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DOI: 10.1542/peds.2008-2780F2009;123;S277Pediatrics

Cooper and Lisa A. SimpsonChristina Bethell, Debra Read, Elizabeth Goodman, Jessica Johnson, John Besl, Julie

the Prevalence of Childhood Overweight and ObesityConsistently Inconsistent: A Snapshot of Across- and Within-State Disparities in

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located on the World Wide Web at:The online version of this article, along with updated information and services, is

of Pediatrics. All rights reserved. Print ISSN: 0031-4005. Online ISSN: 1098-4275.Boulevard, Elk Grove Village, Illinois, 60007. Copyright 2009 by the American Academypublished, and trademarked by the American Academy of Pediatrics, 141 Northwest Point

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SUPPLEMENT ARTICLE

Consistently Inconsistent: A Snapshot of Across- andWithin-State Disparities in the Prevalence ofChildhood Overweight and Obesity

ChristinaBethell, PhD, MBA, MPHa, Debra Read,MPHa, ElizabethGoodman,MDb, Jessica Johnson, BAa, JohnBesl, MAc, JulieCooper, MPAd,

Lisa A. Simpson,MB,BCh,MPH, FAAP

c

aChild and Adolescent Health Measurement Initiative, Department of Pediatrics, Oregon Health and Science University, Portland, Oregon; bFloating Hospital for Children

at Tufts Medical Center, Tufts University School of Medicine, Boston, Massachusetts; cChild Policy Research Center, Cincinnati Childrens Hospital Medical Center,

Cincinnati, Ohio; dChild Health Institute at the University of Washington, Seattle, Washington

The authors have indicated they have no financial relationships relevant to this article to disclose.

ABSTRACT

BACKGROUND. The epidemic of childhood overweight and obesity is characterized byknown disparities. Less is known about how these disparities vary across and within

the state in which a child lives.

OBJECTIVE. To examine the magnitude and patterns of across- and within-state differ-

ences in the prevalence of childhood overweight and obesity according to childrensinsurance type (public versus private), household income level, race (non-Hispanic

black versus non-Hispanic white), and ethnicity (Hispanic versus non-Hispanic).

METHODS. State-level overweight and obesity prevalence rates for children aged 1017

were calculated by using data from the 2003 National Survey of Childrens Health.Statistical significance of across-state variation was assessed. Disparity ratios assessed

within-state equity according to childrens insurance type, income, race, and eth-

nicity. State ranks on overall prevalence and ranks on disparity indices were corre-

lated and regression models were fit to examine within-state consistency, state-level

clustering effects and whether the effect of child characteristics varied across keypopulation subgroups.

RESULTS. Prevalence of childhood overweight and obesity varied significantly across

states. A total of 31 states had a prevalence lower than the national rate of 30.6% (14statistically significant), and 20 had higher rates (9 statistically significant). Within-

state disparity indices ranged from a low of 1.0 (no disparity) to a high of 3.44 (nearly

3.5 times higher). Correlations between state ranks on overall prevalence and theirranks on disparity indices were not significant for the insurance type, income, or race

disparity groups examined. A modest state-clustering effect was found. Compared

with non-Hispanic white children, the effect of lower household income and lower

household education level education were significantly less for non-Hispanic black

and Hispanic children, who were more likely to be overweight or obese regardless of these other factors.

CONCLUSIONS. Disparities in the prevalence of childhood overweight and obesity vary significantly both within and

across states. Patterns of variation are inconsistent within states, highlighting the need for states to undertake state-and population-specific analyses and interventions to address the epidemic. Pediatrics 2009;123:S277S286

CHILDHOOD OVERWEIGHT AND obesity are major public health problems that have the potential to cause enormousmedical, psychosocial, and financial costs and are characterized by known disparities.15 During the periods19761980, 19881994, and 19992002, rates of obesity increased for all children regardless of age, race, ethnicity,or gender.6 The epidemic of childhood overweight and obesity is gaining significant attention from policy makers at

the state and federal levels. Although a rich literature is emerging on disparities in childhood obesity,715 less is known

about the degree to which these disparities exist and vary across and within individual states. Examination of within-

and across-state variation in childhood obesity rates is critical to shaping effective national and state-level policy and

program responses to prevent and reduce overweight and obesity among children.This study builds on previous research that outlined state-level prevalence of overweight and obesity16,17 and the

existence of disparities according to demographic characteristics of children.18 The objective of this study was to

examine the magnitude, statistical significance, and patterns of across- and within-state differences in the prevalence

of childhood overweight and obesity according to childrens insurance type (public versus private), household

