Disaggregated data can reveal systemic inequities. It can also reinforce them — here’s how.
Why It Matters
Community trust in social impact organizations who do disaggregated data collection can be shattered if it is done badly, especially with subjects who are already overpoliced and surveilled.
Toronto Public Health’s (TPH) decision to collect race, ethnicity, income, and housing data from COVID-19 patients to track the virus’s spread goes all the way back to the first few months of the pandemic.
The results are unsurprising to anyone with the most basic understanding of structural racism. Around 73 percent of all COVID cases in Toronto, and 74 percent of those admitted to hospital for treatment, are among racialized people. Some of the city’s poorest neighbourhoods are among the most ethnically diverse and have the highest COVID-19 case and fatality rates.
Yet Toronto Public Health’s decision to collect disaggregated race-based health data was not universally applauded. In June 2020, after TPH published a neighbourhood level COVID map, the Black Public Health Collective condemned the practice in a statement. They pointed out that Black communities
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