Background: Conventional methods for modelling longitudinal growth data focus on the analysis of mean longitudinal trends or the identification of abnormal growth based on cross-sectional standardized z-scores. Latent Class Mixed Modelling (LCMM) considers the underlying heterogeneity in growth profiles and allows for the identification of groups of subjects that follow similar longitudinal trends.
Methods: LCMM was used to identify underlying latent profiles of growth for univariate responses of standardized height, standardized weight, standardized body mass index and standardized weight-for-length/height measurements and multivariate response of joint standardized height and standardized weight measurements from birth to five years for a sample of 1143 children from a South African birth cohort, the Drakenstein Child Health Study (DCHS). Allocations across latent growth classes were compared to better understand the differences and similarities across the classes identified given different composite measures of height and weight as input.
Results: Four classes of growth within standardized height (n1=516, n2=112, n3=187, n4=321) and standardized weight (n1=263, n2=150, n3=584, n4=142), three latent growth classes within Body Mass Index (BMI) (n1=481, n2=485, n3=149) and Weight for length/height (WFH) (n1=321, n2=710, n3=84) and five latent growth classes within the multivariate response of standardized height and standardized weight (n1=318, n2=205, n3=75, n4=296, n5=242) were identified, each with distinct trajectories over childhood. A strong association was found between various growth classes and abnormal growth features such as rapid weight gain, stunting, underweight and overweight.
Conclusions: With the identification of these classes, a better understanding of distinct childhood growth trajectories and their predictors may be gained, informing interventions to promote optimal childhood growth.
Keywords: Anthropometric; Early Childhood Growth; Latent Growth Trajectories.