Cited by 435Obesity, Physical Activity, DietBody Composition Measurement TechniquesStatistical Methods in Epidemiology

The DOI matches its Crossref record; 28 of the 29 references it cites were found in a registry or an index.

Characterizing extreme values of body mass index-for-age by using the 2000 Centers for Disease Control and Prevention growth charts.

Open access

Flegal, Katherine M, Wei, Rong, Ogden, Cynthia L, Freedman, David S, Johnson, Clifford L, Curtin, Lester R·2009·The American Journal of Clinical Nutrition

DOI 10.3945/ajcn.2009.28335

Open this paper in CoChat — free account

Abstract

Background The 2000 Centers for Disease Control and Prevention (CDC) growth charts included lambda-mu-sigma (LMS) parameters intended to calculate smoothed percentiles from only the 3rd to the 97th percentile. Objective The objective was to evaluate different approaches to describing more extreme values of body mass index (BMI)-for-age by using simple functions of the CDC growth charts. Design Empirical data for the 99th and the 1st percentiles of BMI-for-age were calculated from the data set used to construct the growth charts and were compared with estimates extrapolated from the CDC-supplied LMS parameters and to various functions of other smoothed percentiles. A set of reestimated LMS parameters that incorporated a smoothed 99th percentile were also evaluated. Results Extreme percentiles extrapolated from the CDC-supplied LMS parameters did not match well to the empirical data for the 99th percentile. A better fit to the empirical data was obtained by using 120% of the smoothed 95th percentile. The empirical first percentile was reasonably well approximated by extrapolations from the LMS values. The reestimated LMS parameters had several drawbacks and no clear advantages. Conclusions Several approximations can be used to describe extreme high values of BMI-for-age with the use of the CDC growth charts. Extrapolation from the CDC-supplied LMS parameters does not provide a good fit to the empirical 99th percentile values. Simple approximations to high values as percentages…

Integrity signals

Registry

Crossref · 10.3945/ajcn.2009.28335

Cites

28 of 28 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
CDC GROWTH CHARTS FOR THE UNITED STATES: METHODS AND DEVELOPMENT 2000
Kuczmarski, R. J., Ogden, C. L., Guo, S. S., Strawn, L. G., Flegal, K. M., & Mei, Z. (2002) · Cited by 6,426
Self-citation
CDC growth charts: United States.
Kuczmarski, R. J., Ogden, C. L., Grummer‐Strawn, L. M., Flegal, K. M., Guo, S. S., Wei, R. X., Mei, Z., Curtin, L. R., Roche, A. F., & Johnson, C. L. (2000) · PubMed · Cited by 5,298
Self-citation
Statistical Analysis With Missing Data
Ghosh, S., Little, R. J. A., & Rubin, D. B. (1988) · Technometrics · doi:10.2307/1269814 · Cited by 4,730
Prevalence of overweight and obesity among US children, adolescents, and adults, 1999-2002.
Hedley, A. A., Ogden, C., Johnson, C. L., Carroll, M., Curtin, L. R., & Flegal, K. (2004) · Journal of the American Medical Association (JAMA) · doi:10.1001/jama.291.23.2847 · Cited by 4,670
Expert Committee Recommendations Regarding the Prevention, Assessment, and Treatment of Child and Adolescent Overweight and Obesity: Summary Report
Barlow, S. E., & and the Expert Committee (2007) · Pediatrics · doi:10.1542/peds.2007-2329c · Cited by 4,497
Prevalence and Trends in Overweight Among US Children and Adolescents, 1999-2000
Ogden, C. L. (2002) · JAMA · doi:10.1001/jama.288.14.1728 · Cited by 4,060
Self-citation
Smoothing reference centile curves: The lms method and penalized likelihood
Cole, T. J., & Green, P. J. (1992) · Statistics in Medicine · doi:10.1002/sim.4780111005 · Cited by 2,803

Flagged first, then by citations

Show all 29 references

1 of the 29 references couldn’t be matched to a known paper.

