Three ways COVID response could have been better with (more) disaggregated data
Why It Matters
Disaggregated data makes the intersectional nature of the COVID-19 pandemic painfully obvious, but many social purpose organizations don’t have access to critical information – or don’t know what to do with it.
When joblessness rates and food bank use soared during the first year of the COVID-19 pandemic, many Canadian social purpose organizations realized they needed better data in order to respond well.
Researchers in Canada have known for decades about how racialized communities, women, newcomers to Canada, and working-class neighbourhoods all suffer disproportionately bad health compared to their whiter, wealthier counterparts. The pandemic was no exception. Perhaps the starkest example came out of Toronto Public Health’s findings that the city’s most racialized regions – such as Scarborough – had among the highest rates of COVID-19 infections and deaths.
Social impact organizations sometimes struggle to interpret data collected by Statistics Canada, academics, think tanks and private research organizations. But Shalini Sharma, director of research and policy at th
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