Grant: Operating Grant: HCRI: Data Analysis Using Existing Databases and Cohorts

The COVID-19 pandemic and its economic impact has affected the health of Canadians in many ways, particularly for those with lower income. In the aftermath of the COVID-19 pandemic, low-income communities have been greatly affected by financial strain caused by a rise in unemployment, work hours, and inflation, and by cuts to public spending. At the same time, people with lower-income experience more challenges accessing programs and services to support physical activity and social wellbeing. Their communities also receive less investment in community infrastructure (e.g. parks and walking paths) to support healthy, active living. All of these factors may result in more people developing diabetes. However, there are many community factors that could protect health during these times-such as having healthier environments (more walkable, greater amount of greenspace, fewer fast food swamps), lower levels of unemployment, or lower rises in the cost of living. We expect that communities with more of these 'community resilience' factors will have fewer people who develop type 2 diabetes. Using existing health and environmental databases, our team will use traditional epidemiological methods and machine learning methodology to: (1) explore whether rates of developing diabetes and related inequities increased in Canadian cities following the pandemic; and (2) to examine community-level (city- and neighborhood-level) characteristics that provide greater resilience against or susceptibility towards a post-pandemic increase in diabetes development. We expect our findings will inform public policies and urban planning decisions aimed at creating more resilient communities-ultimately, preventing the development of type 2 diabetes, while improving urban population health and health equity.