Sorted by citations

Items 201–300 of 435

Urinary polycyclic aromatic hydrocarbons in relation to anthropometric measures and pubertal development in a cohort of Northern California girls
Dobraca, D., Laurent, C. A., Greenspan, L. C., Hiatt, R. A., Sjödin, A., Kushi, L. H., & Windham, G. C. (2020) · Environmental Epidemiology · doi:10.1097/ee9.0000000000000102 · Cited by 17
Predictors of Severe Obesity in Low-Income, Predominantly Hispanic/Latino Children: The Texas Childhood Obesity Research Demonstration Study
Salahuddin, M., Pérez, A., Ranjit, N., Kelder, S. H., Barlow, S. E., Pont, S. J., Butte, N. F., & Hoelscher, D. M. (2017) · Preventing Chronic Disease · doi:10.5888/pcd14.170129 · Cited by 17
Tracking of obesity among 2‐ to 9‐year‐olds in an electronic heath record database from 2006 to 2018
Freedman, D. S., Goodman, A. B., King, R. J., & Blanck, H. M. (2020) · Obesity Science & Practice · doi:10.1002/osp4.407 · Cited by 15
Self-citation
Obesity and Severe Obesity in Children with Autism Spectrum Disorder: Prevalence and Risk Factors
Pham, D., Silver, S., Haq, S., Hashmi, S. S., & Eissa, M. A. (2020) · Southern Medical Journal · doi:10.14423/smj.0000000000001068 · Cited by 19
“468 Aversion to any food texture 66 (26) 24 (30) 7 (12) 4 (15) 0.”
Influential
Growth After Adenotonsillectomy for Obstructive Sleep Apnea: Revisited
Jensen, A. M., Herrmann, B. W., Mitchell, R. B., & Friedman, N. R. (2021) · The Laryngoscope · doi:10.1002/lary.29863 · Cited by 16
Stability of relative weight category and cardiometabolic risk factors among moderately and severely obese middle school youth
Marcus, M., Foster, G., & Ghormli, L. E. (2014) · Obesity · doi:10.1002/oby.20688 · Cited by 18
“Consequently, Flegal and colleagues (8) have recommended using 120% of the smoothed 95th percentile of BMI as an approximation of severe obesity in order to assess and follow extremely heavy children.”
Background
Characterizing a “Big Data” Cohort of Over 200,000 Low-Income U.S. Infants and Children for Obesity Research: The ADVANCE Early Life Cohort
Boone‐Heinonen, J., Tillotson, C. J., O'Malley, J. P., Cottrell, E., Gaudino, J. A., Amofah, A., Rivo, M. L., Brickman, A. L., Mayer, K. H., McBurnie, M. A., et al. (2017) · Maternal and Child Health Journal · doi:10.1007/s10995-016-2232-5 · Cited by 16
Relationship of BMI z score to fat percent and fat mass in multiethnic prepubertal children
Wilkes, M. A., Thornton, J., Horlick, M., Sopher, A. B., Wang, J., Widen, E. M., Pierson, R. N., & Gallagher, D. (2018) · Pediatric Obesity · doi:10.1111/ijpo.12463 · Cited by 17
Electronic Gaming Characteristics Associated with Class 3 Severe Obesity in Youth Who Attend the Pediatric Weight Management Programs of the COMPASS Network
Phan, T.-L. T., Tucker, J. M., Siegel, R., Christison, A. L., Stratbucker, W., Werk, L. N., Hossain, J., Datto, G. A., Gentile, D. A., & Stubblefield, S. (2018) · Childhood Obesity · doi:10.1089/chi.2018.0156 · Cited by 18
“For the purpose of analyses, we categorized BMI percentile dichotomously into two groups of similar size: class 3 severe obesity (BMI ‡140th percentile above the 95th percentile) or not.(3,20,21)”
Altered neural correlates of episodic memory in adolescents with severe obesity
Pearce, A. L., Mackey, E. R., Cherry, J. B. C., Olson, A. M., You, X., Nadler, E. P., & Vaidya, C. J. (2019) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2019.100727 · Cited by 16
“, 2002), with 8% meeting criteria for severe obesity (BMI 120% above the 95 percentile; Flegal et al., 2009; Hales et al., 2018; Kelly et al., 2013).”
BackgroundInfluential
The age of the obesity onset is a very important factor for the development of metabolic complications and cardiovascular risk in children and adolescents with severe obesity
Szczudlik, E., Stępniewska, A., Bik-Multanowski, M., Brandt-Heunemann, S., Flehmig, B., Małecka‐Tendera, E., Mazur, A., Petriczko, E., Ranke, M. B., Wabitsch, M., et al. (2024) · European Journal of Pediatrics · doi:10.1007/s00431-024-05636-x · Cited by 18
“In the United States, the prevalence is even higher reaching 4–6% of the general pediatric population [4, 5].”
Background
Improved Access to and Impact of Registered Dietitian Nutritionist Services Associated with an Integrated Care Model in a High-Risk, Minority Population
Warner, M. F., Miklos, K. E., Strowman, S. R., Ireland, K. A., & Pojednic, R. M. (2018) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2018.05.004 · Cited by 15
Precisión diagnóstica del índice de masa triponderal (kg/m3) para identificar el fenotipo de riesgo metabólico en pacientes obesos
Yeste, D., Clemente, M., Campos-Martorell, A., Fábregas, A., Mogas, E., Soler, L., & Carrascosa, A. (2020) · Anales de Pediatría · doi:10.1016/j.anpedi.2020.04.004 · Cited by 19
Associations Among Excess Weight Status and Tobacco, Alcohol, and Illicit Drug Use in a Large National Sample of Early Adolescent Youth
Zeller, M. H., Becnel, J. N., Reiter‐Purtill, J., Peugh, J., & Wu, Y. P. (2016) · Prevention Science · doi:10.1007/s11121-016-0639-2 · Cited by 16
“, Bpercentage of the 95th percentile^) are growing in use in an attempt to refine this weight category to improve tracking of BMI change over time and allow further delineation of severely obese sub-groups (Flegal et al. 2009; Kelly et al. 2013; Skinner and Skelton 2014).”
Background
The Prevalence of Abnormal Metabolic Parameters in Obese and Overweight Children
Salvatore, D., Satnick, A., Abell, R., Messina, C. R., & Chawla, A. (2013) · Journal of Parenteral and Enteral Nutrition · doi:10.1177/0148607113496820 · Cited by 15
Racial/Ethnic Differences in the Effectiveness of a Multisector Childhood Obesity Prevention Intervention
Nelson, C. C., Colchamiro, R., Perkins, M. E., Taveras, E. M., Leung‐Strle, P., Kwass, J., & Baidal, J. W. (2018) · American Journal of Public Health · doi:10.2105/ajph.2018.304511 · Cited by 14
