Children Health Issues – Library Research Assignment

You need to read some articles and write annotated bibliographies and essay responses to a couple of questions. Overall ~1400 words.

SELECT ONE topic of interest- some issue or concern that you would like to research in the field of Child and Youth Study.

Don't use plagiarized sources. Get Your Custom Essay on

Just from $13/Page

Order Essay

Examples of topic ideas or one of your own:

Child and youth special needs; early intervention; health issues; infant development; addictions drugs/ alcohol; ADD/ASD autism; adolescent relationships; youth mental health; teen pregnancy; obesity, body image; issues of educational access; indigenous trauma; special populations etc.

Students are required to go Online to the library (I WILL PROVIDE YOU THE ARTICLES. They are about 10-12 pages each)
From (3) three separate journal titles such as; Young Children; Relational Child Care; Early

education; Adolescence; Social work; Child psychology etc. You must reference all resources.

Find (3) three peer reviewed research articles on the topic you have chosen in the child and youth field

Reading & Research Expectations: You will need to review a few articles

NOT WEBSITES check the date (research should be current 2012 forward and peer reviewed).
NOTE: Readings must be minimum 6 pages NOT including their references

You are required to create a reference page of all articles in APA format at the end of your work as per library day discussion.

Part 1:
Select 2 of the three articles chosen

Writing two (2) Bibliographies: 200 words each
Review the three articles and create a brief annotated bibliography (Look this up) of two journal articles by reading the abstracts and skimming the article; Article title at the page top or beginning of the annotation.
– (photocopy the first page of each of the 3 articles and attach to the end of your work).

Part 2
FULLY READ ONE ARTICLE [hopefully your favourite] and fill out the writer’s logic sheet (below) attach this sheet to your work at the end (word count not included)

Part 3
Write complete paragraphs to answer the following: After you read the article and make use of the writers logic work sheet below; I do not want a fully detailed research paper I want you to explain what the author/s want us to know in your paragraphs.
EDIT your words and review your work for clarity. Write a brief review answering these questions, maximum 750 words:

1. What is the main discussion presented in the article?

2. What are the main arguments and conclusions presented? Use p.# to support your

statements, from the writers sheet you have filled out.

3. What new information have I learned from this reading?

4. Final thoughts: What have I learned about ‘how I do research’; ex. need to use time

management better; use my RefWorks resource more effectively…etc.

FINAL CHECK

1. Do not include a title page: YOUR Topic title and your name at the top of p.1

2. Use your name as the file name when uploading to the Hand in on the moodle site

3. Each paragraph should begin with topic sentence; use an introduction; include at least

three supporting sentences in the body of each paragraph and complete your work with a

concluding thought. Paragraphs will be approximately half a page to a page in length.

4. Do not plagiarize! If you use a quote please identify the page # [at the end of the

sentence] that you took it from and place quotation marks around it. Ex. (p.235)

5. Writing Expectations: APA Format

6. Maximum 750 words; choose you words for the best meaning and clarity

7. Double spaced; do not double-double space anywhere on the pages

8. Reference page: identify all resources in APA format

9. Copy/ Attach first page of the all articles to the end of your assignment

10. Attach the filled – out writer’s research sheet (Below) (9 & 10: these pages are not in

the word count)

Grading: 25%

Filling out Writer’s Sheet for the one selected reading 6pt. Clear comprehensive Annotated bibliographies 6pt.

Content and clarity in responses for summary article; sentence structure spelling and grammar 8pt.

Following outline directions and using APA format 5pt.

Please recreate this table/sheet in word and attach to your final paper.

While you Read; keep track of your own ideas on a blank paper

The Logic of the Writer’s argument: fill this out prior to writing your brief article summary and attach it to your WORK:

type your responses in the right hand space provided; You may use this in handwriting if easier and include a photo of your sheets ONLY if it is legible! If not type it up and attach it to the assignment.

Name: ____________________________________________

Look for…… Mark it with…

The main question or issue	Aim/ objective/ main
Fundamental concepts explanations or descriptions	Highlight/underline
Important conclusions (maybe more than one)	Rank these and highlight or quote pp.#
Unclear or faulty logic or/ problematic Assumptions	Question? / Problems
Supporting data or evidence	Evidence note page numbers and quotes or information that you have taken for your paper!!
Author voicing an opinion	View point
Greater implications of the argument	Implication
Title/journal /author/date	Save full reference/ location of resource

Eating Patterns and Obesity in Children

The Bogalusa Heart Study
Theresa A. Nicklas, DrPH, LN, Su-Jau Yang, MS, Tom Baranowski, PhD, Issa Zakeri, PhD, Gerald Berenson, MD

Background: Childhood obesity is a growing public health problem. This study examined the association
between eating patterns and overweight status in children who participated in the Bogalusa
Heart Study.

Methods: A single 24-hour dietary recall was collected on a cross-sectional sample of 1562 children
aged 10 years (65% Euro-American [EA], 35% African American [AA]) over a 21-year
period. Overweight was defined as body mass index greater than the 85th percentile using
Centers for Disease Control and Prevention reference standards. Multivariate logistic
regression was used to investigate the association between eating patterns and overweight.

Results: Consumption of sweetened beverages (58% soft drinks, 20% fruit flavor drinks, 19% tea,
and 3% coffee) (p�0.001); sweets (desserts, candy, and sweetened beverages) (p�0.001);
meats (mixed meats, poultry, seafood, eggs, pork, and beef) (p�0.051); and total
consumption of low-quality foods (p�0.01) were positively associated with overweight
status. Total amount of food consumed, specifically from snacks, was positively associated
with overweight status (p�0.05). There was a lack of congruency in the types of eating
patterns associated with overweight status across four ethnic– gender groups. The percent
variance explained from the eating pattern– overweight models was very small. The
interaction of ethnicity and gender was significantly associated with overweight status (p�0.001).
The odds of being overweight for EA males were 1.2 times higher than for AA females.

Conclusions: These results demonstrate that numerous eating patterns were associated with overweight
status, yet the odds of being overweight were very small. Additional studies are needed to
confirm these findings in a longitudinal sample having multiple days of assessment.
(Am J Prev Med 2003;25(1):9 –16) © 2003 American Journal of Preventive Medicine

Introduction

Obesity among children has increased dramati-cally over the past 3 decades.1,2 Today anestimated one in four children in the United
States is at risk of overweight (body mass index [BMI]
�85th percentile), while 11% are overweight (BMI
�95th percentile). Obese children tend to become
obese adults.3–5 Further, obesity in early life is associ-
ated with several risk factors for coronary heart dis-
ease6,7 and is predictive of coronary heart disease,8

hypertension,9 and diabetes9 in adulthood.
U.S. society has been increasingly characterized as

“obesogenic.”10 Although obesity has a strong genetic
background,11 environmental factors are commonly
considered to be the underlying cause of the increase

in obesity by promoting or exacerbating the prob-
lem.11,12 The dietary causes of obesity are complex and
poorly understood.13 While individual nutrients have
been implicated in obesity,14 –18 few attempts have been
made to identify eating patterns in this regard. Several
studies have shown an association between BMI and
restaurant food consumption,19 –21 soft drink consump-
tion,22,23 increased portion sizes,24 meal patterns and
meal frequency,25–28 diet quality,29 and diet diversity.20

However, most of these studies were conducted with
adults, with very little reported on the eating pattern–
obesity relationship in children. Moreover, several of
the adult findings have yet to be replicated with other
populations.

Secular increases in relative weight and adiposity
have been documented among children in Bogalusa,
Louisiana, over 2 decades.3,30,31 The prevalence of
overweight among these children in 1973–1974 in-
creased approximately twofold by 1994, with the largest
increases observed among 19- to 24-year-olds. Further-
more, the annual increases in relative weight and
obesity from 1983 through 1994 were 50% greater than
those between 1973 and 1982.30 During the 1970s and

From the Children’s Nutrition Research Center, Department of
Pediatrics, Baylor College of Medicine (Nicklas, Yang, Baranowski,
Zakeri), Houston, Texas; and Tulane Center for Cardiovascular
Health, Tulane School of Public Health and Tropical Medicine
(Berenson), New Orleans, Louisiana

Address correspondence to: Theresa A. Nicklas, DrPH, LN, Chil-
dren’s Nutrition Research Center, Department of Pediatrics, Baylor
College of Medicine, 1100 Bates Street, Houston TX 77030. E-mail:
tnicklas@bcm.tmc.edu.

9Am J Prev Med 2003;25(1) 0749-3797/03/$–see front matter
© 2003 American Journal of Preventive Medicine • Published by Elsevier Inc. doi:10.1016/S0749-3797(03)00098-9

1980s, the average increase in the body weight of
children was 2.5 kg, without a significant increase in
height. During the 1980s and 1990s, the increase was 5
kg. In this article, we assess the extent to which eating
patterns (e.g., food consumption and meal patterns)
are related to childhood obesity in the Bogalusa Heart
Study.

Materials and Methods
Population

The Bogalusa Heart Study, which began in 1973, is a
long-term epidemiologic study designed to examine
the early natural history of heart disease in a well-
defined biracial (African-American [AA] and Euro-
American [EA]) pediatric population. Dietary intake
and anthropometric data were obtained on 1562 fifth-
grade students (10-year-olds) in the Bogalusa, Louisi-
ana, school system between 1973 and 1994, and then
the seven cross-sectional surveys were combined. Sam-
ple sizes varied slightly across the surveys; ethnicity
(65% EA, 35% AA) and gender (51% female, 49%
male) distributions were similar for each survey year of
10-year-olds and reflective of the total population.32,33

Dietary Methodology and

Nutrient Database

The 24-hour dietary recall method was used in inter-
viewing the children.34,35 Quality controls included:
(1) a standardized protocol that specified exact tech-
niques for interviewing, recording, and calculating results;
(2) standardized graduated food models for quantifica-
tion of foods and beverages consumed; (3) a product
identification notebook for probing of snack consump-
tion; (4) school lunch assessment to identify all school
lunch recipes, preparation methods, and average por-
tion sizes of menu items reflected in each 24-hour
recall36; (5) follow-up telephone calls to parents to
obtain information on brand names, recipes, and prep-
aration methods of meals served at home; (6) products
researched in the field to obtain updated information
on their ingredients and weights, primarily snack foods,
candy, and fast foods; and (7) the Moore Extended
Nutrients (MENu), formerly known as the Extended
Table of Nutrient Values, for nutrient composition.37

All interviewers participated in rigorous training ses-
sions and pilot studies before the field surveys to
minimize interviewer effects. One 24-hour dietary recall
was collected on each study participant. Duplicate
recalls were obtained from 10% random subsamples of
each study population to assess interviewer variabili-
ty.38,39 The timeframe of the 24-hour recall period
included everything the child consumed from the time
he/she woke up until the time of the interview and
everything after the interview time on the previous day
until the time the child went to bed. The same dietary-
recall interview protocol was followed for all surveys.

Nutrient Database

MENu is a nutrient database that includes more than
5000 core foods and recipes, with values for 97 dietary
components.37 The data bank is a flexible system
permitting continuous updates of existing values and
additions of new single or composite foods. Periodic
updates are made to MENu to reflect nutrient changes
in food products. Nutrient values were obtained from
U.S. Department of Agriculture data, other published
references, manufacturers’ information, and recipe cal-
culation by ingredients. The database includes brand
names of foods, school and family recipes, and foods
commonly consumed by children.

For each survey period, the 24-hour recalls were
analyzed with MENu. The version of MENu used for
that analysis was saved on a tape. Analyses reflected data
retrieved from stored information files specific to each
time period.

Food Groups

The food-grouping scheme was designed for all foods
or entries (core and recipe) appearing in MENu. Food
types were identified for groups (e.g., cheese, as a
major ingredient, was included in a food group list).
Twenty-one major food groups were established, based
on similar source characteristics (e.g., “fruit and fruit
juices” formed one major group; “rice, biscuits, and
cereals” were included in the breads and grains catego-
ry). Composite food items, such as recipes, were as-
signed to food groups according to primary ingredi-
ents. If no single type of food (other than water)
accounted for at least 60% of the weight, the item was
classified as a mixed food. Examples of foods included
in the food groups have been documented previously.40

Four food groups were deleted from the analyses due to
small sample sizes (i.e., formula, vitamins, veal/lamb,
alcohol), resulting in 17 food groups.

Four larger food categories were created and used in
the analyses: FJV (fruit, fruit juices, vegetables); meats
(mixed meats, poultry, seafood, eggs, pork, and beef);
sweets (desserts, candy, and sweetened beverages); and
dairy (milk and cheese). High- and low-quality food
groups were also created. The high-quality food group
reflected foods consumed at least once from meats,
dairy, breads/grains, fruits/fruit juices, and vegetable
groups. The low-quality food groups reflected foods
that were consumed at least once from salty snacks,
candy, desserts, fats/oils, and sweetened beverage
groups.

The eating patterns selected for this study included
food consumption patterns, total gram amount of
food/beverages consumed by meal period, total eating
episodes, number of meals and snacks consumed, and
total gram amount of high- and low-quality foods.
These eating patterns were selected based on an exten-

10 American Journal of Preventive Medicine, Volume 25, Number 1

sive review of the literature41 and the eating patterns
that could actually be extracted from the 24-hour
dietary recall.

Measure of Adiposity

Trained examiners followed rigid protocols that
changed little over time.32 Briefly, height was measured
twice to the nearest 0.1 cm on a standard board, and
weight was measured twice to the nearest 0.1 kg by
using a balance-beam metric scale. For both weight and
height, the two readings were averaged. The children
were clothed in only a hospital gown, underpants, and
socks. BMI (kg/m2) was used as a measure of adiposity.
The age- and gender-specific Centers for Disease Con-
trol and Prevention (CDC) reference standards42 were
used to classify children who were normal weight (BMI
�85th percentile); at risk of overweight (BMI �85th
and �95th percentile); and overweight (BMI �95th
percentile).

Statistical Analysis
Prevalence of Overweight

In the analysis, children at risk for overweight (BMI
�85th percentile to �95th percentile) and those who
were overweight (BMI �95th percentile) were com-
bined to reflect the overweight group. The number and
percentage of 10-year-olds who were overweight were
identified by study year, ethnicity, gender, or ethnicity
by gender groups. The Cochran–Armitage trend test
was applied to examine the trend of the proportions of
overweight 10-year-olds over a 21-year period. Trends in
height among normal-weight and overweight 10-year-
olds were examined separately by ANOVA over the
same period.

