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Novel Approaches for Modelling Gestational Weight Gain Trajectories Using the Super Imposition Translation and Rotation Growth Model for Predicting Neonatal Outcomes

Lucas Malla of the Kemri-Wellcome Trust Research Program in Kenya will apply a novel statistical method to determine how the timing and rates of gestational weight gain during pregnancy affect maternal and child health in Africa to identify risk factors and those at highest risk. Excessive or insufficient gestational weight gain can predict adverse maternal and child health outcomes such as gestational diabetes, preterm birth, and infant mortality. They will use six existing longitudinal datasets with over 40,000 data-points on gestational weight gain and apply a univariate and multivariate SITAR (super-imposition by translation and rotation) growth curve analysis approach to model the gestational weight trajectories. They will then use regression techniques to identify specific parameters (size, timing, and velocity of the weight gain) that can predict specific health outcomes.

More information about Data Science Approaches to Improve Maternal and Child Health in Africa

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