BMI-for-Age and Weight-for-Length in Children 0 to 2 Yearspediatrics.aappublications.org/content/pediatrics/138/1/e20153809... · BMI-for-Age and Weight-for-Length in Children 0 to

ARTICLEPEDIATRICS Volume 138 , number 1 , July 2016 :e 20153809

BMI-for-Age and Weight-for-Length in Children 0 to 2 YearsKayla R. Furlong, MSc, a Laura N. Anderson, PhD, a, b Huiying Kang, c, d Gerald Lebovic, PhD, d, e Patricia C. Parkin, MD, FRCPC, a, e, f, g Jonathon L. Maguire, MD, MSc, FRCPC, d, e, f, g, h, i Deborah L. O’Connor, RD, PhD, h Catherine S. Birken, MD, MSc, FRCPC, a, e, f, g on behalf of the TARGet Kids! Collaboration

abstractOBJECTIVES: To determine the agreement between weight-for-length and BMI-for-age in

children 0 to <2 years by using research-collected data, examine factors that may affect

agreement, and determine if agreement differs between research- and routinely collected

data.

METHODS: Cross-sectional data on healthy, term-born children (n = 1632) aged 0 to <2 years

attending the TARGet Kids! practice-based research network in Toronto, Canada (December

2008–October 2014) were collected. Multiple visits for each child were included. Length

(cm) and weight (kg) measurements were obtained by trained research assistants during

research visits, and by nonresearch staff during all other visits. BMI-for-age z-scores were

compared with weight-for-length z-scores (the criterion measure).

RESULTS: The correlation between weight-for-length and BMI-for-age was strong (r = 0.986,

P < .0001) and Bland-Altman plots revealed good agreement (difference = −0.08, SD =

0.20, P = .91). A small proportion (6.3%) of observations were misclassified and most

misclassifications occurred near the percentile cutoffs. There were no differences by age

and sex. Agreement was similar between research- and routinely collected data (r = 0.99, P

< .001; mean difference −0.84, SD = 0.20, P = .67).

CONCLUSIONS: Weight-for-length and BMI-for-age demonstrated high agreement with low

misclassification. BMI-for-age may be an appropriate indicator of growth in the first 2 years

of life and has the potential to be used from birth to adulthood. Additional investigation is

needed to determine if BMI-for-age in children <2 years is associated with future health

outcomes.

aChild Health and Evaluative Sciences, Research Institute, and fPaediatric Outcomes Research Team, Division

of Paediatric Medicine, Department of Paediatrics, The Hospital for Sick Children, Toronto, Ontario, Canada; bDepartment of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada; cDalla

Lana School of Public Health, eInstitute of Health Policy, Management, and Evaluation, and Departments of gPaediatrics and hNutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada;

and dThe Applied Research Centre of the Li Ka Shing Knowledge Institute, and iDepartment of Pediatrics, St.

Michael’s Hospital, Toronto, Ontario, Canada

Ms Furlong and Dr Anderson conceptualized and designed the study, contributed to the analysis

and interpretation of the data, and drafted the manuscript; Ms Kang contributed to the analysis

and interpretation of the data; Drs Lebovic and Birken conceptualized and designed the study

and contributed to the analysis and interpretation of the data; Drs Parkin, Maguire, and O’Connor

assisted in refi ning the study design and contributed to the analysis and interpretation of the

data; and all authors contributed to the revision of the manuscript, approved the fi nal version

submitted for publication, and agreed to act as guarantors of the work.

DOI: 10.1542/peds.2015-3809

Accepted for publication Apr 22, 2016 To cite: Furlong KR, Anderson LN, Kang H, et al. BMI-for-Age

and Weight-for-Length in Children 0 to 2 Years. Pediatrics.

2016;138(1):e20153809

WHAT’S KNOWN ON THIS SUBJECT: BMI-for-

age growth charts are now available for

growth monitoring in children younger than 2

years, although weight-for-length remains the

recommended approach. If BMI-for-age performs

similarly to weight-for-length, practitioners could

use the same metric from birth to adulthood.