www.pediatrics.org/cgi/doi/10.1542/

peds.2008-2780F

doi:10.1542/peds.2008-2780F

Key Words

childhood overweight and obesity, state

variations, disparities, National Survey of

Childrens Health

Abbreviations

NSCHNational Survey of Childrens

Health

MCHBMaternal and Child Health Bureau

FPLfederal poverty level

COVcoefficient of variation

ICCinterclass correlation

mORmedian odds ratio

aORadjusted odds ratio

Accepted for publication Feb 18, 2009

Address correspondence to Christina Bethell,

PhD, MBA, MPH, Child and Adolescent Health

Measurement Initiative, Department of

Pediatrics, Oregon Health and Science

University, 707 SW Gaines Ave, Mail Code

CDRC-P, Portland, OR 97219. E-mail: bethellc@

ohsu.edu

PEDIATRICS (ISSNNumbers:Print, 0031-4005;

Online, 1098-4275). Copyright 2009by the

AmericanAcademy of Pediatrics

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income level, race (non-Hispanic black versusnon-Hispanic white), and ethnicity (Hispanic versus

non-Hispanic). We also estimate the presence of a state-

clustering effect that may suggest a higher or lower

probability of being overweight or obese for children in

some states after accounting for child-level characteris-

tics. In addition, we sought to enrich already-publishedfindings regarding the effect of child characteristics by

assessing the effect of characteristics such as a childs age,gender, household income, and education according to

different race and ethnicity subgroups. Together, with

the analyses presented here, we sought to help deter-mine how the prevalence of overweight and obesity

among children and youth aged 10 to 17 years in each

state compares to that in other states and if the burden of

overweight and obesity within states falls more on par-

ticular population subgroups. We also compared themagnitude of disparities between key indicators of dis-

advantage: income, health insurance coverage, and race/

ethnicity. Such information will help program and pol-

icy planners, who have limited resources, to address

childhood overweight and obesity and target their effortsto the highest-risk subgroups, in addition to the broader

child and youth population, thereby increasing the effi-

ciency of their actions to halt this epidemic.

METHODS

Data and Variable DescriptionsData for this study were drawn from the 2003 National

Survey of Childrens Health (NSCH) public-use data

file.19,20 The 2003 NSCH is the most recent national dataallowing state-level estimates of overweight and obesity

for children and youth. Data were collected between

January 2003 and July 2004. The NSCH is directed andfunded by the Maternal and Child Health Bureau

(MCHB) of the Health Resources and Services Adminis-tration and is administered by the National Center for

Health Statistics. The prevalence of overweight and obe-

sity was calculated by using the age- and gender-specific

weight-status variable included in the NSCH. This vari-

able classified children and youth as underweight, nor-mal weight, overweight, or obese by using original re-

spondent (primarily mothers) reported information on

childrens age, weight, and height and on the basis of the

Centers for Disease Control and Prevention BMI-for-age

gender-specific growth charts (www.cdc.gov/growthcharts)and recognized guidelines for defining overweight and

obesity.2123 Overweight was defined as85th percentile

of age- and gender-specific BMI and obesity as 95th

percentile for age and gender. The MCHB and National

Center for Health Statistics have also evaluated data-validity issues and determined that NSCH data for esti-

mating overweight and obesity are valid for children and

youth aged 10 to 17 years, especially for purposes of

comparing geographic or population subgroups.19 As

such, data from children in the 10- to 17-year age grouponly are included (unweighted n 46 707; weightedn 31 061 473).

In addition to weight status, variables used in multi-

variate analyses (described below) included child age

(1013 vs 1417 years), gender, and race/ethnicity(non-Hispanic white, non-Hispanic black, Hispanic,

other), household income (100% of the federal pov-

erty level [FPL], 100%199% of the FPL, 200%399%

of the FPL, 400% of the FPL) and education level (less

than high school, high school only, beyond high school),

whether the child had public insurance, private insur-ance, or was uninsured, and whether the child lived in a

household in which English is not the primary language.All variables were reported by a parent or guardian per

NSCH protocols.9

Analytic MethodsFirst, the statistical significance of differences in the overall

overweight/obesity prevalence rates across states for all

children aged 10 to 17 years as well as the significance of

differences between each states prevalence and the na-tional prevalence rate was evaluated using ANOVA and

nested t tests, respectively. A .05 level of significance was

used in these analyses. In conducting statistical tests, SEs

were adjusted to take into account weighting, clustering,

stratification, and increased variability that result from theNSCHs complex sampling design by using the SPSS (SPSS

Inc, Chicago, IL) complex sample module. State-level prev-

alence and results comparing each state to the nation were

configured in a map of the United States created with

ArcGIS software (ESRI, Redlands, CA) (Fig 1).Next, 4 rate-ratio disparity indices were calculated for

each state, and states were ranked according to their

disparity-index score. The 4 disparity indices were (1) an

insurance-type disparity index calculated as the ratio of

the prevalence for publicly versus privately insured chil-dren, (2) a household-income disparity index calculated

as the ratio of the prevalence for children living in

households with the lowest (

100% of the FPL) versushighest (400% of the FPL) incomes, (3) a race disparity

index calculated as the ratio of the prevalence for non-Hispanic black and non-Hispanic white children, and (4)

an ethnicity disparity index calculated as the ratio of the

prevalence for Hispanic versus Non-Hispanic children.

