Year:
Applicant:
Trainee:
Institution:
Email:
hiroshi.mamiya@mcgill.ca
Keywords:
bayesian inference
built environment
correlated exposure
longitudinal study
mixture analysis
obesity
Project ID:
2510016
Approved Project Status:
Project Summary
Studies suggest modifiable urban environmental factors have an impact on health outcomes, including obesity, a significant public health concern in Canada. As such, there is growing interest in funding urban infrastructure initiatives. However, existing studies mainly focus on individual urban features, and thus, a better understanding of the interacting effects between coexisting urban features is needed for more effective policy decisions. This study will estimate the nonlinear associations between many urban features and obesity at high spatial resolution using the Canadian Longitudinal Study on Aging cohort linked with environmetrics. The study will use a Bayesian kernel machine regression approach, which has seen use in other areas of epidemiology, and allows for an estimation of interacting effects between urban features. This is a longitudinal study which will use urban data from the Canadian Urban Environmental Health Research Consortium (CANUE), combined with Body Mass Index.
