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Brief Original Report

Gender-specic 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, Springeld, IL 62794-9664, United Statesb Springeld Public Schools-District 186, 900 W. Edwards, Springeld, IL 62704, United Statesc Department of Family and Community Medicine, Southern Illinois University School of Medicine, 913 N. Rutledge, Springeld, IL 62794-9671, United Statesd Ofce of Community Health and Service, Southern Illinois University School of Medicine, 201 E. Madison, Springeld, IL 62794-9604, United States

a b s t r a c ta r t i c l e i n f o

Available online 5 September 2015

Keywords:

Pediatric obesity

Poverty

Schools

Objective. To assess the gender-specic effectof socioeconomic disadvantageon obesity in elementaryschool

students.

Methods. We evaluatedbody massindex (BMI) data from2,648 rst- and fourth-grade students (1,377 male

and 1,271 female students) in eight elementary schools in Springeld, 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-stratied 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% con

dence 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 adoles-

cents (Trust for America's Health & Robert Wood Johnson Foundation).

Obesity in children and adolescents is dened 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 chil-

dren are more likely to remain obese into adolescence and adulthood

and have a heightened risk of chronic conditions, such as cardiovascular

disease, diabetes, and cancer (The Surgeon General's Vision for a Healthy

& Fit Nation).

Manystudies 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 obe-

sity 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 specically. 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 educationhouseholds or wholive

in neighborhoods with high economic deprivation had an increased risk

Preventive Medicine 81 (2015) 138141

Corresponding author at: 201 E. Madison Room 235, PO Box 19664, Springeld, IL

62794-9664, United States.

E-mail address:wzahnd@siumed.edu(W.E. Zahnd).

http://dx.doi.org/10.1016/j.ypmed.2015.08.021

0091-7435/ 2015 Elsevier Inc. All rights reserved.

Contents lists available at ScienceDirect

Preventive Medicine

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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 andless well-understood.Whilethe literature describing the

association between lower socioeconomic status, race/ethnicity, andobesity is extensive, there is a relative dearth in the literature exploring

these associations stratied by gender. Evidence of association between

gender, race/ethnicity, and SES could point to potential school-based

and public health interventions to signicantly reduce existing U.S.

childhood obesity disparities. Studies examining gender-stratied

effects of poverty on obesity have been performed primarily in young

children (aged 25 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 ofve 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-specic

association between socioeconomic disadvantage and obesity in

elementary school studentsan 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 Springeld,

Illinois.

Methods

We performed a cross-sectional, gender-stratied analysisof aggregated

data collected on 2,648 rst- and fourth-grade students from eight schoolsin Springeld, Illinois Public School District 186 (SPS) in 2012, 2013 and

2014. These data were collected as part of the efforts of the Spring eld

Collaborative for Active Child Health, an academic-community partnership

comprised of SPS, the Springeld 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.

Schoolnursesperformedheight andweight measurementsduringthe 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 de-

ne obese as a gender-specic, 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

thepoverty level areeligible forfree lunch, andstudentswhose familyincome

is between 130% and 185% of the poverty level are eligible for the reduced

lunchrate (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:

otherrace/ethnicity. Therefore, in our analysis, there were ve race/ethnicity

categories: white, African American, Hispanic, Multi-Racial, and other.

Our study was approved by our institutional review board (IRB), the

Springeld Committee on Research Involving Human Subjects.

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-signicant, and the intraclass correlation value was

very low (b0.01), indicating that multilevel models were not necessary to test

our data (Hayes, 2006). We ultimately performed gender-stratied unadjustedand 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, socioeco-

nomic 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 signicantly 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% ofrst-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 wasno 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% condence interval [CI] = 1.092.00). This association

remained after controlling for year of data collection, school, grade level,

and race/ethnicity (OR = 1.49; 95% CI = 1.092.04).

Discussion

We analyzed BMIdata from 2,648 1st- and4th-gradestudents 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 specically. 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 disadvan-taged had 49% higher odds of being obese compared to their non-SD

peers.

Ourndings corroborate other studies suggesting a gender-specic

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 col-

leagues 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 ve

year old girls with greater cumulative social risks (Suglia et al., 2013).

Suglia suggests that unmeasured factors associated with social stress

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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-specic 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, behav-

ioral, and physiological causes of increased risk of obesity in girls and

women of low socioeconomic status.

Ourresults were uniquein that obesity waselevated 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 ndings, however, suggest that socioeconomic

factors play a larger role than race/ethnicity in girls and thus may be an

appropriate factor for consideration in targeted interventions (Singh

et al., 2010a). A review of school-based obesity interventions indicated

gender-specic interventions may be most effective (Kropski et al.,

2008). Other research suggests targeting interventions specically at

low-income students (Kumanyika & Grier, 2006). However, ourndings

indicate the potential utility of considering both gender and socioeco-

nomic status when developing and testing obesity prevention interven-

tions in school-aged children.