Indices for other race and ethnicity groupings were not

included because of small state-level sample sizes forthese groups. The simple rate-ratio disparity index used

here resulted in within- and across-state ranking con-

clusions identical to those arrived at if the commonly

used index-of-disparity formula were used, with the

primary difference being that it is expressed as a numberbelow or above 1 (1 being no difference between 2

groups) versus a percentage below or above 0% (0%

being no difference between 2 groups).20 Our simpler

index was easier to interpret. However, were more than

2 groups compared or more than 1 measure assessed at1 time, the more complex index of disparity would have

been used. In these analyses, states with a subgroup-

specific overweight/obesity prevalence rate that had a

relative SE of 30% and/or had fewer than 25 children

categorized as overweight/obese were eliminated fromthe disparity-indices analyses. Using these criteria, 49

states were included in the insurance-type disparity

analyses, 39 states were included in the household-in-

come disparity analyses, 23 states were included in the

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race disparity analyses, and 21 states were included inthe ethnicity disparity analyses. State ranks on each

disparity index were correlated with state ranks on over-all overweight/obesity prevalence by using Spearmans

statistic. In addition, graphs that plot each states over-

weight/obesity rate for each of the 4 index comparison

groups were created by using SPSS 15.0 (Figs 25).

Two other measures of state variation were calcu-lated. A ratio of the highest versus the lowest state

overweight/obesity prevalence was calculated for all

children and for each of the 8 subgroups used to define

the 4 disparity indices. In addition, the coefficient of

variation (COV) was calculated to estimate the relativespread (or dispersion) across states. The COV is a nor-

malized measure that allows dispersion to be validly

compared across units of analysis with different means

and underlying SDs.

A multilevel (or hierarchical) regression model was fitby using the SAS 9.1 (SAS Institute, Inc, Cary, NC) GLIMIX

procedure to assess the degree to which child overweight

or obesity may be explained by differences between states

(called the clustering effect) adjusting for individual

child-level variables. The presence of a significant cluster-ing effect according to state was estimated by using both

the interclass correlation (ICC) and median odds ratio

(mOR) statistics.24 The ICC was calculated by using the

latent-variable (versus simulation) method.24 The mOR in-

dicates whether the odds of meeting criteria for being over-weight or obese would be higher if a child moved from the

state with the lowest to the highest prevalence rate andafter taking into account within-state variation on this

same measure. These statistics were first calculated on the

basis of an empty model, which estimated the presence

of a state-clustering effect before accounting for child and

family characteristics outlined above. Next, child-levelcharacteristics were added, and the ICC and mOR were

recalculated. In addition to the multilevel model, separate

logistic regression models were fit to assess the adjusted

odds of being overweight or obese according to a childs

race or ethnicity. Separate models were fit for non-His-panic black, non-Hispanic white, and Hispanic children

and included age, gender, race, primary household lan-

guage, household income, and household education as

independent variables. Data from all states were included

in regression analysis.

RESULTS

Across-All-States VariationAcross the 50 US states and the District of Columbia(termed a state here), the prevalence of childhood over-

weight and obesity varied significantly from a low of

20.9% (95% confidence interval [CI]: 17.524.7) in

Utah to a high of 39.5% (95% CI: 35.144.2) in the

FIGURE 1

US map of state-level overweight and obesity prevalence, categorized according to significance of differences from the national estimate (30.6%) ( P .05). Data Source: 2003 NSCH.



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District of Columbia. A total of 31 states had a preva-lence that was lower than the national rate, with 14 that

were also statistically different from this national rate.

Overall, 20 states had a prevalence that was higher thanthe national rate, with 9 that were also statistically dif-

ferent from this national rate (Fig 1).

FIGURE 2

Prevalence of overweight or obesity for publicly versus

privately insured children aged 10 to 17 years according

to disparity ratio quartile (n 49 states).

FIGURE 3

Prevalence of overweight or obesity for children aged 10

to 17 years living in households with incomes of100%

FPL versus

400% FPL according to disparity ratio quar-tile (n 39 states).



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The across-state variation in prevalence of overweightor obesity was significant for each of the 8 population

subgroups used in the disparity-indices comparisons

(P .05). The highest national prevalence of overweight

and obesity was observed for non-Hispanic black chil-dren at 41.2% (95% CI: 38.5 43.9), and the lowest was

for children living in households with income at 400%

of the FPL at 22.9% (95% CI: 21.524.2) (Table 1).

