A causal model of human growth and its estimation using temporally sparse data

Existing models of human growth provide limited insight into underlying mechanisms responsible for inter-individual and inter-population variation in children's growth trajectories. Building on general theories linking growth to metabolic rates, we develop a causal parametric model of height and weight growth incorporating a representation of human body allometry and a process-partitioned representation of ontogeny. This model permits separation of metabolic causes of growth variation, potentially influenced by nutrition and disease, from allometric factors, potentially under stronger genetic control. We estimate model parameters using a Bayesian multilevel statistical design applied to temporally dense height and weight measurements of U.S. children, and temporally sparse measurements of Indigenous Amazonian children. This facilitates a comparison of the contributions of metabolism and allometry to observed cross-cultural variation in the growth trajectories of the two populations, and permits simulation of the effects of healthcare interventions on growth. This theoretical model provides a new framework for exploring the causes of growth variation in our species, while potentially guiding the development of appropriate, and desired, healthcare interventions in societies confronting growth-related health challenges, such as malnutrition and stunting.