Insights
Use of Race-Based Data in the Pandemic: Let’s Move from Stigma to Solutions
With the increased use of race-based data in pandemic planning, we must consider one key caveat: is the data being used to better inform suitable interventions, or as dangerous vitriol to shame and stigmatize already vulnerable populations? We reflect on this question as recent political and media attention has leveraged race-based data to identify “hot spots” in highly racialized communities in Ontario (Grant 2020). Complex social factors, travel patterns and cultural practices such as multigenerational family homes and high proportions of international students have been woven into a narrative that unjustly lays blame, reinforces an anti-immigration rhetoric and risks further marginalization of communities of people of colour (POC).
Of course, there is reason for concern with a rapid increase in COVID-19 cases in the Region of Peel (Brampton, Mississauga and Caledon) and Toronto, ON. Initial reports have suggested that some contributing behaviours may be large social gatherings and lack of adherence to social distancing guidelines. However, a deeper understanding of the social, cultural and economic rationales behind these health behaviours is required for more tailored approaches.
While we acknowledge that a large proportion of the population in Peel and Toronto identifies as POC, this itself is a factor that leads to a higher risk of COVID-19 infections. It must, however, be noted that there is no data to suggest that racial groups vary in their susceptibility to the COVID-19 virus based on their genetic composition. Rather, it is race-based inequities in accessing care that are the more common contributors to such disparities. Race plays a key role in how one interacts with all levels of the healthcare system, from awareness and screening, to acceptance, diagnosis and treatment. However, if we know these facts why have our leaders’ first instincts been to point fingers at entire communities, insinuating noncompliance with public health recommendations?
Rather than blaming cultural communities for the spike in COVID-19 infections, race-based data ought to be used as a launching pad to further refine scientific queries, conduct root cause analyses, assess the unique needs and assets of these communities and, consequently, identify and close potential gaps in the system that created the inequity. We remind our political leaders and media reporters (especially those with a national platform), that logic models for successful health promotion interventions take an ecological perspective that not only focuses on specific and unique community needs but also takes a strength-based approach to build on the capacities, skills and assets of communities (Kretzmann and McKnight 1993).
Such capacity-focused development and program planning requires active engagement and collaborative partnerships with community members and other local stakeholders allied in their efforts to “improve the health of a community” (Center for Community Health and Development n.d.). Ethnographic approaches – rooted in anthropological principles – in the study of health and health behaviours will help decision makers “gain the insider’s view of a particular group or community … giving answers to questions of power, inequality and how some voices are heard above others” (Savage 2006: 385). With these perspectives in mind, interventions must be designed to meet the unique needs of their target populations – the target populations are not meant to meet the needs of the intervention’s biased design parameters. It is unjust to imply that entire racialized communities are to blame, when the healthcare system (including public health screening and strategies) in which they must participate is historically rooted in Eurocentric frameworks that are now widely accepted as inequitable. Clearly, initial public health strategies have been incongruent with the health behaviours and beliefs of the communities that are continuing to have alarmingly high infection rates.
According to the Wellesley Institute, minority groups in Toronto were six to nine times more likely to test positive for COVID-19 when compared to the white population (McKenzie 2020). Similarly, in the Peel region, residents with South Asian, Black and Latin American origins were over-represented in COVID-19 cases while their white counterparts were under-represented in comparison to their proportion in the population (Region of Peel 2020).
An important part of healthcare access and inequity is embedded in the public health units’ approaches to awareness campaigns, screening and interventions. Public health units across the country initially opted for blanket approaches targeted at the majority – “the mainstream” population. But if a one-size-fits-all intervention is not working, then it is the intervention that needs to be revisited and modified to fit the lived reality of individuals in these jurisdictions.
As the two largest municipalities in Ontario, Toronto and the Peel region both have a number of important demographic attributes that should be considered in the context of potential disease patterns:
- Toronto and Peel are racially heterogeneous, with similarly heterogeneous health behaviours and needs.
- In 2018, Toronto (21%) and Peel (10%) held the largest share of Ontario’s population. In contrast, York, the next largest municipality in Ontario, housed only 8% of the total population (Ontario Ministry of Finance 2019).
- Toronto and Peel are rapidly growing communities with the rate of population increase over the next 28 years projected to be 44% and 69% respectively (compared to 38% rate of population increase for Ontario) (Ontario Ministry of Finance 2019).
