Amy Go, president of Chinese Canadian National Council for Social Justice (CCNC-SJ), remembers when the SARS virus came to Canada nearly 20 years ago. At the time, she says she was working at a long term care-home in Markham, ON that catered specifically to South Asian seniors. “Even though there was not one single case of the virus amongst our residents or staff, we were labelled as having SARS,” she says. 

Go says she remembers the “yellow peril fear” that followed in the wake of the SARS virus, as “Chinese workers were told to go home and were laid off, Chinese tenants were locked out by their landlords, and even delivery people would not deliver goods inside to the centre.” 

So when the COVID-19 pandemic struck and its origins were located in China, Go says that she and other members of the Chinese Canadian community immediately knew that Chinese and East Asian Canadians would be targeted right away. “We knew that racialized communities, particularly racialized communities with the high potential of people working in the frontlines, who had no luxury of staying at home would be affected at so many different levels.” 

Go says, “Unfortunately it just turned out to be multiple times worse than what we had anticipated, because the pandemic had not only exposed historical systemic racism in Canada, but made it worse, amplified it, exposed it, revealed it and exacerbated it.” 

But the CCNC-SJ, like most organizations in Canada’s social impact sector, faced a dire lack of disaggregated data that would have helped to shed light on the issues faced by this racialized community and other marginalized populations. 

Lagging behind

Go explains that unlike the US, Canada is “lagging in the collection of disaggregated data.” 

This is because, she explains, “American civil rights legislations mandate, and have been mandating [the government] to collect disaggregated data –– for instance, in particular, surrounding Black American communities –– so they actually have better data. And in Canada, there’s no legislative requirement to collect and use disaggregated data.”

Go says that while her organization doesn’t directly collect such data themselves, “we’ve been speaking up clearly and loudly for the use of disaggregated data” –– a function that is necessary in order to evolve data collection practices, analysis and the using of the data-sets themselves as a tool to measure inequity. 

At the national level, The Canadian Institute for Health Information (CIHI) has been doing exactly that: advocating for disaggregation whilst also collecting and analyzing aggregated data.

CIHI is a national non-profit that works to provide Canadians critical information and resources necessary to navigate the country’s complex healthcare system. According to Dana Riley, the program lead for the organization’s population health team, the organization is “a steward to 28 different databases” as it collects, maintains and analyzes larger datasets relevant to Canadian’s health and healthcare in Canada. 

Riley explains that these databases range in size and type. She says that one of CIHI’s most used databases, for instance, is the discharge abstract database which contains hospitalization records from across all provinces and territories. Riley says that her organization also collects other data on “health, workforce, and data related to physicians and healthcare spending.”

“What we’re able to do as a data steward is bring these data together and then disseminate it back to the [provincial] jurisdictions in a way that can be used to represent populations accurately and inform the decision making in each province or territory,” Riley says.

This is because, she explains, the power to make decisions about data collection, reporting and analysis lies within the jurisdictions of each province and territory. Further, the power to institute policy and affect change within Canada’s healthcare system, also lies in the hands of these jurisdictions. Riley says that therefore, provinces and territories across Canada can be pushed to address issues surrounding healthcare, based on findings from disaggregated data-sets that highlight key facts surrounding gaps in equity faced by marginalized communities. 

However, she says, very few of these databases collect data disaggregated by identities. 

So in early 2020 when the pandemic struck, Riley says, “it highlighted two major gaps — in our ability to collect and disaggregate race-based and Indigenous identity data specifically, as we saw that racialized groups were disproportionately affected by the COVID-19 pandemic.”

At this point, “my colleague and I started talking about race-based and Indigenous identity standards, and how we might facilitate implementation of a standardized approach to collect this type of data across various healthcare settings in Canada,” Riley says. “So when it comes to something like race-based or Indigenous identity standards, we can set the standard for data collection and analysis and we can make space available in our databases for provinces and territories to submit that type of data.”

The result of this conversation between Riley and her colleagues, she explains, was the release of  “a discussion document” in July of 2020 surrounding this very topic.

As a result of and since this document, Riley says that her organization has been engaging with a variety of “stakeholders and partners” invested in improving healthcare for Canadians––such as social impact organizations, “to really understand the issues related to the collection of race-based and Indigenous identity data” and to work together as a collective in advocating for and participating in the disaggregation of data-sets. 

Whilst CIHI’s ability to disaggregate data is evolving, Riley says that one of the things she has learned is that in order for their effort to succeed, “communities need to be involved.” She explains that it is critical to involve populations at the community levels in order to successfully collect and analyze disaggregated data that accurately represents racialized and marginalized populations in Canada and the complex issues they face today due to the COVID-19 pandemic. 

Some provincial-level social impact organizations, too, have begun the work to identify and address social inequities through collecting and using disaggregated data by engaging populations at the community level, in order to stay ahead of the curve amidst the pandemic. 