WHAT THIS STUDY ADDS: Agreement between

weight-for-length and BMI-for-age is very high, with

most misclassifi cations close to the percentile

cutoffs. BMI-for-age appears to be an appropriate

anthropometric alternative in children <2 years.

by guest on February 15, 2019www.aappublications.org/newsDownloaded from

FURLONG et al

Growth monitoring continues

to be the most valuable clinical

and public health tool to monitor

growth and assess the health and

nutritional status of children.1, 2

Growth monitoring of children 0

to 18 years old in primary care is

recommended by numerous expert

bodies worldwide.3–6 In 2006, the

World Health Organization (WHO)

endorsed new growth reference

charts that were constructed from

the monitoring of growth, in a

longitudinal manner, of healthy,

singleton, term-born children in 6

ethnically diverse countries.7, 8

These charts represent ideal

growth in children under optimal

environmental conditions for growth

and have percentile cutoffs that can

be used to classify weight status7, 9

(eg, wasting, overweight, obesity)

that may be practical for growth

monitoring and screening.10–12

Currently, it is recommended that

clinicians assess weight status by

calculating and plotting weight-

for-length in children 0 to <2 years

of age, and then transition to BMI-

for-age in children 2 years of age

and older.3, 13 However, the WHO

Child Growth Standards (2006)

also includes BMI-for-age growth

reference charts for children <2 years

that were not previously available.14

Using 1 tool, such as BMI-for-age,

would give clinicians the ability to

use BMI from birth to adulthood,

track growth trajectories using 1

metric, and avoid the transition

between differing measures after 2

years of age.

The association between weight-for-

length and BMI-for-age in children

<2 years has been explored by others,

but with some limitations.15–17

Similarities in prevalence have been

reported for some (eg, underweight,

overweight, obesity), but not all,

weight status categories when

comparing weight-for-length and

BMI-for-age.16, 17 One study also

reported a good correlation (r = 0.83,

P < .0001) between the 2 measures,

but included children with chronic

diseases and preterm infants in their

comparison.15

Recent studies also suggest

the importance of comparing

agreement between research- and

routinely collected anthropometric

measurements.18, 19 Several pediatric

studies report small differences for

height (∼0.3 – 0.9 cm) and weight

(∼0.01 – 0.04 kg) alone, 20, 21

exemplifying the ability to use

primary care data for population

growth monitoring. It is unknown

whether the agreement between

weight-for-length and BMI-for-age

differs among these data sources.

The primary objective of this study

was to determine the agreement

between weight-for-length and

BMI-for-age in healthy, term-

born children aged 0 to <2 years

using research-collected data.

The secondary objectives were to

examine if age, sex, and weight status

category affect agreement, and if

agreement differs between research-

and routinely collected data.

METHODS

Study Design

Cross-sectional data were collected

through the TARGet Kids! (The

Applied Research Group for Kids)

primary care practice-based

research network in Toronto,

Ontario, Canada. TARGet Kids!

is a collaboration between child

health researchers in the Faculty

of Medicine at the University of

Toronto and primary care physicians

in the university’s Department of

Pediatrics and Department of Family

and Community Medicine. Details

of study recruitment, including

study protocol, have been described

previously.22 The study was

approved by the Hospital for Sick

Children and St Michael’s Hospital

Research Ethics Boards. All parents

of participating children provided

written, informed consent to

participate in the study.

Participants

Children 0 to <2 years were recruited

from 9 pediatric or family medicine

primary care practices during

scheduled well-child visits between

December 2008 and October 2014

(n = 1632). In this study, children

were included if a weight and length

measurement had been obtained

by a trained research assistant on

the same day at any well-child visit

between 0 and 2 years of age. We

excluded children with gestational

age <37 weeks, birth weight <1500 g,

a health condition affecting growth

(eg, failure to thrive, cystic fibrosis),

a chronic illness (except asthma),

severe developmental delay, or the

absence of a parent and/or guardian

fluent in English.

