Three ways COVID response could have been better with (more) disaggregated data

Statistics Canada already provides some disaggregated data, but social sector leaders are looking for more of it to drive better program delivery, donor relations, and community engagement.

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.

var TRINITY_TTS_WP_CONFIG = {"cleanText":"Three ways COVID response could have been better with (more) disaggregated data. 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\u2019s findings that the city\u2019s most racialized regions \u2013 such as Scarborough \u2013 had among the highest rates of COVID-19 infections and deaths. Soc

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