Amanda Norton, Scarlett Rakowska, Tracey Galloway, Kathleen Wilson, Laura Rosella, Matthew Adams
COVID-19 health impacts and risks have been disproportionate across social, economic, and racial gradients (Chen et al., 2021; Thompson et al., 2021; Mamuji et al., 2021; COVID-19 and Ethnicity, 2020). By examining the first five waves of the pandemic in Ontario, we identify if Forward Sortation Area (FSAs)based measures of sociodemographic status and their relationship to COVID-19 cases are stable or vary by time. COVID-19 waves were defined using a time-series graph of COVID-19 case counts by epi-week. Percent Black visible minority, percent Southeast Asian visible minority and percent Chinese visible minority at the FSA level were then integrated into spatial error models with other established vulnerability characteristics. The models indicate that area-based sociodemographic patterns associated with COVID-19 infection change over time. If sociodemographic characteristics are identified as high risk (increased COVID-19 case rates) increased testing, public health messaging, and other preventative care may be implemented to protect populations from the inequitable burden of disease.