Association Between Eating Patterns and
Overweight Status

Data from the seven surveys were analyzed together to
investigate the association between eating patterns and
being overweight. The association was evaluated by
logistic regression analysis via the PROC LOGISTIC
procedure of SAS (version 8.0, SAS Institute Inc., Cary,
North Carolina, 1999). In each logistic regression
model, being overweight was used as a dependent
variable and eating patterns were used as independent
variables. The collinearity among independent vari-
ables was checked first, using the PROC REG proce-
dure of SAS with options VIF and COLLINOINT,
before logistic regression was carried out. Multivariate
models were conducted for overall (N�1562) as well as
separately for each ethnicity– gender group (n�497 for
EA male; n�513, EA female; n�273, AA male; and
n�279, AA female) because ethnicity– gender interac-
tion had significant effects on being overweight. Each

model for overall effects included total calorie intake,
age, study year, ethnicity, gender, and ethnicity �
gender interaction to control for their effects on being
overweight. Likewise, the models for each ethnicity–
gender group were adjusted for total calorie intake,
age, and study year. An association was defined if the
unity was not in the 95% confidence interval (CI) of an
odds ratio (OR). The OR presented in Tables 1 and 2
were calculated depending on the type of eating pat-
tern construct. If the eating pattern was measured as
consumption in grams, then the OR was calculated as
the ratio of odds of being overweight for participants
with the mean amount of increased consumption in
grams, compared to participants with mean gram con-
sumption. The mean gram consumption was the aver-
age amount of food/beverage consumed for each of
the food groups. This average amount consumed re-
flected the average “serving size” of each food group.
For example, the odds of being overweight for a
10-year-old who consumed two average servings of a
sweetened beverage (2�399 g) would be 1.33 times
higher than a 10-year-old who consumed only an aver-
age serving (399 g). If the eating pattern was not
measured in grams, such as the number of eating
episodes, then the OR was calculated as the ratio of
odds of being overweight for a 10-year-old who had
“n�1” eating episodes, compared to that for a 10-year-
old with n eating episodes “(n�0).” For example, the
odds of being overweight for a 10-year-old, African-
American girl who had three meals was 0.56 times lower
than for those who had only two meals.

Results
Trends in Obesity Status

The percentage of children with a BMI in the �50th
percentile significantly decreased (p�0.0001) from
55% in 1973 to 34% in 1994. In contrast, there was a
twofold increase (p�0.0001) in the percentage of chil-
dren with a BMI �85th and �95th percentile and a
five-fold increase (p�0.0001) in the percentage of
children with a BMI �95th percentile (4% to 20%)
over 2 decades. Mean height (cm) significantly in-
creased (p�0.05) for 10-year-old children with a BMI
�50th percentile. In all survey years for children with a
BMI �85th percentile, mean height was significantly
(p�0.0001) higher than those children with a BMI
�85th percentile.

Percentage of Overweight 10-Year-Olds by
Ethnicity and Gender

All surveys were combined and divided into two weight-
status groups (based on BMI): normal weight (�85th
percentile), and overweight (�85th percentile) (Table

Am J Prev Med 2003;25(1) 11

3).43 The overall percentage of overweight among
10-year-olds was 24%, with 76% being normal weight.
The percentage of overweight children by ethnicity
(approximately 24%) and gender (approximately
24%) was equally distributed. There was a significant
(p�0.0001) difference in the ethnic � gender distri-
bution of overweight status. EA males had the highest
prevalence of overweight (27%) and AA males had
the lowest prevalence (19%). The interaction of

ethnicity and gender was significantly associated with
overweight status (p�0.01). Among the four ethnici-
ty– gender groups, the likelihood to be overweight
for EA males was 1.2 times higher than AA females at
10 years of age (data not shown). The percentage of
10-year-olds overweight in Bogalusa (24%) was slightly
higher than among 6- to 11-year-olds in the National
Health and Nutrition Examination Survey (22%), partic-
ularly for EA males.

Table 1. The association between eating-pattern variables and overweight status

Eating-pattern variable OR (95% CI) Mean g Example of food weight (g)

Food groups consumption Ia,b (R2�0.05)
Fats/oils 0.98 (0.91–1.00) 22.0 1/8 c. oil (25.8)
Fruits/fruit juices 0.97 (0.87–1.07) 135.0 1 apple (129.0)
Vegetables 0.98 (0.86–1.12) 161.0 1/8 c. broccoli (23.3)
Breads/grains 1.03 (0.85–1.24) 187.0 1 c. cereal (37.6)
Mixed meats 1.05 (0.96–1.14) 60.0 1 slice of 12� pizza (66.4)
Desserts 0.98 (0.87–1.10) 70.0 1/8 c. ice cream (16.6)
Candy 0.93 (0.82–1.06) 40.0 Snickers Fun Bar (23.7)
Sweetened beverages 1.33 (1.12–1.57)**** 399.0 8 oz. Coke (235.2)
Poultry 1.01 (0.95–1.07) 31.0 1 fried chicken wing (32.0)
Salty snacks 0.95 (0.88–1.02) 12.0 1 bag potato chips (32.0)
Seafood 1.00 (0.97–1.02) 9.0 1 fish stick (6.0)
Condiments 1.00 (0.95–1.05) 7.0 1 Tb mayonnaise (13.8)
Eggs 1.00 (0.95–1.04) 11.0 1 medium egg (44.0)
Milk 1.08 (0.87–1.33) 409.0 1 c. whole milk (244.0)
Pork 1.03 (0.98–1.09) 23.0 1 slice bacon (6.3)
Cheese 1.01 (0.97–1.06) 22.0 1 slice cheese (18.9)
Beef 1.06 (0.99–1.15) 47.0 1 hotdog (22.0)
Food groups consumption IIb,c (R2�0.05)
Fats/oils 0.99 (0.92–1.06)
FJV 0.96 (0.81–1.13)
Breads/grains 1.05 (0.87–1.25)
Meats 1.21 (1.00–1.46)*
Sweets 1.38 (1.12–1.71)*
Salty snacks 0.96 (0.89–1.03)
Condiments 1.00 (0.95–1.05)
Dairy 1.12 (0.91–1.39)
Grams from high- and low-quality foods (R2�0.04)
Gram of high-quality foodsd 1.19 (0.78–1.80)
Gram of low-quality foodse 1.35 (1.08–1.68)*
Gram amountc

Total (R2�0.04) 1.77 (1.02–3.08)*
From breakfast (R2�0.04) 0.96 (0.79–1.16)
From lunch (R2�0.04) 1.14 (0.87–1.49)
From dinner (R2�0.04) 1.26 (0.96–1.63)
From snacks (R2�0.04) 1.24 (1.02–1.50)*
Eating episodef

Total (R2 � 0.04) 0.97 (0.90–1.05)
No. of meals (R2 � 0.04) 0.91 (0.72–1.15)
No. of snacks (R2 � 0.04) 0.98 (0.90–1.05)

*p�0.051; *p�0.05; *p�0.01; ****p�0.001.
aFood group consumption I includes all individual food groups.
bOdds ratio � risk of being overweight if increasing mean gram consumption.
cFood group consumption II includes the four larger food group categories (FIV, dairy, meats, and sweets) and four individual food groups
(fats/oils, breads/grains, salty snacks, condiments).
dHigh-quality foods: fruits/fruit juices, vegetables, breads/grains, meats, dairy.
eLow-quality foods: fats/oils, sweets, salty snacks.
fOdds ratio � risk of being overweight if having one more eating episode.
CI, confidence interval; dairy, milk and cheese; FJV, fruits/fruit juices and vegetables; g, grams; meats, mixed meats, poultry, seafood, eggs, pork,
and beef; OR, odds ratio; sweets, desserts, candy, and sweetened beverages.

12 American Journal of Preventive Medicine, Volume 25, Number 1

Association Between Eating Patterns and
Overweight Status
Total gram amount of food/beverage consumed, par-
ticularly from snacks (p�0.05), and total gram con-
sumption of low-quality foods (p�0.01) were positively
associated with overweight status (Table 1). Consump-
tion of sweets (p�0.001), specifically sweetened bever-

ages (p�0.001), and meats (p�0.051) was positively
associated with overweight status.

Despite these significant associations, the percentage
of variance explained by the model was very small
(Table 1). In the food group consumption model, only
5% of the variance was explained, of which sweetened
beverages alone explained 1%. The combined food

Table 2. The association between eating-pattern variables and overweight status by ethnicity– gender groups

Eating pattern
EA male
OR (95% CI)

EA female
OR (95% CI)

AA male
OR (95% CI)

AA female
OR (95% CI)

Food groups consumption Ia,b R2�0.08 R2�0.10 R2�0.17 R2�0.13
Fats/oils 0.97 (0.85–1.10) 1.00 (0.83–1.19) 0.93 (0.71–1.22) 1.06 (0.86–1.32)
Fruits/fruit juices 1.03 (0.88–1.20) 1.10 (0.92–1.31) 0.97 (0.69–1.41) 0.55 (0.38–0.79)*
Vegetables 0.98 (0.77–1.24) 1.09 (0.87–1.36) 1.05 (0.74–1.49) 0.75 (0.51–1.09)
Breads/grains 1.20 (0.86–1.67) 0.90 (0.62–1.30) 0.62 (0.33–1.16) 1.03 (0.60–1.79)
Mixed meats 1.12 (0.95–1.31) 0.93 (0.78–1.12) 1.06 (0.82–1.37) 0.97 (0.78–1.19)
Desserts 0.89 (0.73–1.09) 1.08 (0.86–1.35) 0.89 (0.65–1.22) 0.89 (0.66–1.21)
Candy 0.94 (0.76–1.18) 0.78 (0.60–1.01) 0.79 (0.51–1.23) 1.00 (0.73–1.35)
Sweetened beverages 1.68 (1.21–2.33)* 1.53 (1.05–2.22)* 1.02 (0.72–1.46) 0.92 (0.65–1.30)
Poultry 0.99 (0.89–1.09) 1.04 (0.94–1.16) 0.97 (0.76–1.23) 0.99 (0.84–1.16)
Salty snacks 0.98 (0.88–1.09) 0.92 (0.80–1.05) 1.15 (0.94–1.42) 0.84 (0.66–1.06)
Seafood 0.97 (0.92–1.02) 1.07 (1.01–1.13)* 0.73 (0.48–1.11) 1.03 (0.95–1.11)
Condiments 1.02 (0.93–1.12) 0.99 (0.90–1.08) 1.02 (0.86–1.22) 0.89 (0.74–1.07)
Eggs 0.97 (0.88–1.06) 1.07 (0.99–1.17) 0.99 (0.85–1.14) 0.91 (0.79–1.05)
Milk 0.96 (0.64–1.46) 1.19 (0.82–1.73) 1.25 (0.68–2.30) 0.93 (0.55–1.59)
Pork 1.04 (0.94–1.14) 1.00 (0.89–1.12) 1.16 (0.99–1.35) 0.98 (0.84–1.13)
Cheese 1.02 (0.95–1.11) 1.04 (0.97–1.13) 0.95 (0.82–1.09) 0.90 (0.77–1.06)
Beef 1.08 (0.92–1.25) 1.06 (0.91–1.24) 1.11 (0.89–1.37) 1.02 (0.88–1.18)
Food groups consumption IIb,c R2�0.05 R2�0.08 R2�0.12 R2�0.11
Fats/oils 0.98 (0.86–1.11) 1.00 (0.84–1.19) 0.94 (0.75–1.17) 1.08 (0.88–1.32)
FJVd 1.05 (0.81–1.36) 1.23 (0.93–1.62) 1.05 (0.64–1.75) 0.43 (0.26–0.73)*
Breads/grains 1.25 (0.92–1.70) 0.93 (0.66–1.31) 0.70 (0.41–1.19) 1.15 (0.48–2.74)
Meatsd 1.26 (0.89–1.78) 1.25 (0.88–1.78) 1.49 (0.87–2.56) 0.98 (0.63–1.52)
Sweetsd 1.65 (1.12–2.44)* 1.65 (1.06–2.57)* 1.01 (0.63–1.59) 0.93 (0.59–1.46)
Salty snacks 0.99 (0.89–1.10) 0.91 (0.80–1.05) 1.16 (0.96–1.42) 0.85 (0.67–1.07)
Condiments 1.01 (0.92–1.11) 1.00 (0.92–1.08) 0.99 (0.84–1.18) 0.93 (0.79–1.10)
Dairyd 1.08 (0.73–1.62) 1.30 (0.90–1.89) 1.27 (0.72–2.26) 0.91 (0.55–1.53)
Gram from high-and

low-quality foods
R2�0.05 R2 � 0.07 R2�0.09 R2�0.06

High-quality foodsd 1.44 (0.70–2.95) 1.89 (0.90–3.96) 1.06 (0.34–3.33) 0.34 (0.12–1.04)
Low-quality foodse 1.69 (1.12–2.53)* 1.60 (1.01–2.53)* 1.00 (0.62–1.63) 0.89 (0.56–1.42)
Gram amount2 R2�0.05 R2�0.07 R2�0.09 R2�0.06
Total 3.17 (1.20–8.41)*

R2�0.05
2.97 (1.03–8.52)*
R2�0.07

1.03 (0.25–4.30)
R2�0.11

0.33 (0.09–1.22)
R2�0.07

From breakfast 1.14 (0.81–1.60) 1.18 (0.83–1.71) 0.67 (0.40–1.10) 0.62 (0.42–0.93)*
From lunch 1.20 (0.75–1.93) 1.38 (0.86–2.23) 1.03 (0.48–2.23) 0.91 (0.47–1.77)
From dinner 1.82 (1.13–2.91)* 1.24 (0.72–2.14) 0.81 (0.43–1.54) 0.95 (0.54–1.67)
From snacks 1.39 (1.00–1.95) 1.43 (0.96–2.14) 1.34 (0.81–2.20) 0.68 (0.41–1.13)
Eating episodef R2�0.04 R2�0.06 R2�0.09 R2�0.06
Total 0.90 (0.79–1.03) 1.07 (0.93–1.24) 1.04 (0.85–1.28) 0.91 (0.76–1.08)

R2�0.04 R2�0.06 R2�01.4 R2�0.07
No. of meals 0.97 (0.63–1.50) 1.23 (0.80–1.89) 0.70 (0.38–1.33) 0.56 (0.33–0.95)*
No. of snacks 0.90 (0.78–1.03) 1.06 (0.91–1.23) 1.07 (0.87–1.31) 0.93 (0.78–1.12)

*p�0.05; *p�0.01.
aFood group consumption I: individual food group consumption as eating pattern variables.
bOdds ratio � risk of being overweight if increasing mean gram consumption.
cFood group consumption II includes the four larger food group categories (FIV, dairy, meats, sweets) and four individual food groups (fats/oils,
breads/grains, salty snacks, condiments).
dHigh-quality foods: fruits/fruit juices, vegetables, breads/grains, meats, dairy.
eLow-quality foods: fats/oils, sweets, salty snacks.
fOdds ratio � risk of being overweight if having one more eating episode.
AA, African American; CI, confidence interval; EA, Euro-American; FJV, fruits/fruit juices and vegetables; meats, mixed meats, poultry, seafood,
eggs, pork, and beef; OR, odds ratio; sweets, desserts, candy, and sweetened beverages; dairy, milk and cheese.

Am J Prev Med 2003;25(1) 13

ategories of meats and sweets (1%) explained very little of
the variance in obesity. Similarly, the percentage of vari-
ance explained by total gram amount of low-quality foods
consumed and the total gram amount of foods/beverages
consumed (particularly from lunch and dinner), the
number of snacking episodes was equally low.

Association Between Eating Patterns and
Overweight Status by Ethnicity and Gender

The association of eating patterns and obesity varied by
ethnicity and gender (Table 2). For EA males, con-
sumption of sweets (p�0.05), especially sweetened bev-
erages (p�0.01); total gram amount of food/beverages
consumed (p�0.05), particularly from the dinner meal
(p�0.05); and total gram amount of low-quality foods
consumed (p�0.05) were positively associated with
obesity. For EA females, consumption of seafood
(p�0.05) and sweets (p�0.05), particularly sweetened
beverages (p�0.05), were positively associated with
obesity. Total gram amount of food consumed
(p�0.05), particularly low-quality foods (p �0.05), was
positively associated with obesity. For AA females, con-
sumption of fruits/fruit juices (p �0.01); FJV (p�0.01);
total gram amount of breakfast consumed (p�0.05);
and total number of meals consumed (p�0.05) were
negatively associated with obesity. Overall, the percent
of variance explained (range 4% to 8%) by the model
was low for all ethnic � gender groups.

