839A novel approach to investigating poor growth in a longitudinal study of infants in PNG. (2nd September 2021)

• Record Type: Journal Article
• Authors: Moreira, Clarissa; Scoullar, Michelle; Peach, Elizabeth; Fidelis, Ruth; Melepeia, Pele; Pomat, William; Siba, Peter; Crabb, Brendan; Robinson, Leanne; Agius, Paul; Beeson, James
• Is Part Of: International journal of epidemiology. Volume 50(2021)Supplement 1
• Publication Date: 2021-09-02
• Subjects: Epidemiology -- Periodicals; 614.4
• DOI: 10.1093/ije/dyab168.455
• Languages: English
• ISSNs: 0300-5771

Abstract

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 χ 2 (2) =15.3, p <0.001) and gained more weight and length monthly (Wald χ 2 (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.