Childhood obesity, poverty and population level estimates to be focus of UNICEF data project
Tackling childhood obesity, addressing poverty and improving child population estimates will be the focus of a data-led project to support the work of the United Nations Children’s Fund.
Researchers in Scotland are set to undertake a data-gathering exercise in order to deliver new ways of tackling these ‘initial’ three problems at local, national and global levels.
Originally announced in the First Minister’s 2018 Programme for Government, the ‘Collaborative’ is a partnership between UNICEF, The Scottish Government and Edinburgh University’s Data Driven Innovation Programme. The £3m project – funded over three years – draws on the strengths of all partners to bring insight and use data to solve problems facing children.
Collaborative projects are identified and selected in a joint review process involving all three partners. Alex Hutchison has been appointed as Delivery Director and is responsible for launching the Collaborative and taking the first three projects forward. The Collaborative was initiated by, and is currently based at, The Data Lab. It is currently recruiting one other team member, with a third planned by the end of the year, but it will leverage the resource and expertise of three partner organisations and The Data Lab.
Alex Hutchison, Delivery Director at the Data for Children Collaborative with UNICEF, said: “It is hugely exciting to be working with UNICEF, The Scottish Government and Edinburgh University’s Data Driven Innovation Programme to deliver the projects. Each project has the potential to make a real, positive impact on children’s lives worldwide. Pairing insights with data enables us to better understand what is driving childhood obesity and child poverty, and more accurate population estimates will help develop a widely scalable approach to tackling hugely important issues.”
Each project is in support of the Convention of the Rights of the Child and the UN’s Sustainable Development Goals and seeks to improve children’s outcomes and wellbeing around the world.
Lucinda Rivers, Head of UNICEF UK in Scotland, said: “UNICEF’s mission is to drive better results for more children, but we can only do this by using the power of evidence to deliver a better understanding of some of the challenges they face. We are grateful to the Scottish Government and the University of Edinburgh for supporting this ambition through their support of the Data for Children Collaborative.
“The world-leading data science and artificial intelligence capabilities now available to UNICEF through this collaboration will help us achieve better results for more children.”
From the Childhood Obesity project which looks to understand what children are eating and how this is influenced in different settings, to improving global population estimates through satellite imagery and big data, as well as using data to better understand drivers of childhood poverty, each of these first projects aims to deliver insights that can enhance the lives of children globally.
Digital Economy Minister Kate Forbes said: “This is an innovative programme that will help to deliver new ways of tackling childhood obesity and child poverty, on a local, national and international scale.
“We are in a very strong position in Scotland to deliver this type of world leading data analysis that helps to save time, money and lives.
“I am very excited about this collaboration, through partnering with organisations that share our vision for the role of data in improving the lives of children both in Scotland and globally.”
Lesley McAra, Director of the Edinburgh Futures Institute at the University of Edinburgh, added: “The Edinburgh Futures Institute is proud to be hosting this data collaborative. Our mission is to undertake challenge-based research, education and engagement that harnesses innovations in digital, data and artificial intelligence for social benefit. We are delighted to be working in partnership with UNICEF and the Scottish Government. The Data Collaborative goes to the heart of the values that we embrace as a University.”
Globally, 40 million children under the age of 5 years old, and 340m between the age of 5-18 years old are obese. Twenty nine per cent of Scottish children are obese. This project seeks to understand what children are eating and how it is influenced in different settings, initially in Scotland with scalability to have global impact. A five-phase activity plan will result in a scorecard tool that will be implemented globally after a pilot programme, with stratified interventions.
The population estimates project seeks to use population estimates to make invisible children visible, so that child services such as Healthcare, Education, Protection and Vaccinations can be planned appropriately for every child. Census population estimates can be improved by using satellite imagery and other big data sources, and a better understanding of populations will allow better allocation of resources. The population project will look at developing countries initially – where population data is of a poorer quality than that in Scotland – and it will look at using innovative, alternative data sources to supplement existing census and administrative data. The project is being done in Scotland because of the nation’s leading data science resources to benefit children worldwide.
Child poverty is multi-dimensional and there is a need to understand the causes, consequences and characteristics in order to explore suitable interventions. The first ever estimate of child poverty in developing countries, dating from 2003, estimated about half of the children to be poor. More recent regional and country studies indicate this value may still be valid. This project will combine household data with non-traditional information such as geo-spatial data to assess child poverty more periodically than is possible relying only on surveys carried out every few years. The project seeks to understand and test whether and (where) access to social services is a barrier or a driver of child poverty in order to improve policy interventions.