Grant: Operating Grant: ECRs in Human Development, Child and Youth Health

Children living with more than two chronic conditions (known as multimorbidity) such as asthma, obesity, mental illness and cardiovascular disease take up a large proportion of healthcare expenditures. The multimorbidity poses a substantial burden for their families, the healthcare system and society. Current knowledge of multi-morbidity in childhood is very limited, and therefore we are unable to develop effective interventions and treatments. Understanding and addressing multimorbidity in children is very important for preventing and improving child health. The main goal is to advance our understanding of multimorbidity development in childhood and adolescence and to inform strategies for preventing multimorbidity. Our innovative study will leverage a large Canadian birth cohort with rich clinical, biological, social determinants and environmental data and will utilize novel and interpretable data-driven approaches. Specific aims include: (a)Aim 1: To characterize the patterns and progression of multimorbidity over time from 5 to 13 years of age. (b)Aim 2: To identify environmental determinants shaping multimorbidity through an exposome approach. (c)Aim 3: To create a prediction tool for predicting multimorbidity. This study is the first to implement state-of-the-art machine learning approaches to understand the progression of multimorbidity from childhood to adolescence. It is also the first research to incorporate the novel data-driven approach to study the modifiable risk factors for multimorbidity. This research will generate new knowledge on multimorbidity and its progression over time in children and identify modifiable risk factors for multimorbidity. Ultimately, it will improve the management and care coordination for children and adolescents leading to a reduction in highly complex care situations in adulthood.