Multi-level modelling of longitudinal child growth data from the Birth-to-Twenty Cohort: a comparison of growth models

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.

• Child growth
• growth models
• mixed effects modelling
• structural and non-structural growth models

Acknowledgements

The Birth to Twenty Research Programme is funded by the Wellcome Trust (UK), Human Sciences Research Council (South Africa), Medical Research Council (South Africa) and University of Witwatersrand, Johannesburg. Esnat Chirwa is funded by a fellowship award provided by the Consortium for Advanced Research Training in Africa (CARTA). CARTA has been funded by the Welcome Trust (UK) (grant: 087547/Z/08/Z), the Department for International Development under the Development Partnerships in Higher Education (DelPHE), the Carnergie Corporation of New York (grant: B 8606), the Ford Foundation (grant: 1100-0399) and the Bill and Melinda Gates Foundation (grant: 51228). Paula Griffiths is funded by a British Academy mid-career fellowship award (reference MD120048).

Supplementary material available online

Appendix 1 and 2