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Distance and percentage distance from median BMI as alternatives to BMI z score

  • David S. Freedman (a1), Jessica G. Woo (a2) (a3), Cynthia L. Ogden (a4), Ji H. Xu (a5) and Tim J. Cole (a6)...

Abstract

BMI z (BMIz) score based on the Centers for Disease Control and Prevention growth charts is widely used, but it is inaccurate above the 97th percentile. We explored the performance of alternative metrics based on the absolute distance or % distance of a child’s BMI from the median BMI for sex and age. We used longitudinal data from 5628 children who were first examined <12 years to compare the tracking of three BMI metrics: distance from median, % distance from median and % distance from median on a log scale. We also explored the effects of adjusting these metrics for age differences in the distribution of BMI. The intraclass correlation coefficient (ICC) was used to compare tracking of the metrics. Metrics based on % distance (whether on the original or log scale) yielded higher ICCs compared with distance from median. The ICCs of the age-adjusted metrics were higher than that of the unadjusted metrics, particularly among children who were (1) overweight or had obesity, (2) younger and (3) followed for >3 years. The ICCs of the age-adjusted metrics were also higher compared with that of BMIz among children who were overweight or obese. Unlike BMIz, these alternative metrics do not have an upper limit and can be used for assessing BMI in all children, even those with very high BMIs. The age-adjusted % from median (on a log or linear scale) works well for all ages, while unadjusted % from median is better limited to older children or short follow-up periods.

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Corresponding author

*Corresponding author: David S. Freedman, fax +1 815-572-8152, email dxf1@cdc.gov

