Practical Application of Linear Growth Measurements in Clinical Research in Low- and Middle-Income Countries

Abstract

Background/Aims: Childhood stunting is a prevalent problem in low- and middle-income countries and is associated with long-term adverse neurodevelopment and health outcomes. In this review, we define indicators of growth, discuss key challenges in their analysis and application, and offer suggestions for indicator selection in clinical research contexts. Methods: Critical review of the literature. Results: Linear growth is commonly expressed as length-for-age or height-for-age z-score (HAZ) in comparison to normative growth standards. Conditional HAZ corrects for regression to the mean where growth changes relate to previous status. In longitudinal studies, growth can be expressed as ΔHAZ at 2 time points. Multilevel modeling is preferable when more measurements per individual child are available over time. Height velocity z-score reference standards are available for children under the age of 2 years. Adjusting for covariates or confounders (e.g., birth weight, gestational age, sex, parental height, maternal education, socioeconomic status) is recommended in growth analyses. Conclusion: The most suitable indicator(s) for linear growth can be selected based on the number of available measurements per child and the child’s age. By following a step-by-step algorithm, growth analyses can be precisely and accurately performed to allow for improved comparability within and between studies.

Factors associated with recovery from stunting among under-five children in two Nairobi informal settlements

“…In this case, the 'failure event is recovery from stunting which is defined by an increase in the height-forage z-score (HAZ) between two time points t 1 and t 2 , such that HAZ(t 2 ) � -2. Apart from its simplicity, the main advantage of this definition is that it reflects a dynamic assessment of growth and can be used consistently over the whole growth trajectory as noted by Wit et al [17]. In addition, the method uses all time points available and accounts for multiple failures during the observation period.…”

“…WHO child height standards). In most recent studies, catch-up in linear growth was defined in relation to changes in height-for-age z-scores which account for the increasing variability in height as children age [15,17,18]. Even in the latter definition, the debate continues that 'recovery from stunting' does not necessarily reflect 'catch-up in linear growth' as some children may have positive changes in height-for-age z-scores, but still remain below the reference mean.…”

“…Early childhood growth trajectories in LMICs have been documented to have non-linear patterns with a decline in growth from after birth up to about 24 months, followed by regular or irregular catch-up to about 5 years [19][20][21]. When multiple measurements are available (at least 3) during a time period of interest, modeling of linear growth that uses more than two time points allows for a more detailed account of the growth trajectories [17,22] Finally, a number of studies of catch-up in linear growth compared the mean growth measurements at different time points between groups of children. In these studies, the mean height-for-age z-score, height-for-age difference or height for a group of children is compared to that of a reference group [9,[13][14][15].…”

“…This approach may show significant differences between groups measured at two different time points as children who were short at early ages are more likely to remain shorter when compared to other children. However, central tendency measurements in child growth are known to identify individual level changes less well [17,23]. Therefore, these studies, while extending our knowledge in this research area, are less able to describe the individual level catch-up growth.…”

“…Secondly, to identify the factors associated with recovery from stunting, we used HAZ slope modelling as suggested by Wit et al [17] when multiple data points (at least 3) are available during the observation time. This approach may have the advantage of accounting for the non-linear individual growth trajectories and smoothing out possible growth irregularities.…”