.4%) 490 (35.6%)
816 (64.2%) 455 (35.8%)
1291 (48.8%) 917 (34.6%) 193 (7.3%) 177 (6.7%) 70 (2.6%)
672 (52.1%) 489 (53.3%) 91 (47.2%) 86 (48.6%) 39 (55.7%)
619 (47.9%) 428 (46.7%) 102 (52.8%) 91 (51.4%) 31 (44.3%)
P-value
Obese students (n = 484)
Non-obese students (n = 2,162)
0.13
P-value 0.008
69 (22.0%) 33 (11.5%) 47 (18.4%) 60 (18.5%) 86 (19.5%) 54 (22.4%) 69 (15.1%) 66 (20.2%)
244 (78.0%) 33 (88.5%) 209 (81.6%) 265 (81.5%) 356 (80.5%) 187 (77.6%) 387 (84.9%) 261 (79.8%)
145 (16.1%) 145 (18.6%) 194 (20.1%)
757 (83.9%) 635 (81.4%) 772 (79.9%)
217 (16.3%) 267 (20.3%)
1111 (83.7%) 1051 (79.7%)
333 (19.6%) 151 (16.0%)
1370 (81.7%) 794 (84.0%)
215 (16.7%) 180 (19.6%) 37 (19.2%) 40 (22.6%) 12 (17.1%)
1076 (83.3%) 737 (80.4%) 156 (80.8%) 137 (77.4%) 58 (82.9%)
0.57
0.08
0.03
0.01
0.94
0.02
0.45
0.22
SD = socioeconomic disadvantage a Chi-square test for independence. b Fisher's exact test. c 3 students were missing on grade level.
may contribute to the increased risk of obesity in girls. The risk of obesity in socioeconomically disadvantaged females extends beyond childhood into adulthood. An analysis of the National Longitudinal Study of Youth found that cumulative poverty in childhood increased the risk of overweight and obesity in young adult women of all races and ethnicities (Hernandez & Pressler, 2014). These authors suggest that the increased risk was due to gender-specific behavioral and physiological factors that occur with long-term social stresses. With the growing evidence of association between poverty and obesity in females, further research is needed to elucidate the sociological, behavioral, and physiological causes of increased risk of obesity in girls and women of low socioeconomic status. Our results were unique in that obesity was elevated in SD girls, even after controlling for race/ethnicity. Previous studies have found elevated levels of obesity in Hispanic and African American girls (Rossen, 2014; Singh et al., 2010a). Our findings, however, suggest that socioeconomic factors play a larger role than race/ethnicity in girls and thus may be an
Table 2 Obesity odds in socioeconomically disadvantaged vs. non-socioeconomically disadvantaged elementary school students in Springfield, Illinois, 2012–2014.
Male Socioeconomic disadvantage School Grade Year of data collection Race/ethnicity Female Socioeconomic disadvantage School Grade Year of data collection Race/ethnicity ⁎⁎ Unadjusted analysis.
Unadjusted odds ratio (95% confidence interval)
Adjusted odds ratio (95% confidence interval)
1.12 (0.88–1.42) ⁎⁎ ⁎⁎ ⁎⁎ ⁎⁎
1.10 (0.82–1.48) 1.01 (0.95–1.07) 1.10 (1.00–1.21) 1.05 (0.89–1.24) 1.19 (1.05–1.36)
1.47 (1.09–2.00) ⁎⁎ ⁎⁎ ⁎⁎ ⁎⁎
1.49 (1.09–2.04) 1.02 (0.96–1.08) 1.10 (1.00–1.20) 1.21 (1.02–1.44) 0.96 (0.83–1.10)
appropriate factor for consideration in targeted interventions (Singh et al., 2010a). A review of school-based obesity interventions indicated gender-specific interventions may be most effective (Kropski et al., 2008). Other research suggests targeting interventions specifically at low-income students (Kumanyika & Grier, 2006). However, our findings indicate the potential utility of considering both gender and socioeconomic status when developing and testing obesity prevention interventions in school-aged children.
