Developmental origins of child neurodevelopment: Novel approaches to clinical risk prediction using an international metadataset.
Lead Research Organisation:
University of Oxford
Department Name: Paediatrics
Abstract
Approximately 1 in 10 children under 5 years of age, globally, are at risk of delayed development. The lifecourse impact of this is substantial with cognitive, motor and language delays, and behavioural problems, during early childhood, associated with poorer educational attainment, lower personal finances, poorer physical health and increased risk of substance abuse and mental illness during adulthood.
There is strong evidence that the period between conception and the child's 3rd birthday is 'a golden window of opportunity' for brain development. Brain stimulation activities during the early postnatal years, when compared with others administered at older ages, have been shown to have the greatest and most enduring beneficial impact on educational, health and economic outcomes through childhood and adult life.
While it is technically challenging to assess cognitive, language and motor skills at birth and during very early life, a number of foetal, birth-related and childhood factors (such as foetal growth, prematurity, child growth, health, and social environment) have been associated with these outcomes during early childhood. However, these associations are not universally observed, and different patterns have been reported from populations in different countries. Moreover, studies have used different techniques to assess child development, limiting the comparability between them.
Understanding, in international populations, the risk factors associated with development delay are key to identifying, as early as possible, children at risk of delay. Where neurodevelopmental assessments are not feasible (such as in the newborn or in resource-limited settings) or limited in sensitivity (such as during infancy), the use of risk profiles presents an alternative, low-cost strategy for the identification children at risk and for the appropriate targeting of interventions. However, as yet, there is no such risk-prediction tool for developmental delay that can be applied during very early life and across international populations.
To address these issues, my project will bring together data already collected from 9 studies to examine, across high-, middle- and low-income countries, the associations between growth, health and environmental influences, in the period between conception and the child's second birthday, on cognitive, motor, language and behavioural outcomes at 2 years. Taken together, this metadataset will represent 8,015 children from 13 countries (Brazil, DRC, Finland, Grenada, Guatemala, India, Italy, Kenya, Pakistan, Thailand, Slovakia, South Africa and the UK). Importantly, child development at 2 years is measured, in all contributing studies, on one standardised development test (the INTER-NDA).
I will undertake statistical analyses to quantify the association between multiple early life influences and developmental delay at 2 years and study how these factors interact with each other to modify risk. I will apply statistical and machine-learning approaches to group risk factors into clusters and use these to construct models to predict, at birth and at 1 year, the risk for developmental delay at 2 years. I will evaluate the performance of this risk-model in an independent dataset (not included in the metadataset, and in which developmental milestones were not assessed at 2 years on the INTER-NDA) of 500 children from the Mysore, India.
This project will significantly benefit researchers and clinicians working in child health, across high-, middle- and low-income countries, by (1) extending the understanding of the risk factors associated with early developmental delay and (2) construction of a tool with which to predict, during early life, the risk of developmental delay. Identifying children at risk of developmental delay early, in order to target treatment strategies within sensitive window of brain development, is key to preventing the loss of developmental potential in children.
There is strong evidence that the period between conception and the child's 3rd birthday is 'a golden window of opportunity' for brain development. Brain stimulation activities during the early postnatal years, when compared with others administered at older ages, have been shown to have the greatest and most enduring beneficial impact on educational, health and economic outcomes through childhood and adult life.
While it is technically challenging to assess cognitive, language and motor skills at birth and during very early life, a number of foetal, birth-related and childhood factors (such as foetal growth, prematurity, child growth, health, and social environment) have been associated with these outcomes during early childhood. However, these associations are not universally observed, and different patterns have been reported from populations in different countries. Moreover, studies have used different techniques to assess child development, limiting the comparability between them.
Understanding, in international populations, the risk factors associated with development delay are key to identifying, as early as possible, children at risk of delay. Where neurodevelopmental assessments are not feasible (such as in the newborn or in resource-limited settings) or limited in sensitivity (such as during infancy), the use of risk profiles presents an alternative, low-cost strategy for the identification children at risk and for the appropriate targeting of interventions. However, as yet, there is no such risk-prediction tool for developmental delay that can be applied during very early life and across international populations.