Discussion

The present investigation adds to previous studies,
using dietary intake data to establish the association of
eating patterns with overweight status in 10-year-old
children. The prevalence of overweight among the
children was 24%, which is comparable to the national
average,1,2 with the highest prevalence shown among

EA males. Overall, several eating patterns were posi-
tively associated with overweight status: consumption of
sweetened beverages, sweets, and meats, and total gram
consumption of low-quality foods. Total gram amount
of food/beverages consumed, particularly from snacks,
was also positively associated. Other studies have shown
an association between soft drink consumption,22,23

low-quality foods,29 and overweight status, while other
studies have not.44,45 In the present study, soft drink
consumption accounted for 50% of the sweetened
beverages consumed, with the remaining percentage
reflecting consumption of tea or coffee with sugar and
fruit drinks. When only soft drink consumption is
considered, the positive association with overweight
status still existed but the percent of variance explained
was very low (1%). The positive association between
sweets and overweight status resulted from consump-
tion of sweetened beverages and not desserts or candy.

The association between total gram amount of bev-
erage/food consumed and overweight status was not
surprising, particularly since the amount of time chil-
dren are spending in physical activity has de-
creased.46,47 A previous study of these same Bogalusa
children showed that the mean total gram amount of
food/beverages consumed increased from 1973 to 1994
(first author, unpublished observations, 2003). If phys-
ical activity is decreasing48 –51 and the total gram
amount of food consumed has increased, it is reason-
able to find an association between total gram amount
consumed and overweight status.

A particularly interesting finding was the lack of
congruency in the types of eating patterns associated
with overweight status across the four ethnic– gender
groups. Data suggest that the eating patterns associated
with obesity may in fact vary by ethnicity and gender.
This initial finding needs to be confirmed in other

Table 3. Percentage of overweight 10-year-olds by ethnicity and gender

Weight status Bogalusa
NHANES II
and NHES III

Normal
n (%)

Overweight
n (%)

Ethnicity
Euro-American 768 (76) 242 (24) —
African American 427 (77) 125 (23) —

Gender
Male 584 (76) 186 (24) 467 (23)
Female 611 (77) 181 (23) 458 (21)

Ethnicity and gendera

Euro-American, male 364 (73) 133 (27) 267 (21)
Euro-American, female 404 (79) 109 (21) 270 (22)
African American, male 220 (81) 53 (19) 257 (27)
African American, female 207 (74) 72 (26) 224 (31)

Total 1195 (76) 367 (24) 1817 (22)
a�2�7.75; p�0.0515.
NHANES II, National Health and Nutrition Examination Survey43; NHES, National Health Examination Survey.43

14 American Journal of Preventive Medicine, Volume 25, Number 1

national surveys with a larger geographic representa-
tion and larger sample size.

An important consideration when interpreting the
results is that the percent variance explained from the
eating pattern– overweight models was extremely small,
suggesting that substantial variance in obesity was un-
explained. Since the eating patterns studied were not
mutually exclusive, it was impossible to determine the
cumulative effect of the significant eating patterns on
overweight status. One can hypothesize that the associ-
ation between eating patterns and overweight status is
not a result of a single eating pattern but from a
combination of eating patterns that are interrelated
and cumulative in their effect on overweight status.

Limitations

The present study was a cross-sectional analysis, and
thus causal inferences cannot be made. Longitudinal
studies are needed to confirm these preliminary find-
ings. A single 24-hour dietary recall was collected on
each participant. One 24-hour dietary recall is not
adequate for characterizing the usual eating patterns of
an individual, but is sufficient for charactering the
eating patterns of large groups of children.52 Despite
this limitation, several eating pattern correlates of
obesity in 10-year-old children were detected. The
sample size in this study was 1562 children, which is
larger than most regionally specific studies but smaller
than national surveys. These findings are specific to
Bogalusa 10-year-old children and may not be represen-
tative of the nation as a whole. However, the dietary
intakes of Bogalusa children are comparable to na-
tional surveys.53–55 The overweight group defined in
this study was those children at risk for overweight or
already overweight. Although this approach has been
used in other published studies, the findings need to be
interpreted with some caution and replicated in larger
studies. The percentage of children who were over-
weight in this study (11%) is comparable to national
averages (11%).56 Another major limitation is the lack
of physical activity data on this cohort of children.
Others have found an association between physical
activity and overweight status.57–59 Energy expenditure
from physical activity directly influences the overall
energy balance equation.58 This may explain, in part,
the low percentage of variance of the single eating
pattern variables investigated in this study.

Application and Implications

These results have important implications for obesity-
prevention research targeting children. These associa-
tions were poorly explained by a single eating pattern,
and the pattern of significant relationships varied by
ethnicity and gender. Further research with multiple
days of assessment is needed to better understand the

associations among eating patterns and overweight
status among children.

We are grateful to Margaret Moore for development and
Catherine Champagne, PhD, for maintenance of the Moore
Extended Nutrients (MENu) database and Pamelia Harris for
help in preparing the manuscript. We also extend a special
thanks to the children and young adults of Bogalusa without
whom this work could not be accomplished.

This research was supported by the National Institutes of
Health, Evolution of Cardiovascular Risk with Normal Aging
National Institute on Aging (AG 16592) and the U.S. Depart-
ment of Agriculture, Food Assistance and Nutrition Research
Program. Partial support was received from the Kellogg’s
Company, the Sugar Association, and MARS Inc.

This work is a product of the U.S. Department of Agricul-
ture (USDA/ARS) Children’s Nutrition Research Center,
Department of Pediatrics, Baylor College of Medicine, Hous-
ton TX, and had been funded in part with federal funds from
the USDA/ARS (Cooperative Agreement 58-6250-6001). The
contents of this publication do not necessarily reflect the
views or policies of the USDA, nor does mention of trade
names, commercial products, or organizations imply endorse-
ment from the U.S. government.

References
1. Flegal KM. The obesity epidemic in children and adults: current evidence

and research issues. Med Sci Sports Exerc 1999;31:509 –14.
2. Rippe J. The obesity epidemic: challenges and opportunities. J Am Diet

Assn 1998;98:5.
3. Webber LS, Wattigney WA, Srinivasan SR, Berenson GS. Obesity studies in

Bogalusa. Am J Med Sci 1995;310(suppl):S53–61.
4. Guo SS, Roche AF, Chumlea WC, Gardner JD, Siervogel RM. The predic-

tive value of childhood body mass index values for overweight at age 35 y.
Am J Clin Nutr 1994;59:810 –9.

5. Webber LS, Cresanta JL, Croft JB, Srinivasan SR, Berenson GS. Transitions
of cardiovascular risk from adolescence to young adulthood—the Bogalusa
Heart Study: II. Alterations in anthropometric blood pressure and serum
lipoprotein variables. J Chronic Dis 1986;39:91–103.

6. Johnston FE. Health implications of childhood obesity. Ann Intern Med
1985;103:1068 –72.

7. Aristimuno GG, Foster TA, Voors AW, Srinivasan SR, Berenson GS.
Influence of persistent obesity in children on cardiovascular risk factors:
the Bogalusa Heart Study. Circulation 1984;69:895–904.

8. Hoffmans MD, Kromhout D, Coulander CD. Body mass index at the age of
18 and its effects on 32-year mortality from coronary heart disease and
cancer. A nested case-control study among the entire 1932 Dutch male
birth cohort. J Clin Epidemiol 1989;42:513–20.

9. Abraham S, Collins G, Nordsieck M. Relationship of childhood weight
status to morbidity in adults. HSMHA Rep 1971;86:273–84.

10. Swinburn B, Egger G, Raza F. Dissecting obesogenic environments: the
development and application of a framework for identifying and prioritiz-
ing environmental interventions for obesity. Prev Med 1999;29:563–70.

11. Hebebrand J, Wulftange H, Goerg T, et al. Epidemic obesity: are genetic
factors involved via increased rates of assortative mating? Int J Obes Relat
Metab Disord 2000;24:345–53.

12. Hill JO, Melanson EL, Wyatt HT. Dietary fat intake and regulation of
energy balance: implications for obesity. J Nutr 2000;130(suppl):S284 –88.

13. U.S. Department of Agriculture. Childhood obesity: causes and prevention.
Proceedings of a symposium sponsored by the Center for Nutrition Policy
and Promotion. Washington DC, October 27, 1998. Available at: www.nal.
usda.gov:8001/Training/obesity . Accessed July 12, 2002.

14. Zemel MB, Shi H, Greer B, Dirienzo D, Zemel PC. Regulation of adiposity
by dietary calcium. FASEB J 2000;14:1132–8.

15. Roberts SB, McCrory MA, Saltzman E. The influence of dietary composi-
tion on energy intake and body weight. J Am Coll Nutr 2002;21(suppl):
S140 –45.

Am J Prev Med 2003;25(1) 15

16. Gibson SA. Are high-fat, high-sugar foods and diets conducive to obesity?
Int J Food Sci Nutr 1996;47:405–15.

17. Macdiarmid JI, Vail A, Cade JE, Blundell JE. The sugar–fat relationship
revisited: differences in consumption between men and women of varying
BMI. Int J Obes Relat Metab Disord 1998;22:1053–61.

18. Bray GA, Popkin BM. Dietary fat intake does affect obesity! Am J Clin Nutr
1998;68:1157–73.

19. Clemens LH, Slawson DL, Klesges RC. The effect of eating out on quality
of diet in premenopausal women. J Am Diet Assn 1999;99:442–4.

20. McCrory MA, Fuss PJ, McCallum JE, et al. Dietary variety within food
groups: association with energy intake and body fatness in men and
women. Am J Clin Nutr 1999;69:440 –7.

21. Coll M, Meyer A, Stunkard AJ. Obesity and food choices in public places.
Arch Gen Psychiatry 1979;36:795–7.

22. Ludwig DS, Peterson KE, Gortmaker SL. Relation between consumption of
sugar-sweetened drinks and childhood obesity: a prospective, observational
analysis. Lancet 2001;357:505–8.

23. Dennison BA, Rockwell HL, Baker SL. Excess fruit juice consumption by
preschool-aged children is associated with short stature and obesity.
Pediatrics 1997;99:15–22.

24. Hill JO, Peters JC. Environmental contributions to the obesity epidemic.
Science 1998;280:1371–4.

25. Ceru-Bjork C, Andersson I, Rossner S. Night eating and nocturnal eating—
two different or similar syndromes among obese patients? Int J Obes Relat
Metab Disord 2001;25:365–72.

26. Speechly DP, Rogers GG, Buffenstein R. Acute appetite reduction associ-
ated with an increased frequency of eating in obese males. Int J Obes
1999;23:1151–9.

27. Fabry P, Fodo J, Hejl Z, Braun T, Zvolankova K. The frequency of meals: its
relation to overweight, hypercholesterolaemia, and decreased glucose
tolerance. Lancet 1964;2:614 –5.

28. Kant A. Frequency of eating occasions and weight change in NHANES I
Epidemiologic Follow-up Study. Int J Obes 1995;19:468 –74.

29. Basiotis PP, Lino M, Anand RS. Report card on the diet quality of African
Americans. Fam Econ Nutr Rev 1998;11:61–3.

30. Freedman DS, Srinivasan SR, Valdez RA, et al. Secular increases in relative
weight and adiposity among children over two decades: the Bogalusa Heart
Study. Pediatrics 1997;99:420 –6.

31. Gidding SS, Bao W, Srinivasan SR, Berenson GS. Effects of secular trends
in obesity on coronary risk factors in children: the Bogalusa Heart Study.
J Pediatr 1995;127:868 –74.

32. Berenson GS. Cardiovascular risk factors in children: the early natural
history of atherosclerosis and essential hypertension. New York: Oxford
University Press, 1980.

33. Harsha DW. Body composition and obesity in a population of children and
their influence on cardiovascular risk. In: Berenson GS, ed. Causation of
cardiovascular risk factors in children: perspectives on cardiovascular risk
in early life. New York: Raven Press, 1986:190 –222.

34. Frank GC, Berenson GS, Schilling PE, Moore MC. Adapting the 24-hr.
recall for epidemiologic studies of school children. J Am Diet Assn
1977;71:26 –31.

35. Farris RP, Nicklas TA. Characterizing children’s eating behavior. In:
Suskind RM, Suskind LL, eds. Textbook of pediatric nutrition. 2nd ed. New
York: Raven Press, 1993;505–16.

36. Nicklas TA, Forcier JE, Webber LS, Berenson GS. School lunch assessment
to improve accuracy of 24-hour dietary recall for children. J Am Diet Assn
1991;91:711–3.

37. Moore MC, Goodloe MH. Extended table of nutrient values (ETNV), 2nd
rev. ext. ed. New Orleans LA: International Dietary Information Founda-
tion, Louisiana State University Medical Center, 1982.

38. Farris RP, Frank GC, Webber LS, Berenson GS. Quality controls for
processing dietary data. Diet J Software 1984;2:3–4.

39. Frank GC, Hollatz AT, Webber LS, Berenson GS. Effect of interviewer
recording practices on nutrient intake—Bogalusa Heart Study. J Am Diet
Assn 1984;84:1432–36, 1439.

40. Nicklas TA, Farris RP, Johnson CC, Webber LS, Berenson GS. Food sources
of nutrients: a tool for dietary management and health. The Bogalusa
Heart Study. 1973–1983. Tulane Center for Cardiovascular Health, New
Orleans LA, 1990.

41. Nicklas TA, Baranowski T, Cullen KW, Berenson G. Eating patterns, dietary
quality, and obesity. J Am Coll Nutr 2001;20:598 –608.

42. National Center for Health Statistics. National Health and Nutrition
Examination Survey, CDC growth charts: United States. Available at:
www.cdc.gov/nchs/about/major/nhanes/growthcharts/datafiles.htm. Ac-
cessed on July 12, 2002.

43. Troiano RP, Flegal KM, Kuczmarski RJ, Campbell SM, Johnson CL.
Overweight prevalence and trends for children and adolescents: the
National Health and Nutrition Examination Surveys, 1963–1991. Arch
Pediatr Adolesc Med 1995;149:1085–91.

44. Bouchard C. Can obesity be prevented? Nutr Rev 1996;54(suppl):S125–
130.

45. Seidell JC. Dietary fat and obesity: an epidemiologic perspective. Am J Clin
Nutr 1998;67:546 –50.

46. Pratt M, Macera CA, Blanton C. Levels of physical activity and inactivity in
children and adults in the United States: current evidence and research
issues. Med Sci Sports Exerc 1999;31:526 –33.

47. Kann L, Kinchen SA, Williams BI, et al. State and local YRBSS coordinators.
Youth Risk Behavior Surveillance—United States. MMWR Surveill Summ
1998;47:1–89.

48. Porkka KV, Raitakari OT, Leino A, et al. Trends in serum lipid levels during
1980 –1992 in children and young adults. The Cardiovascular Risk in Young
Finns Study. Am J Epidemiol 1997;146:64 –77.