Limitations and strengths There were some limitations to our study. Specifically, we were not able to account for additional social risk factors for childhood obesity, such as parent education levels and single parent households, and we were not able to consider neighborhood contextual factors that may play a role in obesity prevalence, such as access to parks and other recreation facilities. Additionally, we utilized a convenience sample of students who were assessed as part of our collaborative's evaluation. Thus, our results may not be representative of students in the same grades at other schools in our district, state or nation. However, there were strengths to our study. First, we used a student-specific socioeconomic indicator—eligibility for free/reduced rate school lunch—which categorizes students individually in the context of poverty level. Other studies that evaluated the relationship between socioeconomic factors and childhood obesity often used neighborhood level socioeconomic measures as a proxy measure for individual-level socioeconomic status. An additional strength of our study was our use of height and weight measures conducted by trained school nurses using standardized techniques to determine BMI, which is a more reliable method than self-report or parental report. Also, we assessed data from elementary school-aged students whereas most other studies examined students in preschool or adolescence. The agegroup we examined may be the most appropriate for interventions, as elementary schools provide cost-effective infrastructure for childhood obesity interventions and students spend 6–8 hours a day at school (Budd & Volpe, 2006; Wang et al., 2003).
W.E. Zahnd et al. / Preventive Medicine 81 (2015) 138–141
Conclusions Our study found that, even after controlling for factors such as race/ ethnicity, obesity prevalence was elevated in SD female elementary school students compared to their non-SD classmates. This association was not seen in male students. This suggests that interventions targeted at SD female students could be helpful to prevent and reduce childhood obesity. Future research should be initiated to help determine the causes of increased obesity in SD girls. Conflict of interest The authors declare that there are no conflicts of interest. Acknowledgments This study was funded, in part, by Healthy Kids, Healthy Families funding from Blue Cross Blue Shield of Illinois. The authors wish to thank Melissa Cleer and Donna Treadwell for their contributions as project coordinators for the Springfield Collaborative for Active Child Health and to all partner organizations involved in the work of the Collaborative. The authors also wish to acknowledge Steve Scaife for his assistance in data management and Dr. Steve Verhulst and Georgia Mueller-Luckey for their statistical guidance. References Barlow, S.E., the Expert Committee, 2007. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics 120 (S4), S164–S192. Budd, G.M., Volpe, S.L., 2006. School-based obesity prevention: research, challenges and recommendations. J. Sch. Health 76 (10), 485–495. Clarke, P., O'Malley, P.M., Johnston, L.D., Schluenberg, J.E., 2009. Social disparities in BMI trajectories across adulthood by gender, race/ethnicity and lifetime socio-economic position: 1986-2004. Int. J. Epidemiol. 38 (2), 499–509.
141
Gordon-Lausen, P., Adair, L.S., Popkin, B.M., 2003. The relationship of ethnicity, socioeconomic factors, and overweight in US adolescents. Obes. Res. 11 (1), 121–129. Hayes, A.F., 2006. A primer on multilevel modeling. Hum. Commun. Res. 32, 385–410. Hernandez, D.C., Pressler, E., 2014. Accumulation of childhood poverty on young adult overweight and obese status: race/ethnicity and gender disparities. J. Epidemiol. Community Health 68 (5), 478–484. Kropski, J.A., Keckley, P.H., Jensen, G.L., 2008. School-based obesity prevention programs: an evidence-base review. Obesity (Silver Spring) 16 (5), 1009–1018. Kumanyika, S., Grier, S., 2006. Targeting interventions for ethnic minority and lowincome populations. Future Child. 