Extreme value analysis for dietary intake based on weight class
Panagoulias, D. P., Sotiropoulos, D. N., & Tsihrintzis, G. A. (2022) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa56318.2022.9904418 · Cited by 14
“Extreme values of body mass index for age by using the 2000 Centers for Disease Control and Prevention growth charts are examined in [12].”
Method
Evaluation of BMI Metrics to Assess Change in Adiposity in Children with Overweight and Moderate and Severe Obesity
Barlow, S. E., Salahuddin, M., Durand, C. P., Pont, S. J., Hoelscher, D. M., & Butte, N. F. (2020) · Obesity · doi:10.1002/oby.22858 · Cited by 15
Comparing US paediatric and adult weight classification at the transition from late teenage to young adulthood
Maring, B., Greenspan, L. C., Chandra, M., Daniels, S. R., Sinaiko, A. R., Prineas, R. J., Parker, E. D., Adams, K. F., Daley, M. F., Sherwood, N. E., et al. (2015) · Pediatric Obesity · doi:10.1111/ijpo.274 · Cited by 14
“As such, expressing BMI as a percentage above the 95th BMI percentile enables specific BMI tracking for severely obese children and adolescents (26,28) and has been shown in preliminary studies to be associated with clinically relevant health risk (22).”
BackgroundMethod
Severe Obesity and Sleep-Disordered Breathing as Risk Factors for Emergence Agitation in Pediatric Ambulatory Surgery
Reynolds, T., Sankaran, S., Chimbira, W. T., Phan, T., & Nafiu, O. O. (2017) · Journal of PeriAnesthesia Nursing · doi:10.1016/j.jopan.2016.09.007 · Cited by 14
“120% of the 95th age and sexspecific BMI percentile).(16)”
Who benefits from the intervention? Correlates of successful BMI reduction in the Texas Childhood Obesity Demonstration Project (TX‐CORD)
Barlow, S. E., Durand, C. P., Salahuddin, M., Pont, S. J., Butte, N. F., & Hoelscher, D. M. (2020) · Pediatric Obesity · doi:10.1111/ijpo.12609 · Cited by 14
“Use of the %BMIp95 as the outcome measure avoids the well-described limitations of BMI percentile and z-score among participants with obesity and severe obesity.(11,12) Using this metric, class 1 obesity comprises BMIs from the 95th percentile to <120% of the 95th, class 2 is 120% to <140% of the 95th, and class 3 is…”
Background
Childhood Obesity: An Evidence-Based Approach to Family-Centered Advice and Support
Kaufman, T. K., Lynch, B. A., & Wilkinson, J. M. (2020) · Journal of Primary Care & Community Health · doi:10.1177/2150132720926279 · Cited by 14
Changes in the Recreational Built Environment and Youth Body Mass Index
White, M. J., McClure, E. S., Killeen, J., Howard, J., Skinner, A. C., Spears, T. G., & Perrin, E. M. (2020) · Academic Pediatrics · doi:10.1016/j.acap.2020.09.004 · Cited by 14
Satisfaction and acceptability of paediatric weight management services amongst parents and carers: A mixed‐methods study
McMaster, C. M., Cohen, J. E., Alexander, S., Neal, R., Gow, M. L., Calleja, E., Signorelli, C., Tan, E. J., Williams, K., Sim, K. A., et al. (2020) · Clinical Obesity · doi:10.1111/cob.12391 · Cited by 12
Dietary patterns and associations with BMI in low-income, ethnic minority youth in the USA according to baseline data from four randomised controlled trials
LeCroy, M. N., Nicastro, H. L., Truesdale, K. P., Matheson, D., Ievers‐Landis, C. E., PRATT, C. A., Jones, S., Sherwood, N. E., Burgess, L. E., Robinson, T. N., et al. (2020) · British Journal Of Nutrition · doi:10.1017/s0007114520003852 · Cited by 13
Feasibility/acceptability of an acceptance‐based therapy intervention for diverse adolescent girls with overweight/obesity
Cardel, M. I., Lee, A. M., Chi, X., Newsome, F. A., Miller, D. R., Bernier, A. V., Thompson, L. A., Gurka, M. J., Janicke, D. M., & Butryn, M. L. (2021) · Obesity Science & Practice · doi:10.1002/osp4.483 · Cited by 13
Are the Recent Secular Increases in Waist Circumference among Children and Adolescents Independent of Changes in BMI?
Freedman, D. S., Kit, B. K., & Ford, E. S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141056 · Cited by 13
“Severe obesity is defined as a BMI 120% of the 95 percentile in the CDC growth charts [33].”
BackgroundSelf-citation
Severe Obesity and High Blood Pressure Among Children, Philadelphia Health Centers, 2010
Nguyen, J. V. L., Robbins, J. M., Houck, K. L., Nobis, E. A., Inman, K. A., Khan, K. S., & Robbins, S. W. (2013) · Journal of Primary Care & Community Health · doi:10.1177/2150131913506437 · Cited by 13
“6) Gender Female 346 9 (6-12) 15 (11-19) Male 345 6 (4-10) 20 (16-25) Race/ethnicity Black 400 9 (6-12) 19 (15-23) Hispanic 113 6 (3-12) 11 (6-18) Asian 58 9 (3-19) 28 (17-41) White 25 4 (0-20) 28 (12-49) Other/unknown 95 6 (2-13) 12 (6-20) Age in years 3-5 156 6 (3-11) 17 (11-23) 6-8 123 7 (3-13) 12 (7-19) 9-12 172 8…”
Effects of trimming weight-for-height data on growth-chart percentiles
Flegal, K. M., Carroll, M. D., & Ogden, C. L. (2012) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.112.041822 · Cited by 13
“Rather, the CDC LMS parameters were back-calculated from the previously smoothed percentiles and not from any other features of the data (2, 8).”
Self-citation
Severe obesity in children 17 to 24 months of age: a cross-sectional study of TARGet Kids! and Better Outcomes Registry & Network (BORN) Ontario
Satkunam, M., Anderson, L. N., Carsley, S., Maguire, J. L., Parkin, P. C., Sprague, A. E., Ball, G. D. C., & Birken, C. S. (2018) · Canadian Journal of Public Health · doi:10.17269/s41997-018-0065-2 · Cited by 13
Cutoff values of body mass index for severe obesity in Korean children and adolescents: the 99th percentile versus 120% of the 95th percentile
Choe, J., Kim, J. H., & Moon, J. S. (2023) · Annals of Pediatric Endocrinology & Metabolism · doi:10.6065/apem.2244058.029 · Cited by 13
Prevalence of Overweight and Obesity Among African American Children and Adolescents: Risk Factors, Health Outcomes, and Prevention/Intervention Strategies
