Creating the conditions for robust early language development for all—Part 1: Evidence‐informed child language surveillance in the early years
Authors: Reilly Sheena , McKean Cristina 
Published: July 2023

AbstractBackgroundThe emergence of language in the early years is a major developmental accomplishment that underpins learning, enables social interaction and, later, is an indicator of well‐being. Learning language is an effortless process for most, but can be challenging for others. There is a need to act early. First, because there are several social, environmental and family factors known to influence how language develops during the critical early years. Second, there is a robust association between a child's socio‐economic circumstances and language outcomes. Put simply, children living in less advantaged circumstance have poorer language outcomes, which are apparent very early and persist across the lifespan. Third, children with demonstrated weaknesses in language learning in early childhood have poorer educational, employment, mental health and quality‐of‐life outcomes across the lifespan. Acting early to counter these impacts is important; however, there are several well‐documented challenges in accurately identifying in the early years children who are at later risk of developmental language disorder (DLD) and to deliver prevention and intervention programmes to scale. This is critical because many services do not currently reach those who need them most; as many as 50% of children in need may not be receiving support.AimTo determine whether an improved surveillance system, based on best evidence, could be developed for the early years.Methods & ProceduresWe summarised findings from longitudinal, population or community studies that: (1) adopted bioecological models, (2) repeatedly measured language (including the early years) and (3) adopted similar methodologies, to identify factors that influence language outcomes.Main contributionThe evidence confirmed that language development is not always stable but is characterized by distinct trajectories and each has distinguishing social, environmental features. Children in the change or fluctuating groups tend to live in less advantageous circumstances that may not always support and enable language development. Risk factors tend to cluster and accumulate across the early years and beyond, thereby markedly increasing the likelihood of poorer language outcomes later in life.Conclusions & ImplicationsIn this the first of two papers, designed to be read together, we integrate research on the social determinants of child language and propose they be embedded into surveillance models. This has the potential to reach more children and those living in disadvantaged circumstances. In the accompanying paper we combine this information with evidence‐informed early prevention/intervention approaches and propose the design and implementation of an early language public health framework.What this paper addsWhat is already known on the subject
There are several well‐documented challenges in accurately identifying in the early years children who are at later risk of DLD and reaching those most in need of language support.What this study adds to existing knowledge
A combination of child, family and environmental determinants, collectively and cumulatively, play out over time and dramatically increase the risk of later language problems, in particular those children living in disadvantaged circumstances. We propose an improved surveillance system that incorporates these determinants be developed and that this be part of a whole of system approach to child language in the early years.What are the potential or actual clinical implications of this work?
Clinicians intuitively act to prioritize children with multiple features or risks; however, they can only do so for those who present or are identified to be at risk. Given many children with language problems are not being reached by many early language services, it is reasonable to ask if this knowledge can be integrated to improve reach. Or is a different surveillance model required?