Using the WHO 2006 child growth standard to assess the growth and nutritional status of rural south Indian infants

Auxology of small samples: A method to describe child growth when restrictions prevent surveys

Background

Child growth in populations is commonly characterised by cross-sectional surveys. These require data collection from large samples of individuals across age ranges spanning 1–20 years. Such surveys are expensive and impossible in restrictive situations, such as, e.g. the COVID pandemic or limited size of isolated communities. A method allowing description of child growth based on small samples is needed.

Methods

Small samples of data (N~50) for boys and girls 6–20 years old from different socio-economic situations in Africa and Europe were randomly extracted from surveys of thousands of children. Data included arm circumference, hip width, grip strength, height and weight. Polynomial regressions of these measurements on age were explored.

Findings

Polynomial curves based on small samples correlated well (r = 0.97 to 1.00) with results of surveys of thousands of children from same communities and correctly reflected sexual dimorphism and socio-economic differences.

Conclusions

Fitting of curvilinear regressions to small data samples allows expeditious assessment of child growth in a number of characteristics when situations change rapidly, resources are limited and access to children is restricted.

WHO Child Growth Standards Are Often Incorrectly Applied to Children Born Preterm in Epidemiologic Research

In epidemiologic research, there is no standard approach for accounting for gestational age (GA) at birth when interpreting postnatal anthropometric data in analyses of cohorts that include children born preterm (CBP). A scoping review was conducted to describe analytical approaches to account for GA at birth when applying the WHO Growth Standards (WHO-GS) to anthropometric data in epidemiologic studies. We searched PubMed, Scopus, MEDLINE, Embase, and Web of Science for studies that applied WHO-GS, included CBP in the study population, had access to data within 1 mo of age, and were published between 2006 and 2015 in English. Of the 80 included studies that used the WHO-GS, 80% (64 of 80) included all children regardless of GA, whereas 20% (16 of 80) restricted analyses that used WHO-GS to term-born children. Among the 64 studies that included all children, 53 (83%) used chronological age and 11 (17%) used corrected age for CBP. Of the 53 studies that used chronological age, 12 (23%) excluded data that were likely contributed by CBP (e.g., very low birth weight or extremely low outlying z scores) and 19 (36%) adjusted for or stratified by GA at birth in regression analyses. In summary, researchers commonly apply WHO-GS to CBP, usually based on chronological age. Methodologic challenges of analyzing data from CBP in the application of WHO-GS were rarely explicitly addressed. Further efforts are required to establish acceptable approaches to account for heterogeneity in GA at birth in the analysis of post-term anthropometric data in epidemiologic research.

Auxological perspectives on 'growth' in DOHaD

David Barker established growth as a seminal link between early development and later health attainment and disease risk. This was nothing less than a paradigm shift in health and medicine, turning the focus of disease causality away from contemporary environmental influences to earliest growth as a time when functional anatomy and physiology sets in place critical structures and function for a lifetime. Barker's prodigious work investigated time- and place-specific interactions between maternal condition and exogenous environmental influences, focusing on how growth unfolds across development to function as a mechanistic link to ensuing health. Subsequent applications do not always attend to the specificity and sensitivity issues included in his original work, and commonly overlook the long-standing methods and knowledge base of auxology. Methodological areas in need of refinement include enhanced precision in how growth is represented and assessed. For example, multiple variables have been used as a referent for 'growth,' which is problematic because different body dimensions grow by different biological clocks with unique functional physiologies. In addition, categorical clinical variables obscure the spectrum of variability in growth experienced at the individual level. Finally, size alone is a limited measure as it does not capture how individuals change across age, or actually grow. The ground-breaking notion that prenatal influences are important for future health gave rise to robust interest in studying the fetus. Identifying the many pathways by which size is realized permits targeted interventions addressing meaningful mechanistic links between growth and disease risk to promote health across the lifespan.

Analytical strategies in human growth research

Human growth research requires knowledge of longitudinal statistical methods that can be analytically challenging. Even the assessment of growth between two ages is not as simple as subtracting the first measurement from the second, for example. This article provides an overview of the key analytical strategies available to human biologists in increasing order of complexity, starting with a review on how to express cross-sectional measurements of size, before covering growth (conditional regression models, regression with conditional growth measures), growth curves (individual growth curves, mixed effects growth curves, latent growth curves), and patterns of growth (growth mixture modeling). The article is not a statistical treatise and has been written by a human biologist for human biologists; as such, it should be accessible to anyone with training in at least basic statistics. A summary table linking each analytical strategy to its applications is provided to help investigators match their hypotheses and measurement schedules to an analysis plan. In addition, worked examples using data on non-Hispanic white participants in the Fels Longitudinal Study are used to illustrate how the analytical strategies might be applied to gain novel insight into human growth and its determinants and consequences. All too often, serial measurements are treated as cross-sectional in analyses that do not harness the power of longitudinal data. The broad goal of this article is to encourage the rigorous application of longitudinal statistical methods to human growth research.

Comparative study of four growth models applied to weight and height growth data in a cohort of US children from birth to 9 years

BACKGROUND/AIMS

The objective of our study was to compare the fit of four growth models for weight and height in contemporary US children between birth and 9 years.

METHODS

In Project Viva, we collected weight and height growth data between birth and 9 years. We compared the Jenss model, the adapted Jenss model that adds a quadratic term, and the Reed 1st and 2nd order models. We used the log likelihood ratio test to compare nested models and the Akaike (AIC)/Bayesian information criterion (BIC) to compare nonnested models.