16 (1), 187–207. Li, J., Hooker, N.H., 2010. Childhood obesity and schools: evidence from the national survey of children's health. J. Sch. Health 80 (2), 96–103. Ogden, C.L., Carroll, M.D., Kit, B.K., Flegal, K.M., 2014. Prevalence of childhood and adult obesity, 2011–2012. JAMA 311 (8), 806–814. Rossen, L.M., 2014. Neighbourhood economic deprivation explains racial/ethnic disparities in overweight and obesity among children and adolescents in the U.S.A. J. Epidemiol. Community Health 68 (2), 123–129. Shih, M., Dumke, K.A., Goran, M.I., Simon, P.A., 2013. The association between community-level economic hardship and childhood obesity prevalence in Los Angeles. Pediatr. Obes. 8 (6), 411–417. Singh, G.K., Siahpush, M., Kogan, M.D., 2010a. Rising social inequalities in US childhood obesity, 2003–2007. Ann. Epidemiol. 20 (1), 40–52. Singh, G.K., Siahpush, M., Kogan, M.D., 2010b. Neighborhood socioeconomic conditions, built environments, and childhood obesity. Health Aff. 29 (3), 503–5112. Suglia, S.F., Durante, C.S., Chambers, E.C., Boynton-Jarrett, R., 2013. Social and behavioral risk factors for obesity in early childhood. J. Dev. Behav. Pediatr. 34 (8), 549–556. The Surgeon General's Vision for a Healthy and Fit Nation. 108. Department of Health and Human Services, Office of the Surgeon General, Rockville, MD: U.S, pp. 712–718. Trust for America's Health and Robert Wood Johnson Foundation, d. The State of Obesity in Illinois. Available at, http://stateofobesity.org/states/il/ (Accessed on 20 March 2015). United States Department of Agriculture, d. National School Lunch Program (NSLP). Available at, http://www.fns.usda.gov/nslp/national-school-lunch-program-nslp (Accessed on 22 May 2015). Wang, L.Y., Yang, Q., Lowry, R., Wechsler, H., 2003. Economic analysis of a school-based obesity prevention program. Obes. Res. 11, 1313–1324. Wang, C.Y., Gortmaker, S.L., Taveras, E.M., 2011, Feb. Trends and racial/ethnic disparities in severe obesity among US children and adolescents, 1976–2006. Int. J. Pediatr. Obes. 6 (1), 12–20.
Contents lists available at ScienceDirect
Preventive Medicine journal homepage: www.elsevier.com/locate/ypmed
Brief Original Report
Gender-specific relationships between socioeconomic disadvantage and obesity in elementary school students Whitney E. Zahnd a,⁎, Valerie Rogers b, Tracey Smith c, Susan J. Ryherd a, Albert Botchway a, David E. Steward d a
Center for Clinical Research, Southern Illinois University School of Medicine, 201 E. Madison, Springfield, IL 62794-9664, United States Springfield Public Schools-District 186, 900 W. Edwards, Springfield, IL 62704, United States Department of Family and Community Medicine, Southern Illinois University School of Medicine, 913 N. Rutledge, Springfield, IL 62794-9671, United States d Office of Community Health and Service, Southern Illinois University School of Medicine, 201 E. Madison, Springfield, IL 62794-9604, United States b c
a r t i c l e
i n f o
Available online 5 September 2015 Keywords: Pediatric obesity Poverty Schools
a b s t r a c t Objective. To assess the gender-specific effect of socioeconomic disadvantage on obesity in elementary school students. Methods. We evaluated body mass index (BMI) data from 2,648 first- and fourth-grade students (1,377 male and 1,271 female students) in eight elementary schools in Springfield, Illinois, between 2012 and 2014. Other factors considered in analysis were grade level, year of data collection, school, race/ethnicity, gender, and socioeconomic disadvantage (SD). Students were considered SD if they were eligible for free/reduced price lunch, a school-based poverty measure. We performed Fisher's exact test or chi-square analysis to assess differences in gender and obesity prevalence by the other factors and gender-stratified logistic regression analysis to determine if SD contributed to increased odds of obesity. Results. A higher proportion of SD female students (20.8%) were obese compared to their non-SD peers (15.2%) (p = 0.01). Unadjusted and adjusted logistic regression analysis indicated no difference in obesity in SD and non-SD male students. However, in both unadjusted and adjusted analyses, SD female students had higher odds of obesity than their peers. Even after controlling for grade level, school, year of data collection, and race/ethnicity, SD female students had 49% higher odds of obesity than their non-SD classmates (odds ratio:1.49; 95% confidence interval: 1.09–2.04). Conclusions. Obesity was elevated in SD female students, even after controlling for factors such as race/ ethnicity, but such an association was not seen in male students. Further study is warranted to determine the cause of this disparity, and interventions should be developed to target SD female students. © 2015 Elsevier Inc. All rights reserved.
