Baby sign but not spontaneous gesture predicts later vocabulary in children with Down Syndrome.

Early spontaneous gesture, specifically deictic gesture, predicts subsequent vocabulary development in typically developing (TD) children. Here, we ask whether deictic gesture plays a similar role in predicting later vocabulary size in children with Down Syndrome (DS), who have been shown to have difficulties in speech production, but strengths in spontaneous gesture and baby sign use. We compared the gestures and baby signs produced by twenty-three children with DS (Mage = 2;6) and twenty-three TD children (Mage = 1;6), in relation to their expressive spoken vocabulary size one year later. Children with DS showed significant deficits in gesture production, particularly for deictic gestures, but strengths in baby sign production, compared to their typically developing peers. More importantly, it was the baby signs produced by children with DS, but not deictic gestures, that predicted their spoken vocabulary size one year later. Our results further highlight the important role baby signs can play in language development in children with developmental disorders.

Developmental impacts of the COVID-19 pandemic on young children: a conceptual model for research with integrated administrative data systems.

The COVID-19 pandemic made its mark on the entire world, upending economies, shifting work and education, and exposing deeply rooted inequities. A particularly vulnerable, yet less studied population includes our youngest children, ages zero to five, whose proximal and distal contexts have been exponentially affected with unknown impacts on health, education, and social-emotional well-being. Integrated administrative data systems could be important tools for understanding these impacts. This article has three aims to guide research on the impacts of COVID-19 for this critical population using integrated data systems (IDS). First, it presents a conceptual data model informed by developmental-ecological theory and epidemiological frameworks to study young children. This data model presents five developmental resilience pathways (i.e. early learning, safe and nurturing families, health, housing, and financial/employment) that include direct and indirect influencers related to COVID-19 impacts and the contexts and community supports that can affect outcomes. Second, the article outlines administrative datasets with relevant indicators that are commonly collected, could be integrated at the individual level, and include relevant linkages between children and families to facilitate research using the conceptual data model. Third, this paper provides specific considerations for research using the conceptual data model that acknowledge the highly-localised political response to COVID-19 in the US. It concludes with a call to action for the population data science community to use and expand IDS capacities to better understand the intermediate and long-term impacts of this pandemic on young children.