FIGURE 4

Prevalence of overweight or obesity for black, non-His-

panic(NH) versus white,non-Hispanicchildren aged10 to

17 years according to disparity ratio quartile (n 23

states).

FIGURE 5

Prevalence of overweight or obesity for Hispanic versus

non-Hispanic children aged 10 to 17 years according todisparity ratio quartile (n 21 states).



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Despite the high prevalence rate for non-Hispanic black

children, this same group had the lowest high versus low

state prevalence ratio (1.52) and the lowest measure ofrelative spread (COV: 10.1), indicating that this popula-

tion of children has the most consistently high preva-

lence rate across states, despite other ways in which

states may vary (eg, poverty and education levels, etc).

In contrast, non-Hispanic white children had the great-est high versus low state prevalence ratio (2.95) and the

highest measure of relative spread (COV: 16.9), indicat-ing that prevalence for this population of children is the

most inconsistent across states. Across all states and dis-

parity subgroups, the highest overall prevalence of over-

weight and obesity was observed for Hispanic childrenliving in Delaware at 56.6% (95% CI: 42.669.7). The

lowest was observed for non-Hispanic white children

living in the District of Columbia at 12.7% (95% CI:

8.318.8) (Table 1).

On a national level, disparities in overweight andobesity were lowest for ethnicity (1.28) and highest for

household income (1.74), which suggests that children

and adolescents aged 10 to 17 from families with an

income below the FPL were 74% more likely to be

overweight or obese than those from families with ahousehold income at 400% FPL (39.8% vs 22.9%,

respectively). The national race and insurance-type dis-

parity indices were 1.55 and 1.48, respectively.

Within-State VariationsAcross-state patterns of variation differed from those

that emerged when each state was evaluated individu-

ally. At a state-specific level, the 4 disparity-index scoresranged from a low of 1.0 (no disparity) to a high of 3.44.

For insurance type (public versus private), the within-

state disparity index ranged from 1.00 in Nevada to 2.20

in Illinois. For household income, the within-state dis-

parity index ranged from 1.00 in Louisiana to 2.98 in

Wisconsin. For race (non-Hispanic black versus non-

Hispanic white), the within-state disparity index ranged

from 1.14 in Tennessee to 3.44 in the District of Colum-

bia. For ethnicity (Hispanic versus non-Hispanic), the

within-state disparity index ranged from 1.16 in the

District of Columbia to 2.08 in Idaho (Table 2).

As displayed in Table 2 and Figs 2 through 5, no

state ranked among the highest or lowest on its overall

overweight/obese prevalence and on each of the dis-parity-index scores (insurance, income, race, ethnic-

ity). For example, Pennsylvania ranked 23rd in overall

prevalence of overweight/obesity but was ranked first

(ie, lowest rate or best) for prevalence among low-

income children (26.7%). Consistent with this, Penn-

sylvania had the second lowest income disparity in-

dex. In contrast, North Carolina ranked 42nd in

overall prevalence but was ranked 8th for prevalence

among high-income children. Similarly, this is re-

flected by the fact that North Carolina had the third

highest income disparity index (Table 2; Fig 3). In

addition, although the District of Columbia had the

highest overall rate of childhood overweight and obe-

sity among all states and the highest disparity between

non-Hispanic black and non-Hispanic white children,

it also had the lowest rate for non-Hispanic white

children and the lowest disparity between Hispanic

and Non-Hispanic children (Figs 4 and 5). Similarly,

Michigan had the fifth highest disparity between

lower and higher income children but the fourth low-

est disparity between non-Hispanic black and non-

Hispanic white children (Table 2; Figs 3 and 4). These

observations are supported by the lack of any signifi-

cant correlation between the overall prevalence rank

across states and their rank on 3 of the 4 disparity

indices calculated. The exception was a significant

TABLE 1 Summary of Across- andWithin-State Variation in the Prevalence of Obesity/Overweight Among US Children Aged 1017 Years

Population Nation, %

(95% CI)

Range AcrossStates, % (95% CI)a High State

vsLow

State

Ratio

Across-State

Spread, COV

Index of Disparity Between

Comparison Groups (Index

Range Across States)

Low High Nation State Range

All children, aged 1017 y 30.6 (29.731.5) UT: 20.9 (17.524.7) DC: 39.5 (35.144.2) 1.89 14.0 NA

Children according to type of

health insuranceb

Public-sector insurance 39.6 (37.441.8) NV: 25.9 (17.636.4) IL: 55.6 (43.267.3) 2.15 16.2 1.48 1.00 (NV) to 2.20 (IL)