RESULTS

For weight and height, the adapted Jenss model had a better fit than the Jenss model (for weight: p < 0.0001), and the Reed 2nd order model had a better fit than the Reed 1st order model (for weight: p < 0.0001). Compared with the Reed 2nd order model, the adapted Jenss model had a better fit for both weight (adapted Jenss vs. Reed 2nd order, AIC: 66,974 vs. 82,791, BIC: 67,066 vs. 82,883) and height (adapted Jenss vs. Reed 2nd order, AIC: 87,108 vs. 87,612, BIC: 87,196 vs. 87,700).

CONCLUSIONS

In this pre-birth study of children aged 0-9 years, for both weight and height the adapted Jenss model presented the best fit of all four tested models.

Postnatal growth velocity and overweight in early adolescents: a comparison of rural and urban African boys and girls

Objectives

To compare growth velocity of two African child cohorts and examine the relationship between postnatal growth velocity in infancy/early childhood and the risk of overweight/stunting in early adolescence.

Methods

The study used data from two child cohorts from urban (Birth to Twenty Cohort, South Africa) and rural (Lungwena Child Survival Study, Malawi) African settings. Mixed effect modelling was used to derive growth and peak growth velocities. T-tests were used to compare growth parameters and velocities between the two cohorts. Linear and logistic regression models were used to determine the relationship between growth velocity and early adolescent (ages 9–11 years) body mass index and odds of being overweight.

Results

Children in the BH cohort were significantly taller and heavier than those in the Lungwena cohort, and exhibited faster weight and height growth velocity especially in the first year of life (P < 0.05). No significant association was shown between baseline weight (α_w) and overweight in early adolescence (OR = 1.25, CI = 0.67, 2.34). The weight growth velocity parameter β_w was highly associated with odds of being overweight. Association between overweight in adolescence and weight velocity was stronger in infancy than in early childhood (OR at 3 months = 4.80, CI = 2.49, 9.26; OR at 5 years = 2.39, CI = 1.65, 3.47).

Conclusion

High weight and height growth velocity in infancy, independent of size at birth, is highly associated with overweight in early adolescence. However, the long term effects of rapid growth in infancy may be dependent on a particular population's socio-economic status and level of urbanization. Am. J. Hum. Biol. 26:643–651, 2014. © 2014 The Authors American Journal of Human Biology Published by Wiley Periodicals, Inc.

Integrating nutrition and early child-development interventions among infants and preschoolers in rural India

This article describes the development, design, and implementation of an integrated randomized double-masked placebo-controlled trial (Project Grow Smart) that examines how home/preschool fortification with multiple micronutrient powder (MNP) combined with an early child-development intervention affects child development, growth, and micronutrient status among infants and preschoolers in rural India. The 1-year trial has an infant phase (enrollment age: 6-12 months) and a preschool phase (enrollment age: 36-48 months). Infants are individually randomized into one of four groups: placebo, placebo plus early learning, MNP alone, and MNP plus early learning (integrated intervention), conducted through home visits. The preschool phase is a cluster-randomized trial conducted in Anganwadi centers (AWCs), government-run preschools sponsored by the Integrated Child Development System of India. AWCs are randomized into MNP or placebo, with the MNP or placebo mixed into the children's food. The evaluation examines whether the effects of the MNP intervention vary by the quality of the early learning opportunities and communication within the AWCs. Study outcomes include child development, growth, and micronutrient status. Lessons learned during the development, design, and implementation of the integrated trial can be used to guide large-scale policy and programs designed to promote the developmental, educational, and economic potential of children in developing countries.

Multi-level modelling of longitudinal child growth data from the Birth-to-Twenty Cohort: a comparison of growth models

Background: Different structural and non-structural models have been used to describe human growth patterns. However, few studies have compared the fitness of these models in an African transitioning population.

Aim: To find model(s) that best describe the growth pattern from birth to early childhood using mixed effect modelling.

Subjects and methods: The study compared the fitness of four structural (Berkey-Reed, Count, Jenss-Bayley and the adapted Jenss-Bayley) and two non-structural (2nd and 3rd order Polynomial) models. The models were fitted to physical growth data from an urban African setting from birth to 10 years using a multi-level modelling technique. The goodness-of-fit of the models was examined using median and maximum absolute residuals, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC).

Results: There were variations in how the different models fitted to the data at different measurement occasions. The Jenss-Bayley and the polynomial models did not fit well to growth measurements in the early years, with very high or very low percentage of positive residuals. The Berkey-Reed model fitted consistently well over the study period.

Conclusion: The Berkey-Reed model, previously used and fitted well to infancy growth data, has been shown to also fit well beyond infancy into childhood.

Modeling physical growth using mixed effects models

This article demonstrates the use of mixed effects models for characterizing individual and sample average growth curves based on serial anthropometric data. These models are advancement over conventional general linear regression because they effectively handle the hierarchical nature of serial growth data. Using body weight data on 70 infants in the Born in Bradford study, we demonstrate how a mixed effects model provides a better fit than a conventional regression model. Further, we demonstrate how mixed effects models can be used to explore the influence of environmental factors on the sample average growth curve. Analyzing data from 183 infant boys (aged 3-15 months) from rural South India, we show how maternal education shapes infant growth patterns as early as within the first 6 months of life. The presented analyses highlight the utility of mixed effects models for analyzing serial growth data because they allow researchers to simultaneously predict individual curves, estimate sample average curves, and investigate the effects of environmental exposure variables.