Sutherland, M. E. (2021) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-020-00890-9 · Cited by 15
“[8] suggested the use of the 95th percentile as a better way of quantifying youths at higher levels of obesity.”
Method
Characteristics of paediatric weight management in the United States: Associations with program retention and BMI outcomes in the paediatric obesity weight evaluation registry ( POWER )
Tucker, J. M., Stratbucker, W., King, E. C., Cuda, S. E., Negrete, S., Sweeney, B. R., Kumar, S., Borzutzky, C., Binns, H. J., Kirk, S., et al. (2021) · Pediatric Obesity · doi:10.1111/ijpo.12848 · Cited by 12
Trends in relative weight over one year in low-income urban youth
Lawman, H. G., Mallya, G., Veur, S. V., McCoy, T., Colby, L., Sanders, T. A., Wylie-Rosett, J., & Foster, G. D. (2014) · Obesity · doi:10.1002/oby.20928 · Cited by 12
“Severe pediatric obesity The third principal finding described trends in those considered severely obese based on the new recommended cutoff of 120% of the 95th percentile (13) rather than the 99th percentile (21,22).”
BackgroundMethod
Assessing adiposity using BMI z‐Score in children with severe obesity
Woo, J. G., & Cole, T. J. (2017) · Obesity · doi:10.1002/oby.21800 · Cited by 12
“also proposed 120% of the 95th BMI percentile as a new threshold for severe obesity (4).”
Background
What Is the Relationship Between Dairy Intake and Blood Pressure in Black and White Children and Adolescents Enrolled in a Weight Management Program?
DellaValle, D. M., Carter, J., Jones, M., & Henshaw, M. H. (2017) · Journal of the American Heart Association · doi:10.1161/jaha.116.004593 · Cited by 12
“This clinical index is useful when looking at change in obesity status over time, especially when BMI for age is ≥99th percentile.(5,6) Waist and hip circumference measurements were also taken using standardized methods, and waist:hip ratios were calculated for all participants.”
Method
Prevalence of severe childhood obesity in Wales UK
Beynon, C., & Bailey, L. (2019) · Journal of Public Health · doi:10.1093/pubmed/fdz137 · Cited by 12
Implementation of a School Nurse-led Intervention for Children With Severe Obesity in New York City Schools
Schroeder, K., Jia, H., Wang, Y. C., & Smaldone, A. M. (2017) · Journal of Pediatric Nursing · doi:10.1016/j.pedn.2017.02.030 · Cited by 17
“Nearly 4% of American children meet criteria for severe obesity (Skelton, Cook, Auinger, Klein, & Barlow, 2009), with a BMI for age and sex at the 99th percentile or 120% of the 95th percentile (Flegal et al., 2009; Kelly et al., 2013).”
Background
Contemporary surgical management of the obese adolescent
Barnett, S. J. (2011) · Current Opinion in Pediatrics · doi:10.1097/mop.0b013e3283465923 · Cited by 11
Targeted dietary approaches for the management of obesity and severe obesity in children and adolescents: A systematic review and meta‐analysis
Southcombe, F., Lin, F., Krstic, S., Sim, K. A., Dennis, S. M., Lingam, R., & Denney‐Wilson, E. (2022) · Clinical Obesity · doi:10.1111/cob.12564 · Cited by 11
“Recent advancements in the use of BMI to express obesity and severe obesity in children will likely strengthen future reporting.(153) Lastly, the energy target definitions used within this systematic review were applied post-hoc and represent broad groupings”
Diabetes and Prediabetes are Significantly Higher in Morbidly Obese Children Compared With Obese Children
Propst, M. S., Colvin, C., Griffin, R. L., Sunil, B., Harmon, C. M., Yannam, G., Johnson, J. E., Smith, C. B., Lucas, A. P., Diaz, B. T., et al. (2015) · Endocrine Practice · doi:10.4158/ep14414.or · Cited by 11
Which Propensity Score Method Best Reduces Confounder Imbalance? An Example From a Retrospective Evaluation of a Childhood Obesity Intervention
Schroeder, K., Jia, H.-M., & Smaldone, A. (2016) · Nursing Research · doi:10.1097/nnr.0000000000000187 · Cited by 8
“…medical record data to examine the effect of a school nurse-led obesity program on BMI percentile change at one year of kindergarten to 5th grade students who met the criteria for severe obesity (BMI for age and gender at the 99th percentile or 120% of the 95th percentile; Flegal et al., 2009).”
Background
Trends and Disparities in the Prevalence of Childhood Obesity in South Texas between 2009 and 2015
Foster, B. A., Maness, T., & Aquino, C. (2017) · Journal of Obesity · doi:10.1155/2017/1424968 · Cited by 11
Bariatric surgery in adolescents with severe obesity: Review and state of the art in France
Coutant, R., Bouhours‐Nouet, N., Donzeau, A., Fauchard, M., Decrequy, A., Malka, J., Riquin, É., Beaumesnil, M., Sallé, A., Briet, C., et al. (2017) · Annales d Endocrinologie · doi:10.1016/j.ando.2017.03.002 · Cited by 11
“[3] Flegal KM, Wei R, Ogden CL, Freedman DS, Johnson CL, et al.”
Influential
Rationale and design of “Hearts & Parks”: study protocol for a pragmatic randomized clinical trial of an integrated clinic-community intervention to treat pediatric obesity
Armstrong, S., Windom, M. O., Bihlmeyer, N. A., Li, J. S., Shah, S. H., Story, M. T., Zucker, N. L., Kraus, W. E., Pagidipati, N. J., Peterson, E. D., et al. (2020) · BMC Pediatrics · doi:10.1186/s12887-020-02190-x · Cited by 11
“we will assess and report child relative BMI, expressed as a standard deviation score (zBMI) and as a percent of the BMI value at the 95th percentile (%95th BMI) to evaluate and track obesity [44].”
Method
Changes in Eating Behaviors of Children with Obesity in Response to Carbohydrate-Modified and Portion-Controlled Diets
Kirk, S., Woo, J. G., Brehm, B. J., Daniels, S. R., & Saelens, B. E. (2017) · Childhood Obesity · doi:10.1089/chi.2017.0020 · Cited by 10
“Categorical weight status criteria were based on BMI percent of 95th percentile as follows: Obesity-Class 1 (OB1): 100%–120% of 95th percentile for BMI and BMI <35 kg/m(2); Severe Obesity-Class 2 (OB2): 121%–140% of 95th percentile or BMI = 35–39 kg/m(2), whichever is lower; and Severe Obesity-Class 3 (OB3): >140% of…”