Data Collection

Trained research assistants at each

primary care practice collected

demographic information from

parents by using a standardized

data collection form adapted from

the Canadian Community Health

Survey.23 Demographic information

included age, sex, and maternal

ethnicity. Ethnicity was classified

using a close-ended maternal

ethnicity question designed and

validated by the TARGet Kids!

Collaboration that states: “What

were the ethnic or cultural origins of

your child’s ancestors? (An ancestor

is usually more distinct than a

grandparent.)”24 Response categories

(described elsewhere)22, 24 were

then collapsed into the following

subcategories: European; East, South,

and Southeast Asian; African and

Caribbean; Latin American; West

Asian, Arab, and North African; and

Mixed.

Anthropometric Measurements

Research-collected data included

weight and length measurements

obtained by trained research

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PEDIATRICS Volume 138 , number 1 , July 2016

assistants during a scheduled

well-child visit (ie, a research

visit). Standardized measurement

techniques were used for all

research-collected data; weight (kg)

was measured using a precision

digital scale (± 0.025%; Seca,

Hamburg, Germany), and length (cm)

was measured to the nearest 0.1

cm with a calibrated length board.

Routinely collected data included

weight and length measurements

performed without the presence

of a trained research assistant, and

abstracted from the primary care

health records of these enrolled

children from any other health care

visit. The method of weight and

length ascertainment, including

adherence to recommended

protocols, including the use of

standardized equipment, calibrated

length boards, measurement by

various team members, such as clinic

nurse, physician, or other health care

professional during these visits was

unknown. Research and routine visits

did not occur on the same date. For

both research and routine visits, data

from multiple visits were available

for each child and were included (ie,

repeated measures).

BMI (kg/m2) was calculated as

weight divided by the square of

length.25, 26 Age- and sex-specific

percentiles and the corresponding

z-scores were determined by

using the WHO Child Growth

Standards (2006) for both weight-

for-length and BMI-for-age.3, 27

Percentiles and z-scores were

electronically computed by using

WHO Anthro software (www. who.

int/ childgrowth/ software/ en/ ).

Z-scores were classified into weight

status categories by using the

following percentile cutoffs: severely

underweight (z < 0.1st), underweight

(z < 3rd), normal (3rd ≤ z ≤85th

percentile), at-risk overweight

(z >85th), overweight (z >97th), and

obese (z >99.9th). These cutoffs are

used to describe growth in children 0

to <2 years of age by using the WHO

Child Growth Standards.3

Data Cleaning and Identifying Outliers

Data were assessed for quality by

first identifying weight-for-length

and BMI-for-age z scores < −4.0

and > −5.0.1 For observations with

these values, we reviewed health

records to compare with available

data on previous or subsequent

well-child visits within 2 years. Data

points were removed if there was

a ±1 SD difference from a previous

or subsequent visit. If there was no

previous or subsequent visit, the data

point was also removed.

Statistical Analysis

Descriptive statistics, including

frequency distributions for

categorical variables (eg, age, sex,

ethnicity) and mean (±SD) and

median (interquartile range) for

continuous variables (eg, age,

weight-for-length, and BMI-for-age)

are presented. A Pearson χ2 test

was used to detect the difference in

proportions between weight status

categories.

For our primary analysis, the overall

degree of agreement between

weight-for-length and BMI-for-

age (as continuous variables)

for research-collected data were

evaluated by using a Pearson

correlation coefficient and visually

examined agreement by using a

Bland-Altman plot (with 95% limits

of agreement).