49. Kann L, Warren CW, Harris WA, et al. Youth risk behavior surveillance—
United States, 1993. J Sch Health 1995;65:163–71.

50. Grunbaum JA, Kann L, Kinchen SA, et al. Youth risk behavior surveil-
lance—United States, 2001. J Sch Health 2002;72:313–28.

51. Lowry R, Wechsler H, Kann L, Collins JL. Recent trends in participation in
physical education among US high school students. J Sch Health 2001;71:
145–52.

52. Thompson FE, Byers T. Dietary assessment resource manual. J Nutr
1994;124(suppl):S2245–317.

53. Nicklas TA. Dietary studies of children and young adults (1973–1988): the
Bogalusa Heart Study. Am J Med Sci 1995;310(suppl):S101–8.

54. Nicklas TA, Farris RP, Srinivasan SR, Webber LS, Berenson GS. Nutritional
studies in children and implications for change: the Bogalusa Heart Study.
J Adv Nurs 1989;2:451–74.

55. Nicklas TA, Johnson CC, Myers L, Webber L, Berenson GS. Eating patterns,
nutrient intakes and alcohol consumption patterns of young adults: the
Bogalusa Heart Study. Med Exerc Nutr Health 1995;4:316 –24.

56. Ogden CL, Flegal KM, Carroll MD, Johnson CL. Prevalence and trends in
overweight among children and adolescents, 1999 –2000. JAMA 2002;288:
1728 –32.

57. National Center for Chronic Disease Prevention and Health Promotion.
Physical activity and health: a report of the Surgeon General. Atlanta, GA:
Centers for Disease Control and Prevention, 1996.

58. Barlow SE, Dietz WH. Obesity evaluation and treatment: expert committee
recommendations. The Maternal and Child Health Bureau, Health Re-
sources and Services Administration and the Department of Health and
Human Services. Pediatrics 1998;102:E29.

59. Dietz W. Physical activity and childhood obesity. Nutrition 1991;7:295–6.

16 American Journal of Preventive Medicine, Volume 25, Number 1

Prevalence of obesity and extreme obesity in
children aged 3–5 years
J. C. Lo1,2, B. Maring2, M. Chandra1, S. R. Daniels3, A. Sinaiko4, M. F. Daley5,
N. E. Sherwood6, E. O. Kharbanda6, E. D. Parker6, K. F. Adams6, R. J. Prineas7,
D. J. Magid5, P. J. O’Connor6 and L. C. Greenspan8
1Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA; 2Department of Medicine, Kaiser Permanente
Oakland Medical Center, Oakland, CA, USA; 3University of Colorado School of Medicine, Denver, CO, USA; 4Department of
Pediatrics, University of Minnesota, Minneapolis, MN, USA; 5Institute for Health Research, Kaiser Permanente Colorado, Denver,
CO, USA; 6HealthPartners Institute for Education and Research, Minneapolis, MN, USA; 7Division of Public Health Sciences,
Wake Forest University School of Medicine, Winston-Salem, NC, USA; 8Department of Pediatrics, Kaiser Permanente San
Francisco Medical Center, San Francisco, CA, USA

Received 26 October 2012; revised 20 December 2012; accepted 1 January 2013

What is already known about this subject
• The prevalence of obesity in the United States has

increased dramatically over the past three decades

• There is a growing spectrum of severe obesity among

children and adolescents.
• Obesity trends and race/ethnic differences may be evident

at a young ag

What this study adds
• Among children aged 3–5 years, the prevalence of obesity

and severe obesity was higher in boys than in girls, and
highest among children of Hispanic ethnicity.

• Within this young age group, higher body mass index
(BMI) was associated with greater height percentile.

• Among obese children aged 5 years, provider recognition
of obesity or elevated BMI was high, approaching 80% of
children.

Summary
Background: Early childhood adiposity may have significant later health effects. This study examines the
prevalence and recognition of obesity and severe obesity among preschool-aged children.

Methods: The electronic medical record was used to examine body mass index (BMI), height, sex and
race/ethnicity in 42 559 children aged 3–5 years between 2007 and 2010. Normal or underweight (BMI < 85th percentile); overweight (BMI 85th–94th percentile); obesity (BMI � 95th percentile); and severe obesity (BMI � 1.2 ¥ 95th percentile) were classified using the 2000 Centers for Disease Control and Prevention growth charts. Provider recognition of elevated BMI was examined for obese children aged 5 years.

Results: Among 42 559 children, 12.4% of boys and 10.0% of girls had BMI � 95th percentile. The
prevalence was highest among Hispanics (18.2% boys, 15.2% girls), followed by blacks (12.4% boys, 12.7%
girls). A positive trend existed between increasing BMI category and median height percentile, with obesity
rates highest in the highest height quintile. The prevalence of severe obesity was 1.6% overall and somewhat
higher for boys compared with girls (1.9 vs. 1.4%, P < 0.01). By race/ethnicity, the highest prevalence of severe obesity was seen in Hispanic boys (3.3%). Among those aged 5 years, 77.9% of obese children had provider diagnosis of obesity or elevated BMI, increasing to 89.0% for the subset with severe obesity.

Conclusions: Obesity and severe obesity are evident as early as age 3–5 years, with race/ethnic trends
similar to older children. This study underscores the need for continued recognition and contextualization of
early childhood obesity in order to develop effective strategies for early weight management.

Keywords: Children, obesity, preschool, severe obesity.

Address for correspondence: Dr JC Lo, Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA
94612, USA. E-mail: joan.c.lo@kp.org

PEDIATRICOBESITY ORIGINALRESEARCH doi:10.1111/j.2047-6310.2013.00154.x

O
R

IG
IN

A
L

R
E

S
E

A
R

C
H

Introduction

The current paediatric obesity epidemic in the United
States (1) includes approximately 12 million (17%

)

children aged 2–19 years (2), with obesity defined as
body mass index (BMI) at or above the 95th percen-
tile using the year 2000 Centers for Disease Control
and Prevention (CDC) BMI-for-age growth charts
(3,4). While the prevalence of obesity in children
appears to have plateaued in recent years (3–5), it is
now roughly triple what it was over three decades
ago (6,7). Moreover, there is a growing spectrum of
severe obesity, previously classified using a threshold
of BMI � 99th percentile (1,8,9) and now using the
more recently accepted threshold of BMI � 120% of
the 95th BMI percentile (10–12), adjusted for age
and sex. Using these definitions, studies in selected
paediatric populations have found that the preva-
lence of severe obesity in children ranges from 3.8 to
7.7% (8,11–13), with severe obesity beginning as
early as the preschool years (8,11,12).

The age at which excess weight gain occurs has
important implications for child health and develop-
ment (14). The range of normal BMI changes during
childhood growth, with values generally lowest
during ages 4–6 years (adiposity nadir) followed by
an increase (adiposity rebound) and subsequent
steady rise into the adolescent years (15–17). Early
adiposity rebound is associated with greater adipos-
ity in mid-childhood (15,16), which, in turn, has been
associated with accelerated growth, advanced bone
age, earlier pubertal transition and adult obesity
(16,18–25). In older children, both overweight and
obesity contribute to an increased risk of metabolic
syndrome, type 2 diabetes, hypertension, dyslipidae-
mia, non-alcoholic steatohepatitis and obstructive
sleep apnoea (1,26). Thus, early recognition of
severe obesity is an important step towards prevent-
ing long-term adverse health consequences.

There are few epidemiologic studies from large
healthcare delivery systems examining obesity rates
in preschool-aged children. Recent implementation
of sophisticated electronic health information tech-
nology provides the unique opportunity to monitor
onset and progression of overweight and obesity in
large community populations, as well as the futu

ability to link early anthropometric findings to clinical
outcomes. The primary objective of this study was to
examine anthropometric findings obtained in clinical
practice from a large contemporary cohort of chil-
dren aged 3–5 years to (i) determine the prevalence
of obesity and severe obesity and differences by
race/ethnicity, sex and stature; (ii) compare estimates
using BMI percentile, Z-score or percentage above

the 95th BMI percentile as thresholds for extremely
high BMI-for-age; (iii) determine whether children
with BMI � 95th percentile were recognized by their
provider and (iv) to review the anthropometric
records of individuals whose measured weight
and/or BMI values were flagged by the CDC growth
chart programme (27) as being biologically implausi-
ble values (28) to determine whether these were
actually children with extreme obesity.

Methods
Kaiser Permanente Northern California (KPNC) is

large integrated healthcare delivery system providing
care to over three million members in Northern Cali-
fornia, of whom approximately 6% are children under
the age of 6 years. This study examines data derived
from 42 620 3- to 5-year-old children from a retro-
spective cohort of 160 300 KPNC children aged
3–17 years who had BMI and blood pressure
obtained during a well-child visit between 1 January
2007 and 31 December 2010. All children had at
least 6 months health plan membership prior to the
index visit and received care within three large KPNC
sub-regions. The Institutional Review Board at
HealthPartners Institute for Education and Research
approved the study with ceding of oversight authority
by the KPNC Institutional Review Board. A waiver of
informed consent was obtained because of the
nature of the study.

The electronic medical record was used to obtain
information on age, sex, race/ethnicity and meas-
ured height and weight, with BMI calculated as
weight (kg) divided by height (m) squared. The year
2000, CDC growth charts were used to calculate
age- and sex-specific BMI, height and weight per-
centiles, and BMI standard deviation (Z) scores using
publically available programmes provided by the
CDC (27). Both BMI percentile and the BMI percent-
age above the 95th BMI percentile were used to
categorize higher degrees of obesity, including a
threshold of 1.2 times the 95th BMI percentile to
define severe obesity. Subsequent outpatient visits
and recognition of obesity and/or elevated BMI within
the ensuing year were examined using electronic
medical record diagnoses (International Classifica-
tion of Diseases, Ninth revision codes 278.00 for
obesity, 278.02 for overweight, V85.54 for
BMI � 95th percentile and V85.53 for BMI 85th–
94th percentile).

We used three primary methods for data validation
because of potential errors in height and weight
measurements extracted from the electronic medical
record. First, we excluded children in whom the

168 | J. C. Lo et al.
O

R
IG

IN
A

L
R

E
S

E
A

R
C

height was flagged as a ‘biologically implausible
value’ by the CDC programme (28) (n = 30) and chil-
dren whose index visit measurement included a
height <61 cm (24 in.), weight <4.5 kg (10 lb) or BMI < 10 kg m-2 (n = 3).

Among the remaining 42 587 children with well-
child visit data, we examined growth charts for chil-
dren flagged with high biologically implausible values
for weight and/or BMI (n = 237); and only 14 were
found to be erroneous and excluded. Three children
with missing BMI percentile because of erroneously
high BMI > 150 kg m-2 were also excluded. Finally,
we reviewed growth charts for obese children with
BMI or height differing by more than 15% or weight
by more than 20% per year at a prior or subsequent
visit within 2 years (requiring age �2 years) who also
had an (A) index BMI � 99th percentile, (B) index
BMI � 1.2 times the 95th percentile, (C) index BMI
Z-score � 2.5 or (D) index BMI 95th–98th percentile
with another BMI < 95th percentile (n = 193) of whom only 11 were erroneous and excluded. Height, weight or BMI values that were part of a pattern of rapid growth or weight gain persisting over two or more measurements were considered valid. If the anthropometric data provided insufficient informa- tion, or if no prior/subsequent measurements were available (n = 35), progress note documentation of obesity and/or elevated BMI was also examined.

In total, 430 growth charts were reviewed by a
paediatric endocrinologist (Louise Greenspan MD)
with exclusion of 28 children (0.06% of the entire
cohort) because of erroneous or inconsistent values.
The final study cohort included 42 559 3- to 5-year-
old children.

Statistical methods

All analyses were conducted using SAS version 9.1
(SAS Institute, Cary, NC, USA). Differences between
subgroups were compared using the chi-squared test
for categorical data. The Cochrane–Armitage test
was used to examine the trend in proportions across
categories. Linear regression techniques were used
to examine the relationship between median height
percentile and BMI percentile category. A P-value of
<0.05 was considered statistically significant.

Results
Among the final cohort of 42 559 children, 7934
(18.6%) were 3 years old, 23 351 (54.9%) were 4
years old and 11 274 (26.5%) were 5 years old
(Table 1). Half of the cohort (49.0%) was female. The
cohort was racially and ethnically diverse, consisting Ta

b
le

1
B

M
I

as
si

fic
at

io
n

am
o

n
g

ch
ild

re
n

ag
ed

3
–5

ye
ar

s
w

ith
a

w
el

l-
ch

ild
vi

si
t

d
u
rin

g
2

0
7

–2

0
1
0

B
M

I
p

e
rc

e
n
ti
le

B
M

I
%

B
M
I
%
B
M

I
Z

S
c
o

5
th

8
5
th

–9
4

�

9
5

�
9
7
th

�
1

¥
9
5
th

p
e
rc

e
n
ti
le

�
1
.2

¥
9
5
th
p
e
rc
e
n
ti
le

2
.0

0
–2

9
9

�
3
.0

B
O

Y
S

(

2
1

2
0
)

1
5
,9

4
6

(

7
3

%
)

3
,0

7
6

(

1
4

%
)

2
,6

9
8

(1
2
.4

%
)

1
,9

8
0

(9
.1

%
)

2
6

(4
.3

%
)

0
3

(1
.9

%
)

1
,2

7
6

(5
.9

%
)

4
2
8

(2
.0

%
)

g
e

in
ye

ar
s

(n
)

3
ye

ar
s

(4
,0

4
2
)

2
,9

4
5

(7
2
.9

%
)

5
7
6

(1
4
.3

%
)

5
2
1

(1
2
.9

%
)

3
8

(9
.5

%
)

1
4
1

(3
.5

%
)

4
3

(1
.1

%
)

2
4

(6
.1

%
)

8
6

(2
.1

%
)

4
ye

ar
s

(1
1
,9

4
7
)

8
,8

9
8

(7
4
.5

%
)

1
,6

5
0

(

1
3

%
)

1
,3

9
9

(1
1
.7

%
)

1
,0

2
3

(8
.6

%
)

3
4

(3
.6

%
)

1
8
7

(1
.6

%
)

6
4
1

(5
.4

%
)

2
3
4

(2
.0
%
)

5
ye

ar
s

(5
,7

3
1
)

4
,1

0
3

(7
1
.6

%
)

8
5
0

(1
4
.8

%
)

7
7

(1
3
.6

%
)

5
7
3

(1
0
.0

%
)

3
5
1

(6
.1
%
)

1
7
3

(3
.0

%
)

3
8
8

(6
.8

%
)

1
0
8

(1
.9
%
)

G
IR

L
S

(2
0
,8

3
9

)

1
5
,5

7
3

(7
4
.7

%
)

3
,1

8
9

5
.3

%
)

2
,0

7
7
(1
0
.0
%
)
1
,3
3
8

(6
.4

%
)

7
0
5

(3
.4

%
)

2
8
5

(1
.4

%
)
1
,0
2
1

(

4
.9

%
)

6
1

(0
.3

%
)
A
g
e
in
ye
ar
s

(N
)

3
ye
ar
s

(3
,8

9
2
)

2
,8

2
6

(7
2
.6

%
)

3
0

(1
6
.2

%
)

4
3
6

(1
1
.2

%
)

3
1
2

(8
.0

%
)

1
4
7

(3
.8

%
)
3
9

(1
.0

%
)

