Abstract
Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 − -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset.
Highlights
In developing countries, small size at birth is common, reflecting combined effects of inadequate intrauterine growth and preterm birth
We illustrate the use of partial least squares (PLS) regression analysis to quantify associations between materno-infant and household socioeconomic characteristics and multiple measures of newborn size, drawing on a large population cohort of infants whose mothers participated in a maternal vitamin A and beta-carotene supplementation in rural, northern Bangladesh [6]
Our study revealed that all the maternal variables examined, except vitamin A and β- carotene supplement receipt during pregnancy, were significantly associated with birth size
Summary
Small size at birth is common, reflecting combined effects of inadequate intrauterine growth and preterm birth Both birth conditions are associated with increased risks of poor postnatal growth [1] and infant mortality [2], and diverse maternal nutritional, health and socioeconomic factors [3,4,5]. The variables are interrelated to some extent and multicollinearity often exists Routine statistical approaches such as multiple linear regression or principal component regression (PCR) are usually challenged with multiple testing. We propose through this study that PLS regression has the potential to be a useful method to predict multiple facets of a health outcome—in this instance, infant size at birth from maternal, infant and other household factors in a rural South Asian population [6]. We compare the performance of PLS regression with PCR and the individual predictive ability of each these two methods
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