Private-sector insurance 26.7 (25.827.7) WY: 20.2 (17.123.7) LA: 33.9 (29.838.3) 1.68 13.0

Children according to family

incomeb

100% FPL 39.8 (36.942.8) PA: 26.7 (18.337.1) DE: 54.3 (41.266.7) 2.03 15.2 1.74 1.00 (LA) to 2.98 (WI)

400% FPL 22.9 (21.524.2) SD: 15.6 (10.821.9) LA: 36.6 (30.643.0) 2.35 16.1

Children according to raceb

Non-Hispanic black 41.2 (38.543.9) MI: 35.6 (27.344.9) NJ: 54.0 (42.864.9) 1 .52 10.1 1.55 1.14 (TN) to 3 .44 (DC)

Non-Hispanic white 26.6 (25.727.6) DC: 12.7 (8.318.8) KY: 37.5 (33.541.6) 2 .95 16.9

Children according to ethnicityb

Hispanic 37.7 (34.640.9) CT: 32.4 (22.344.4) DE: 56.6 (42.669.7) 1.75 15.3 1.28 1.16 (DC) to 2.08 (ID)

NonHispanic 29.5 (28.630.5) CO: 19.9 (16.424.0) DC: 39.0 (34.343.9) 1.96 14.6

a Differences across states significant at the P .05 level.b Analyses included only those states with samples sizes of25 or with relative standard error of30%. Insurance-type analysis: 49 states; income analysis: 39 states; race analysis: 23

states; ethnicity analysis: 21 states.



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TABLE 2 StatesWith the Highest andLowest Disparity Indices ComparedWith Their Overall National Rank

State Prevalence of Overweight

or Obesity: Children Aged

1017 y, % (95%CI)

State-Level

Disparity

Index

States Overall

Rank

Nationallya

Insurance disparity index (public vs privately insured)

(n 49 states)

Lowest 5 states

Nevada 26.6 (23.430.1) 1.00 12

Arkansas 32.9 (29.236.8) 1.07 40

Pennsylvania 29.3 (26.132.6) 1.08 23

Louisiana 35.6 (32.139.3) 1.13 46

South Carolina 36.1 (32.639.8) 1.16 47

Highest 5 states

North Carolina 33.9 (30.337.7) 1.87 42

Nebraska 26.3 (22.930.1) 1.91 10

North Dakota 26.9 (23.530.6) 1.93 14

Georgia 31.7 (27.835.9) 1.98 37

Illinois 31.2 (27.535.1) 2.20 35

Income disparity index (100% vs400% FPL)

(n 39 states)

Lowest 5 states

Louisiana 35.6 (32.139.3) 1.00 45

Pennsylvania 29.3 (26.132.6) 1.18 23Arkansas 32.9 (29.236.8) 1.28 40

California 30.0 (26.434.0) 1.32 29

Alabama 34.6 (31.038.3) 1.37 43

Highest 5 states

Michigan 28.8 (25.632.2) 2.36 20

South Dakota 25.8 (22.329.7) 2.40 9

North Carolina 33.9 (30.337.7) 2.43 42

New Mexico 28.9 (25.232.9) 2.47 21

Wisconsin 29.4 (25.833.2) 2.98 24

Race disparity index (non-Hispanic black vs non-Hispanic

white) (n 23 states)

Lowest 5 states

Tennessee 35.3 (31.539.3) 1.14 44

Kentucky 38.2 (34.542.1) 1.16 50

Louisiana 35.6 (32.139.3) 1.27 46Michigan 28.8 (25.632.2) 1.32 20

Arkansas 32.9 (29.236.8) 1.37 40

Highest 5 states

Maryland 29.9 (26.533.6) 1.77 26

Florida 32.5 (28.936.3) 1.78 39

Connecticut 27.3 (24.130.9) 1.94 18

New Jersey 31.5 (27.935.3) 2.14 36

District of Columbia 39.5 (35.144.2) 3.44 51

Ethnicity disparity index (Hispanic vs non-Hispanic)

(n 21 states)

Lowest 5 states

District of Columbia 39.5 (35.144.2) 1.16 51

New Jersey 31.5 (27.935.3) 1.19 36

Connecticut 27.3 (24.130.9) 1.21 18

Florida 32.5 (28.936.3) 1.23 39

New York 30.9 (27.434.7) 1.24 33

Highest 5 states

Oregon 26.5 (23.329.9) 1.63 11

Virginia 30.5 (27.134.1) 1.65 31

Delaware 35.5 (31.939.2) 1.67 45

Massachusetts 28.9 (25.532.6) 1.67 22

Idaho 25.6 (22.329.2) 2.08 7

a 1 best or lowest rate.



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negative correlation in state overall prevalence ranksand their Hispanic/non-Hispanic disparity index (r

0.32; P .04).

