Abstract
In many countries, the monitoring of child growth does not occur in a regular manner, and instead, we may have to rely on sporadic observations that are subject to substantial measurement error. In these countries, it can be difficult to identify patterns of poor growth, and faltering children may miss out on essential health interventions. The contribution of this paper is to provide a framework for pooling together multiple datasets, thus allowing us to overcome the issue of sparse data and provide improved estimates of growth. We use data from multiple longitudinal growth studies to construct a common correlation matrix that can be used in estimation and prediction of child growth. We propose a novel 2‐stage approach: In stage 1, we construct a raw matrix via a set of univariate meta‐analyses, and in stage 2, we smooth this raw matrix to obtain a more realistic correlation matrix. The methodology is illustrated using data from 16 child growth studies from the Bill and Melinda Gates Foundation's Healthy Birth Growth and Development knowledge integration project and identifies strong correlation for both height and weight between the ages of 4 and 12 years. We use a case study to provide an example of how this matrix can be used to help compute growth measures.
Highlights
The study of physical growth in children is a challenging and complex topic that must consider a variety of genetic, physiological, and socio-economic factors
We propose a novel 2-stage approach, which uses data from multiple studies to construct a common correlation matrix that can be used in estimation and prediction of child growth
We have outlined a method for obtaining a single correlation matrix by combining a set of matrices from different studies
Summary
The study of physical growth in children is a challenging and complex topic that must consider a variety of genetic, physiological, and socio-economic factors. There is a great deal of interest in being able to understand the factors that drive growth faltering in young children, in order to develop better preventative measures.[3,4,5]. To adequately monitor the health of young children, it is imperative that we are able to accurately model their growth across their formative years. Children are measured regularly to track their progress, but there are many places where such monitoring does not occur in a consistent or regular manner.[6] In these countries, a child's height and weight may only be measured sporadically, and these measurements may be subject to a great deal of measurement error.
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