Using data from multiple studies to develop a child growth correlation matrix

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.

Keywords: SDS; child health; correlation; growth.

Figure 1

A heat map showing the extent of the correlation matrices for individual studies. The colour corresponds to the number of matrices that cover that particular age range. Darker red means more studies are available, while paler yellow means fewer studies are available. HAZ, height‐for‐age Z‐scores [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 2

Week‐by‐week height‐for‐age Z‐scores correlation matrices for each of the 16 studies [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 3

Week‐by‐week weight‐for‐age Z‐scores correlation matrices for each of the 16 studies [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 4

Week‐by‐week height‐for‐age Z‐scores (HAZ) correlation matrix obtained via meta‐analysis of 16 studies. The left panel displays the unsmoothed estimates obtained from the univariate meta‐analysis, while the right panel displays the final smoothed matrix. Dark red corresponds to high correlation, while blue corresponds to lower correlation [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 5

Week‐by‐week weight‐for‐age Z‐scores (WAZ) correlation matrix obtained via meta‐analysis of 16 Gates studies. The left panel displays the unsmoothed estimates obtained from the univariate meta‐analysis, while the right panel displays the final smoothed matrix. Dark red corresponds to high correlation, while blue corresponds to lower correlation [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 6

The lower and upper bounds of the height‐for‐age Z‐scores (HAZ) correlation matrix, calculated by smoothing the incomplete lower and upper confidence surfaces [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 7

The lower and upper bounds of the weight‐for‐age Z‐scores (WAZ) correlation matrix, calculated by smoothing the incomplete lower and upper confidence surfaces [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 8

The uncertainty surface for our estimated height‐for‐age Z‐scores (HAZ) and weight‐for‐age Z‐scores (WAZ) matrices, displayed as the difference between lower bounds and upper bounds [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 9

Visualisation of Z‐scores and conditional standard deviation score (cSDS) for a single child from cntt. The blue points represent observed Z‐scores, and the red line displays the cSDS computed between each pair of consecutive points. HAZ, height‐for‐age Z‐scores [Colour figure can be viewed at wileyonlinelibrary.com]