State Clustering and Consistency of Effects Across Race andEthnicity GroupsIn multilevel analyses, a small state-clustering effect was

observed after accounting for within-state variation inoverweight/obesity prevalence. The mOR was 1.17 (ICC:0.9%) before taking individual child characteristics into

account and decreased only modestly to 1.13 (ICC:

0.5%) after these characteristics were considered. Indi-

vidual-level variables significantly associated with in-

creased odds of overweight/obesity included male gen-der with an adjusted odds ratio (aOR) of 1.55 (95% CI:

1.441.68), non-Hispanic black race with an aOR of 1.65

(95% CI: 1.481.85), Hispanic race/ethnicity with an

aOR of 1.34 (95% CI: 1.141.57), and lower household

income with an aOR of 1.38 (95% CI: 1.181.62) for

100% of the FPL, an aOR of 1.45 (95% CI: 1.291.64)

for 100% to 299% of the FPL, and an aOR of 1.24 (95%

CI: 1.141.36) for 300% to 399% of the FPL. Odds were

also higher for children living in households with high

school only as the highest level of education in thehousehold (aOR: 1.38 [95% CI: 1.261.51]). Odds de-

creased for each increase in year of age (aOR: .87 [95%

CI: .85.88]) and were not significant for the primary-

household-language or insurance-type variables.

When separate models were run for different race/ethnicity groups, differences in the effect of household

income and education did emerge. Specifically, the pos-

itive effect of lower household income and education

level on the probability of being overweight or obese was

substantially higher and significant for non-Hispanic

white children compared with non-Hispanic black andHispanic children, where income effects were not statis-

tically significant and education effects were minimal

after accounting for other factors. This indicates that

these 2 race/ethnicity subgroups were more likely to beoverweight or obese regardless of these other factors.

Information on state-by-state results and more details

from the hierarchical linear models (HLM) and logistic

regression analyses are available from Dr Bethell.

LIMITATIONSThe findings presented here are limited in that not allstates were possible to include in the disparity analyses

because of small NSCH sample sizes for 1 or more of the

disparity subgroups. It is important to point out that

those states not included in 1 or more of the disparity-

index calculations were disproportionately those withsignificantly lower prevalence overall and, as would be

expected, are more demographically homogenous, with

relatively fewer children represented among disadvan-

taged subgroups. In addition, for simplicity and as a

beginning step, only 2 comparison groups for each dis-parity variable of interest were evaluated. More detailed

analyses of each of these dimensions of disadvantage

would be useful. Also, analyses related to state-cluster-

ing effects were preliminary and further work to fully

develop robust multilevel models that include salientstate-level policy variables and information about state-

level programs. Parent-reported height and weight data

were used and are known to err on the side of under-

estimating their childs weight.25 As such, prevalence

results may be somewhat lower than if more objective

measures of height and weight were used.

DISCUSSIONThis study demonstrates significant variation across and

within states in the prevalence and disparities in child-

hood overweight and obesity among populations of chil-dren in 2003. Although a pattern of disparities was

shown across states, the magnitude and patterns of dis-

parity were inconsistent when each state was examined

separately. Often, a single state was both among the

highest and lowest in its disparity-index score dependingon which disparity group was examined (eg, Michigan:

4th lowest race disparity index and 5th highest income

disparity index). These inconsistencies were reflected in

the modest, but potentially important, state-clustering

effect that was found. The clustering effect indicates thatchildren with similar individual-level characteristics vary

in their probability of being overweight or obese depend-

ing on the state in which they live. These findings point

to opportunities for states to learn from each other and

to identify policies, programs, or other factors that con-tribute to both lower overweight and obesity rates and

higher equity in some states and population groups.

Our findings are consistent with those reported in the

study by Singh et al,17 who used the NSCH data that

indicate that the child-level characteristics similar tothose considered here account for only a portion of

overall variation in overweight/obesity.18 As such, fur-

ther studies are needed to assess additional child-, com-munity-, and state-level factors that contribute to higher

or lower prevalence of overweight or obesity within andacross states. This study adds a layer of depth to the

previous analyses by assessing within-state comparisons

and through its focus on disparities. Of particular interest

was the finding that some states that have an overall

lower prevalence also have higher disparity indices. Forinstance, Oregon and South Dakota rank 11th and 9th in

overall prevalence nationally, respectively, but are

among the states with the highest ethnicity and income

disparities index scores. This suggests that these states

would benefit from targeted obesity interventions. Onthe other hand, states such as Louisiana, which has 1 of

the higher prevalence rates overall but some of the

lowest disparity indices, would benefit from generalized

interventions across the state. States should examine

their specific profile of overweight and obesity preva-lence to develop the most effective antiobesity cam-

paigns at the state level.