Introduction Obesity prevalence among American children is now at 17%, more than triple the rate of a generation ago (Ogden et al., 2014). Rates of obesity in Illinois children are particularly high, ranking in the top quartile of states for obesity in low-income preschoolers and adolescents (Trust for America's Health & Robert Wood Johnson Foundation). Obesity in children and adolescents is defined as ≥ 95th percentile of Center for Disease Control and Prevention (CDC) 2000 growth rate charts. The BMI-for-age percentile growth categories and related percentiles are the most commonly used metric for childhood size and growth patterns (Barlow & the Expert Committee, 2007). Obese children are more likely to remain obese into adolescence and adulthood and have a heightened risk of chronic conditions, such as cardiovascular
⁎ Corresponding author at: 201 E. Madison Room 235, PO Box 19664, Springfield, IL 62794-9664, United States. E-mail address: [email protected] (W.E. Zahnd).
http://dx.doi.org/10.1016/j.ypmed.2015.08.021 0091-7435/© 2015 Elsevier Inc. All rights reserved.
disease, diabetes, and cancer (The Surgeon General's Vision for a Healthy & Fit Nation). Many studies have indicated that racial, ethnic, socioeconomic status (SES), and gender factors can individually contribute to an increased likelihood of obesity in children (Ogden et al., 2014; Singh et al., 2010a). Although some U.S. subgroups show improved childhood obesity trends, minority and low SES populations continue to struggle with obesity disparities. Overweight and obesity rates tend to be higher for minority children across SES parameters (Shih et al., 2013). Studies have shown that Hispanic and African American children were more likely to be obese than their white or Asian peers (Rossen, 2014; Singh et al., 2010a). Other recent studies have found obesity disparities among African American girls specifically. Wang et al. reported that severe obesity occurrence increased among U.S. youth, with higher prevalence among non-Hispanic black girls and Hispanic boys (Wang et al., 2011). Wang also found that non-Hispanic black girls aged 12– 19 years showed the highest prevalence of severe obesity. Furthermore, children from low-income and/or low education households or who live in neighborhoods with high economic deprivation had an increased risk
W.E. Zahnd et al. / Preventive Medicine 81 (2015) 138–141
of obesity compared to children from higher income households or neighborhoods (Shih et al., 2013; Singh et al., 2010b). The children of parents with only a high school diploma compared to those with a college degree, as well as children living in poverty compared to children in families with incomes N 400% of the poverty level, showed higher odds of being obese or overweight. (Singh et al., 2010b) However, the combined relationship of obesity with gender and SES is complex and less well-understood. While the literature describing the association between lower socioeconomic status, race/ethnicity, and obesity is extensive, there is a relative dearth in the literature exploring these associations stratified by gender. Evidence of association between gender, race/ethnicity, and SES could point to potential school-based and public health interventions to significantly reduce existing U.S. childhood obesity disparities. Studies examining gender-stratified effects of poverty on obesity have been performed primarily in young children (aged 2–5 years) and adolescents or have studied the effect of childhood poverty on adult obesity (Clarke et al., 2009; Gordon-Lausen et al., 2003; Hernandez & Pressler, 2014; Suglia et al., 2013). A study by Suglia and colleagues found that cumulative social risks, such as poverty-related factors (e.g. housing and food insecurity), in girls under the age of five increased their obesity risk (Suglia et al., 2013). Gordon-Larsen and colleagues reported disparities in obesity, race, and SES combined, but the only clear association was found between low SES and obesity in adolescent girls (Gordon-Lausen et al., 2003). A study by Clarke and colleagues found increased obesity in adult women associated with childhood poverty (Clarke et al., 2009). Our study is unique in that it evaluates the gender-specific association between socioeconomic disadvantage and obesity in