The use of weight adjusted for height rather than body mass index to assess growth trajectory: Results from a population‐based cohort. (18th October 2018)

• Record Type: Journal Article
• Authors: Araújo, Joana; Ramos, Elisabete; Mishra, Gita D.; Severo, Milton
• Is Part Of: Statistics in medicine. Volume 38:Number 5(2019)
• Page Start: 855
• Page End: 865
• Publication Date: 2018-10-18
• Subjects: fractional polynomial -- growth modeling -- linear spline -- polynomial -- weight; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8007
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : We compared different growth models parameterizations regarding (i) adjustment of weight‐for‐height, as denoted by body mass index (BMI); (ii) adjustment for different covariates, ie, age or height; and (iii) the use of different smoothing methods, ie, polynomial, fractional polynomial, or linear splines. A total of 11 459 measurements of weight and height from 719 participants were used, obtained from the EPITeen cohort at 13, 17, and 21 years, and extracted from child health books. The individual growth curves were modeled using mixed‐effects polynomial, fractional polynomial, and linear splines, and each model parameterization included as covariate age or height. The goodness‐of‐fit of the model parametrizations was compared using the relative squared error (RSE) and the relative absolute error (RAE). The adjustment of weight‐for‐height as denoted by BMI was found to be biased, especially for extreme values of height and presented the worst fit indexes from all model parameterizations tested (RSE = 12.46%; RAE = 22.63%). Regardless of the smoothing method, the weight‐for‐height retrieved the best fit indexes in comparison to the adjustment for age. With regard to the smoothing methods and comparing weight‐for‐height model parameterizations, the fractional polynomial model performed better (RSE = 0.75%; RAE = 5.70%), followed by linear splines (RSE = 0.77%; RAE = 5.82%), and conventional polynomial (RSE = 0.91%; RAE = 6.82%). Therefore, growth modeling in pediatric age should be based on the modeling of weight‐for‐height because the use of BMI leaves residual confounding for height. Regarding the smoothing methods, although differences were relatively small, the fractional polynomials performed better in comparison to conventional polynomials and linear splines.