Method
Social Determinants of Health and Body Mass Index in American Indian/Alaska Native Children
Fyfe‐Johnson, A. L., Reid, M. M., Jiang, L., Chang, J. J., Huyser, K. R., Hiratsuka, V. Y., Johnson-Jennings, M., Conway, C., Goins, R. T., Sinclair, K., et al. (2022) · Childhood Obesity · doi:10.1089/chi.2022.0012 · Cited by 12
Youth Whose Weight Exceeds Healthy Guidelines Are High-Risk Targets for Tobacco Prevention Messaging and Close Monitoring of Cigarette Use
Zeller, M. H., Reiter‐Purtill, J., Peugh, J., Wu, Y. P., & Becnel, J. N. (2015) · Childhood Obesity · doi:10.1089/chi.2014.0113 · Cited by 11
“, ‘‘percentage of the 95th percentile’’) are growing in acceptance to refine the classification of the 99th percentile to more accurately track BMI percentile changes over time in youth who are severely obese and allow further delineation of severely obese subgroups.(8,52,53) Post-hoc examination of the present sample…”
Background
Weight Loss Surgery in Adolescents
Christison, A. L., & Gupta, S. K. (2017) · Nutrition in Clinical Practice · doi:10.1177/0884533617712702 · Cited by 10
Impact of body mass index on high blood pressure among obese children in the western region of Saudi Arabia
Al-Agha, A. E., & Mahjoub, A. (2018) · Saudi Medical Journal · doi:10.15537/smj.2018.1.20942 · Cited by 10
Clinical-Community Collaboration: A Strategy to Improve Retention and Outcomes in Low-Income Minority Youth in Family-Based Obesity Treatment
Tripicchio, G. L., Ammerman, A. S., Ward, D. S., Faith, M. S., Truesdale, K. P., Burger, K. S., Dean, K. M., Dumenci, L., & Davis, A. M. (2018) · Childhood Obesity · doi:10.1089/chi.2017.0266 · Cited by 11
Sustained improvements in fitness and exercise tolerance in obese adolescents after a 12 week exercise intervention
Gow, M. L., van Doorn, N., Broderick, C. R., Hardy, L. L., Ho, M. M., Baur, L. A., Cowell, C. T., & Garnett, S. P. (2015) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2015.04.001 · Cited by 10
Obesity, Diabetes and Gastrointestinal Malignancy: The role of Metformin and other Anti-diabetic Therapy
McFarlane, S. (2018) · Global Journal of Obesity Diabetes and Metabolic Syndrome · doi:10.17352/2455-8583.000032 · Cited by 10
Associations between obesity severity and medical comorbidities for children with obesity in low intensity hospital intervention
Tsao‐Wu, M., Williams, R. J., Hendy, H. M., & Novick, M. B. (2019) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2019.11.001 · Cited by 11
Negative childhood experiences and disordered eating in adolescents in a weight management program: The role of depressive symptoms
Darling, K. E., Ranzenhofer, L. M., Hadley, W. S., Villalta, D., Kasper, V. L., & Jelalian, E. (2020) · Eating Behaviors · doi:10.1016/j.eatbeh.2020.101402 · Cited by 10
Extreme percentiles of the 2000 Centers for Disease Control and Prevention BMI chart and the LMS method
Cole, T. J. (2010) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.2009.28928 · Cited by 10
Distribution of comorbidities as primary diagnoses by obesity class among patients in a large US paediatric healthcare system
Nussbaum, B. M., Mathew, M. S., Atem, F., Barlow, S. E., Gupta, O. T., & Messiah, S. E. (2021) · Clinical Obesity · doi:10.1111/cob.12478 · Cited by 9
“Obesity classes were categorised based on the following BMI percentiles adjusted for age at time of measure and sex: No obesity = <95th percentile (including overweight = ≥85th percentile and <95th percentile), class I = ≥95th percentile and <120% of the 95th percentile, class II = ≥120% of the 95th percentile and <14…”
Method
More than just body mass index: Using the Edmonton obesity staging system for pediatrics to define obesity severity in a multi‐ethnic Australian pediatric clinical cohort
Southcombe, F., Vivekanandarajah, S., Krstic, S., Lin, F., Chay, P., Williams, M., Khan, J. R., Hu, N., Eapen, V., Dennis, S., et al. (2023) · Obesity Science & Practice · doi:10.1002/osp4.648 · Cited by 9
An Extreme Value Analysis-Based Systemic Approach in Healthcare Information Systems: The Case of Dietary Intake
Panagoulias, D. P., Sotiropoulos, D. N., & Tsihrintzis, G. A. (2022) · Electronics · doi:10.3390/electronics12010204 · Cited by 9
Pregnancy health in a multi-state U.S. population of systemically underserved patients and their children: PROMISE cohort design and baseline characteristics
Boone‐Heinonen, J., Scott, K. L., Springer, R. J., Schmidt, T. D., Vesco, K. K., Booman, A., Dinh, D., Fortmann, S. P., Foster, B. A., Hauschildt, J., et al. (2024) · BMC Public Health · doi:10.1186/s12889-024-18257-8 · Cited by 9
The omentum of obese girls harbors small adipocytes and browning transcripts
Tarabra, E., Nouws, J., Vash‐Margita, A., Nadzam, G. S., Goldberg, R. L., Name, M. A. V., Pierpont, B. M., Knight, J., Shulman, G. I., & Caprio, S. (2020) · JCI Insight · doi:10.1172/jci.insight.135448 · Cited by 10
Psychopathology and Adolescent Bariatric Surgery: A Topical Review to Support Psychologists in Assessment and Treatment Considerations
Burton, E. T., Mackey, E. R., Reynolds, K., Cadieux, A. M., Gaffka, B. J. S., & Shaffer, L. A. (2020) · Journal of Clinical Psychology in Medical Settings · doi:10.1007/s10880-020-09717-5 · Cited by 11
Effect of Fat Loss on Arterial Elasticity in Obese Adolescents With Clinical Insulin Resistance: RESIST Study
Ho, M. M., Gow, M. L., Baur, L. A., Benitez‐Aguirre, P., Tam, C. S., Donaghue, K. C., Craig, M. E., Cowell, C. T., & Garnett, S. P. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2014-1944 · Cited by 9
“Body mass index (BMI), expressed as a percentage of the 95th centile (BMI%95 centile), was calculated from ageand sex-specific reference values (22).”
Method
Creating Equal Opportunities for a Healthy Weight: Workshop Summary
Nutrition Board (2013) · Cited by 9
Prevalence of severe obesity and its association with elevated blood pressure among children and adolescents in Shandong, China