elementary school students—an understudied population, but a key population for preventive interventions. The objective of this study was to examine the association of socioeconomic disadvantage and obesity in elementary school male and female students in Springfield, Illinois. Methods We performed a cross-sectional, gender-stratified analysis of aggregated data collected on 2,648 first- and fourth-grade students from eight schools in Springfield, Illinois Public School District 186 (SPS) in 2012, 2013 and 2014. These data were collected as part of the efforts of the Springfield Collaborative for Active Child Health, an academic-community partnership comprised of SPS, the Springfield Urban League, the Illinois Department of Public Health, the Southern Illinois University School of Medicine and other informal community partners. The collaborative is active in eight of SPS's elementary schools. Its aim is to prevent and reduce childhood obesity and promote physical activity and proper nutrition through collaboration, education, and evaluation. School nurses performed height and weight measurements during the fall of each respective year. Body mass index (BMI) was calculated using child height, weight, gender, and age at date of measurement. CDC criterion was used to define “obese” as a gender-specific, BMI-for-age percentile greater than or equal to the 95th percentile based upon the 2000 CDC growth charts (Barlow & the Expert Committee, 2007). Socioeconomic disadvantage (SD) was determined by eligibility for free or reduced rate lunch, a measure of poverty previously used in research exploring the association between socioeconomic status and obesity (Li & Hooker, 2010). Eligibility for free or reduced rate lunch is based upon a student's family's income. Students whose family income is ≤130% of the poverty level are eligible for “free lunch,” and students whose family income is between 130% and 185% of the poverty level are eligible for the “reduced lunch” rate (United States Department of Agriculture). Other factors included in our study analysis were school, gender, grade level, year of data collection, and race/ethnicity. SPS students' race/ethnicity was categorized as white, African American, Hispanic, Asian, American Indian, Native Hawaiian, or multi-racial. There were few students in the Asian, American Indian, and Native Hawaiian groups. We collapsed those race/ethnicity groups into one group: “other” race/ethnicity. Therefore, in our analysis, there were five race/ethnicity categories: white, African American, Hispanic, Multi-Racial, and “other”. Our study was approved by our institutional review board (IRB), the Springfield Committee on Research Involving Human Subjects.
139
Statistical analyses We performed Fisher's exact test or chi-square test of independence to assess differences in gender and obesity proportions by school, year of data collection, grade, SD, and race/ethnicity. We tested a multilevel model to account for clustering effects due to having students “clustered” in schools. The resulting likelihood ratio test indicated that the random effect was non-significant, and the intraclass correlation value was very low (b 0.01), indicating that multilevel models were not necessary to test our data (Hayes, 2006). We ultimately performed gender-stratified unadjusted and adjusted logistic regression to assess the effect of SD on obesity. Analyses were performed in SPSS 22 (IBM Corporation). Adjusted models controlled for school, year of data collection, grade level, and race/ethnicity.
Results There were no differences in the year of data collection, socioeconomic status, or race/ethnicity by gender (Table 1). However, the proportion of male and female students differed by grade (p = 0.02). The prevalence of obesity significantly varied by school, grade level, and socioeconomic status, but not by year of data collection or race. Obesity prevalence differed by grade, as 16.3% of first-grade students and 20.3% of fourth-grade students were obese (p = 0.01) (Table 1). A higher proportion of SD students (19.6%) were obese compared to non-SD students (16.0%) (p = 0.02). There was no difference in obesity prevalence between SD and non-SD male students (18.4% and 16.7%, respectively; p = 0.46). However, obesity was more prevalent in SD female