It is widely understood that public health and envi-

ronmental strategies, such as those in schools, neighbor-

hoods, and recreational settings, are fundamental toaddressing the childhood overweight and obesity epi-

demic.2628 In addition, the development and adoption of

effective approaches for minority children and youth or

children from low-income communities will not occur



10/12

unless front-line providers serving those communities

are engaged, tools are customized for these settings, and

policies supporting their use are promulgated. The first

step in addressing overweight and obesity is appropri-

ately identifying and preventing its development. This is

often done in the context of a preventive care visit.

However, Perry and Kenney29 found that, overall, only

41.1% of all children had a preventive visit in the pre-

vious year. Authors of this and other studies have ex-

amined providers delivery of obesity-related services,

including BMI percentile measurement and counseling

on nutrition and physical activity, and found extremely

low rates, especially among minority and nonEnglish-

speaking and lower-income children.17,29,30 The role of

health services and health policy in addressing childhood

disparities is now well recognized, and research and

practice over the last decade have yielded important

lessons about approaches that are likely to be successful

in overcoming disparities in other areas. Similarly, na-

tional efforts are being made to integrate a focus on

disparities into all quality-improvement efforts. Specific

to overweight and obesity, the National Initiative for

Childrens Healthcare Qualityled Childhood Obesity

Action Network will connect health care providers with

national clinical leaders and up-to-date research to gain

the skills necessary to combat this epidemic.31

Since 2003, when the data used in this study were

collected, states have moved ahead and implemented a

number of policies and programs to address the epi-

demic. Release of the 2007 NSCH in 2009 will allow a

comparison of rates across the 2 years, as well as exam-

ination of whether state actions are at all associated with

the direction of the epidemic overall and in individual

states. These national and state-level data, along with

already available findings from the 2003 NSCH, are ex-

pected to be available for easy searching on the Data

Resource Center for Child and Adolescent Health Web

site by Spring 2009 (www.childhealthdata.org).32 Al-

though the repeated NSCH surveillance data are useful,

such data do not allow for in-depth within-state studies.

Such longitudinal data, such as those being collected in

Arkansas under the auspices of Act 1220 of 2003, will be

necessary to fully evaluate the efficacy of policy and

community-level interventions.

CONCLUSIONS

This study demonstrates variation within and acrossstates according to population subgroups in the preva-

lence of childhood overweight and obesity. These pat-

terns are not consistent across the nation and indicate

that specific state-level responses will be necessary to

effectively combat the obesity epidemic. As state policy

and program leaders wrestle with the task of deciding

whether to focus their efforts on high-risk groups or the

population as a whole, the data presented here can assist

decision-makers in determining whether to allocate re-

sources by targeting children and families who are dis-

proportionately affected by the epidemic or developing

more population-wide interventions.

ACKNOWLEDGMENTS

This study was supported through the federal MCHB-supported Child and Adolescent Health Measurement

Initiative Data Resource Center for Child and Adolescent

Health (www.childhealthdata.org) under cooperative

agreement 1-US9-MC06980-01 and by Nemours Health

and Prevention Services.

REFERENCES1. Ogden CL, Carroll MD, Curtin LR, Macdowell MA, Tabak CJ,

Flegal KM. Prevalence of overweight and obesity in the United

States, 19992004. JAMA. 2006;295(13):15491555

2. Thompson D, Edelsberg J, Kinsey KL, Oster G. Estimated eco-

nomic costs of obesity to U.S. business. Am J Health Promot.

1998;13(2):120127

3. Thorpe KE, Florence CS, Howard DH, Joski P. The impact of

obesity on rising medical spending. Health Aff (Millwood). 2004;

(suppl Web exclusives):W4480W4486

4. Thorpe KE. Factors accounting for the rise in health-care

spending in the United States: the role of rising disease preva-

lence and treatment intensity. Public Health. 2006;120(11):

100210075. Finkelstein EA, Fiebelkorn IC, Wang G. State-level estimates of

annual medical expenditures attributable to obesity. Obes Res.

2004;12(1):18 24

6. Wang Y, Beydoun MA. The obesity epidemic in the United

States: gender, age, socioeconomic, racial/ethnic, and geo-

graphic characteristicsa systematic review and meta-

regression analysis. Epidemiol Rev. 2007;29:628

7. Yancey AK, Kumanyika SK. Bridging the gap: understanding

the structure of social inequities in childhood obesity. Am J Prev

Med. 2007;33(4 suppl):S172S174

8. Drewnowski A, Darmon N, Briend A. Replacing fats and sweets

with vegetables and fruit: a question of cost. Am J Public Health.