Zhang, Y.-X., Wang, S., & Li, S. (2017) · Blood Pressure Monitoring · doi:10.1097/mbp.0000000000000292 · Cited by 9
The Team to Address Bariatric Care in Canadian Children (Team ABC3): Team Grant Research Proposal
Team ABC (2017) · BMC Research Notes · doi:10.1186/s13104-017-2506-z · Cited by 9
“This classification system was proposed recently [19, 80], but lacked empirical data.”
Background
The impact of interval training on adiponectin to leptin ratios and on blood pressures in severely obese adolescent girls: A randomized controlled trial
Racil, G., Aouichaoui, C., Hawani, A., Signorelli, P., Chamari, K., Migliaccio, G. M., Trabelsi, Y., & Padulo, J. (2024) · Journal of Sports Sciences · doi:10.1080/02640414.2024.2369447 · Cited by 8
“After that, the percentage of the 95th percentile of body mass index (BMIp95) was calculated as suggested by Flegal et al. (2009a) and participants with a BMI ≥ 120% of the 95th percentile were kept for the study.”
Diabetes Distress and Unmet Support Needs Hinder Optimal Care for Adolescents With Type 2 Diabetes: A Mixed Methods Study
Spajic, D., Curran, J. A., Luu, Y., Shah, M. A. E., Subramani, G., James, R., Oxlad, M., Speight, J., & Peña, A. S. (2025) · Pediatric Diabetes · doi:10.1155/pedi/5574666 · Cited by 8
“Overweight and obesity were classi ﬁ ed as having a BMI percentile between 85 and 95 and over 95, respectively [25].”
Is severity of obesity associated with diagnosis or health education practices?
Leventer‐Roberts, M., Patel, A. I., & Trasande, L. (2012) · International Journal of Obesity · doi:10.1038/ijo.2012.1 · Cited by 8
High body mass index, overweight, and obesity in children: Definitions, terminology, and interpretation
Flegal, K. M., & Ogden, C. L. (2010) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199572915.003.0001 · Cited by 8
Self-citation
The Effectiveness of Nutrition Specialists on Pediatric Weight Management Outcomes in Multicomponent Pediatric Weight Management Interventions: A Systematic Review and Exploratory Meta-Analysis
Thompson, K. L., Chung, M., Handu, D., Gutschall, M. D., Jewell, S., Byham-Gray, L. D., & Parrott, J. S. (2019) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2018.12.008 · Cited by 8
“However, a substantial body of evidence indicates that BMI z scores are often misleading and/or inaccurate for severely obese children and adolescents whose weights fall above 120% of the 95th BMI percentile or whose BMI z scores are >3.(137,143,146-148) Measurements of changes in fat mass and fatfree mass over the co…”
Background
Obesity in Pediatric Oncology: Assessment, Treatment Strategies, and Knowledge Gaps
Frazier, A. L., Orjuela‐Grimm, M., & Dietz, W. H. (2019) · JNCI Monographs · doi:10.1093/jncimonographs/lgz024 · Cited by 8
Diagnostic accuracy of the tri-ponderal mass index in identifying the unhealthy metabolic obese phenotype in obese patients
Yeste, D., Clemente, M., Campos-Martorell, A., Fábregas, A., Mogas, E., Soler, L., & Carrascosa, A. (2021) · Anales de Pediatría (English Edition) · doi:10.1016/j.anpede.2020.04.017 · Cited by 8
Health-Related Quality of Life across Recent Pediatric Obesity Classification Recommendations
Black, W. R., Borner, K. B., Beauchamp, M. T., Davis, A. M., Gillette, M. L. D., Sweeney, B. R., & Hampl, S. E. (2021) · Children · doi:10.3390/children8040303 · Cited by 8
“, above the 97th percentile and below the 3rd percentile) [2,9,10] and their use has been cautioned against when representing the weight status of those outside this range [9,11].”
BackgroundResult
Have States Reduced Obesity by Legislating More Physical Activity in Elementary School?
von Hippel, P. T., & Frisvold, D. (2023) · Milbank Quarterly · doi:10.1111/1468-0009.12604 · Cited by 11
Interventions for managing weight change following paediatric acquired brain injury: a systematic review
Shah, E., Lodh, R., Siddell, P., & Morrall, M. C. H. J. (2016) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.13182 · Cited by 8
“Questions exist about the suitability of BMI for classifying children at extreme ends of this spectrum due to techniques used in the compilation of such measures.(44) There was no clear consensus in the type of interventions that should be employed.”
Method
Suboptimal Clinical Documentation in Young Children with Severe Obesity at Tertiary Care Centers
Brady, C. C., Thaker, V., Lingren, T., Woo, J. G., Kennebeck, S., Namjou‐Khales, B., Roach, A., Bickel, J. P., Patibandla, N., Savova, G., et al. (2016) · International Journal of Pediatrics · doi:10.1155/2016/4068582 · Cited by 10
Obesity Cardiometabolic Comorbidity Prevalence in Children in a Rural Weight-Management Program
O’Hara, V. M., Browne, N. T., Fathima, S., Sorondo, B., Bayleran, J. K., Johnston, S. V., & Hastey, K. (2017) · Global Pediatric Health · doi:10.1177/2333794x17729303 · Cited by 8
Addressing Pediatric Obesity in Clinic
Pujalte, G. G. A., Ahanogbe, I., Thurston, M. J., White, R. O., & Roche‐Green, A. (2017) · Global Pediatric Health · doi:10.1177/2333794x17736971 · Cited by 8
Child Obesity and Health
Wang, Y., & Wang, L. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-803678-5.00061-8 · Cited by 8
Interplay between preclinical indices of obesity and neural signatures of fluid intelligence in youth
Ward, T. W., Schantell, M. D., Dietz, S. M., Ende, G. C., Rice, D. L., Coutant, A. T., Arif, Y., Wang, Y., Calhoun, V. D., Stephen, J. M., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06924-w · Cited by 8
“The LMS parameters for BMI were derived from equations for smoothed percentiles 109,110 based on Cole ’ s LMS method 71,72 .”
Association of health behaviors in life’s essential 8 and hypertension in adolescents: a cross-sectional study from the NHANES database
Zhang, Z., Wu, X., Qu, Y., Zhang, D., & Li, W. (2024) · BMC Cardiovascular Disorders · doi:10.1186/s12872-024-04205-2 · Cited by 8
