Summary
Health inequality is a major societal challenge, and evidence suggests that health inequality is established in childhood and may even transcend generations. Childhood is a sensitive period with rapid growth and development, and adversity during this period may have long-lasting health effects. More importantly, multiple forms of adversity intersect with each other, and disadvantaged children are often exposed to adversity across multiple biological, health, social, neighbourhood and environmental layers, but the childhood adversity literature has almost exclusively focused on social adversity. This is a major gap in our understanding, and empirical data which transcends multitude layers of adversity and follows individuals over entire life courses or across generations is lacking.
With LAYERS, I am in a unique position to meet this challenge by creating a unified data infrastructure for life course analyses of multiple layers of childhood adversity in 2M people over three generations combined with an interdisciplinary fusion of methods from data science, epidemiology, econometrics, and systems science within a newly developed complex systems framework. This innovative combination of data and methods will allow for a systematic generation of knowledge on the patterns of health inequality that emerge in early life and transcend generations, the mechanisms which generate these patterns, and the dynamics that make them change over time. To translate these insights into actionable public health, LAYERS will establish a real-world policy data lab which integrates evidence from nationwide policies and simulations.
Combined, these interconnected elements will redirect the next frontier in health inequality research towards the multiple layers of adversity which generate inequality over the life course and across generations. Ultimately, this will help us break vicious circles of adversity by identifying children and families who would benefit from targeted support.
With LAYERS, I am in a unique position to meet this challenge by creating a unified data infrastructure for life course analyses of multiple layers of childhood adversity in 2M people over three generations combined with an interdisciplinary fusion of methods from data science, epidemiology, econometrics, and systems science within a newly developed complex systems framework. This innovative combination of data and methods will allow for a systematic generation of knowledge on the patterns of health inequality that emerge in early life and transcend generations, the mechanisms which generate these patterns, and the dynamics that make them change over time. To translate these insights into actionable public health, LAYERS will establish a real-world policy data lab which integrates evidence from nationwide policies and simulations.
Combined, these interconnected elements will redirect the next frontier in health inequality research towards the multiple layers of adversity which generate inequality over the life course and across generations. Ultimately, this will help us break vicious circles of adversity by identifying children and families who would benefit from targeted support.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101124807 |
| Start date: | 01-05-2024 |
| End date: | 30-04-2029 |
| Total budget - Public funding: | 2 000 000,00 Euro - 2 000 000,00 Euro |
Cordis data
Original description
Health inequality is a major societal challenge, and evidence suggests that health inequality is established in childhood and may even transcend generations. Childhood is a sensitive period with rapid growth and development, and adversity during this period may have long-lasting health effects. More importantly, multiple forms of adversity intersect with each other, and disadvantaged children are often exposed to adversity across multiple biological, health, social, neighbourhood and environmental layers, but the childhood adversity literature has almost exclusively focused on social adversity. This is a major gap in our understanding, and empirical data which transcends multitude layers of adversity and follows individuals over entire life courses or across generations is lacking.With LAYERS, I am in a unique position to meet this challenge by creating a unified data infrastructure for life course analyses of multiple layers of childhood adversity in 2M people over three generations combined with an interdisciplinary fusion of methods from data science, epidemiology, econometrics, and systems science within a newly developed complex systems framework. This innovative combination of data and methods will allow for a systematic generation of knowledge on the patterns of health inequality that emerge in early life and transcend generations, the mechanisms which generate these patterns, and the dynamics that make them change over time. To translate these insights into actionable public health, LAYERS will establish a real-world policy data lab which integrates evidence from nationwide policies and simulations.
Combined, these interconnected elements will redirect the next frontier in health inequality research towards the multiple layers of adversity which generate inequality over the life course and across generations. Ultimately, this will help us break vicious circles of adversity by identifying children and families who would benefit from targeted support.
Status
SIGNEDCall topic
ERC-2023-COGUpdate Date
12-03-2024
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