This team focuses on quantitative research on a broad range of topics related to population health registries focused on the theme of how individual-level characteristics or environmental risk factors affect various health outcomes such as quality of life, chronic conditions, hospitalization, and mortality. To implement pragmatic linkage projects, we are using high-quality European and international population health surveys, longitudinal cohorts, and administrative health data as well as advanced statistical techniques for cross-sectional and longitudinal data. Each project can generate multiple research questions and manuscripts.
Survey of Health, Ageing and Retirement in Europe (SHARE)
Chronic diseases are the leading cause of death globally. The aim of this project is to gain a better understanding of the effects of a broad range of risk factors (i.e., childhood characteristics, social determinants of health, and health-related modifiable behaviours) on the onset and development of chronic conditions. We are also interested in the effects of chronic conditions on quality of life and healthcare utilization across the life course. To assess the complex relationships between the onset and development of chronic conditions, their determinants, and sequela, we take advantage of the Survey of Health, Ageing and Retirement in Europe and apply advanced statistical techniques such as multilevel structural equation modelling techniques and latent growth curve analysis.
Canadian Health Linkage
The aim of this project is to assess the role of various individual and environmental risk factors to develop more targeted strategies and reduce the overall volume of emergency department visits and hospitalizations, in particular non-urgent or preventable hospital encounters. This project uses high quality Canadian administrative health databases on emergency department visits and hospital stays that are linked to national population health surveys, census cohorts, and various standardized indicators of social, built, and natural environmental risk factors. Depending on the specific research question, pertinent statistical techniques may involve multilevel or geographically-weighted regression techniques, structural equation modeling, or two-stage time series analysis.