“Percentile categories were defined as normal ( ≥ 5th to 85th percentile), overweight ( ≥ 85th to 95th percentile), obesity ( ≥ 95th percentile), and severe obesity ( ≥ 120% of the 95th percentile) [15].”
Effect of Relative Weight Group Change on Nuclear Magnetic Resonance Spectroscopy Derived Lipoprotein Particle Size and Concentrations among Adolescents
Jago, R., Drews, K. L., Otvos, J. D., Foster, G. D., Marcus, M. D., Buse, J. B., Mietus‐Snyder, M., & Willi, S. M. (2014) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2013.12.029 · Cited by 7
The rehabilitation of children and adolescents with severe or medically complicated obesity: an ISPED expert opinion document
Childhood Obesity Study Group of the Italian Society for Pediatric Endocrinology and Diabetology (ISPED), Grugni, G., Licenziati, M. R., Valerio, G., Crinò, A., Maffeis, C., Tanas, R., & Morino, G. S. (2016) · Eating and Weight Disorders - Studies on Anorexia Bulimia and Obesity · doi:10.1007/s40519-016-0305-5 · Cited by 8
Self-Management Behaviors in Obese Patients Undergoing Surgery Based on General and Specific Adherence Scales
Sobhani, Z., Amini, M., Zarnaghash, M., Hosseini, S. V., & Foroutan, R. (2019) · WORLD JOURNAL OF PLASTIC SURGERY · doi:10.29252/wjps.8.1.85 · Cited by 7
Circulating Extracellular Vesicles Impair Mesenchymal Stromal Cell Differentiation Favoring Adipogenic Rather than Osteogenic Differentiation in Adolescents with Obesity
Peruzzi, B., Urciuoli, E., Mariani, M., Chioma, L., Tomao, L., Montano, I., Algeri, M., Luciano, R., Fintini, D., & Manco, M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms24010447 · Cited by 7
“Body mass index (BMI) and sex- and age-specific standard deviation scores (SDS) of BMI were calculated [56].”
Method
Adiposity, cardiovascular, and health-related quality of life indicators and the reallocation of waking movement behaviors in preschool children with overweight and obesity: An isotemporal data analysis
Dooley, E. E., Gabriel, K. P., Kohl, H. W., Durand, C. P., Hoelscher, D. M., & Byrd-Williams, C. E. (2020) · PLoS ONE · doi:10.1371/journal.pone.0242088 · Cited by 7
The regulatory state of nonalcoholic steatohepatitis and metabolism
Omokaro, S. O., & Golden, J. K. (2020) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.113 · Cited by 7
Review of patients with achondroplasia: a single-center's experience with follow-up and associated morbidities
Soğukpınar, M., Demir, G. Ü., Ütine, G. E., Gonc, E. N., Özön, Z. A., & Şimşek‐Kiper, P. Ö. (2024) · European Journal of Pediatrics · doi:10.1007/s00431-024-05643-y · Cited by 6
“Body mass index (BMI) was assessed according to the Centers for Disease Control and Prevention (CDC) guidelines for those aged 2 to 20 years [15].”
Method
Management of Obesity in Children and Adolescents: Staged Care Approach
Chung, S. (2012) · The Korean Journal of Obesity · doi:10.7570/kjo.2012.21.2.84 · Cited by 6
Childhood Obesity in an Inner-City Primary Care Population: A Longitudinal Study
Robbins, J. M., Benson, B. J., Esangbedo, I. C., Ward, R. L., & Haden, R. N. (2016) · Journal of the National Medical Association · doi:10.1016/j.jnma.2016.05.006 · Cited by 7
Clinical, Social, and Genetic Factors Associated with Obesity at 12 Months of Age
Hazrati, S., Wong, W. S. W., Huddleston, K., Yui, Y., Gilchrist, N., Solomon, B. D., Niederhuber, J. E., & Hourigan, S. K. (2018) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2017.12.042 · Cited by 6
Weight-related quality of life and temperament as predictors and moderators of outcomes among treatment-seeking, low-income, ethnically diverse children with obesity
Frerichs, L., Smith, N., Hoffman, J., Gaskin, K., Skinner, A. C., & Armstrong, S. (2018) · Translational Behavioral Medicine · doi:10.1093/tbm/iby115 · Cited by 6
The Longitudinal Relation of Childhood Height to Subsequent Obesity in a Large Electronic Health Record Database
Freedman, D. S., Goodman, A. B., King, R. J., & Daymont, C. B. (2020) · Obesity · doi:10.1002/oby.22901 · Cited by 6
Self-citation
Estimation of Body Fat Percentage for Clinical Pharmacokinetic Studies in Children
Green, T. P., Binns, H. J., Wu, H., Ariza, A. J., Perrin, E. M., Quadri, M., Hornik, C. P. V., & Cohen‐Wolkowiez, M. (2020) · Clinical and Translational Science · doi:10.1111/cts.12896 · Cited by 7
Psychosocial outcomes after adolescent metabolic and bariatric surgery: a narrative review of the literature
Boles, R. E., & Moore, J. M. (2024) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2024.09.003 · Cited by 6
Association between BMI Change, Transaminases, and Other Metabolic Parameters in Children with Nonalcoholic Fatty Liver Disease
Flores, A., Quirós‐Tejeira, R. E., Lyden, E. R., McGill, B., & Dike, C. R. (2024) · Journal of Obesity · doi:10.1155/2024/6997280 · Cited by 5
“Flegal et al. also utilized BMI Z -score as their primary outcome which has been found to be less reliable for extreme values [16].”
Studying the Relationship between Severe Dental Caries in Childhood and Body Mass Index in Children
Kızılcı, E., Duman, B., Demiroğlu, C., & Ayhan, B. (2024) · Annals Journal of Dental and Medical Assisting · doi:10.51847/l1xzmq8pef · Cited by 5
“Fortunately, the Centers for Disease Control (CDC) has prepared BMI curves based on developmental age, separated by sex [18].”
Breastfeeding in Infancy Is Associated with Body Mass Index in Adolescence: A Retrospective Cohort Study Comparing American Indians/Alaska Natives and Non-Hispanic Whites
Zamora‐Kapoor, A., Omidpanah, A., Nelson, L. A., Kuo, A. A., Harris, R. M., & Buchwald, D. (2017) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2016.11.013 · Cited by 5
Use of Metformin for Weight Management in Children and Adolescents With Obesity in the Clinical Setting
Kyler, K. E., Kadakia, R., Palac, H., Kwon, S., Ariza, A. J., & Binns, H. J. (2018) · Clinical Pediatrics · doi:10.1177/0009922818803404 · Cited by 6