Limitations and strengths

There were some limitations to our study. Specically, 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-specic socioeconomic indicatoreligibility for free/reducedrate school lunchwhich 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 wasour use of height and weight measures conducted by trained

school nursesusingstandardizedtechniques to determineBMI, 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 age-

group we examined may be the most appropriate for interventions, as

elementary schools provide cost-effective infrastructure for childhood

obesity interventions and students spend 68 hours a day at school

(Budd & Volpe, 2006; Wang et al., 2003).

Table 1

Student demographics by gender and obesity status of elementary school students in Springeld, Illinois, 20122014.

All students

(n= 2,648)

Male students

(n= 1,378)

Female students

(n= 1,271)

P- va lu e O bese stu dents

(n= 484)

Non-obese students

(n= 2,162)

P-value

Schoola 0.13 0.008

A 313 (11.8%) 173 (55.3%) 140 (44.7%) 69 (22.0%) 244 (78.0%)

B 288 (10.9%) 151 (52.4%) 137 (47.6%) 33 (11.5%) 33 (88.5%)

C 256 (9.7%) 127 (49.6%) 129 (50.4%) 47 (18.4%) 209 (81.6%)

D 325 (12.3%) 157 (48.3%) 168 (51.7%) 60 (18.5%) 265 (81.5%)

E 442 (16.7%) 249 (56.3%) 193 (43.7%) 86 (19.5%) 356 (80.5%)F 241 (9.1%) 111 (46.1%) 130 (53.9%) 54 (22.4%) 187 (77.6%)

G 456 (17.2%) 244 (53.5%) 212 (46.5%) 69 (15.1%) 387 (84.9%)

H 327 (12.3%) 165 (50.5%) 162 (49.5%) 66 (20.2%) 261 (79.8%)

Year of data collectiona 0.57 0.08

2012 902 (34.1%) 459 (50.9%) 443 (49.1%) 145 (16.1%) 757 (83.9%)

2013 780 (29.5%) 417 (53.5%) 363 (46.5%) 145 (18.6%) 635 (81.4%)

2014 966 (36.5%) 501 (51.9%) 465 (48.1%) 194 (20.1%) 772 (79.9%)

Gradeb,c 0.03 0.01

1st grade 1328 (50.2%) 662 (49.8%) 666 (50.2%) 217 (16.3%) 1111 (83.7%)

4th grade 1318 (49.8%) 714 (54.2%) 604 (45.8%) 267 (20.3%) 1051 (79.7%)

Socioeconomic status 0.94 0.02

SD 1703 (64.3%) 887 (64.4%) 816 (64.2%) 333 (19.6%) 1370 (81.7%)

Non-SD 945 (35.7%) 490 (35.6%) 455 (35.8%) 151 (16.0%) 794 (84.0%)

Race/Ethnicitya 0.45 0.22

White 1291 (48.8%) 672 (52.1%) 619 (47.9%) 215 (16.7%) 1076 (83.3%)

African American 917 (34.6%) 489 (53.3%) 428 (46.7%) 180 (19.6%) 737 (80.4%)

Hispanic 193 (7.3%) 91 (47.2%) 102 (52.8%) 37 (19.2%) 156 (80.8%)Multi-Racial 177 (6.7%) 86 (48.6%) 91 (51.4%) 40 (22.6%) 137 (77.4%)

Other 70 (2.6%) 39 (55.7%) 31 (44.3%) 12 (17.1%) 58 (82.9%)

SD = socioeconomic disadvantagea Chi-square test for independence.b Fisher's exact test.c 3 students were missing on grade level.

Table 2

Obesity odds in socioeconomically disadvantaged vs. non-socioeconomically disadvan-

taged elementary school students in Springeld, Illinois, 20122014.

Unadjusted odds ratio

(95% condence interval)

Adjusted odds ratio

(95% condence interval)

Male

Socioeconomic disadvantage 1.12 (0.881.42) 1.10 (0.821.48)

School 1.01 (0.951.07)

Grade 1.10 (1.001.21)

Year of data collection 1.05 (0.891.24)

Race/ethnicity 1.19 (1.051.36)

Female

Socioeconomic disadvantage 1.47 (1.092.00) 1.49 (1.092.04)

School 1.02 (0.961.08)

Grade 1.10 (1.001.20)

Year of data collection 1.21 (1.021.44)

Race/ethnicity 0.96 (0.831.10)

Unadjusted analysis.

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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.

Conict of interest

The authors declare that there are no conicts 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 Springeld 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.

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