Socio-demographic data collection and equity in covid-19 in Toronto

Toronto, Ontario, Canada is home to 8% of Canada’s population and 11% of Canada’s coronavirus cases [1]. There is significant income inequality; 25% of children and 20% of adults live in poverty [2]. 52% of the population is racialized. Income and race are risk factors for covid-19 so a pandemic strategy needs to be equitable to be effective [1]. To flatten the curve, we needed to focus on who is under the curve, but, at the start of the pandemic, little routine socio-demographic data was being collected by public health. Reports of higher rates covid-19 in Black populations in the USA and UK and the rise of Black Lives Matter in spring 2020 led Toronto communities to question whether similar disparities were present locally. An open letter to the Government of Ontario calling for race based data collection [3], newspaper op-eds and multi-media interviews crystalized in the development of a backbone organization the Black Health Equity Working Group (BHEWG) which linked Black communities, academics, service providers and policy specialists. BHEWG developed a strategy for the collection and use of sociodemographic data including race/ethnicity and income in which initial analysis of existing area-based data was used as way of highlighting the need for individual level data collection at testing, tracing and hospitalization. A longer-term goal was for socio-demographic data collection when people renew their Ontario Health Insurance Plan cards. The strategy included suggested tools for data collection and the development of a data governance framework (available on request). The aim was to use data to improve equity by changing practice in all parts of the system involved in pandemic: public health units, City of Toronto, the Province of Ontario and Federal Government. Encouraging government analysts and policy organizations to use existing area based data from the census to map disparities was a vital first step. These analyses reported covid-19 rates 10 times higher in some areas and the best predictors were the percentage of racialized populations in an area and income [4].