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

Chirwa ED, Griffiths PL, Maleta K, Norris SA, Cameron N - Ann. Hum. Biol. (2013)

Bottom Line: To find model(s) that best describe the growth pattern from birth to early childhood using mixed effect modelling.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.

View Article: PubMed Central - PubMed

Affiliation: Wits/MRC Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand , Johannesburg , South Africa .

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.

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Graphs of weight profiles and growth models fitted to weight from birth to 10 years.
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f1: Graphs of weight profiles and growth models fitted to weight from birth to 10 years.

Mentions: Weight and height profiles for a random sample of boys and girls have been shown in the first graphs of Figures 1 and 2. The weight profiles show some rapid weight gain in the first year of life. A similar trend is shown by the height profiles.Figure 1.


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

Chirwa ED, Griffiths PL, Maleta K, Norris SA, Cameron N - Ann. Hum. Biol. (2013)

Graphs of weight profiles and growth models fitted to weight from birth to 10 years.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219852&req=5

f1: Graphs of weight profiles and growth models fitted to weight from birth to 10 years.
Mentions: Weight and height profiles for a random sample of boys and girls have been shown in the first graphs of Figures 1 and 2. The weight profiles show some rapid weight gain in the first year of life. A similar trend is shown by the height profiles.Figure 1.

Bottom Line: To find model(s) that best describe the growth pattern from birth to early childhood using mixed effect modelling.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.

View Article: PubMed Central - PubMed

Affiliation: Wits/MRC Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand , Johannesburg , South Africa .

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.

Show MeSH