2
2
8

(5
.9
%
)
2
6

(0
.7

%
)
4
ye
ar
s

(1
1
,4

0
4
)

8
,6

4
0

(7
5
.8

%
)

1
,7

3
4

(1
5
.2

%
)
1
,0
3
0

(9
.0

%
)

6
3
3

(5
.6

%
)

3
1
4

(2
.8

%
)

1
2
8

(1
.1
%
)

4
7
3

(4
.2

%
)

2
9

(0
.3
%
)
5
ye
ar
s

(5
,5

4
3
)

4
,1
0
7

4
.1

%
)

8
2
5

(1
4
.9

%
)

6
1
1

(1
1
.0

%
)

3
9
3

(7
.1

%
)

2
4
4

(4
.4

%
)

1
1
8

(2
.1
%
)

3
2
0

(5
.8

%
)

6
(0

.1
%

)
B
M

I< 8

5
th

p
er

n
til

e,
n
o

rm
al

o
r

n
d

er
w

ei
g

h
t;

B
M

I
8

5
th
–9
4

th
(9

4
.9

)
p

er
ce

n
til

e,
o

ve
rw

ei
g
h
t;
B
M

I�
9

5
th
p
er

ce
n
til

e,
o

b
es

ity
;

B
M

I�
1

.2
¥

9
5

th
p

er
ce
n
til

e,
se

ve
re

o
b

es
ity

.
N

u
m

b
er

s
re

p
re

se
n
t

ro
w

p
er

n
ta

g
es

.
B

M
I,

b
o

d
y

m
as

s
in

d
ex

Obesity in young children | 169

O
R
IG
IN
A
L
R
E
S
E
A
R

C
H

of 26.1% white, 5.9% black, 24.4% Hispanic, 21.8%
Asian (including Pacific Islanders) and 21.8% of other
or unknown race/ethnicity. There were 4775 (11.2%)
children who met the criteria for obesity defined by
BMI � 95th percentile. Overall and within each of the
age stratum, the proportion with BMI � 95th percen-
tile was slightly, but significantly greater for boys than
for girls (12.4% vs. 10.0% overall, P < 0.05 for all comparisons). The prevalence of obesity was signifi- cantly lower in 4-year-old children (boys, 11.7%; girls, 9.0%) compared with 3-year-old children (boys, 12.9%; girls, 11.2%) and 5-year-old children (boys, 13.6%; girls, 11.0%) (all P < 0.05), possibly reflecting the adiposity nadir. Across racial/ethnic subgroups (Fig. 1), the prevalence of obesity was highest among Hispanics (boys, 18.2%; girls, 15.2%) fol- lowed by blacks (boys, 12.4%; girls, 12.7%, P < 0.05 for within gender comparisons). Asian girls tended to have a lower prevalence (6.9%) compared with white girls (7.9%; P = 0.05), and Asian boys had a higher prevalence (10.9%) compared with white boys

(8.6%; P < 0.001). A total of 7.8% of children had BMI � 97th percentile, with a greater proportion among boys compared with girls (9.1 vs. 6.4%, P < 0.001) and the highest proportions in Hispanic children (13.9% for boys; 10.4% for girls, Fig. 1). Approximately, half (49.2%) of the children with BMI � 97th percentile also had a BMI � 1.1 ¥ 95th percentile. When categorized by BMI Z-score �3.00, only 0.3% of girls were identified compared with 2.0% of boys (P < 0.001), with almost no girls iden- tified by age 5 years (0.1%).

Using BMI-for-age criteria for severe obesity, 1.6%
of children had BMI � 1.2 times the 95th BMI per-
centile (1.9% for boys and 1.4% for girls, P < 0.001). This prevalence was significantly lower than that ascertained when using a threshold of BMI � 99th percentile (3.9% overall, 5.1% for boys and 2.7% for girls, P < 0.001). For both thresholds, a male pre- dominance was seen. Across racial/ethnic sub- groups, Hispanic boys had the highest prevalence of severe obesity (3.3%, P < 0.01 compared with boys of white, black or Asian race/ethnicity, and compared with Hispanic and black girls). Among girls, Hispanic and black girls had the highest prevalence of severe obesity (2.2 and 1.7%, respectively, P < 0.05 when compared with white and Asian girls; P = 0.30 when comparing Hispanic and black girls).

Figure 2 shows the median height percentile by
BMI percentile subgroup for each age by gender.
Among children with BMI < 85th percentile, the median height percentile was around the 50th per- centile. However, increasing BMI percentile category was associated with an increase in the median height percentile (significant linear trend, P < 0.001) within each age–sex stratum. Figure 3 shows the increas- ing proportions of children with BMI at or above the 95th and 97th percentiles across increasing height percentile in quintiles, (P < 0.001, test for trend).

Included in this study were 223 children with
extreme BMI (n = 156) or both extreme BMI and
weight (n = 67) values that were correct despite iden-
tification by the CDC-based programme as high ‘bio-
logically implausible values’. As shown in Table 2,
these included 3.5-fold more boys than girls and the
majority were of Hispanic or Asian race/ethnicity. All
223 children had BMI and weight percentile values in
the 99th-100th percentile range, BMI Z-score above
2.5, and BMI exceeding 1.2 times the 95th BMI
percentile for age and sex (Table 2). Each age–sex
group also demonstrated extremely high median
height percentiles (>80th percentile). Although 0.9%
had Prader–Willi syndrome, most did not demon-
strate obesity-related conditions. The most promi-
nent comorbidity was asthma, identified in 17.5% of

Figure 1 The proportion of children by sex and race/
ethnicity according to body mass index (BMI) classification
(BMI � 95th percentile, obesity; BMI � 1.2 ¥ 95th percen-
tile, severe obesity).

170 | J. C. Lo et al.
O

R
IG
IN
A
L
R
E
S
E
A
R
C
H
© 2013 The Authors
Pediatric Obesity © 2013 International Association for the Study of Obesity. Pediatric Obesity 9, 167–175

this subgroup based on coded visit diagnoses in the
prior 6 months.

We examined the frequency of overweight and
obesity recognition over the ensuing year after the
index well-child visit in the 1389 (12.3%) of 11 274
children aged 5 years with BMI � 95th percentile
because this age represents the beginning of the
BMI trajectory beyond the adiposity nadir. The major-
ity (86.9%) had continuous membership for at least 1
year after the well-child visit, with one or more sub-
sequent ambulatory visits among 76.5% overall and
80.2% obese children. Among these 1389 children,
71.0% had a visit diagnosis of either obesity or
elevated BMI � 95th percentile and an additional
6.9% had a diagnosis relating to overweight (or BMI
85th–94th percentile), all of whom received routine
nutrition and/or exercise counselling. There were
573 boys and 393 girls aged 5 years with BMI �
97th percentile in whom recognition of elevated
BMI � 95th percentile and/or obesity was docu-

mented in 77.6%. Among the subset of 173 boys
and 118 girls with severe obesity (BMI � 1.2 ¥ 95th
BMI-for-age percentile), recognition of elevated
BMI � 95th percentile and/or obesity was higher at
89.0%.

Discussion
In this cohort of more than 40 000 preschool-aged
children (3–5 years old), 12.4% of boys and 10.0% of
girls met criteria for obesity (BMI � 95th percentile).
While fewer children met criteria for severe obesity
defined by BMI � 1.2 times the 95th percentile
(1.6%), the majority of obese children (69.5%)
exceeded the 97th BMI percentile. Across increasing
BMI strata, the prevalence was highest among His-
panic boys. These findings complement data from
other studies demonstrating that the prevalence of
high BMI-for-age in older children is greatest among
Hispanic boys and black girls (5,8). A study of 2452
younger urban children similarly found that a disparity

Figure 2 Median height percentile by body mass index
(BMI) percentile in boys and girls. Significant differences in
median height percentile were seen across BMI categories
for each age–sex stratum, with a significant linear trend
(P < 0.001 within each age–sex category).

Figure 3 Proportion of children with body mass index
(BMI) � 95th percentile (BMI 95th–96th percentile or
BMI � 97th percentile) by increasing height quintile in boys
and girls aged 3–5 years old. P < 0.001 for trend across increasing height category.

Obesity in young children | 171

O
R
IG
IN
A
L
R
E
S
E
A
R
C
H
© 2013 The Authors
Pediatric Obesity © 2013 International Association for the Study of Obesity. Pediatric Obesity 9, 167–175

between Hispanic and non-Hispanic children can be
identified as early as 3 years of age (29).

This study found that in the context of routine
paediatric care, a relatively high percentage of obese
5-year-old children had their obesity recognized by
their clinical caregivers, as ascertained from the elec-
tronic medical record; it is possible that recognition
rates would have been even higher had all clinical
encounter notes been systematically reviewed for
this study. The proportion of children with subse-
quent outpatient visits during the ensuing year was
also extremely high, suggesting there are opportuni-
ties to initiate preventive measures at this early age.

Both the 99th BMI-for-age percentile threshold and
a BMI threshold of 1.2 times the 95th percentile have
been used as measures of severe obesity (1,8–12).
While the 99th percentile identified a larger propor-
tion of children, concerns have been raised about the
accuracy of this threshold when using the CDC
growth charts, given the lack of fit to empiric data
(10). Furthermore, use of BMI percentile for stratifi-
cation of extremely high BMI values is limited by
contraction of percentile values at the upper bound
where large changes in BMI result in minimal change
in BMI percentile (30,31). The use of BMI Z-score is
also limited by an upper bound, depending on age
and sex (31). Almost no girls aged 5 years had a
Z-score �3.00, because of the fact that Z-scores
beyond 4.00 are undefined for girls and boys over
the age of 4 and 6 years, respectively (31). These
findings highlight challenges in classification of
extremely high BMI-for-age in children, particularly
for contemporary populations who represent a right-
ward shift from historic populations used for devel-
opment of the year 2000 CDC growth charts (17).
Classifying high BMI values as a percentage of the
95th percentile has been proposed as a more flexible
approach to characterizing obesity severity based on
evidence that this approach may provide a better fit
to empiric data than BMI percentile estimates above
the 97th percentile (10). The currently preferred defi-
nition of severe obesity is a BMI �1.2 times the 95th
percentile when using the CDC growth charts (10–
12), a threshold that demonstrates better approxi-
mation of the empiric 99th BMI percentile (10).
Consistent with these recommendations, a new
growth chart based on BMI percentage above the
95th percentile has recently been proposed for clini-
cal tracking of severely obese children (32). Our
study found that using a BMI threshold of
�1.2 ¥ 95th percentile identified 1.9% of boys and
1.4% of girls who met criteria for severe obesity.
Because the range of BMI values within this young
age group is relatively narrow compared with olderTa

b
le

2
D

em
o
g
ra

p
h
ic

an
d

an
th

ro
p

o
m

et
ric

fin
d

in
g
s

in
th

e
2
2
3

ch
ild
re
n

w
ith

va
lid

m
ea

su
re

m
en

ts
o
fw

ei
g

h
t

an
d
B
M

d
es

p
ite

d
es

ig
n
at

io
n

as
h
ig

h
‘b

io
lo

g
ic

al
ly

im
p

u
si

b
le

va
lu

es
’

u
si
n
g

th
e

Ye
ar

0
0

C
en

te
rs

fo
r

D
is

ea
se

C
o
n
tr

o
la

n
d

P
re

ve
n
tio

n
g
ro

w
th

ch
ar

t
re

fe
re

n
ce

d
at

e
d

ia
n

(IQ
R

)
W

e
ig

h
t

(k
g

)
M

e
d
ia
n
(IQ
R

)
H

e
ig
h
t

(c
m

)
M
e
d
ia
n
(IQ
R
)
H
e
ig
h
t
p
e
rc
e
n
ti
le

M
e
d

ia
n
(IQ
R

)
B

M
I
(k
g

m
-2

)
�

1
.5

¥
9
5
th
B
M
I
p
e
rc
e
n
ti
le

n
(%

)

R
a
c
e
/e

th
n
ic

it
y

B
o
ys

(n
=

1
7
3
)

3
1
.5

(2
8
.0

–3
4
.5

)
1
1
1
.1

(1
0
6
.7

–1
1
4
.3

)
8
2
.1

(6
5
.4

–9
2
.1

)
2
5
.1

(2
4
.1

–2
6

)
4
2

(2
4
.3

%
)

1
2
.1

%
W

h
ite

4
.1

%
B

la
ck

4
3
.9

%
H

is
p

an
ic

2
4
.9

%
A

si
an

A
g
e

3
(n

=
2
0
)

2
7
.9

(2
5
.4

–3
0
.3

)
1
0
5
.7

(1
0
1
.6

–1
0
6
.7

)
9
0
.7

(7
8
.1

–9

3
.1

)
2
5
.3

(2
3
.5

–2
7

.0
)

5
(2

5
.0

%
)
A
g
e

4
(n

=
9
3
)

2
9
.7

(2
7
.0

–3
3
.6

)
1
0
8
.5

(1
0
5
.4

–1
1
3
.0

)
8
1
.5

(6
4
.9

–9
3
.1

)
2
4
.6

(2
3
.8

–2
6

.9
)

2
4

(2
5
.8

%
)
A
g
e

5
(n

=
6
0
)

3
4
.0

(3
2
.3

–3
7
.7

)
1
1
4
.9

(1
1
3
.0

-1
1
7
.8

)
8
0
.3

(6
8
.9

–8
8
.5

)
2
5
.5

(2
4
.8

–2
6

.7
)

1
3

(2
1
.7

%
)

G
irl

s
(n

=
5
0
)

3
2
.1

(2
8
.6

–3
5
.5

)
1
0
8
.6

(1
0
4
.8

–1
1
4
.3

)
8
4
.9

(7
1
.0

–9
1
.8

)
2
6
.5

(2
5
.7

–2
7
.7
)
1
4
(2
8
.0
%
)

6
.0

%
W
h
ite
6
.0
%
B
la
ck

6
0
.0

%
H
is
p
an
ic

1
4
.0

%
A
si
an
A
g
e
3
(n

=
1
3
)

2
7
.7

(2
6
.2

–2
8

)
1
0
3
.5

(1
0
0
.3

–1
0
5
.4

)
9
1
.4

(6
0
.3

–9
1
.8

)
2
5
.7

(2
5
.2

–2
6

.4
)

3
(2

3
.1
%
)
A
g
e
4
(n
=
2
0
)

3
1
.1

(2
8
.7

–3
3
.0

)
1
0
6
.6

(1
0
5
.4

–1
1
2
.4

)
8
2
.6

(7
3
.4

–9
5
.1

)
2
6
.4

(2
5
.4
–2
7

.2
)

5
(2
5
.0
%
)
A
g
e
5
(n

=
1
7
)

3
5
.6

(3
4
.8

–3
8
.7

)
1
1
4
.3

(1
1
3
.0

–1
1
8
.1

)
8
5
.1

(7
4
.5

–9
0
.1

)
2
7
.5

(2
6
.6

–2
8

.6
)

6
(3

5
.3
%
)

*A
ll

ch
ild
re
n

h
ad

B
M

I
an

d
w

ei
g
h
t
va
lu

es
in

th
e
9
9

th
-1

0
0
th
p
er
ce
n
til

e
ra

n
g

e
an

d
a

B
M

I
va

lu
e

�
1
.2
¥
9
5

th
B

M
I
p

er
ce
n
til

e.
P

er
ce
n
ta
g
e

n
u
m

b
er
s
re
p
re
se
n
t
ro
w
p
er

ce
n
ta

g
es
.
B
M
I,
b
o
d
y
m
as
s
in
d
ex

;
IQ

R
,

in
te

rq
u
ar

til
e

ra
n
g

172 | J. C. Lo et al.
O

R
IG
IN
A
L
R
E
S
E
A
R
C
H
© 2013 The Authors
Pediatric Obesity © 2013 International Association for the Study of Obesity. Pediatric Obesity 9, 167–175

children where the growth curves widen consider-
ably, use of lower thresholds such as the 97th per-
centile or 1.1 ¥ 95th percentile may also be helpful in
targeting young obese children at risk for progression
to more severe degrees of obesity.