- Peel has a larger proportion of employment in sectors that require in-person attendance in the workplace such as manufacturing and transportation/warehousing (Region of Peel 2019), thus leading to higher risks of contracting the virus.
Early in the pandemic, we often heard that COVID-19 was “the great equalizer”, impacting individuals from all walks of life. In recent months, we have seen that the impact of COVID-19 has been widespread but differential. Dr. Kwame McKenzie very eloquently stated on CBC’s The Sunday Magazine that the pandemic has functioned as a “social x-ray” bringing to light pre-existing cracks in our society (CBC 2020). Socio-demographic and race-based data has helped reinforce the fact that POC populations face an increased risk of COVID-19 infection and its related socio-economic repercussions.
As the pandemic unfolds, we see some communities left behind, where a blanketed approach has been ineffective. What may work for the majority of community settings may not be effective or sustainable in marginalized communities. We must acknowledge that it is not the people that fail, rather, it is the system that is failing specific communities. The creation of effective and sustainable health interventions for high-risk populations often requires intensive community engagement rather than a prescriptive approach.
Blaming and shaming are not effective public health strategies – they lead to stigmatization and push communities further to the margins. Rather, we must engage with POC communities to understand the health behaviours, beliefs and circumstances that create their lived reality. If we are truly in this together, listening, learning and refining our mainstream public health strategies will allow us to reduce harm and save more lives.
About the Author(s)
Sawinder Kaur Dhillon, BHSc (Hons), MBA, is a community advocate with an interest in health equity and has professional experience in healthcare administration and consulting.
Ripudaman Singh Minhas, MD, MPH, is a developmental pediatrician and director of Pediatrics Research at Unity Health Toronto in Toronto, ON.
Sawinder and Ripudaman are married to one another and currently reside in Brampton, ON, the city where they grew up. They proudly identify as people of colour.
Follow them on Twitter: @dhillonsawinder @ripudamanminhas
References
CBC. 2020, September 13. “How the Pandemic is Reordering Society.” The Sunday Magazine. Retrieved September 16, 2020. <https://www.cbc.ca/radio/sunday/the-sunday-magazine-for-september-13-2020-1.5718668>.
Center for Community Health and Development. n.d. Chapter 1: Section 3 – Our Model of Practice: Building Capacity for Community and System Change. Community Toolbox. Retrieved September 27, 2020. <https://ctb.ku.edu/en/table-of-contents/overview/model-for-community-change-and-improvement/building-capacity/main>.
Grant, K. 2020, September 10. Peel Has More Active COVID-19 Cases Than Any Other Public-Health Unit. The Globe and Mail. Retrieved September 10, 2020. <https://www.theglobeandmail.com/canada/article-brampton-emerges-as-ontarios-coronavirus-epicentre/>.
Kretzmann, J. P. and J. L. McKnight. 1993. Building Communities from the Inside Out: A Path Toward Finding and Mobilizing a Community’s Assets, pp. 1–11. ACTA Publications.
McKenzie, K. 2020, August 13. Toronto and Peel Have Reported Race-Based and Socio-Demographic Data – Now We Need Action. The Wellesley Institute. Retrieved September 16, 2020. <https://www.wellesleyinstitute.com/healthy-communities/toronto-and-peel-have-reported-race-based-and-socio-demographic-data-now-we-need-action/>.
Ontario Ministry of Finance. 2019. Ontario Population Projections, 2018-2046: Table 4 - Historical and Projected Population by Census Division, Selected Years – Reference Scenario. Retrieved September 16, 2020. <https://www.fin.gov.on.ca/en/economy/demographics/projections/#tables>.
Region of Peel. 2019, August. 2018 Employment Survey Bulletin. Retrieved September 27, 2020. <https://www.peelregion.ca/planning/pdc/pdf/2018-employment-survey-bulletin.pdf>.
Region of Peel. 2020, August 7. COVID-19 and the Social Determinants of Health: Race and Occupation. Novel Coronavirus (COVID-19) Peel Health Surveillance. Retrieved September 27, 2020. <https://www.peelregion.ca/coronavirus/_media/COVID-19-race-and-occupation.pdf>.
Savage, J. 2006. Ethnographic Evidence – The Value of Applied Ethnography in Healthcare. Journal of Research in Nursing. 11(5): 383–93. doi:10.1177/ 1744987106068297.
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