Addressing the single-risk single-disease framework through deep learning and agent-based modelling: applications in multimorbidity
Description: It is increasingly common for individuals to have two or more chronic conditions, which is often referred to as multimorbidity. As society ages, increased occurrences of multimorbidity is a challenge for health service providers. Multimorbidity is influenced by the conditions in which people grow, age, work and live. These conditions, also considered risk factors, are referred to as the social determinants of health. Existing research has focused on a single risk factor for a single disease. It is important to develop new methods to study multimorbidity, to account for the combined influence between the risk factors. By applying novel research methodology, I will develop a risk metric to characterize the combined influence of the social determinants of health. I will evaluate the effect of the social risk metric on the development of chronic conditions and multimorbidity. I will then use the risk metric to demonstrate the long-term impact of policy intervention which targets the social determinants of health on the development of multimorbidity. Understanding the combined influence of the social determinants of health will help identify individuals at risk of developing multimorbidity. Developing a simulation model to demonstrate the effect of policy interventions will assist in policy development and enable improved cross-government collaboration for chronic disease prevention.
Amount: CA$143,300
Funding type: fellowship
Funder scheme: Doctoral Research Award: Canada Graduate Scholarships
Start date: 2022-05-01
End date: 2026-04-30
Investigator: Ingrid Giesinger
Institution: University of Toronto
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Funded outputs count: 0
Funder award ID: 181384_1
OpenAlex ID: G6187006842