Most children initially flagged as having biologically
implausible high values for BMI and/or weight had, in
fact, valid measurements of these indices. More than
three quarters were boys, of whom a large propor-
tion were Hispanic (44%) or Asian (25%). The CDC
programme uses fixed exclusion ranges provided by
the World Health Organization to define ‘biologically
implausible values’ with the goal of removing these
excessively high values that may be due to entry
errors or mis-measurement (27,28). However, our
findings demonstrate that values labelled as implau-
sible should not be automatically excluded in young
children without more careful evaluation of growth
trajectory. Other investigators have used algorithms
based on incremental weight or height values below
the first or above the 99th percentiles to identify
potential errors in the electronic medical record (33);
using those methods, our study identified no addi-
tional children for exclusion. In this study, we used
data from prior and/or subsequent clinical visits,
where inconsistencies in measured height, weight
and BMI within specific subgroups were used to
determine the need for individual growth chart
review.

Level of BMI was directly associated with height
percentile, consistent with the known interrelation-
ship of BMI and linear growth; heavier children were
taller and those in the highest BMI category had the
tallest stature. The increasing proportion of children
with BMI � 95th percentile or �97th percentile
across increasing height quintiles is consistent with
data from others who observed a more than 10-fold
increase in obesity prevalence across lowest to
highest height quintile for children 3–10 years of age
(34). This early association between BMI and growth
may have important implications for subsequent
pubertal development, final adult height and adult
adiposity. One of the challenges associated with
assessment of adiposity status during the early years
of growth is that changes in BMI represent both
changes in adiposity and changes in lean mass.
However, calculated BMI is strongly correlated with
percent body fat measured by dual energy X-ray
absorptiometry (35); and differences in BMI at a
given height are associated with differences in body
fatness. Additional demographic variation in the rela-
tionship of BMI to body fat may also be relevant
when comparing BMI-for-age across different popu-
lation subgroups (36).

It is not clear whether obesity at this early age is
related to differences in behaviours and feeding pat-
terns, e.g., breast milk vs. formula and the timing of
introduction of solid foods, particularly in a racially
and ethnically diverse cohort. Parental obesity,
maternal conditions, environment and behaviour are
also known to modulate the risk of childhood obesity
(37,38), but these contextual factors could not be
ascertained in this study because of the limitations of
the electronic medical record. Although this study did
not include children under the age of 3, obesity
trends may begin as early as infancy where exami-
nation of weight-for-length has been used to esti-
mate the prevalence of infant obesity (39).

In summary, the high prevalence of early onset
obesity is of great concern from a public health per-
spective, because of the strong tracking effect of
childhood obesity and relation of childhood obesity
to adult metabolic complications and other clinical
comorbidities. This concern is underscored by the
finding that extreme obesity is significantly higher in
specific ethnic groups and, in particular, Hispanics.
While future research is needed to track the long-
term outcomes among these children, the early rec-
ognition of obesity by healthcare providers and the
high proportion with follow-up care within this young
age group may provide an important framework to
support delivery of effective and timely interventions
for obesity management and prevention at an early
age.

Conflicts of interest
statement
No conflict of interest was declared.

Acknowledgements
JL, BM, MC, SD, AS and LG conceived the project.
MC conducted the data analyses. JL, BM and LG
drafted the initial manuscript. All authors provided
critical input on data analysis and interpretation,
revised the manuscript for important intellectual
content and approved the final version. The authors
would like to acknowledge Joel Gonzalez for his
support with manuscript preparation. This study was
funded by the National Heart, Lung and Blood Insti-
tute at the National Institutes of Health
1RO1HL093345 to HealthPartners Research Foun-
dation (Patrick O’Connor, Principal Investigator) and
conducted within the Cardiovascular Research
Network, a consortium of research organizations
affiliated with the HMO Research Network and spon-
sored by the National Heart Lung and Blood Institute
(U19 HL91179-01).

Obesity in young children | 173

O
R
IG
IN
A
L
R
E
S
E
A
R
C
H
© 2013 The Authors
Pediatric Obesity © 2013 International Association for the Study of Obesity. Pediatric Obesity 9, 167–175

References

1. Barlow SE. Expert committee recommendations
regarding the prevention, assessment, and treatment of
child and adolescent overweight and obesity: summary
report. Pediatrics 2007; 120(Suppl. 4): S164–S192.
2. Ogden CL, Lamb MM, Carroll MD, Flegal KM. Obesity
and socioeconomic status in children and adolescents:
United States, 2005–2008. NCHS Data Brief 2010; 51:
1–8.
3. Ogden CL, Carroll MD, Curtin LR, Lamb MM, Flegal
KM. Prevalence of high body mass index in US children
and adolescents, 2007–2008. JAMA 2010; 303: 242–249.
4. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of
obesity and trends in body mass index among US children
and adolescents, 1999–2010. JAMA 2012; 307: 483–490.
5. Ogden CL, Carroll MD, Flegal KM. High body mass
index for age among US children and adolescents, 2003–
2006. JAMA 2008; 299: 2401–2405.
6. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin
LR, Flegal KM. Prevalence of overweight and obesity
among US children, adolescents, and adults, 1999–2002.
JAMA 2004; 291: 2847–2850.
7. Ogden CL, Carroll MD. Prevalence of Obesity Among
Children and Adoloescents: United States, Trends 1963–
1965 Through 2007–2008. NCHS Health E-Stat 2010;
June: 1–5.
8. Skelton JA, Cook SR, Auinger P, Klein JD, Barlow SE.
Prevalence and trends of severe obesity among US chil-
dren and adolescents. Acad Pediatr 2009; 9: 322–329.
9. Freedman DS, Mei Z, Srinivasan SR, Berenson GS,
Dietz WH. Cardiovascular risk factors and excess adiposity
among overweight children and adolescents: the Bogalusa
Heart Study. J Pediatr 2007; 150: 12–17.
10. Flegal KM, Wei R, Ogden CL, Freedman DS, Johnson
CL, Curtin LR. Characterizing extreme values of body
mass index-for-age by using the 2000 Centers for Disease
Control and Prevention growth charts. Am J Clin Nutr
2009; 90: 1314–1320.
11. Koebnick C, Smith N, Coleman KJ, et al. Prevalence
of extreme obesity in a multiethnic cohort of children and
adolescents. J Pediatr 2010; 157: 26–31 e2.
12. Wang YC, Gortmaker SL, Taveras EM. Trends and
racial/ethnic disparities in severe obesity among US chil-
dren and adolescents, 1976–2006. Int J Pediatr Obes
2011; 6: 12–20.
13. Benson L, Baer HJ, Kaelber DC. Trends in the diag-
nosis of overweight and obesity in children and adoles-
cents: 1999–2007. Pediatrics 2009; 123: e153–e158.
14. Dietz WH. Critical periods in childhood for the devel-
opment of obesity. Am J Clin Nutr 1994; 59: 955–959.
15. Rolland-Cachera MF, Deheeger M, Bellisle F, Sempe
M, Guilloud-Bataille M, Patois E. Adiposity rebound in chil-
dren: a simple indicator for predicting obesity. Am J Clin
Nutr 1984; 39: 129–135.
16. Rolland-Cachera MF, Deheeger M, Maillot M, Bellisle
F. Early adiposity rebound: causes and consequences for
obesity in children and adults. Int J Obes (Lond) 2006;
30(Suppl. 4): S11–S17.

17. Kuczmarski RJ, Ogden CL, Guo SS, et al. 2000 CDC
Growth Charts for the United States: methods and devel-
opment. Vital Health Stat 11 2002; 246: 1–190.
18. Demerath EW, Jones LL, Hawley NL, et al. Rapid
infant weight gain and advanced skeletal maturation in
childhood. J Pediatr 2009; 155: 355–361.
19. Forbes GB. Nutrition and growth. J Pediatr 1977; 91:
40–42.
20. Mamun AA, Hayatbakhsh MR, O’Callaghan M, Wil-
liams G, Najman J. Early overweight and pubertal matura-
tion – pathways of association with young adults’
overweight: a longitudinal study. Int J Obes 2009; 33:
14–20.
21. Must A, Naumova EN, Phillips SM, Blum M, Dawson-
Hughes B, Rand WM. Childhood overweight and matura-
tional timing in the development of adult overweight and
fatness: the Newton Girls Study and its follow-up. Pediat-
rics 2005; 116: 620–627.
22. Sandhu J, Ben-Shlomo Y, Cole TJ, Holly J, Davey
Smith G. The impact of childhood body mass index on
timing of puberty, adult stature and obesity: a follow-up
study based on adolescent anthropometry recorded at
Christ’s Hospital (1936–1964). Int J Obes 2006; 30: 14–22.
23. Freedman DS, Khan LK, Serdula MK, Dietz WH, Srini-
vasan SR, Berenson GS. The relation of menarcheal age to
obesity in childhood and adulthood: the Bogalusa heart
study. BMC Pediatr 2003; 3: 3.
24. Whitaker RC, Pepe MS, Wright JA, Seidel KD, Dietz
WH. Early adiposity rebound and the risk of adult obesity.
Pediatrics 1998; 101: E5.
25. Williams SM, Goulding A. Early adiposity rebound is an
important predictor of later obesity. Obesity (Silver Spring)
2009; 17: 1310.
26. Daniels SR, Jacobson MS, McCrindle BW, Eckel RH,
Sanner BM. American Heart Association Childhood
Obesity Research Summit: executive summary. Circulation
2009; 119: 2114–2123.
27. A SAS program for the CDC growth charts. [WWW
document]. URL http://www.cdc.gov/nccdphp/dnpao/
growthcharts/resources/sas.htm (accessed October
2011).
28. Cut-offs to define outliers in the 2000 CDC Growth
Charts. [WWW document]. URL http://www.cdc.gov/
nccdphp/dnpa/growthcharts/resources/BIV-cutoffs
(accessed April 2012).
29. Whitaker RC, Orzol SM. Obesity among US urban
preschool children: relationships to race, ethnicity, and
socioeconomic status. Arch Pediatr Adolesc Med 2006;
160: 578–584.
30. Cole TJ, Faith MS, Pietrobelli A, Heo M. What is the
best measure of adiposity change in growing children:
BMI, BMI %, BMI Z-score or BMI centile? Eur J Clin Nutr
2005; 59: 419–425.
31. Woo JG. Using body mass index Z-score among
severely obese adolescents: a cautionary note. Int J
Pediatr Obes 2009; 4: 405–410.
32. Gulati AK, Kaplan DW, Daniels SR. Clinical tracking of
severely obese children: a new growth chart. Pediatrics
2012; 130: 1136–1140.

174 | J. C. Lo et al.
O

R
IG
IN
A
L
R
E
S
E
A
R
C
H
© 2013 The Authors
Pediatric Obesity © 2013 International Association for the Study of Obesity. Pediatric Obesity 9, 167–175

33. Smith N, Coleman KJ, Lawrence JM, et al. Body
weight and height data in electronic medical records of
children. Int J Pediatr Obes 2010; 5: 237–242.
34. Freedman DS, Khan LK, Serdula MK, Dietz WH,
Srinivasan SR, Berenson GS. Inter-relationships among
childhood BMI, childhood height, and adult obesity: the
Bogalusa Heart Study. Int J Obes Relat Metab Disord
2004; 28: 10–16.
35. Lindsay RS, Hanson RL, Roumain J, Ravussin E,
Knowler WC, Tataranni PA. Body mass index as a measure
of adiposity in children and adolescents: relationship to
adiposity by dual energy x-ray absorptiometry and to car-
diovascular risk factors. J Clin Endocrinol Metab 2001; 86:
4061–4067.

36. Daniels SR, Khoury PR, Morrison JA. The utility of
body mass index as a measure of body fatness in children
and adolescents: differences by race and gender. Pediat-
rics 1997; 99: 804–807.
37. Reilly JJ, Armstrong J, Dorosty AR, et al. Early life risk
factors for obesity in childhood: cohort study. BMJ 2005;
330: 1357.
38. Salsberry PJ, Reagan PB. Dynamics of early child-
hood overweight. Pediatrics 2005; 116: 1329–1338.
39. McCormick DP, Sarpong K, Jordan L, Ray LA, Jain S.
Infant obesity: are we ready to make this diagnosis? J
Pediatr 2010; 157: 15–19.

Obesity in young children | 175

O
R
IG
IN
A
L
R
E
S
E
A
R
C
H
© 2013 The Authors
Pediatric Obesity © 2013 International Association for the Study of Obesity. Pediatric Obesity 9, 167–175

ORIGINAL PAPER

Prevalence of overweight and obesity in children aged
6–13 years—alarming increase in obesity in Cracow, Poland

Aneta Bac & Renata Woźniacka & Stanislaw Matusik &
Joanna Golec & Edward Golec

Received: 15 April 2011 /Accepted: 14 June 2011 /Published online: 7 July 2011
# The Author(s) 2011. This article is published with open access at Springerlink.com

Abstract This study in children aged 6–13 years (n=1,499)
was performed between October 2008 and March 2009.
Height and weight measurements were taken to calculate
BMI. The prevalence of overweight and obesity was
determined by means of IOTF cut-offs with respect to age.
Alarming is the fact that the percentage of obese children in
Cracow increased dramatically from 1.04% in boys and
0.20% in girls in 1971 to 7% in boys and 3.6% in girls in
2009. In this report, a higher percentage of overweight boys
was observed in rural boys (28.14%) than in urban ones
(27.31%). Obesity was identified in an almost twice as high
percentage of urban boys (7.78%) as in rural ones (3.52%).
A higher percentage of overweight girls was registered in
rural areas (16.49%) than in urban ones (16.09%). Obesity
was prevailing in rural girls (4.12%) relative to their urban
counterparts (3.44%). The highest number of overweight

urban boys was diagnosed in the group of 12-year-olds
(n=48) and rural boys in the group of 10-year-olds (n=39),
as well as in urban girls aged 11 (n=17) and rural girls aged
9 (n=9). The highest number of obesity was observed in
rural boys aged 12 (n=3) and in urban boys aged 9 and 10
(n=9 in both groups). In the group of girls, obesity prevailed
in urban 9-year-olds (n=5) and in rural 7-year-olds (n=5).
Conclusions: Overweight and obesity affect boys almost
twice as frequently as girls. Obesity is twice as frequent in
urban boys as in their rural peers.