students compared to their non-SD peers (20.8% and 15.2%, respectively; p = 0.01). Unadjusted logistic regression indicated no difference in non-SD male students compared to SD male students (Table 2). This remained after controlling for school, year of data collection, grade level, and race/ethnicity. In female students, unadjusted logistic regression yielded increased odds of obesity in SD female students (odds ratio [OR] = 1.47; 95% confidence interval [CI] = 1.09–2.00). This association remained after controlling for year of data collection, school, grade level, and race/ethnicity (OR = 1.49; 95% CI = 1.09–2.04). Discussion We analyzed BMI data from 2,648 1st- and 4th-grade students over a 3-year time period. These analyses indicated gender difference by grade, but not by any other factors. Obesity prevalence differed by school and by socioeconomic status, as a higher percentage of SD students were obese compared to non-SD students overall and among female students specifically. Logistic regression analysis indicated that there were no differences in likelihood of obesity in SD and non-SD male students, even after controlling for relevant factors. However, analysis of female students indicated that SD female students had a higher chance of being obese compared to their non-SD peers. The increased likelihood of obesity was maintained in adjusted analysis. After controlling for race/ethnicity, grade level, year of data collection, and school, female students who were socioeconomically disadvantaged had 49% higher odds of being obese compared to their non-SD peers. Our findings corroborate other studies suggesting a gender-specific link between lower socioeconomic status and obesity. Using data from the nationally representative 2007 National Survey of Children's Health, a study by Singh and colleagues, found that adolescent girls who lived in neighborhoods with poorer socioeconomic conditions were two to four times more likely to be overweight or obese than girls from wealthier neighborhoods (Singh et al., 2010a). Another study by Suglia and colleagues used data from the Fragile Families and Child Wellbeing Study, a study that surveyed families of preschool children in twenty U.S. cities, and found that there was a greater risk of obesity in five year old girls with greater cumulative social risks (Suglia et al., 2013). Suglia suggests that unmeasured factors associated with social stress
140
W.E. Zahnd et al. / Preventive Medicine 81 (2015) 138–141
Table 1 Student demographics by gender and obesity status of elementary school students in Springfield, Illinois, 2012–2014. All students (n = 2,648) Schoola A B C D E F G H Year of data collectiona 2012 2013 2014 Gradeb,c 1st grade 4th grade Socioeconomic status SD Non-SD Race/Ethnicitya White African American Hispanic Multi-Racial Other
Male students (n = 1,378)
Female students (n = 1,271)
313 (11.8%) 288 (10.9%) 256 (9.7%) 325 (12.3%) 442 (16.7%) 241 (9.1%) 456 (17.2%) 327 (12.3%)
173 (55.3%) 151 (52.4%) 127 (49.6%) 157 (48.3%) 249 (56.3%) 111 (46.1%) 244 (53.5%) 165 (50.5%)
140 (44.7%) 137 (47.6%) 129 (50.4%) 168 (51.7%) 193 (43.7%) 130 (53.9%) 212 (46.5%) 162 (49.5%)
902 (34.1%) 780 (29.5%) 966 (36.5%)
459 (50.9%) 417 (53.5%) 501 (51.9%)
443 (49.1%) 363 (46.5%) 465 (48.1%)
1328 (50.2%) 1318 (49.8%)
662 (49.8%) 714 (54.2%)
666 (50.2%) 604 (45.8%)
1703 (64.3%) 945 (35.7%)
887 (64.4%) 490 (35.6%)
816 (64.2%) 455 (35.8%)
1291 (48.8%) 917 (34.6%) 193 (7.3%) 177 (6.7%) 70 (2.6%)
672 (52.1%) 489 (53.3%) 91 (47.2%) 86 (48.6%) 39 (55.7%)
619 (47.9%) 428 (46.7%) 102 (52.8%) 91 (51.4%) 31 (44.3%)
P-value
Obese students (n = 484)
Non-obese students (n = 2,162)
0.13
P-value 0.008
69 (22.0%) 33 (11.5%) 47 (18.4%) 60 (18.5%) 86 (19.5%) 54 (22.4%) 69 (15.1%) 66 (20.2%)
244 (78.0%) 33 (88.5%) 209 (81.6%) 265 (81.5%) 356 (80.5%) 187 (77.6%) 387 (84.9%) 261 (79.8%)
145 (16.1%) 145 (18.6%) 194 (20.1%)
757 (83.9%) 635 (81.4%) 772 (79.9%)
217 (16.3%) 267 (20.3%)
1111 (83.7%) 1051 (79.7%)
333 (19.6%) 151 (16.0%)
1370 (81.7%) 794 (84.0%)
215 (16.7%) 180 (19.6%) 37 (19.2%) 40 (22.6%) 12 (17.1%)
1076 (83.3%) 737 (80.4%) 156 (80.8%) 137 (77.4%) 58 (82.9%)
0.57
0.08
0.03
0.01
0.94
0.02
0.45
0.22
SD = socioeconomic disadvantage a Chi-square test for independence. b Fisher's exact test. c 3 students were missing on grade level.