Authors: Flegal, K. M., Wei, R., Ogden, C. L., Freedman, D. S., Johnson, C. L., & Curtin, L. R.
Affiliations: Centers for Disease Control and Prevention (US)
National Center for Health Statistics (US)
Corresponding author: Flegal, Katherine M
ORCID: Flegal, Katherine M
Wei, Rong
Ogden, Cynthia L
Freedman, David S
Venue: The American Journal of Clinical Nutrition
Publisher: Elsevier BV
Published: 2009-11
Volume: 90
Issue: 5
Pages: 1314-1320
ISSN: 0002-9165 (ISSN-L)
DOI: 10.3945/ajcn.2009.28335
OpenAlex: W2130113732
Cited by: 435
References: 29
Citations by year: 2026: 8 · 2025: 10 · 2024: 27 · 2023: 14 · 2022: 25 · 2021: 32 · 2020: 44 · 2019: 33 · 2018: 38 · 2017: 42 · 2016: 30 · 2015: 26 · 2014: 29 · 2013: 28 · 2012: 28
FWCI: 16.06
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 4862 d)
Locations: American Journal of Clinical Nutrition (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
Health Sciences › Medicine › Physiology (medical) › Body Composition Measurement Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords: body mass index for age, CDC growth charts, 99th percentile, overweight children, childhood obesity
MeSH terms: Adolescent, Centers for Disease Control and Prevention, U.S., Child, Child, Preschool, Female, Growth, Humans, Male, Nutrition Surveys, Thinness, United States, Body Mass Index, Overweight, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Characterizing extreme values of body mass index-for-age by using the 2000 Centers for Disease Control and Prevention growth charts.

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks