Montreal as Canada’s COVID-19 epicenter

Some factors that can help to explain why six Montreal boroughs are Canada’s COVID-19 epicenter

May 28, 2020

En français

In the middle of the COVID-19 pandemic, federal health senior officials and some provincial chief medical officers, including those from Quebec, refuse to collect comprehensive socio-demographic data on health. This is despite the fact that 81% of all COVID-19 cases in Canada have resulted from community transmission, that some communities across Canada with a large percentage of Black residents present increased COVID-19 rates, and that the lowest income group in Toronto had the highest rate of COVID-19 cases.

We know that an early spring break, the return of snowbirds, and the (pre-COVID-19) effect of precarious employment conditions on long-term care home workers facilitated the initial spread of the SARS-CoV-2 virus in Montreal (COVID-19 being the disease).

Anecdotal accounts indicate that certain social groups are being disproportionately affected by COVID-19. Nonetheless, we are still missing substantive analyses to better understand why specifically Montreal became Canada’s COVID‑19 epicenter.

Previous research indicates that disparities caused by factors such as employment, income, education, gender, race and ethnicity, including experiences of discrimination, racism and historical trauma, create health inequalities. These factors are known as social determinants of health.

Using descriptive statistics and 2016 Montreal statistics and census data, I show that the six Montreal boroughs most affected by COVID‑19 exhibit a range of disadvantageous socio-demographic factors. Used as hypotheses, these indicators can reveal potential rich areas of further data collection and research on the social determinants of health, particularly when we consider that a second wave of COVID-19 is expected soon (see map – will open a pop-up window).

Socio-Economic Determinants of Health

The boroughs most affected by COVID-19 are Montréal-Nord (MN), Ahuntsic-Cartierville (AC), Côte-des-Neiges–Notre-Dame-de-Grâce (CDN-NDG), Mercier–Hochelaga-Maisonneuve (MHM), Rivière-des-Prairies–Pointe-aux-Trembles (RPPT), and Villeray–Saint-Michel–Parc-Extension (VSMP) in that order (as of May 14). A characteristic common to these boroughs–and considering data from all the island of Montreal–is low income. For example, the median and average after-tax income of the three boroughs with lower incomes (CND-NDG, MN and VSMP) were $22,553 and $22,840 in 2016, resulting in a difference of $6,204 and $20,728 compared to the island of Montreal (see map).

The boroughs also presented the largest number of single-parent families in the island of Montreal (45%), where 95.4% were sole providers and 82.7% women (see map). This is significant since research tells us that single parents, particularly single mothers, face multiple challenges.

These boroughs also had the cheapest rental rates in the island of Montreal ($731 in average; see map), and still some households spent more than 30% of their income on rent, particularly CDN-NDG (10.3% of all the island of Montreal households and 36% of all CDN-NDG households), VSMP (7.9% and 31.9% respectively), MHM (7.2% and 28.2%), and AC (6.4% and 28.5%). Montreal Nord presented a low percentage of households (4.6%) spending more than 30% on rent when considering all the island of Montreal households, but a high percentage (34.3) when considering only the borough’s households (see map).

Other less COVID-19 affected boroughs also had noteworthy percentages of cheapest rental rates, such as Ville-Marie (8.4%), Rosemont–La Petite-Patrie (8.3%), and Plateau-Mont-Royal (8.2%). With the exception of Rosemont ($734), the average monthly rent in the other two boroughs was higher ($892). This higher expenditure may be explained by renters with higher incomes being more willing to spend more of their income to acquire a higher social status by living, for example, in the Plateau, than because of limited economic possibilities.

Most of these boroughs also had the largest number of households with more than one-person per room considering the island of Montreal households and per-borough/municipality households: CDN-NDG (with 0.5% and 5.3% of cases respectively), VSMP (0.33% and 4.4%), AC (0.27% and 4%), and MN (0.17% and 4.2%) (see map). Only Saint-Laurent and Saint-Léonard, with medium COVID-19 rates, also presented relatively high rates of households with more than one-person per room (0.29% and 6.7%, and 0.2% and 5.3% respectively).

These boroughs also had the largest percentage of dwellings of insufficient size considering only their total number of households, particularly CDN-NDG (14.3%), VSMP (11.9%), MN (11.5%), and AC (10.7%) (see map). Again, Saint-Laurent (14.8%) and Saint-Léonard (12.1%) rank relatively high in this indicator, but they are not strongly affected by COVID-19. The two last indicators point to the potential effect of low income on housing quality and the impossibility of maintaining at-home social distance if someone contracts the virus, thereby putting all household members at immediate health and economic risk.

Although some boroughs had the largest number of dwellings in need of major repairs (CDN-NDG with 7,825 units, VSMP with 5,835 units, and MHM with 5,455 units) (see map) and social/community housing (MHM and CDN-NDG; see map), these conditions are not enough to potentially suggest a higher risk for COVID-19. This is because other less affected boroughs present similar or even larger numbers of these indicators.

For example, Rosemont–la Petite-Patrie and the Plateau-Mont-Royal had a large number of dwellings in need of repair (9.4% and 8.3% respectively), but their COVID-19 rate infections are relatively lower, particularly in the Plateau (159% lower than in Montreal Nord). And the Sud-Ouest and Ville-Marie contain the larger number of social housing units in the island and are not strongly affected by COVID-19 (e.g. the Sud-Ouest has twice as many community/social houses than Ahuntsic-Cartierville, but the latter has 130% more COVID-19 cases than the former).

Similarly, there are few boroughs that exhibited some negative economic indicators but have median COVID-19 rates, such as Saint-Laurent, Saint-Leonard, and Rosemont–La Petite-Patrie. What the previously analyzed indicators suggest then, is that it is the interlocking of various negative socio-economic factors, as part of the social determinants of health, which may increase the risk of COVID‑19.

Living in one of the boroughs most affected by COVID‑19 implies living in one of the island of Montreal’s most underprivileged areas. This is not limited to a low after-tax income but includes a range of diverse socio-economic factors, such as dwelling conditions, rental prices, family type, and the effect of gender in family composition and economic opportunities. Nonetheless, it is important to note that living in one of these boroughs does not necessarily mean that someone is experiencing a number of disadvantages and/or will develop COVID-19 (i.e. there is not causation).

Nevertheless, if someone is a so-called “essential worker,” lives in one of the most COVID-19 affected boroughs, and experiences an unequal socio-economic context, they may face higher challenges than others. One of these challenges could be facing the decision of staying at home and (the impossibility of) maintaining social distance versus putting food on the table and paying rent, while facing an increased risk of viral contagion. Either decision can trigger a series of negative events that may hinder their (and others’) economic capacity and survival.

Economic indicators are not enough, however, to explain the extent to which people’s health is affected. Being new in the country or belonging to a minority group, as well as level of education, may also shape this experience.

Indigenous Peoples, Immigrants, Visible Minorities, and Education

First Nations, Inuit, and Métis persons constituted only 0.68 % of the island of Montreal population in 2016 (n=13,100). However, their presence was relatively significant in MHM (36.8% live there) (see map). Another borough with a significant presence of Indigenous population was Rosemont–La Petite-Patrie (1,245), which has a medium COVID-19 rate. Moreover, we know that close to 24% of off-reserve indigenous peoples live in poverty, and the rate increases for lone parent families (51.2%).

Some of the most COVID-19 affected boroughs had a large proportion of immigrants, particularly CDN-NDG (3.9% of the island of Montreal immigrant population and 45.8% of the boroughs’ immigrant population), VSMP (3.1% and 41.9% respectively), and AC (2.7% and 38.7%). A peculiar exception is MN, which had a relatively low percentage of immigrants considering the island of Montreal population, but a large percentage considering only the borough’s population (1.8% and 40.4% respectively). There are other boroughs that have not been significantly affected by COVID‑19 but also had a large proportion of immigrants, such as Saint-Laurent and Saint-Léonard, with a medium COVID-19 impact (2.7% and 52.8%, and 1.9% and 47.7% respectively) (see map).

Some boroughs also had the largest number of refugees in the island of Montreal: VSMP (13.8%), AC (10.6%), and CDN-NDG (9.2%). Saint-Laurent, however, also had a relatively large percentage of refugees (9.8%) (see map). When we consider the concentration of refugees vis-à-vis the borough’s immigrant population, the ranking varies, and the other strongly COVID-19 affected boroughs present a rate increase: VSMP (22.2%), RPPT (20.6%), AC (19.8%), MN (18.5%), MHM (17.8%), and CDN-NDG (11.3%).

Research shows that it can take new immigrants and refugees at least 10 years to achieve income parity with the average Canadian born person. When we consider this population (landed between 2011 and 2016), some boroughs had the larger proportion of recent immigrants: CDN-NDG (14.1%), AC (9.4%), and VSMP (8.6%). Only Saint-Laurent, with a medium COVID‑19 impact, had a similar percentage of recent immigrants (8.4%, see map).

In addition, many of these boroughs had the largest proportion of visible minorities in the island of Montreal (as a reference, one in three Montrealers was a visible minority in 2016): CDN-NDG (4%), VSMP (3.5%), AC (2.5%), and MD (2.5%). MHM and RPPT, however, had lower percentages (1.5% and 1.4% respectively), similar to other boroughs less affected by COVID‑19, such as Saint-Léonard (1.7%), Rosemont–La Petite-Patrie (1.4%), and La Salle (1.5%). Saint-Laurent is the only borough that had a relatively large percentage of visible minorities in the island of Montreal (2.7%) and has not been strongly affected by COVID-19 (see map).

It is important to note that the immigrant or refugee status in Canada does not automatically locate someone at an economic disadvantage, nor are all immigrants and refugees visible minorities. Therefore, we cannot assume a causal relationship between these statuses and a poor economic insertion or a negative impact on health. For instance, the presence of indigenous peoples, immigrants, and refugees in these boroughs does not necessarily suggest a direct correlation with high COVID-19 levels, as Saint-Laurent demonstrates.

Nonetheless, it is also true that, since the late 1970s, the economic fate of many immigrants and refugees in Canada is negatively impacted by numerous factors, and in some cases despite their education level. For example, at the borough level, 40.3% of the most affected boroughs’ inhabitants 15 years and over had a high school diploma (see map), whereas 38.2% had a college or Cégep degree (see map). Slightly more than 40% of this population had a university certificate or diploma below the Bachelor level (see map). And 33% percent of this population had a bachelor’s degree considering all the island of Montreal inhabitants with this degree (see map).

Taking into account that one in three persons with a Bachelor’s degree in the island live in the six most COVID-19 affected boroughs, and that many nonetheless score low in various socio-economic factors, we need to consider how this may signal a precarious economic and employment insertion and the negative effect of this on health.

Anecdotal accounts about refugee claimants actively working on the front line of the pandemic, whether as trained orderlies or doing low-skilled jobs, exemplify the difficulties of economic integration when considering immigration, gender, race, and ethnicity status.

The Interlocking of the Social Determinants of Health

It is only when we determine the effect of a mosaic of social determinants of health that we can forecast and prevent health inequalities. Previous research on economic inclusion has shown that gender, visible minority status, age, and [a shorter] length of stay in Canada are strong predictors of economic exclusion, particularly for young new immigrants and racialized women.

This helps to explain, for instance, why more women than men have been affected by COVID-19 (e.g. the first round of pandemic layoffs occurred in industries dominated by women, all sectors characterized by physical contact).

Consequently, when we incorporate multiple indicators into the analysis, we can identify a strong potential correlation between high levels of COVID-19 and low scores in these boroughs across various socio-economic indicators, a significant presence of immigrants, refugees, and visible minorities, and good levels of education that, nonetheless, have a lower return rate in employment, income, and possibly type of work. It is this interlocking of various negative factors that produces diverse types of immediate and long-term social disadvantages.

Although more sophisticated analyses are required to determine robust correlations between various socio-demographic factors and increased health risks (including a careful weighting of those who have not been tested for COVID-19 yet), this analysis clearly indicates that the most COVID-19 affected boroughs in the island of Montreal present negative rates across numerous social determinants of health. These findings, as hypotheses, strongly emphasize the urgency of collecting comprehensive scientific data to better predict and manage current and future health inequalities, such as a second COVID-19 second wave.

It is imperative to depoliticize the COVID-19 pandemic if we really want to be “in this together.” Doing so will generate prosperous economies and save numerous lives.

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