Summary
Exposure to war-related traumatic events as well as displacement have been found to increase refugee children’s vulnerability for the development of mental health problems. However, there is substantial variability, with some presenting with mental health disorders, while others show remarkable resilience. One reason for this observed variability is the complex interplay between individual traits, such as genetic factors, and characteristics of the environment in determining mental health outcomes. However, the extent and nature to which individual and environmental factors causally interact to determine mental health outcomes in children remains unclear as most studies rely on correlational designs. Hence, we propose the development of an innovative statistical approach, combining advanced machine learning techniques and concepts of statistical causality, to investigate the complex and interactive causal relations between the various determinants of mental health across multiple domains. In order to achieve our objective, this new statistical approach will be applied to recently collected data from a unique longitudinal study on Syrian refugee children based in Lebanon (BIOPATH). The available data includes comprehensive demographic, genetic, epigenetic, neuroendocrine, social, psychological, and environmental variables collected from Syrian refugee children and their caregivers at two time-points (N=1600 at the baseline and N=1009 at a one year follow-up). The Experienced Researcher, Dr Claudinei Biazoli, a trained psychiatrist expertise in developing innovative models and analysing complex data, will acquire knowledge and skills in Developmental Psychology, Behavioral and Psychiatric genetics under the supervision of Prof Michael Pluess, a leader researcher in the field of Differential Susceptibility and Principal Investigator of the BIOPATH study, with additional support from Dr Robert Keers, with expertise in the analysis of whole-genome data.
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| Web resources: | https://cordis.europa.eu/project/id/896021 |
| Start date: | 14-09-2020 |
| End date: | 13-09-2022 |
| Total budget - Public funding: | 224 933,76 Euro - 224 933,00 Euro |
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Original description
Exposure to war-related traumatic events as well as displacement have been found to increase refugee children’s vulnerability for the development of mental health problems. However, there is substantial variability, with some presenting with mental health disorders, while others show remarkable resilience. One reason for this observed variability is the complex interplay between individual traits, such as genetic factors, and characteristics of the environment in determining mental health outcomes. However, the extent and nature to which individual and environmental factors causally interact to determine mental health outcomes in children remains unclear as most studies rely on correlational designs. Hence, we propose the development of an innovative statistical approach, combining advanced machine learning techniques and concepts of statistical causality, to investigate the complex and interactive causal relations between the various determinants of mental health across multiple domains. In order to achieve our objective, this new statistical approach will be applied to recently collected data from a unique longitudinal study on Syrian refugee children based in Lebanon (BIOPATH). The available data includes comprehensive demographic, genetic, epigenetic, neuroendocrine, social, psychological, and environmental variables collected from Syrian refugee children and their caregivers at two time-points (N=1600 at the baseline and N=1009 at a one year follow-up). The Experienced Researcher, Dr Claudinei Biazoli, a trained psychiatrist expertise in developing innovative models and analysing complex data, will acquire knowledge and skills in Developmental Psychology, Behavioral and Psychiatric genetics under the supervision of Prof Michael Pluess, a leader researcher in the field of Differential Susceptibility and Principal Investigator of the BIOPATH study, with additional support from Dr Robert Keers, with expertise in the analysis of whole-genome data.Status
CLOSEDCall topic
MSCA-IF-2019Update Date
28-04-2024
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