Stephanie Dizonno

Influenza puts pressure on the Canadian healthcare system every year, and seasonally accounts for thousands of hospital related visits. However, the extent of publications that discuss the implication of socioeconomic status and rates of influenza on a granular spatial extent are limited. This study’s purpose is to explore the spatial patterns of influenza and identify the relationships between socioeconomic status and rate of influenza by Forward Sortation Area (FSA) in Ontario. Preliminary background research based on Twitter data indicates the important role that pharmacy accessibility plays in influenza outcomes. Using the Enhanced Two-Step Floating Catchment Area (E2SFCA) model, pharmacy accessibility is constructed for all of Ontario using FSA’s. Using calculated accessibility scores, as well as the socioeconomic census variables, an Ordinary Least Square (OLS) linear regression and Geographically Weighted Regression (GWR) are performed to determine significant risk factors of influenza and the spatial variation of such relationships. This study provides implications for using enhanced data-mining techniques in public health research by integrating non-traditional social media data into influenza research.