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RACE AND ETHNICITY DATA COLLECTION DURING COVID-19 IN CANADA: IF YOU ARE NOT COUNTED YOU CANNOT COUNT ON THE PANDEMIC RESPONSE 

Kwame McKenzie, CEO, Wellesley Institute | November 12, 2020

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Abstract

This paper discusses arguments for race and ethnicity data collection during COVID-19 and theories of how and why differences risk and the impacts of COVID-19 are related to race and ethnicity.  It summarizes Canadian data on race ethnicity and COVID-19 before presenting ways to promote equity. There have been calls for race and ethnicity data collection to identify health disparities and promote heath equity in Canada for decades. They have not been heeded. COVID-19 has acted like a social x-ray, highlighting problems in our body politic. The higher rates and greater impacts of COVID-19 on racialized populations in Canada could be attributed, in part, to a lack of available data to identify inequities. But the fact that these data were not being collected; the fact that they still are not collected in most provinces; and, the fact the knowledge of disparities not has led to significant change in the pandemic response points to an underlying systemic resistance to pursuing health equity.  Collecting race and ethnicity data, developing appropriate processes for governance and analysis, and ensuring that data is used for action are vital parts of a health system fit for Canada in the 21st century.  But it will only happen if there is legislative change to deal with the systemic resistance to health equity for racialized people.  It should not be legal to set up health care or pandemic strategies that predictably do not meet the needs of Canada’s diverse populations. It should not be legal to be deliberately blind to health disparities. 

Canada is a multi-cultural country which claims diversity as a strength.  There is social and cultural heterogeneity rarely seen in high income countries. And this has been used to develop a high standard of living. Canada consistently ranks as one of the best places to live in the world. 

But this is not the case for all.  There are significant socio-economic disparities and these lead to health disparities. Indigenous and racialized groups, particularly Canada’s Black populations, have an increased risk of a number of illnesses, poorer access to care and worse health outcomes.(1, 2)

The COVID-19 pandemic feeds on and exacerbates existing inequalities. Because of this, Canada’s social and health disparities have major implications for the development of evidence-based effective pandemic response. A one size fits all strategy is unlikely to work in a population with diverse needs. 

Numbers have been vital in the fight against COVID-19. Countries have relied on the number of cases and the R- number to monitor the effectiveness of pandemic interventions and to decide when to move through lockdown phases.  And the same numbers can be used to identify whether our interventions are working for everyone. To do this we need to collect socio-demographic data which can be disaggregated during analysis. If you are part of an aggregated sum you can be invisible in the numbers, your story will not be told, your needs will not drive policy action and your needs will not be met.  In addition, disaggregated data are particularly vital in pandemics because the need for collective action.  A response is as strong as its weakest link. 

Good sociodemographic data and race based data are important tools for health equity but data collection it is not an end in itself, it has to be linked to action.  

There are valid concerns about governance, accountability and protections against misuse of data.  These issues need to be addressed because data is vital to the proper functioning of public health. Every doctor must take a history from their patient to ensure they make the right diagnosis and identify the most effective treatment.  In public health, the patients are the population and the history is data. Public health needs good data to develop effective equitable interventions for communities and populations. We have already seen in Ontario that disaggregated data can save lives. Once Cancer Care Ontario were able to show that Black women were not being screened for cancers they were able to deploy one of the legion of evidence based strategies available to decrease disparities(3). 

Collecting race and ethnicity data is now considered standard practice in health worldwide. Countries such as the UK and even our much-maligned neighbors to the south are able to report race and ethnic disparities in health. But Canada has lagged behind in the collection of these data.  Race and ethnicity data is rarely routinely collected or reported at the Federal, Provincial or local level.  This is despite evidence that it is feasible, there are Canadian evidence based tools to aid collection, there is a wealth of evidence that these data are useful in improving the quality of health systems in general and that they can be important specifically in pandemics.  A study by Public Health Ontario during the H1N1 influenza pandemic reported that those who identified as South-East Asian were 3 times more likely to be infected, those who identified as South-Asian group were 6 times more likely to be infected and Ontario’s Black population was 10 times more likely to be infected (4). And, because Indigenous populations were at such high risk, the Ontario Government culturally adapted their public health response to try to improve outcomes.

Federal bodies such as Statistics Canada and health providers, planners and funders at all levels have resisted developing good race and ethnicity data streams. Because of this Canada went into the COVID-19 pandemic unable to identify or monitor crucial factors for the effectiveness and equity of our pandemic response.

During the response, they did not use the data they already had at an area level or attempt data linkages to try to understand whether there were race or ethnic differences in rates of COVID-19.

Later decisions to analyze existing data and collect race and ethnicity data during the COVID-19 response followed reports of clear race and ethnicity differences in illness rates from the USA and the UK, more acceptance of the concept of anti-Black and anti-Indigenous systemic racism in Canada and pressure from community organizations. 

Manitoba was the first province to start collecting race and ethnicity data in its COVID-19 response (5). 

Three public health units in Ontario; Peel, Middlesex-London and Toronto started collecting data between April and May 2020 and then the province of Ontario followed suit (6).  Quebec initially said it would consider collecting race and ethnicity data for its COVID-19 response and then did not (7). Local Black entrepreneurs and community groups eventually launched their own website in and app in August 2020 to try to get data collected.  They hoped this would spur their government to action (8). 

By the end of the first wave the collection of race and ethnicity data in COVID-19 was not widespread.  Most Federal COVID-19 linked programs were not collecting these data, and only 2 provinces were routinely collecting data.  There were no adaptations of the public health or social pandemic response. 

Why would race and ethnicity impact the rates of COVID-19 and the risks of harm? 

Canada’s COVID-19 response has been good.  In fact, our death rate of 23 per 100,000 is better than many other high-income countries. But it is worse than many others such as Germany (9)

One reason for this is that our initial response was focused on flattening the curve not who was under the curve. The focus on public health interventions for the whole population had some success but countries that were more successful added specific public health measures to protect at risk populations.  

Long term care is perhaps the best example. CIHI has reported that 81% of first wave deaths in Canada were in long term care homes.  Countries that had central control of long term care or developed clear early guidance for long term care at the time of their lockdowns did a better job at protecting this at-risk group and had much lower death rates. It has been calculated that at 4528 lives could have been saved if Canada’s first wave pandemic response was as good as Germany’s and much of that is because of their better performance in long-term care (10).  

Focusing on who is under the curve as well as flattening the curve produces better outcomes.

Socio-demographic data is useful for understanding who is under the curve and once disparities in rates of infection have been identified, public health and social interventions can then be improved to ensure they equitably decrease risk. Subsequent data collection can monitor the effectiveness of interventions.  

The Canadian Medical Association has calculated that 85 per cent of our risk of illness is linked to social determinants such as income, housing, education, systemic racism and access to healthcare.  15% is linked to biology (11).  The COVID-19 pandemic adversely impacts health in four main ways:  

  1. the disease itself; 
  2. the public health response;
  3. changes in health services; and,
  4. the economic downturn.