Keywords Children . Overweight . Obesity. Urban . Rural

Introduction

The prevalence of adult overweight and obesity has been
increasing all over the world. This tendency has been
observed for over 30 years [14]. In the United States, in the
years 1980 and 2002, the number of obese people above
20 years of age doubled [18]. In Thailand, in the period
1991–2004, the incidence of overweight in adult men
increased from 13% to 22.4%, whereas in women the value
rose from 23.2% to 34.3% [1]. Unfortunately, the problem
of progressive overweight and obesity affects children as
well. In the United States, in the years 1980–2002, the
prevalence of overweight in children and adolescents
tripled [18]. In Germany, the data collected in the years
1985–1997 (Arbeitsgemeinschaft Adipositas im Kindesund
Jugendalter 2000) show that the prevalence of overweight
in children increased twice [14]. Subsequently, German
children and adolescent health surveys (KIGGS 2006)
carried out in children aged 3–17 years revealed the
prevalence of overweight in 15% of the examined cases
out of which 6.3% were diagnosed as obese [14]. In

A. Bac : J. Golec : E. Golec
Orthopedics Rehabilitation Department,
Faculty of Motor Rehabilitation,
The Bronisław Czech University of Physical Education,
Kraków, Poland

R. Woźniacka (*)
Department of Anatomy, Faculty of Motor Rehabilitation,
The Bronisław Czech University of Physical Education,
Al. Jana Pawła II 78,
31–571 Kraków, Poland
e-mail: renatawozniacka@wp.pl

S. Matusik
Statistic and Computer Science Division,
The Bronisław Czech University of Physical Education,
Kraków, Poland

E. Golec
Traumatic Surgery and Orthopedics Clinic, 5th Military Clinical
Hospital and Policlinic, Independent Public Healthcare Facility,
Kraków, Poland

Eur J Pediatr (2012) 171:245–251
DOI 10.1007/s00431-011-1519-1

Thailand, in the group of children aged 6–12 years, the
incidence of overweight rose from 5.8% in 1997 to 6.7% in
2001 [1].

In some of the European countries, the percentage of
children diagnosed as overweight and obese is high.
However, this tendency is different depending on the
country. The highest rates of obesity are observed in
eastern and southern European countries [13]. In Malta
and southern Italy overweight or obesity are diagnosed in
35% of children, whereas the same conditions are
observed in 15% of cases in Scandinavia and in 12% in
the Netherlands [15].

In the year 2000, a research that was carried out in
Cracow, the third largest city in Poland, revealed that
overweight and obesity were identified in 15.13% of boys
and 11.79% of girls. During the 1971–2000 period, a
gradual increase in overweight and obesity in children aged
7–18 years (n=3,733) was observed. The most remarkable
increase was registered in boys aged 7–12 (n=859) and in
girls aged 7–10 (n=483) [6].

In many countries, including Poland, a gradual increase
in the number of overweight and obese children was an
incentive to carry out a research in order to determine the
extent of adiposity in the young population. In Cracow,
such a study was performed in a group of children aged 6–
13, inhabitants of the city proper as well as neighbouring
rural areas. The purpose was to estimate the prevalence of
overweight and obesity in preschool and school children
with reference to gender and place of residence.

Material and method

At the end of 2008 and the beginning of 2009, a sample
of 1,499 children aged 6–13 years, residences of Cracow
and neighbouring rural areas, was selected. The examined
group consisted of 748 boys (549 urban and 199 rural
ones) and 747 girls (553 urban and 194 rural ones). The
group of rural subjects comprised the children between
the ages of 7 and 12 years. The urban research group
encompassed 6-year-olds and 13-year-olds. The measurements
were taken according to the Martin-Saller method [17]. The
measurement of weight and height allowed the calculation of
body mass index (BMI) for every examined child.

BMI ¼ weight kilogrammesð Þ=height metresð Þ2

The BMI is widely used to evaluate the incidence of
overweight and obesity. In the case of adults, the ratio
above 25 kg/m2 indicates overweight and above 30 kg/
m2—obesity. [14]

BMI values estimated in children differ depending on
gender and age of the examined subjects but the correlation

is not the same as in adults [21]. Therefore, as far as
children are concerned, it is best to use cut-off points
applicable for children and adolescents that correspond with
their age and gender. In the examination performed the
prevalence of overweight and obesity in children was
determined by means of the International Obesity Task
Force cut-offs with respect to age [8]. This method is
applied in many international research centres. It allows for
a comparison of the results of the authors’ own research
with the data provided in worldwide publications.

Additionally, overweight and obesity values in girls were
determined according to age groups corresponding with the
degree of sexual maturity. They were divided into two age
groups: childhood (7–10 years) and early adolescence (11–
13 years). Taking into consideration the difference in the
pubertal progress in boys and girls, the division allows for
other age categories: childhood and early adolescent. The
examined subjects were grouped according to the criteria
established by Chrzanowska et al. [6].

Statistical analysis

Sex-related differences in all anthropometric indicators
were analysed by the Student’s t test. The chi-square test
was used to determine the differences between groups. The
significance level was 0.05.

The Mann–Whitney–Wilcoxon test (also called the
Wilcoxon rank-sum test or W test) was used to determine
whether the medians of the BMI in boys and girls
included in data sets were equal. It was used instead of
the Student’s t test in a case of non-normal distributions of
variables.

Results

The comparison of body height in the examined subjects
did not reveal any statistically significant differences in

Table 1 Median values, ranges, mean values and standard deviation
of weight and height in all boys and girls

All

Number Med. Ranges Mean SD

Boys 748

Height [cm] 140.0 103.5–177.0 139.9 12.9

Weight [kg] 35.0 16.1–106.5 37.3 11.58

Girls 747

Height [cm] 138.8 103.3–170.7 139.2 13.9

Weight [kg] 33.8 16.1–83.0 35.5 11.19

Med. median values, SD standard deviation

246 Eur J Pediatr (2012) 171:245–251

gender groups and in place of residence groups. In terms of
BMI, girls significantly differed from boys (p<0.01). The differences in body mass in gender groups with respect to place of residence were not statistically significant. Mean values, median values and standard deviation of weight and height in gender groups and in place of residence groups are presented in Tables 1 and 2.

Descriptive statistics of BMI in the group of boys and
girls was presented in Tables 3 and 4. Median values,
ranges, mean values and standard deviation were shown
with respect to gender and place of residence depending on
age categories of the examined children.

The Wilcoxon test was used to compare BMI values in
age categories with reference to place of residence.
Changes were not statistically significant.

On the basis of BMI values obtained in the examined
group, overweight and obese children were distinguished.
The classification was done following the IOTF criteria
[8]. Table 5 presents overweight and obesity in boys. The
results were put forward separately for urban and rural
children. The prevalence of overweight in the groups of
urban and rural boys is not statistically significant. A
slightly higher percentage of overweight boys was
observed in rural ones (28.14%). In the case of urban
boys, those overweight were diagnosed in 27.31% of the
examined subjects. On the other hand, obesity was
observed in almost twice as high percentage of urban
boys (7.78%) as in rural ones (3.52%). This difference is
statistically significant (p=0.05).

The prevalence of overweight and obesity in the group
of girls according to age and place of residence was shown
in Table 6. A higher percentage of girls diagnosed as
overweight was observed in rural areas and equalled
16.49% as opposed to 16.09% in the case of urban girls.
Obesity was also prevailing in a higher number of rural
girls and was 4.12% as opposed to 3.44% in urban girls.
The prevalence of overweight and obesity in the group of
girls was not statistically significant.

The highest number of overweight boys was diagnosed
in the group of 12-year-olds regardless of place of
residence. Half of the 12-year-old rural boys were over-
weight, while in the case of their urban peers the number
was 48. The incidence of overweight in 12-year-olds was
almost twice as high as in the remaining age groups.
However, it must be mentioned that in the group of 13-year-
old boys the number of overweight children decreases
remarkably and totalled nine subjects. In 12-year-old rural
boys, the number of obesity was also the highest and

Table 2 Median values, ranges, mean values and standard deviation of weight and height in boys and girls from urban and rural areas

Urban Rural

Number Med. Ranges Mean SD Number Med. Ranges Mean SD

Boys 549 199

Height [cm] 140.1 103.5–172.2 140.7 13.3 138.5 116–177 139.8 11.6

Weight [kg] 35.5 16.1–106.5 37.5 11.8 34 20.8–79.6 36.7 10.9

Girls 553 194

Height [cm] 138.6 103.3–170.7 138.9 14.3 139.3 115–169 139.8 12.5

Weight [kg] 33.7 16.1–83 35.56 11.7 34.7 17.7–65.3 35.18 9.63

Med. median values, SD standard deviation

Table 3 Median values, ranges, mean values and standard deviation
of BMI in all examined groups

Age All

Number Med. Ranges Mean SD

Boys 748 17.87 13.1–38.2 18.62 3.29

6 33 16.46 14.4–19.7 16.51 1.34

7 94 16.59 13.2–24.4 17.16 2.22

8 108 17.46 13.1–26.4 17.65 2.37

9 116 17.34 13.3–27.0 18.11 2.69

10 103 17.87 13.7–34.7 19.18 3.60

11 108 18.56 13.8–34.3 19.13 3.47

12 137 19.49 14.2–38.2 20.13 3.80

13 49 19.28 13.8–30.8 19.87 3.52

Girls 747 17.39 11.9–32.0 17.86 2.92

6 35 16.05 13.4–28.4 16.80 2.70

7 83 16.00 11.9–23.3 16.37 2.36

8 119 16.18 13.4–27.3 16.75 2.28

9 97 17.14 13.4–25.5 17.53 2.55

10 113 17.42 13.4–28.8 17.77 2.62

11 134 17.84 13.6–26.8 18.30 3.21

12 118 19.05 14.5–32.0 19.43 2.85

13 48 19.21 15.3–27.8 19.86 3.12

Med. median values, SD standard deviation

Eur J Pediatr (2012) 171:245–251 247

equalled three cases. The highest number of obese urban
children (n=9) was observed in 9- and 10-years-olds.

The examination did not register such big differences in the
prevalence of overweight in urban girls as it did in the case of
boys. The highest number of overweight girls was observed in
11-year-olds (n=17) and the lowest in 6-, 10- and 13-year-
olds (n=9 in all three age categories). The highest number of
obese urban girls was in the group of 9-year-olds (n=5).
Noticed remarkable changes in the prevalence of overweight
and obesity depending on the age was registered in rural
girls. The highest number of overweight children was in the
group of 9-year-olds and it equalled nine subjects. However,

no instances of obesity were registered. In 7-year-olds, the
number of obese girls was higher than that of overweight
children and totalled five and four cases, respectively.

Taking into consideration the degree of sexual maturity,
the incidence of obesity was more frequent in younger
children regardless of the gender. A higher percentage of
overweight was also registered in younger children with the
exception of urban girls.

The greatest difference in overweight incidence in the
examined sample was observed in gender groups. Regardless
of place of residence, a higher percentage of overweight
children was in the group of boys. Obesity was more prevalent

Age Urban Rural

Number Med. Ranges Mean SD Number Med. Range Mean SD

Boys 549 17.95 13.1–38.2 18.68 3.36 199 17.72 13.9–33.7 18.44 3.09

6 33 16.46 14.4–19.7 16.51 1.34 – – – – –

7 63 16.39 13.2–24.4 17.31 2.52 31 16.96 13.9–19.4 16.85 1.49

8 72 17.07 13.1–26.4 17.57 2.57 36 17.63 14.5–23.6 17.80 1.98

9 85 17.47 13.3–27.0 18.40 2.93 31 17.03 14.4–20.9 17.28 1.70

10 64 17.90 13.7–34.7 19.32 3.85 39 17.76 15.3–30.5 18.93 3.22

11 78 18.55 13.8–34.3 19.17 3.52 30 18.56 14.4–30.8 19.03 3.46

12 106 19.54 14.2–38.2 19.93 3.69 32 19.16 15.1–33.7 20.71 4.21

13 48 19.28 13.8–30.8 19.72 3.58 – – – – –

Girls 553 17.60 12.6–32.0 18.03 3.01 194 17.23 11.9–27.0 17.68 2.61

6 35 16.05 13.4–28.4 16.80 2.70 – – – – –

7 53 16.16 12.6–20.8 16.25 1.97 30 15.84 11.9–23.3 16.59 2.94

8 89 16.45 13.4–27.3 16.75 2.25 30 16.02 13.9–22.4 16.71 2.41

9 67 16.91 13.4–25.5 17.53 2.68 30 17.43 13.5–22.2 17.53 2.26

10 80 17.22 13.4–28.8 17.58 2.74 33 17.65 14.2–23.7 18.22 2.31

11 96 18.06 13.8–26.7 18.50 3.46 38 17.19 13.6–26.8 17.78 2.40

12 85 19.17 14.5–32.0 19.58 2.91 33 18.59 15.5–27.0 19.04 2.68

13 48 19.21 15.3–27.8 19.86 3.12 – – – – –

Table 4 Median values,
ranges, mean values and
standard deviation of BMI
in boys and girls from urban
and rural areas

Med. median values, SD
standard deviation

Age Urban Rural

Number OW(n) OB(n) Number OW(n) OB(n)

6 33 7 0 – – –

7 63 12 8 31 9 0

8 72 20 6 36 10 1

9 85 20 9 31 5 0

10 64 16 9 39 10 2

11 78 19 3 30 6 1

12 106 48 6 32 16 3

13 48 9 2 – – –

Total 549 151 (27.31%) 43 (7.78%) 199 56 (28.14%) 7 (3.52%)

Childhood 501 142 41 199 56 7

Early adolescent 48 9 2 – – –

Table 5 Prevalence of
overweight and obesity in
boys based on IOTF criteria [8]
by place of residence and age

IOTF International Obesity Task
Force, OW(n) number of over-
weight, OB(n) number of obesity

248 Eur J Pediatr (2012) 171:245–251

in urban boys in comparison with other groups in which the
values were similar (Fig. 1).

Discussion

Overweight and obesity pose a serious social and health
problem in many developed and developing countries [2,
9, 19]. Despite economic, social, cultural and religious
differences among them, overweight and obesity are
becoming a widespread issue common for all infrequently
distant countries.

The authors’ own research on the incidence of overweight
and obesity that was carried out in 2009 in the group of
children from Cracow diagnosed 28% of boys as overweight
and 7% as obese. In girls, 16.2% were overweight and 3.6%
obese. Comparing the authors’ own results with the ones
acquired on the basis of a research project performed in three
cycles in Cracow in 1971, 1983 and 2000 [6] a distinct
increase in the prevalence of overweight and obesity in
children and adolescents can be observed. In the years 1971–

2000, the incidence of overweight and obesity in children
from Cracow doubled from 7.5% to 15.2% in boys and from
6.5% to 11.8% in girls. The results obtained in 2009 revealed
a further increase in the percentage of overweight and obese
children reaching 35% in boys (28% overweight (OW) and
7% obesity (OB)) and almost 20% in girls (16.2% OW and
3.6% OB) [6]. A higher percentage of obese children
regardless of the gender of the examined subjects was
observed in younger groups (6–12 years of age in boys and
6–10 years in girls). Similar correlations are found in the
publication by Jebb et al. [11] or Chrzanowska et al. [6].
Alarming is the fact that the percentage of obese children
increased dramatically from 1.04% in boys and 0.20% in
girls in 1971 to 7% in boys and 3.6% in girls in 2009 [6]. A
quick increase in the prevalence of obesity has been
observed in Germany [14]. During the period of 2003–
2006, the number of children diagnosed as obese doubled.
Similarly, disturbing statistics are reported by Zimmerman et
al. [23]. In Switzerland in the years 1990–2002, the
percentage of obese children aged 6–12 years increased
from 0.61% to 3.8% in boys and from 0.8% to 3.7% in girls.

