There is a substantial body of evidence highlighting the importance of the social determinants of health in shaping the health of urban populations in Canada. The low socio-economic status of marginalized, disadvantaged, and precarious populations in urban settings has been linked to adverse health outcomes including chronic and infectious disease, negative health behaviours, barriers to accessing health care services, and overall mortality. Given the dynamic complexities and inter-relationships surrounding the underlying drivers of population health outcomes and inequities, it is difficult to assess program and policy intervention tradeoffs, particularly when such interventions are studied with static models. To address this challenge, we have adopted a systems science approach and developed a simulation model for the City of Toronto, Canada, utilizing system dynamics modelling methodology. The model simulates changes in health, social determinants, and disparities from 2006 and projects forward to 2046 under different assumptions. Most of the variables in the model are stratified by ethnicity, immigration status, and gender, and capture the characteristics of adults aged 25–64. Intervention areas include health care access, behaviour, income, housing, and social cohesion. The model simulates alternative scenarios to help demonstrate the relative impact of different interventions on poor health outcomes such as chronic disease rates, disability rates, and mortality rate. It gives insight into how much, and how quickly, interventions can reduce mortality and morbidity. We believe this will serve as a useful learning tool to allow diverse stakeholders and policy makers to ask “what if” questions and map effective policy directions for complex population health problems, and will enable communities to think about their health futures.
