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 accessAbstract
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…
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Trends over time in obesity prevalence among US youth by age, race and Hispanic origin, income, and education of household head have been published,1,5,13 but trends in obesity and severe obesity prevalence by urbanization level have not been well studied.”
From Differences in Obesity Prevalence by Demographics and Urbanization in US Children and Adolescents, 2013-2016 (2018) · cites this paper as [13]
In the citing paper
“Trends over time in obesity prevalence among US youth by age, race and Hispanic origin, income, and education of household head have been published,1,5,13 but trends in obesity and severe obesity prevalence by urbanization level have not been well studied.”
From Differences in Obesity Prevalence by Demographics and Urbanization in US Children and Adolescents, 2013-2016 (2018) · cites this paper as [13]
Citations
Cites
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-citationCDC 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-citationStatistical 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
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-citationSmoothing 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
1 of the 29 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 401–435 of 435
Johnston, C. A., & Moreno, J. P. (2012) · CRC Press eBooks · doi:10.1201/b12085-10
Simpson, J. C., Wicken, A., Gallagher, S., Adams, J., Duncanson, M., Oben, G., & Morris, S. (2017) · Otago University Research Archive (University of Otago)
Metabolic characteristics of Appalachian children
Gravelle, B. L., & Hagen, T. W. (2017) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2017.09.002
Excess Adiposity in Youth: Subclinical Cardiovascular Disease and Future Implications
Fyfe‐Johnson, A. L. (2017) · University of Minnesota Digital Conservancy (University of Minnesota)
Tripicchio, G. L. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/fb1r-yq94
Efficacy of Bariatric Surgery Among Adolescent Patients with Super-Obesity
King, N. A., Meknat, A., Guevara, D. E., Awwa, G. A., Herron, D. M., & Fernandez-Ranvier, G. G. (2019) · Bariatric Surgical Practice and Patient Care · doi:10.1089/bari.2019.0056
Childhood obesity prevention programs for young children: A meta-analysis
Heidari, Z. (2019) · ThinkTech (Texas Tech University)
Malhotra, S. (2020) · Springer eBooks · doi:10.1007/978-3-030-52215-5_19
Dooley, E. E. (2019) · Digital Commons-TMC (Texas Medical Center)
Tonelli, F., Cotti, S., Leoni, L., Besio, R., Gioia, R., Marchese, L., Giorgetti, S., Villani, S., Gistelinck, C., Wagener, R., et al. (2021) · Yearbook of pediatric endocrinology · doi:10.1530/ey.18.5.11
Joseph, J. A., Paul, D. P. A. D., & Shalni, A. A. (2021)
(2011) · Current Opinion in Endocrinology Diabetes and Obesity · doi:10.1097/med.0b013e3283432fa7
Cirugía bariátrica en pediatría, ¿qué impacto tiene? Revisión de la literatura
Coronado-Sarmiento, J. F., Palencia-Palencia, J. I., & Barraza-Leones, O. C. (2022) · Revista Colombiana de Cirugía · doi:10.30944/20117582.923
Bham, S. Q., Zahra, F., Saeed, F., Sharif, U. H. A., Khatoon, A., & Bukhari, S. I. (2022) · Pakistan BioMedical Journal · doi:10.54393/pbmj.v5i7.647 · Cited by 1
Haselden, L., Carter, J., Jones, M., Henshaw, M. H., & DellaValle, D. M. (2018) · Archives of Cardiology and Cardiovascular Diseases · doi:10.22259/2638-4744.0101009
Pediatric Obesity Prevalence in the U.S. Military Health System, Fiscal Years 2012-2018
Bleach, C. C., Brooks, D. I., & Larson, N. S. (2023) · Military Medicine · doi:10.1093/milmed/usad418