Collecting disaggregated data on the frontlines

In case of a medical emergency, one calls 911. But what do you do in case of any other emergency –– especially in the face of the different challenges that the COVID-19 pandemic has brought? According to Carrie Moody, director of strategic solutions at FindHelp Information Services, you call 211.

Moody’s organization FindHelp is the body that services and operates the 211 phone lines across Canada, provided by 211 Central. In Ontario, the provincial branch of this organization operates a 24/7 free helpline that one can call in order to be connected with key resources and services in one’s own community or locale within the province. The services that Ontario 211 connects callers with range from resources in the areas of mental health, housing, childcare, cultural supports, transport and more.

So when the pandemic struck and the calls for help came flooding in, Moody says her organization saw an opportunity for them to put their position as ‘frontline workers’ to good use. 

“One of the things that we realized is that we can provide a better referral, and we can provide better information, if we know a little bit more about the person that we’re working with,” she says, “So we realized also that if we can get that information out of the communities that we’re connecting with, we can inform and drive our mission in a way that’s much more purposeful and helps us to create a more equitable and accessible system of services, through information and problem solving that comes with the kinds of data that we collect.”

Moody’s organization is composed of ‘frontline’ respondents, who answer the phone and help callers connect to the services and support they need. She explains, “One of the things that we always talk about is that our frontline staff have so much knowledge and information from their experience responding to people’s calls. So that got us thinking about ‘How do we provide some kind of backbone, so that their knowledge is highlighted in the community and can be used to help improve peoples’ lives?”. 

The kinds of data that the organization currently collects at the frontline levels, according to Moody, can primarily be broken down into “needs data and unmet-needs data” based on whether Ontario 211 is able to connect the caller with the service they are seeking, and therefore meet the needs of the caller. 

Moody explains that due to reasons such as hesitancy from especially racialized and marginalized communities when it comes to giving away personal identity-based information , “currently, we can break it [datasets] down by a couple of demographic indicators, but not a large enough group of indicators to help us really solve problems.”

So earlier this year, the organization announced a new strategic plan for 2021-23 to address this inability to disaggregate data, according to Moody, who says that they are now working on building capacity for data collection and being more purposeful about using it.

“Right now, we can collect age really, really well. But we want to be able to collect things like gender, race, Indigeneity, ability, all those other elements that make up our intersecting identities, so that we can so that we can actually tie the needs and unmet needs data with these demographic elements, and really focus in on how we solve problems within particular communities,” she explains. 

Whilst the organization has historically faced challenges with collecting personal data from callers, especially due to hesitancy when giving away such information, Moody says that the strategic plan aims to address this issue as well –– by creating greater levels of awareness amongst frontline staff about why this data is important to collect, and ergo amongst callers about what is going to be done with this information and why it is necessary to collect. 

Meanwhile, on the Western coast of Canada, the British Columbia Non-Profit Housing Association (BCNPHA) is busy leading the effort to use the disaggregated data they have collected in order to improve life for people living with housing and homelessness issues in the province. 

Erika Sagert, policy manager at BCNPHA, says the organization works with data sets in a few different ways. 

Sagert recalls when her organization was hired to conduct the 2020 Metro Vancouver homeless count. “We had, for the first time, a race-based question in the survey and it provided some critical information, particularly around homelessness for people of colour and Indigenous people. There’d always been an Indigenous identity-related question for many, many years in the homeless count but it was the first time in 2020 that we had a more extended question,” she explains. 

Sagert says one of the things her organization found was that Black people in Metro Vancouver were actually 3.7 times more likely to experience homelessness, compared to what their presence in the general population predicts.

Sagert explains that without disaggregating large data-sets like this one, one might perhaps get a sense of “what’s going on” but miss certain key pieces of information in the process –– “which is that certain groups face different barriers to housing…Now that we have collected this information, we have very solid evidence that there needs to be attention placed on homelessness for certain groups especially.”

The BCNPHA is now using their research to advocate for marginalized communities by “working closely with all levels of government” and other “non-profit members, who represent around 70,000 units of affordable housing across the province,” according to Sagert.

Sagert insists that the BCNHPA’s work right now is only the beginning of the disaggregated data-collection journey in Canada. While she says it is therefore too soon to see any large-scale positive impacts on the ground yet, she explains, “Basically, anybody who is receiving government services is accessing housing supports, mental health services… every person that accesses any services directly benefits from the government and our [social impact] sector having better data that’s more reflective of the diversity of experiences that are out there.”

“If you’re trying to fully represent the hundreds and thousands of individuals and families within Canada who have been impacted by things like homelessness, poverty, discrimination, you need to collect and disaggregate data,” she says. 

Sagerts explains, “If we don’t understand what’s going on, because we’re not asking the right questions without collecting the right data, we as a sector are not going to be very effective.”

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