To address these issues, my project will bring together data already collected from 9 studies to examine, across high-, middle- and low-income countries, the associations between growth, health and environmental influences, in the period between conception and the child's second birthday, on cognitive, motor, language and behavioural outcomes at 2 years. Taken together, this metadataset will represent 8,015 children from 13 countries (Brazil, DRC, Finland, Grenada, Guatemala, India, Italy, Kenya, Pakistan, Thailand, Slovakia, South Africa and the UK). Importantly, child development at 2 years is measured, in all contributing studies, on one standardised development test (the INTER-NDA).
I will undertake statistical analyses to quantify the association between multiple early life influences and developmental delay at 2 years and study how these factors interact with each other to modify risk. I will apply statistical and machine-learning approaches to group risk factors into clusters and use these to construct models to predict, at birth and at 1 year, the risk for developmental delay at 2 years. I will evaluate the performance of this risk-model in an independent dataset (not included in the metadataset, and in which developmental milestones were not assessed at 2 years on the INTER-NDA) of 500 children from the Mysore, India.
This project will significantly benefit researchers and clinicians working in child health, across high-, middle- and low-income countries, by (1) extending the understanding of the risk factors associated with early developmental delay and (2) construction of a tool with which to predict, during early life, the risk of developmental delay. Identifying children at risk of developmental delay early, in order to target treatment strategies within sensitive window of brain development, is key to preventing the loss of developmental potential in children.
Technical Summary
Approximately 250 million children under 5 years, worldwide, are at risk of delayed development. Early neurodevelopmental delays (ENDs) are associated with poorer educational, economic, and health outcomes across the lifecourse. As developmental interventions during this period have enduring positive impacts, identifying children at risk early is key to targeting interventions within this window of sensitivity.
While it technically challenging to assess neurocognitive skills during early infancy, the use of risk profiles presents an alternative, low-cost strategy for the identification of children at risk. However, as yet, there is no such tool to identify, at scale and across international populations, children at high risk of ENDs. Multiple prenatal, perinatal and postnatal risk factors (including prematurity, growth, and health) have been associated with ENDs. However, patterns of associations vary between populations limiting the generalisability of findings.
Understanding risk factors associated with developmental delay, is key to identifying, as early as possible, children at high risk of delay. To study this, I will bring together already collected data from 9 studies, representing 8,015 children from 13 high-, middle- and low-income countries. For all children, neurodevelopment at 2 years was measured on the INTER-NDA test. I will examine associations between early risk factors and ENDs using hierarchical association analyses; identify risk clusters and construct models to predict risk of developmental delay during early childhood. I will validate these models in an independent study from Mysore, India (n=500). My hypothesis is that these risk models will satisfactorily predict ENDs, and if proven, I will translate these findings into a tool for early identification of developmental delay.
This project leverages on established collaborations with data contributors and the epidemiological expertise of Southampton's MRC Lifecourse Epidemiology Unit.
While it technically challenging to assess neurocognitive skills during early infancy, the use of risk profiles presents an alternative, low-cost strategy for the identification of children at risk. However, as yet, there is no such tool to identify, at scale and across international populations, children at high risk of ENDs. Multiple prenatal, perinatal and postnatal risk factors (including prematurity, growth, and health) have been associated with ENDs. However, patterns of associations vary between populations limiting the generalisability of findings.
Understanding risk factors associated with developmental delay, is key to identifying, as early as possible, children at high risk of delay. To study this, I will bring together already collected data from 9 studies, representing 8,015 children from 13 high-, middle- and low-income countries. For all children, neurodevelopment at 2 years was measured on the INTER-NDA test. I will examine associations between early risk factors and ENDs using hierarchical association analyses; identify risk clusters and construct models to predict risk of developmental delay during early childhood. I will validate these models in an independent study from Mysore, India (n=500). My hypothesis is that these risk models will satisfactorily predict ENDs, and if proven, I will translate these findings into a tool for early identification of developmental delay.
This project leverages on established collaborations with data contributors and the epidemiological expertise of Southampton's MRC Lifecourse Epidemiology Unit.