On the basis of a continuous research within China
Health and Nutrition Survey performed on school children
and carried out in eight provinces in the years 1991–1997,
the occurrence of obesity depending on the place of
residence was determined. In urban areas, the incidence of
overweight increased from 7.7% to 12.4%, whereas in rural
areas the increase was lower and changed from 5.9% to
6.4% [22]. Chrzanowska and Łaska-Mierzejewska [7]
analysed the prevalence of overweight and obesity in girls
living in rural areas in four regions of Poland. They
observed that the incidence of overweight and obesity had
increased by 7.4% in the years 1987–2001.

Many authors seek the causes of an increasing prevalence
of overweight and obesity in economic and cultural changes
that have taken place in Poland following the emergence of a

Fig. 1 Prevalence of overweight and obesity in gender groups by
place of residence

Age Urban Rural
Number OW(n) OB(n) Number OW(n) OB(n)

6 35 9 2 – – –

7 53 10 2 30 4 5

8 89 12 2 30 5 1

9 67 10 5 30 9 0

10 80 9 2 33 7 0

11 96 17 4 38 2 1

12 85 14 1 33 5 1

13 48 9 1 – – –

Total 553 90 (22.2%) 19 (3.8%) 194 32 (16.49%) 8 (4.12%)

Childhood 324 50 13 123 25 6

Early adolescent 229 40 6 71 7 2

Table 6 Prevalence of
overweight and obesity in
girls based on IOTF criteria [8]
by place of residence and age

IOTF International Obesity
Task Force, OW(n) number
of overweight, OB(n) number
of obesity

Eur J Pediatr (2012) 171:245–251 249

free market economy as well as in the media influence
and nutritional changes, mainly a growing fast food
availability [3, 12]. An increase in the occurrence of
overweight and obesity in children since 2000 has been
partly connected with subsequent changes that followed
the accession of Poland to the European Union and the
opening of borders. People in big cities such as Cracow
have experienced a visible improvement in the standard of
living and have become attracted to a more consumptive
lifestyle which may lead to the growing prevalence of
overweight and obesity. The paper of Jebb and Lambert
[10] stressed that the data on overweight and obesity in
children and adolescents are dominated by studies in
Northern and Western Europe and that their values are
closest to US data due to a similar economic level of those
countries. At the same time, authors showed that in the
available literature there are few reports from European
countries with lower economic status. Our research
presents data from Eastern Europe which complement
the knowledge on the prevalence and trends of overweight
and obesity in European children and adolescents.

In the authors’ own research, overweight and obesity
reach higher values in the group of boys. The earlier
research performed on the population of Cracow confirms
this observation although the differences were not as big as
in 2009. In children from Warsaw, overweight was twice as
frequent in boys as in girls [4]. In the Czech Republic,
another East European country which has undergone the
process of transformation in recent years, the situation is the
same as in Poland [20]. In Hungary, obesity was more
common in men but at the same time more women were
diagnosed as overweight [2].

In countries such as China [21, 22] and Thailand [1], the
incidence of overweight and obesity is more frequently
observed in boys than in girls, whereas in the United Arab
Emirates there are more girls diagnosed as overweight and
obese [16]. In western countries with high economic status,
the tendency is opposite [5, 11].

Conclusions

The research performed and the analysis of the findings
permit the conclusion that overweight and obesity occur
in a remarkably high percentage of children in Cracow,
particularly in younger age groups. Overweight and
obesity are almost twice as frequent in boys as in girls.
The differences related to place of residence are small
and statistically insignificant excepting obesity which
occurs twice as frequently in urban boy as in their rural
peers (p=0.05).

A gradual increase in prevalence of overweight and
obesity has been observed in Cracow since 1971. A

dramatic increase in obesity, registered in the authors’
own research in the studies carried out in various centres all
over the world, is particularly alarming.

Open Access This article is distributed under the terms of the
Creative Commons Attribution Noncommercial License which per-
mits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source are credited.

References

1. Aekplakorn W, Mo-Suwan L (2009) Prevalence of obesity in
Thailand. Obes Rev 10(6):589–592

2. Antal M, Peter S, Biro L, Nagy K, Regőly-Merei A, Arato G,
Szabo C, Martos E (2009) Prevalence of underweight,
overweight and obesity on the basis of body mass index and
body fat percentage in Hungarian schoolchildren: representative
survey in Metropolitan Elementary Schools. Ann Nutr Metab
54:171–176

3. Bielicki T, Szklarska A, Kozieł S, Welon Z (2003) Transformacja
ustrojowa w Polsce w świetle antropologicznych badań 19-letnich
mężczyzn. Wrocław: Monographs of Institute of Antropology,
Polish Academy of Sciences 23:62–69

4. Charzewska J, Chabros E, Jajszczyk B, Rogalska-Niedźwiedź M,
Chwojnowska Z (2004) Wysokość ciała młodzieży z Warszawy na
tle kolejnych okresów ekonomii. In: Kaczanowski K, Bergman P,
Charzewska J, Piechaczek H (eds) Trendy sekularne na tle
zmian cywilizacyjnych. AWF, Warszawa, pp 75–84

5. Chinn S, Rona RJ (2001) Prevalence and trend in overweight and
obesity in three cross sectional studies of British Children 1974–
94. Br Med J 322:24–26

6. Chrzanowska M, Kozieł S, Ulijaszek SJ (2007) Changes in BMI
and the prevalence of overweight and obesity in children and
adolescents in Cracow, Poland, 1971–2000. Econ Hum Biol
5:370–378

7. Chrzanowska M, Łaska-Mierzejewska T (2009) Nadwaga i
otyłość w populacji dziewcząt wiejskich w Polsce—zmiany
w okresie 1987–2001. Anthropol Rev suppl 6:30

8. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH (2000) Establishing
a standard definition for child overweight and obesity worldwide:
international survey. BMJ 320:1240–1243

9. de Assis MAA, Rolland-Cachera MF, Crosseman S, de Vescocelos
FAG, Luna MEP, Calvo MCM, Barros MVG, Pires MMS, Bellisle F
(2005) Obesity, overweight and thinness in schoolchildren of the city
of Florianopolis, Southern Brazil. Eur J Clin Nutr 59:1015–1021

10. Jebb SA, Lambert J (2000) Overweight and obesity in European
children and adolescents. Eur J Pediatr 159(Suppl 1):2–4

11. Jebb SA, Rennie KL, Cole TJ (2003) Prevalence of overweight
and obesity among young people in Great Britain. Public Health
Nutrition 7(3):461–465

12. Kozieł S, Welon Z, Bielicki T, Szklarska A, Ulijaszek S (2004)
The effect of the economic transition on the body mass index of
conscripts in Poland. Econ Hum Biol 2:97–106

13. Livingstone B (2000) Epidemiology of childhood obesity in
Europe. Eur J Pediatr 159(Suppl 1):14–34

14. Lob-Corzilius T (2007) Overweight and obesity in childhood—a
special challenge for public health. Int J Hyg Environ Health
210:585–589

15. Lobstein T, Baur L, Uauy R (2004) Childhood obesity in children
and young people: a crisis in public health. Report to the world
health organization. Obes Rev 5(suppl 1):4–85

250 Eur J Pediatr (2012) 171:245–251

16. Malik M, Bakir A (2007) Prevalence of overweight and obesity
among children in the United Arab Emirates. Obes Rev 8(1):15–20

17. Martin R, Saller K (1957) Lehrbuch der Anthropologie. Fischer,
Stuttgart

18. Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ,
Flegal KM (2006) Prevalence of overweight and obesity in the
United States, 1999–2004. JAMA 295(13):1549–1555

19. Rolland-Cachera MF, Castetbon K, Arnault N, Bellisle F, Romano
MC, Lehingue Y, Frelut ML, Hercberg S (2002) Body mass index
in 7-9-y-old French children: frequency of obesity, overweight and
thinness. Int J Obes 26:1610–1616

20. Vignerova J, Humenikova L, Brabec M, Riedlova J, Blaha P
(2007) Long-term changes in body weight, BMI, and adiposity

rebound among children and adolescents in the Czech republic.
Econ Hum Biol 5:409–425

21. Wang Y, Wang JQ (2002) A comparison of international
references for the assessment of child and adolescent overweight
and obesity in different populations. Am J Clin Nutr 56:973–
982

22. Zhou H, Yamauchi T, Natsuhara K, Yan Z, Lin H, Ichimaru N,
Kim SW, Ishii M, Ohtsuka R (2006) Overweight in urban
schoolchildren assessed by body mass index and body fat mass
in Dalian, China. J Physiol Anthropol 25(1):41–48

23. Zimmermann MB, Gűbelli C, Pűntener C, Molinari L (2004)
Overweight and obesity in 6–12 years old children in Switzerland.
Swiss Med Wkly 134:523–528

Eur J Pediatr (2012) 171:245–251 251

Prevalence of overweight and obesity in children aged 6–13&newnbsp;years—alarming increase in obesity in Cracow, Poland

Abstract
Introduction
Material and method
Statistical analysis
Results
Discussion
Conclusions
References

What Will You Get?

We provide professional writing services to help you score straight A’s by submitting custom written assignments that mirror your guidelines.

Premium Quality

Get result-oriented writing and never worry about grades anymore. We follow the highest quality standards to make sure that you get perfect assignments.

Experienced Writers

Our writers have experience in dealing with papers of every educational level. You can surely rely on the expertise of our qualified professionals.

On-Time Delivery

Your deadline is our threshold for success and we take it very seriously. We make sure you receive your papers before your predefined time.

24/7 Customer Support

Someone from our customer support team is always here to respond to your questions. So, hit us up if you have got any ambiguity or concern.

Complete Confidentiality

Sit back and relax while we help you out with writing your papers. We have an ultimate policy for keeping your personal and order-related details a secret.

Authentic Sources

We assure you that your document will be thoroughly checked for plagiarism and grammatical errors as we use highly authentic and licit sources.

Moneyback Guarantee

Still reluctant about placing an order? Our 100% Moneyback Guarantee backs you up on rare occasions where you aren’t satisfied with the writing.

Order Tracking

You don’t have to wait for an update for hours; you can track the progress of your order any time you want. We share the status after each step.

Order Now Talk to Us

Areas of Expertise

Although you can leverage our expertise for any writing task, we have a knack for creating flawless papers for the following document types.

Essay

Thesis

Presentation

Dissertation

Term Paper

Research Paper

Book Review

Assignment

Report

Case Study

Letter

Article

Coursework

Speech

Q & A

Critical Thinking

Areas of Expertise

Although you can leverage our expertise for any writing task, we have a knack for creating flawless papers for the following document types.

Essay

Thesis

Presentation

Dissertation

Term Paper

Research Paper

Book Review

Assignment

Report

Case Study

Letter

Article

Coursework

Speech

Q & A

Critical Thinking

Trusted Partner of 9650+ Students for Writing

From brainstorming your paper's outline to perfecting its grammar, we perform every step carefully to make your paper worthy of A grade.

Preferred Writer

Hire your preferred writer anytime. Simply specify if you want your preferred expert to write your paper and we’ll make that happen.

Grammar Check Report

Get an elaborate and authentic grammar check report with your work to have the grammar goodness sealed in your document.

One Page Summary

You can purchase this feature if you want our writers to sum up your paper in the form of a concise and well-articulated summary.

Plagiarism Report

You don’t have to worry about plagiarism anymore. Get a plagiarism report to certify the uniqueness of your work.

Free Features $66FREE

Most Qualified Writer $10FREE
Plagiarism Scan Report $10FREE
Unlimited Revisions $08FREE
Paper Formatting $05FREE
Cover Page $05FREE
Referencing & Bibliography $10FREE
Dedicated User Area $08FREE
24/7 Order Tracking $05FREE
Periodic Email Alerts $05FREE

Our Services

Join us for the best experience while seeking writing assistance in your college life. A good grade is all you need to boost up your academic excellence and we are all about it.

On-time Delivery
24/7 Order Tracking
Access to Authentic Sources

Academic Writing

We create perfect papers according to the guidelines.

Professional Editing

We seamlessly edit out errors from your papers.

Thorough Proofreading

We thoroughly read your final draft to identify errors.

Thorough Proofreading

We thoroughly read your final draft to identify errors.

Delegate Your Challenging Writing Tasks to Experienced Professionals

Work with ultimate peace of mind because we ensure that your academic work is our responsibility and your grades are a top concern for us!

Check Out Our Sample Work

Dedication. Quality. Commitment. Punctuality

It May Not Be Much, but It’s Honest Work!

Here is what we have achieved so far. These numbers are evidence that we go the extra mile to make your college journey successful.

0+

Happy Clients

0+

Words Written This Week

0+

Ongoing Orders

0%

Customer Satisfaction Rate

Process as Fine as Brewed Coffee

We have the most intuitive and minimalistic process so that you can easily place an order. Just follow a few steps to unlock success.

Call Us +1 (877) 657-8180 Discuss Order Details Now

See How We Helped 9000+ Students Achieve Success

We Analyze Your Problem and Offer Customized Writing

We understand your guidelines first before delivering any writing service. You can discuss your writing needs and we will have them evaluated by our dedicated team.

Clear elicitation of your requirements.
Customized writing as per your needs.

We Mirror Your Guidelines to Deliver Quality Services

We write your papers in a standardized way. We complete your work in such a way that it turns out to be a perfect description of your guidelines.

Proactive analysis of your writing.
Active communication to understand requirements.

We Handle Your Writing Tasks to Ensure Excellent Grades

We promise you excellent grades and academic excellence that you always longed for. Our writers stay in touch with you via email.

Thorough research and analysis for every order.
Deliverance of reliable writing service to improve your grades.

Place an Order Start Chat Now

Children Health Issues – Library Research Assignment

What Will You Get?

Premium Quality

Experienced Writers

On-Time Delivery

24/7 Customer Support

Complete Confidentiality

Authentic Sources

Moneyback Guarantee

Order Tracking

Areas of Expertise

Essay

Thesis

Presentation

Dissertation

Term Paper

Research Paper

Book Review

Assignment

Report

Case Study

Letter

Article

Coursework

Speech

Q & A

Critical Thinking

Areas of Expertise

Essay

Thesis

Presentation

Dissertation

Term Paper

Research Paper

Book Review

Assignment

Report

Case Study

Letter

Article

Coursework

Speech

Q & A

Critical Thinking

Trusted Partner of 9650+ Students for Writing

Preferred Writer

Grammar Check Report

One Page Summary

Plagiarism Report

Free Features $66FREE

Our Services

Academic Writing

Professional Editing

Thorough Proofreading

Thorough Proofreading

Delegate Your Challenging Writing Tasks to Experienced Professionals

Check Out Our Sample Work

It May Not Be Much, but It’s Honest Work!

0+

Happy Clients

0+

Words Written This Week

0+

Ongoing Orders

0%

Customer Satisfaction Rate

Process as Fine as Brewed Coffee

Share Your Requirements

Place Order & Deposit Funds

Release Payment to Your Writer

See How We Helped 9000+ Students Achieve Success

We Analyze Your Problem and Offer Customized Writing

We Mirror Your Guidelines to Deliver Quality Services

We Handle Your Writing Tasks to Ensure Excellent Grades