Olunusi, P. A., & Asunmo, M. R. (2023) · International Journal of Home Economics Hospitality and Allied Research · doi:10.57012/ijhhr.v2n2.016
Characteristics of adolescents referred for bariatric surgery in Abu Dhabi, United Arab Emirates
Beck, R. H., Afrooz, I., Masalawala, M. S., Watad, R., Shaban, T. A., & Deeb, A. (2024) · Frontiers in Pediatrics · doi:10.3389/fped.2024.1297251
Effects of green tea polyphenols on inflammation and iron status
Jorgenson, M. C. (2022) · doi:10.31274/td-20240329-263
Ng, W., Khaider, K. B., Fan, Q., & Chew, C. S. E. (2024) · Singapore Medical Journal · doi:10.4103/singaporemedj.smj-2024-006
Ng, W., Khaider, K. B., Fan, Q., & Chew, C. S. E. (2024) · Proceedings of Singapore Healthcare · doi:10.1177/20101058241289617
“37”
Serum Adipsin Levels in Obese and Normal Weight Adolescents with Polycystic Ovary Syndrome
Emiral, Ç., Buluş, A. D., Yaşartekin, Y., & Yardım-Akaydin, S. (2024) · Journal of Gazi University Health Sciences Institute · doi:10.59124/guhes.1604306 · Cited by 1
Soltero, E. G., Partida, M., Mihail, S., Villanueva, L., Lopez, C., Musaad, S. M. A., Connor, T. O. A., REDONDO, M. J., Buman, M. P., & Debbe, T. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5124511
Assessment of Nutritional Status in Children and Adolescents
(2025) · American Academy of PediatricsItasca, IL eBooks · doi:10.1542/9781610027700-ch23
Evaluation of BMI Growth Charts for Children Living with Severe Obesity
Engsner, M., Ciba, I., Aydın, B. K., Stenlid, R., Söderhäll, J., Bergsten, P., & Forslund, A. H. (2025) · Childhood Obesity · doi:10.1089/chi.2024.0423
“The fi rst CDC correction for high BMI was presented by Flegal et al. in 2009, 20 which mathematically calculates the percentage above the 95th percentile (Fig.”
InfluentialChaves, E. A., & Kunkler, E. C. (2025) · Obesity Pillars · doi:10.1016/j.obpill.2025.100227
“” (Participant 16).”
InfluentialObesity—Findings from the Bogalusa Heart Study
Freedman, D. S., & Blanck, H. M. (2011) · Springer eBooks · doi:10.1007/978-94-007-1451-9_7
Self-citationBoone‐Heinonen, J., Lyon-Scott, K., Springer, R. J., Schmidt, T. D., Vesco, K. K., Booman, A., Dinh, D., Fortmann, S. P., Foster, B. A., Hauschildt, J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26696903
The Science of Growth Monitoring: Beyond the Basics
Liegl, M., & Pan, A. Y. (2026) · Children · doi:10.3390/children13020162
“Flegal et al. [8] employed the CDC-supplied LMS parameters for extrapolation for the 99th percentile and the fit to the empirical data was poor.”
Soltero, E. G., Partida, M., Mihail, S., Villanueva, L., Lopez, C., Musaad, S. M. A., O’Connor, T. M., REDONDO, M. J., Buman, M. P., & Thompson, D. I. (2026) · JMIR Research Protocols · doi:10.2196/75331
“…and weight will be measured to the nearest 0.1 cm and 0.1 kg, respectively, and used to calculate sex-and age-adjusted BMI as well as the percentage of participants in the 95th percentile, which is more sensitive to detecting clinical changes in BMI in populations with severe obesity [38-40].”
InfluentialWang, H., Su, J. R., Wang, J., Mo, R., Yu, Z., Li, H., & Ye, J. (2026) · Bratislavské lekárske listy/Bratislava medical journal · doi:10.1007/s44411-026-00727-7
Assessment of Sleep Architecture in Obese Children With Obstructive Sleep Apnea
Gedamu, H. Y., Ulualp, S. O., Wani, A., Chapel, A. C., Lenes‐Voit, F., & Mitchell, R. B. (2026) · The Laryngoscope · doi:10.1002/lary.70920
Metadata
- 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
- 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)
- 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
- 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