For our secondary analysis, we

evaluated the Pearson correlation

coefficients stratified by age, sex,

and weight status categories. The

McNemar χ2 test was used to test

the difference in the proportion of

observations classified into each

weight status category by using

weight-for-length or BMI-for-age

(eg, an observation classified as

normal weight status using weight-

for-length, but overweight using

BMI-for-age). A scatterplot of


was graphed to visually inspect

misclassification between growth

status categories. Sensitivity,

specificity, positive predictive values

(PPV), and negative predictive

values (NPV) were also calculated

to determine the influence of weight

status category by using weight-

for-length as the criterion measure.

Weight-for-length was selected as

the criterion measures because it is

the currently recommended method

of growth monitoring in this age

group.3, 13

All descriptive statistics and analyses

described previously were then

conducted for routinely collected

data to determine the difference in

agreement between research- and

routinely collected data. All data were

analyzed by using R v.3.0.3 (Murray

Hill, NJ). All hypotheses were

2-sided and P < .05 was considered

statistically significant.

RESULTS

A total of 1632 children aged 0 to <2

years were enrolled in TARGet Kids!

between October 2008 and October

2014. Measurements from 3517

research well-child visits from these

children were available for analysis.

After the exclusion of missing data

points (height, weight, gestational

age, and birth weight) and nonvalid

z scores, 2190 observations remained

for inclusion in the analysis (see

Fig 1). Children were of mainly

European descent (62.8%), although

the population was ethnically

diverse and boys comprised 53.6%

of the children (Table 1). Of the

observations included, the median

age was 13.6 months (SD = 5.06).

The mean weight-for-length z score

was −0.06 (SD = 1.09); the mean

BMI-for-age z score was −0.14

(SD = 1.12, Table 1). The proportion of

observations classified with a normal

weight status by using weight-for-

length was 82.9% (n = 1815). For

BMI-for-age, 80.5% (n = 1764) of


FURLONG et al

observations were classified with a

normal weight status (Fig 2).

Primary Analysis

The Pearson correlation between


was strong, positive, and statistically

significant (r = 0.985, P < .001). The

Bland-Altman plot revealed that the

mean of the differences between


was near 0 (difference = −0.079,

SD = 0.19) and the difference was not

statistically significant (P = .68). The

magnitude of the limits of agreement

was < |0.5| (−0.46 to 0.31) and most

observations were within the 95%

confidence limits (Fig 3).

Secondary Analysis

In the secondary analysis stratified

by age and sex, weight-for-length

and BMI-for-age were strongly and

positively correlated in each of the

age and sex categories (r ≥ 0.979

for all coefficients, see Table 2).

When stratified by weight status

categories, the strongest correlation

was observed in the normal

weight category (r = 0.97), but all

correlations were strong and positive

(underweight [r = 0.89], at-risk

overweight [r = 0.85], and overweight

[r = 0.87]). Figure 4 illustrates the

classification of observations into

weight status categories. The overall

rate of misclassification was 6.3%

(n = 138/2190). Misclassifications

occurred near the cutoffs. The

McNemar χ2 test revealed that

the proportion of observations

misclassified was statistically

significant for wasting (P < .001),

but not for other weight status

categories (P > .05 for all other

categories).

4

FIGURE 1Participation recruitment and enrollment fl owchart from October 2008 to October 2014. aNonvalid z score includes z scores < −4.0 or >5.0 that (1) could not be compared with other well-child visits to determine authenticity and/or biological plausibility, or (2) were ≥1 SD of a z score of a previous and/or subsequent well-child visit within 2 years.

TABLE 1 Baseline Characteristics of Children 0 to <2 Years of age in TARGet Kids!

Characteristics Total Observations (n = 2190)

Anthropometric measurement

Weight-for-length, z, mean ± SD −0.06 ± 1.09

BMI-for-age, z, mean ± SD −0.14 ± 1.12

Age, mo, mean ± SD 13.57 ± 5.06

0 to <6, n (%) 156 (7.1)

6 to <12, n (%) 535 (24.4)

12 to <18, n (%) 845 (38.6)

18 to 23, n (%) 654 (29.9)

Sex, a n (%)

Girls 767 (47)

Boys 865 (53)

Ethnicity, a n (%)

European, white 1025 (62.8)

East, South, Southeast Asian 223 (13.7)

African and Caribbean 82 (5.1)

Latin American 49 (3.0)

West Asian/Arab/North African 26 (1.6)

Mixed ethnicity 83 (5.1)

Other, Aboriginal 9 (0.5)

Missing 135 (8.3)

Values of n represent absolute number of observations, proportion expressed as percent is presented in parentheses.a Sex and ethnicity are reported per child (n = 1632).

FIGURE 2Proportion (%) of observations classifi ed into weight status categories by using weight-for-length and BMI-for-age.



Among those identified by using

weight-for-length, most observations

(sensitivity ≥0.77 for all weight

status categories) were correctly

classified in the same weight category

by using BMI-for-age. Sensitivity was

the highest for wasting (0.92) and

lowest for at-risk overweight (0.77,

Table 3), whereas specificity was

high in all categories (≥ 0.97). Within

each weight status category, at least

75% were correctly identified in

the same category; the lowest rates

were observed for wasting (0.75) and

at-risk overweight (0.83, Table 3).

The category with the highest PPV

was overweight (0.89). NPVs were

high and similar for wasting, at-risk

overweight, and overweight (≥ 0.97,

See Table 3).

We performed all described analyses

by using only routinely collected

data and found similar results when

compared with research-collected

data (see Supplemental Tables 4,

5, and 6). The Pearson correlation

coefficient between weight-for-

length and BMI-for-age was 0.99 (P <

.001). The mean difference between


was −0.084 (SD = 0.20) and was not

statistically significant as determined

by a Bland-Altman plot (P = .67, data

not shown). Pearson correlation

coefficients were similar among age

and sex categories (see Supplemental

Table 5). Sensitivity and PPVs were

highest among wasting (0.92) and

overweight (0.92) observations,

respectively, whereas specificity was

similar and high in all categories (see

Supplemental Table 6). Analyses

were conducted on 1 randomly

selected observation per child

and the results were similar (data

available on request).

DISCUSSION

Our results indicate high agreement

between weight-for-length and BMI-

for-age with low misclassification

overall. The Bland-Altman plots were

symmetrical on visual inspection and

no systematic bias was identified. Our

results demonstrated high specificity

(≥97%) and most of those identified

in any weight status category were

correctly classified (≥75%). Most

misclassifications occurred near the

cutoffs and misclassification was not

statistically significant for any weight

status category, except for wasting.

The agreement between weight-for-

length and BMI-for-age was similarly

high in routinely collected data,

indicating the potential for routinely

collected data to be used for growth

monitoring and for research and

public health purposes.

Previous research has supported

the use of BMI-for-age for growth

5

FIGURE 3Bland-Altman plot demonstrating the agreement between weight-for-length and BMI-for-age. Limits of agreement minus estimate of difference (weight-for-length z score minus BMI-for-age z score) = −0.079; SD of the differences = 0.19, 95% CI −0.47 to 0.31, P = .68.

TABLE 2 Pearson Correlation Between Weight-for-Length and BMI-for-Age

Variable n Pearson r P

Overall 2190 0.985 <.001

Age, mo

0 to <6 156 0.99 <.001

6 to <12 535 0.99 <.001

12 to < 18 845 0.98 <.001

18 to 23 654 0.99 <.001

Sex

Girls 1016 0.99 <.001

Boys 1174 0.98 <.001

Growth category

Wasting, <3rd 84 0.89 <.001

Normal, ≥3rd and ≤85th 1759 0.97 <.001

At-risk overweight, >85th 271 0.85 <.001

Overweight, >97th 76 0.87 <.001

Values of n represent the absolute number of observations in each category. Total n indicated in each row.


FURLONG et al

monitoring in the first 2 years of

life. Nash et al15 reported a Pearson

correlation coefficient of 0.83 (P <

.0001) between weight-for-length

and BMI-for-age in a small (n =

547) population recruited from

a pediatric tertiary care setting.