References

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1. Kuczmarski, RJ, Ogden, CL, Guo, SS, et al. (2002) 2000 CDC Growth Charts for the United States: methods and development. Vital Health Stat 11, 1190.
2. Ogden, CL, Kuczmarski, RJ, Flegal, KM, et al. (2002) Centers for Disease Control and Prevention 2000 growth charts for the United States: improvements to the 1977 National Center for Health Statistics version. Pediatrics 109, 4560.
3. Flegal, KM & Cole, TJ (2013) Construction of LMS parameters for the Centers for Disease Control and Prevention 2000 growth charts. Natl Health Stat Report 9, 13.
4. Ogden, CL & Flegal, KM (2010) Changes in terminology for childhood overweight and obesity. Natl Health Stat Report 25, 15.
5. Cole, TJ (1990) The LMS method for constructing normalized growth standards. Eur J Clin Nutr 44, 4560.
6. Cole, TJ & Green, PJ (1992) Smoothing reference centile curves: the LMS method and penalized likelihood. Stat Med 11, 13051319.
7. Ragland, DR (1992) Dichotomizing continuous outcome variables: dependence of the magnitude of association and statistical power on the cutpoint. Epidemiology 3, 434440.
8. Royston, P, Altman, DG & Sauerbrei, W (2006) Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med 25, 127141.
9. Freedman, DS, Butte, NF, Taveras, EM, et al. (2017) The limitations of transforming very high body mass indexes into z-scores among 8.7 million 2- to 4-year-old children. J Pediatr 188, 5056.
10. Freedman, DS, Butte, NF, Taveras, EM, et al. (2017) BMI z-scores are a poor indicator of adiposity among 2- to 19-year-olds with very high BMIs, NHANES 1999–2000 to 2013–2014. Obesity (Silver Spring) 25, 739746.
11. Woo, JG (2009) Using body mass index Z-score among severely obese adolescents: a cautionary note. Int J Pediatr Obes 4, 405410.
12. Freedman, DS, Butte, NF, Taveras, EM, et al. (2017) Longitudinal changes in BMI z-scores among 45 414 2–4-year olds with severe obesity. Ann Hum Biol 44, 687692.
13. Júlíusson, PB, Roelants, M, Benestad, B, et al. (2018) Severe obesity is a limitation for the use of body mass index standard deviation scores in children and adolescents. Acta Paediatr 107, 307314.
14. Flegal, KM, Wei, R, Ogden, CL, et al. (2009) 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 90, 13141320.
15. Berkey, CS & Colditz, GA (2007) Adiposity in adolescents: change in actual BMI works better than change in BMI z score for longitudinal studies. Ann Epidemiol 17, 4450.
16. Cole, TJ, Faith, MS, Pietrobelli, A, et al. (2005) What is the best measure of adiposity change in growing children: BMI, BMI%, BMI z-score or BMI centile? Eur J Clin Nutr 59, 419425.
17. Gulati, AK, Kaplan, DW & Daniels, SR (2012) Clinical tracking of severely obese children: a new growth chart. Pediatrics 130, 11361140.
18. Chambers, M, Tanamas, SK, Clark, EJ, et al. (2017) Growth tracking in severely obese or underweight children. Pediatrics 140, e20172248.
19. Centers for Disease Control and Prevention (CDC) (2016) Modified z-scores in the CDC growth charts. https://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/biv-cutoffs.pdf (accessed October 2017).
20. Waterlow, JC (1972) Classification and definition of protein-calorie malnutrition. BMJ 3, 566569.
21. Gomez, F, Galvan, RR, Frenk, S, et al. (1956) Mortality in second and third degree malnutrition. J Trop Pediatr (Lond) 2, 7783.
22. Freedman, DS & Berenson, GS (2017) Tracking of BMI z scores for severe obesity. Pediatrics 140, e20171072.
23. Berenson, GS, McMahan, CA, Voors, AW, et al. (1980) Cardiovascular Risk Factors in Children: the Early Natural History of Atherosclerosis and Essential Hypertension. New York: Oxford University Press.
24. Webber, LS, Cresanta, JL, Croft, JB, et al. (1986) 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 39, 91103.
25. Freedman, DS, Lawman, HG, Galuska, DA, et al. (2018) Tracking and variability in childhood levels of BMI: the Bogalusa Heart Study. Obesity (Silver Spring) 26, 11971202.
26. Centers for Disease Control and Prevention (CDC) (2016) A SAS Program for the 2000 CDC growth charts. https://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm (accessed September 2019).
27. Centers for Disease Control and Prevention (CDC) Percentile data files with LMS values. http://www.cdc.gov/growthcharts/percentile_data_files.htm (accessed September 2019).
28. Cole, TJ & Altman, DG (2017) Statistics notes: percentage differences, symmetry, and natural logarithms. BMJ 358, j3683.
29. Nickerson, CAE (1997) A note on ‘A concordance correlation coefficient to evaluate reproducibility’. Biometrics 53, 15031507.
30. McGraw, KO & Wong, SP (1996) Forming inferences about some intraclass correlation coefficients. Psychol Methods 1, 3046.
31. R Core Team (2019) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.r-project.org/
32. Bates, D, Maechler, M, Bolker, B, et al. (2018) lme4: Linear Mixed-Effects Models Using ‘Eigen’ and S4. http://cran.r-project.org/web/packages/lme4/index.html (accessed September 2019).
33. Kelly, AS & Daniels, SR (2017) Rethinking the use of body mass index z-score in children and adolescents with severe obesity: time to kick It to the curb? J Pediatr 188, 78.
34. Wang, Y, Cai, L, Wu, Y, et al. (2015) What childhood obesity prevention programmes work? A systematic review and meta-analysis. Obes Rev 16, 547565.
35. Hampl, S, Odar Stough, C, Poppert Cordts, K, et al. (2016) Effectiveness of a hospital-based multidisciplinary pediatric weight management program: two-year outcomes of PHIT Kids. Child Obes 12, 2025.
36. McCormick, EV, Dickinson, LM, Haemer, MA, et al. (2014) What can providers learn from childhood body mass index trajectories: a study of a large, safety-net clinical population. Acad Pediatr 14, 639645.
37. Baughcum, AE, Gramling, K & Eneli, I (2015) Severely obese preschoolers in a tertiary care obesity program: characteristics and management. Clin Pediatr 54, 346352.
38. O’Connor, EA, Evans, CV, Burda, BU, et al. (2017) Screening for obesity and intervention for weight management in children and adolescents. Evidence report and systematic review for the US Preventive Services Task Force (USPSTF). JAMA 317, 24272427.
39. Dwyer, T & Blizzard, CL (1996) Defining obesity in children by biological endpoint rather than population distribution. Int J Obes 20, 472480.
40. Williams, DP, Going, SB, Lohman, TG, et al. (1992) Body fatness and risk for elevated blood pressure, total cholesterol, and serum lipoprotein ratios in children and adolescents. Am J Public Health 82, 358363.
41. Freedman, DS, Katzmarzyk, PT, Dietz, WH, et al. (2009) Relation of body mass index and skinfold thicknesses to cardiovascular disease risk factors in children: the Bogalusa Heart Study. Am J Clin Nutr 90, 210216.
42. Steinberger, J, Jacobs, DR, Raatz, S, et al. (2005) Comparison of body fatness measurements by BMI and skinfolds vs dual energy X-ray absorptiometry and their relation to cardiovascular risk factors in adolescents. Int J Obes 29, 13461352.
43. WHO Multicentre Growth Reference Study Group (2006) WHO Child Growth Standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development. Geneva: WHO. https://www.who.int/childgrowth/standards/technical_report/en/
44. Rigby, RA & Stasinopoulos, DM (2014) Automatic smoothing parameter selection in GAMLSS with an application to centile estimation. Stat Methods Med Res 23, 318332.
45. Hales, CM, Fryar, CD, Carroll, MD, et al. (2018) Trends in obesity and severe obesity Prevalence in US youth and adults by sex and age, 2007–2008 to 2015–2016. JAMA 319, 17231725.
46. Kelly, AS, Barlow, SE, Rao, G, et al. (2013) Severe obesity in children and adolescents: identification, associated health risks, and treatment approaches: a scientific statement from the American Heart Association. Circulation 128, 16891712.

Keywords

Distance and percentage distance from median BMI as alternatives to BMI z score

  • David S. Freedman (a1), Jessica G. Woo (a2) (a3), Cynthia L. Ogden (a4), Ji H. Xu (a5) and Tim J. Cole (a6)...

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