Multi-level modelling of longitudinal child growth data from the Birth-to-Twenty Cohort: a comparison of growth models. (9th October 2013)

• Record Type: Journal Article
• Authors: Chirwa, Esnat D.; Griffiths, Paula L.; Maleta, Ken; Norris, Shane A.; Cameron, Noel
• Is Part Of: Annals of human biology. Volume 41:Number 2(2014:Mar./Apr.)
• Page Start: 166
• Page End: 177
• Publication Date: 2013-10-09
• Subjects: Child growth -- growth models -- mixed effects modelling -- structural and non-structural growth models; Human biology -- Periodicals; 612
• DOI: 10.3109/03014460.2013.839742
• Languages: English
• ISSNs: 0301-4460

Abstract

Abstract: Background : Different structural and non-structural models have been used to describe human growth patterns. However, few studies have compared the fitness of these models in an African transitioning population. Aim : To find model(s) that best describe the growth pattern from birth to early childhood using mixed effect modelling. Subjects and methods : The study compared the fitness of four structural (Berkey-Reed, Count, Jenss-Bayley and the adapted Jenss-Bayley) and two non-structural (2nd and 3rd order Polynomial) models. The models were fitted to physical growth data from an urban African setting from birth to 10 years using a multi-level modelling technique. The goodness-of-fit of the models was examined using median and maximum absolute residuals, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Results : There were variations in how the different models fitted to the data at different measurement occasions. The Jenss-Bayley and the polynomial models did not fit well to growth measurements in the early years, with very high or very low percentage of positive residuals. The Berkey-Reed model fitted consistently well over the study period. Conclusion : The Berkey-Reed model, previously used and fitted well to infancy growth data, has been shown to also fit well beyond infancy into childhood.