Nash et al15 included children with

chronic conditions affecting growth

(eg, cystic fibrosis, failure to thrive,

congenital defects) and 18% of the

sample constituted preterm infants.

Our exclusion of children with chronic

disease may have resulted in a higher

correlation coefficient (0.99, P < .001).

Nash et al15 also reported fewer

children identified as at-risk

overweight (ie, ≥85th percentile) by

using BMI-for-age (12.5%) compared

with weight-for-length (18.2%).15

We did not identify any differences

in prevalence at this cutoff or at

other cutoffs. Our sample size was

larger and included only healthy

children, which may account for this

difference. De Onis et al17 reported

a similar prevalence for weight-for-

length and BMI-for-age in overweight

in children <5 years, whereas Mei

et al16 reported no difference for

wasting and overweight children in

the same age category.

The potential for routinely collected

data to be used for research and

public health surveillance has been

recently demonstrated, particularly

for weight and length measurements

in children.20, 21 Both studies report

high agreement for weight and

length between research- and

routinely collected data.20, 21 Our

results align with these findings:

that routinely collected data

appears to be an accurate source

of information.

Our overall rate of misclassification

was low (∼6%), but this rate was

different between weight status

categories. PPVs were lowest

for wasting, at-risk overweight,

normal, and overweight (see Table

3). For wasting, as high as 25% of

those identified as wasted by using

BMI-for-age were not identified as

wasted by using weight-for-length.

For normal, at-risk overweight,

and overweight, the proportion of

misclassified children was 17%,

13%, and 11%, respectively. These

percentages indicate that the 2

measures may not be entirely

interchangeable, although it appears

that misclassification is occurring

near the percentile cutoffs (see Fig 3).

In the future, it will be necessary to

determine how these differences in

classification affect longitudinal child

health outcomes.

Our study has several strengths.

Children in this study were recruited

as part of TARGet Kids!, a primary

care practice-based research-based

research network, which is an

6

FIGURE 4Scatterplot of weight-for-length and BMI-for-age comparing misclassifi cation between growth status categories. Red dots are those misclassifi ed (eg, identifi ed in one category by weight-for-length and in a different category by BMI-for-age). Misclassifi cation rate was 6.3% of total observations included (n = 138/2190).

TABLE 3 Unadjusted Sensitivity, Specifi city, PPV, and NPV by Using BMI-for-Age Compared With Weight-for-Length by Weight Status Category

Variable Weight-for-Length (>Cutoff) Test Characteristics

Yes No Sensitivity Specifi city PPV NPV

Wasting, <3rd

BMI-for-age Yes 77 260.92 0.99 0.75 0.99

No 7 2080

Normal, ≥3rd and ≤85th

BMI-for-age Yes 1703 560.86 0.97 0.87 0.96

No 61 370

At-risk overweight, >85th

BMI-for-age Yes 209 430.77 0.98 0.83 0.97

No 62 1876

Overweight, >97th

BMI-for-age Yes 63 80.83 0.99 0.89 0.99

No 13 2106

Weight-for-length was used as the criterion measure.



