Promote Social Justice: Better Health Equity
Serious health care gaps exist in access, cost, and quality for people based on race, ethnicity, gender, gender identity, age, sexual orientation or other demographic and socio-economic factors. Ontario has the opportunity to use data to identify health outcome disparities that are the result of inequities and societal factors that influence health. Ontario can use and share this data to identify health inequities, find their root causes, craft targeted interventions, and promote social justice across the province.
Race-Based Data
Challenge: The Public Health Agency of Canada, Alliance for Healthier Communities, and the Toronto Board of Health have identified Anti-Black racism as a public health crisis. During the pandemic, this was especially apparent across racialized communities, which had significantly higher rates of COVID-19 infections, hospitalizations, ICU admissions, and deaths because Ontario does not collect, share, and use race-based health data.
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How data was used: When vaccines became available, the Wellesley Institute examined public health data and found that Black residents in Toronto had up to 7 times the rate of COVID-19 infection compared to areas that were mostly white.
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Result: The City of Toronto partnered with Canada’s top Black scientists and community organizations to address vaccine access and trust within Black communities. Due to the data and partnership, between July and December 2020, the COVID-19 rates in Black communities dropped from 7 to 2 times the amount of Ontario’s white population.
Homelessness Cohorts
Challenge: People experiencing homelessness face substantial barriers in accessing health care, such as medical and surgical care, mental health care, prescription medications, eyeglasses, and dental care. Researchers and policymakers have struggled to measure the homeless population to provide care to this underserved group. Not being able to measure this vulnerable population accurately means that programs and funding cannot be adequately allocated or evaluated.
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How data was used: In 2019, using administrative data held at ICES, a team of researchers at ICES Western led by Lucie Richard and Dr. Salimah Shariff developed an algorithm that enabled them to follow those experiencing homelessness over time and provided a reliable and less costly way to identify health needs, services and outcomes for a high-risk and understudied population.
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Result: This group of people, "homeless cohort", can be linked to ICES data to examine the impact of healthcare interventions on homelessness. Researchers are working with teams across Canada to cross-validate the algorithm for national use.
Socioeconomic Data
Challenge: With increased interest to collect more socioeconomic data and proposed legislative changes that will allow data from other ministries, including social services, to be integrated with the ICES data repository, data on race, ethnicity, immigration, and other social determinants of health must be governed, used, and collected responsibly and reported back to the communities who are directly impacted.
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How data was used: To address this need, ICES has developed explicit guidance on an approach to anti-racist and community-driven health research and analytics in consultation with members of the public, equity experts, researchers, and key stakeholders. The aim of this guidance is to ensure that race and related data held at ICES are used to demonstrate the role of systemic racism and other forms of oppression in health inequities. Ongoing efforts have been underway, including recommending conditional use of assigned-race variables and algorithms in the ICES data repository and phasing out those that assign race based on country of origin and language alone.
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Result: The Guidance Document and Framework will be released internally and externally in 2023, with committed actions prioritized over the short and long term that ICES will take towards establishing an anti-racist research agenda and appropriate use of race and related data across the organization.