New preterm infant growth curves influence of gender and race on birth size

2021 ◽  
Author(s):  
Sue A. Groveman
Author(s):  
Mariela Bernabe-García ◽  
Ignacia Cisneros-Silva ◽  
Eduardo Rangel-Baltazar ◽  
María Luisa Cuevas-Urióstegui ◽  
Samuel Flores-Huerta
Keyword(s):  

Microbiome ◽  
2017 ◽  
Vol 5 (1) ◽  
Author(s):  
Alex Grier ◽  
Xing Qiu ◽  
Sanjukta Bandyopadhyay ◽  
Jeanne Holden-Wiltse ◽  
Haeja A. Kessler ◽  
...  

2015 ◽  
Vol 101 (3) ◽  
pp. F230-F234 ◽  
Author(s):  
Mandy B Belfort ◽  
Robyn T Cohen ◽  
Lawrence M Rhein ◽  
Marie C McCormick
Keyword(s):  

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Clarissa Moreira ◽  
Michelle Scoullar ◽  
Elizabeth Peach ◽  
Ruth Fidelis ◽  
Pele Melepeia ◽  
...  

Abstract Background Children in Papua New Guinea (PNG) experience high rates of malnutrition and poor growth - nearly half of children under 5 are stunted and 16% wasted. Methods We investigated predictors of infant growth over the first year of life using longitudinal data from mothers and infants in PNG. Between 2015 and 2018, 699 pregnant women were enrolled. At delivery, one, 6- and 12-months post-partum blood samples and anthropometric measurements were taken from mothers and infants. Using structural equation modelling with full information maximum likelihood, multivariate latent growth curve (LGC) modelling for infant weight and length (i.e. simultaneous estimation) was undertaken, and maternal factors that influenced growth investigated. Results A quadratic function for growth (weight and height) was estimated. Boys were larger at birth (49cm, 3.2kg vs. 48cm, 3.0kg; Wald χ2(2) =15.3, p<0.001) and gained more weight and length monthly (Wald χ2(4) =68.4, p<0.001). Maternal height, MUAC and number of antenatal healthcare visits were associated with birth weight and length, but not growth. Maternal nutrition and infections, breastfeeding and complementary feeding were not associated with birth size or growth. Conclusions Maternal height and MUAC and antenatal healthcare were associated with birth size and no maternal factors were associated with growth. Prenatal interventions to improve postnatal infant growth may be challenging in this environment Key messages Compared to conventional LGC analysis, multivariate LGC modelling using SEM provides less biased estimates of infant growth and factors associated with growth, particularly in the presence of missing data and infant-specific weight and height heterogeneity.


PEDIATRICS ◽  
1971 ◽  
Vol 47 (5) ◽  
pp. 915-916
Author(s):  
John Wingerd ◽  
Edgar J. Schoen ◽  
Irene L. Solomon

On infant growth curves drawn by the usual technique (Fig. 1, left), it is often difficult to distinguish among the centile lines. The lines for the first few months of life are so steep, and so close together, that a chart must be made inconveniently large to be readable. Plotting the logarithm of the height or weight against age improves distinction only slightly. A more satisfactory graphic representation of growth data is suggested by the method of Rao.1 A modification of Rao's method, applicable to the longitudinal analysis of growth data, was recently described by one of us (J.W.).2


PEDIATRICS ◽  
2017 ◽  
Vol 139 (3) ◽  
pp. e20162045 ◽  
Author(s):  
Tanis R. Fenton ◽  
Hilton T. Chan ◽  
Aiswarya Madhu ◽  
Ian J. Griffin ◽  
Angela Hoyos ◽  
...  

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