ethnically diverse cohort of children

with research-collected data from

numerous well-child visits in the first

2 years of life.22 Second, children

with conditions affecting growth, and

those born preterm and very low

birth weight were excluded from our

analysis. Furthermore, we verified

the data quality of both research-

and routinely collected data by

identifying outlier z scores and then

determining both their biological

plausibility and consistency with

measurements from other well-child

visits. In addition, we have used

the WHO recommended guidelines

for classifying weight status, which

is applicable to all children 0 to

<2 years worldwide, regardless of

socioeconomic status, ethnicity, or

feeding patterns.7

One potential limitation of our study

is that data were combined for

children <3rd percentile (ie, wasting

[<0.1st] + severe wasting [>3rd]),

as well as for those >97th percentile

(ie, overweight [>97th] + obesity

[>99.9th]). A small sample size at

these extreme values impeded our

ability to examine the agreement

in these weight status categories

separately. Additionally, we excluded

those children for whom birth weight

and gestational age were not known

(n = 954); however, their inclusion

may have provided additional

insight. Second, although we have

used the recommended percentile

cutoffs by the WHO to define weight

status categories, the validity of

these categories in younger children

remains poorly understood. Many of

these cutoffs were validated for older

children only or chosen as statistical,

rather than clinical, criteria.25, 28–30

Although weight-for-length was

used as the criterion measure,

it may be argued that a more

accurate and proximate measure

of body fat (eg, skinfold test, dual

energy x-ray absorptiometry) be

considered a “gold standard” to

assess weight status.31–33 We did

not collect data on these measures

in our study. Importantly, though,

weight-for-length is the currently

recommended measure to use for

growth monitoring by the American

Academy of Pediatrics4 and the

Canadian Pediatric Society3 in

children <2 years. Indeed, weight-

for-length in children <2 years as a

marker of weight status has been

associated with obesity34, 35 and

cardiometabolic outcomes36 at other

points throughout childhood.

There is also evidence to support

BMI-for-age as a marker of weight

status, 37–39 and it performs similarly

to dual energy x-ray absorptiometry

to predict cardiovascular outcomes

throughout childhood.38, 40 Last, the

generalizability of our results is

not known. Although we excluded

preterm and very low birth

weight infants, we did not have

enough data to compare certain

demographic characteristics of our

population with those of the WHO

Multicentre Growth Reference Study

that represents ideally growing

children (eg, singleton birth rate,

smoking mothers, and breastfeeding

practices).

CONCLUSIONS

We have demonstrated high

agreement with limited

misclassification between weight-

for-length and BMI-for-age in healthy

children 0 to <2 years and found

that agreement is similar between

research- and routinely collected

data. If BMI-for-age were to replace

weight-for-length as the weight

status standard for children 0 to <2

years, this may enable improved

monitoring of longitudinal growth

patterns in young children. Future

studies are required to examine

unmeasured weight status categories,

including severe wasting and obesity,

where classification and agreement

may be lower, and to determine the

predictive ability of BMI-for-age

for long-term health outcomes as

compared with weight-for-length.

ACKNOWLEDGMENTS

*TARGet Kids! Collaboration.

Co-Leads: Catherine S. Birken,

Jonathon L. Maguire; Advisory

Committee: Eddy Lau, Andreas

Laupacis, Patricia C. Parkin, Michael

Salter, Peter Szatmari, Shannon

Weir; Scientific Committee: Kawsari

Abdullah, Yamna Ali, Laura N.

