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Scalable Transdiagnostic Early Assessment of Mental Health (STREAM)

Project overview

Worldwide, over 250 million children are at risk of not obtaining their developmental potential due to exposure to adverse circumstances. India and Malawi house some of the most disadvantaged populations in the world, with over 10% of all children aged 2 to 9 years estimated to have neurodevelopmental disorders.

However, social and economic barriers to access qualified health personnel mean that most of these children do not receive any assessment of neurodevelopment or a clinical diagnosis when needed. Moreover, many parents are unaware of developmental milestones, so clinical opinion is sought only when symptoms become more pronounced and begin to impact daily life with a lost opportunity for early interventions.

This avoidable delay is an unfolding tragedy in light of evidence showing that frontline worker delivered interventions can lead to better behavioural and social outcomes and improve long term developmental trajectories. Scalable methods to assess child neurodevelopment and mental health would promote early referral to specialist facilities, ultimately connecting families with affordable, community-based interventions.

Directly measuring neurodevelopment allows us to identify the most vulnerable children as early as possible, allowing limited resources to be focused on those most likely to benefit from preventive approaches. Taken together, focusing on brain development in early childhood is critical to revolutionising global mental health of young children.

The Birkbeck project lead for this project is Professor Emily Jones and it has been funded by the Medical Research Council (MRC).

Research aims

We will realise this goal by developing a Scalable Transdiagnostic Assessment of Mental Health (STREAM), a mobile platform usable in the home or in a routine health facility by non-specialist workers.

STREAM will be delivered on a tablet PC and will collect different types of data from 4000 children in India and Malawi covering these areas:

  • First, parents will be asked simple questions about their child's everyday behaviour, based on established questionnaires that have been validated in low income settings.
  • Second, gamified tasks designed to measure motor, social, and cognitive abilities will be administered on the tablet. Additionally, novel low-cost eye-tracking technology on the same tablet PC will be used to monitor the child's eye movements in simple tasks, such as those assessing preference for social versus non-social images, and measuring how quickly attention shifts to new objects appearing on the screen.
  • Finally, a segment of parent and child interaction will be recorded using the inbuilt camera, and used to code for signs of atypical behaviour.

 Outputs and outcomes

  • This combination of multiple measures will provide independent channels of data collected on a single platform, significantly improving on current assessment methods that often rely on one technique and expensive, highly-skilled, but scarce, human resources.
  • STREAM will be designed such that it will require minimal training to be administered by non-specialist workers in low and middle income countries, thereby promoting task-sharing, a concept endorsed by the World Health Organization to reach wider populations. This task-sharing approach reduces the burden on the small number of highly-skilled mental-health and child development professionals in these low resource settings.
  • STREAM can also help develop community awareness and, in the longer term, address the barrier of low demand for services in these areas.
  • The development and application of the STREAM platform involves collaborations across the breadth of basic and applied sciences. Our network comprising clinicians, neuroscientists, public health specialists and data scientists spread across UK, India and Malawi is optimally suited to leading this challenge because of our combined expertise deploying novel technologies to measure early childhood neurodevelopment in low-resource settings.