may contribute to the increased risk of obesity in girls. The risk of obesity in socioeconomically disadvantaged females extends beyond childhood into adulthood. An analysis of the National Longitudinal Study of Youth found that cumulative poverty in childhood increased the risk of overweight and obesity in young adult women of all races and ethnicities (Hernandez & Pressler, 2014). These authors suggest that the increased risk was due to gender-specific behavioral and physiological factors that occur with long-term social stresses. With the growing evidence of association between poverty and obesity in females, further research is needed to elucidate the sociological, behavioral, and physiological causes of increased risk of obesity in girls and women of low socioeconomic status. Our results were unique in that obesity was elevated in SD girls, even after controlling for race/ethnicity. Previous studies have found elevated levels of obesity in Hispanic and African American girls (Rossen, 2014; Singh et al., 2010a). Our findings, however, suggest that socioeconomic factors play a larger role than race/ethnicity in girls and thus may be an
Table 2 Obesity odds in socioeconomically disadvantaged vs. non-socioeconomically disadvantaged elementary school students in Springfield, Illinois, 2012–2014.
Male Socioeconomic disadvantage School Grade Year of data collection Race/ethnicity Female Socioeconomic disadvantage School Grade Year of data collection Race/ethnicity ⁎⁎ Unadjusted analysis.
Unadjusted odds ratio (95% confidence interval)
Adjusted odds ratio (95% confidence interval)
1.12 (0.88–1.42) ⁎⁎ ⁎⁎ ⁎⁎ ⁎⁎
1.10 (0.82–1.48) 1.01 (0.95–1.07) 1.10 (1.00–1.21) 1.05 (0.89–1.24) 1.19 (1.05–1.36)
1.47 (1.09–2.00) ⁎⁎ ⁎⁎ ⁎⁎ ⁎⁎
1.49 (1.09–2.04) 1.02 (0.96–1.08) 1.10 (1.00–1.20) 1.21 (1.02–1.44) 0.96 (0.83–1.10)
appropriate factor for consideration in targeted interventions (Singh et al., 2010a). A review of school-based obesity interventions indicated gender-specific interventions may be most effective (Kropski et al., 2008). Other research suggests targeting interventions specifically at low-income students (Kumanyika & Grier, 2006). However, our findings indicate the potential utility of considering both gender and socioeconomic status when developing and testing obesity prevention interventions in school-aged children.
Limitations and strengths There were some limitations to our study. Specifically, we were not able to account for additional social risk factors for childhood obesity, such as parent education levels and single parent households, and we were not able to consider neighborhood contextual factors that may play a role in obesity prevalence, such as access to parks and other recreation facilities. Additionally, we utilized a convenience sample of students who were assessed as part of our collaborative's evaluation. Thus, our results may not be representative of students in the same grades at other schools in our district, state or nation. However, there were strengths to our study. First, we used a student-specific socioeconomic indicator—eligibility for free/reduced rate school lunch—which categorizes students individually in the context of poverty level. Other studies that evaluated the relationship between socioeconomic factors and childhood obesity often used neighborhood level socioeconomic measures as a proxy measure for individual-level socioeconomic status. An additional strength of our study was our use of height and weight measures conducted by trained school nurses using standardized techniques to determine BMI, which is a more reliable method than self-report or parental report. Also, we assessed data from elementary school-aged students whereas most other studies examined students in preschool or adolescence. The agegroup we examined may be the most appropriate for interventions, as elementary schools provide cost-effective infrastructure for childhood obesity interventions and students spend 6–8 hours a day at school (Budd & Volpe, 2006; Wang et al., 2003).