Anderson, Imaan Bayoumi, Catherine

S. Birken, Cornelia M. Borkhoff,

Sarah Carsley, Shiyi Chen, Yang Chen,

Denise Darmawikarta, Cindy-Lee

Dennis, Karen Eny, Stephanie Erdle,

Kayla Furlong, Kanthi Kavikondala,

Christine Koroshegyi, Christine

Kowal, Grace Jieun Lee, Jonathon

L. Maguire, Dalah Mason, Jessica

Omand, Patricia C. Parkin, Navindra

Persaud, Lesley Plumptre, Meta van

den Heuvel, Shelley Vanderhout,

Peter Wong, Weeda Zabih; Site

Investigators: Murtala Abdurrahman,

Barbara Anderson, Kelly Anderson,

Gordon Arbess, Jillian Baker, Tony

Barozzino, Sylvie Bergeron, Dimple

Bhagat, Nicholas Blanchette, Gary

Bloch, Joey Bonifacio, Ashna Bowry,

Anne Brown, Jennifer Bugera,

Douglas Campbell, Sohail Cheema,

Elaine Cheng, Brian Chisamore,

Ellen Culbert, Karoon Danayan,

Paul Das, Mary Beth Derocher,

Anh Do, Michael Dorey, Kathleen

Doukas, Anne Egger, Allison Farber,

Amy Freedman, Sloane Freeman,

Keewai Fung, Sharon Gazeley, Donna

Goldenberg, Charlie Guiang, Dan

Ha, Curtis Handford, Laura Hanson,

Hailey Hatch, Teresa Hughes, Sheila

Jacobson, Lukasz Jagiello, Gwen

Jansz, Paul Kadar, Tara Kiran, Lauren

Kitney, Holly Knowles, Bruce Kwok,

Sheila Lakhoo, Margarita Lam-

Antoniades, Eddy Lau, Fok-Han

Leung, Alan Li, Jennifer Loo, Joanne

Louis, Sarah Mahmoud, Roy Male,

Vashti Mascoll, Rosemary Moodie,

Julia Morinis, Maya Nader, Sharon

Naymark, Patricia Neelands, James

Owen, Jane Parry, Michael Peer, Kifi

Pena, Marty Perlmutar, Navindra

Persaud, Andrew Pinto, Tracy Pitt,

Michelle Porepa, Vikky Qi, Nasreen


FURLONG et al

Ramji, Noor Ramji, Jesleen Rana,

Alana Rosenthal, Katherine Rouleau,

Janet Saunderson, Rahul Saxena,

Vanna Schiralli, Michael Sgro, Susan

Shepherd, Hafiz Shuja, Barbara

Smiltnieks, Cinntha Srikanthan,

Carolyn Taylor, Suzanne Turner,

Fatima Uddin, Joanne Vaughan,

Thea Weisdorf, Sheila Wijayasinghe,

Peter Wong, Anne Wormsbecker,

Ethel Ying, Elizabeth Young, Michael

Zajdman, Ian Zenlea; Research

Team: Charmaine Camacho, Arthana

Chandraraj, Dharma Dalwadi,

Ayesha Islam, Thivia Jegathesan,

Tarandeep Malhi, Megan Smith,

Laurie Thompson; Applied Health

Research Center: Christopher Allen,

Bryan Boodhoo, Judith Hall, Peter

Juni, Gerald Lebovic, Karen Pope,

Jodi Shim, Kevin Thorpe; Mount Sinai

Services Laboratory: Azar Azad.

We thank all of the participating

families for their time and

involvement in TARGet Kids! and are

grateful to all practitioners who are

currently involved in the TARGet Kids!

practice-based research network.

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ABBREVIATIONS

NPV: negative predictive value

PPV: positive predictive value

TARGet Kids!: The Applied

Research Group

for Kids!

WHO: World Health Organization

Address correspondence to Catherine Birken, MD, Child Health and Evaluative Sciences, Research Institute, Division of Paediatric Medicine, Department of

Paediatrics, The Hospital for Sick Children, Rm 109801, 10th Fl, Peter Gilgan Centre for Research and Learning, 686 Bay St, Toronto, ON M5H 0A4 Canada. E-mail:

[email protected]

PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).

Copyright © 2016 by the American Academy of Pediatrics

FINANCIAL DISCLOSURE: The authors have indicated they have no fi nancial relationships relevant to this article to disclose.

FUNDING: Funding of the TARGet Kids! research network was provided by the Canadian Institutes of Health Research Institute of Human Development, Child and

Youth Health, the Canadian Institutes of Health Research Institute of Nutrition, Metabolism and Diabetes, the SickKids Foundation, and the St. Michael’s Hospital

Foundation. The Paediatric Outcomes Research Team is supported by a grant from The Hospital for Sick Children Foundation. The funding agencies had no role

in the design and conduct of the study, the collection, management, analysis and interpretation of the data, or the preparation, review, and approval of the

manuscript.

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential confl icts of interest to disclose.



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DOI: 10.1542/peds.2015-3809 originally published online June 24, 2016; 2016;138;Pediatrics

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