Methodological Developments in Growth Modeling

Abstract

Appropriate modeling of human growth is needed to detect and counter poverty, poor health and nutrition, and lack of early stimulation. This presentation highlights three recent societal trends that impact on the statistical methodology of growth models. First, the spread of smartphones removes important barriers on information access for low and middle income countries (LMIC). A second trend is personalization of interventions for individuals. Finally, the impact of peers on growth is likely to be an important factor but has hardly been studied. This paper proposes curve matching as a technology that will help to signal abnormal growth and development in due time, to evaluate the effectiveness of interventions for a given child, and thus contribute to reduce the number of children who fail to reach their potential in cognitive development.