Background: Modeling the growth curve of height has a significant role in understanding the growth trajectories over time and generated mathematical functions that depict the expected height of children at a particular age. However, modeling the mathematical growth functions for physical height is not well studied in low- and middle-income countries. Modeling and identifying nonlinear growth curves that adequately describe the growth trajectories in low- and middle-income countries were the aims of this study.
Methods: The data were obtained from the Young Lives study. Longitudinal measures of height from infancy to middle-adolescence were collected from low- and middle-income countries. A number of nonlinear growth trajectories were studied through the family of three-parameter nonlinear mixed-effects models.
Results: This study examined the performances of different growth curves for the height growth trajectories. The Logistic curve was chosen among the three-parameter nonlinear growth curves for modeling the growth trajectories from infancy to middle-adolescence. Gender and country have significant effects on the three parameters of growth curves. Males had higher asymptotic height and a lower rate of growth than females. Females reached asymptotic height earlier and shorter at asymptotic height than males. Children with low asymptotic height grow faster than those with higher asymptotic height. Compared to Ethiopian children, Indian and Peruvian children had lower asymptotic height, but Vietnamese children had higher asymptotic height. Ethiopian children approached adult height earlier than Indian children, but later than Peruvian children. However, there was no significant difference in the rate of growth between Ethiopian and Vietnamese children.
Conclusions: This study concludes that the Logistic growth curve was found to be the best growth curve to describe the height growth trajectories. Children in Ethiopia, India, Peru and Vietnam showed different growth parameters. Further enhancements may be attained with the incorporation of other plausible covariates.