W.E. Zahnd et al. / Preventive Medicine 81 (2015) 138–141
Conclusions Our study found that, even after controlling for factors such as race/ ethnicity, obesity prevalence was elevated in SD female elementary school students compared to their non-SD classmates. This association was not seen in male students. This suggests that interventions targeted at SD female students could be helpful to prevent and reduce childhood obesity. Future research should be initiated to help determine the causes of increased obesity in SD girls. Conflict of interest The authors declare that there are no conflicts of interest. Acknowledgments This study was funded, in part, by Healthy Kids, Healthy Families funding from Blue Cross Blue Shield of Illinois. The authors wish to thank Melissa Cleer and Donna Treadwell for their contributions as project coordinators for the Springfield Collaborative for Active Child Health and to all partner organizations involved in the work of the Collaborative. The authors also wish to acknowledge Steve Scaife for his assistance in data management and Dr. Steve Verhulst and Georgia Mueller-Luckey for their statistical guidance. References Barlow, S.E., the Expert Committee, 2007. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics 120 (S4), S164–S192. Budd, G.M., Volpe, S.L., 2006. School-based obesity prevention: research, challenges and recommendations. J. Sch. Health 76 (10), 485–495. Clarke, P., O'Malley, P.M., Johnston, L.D., Schluenberg, J.E., 2009. Social disparities in BMI trajectories across adulthood by gender, race/ethnicity and lifetime socio-economic position: 1986-2004. Int. J. Epidemiol. 38 (2), 499–509.
141
Gordon-Lausen, P., Adair, L.S., Popkin, B.M., 2003. The relationship of ethnicity, socioeconomic factors, and overweight in US adolescents. Obes. Res. 11 (1), 121–129. Hayes, A.F., 2006. A primer on multilevel modeling. Hum. Commun. Res. 32, 385–410. Hernandez, D.C., Pressler, E., 2014. Accumulation of childhood poverty on young adult overweight and obese status: race/ethnicity and gender disparities. J. Epidemiol. Community Health 68 (5), 478–484. Kropski, J.A., Keckley, P.H., Jensen, G.L., 2008. School-based obesity prevention programs: an evidence-base review. Obesity (Silver Spring) 16 (5), 1009–1018. Kumanyika, S., Grier, S., 2006. Targeting interventions for ethnic minority and lowincome populations. Future Child. 16 (1), 187–207. Li, J., Hooker, N.H., 2010. Childhood obesity and schools: evidence from the national survey of children's health. J. Sch. Health 80 (2), 96–103. Ogden, C.L., Carroll, M.D., Kit, B.K., Flegal, K.M., 2014. Prevalence of childhood and adult obesity, 2011–2012. JAMA 311 (8), 806–814. Rossen, L.M., 2014. Neighbourhood economic deprivation explains racial/ethnic disparities in overweight and obesity among children and adolescents in the U.S.A. J. Epidemiol. Community Health 68 (2), 123–129. Shih, M., Dumke, K.A., Goran, M.I., Simon, P.A., 2013. The association between community-level economic hardship and childhood obesity prevalence in Los Angeles. Pediatr. Obes. 8 (6), 411–417. Singh, G.K., Siahpush, M., Kogan, M.D., 2010a. Rising social inequalities in US childhood obesity, 2003–2007. Ann. Epidemiol. 20 (1), 40–52. Singh, G.K., Siahpush, M., Kogan, M.D., 2010b. Neighborhood socioeconomic conditions, built environments, and childhood obesity. Health Aff. 29 (3), 503–5112. Suglia, S.F., Durante, C.S., Chambers, E.C., Boynton-Jarrett, R., 2013. Social and behavioral risk factors for obesity in early childhood. J. Dev. Behav. Pediatr. 34 (8), 549–556. The Surgeon General's Vision for a Healthy and Fit Nation. 108. Department of Health and Human Services, Office of the Surgeon General, Rockville, MD: U.S, pp. 712–718. Trust for America's Health and Robert Wood Johnson Foundation, d. The State of Obesity in Illinois. Available at, http://stateofobesity.org/states/il/ (Accessed on 20 March 2015). United States Department of Agriculture, d. National School Lunch Program (NSLP). Available at, http://www.fns.usda.gov/nslp/national-school-lunch-program-nslp (Accessed on 22 May 2015). Wang, L.Y., Yang, Q., Lowry, R., Wechsler, H., 2003. Economic analysis of a school-based obesity prevention program. Obes. Res. 11, 1313–1324. Wang, C.Y., Gortmaker, S.L., Taveras, E.M., 2011, Feb. Trends and racial/ethnic disparities in severe obesity among US children and adolescents, 1976–2006. Int. J. Pediatr. Obes. 6 (1), 12–20.