2004;94(9):15551559

9. Freedman DS, Khan LK, Serdula MK, Ogden CL, Dietz WH.

Racial and ethnic differences in secular trends for childhood

BMI, weight, and height. Obesity (Silver Spring). 2006;14(2):

301308

10. Goodman E, Slap GB, Huang B. The public health impact of

socioeconomic status on adolescent depression and obesity.

Am J Public Health. 2003;93(11):1844 1850

11. Koplan P, Liverman CT, Kraak VA, eds. Preventing Childhood

Obesity: Health in the Balance. Washington, DC: Institute of

Medicine, National Academies Press; 2005

12. Daniels S. The consequences of childhood overweight and

obesity. Future Child. 2006;16(1):4767

13. Kumanyika S, Grier S. Targeting interventions for ethnic mi-

nority and low-income populations. Future Child. 2006;16(1):

187207

14. Institute of Medicine. Progress in Preventing Childhood Obesity:How Do We Measure Up? Washington, DC: National Academies

of Sciences; 2006

15. Institute of Medicine, Committee on Prevention of Obesity in

Children and Youth. Preventing Childhood Obesity: Health in the

Balance. Washington, DC: National Academies Press; 2005

16. US Department of Health and Human Services, Health Re-

sources and Services Administration, Maternal and Child

Health Bureau. The National Survey of Childrens Health 2003.

Rockville, MD: US Department of Health and Human Services;

2005

17. Singh GK, Kogan MD, van Dyck PC. A multilevel analysis of

state and regional disparities in childhood and adolescent obe-

sity in the United States. J Community Health. 2008;33(2):

90102



11/12

18. Tudor-Locke C, Kronenfeld JJ, Kim SS, Benin M, Kuby M. A

geographical comparison of prevalence of overweight school-

aged children: the National Survey of Childrens Health 2003.

Pediatrics. 2007;120(4). Available at: www.pediatrics.org/cgi/

content/full/120/4/e1043

19. Blumberg SJ, Olson L, Frankel MR, Osborn L, Srinath KP,

Giambo P. Design and operation of the National Survey of

Childrens Health, 2003. Vital Health Stat 1. 2005;(43):1131

20. Pearcy, J, Keppel, K. A summary measure of health disparity.

Public Health Rep. 2002;117(3):273280

21. Kuczmarski RJ, Ogden CL, Guo SS, et al. 2000 CDC growth

charts for the United States: methods and development. Vital

Health Stat 11. 2002;(246):1190

22. Barlow SE; Expert Committee. Expert committee recommen-

dations regarding the prevention, assessment, and treatment of

child and adolescent overweight and obesity: summary report.

Pediatrics. 2007;120(suppl 4):S164 S192

23. Krebs NF, Himes JH, Jacobson D, Nicklas TA, Guilday P, Styne

D. Assessment of child and adolescent overweight and obesity.

Pediatrics. 2007;120(suppl 4):S193S228

24. Merlo J, Chaix B, Ohlsson H, et al. A brief conceptual tutorial

of multilevel analysis in social epidemiology: using measures of

clustering in multilevel logistic regression to investigate con-

textual phenomena. J Epidemiol Community Health. 2006;60(4):290297

25. Maynard LM, Gluska DA, Blank HM, Serdula MK. Maternal

perceptions of weight status of children. Pediatrics. 2003;111(5

pt 2):12261231

26. Burdette HL, Whitaker RC. A national study of neighborhood

safety, outdoor play, television viewing, and obesity in pre-

school children. Pediatrics. 2005;116(3):657 662

27. Lumeng JC, Appugliese D, Cabral HJ, Bradley RH, Zuckerman

B. Neighborhood safety and overweight status in children. Arch

Pediatr Adolesc Med. 2006;160(1):2531

28. Yancey AK, Kumanyika SK, Ponce NA, et al. Population-based

interventions engaging communities of color in eating and

active living; a review. Prev Chronic Dis. 2004;1(1):A09

29. Perry CD, Kenney GM. Preventive care for children in low-

income families: how well do Medicaid and state childrens

health insurance programs do? Pediatrics. 2007;120(6). Avail-

able at: www.pediatrics.org/cgi/content/full/120/6/e1393

30. Branner CM, Koyam T, Jensen GL. Racial and ethnic differ-

ences in obesity prevention counseling: national prevalence of

clinician practices. Obesity (Silver Spring). 2008;16(3):690694

31. National Initiative for Childrens Health Care Quality, Child-

hood Obesity Action Network. Home page. Available at:

www.nichq.org/NICHQ/Programs/ConferencesAndTraining/

ChildhoodObesityActionNetwork.htm. Accessed July 23, 2008

32. Child and Adolescent Health Measurement Initiative. Data

resource center for child and adolescent health, National Sur-vey of Childrens Health. Available at: www.nschdata.org/

Content/Default.aspx. Accessed July 27, 2008



12/12

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