COVID-19 Risks in British Columbia’s Neighbourhoods

The risk of contracting coronavirus varies between people and places, which makes some British Columbians more likely to develop COVID-19 than others. There are several reasons for this, many of which are related to personal risk factors such as socio-economic factors, occupational hazards, and our behaviours. These personal risk factors can become amplified when we spend time in places that may also result in an increased risk of transmission. By scrolling through the maps on this page, you can see how our research team based at Simon Fraser University has been exploring these issues of increased risk of transmission. These maps add important new insights into the ongoing dialogue about how to manage the COVID-19 pandemic in British Columbia and where policy and public health efforts should be focused.

These maps show the results of models our team developed to help explore COVID-19 risks and overall vulnerability to transmission across the province. These models were created by identifying potential risk variables, reviewing the state of evidence that surrounds each variable we may want to include, and identifying an appropriate data source for each variable that warrants inclusion. Our team undertook a careful process for considering the appropriateness of each risk variable by reviewing the scientific and popular literature. Click on the links in this paragraph to see examples of some of the sources we consulted in this process. The first map shows our model of the relative risk of developing COVID-19 based on the key characteristics of the people who live in a neighbourhood. This model considers (as key characteristics) people who are:  living in a crowded house ;  living with a large household ;  not knowing English ;  working outside the home ;  employed in a high risk industry ; a  public transit user ; and who are  socio-economically disadvantaged . The second map shows our model of the relative risk of COVID-19 transmission based on the activities that happen within certain types of places in a neighbourhood. This model considers the:  density of bars & restaurants;   percentage of farmland ;  density of food processing sites ;  density of long-term care beds ;  density of homeless shelters ;  density of daycares ;  density of schools ;  presence of universities with residences ;  density of places of worship ;  density of tourism destinations ;  presence of prisons ;  presence of major mines ; score for  international connectivity  (e.g., presence of airports). The third map shows our overall model of relative vulnerability to COVID-19 transmission risks in neighbourhoods by combining the scores from the first and second models.

There are many points to keep in mind when interpreting what you see in these maps. First, these maps have been created using models that we built specifically for this study. While we have drawn on the best information and science available to us to create these models, we acknowledge that all models have limitations and can never fully capture all the complexity that surrounds an issue. This is why they must be considered with other types of evidence, which can also include other maps (such as  these  from the BCCDC). Second, much of the information we have used to make these maps come from the Canadian census. This means that the variables we have included here are limited to ones we can explore with data recorded by Statistics Canada or that is publicly available. Related to this, we use ‘dissemination areas’ to depict neighbourhoods in our maps, which are the smallest standard geographic unit for which census data is disseminated by Statistics Canada. Third, our models explore risk and in no way should they be used to attribute blame for COVID-19 transmission, spread, or outbreaks to specific people or places in particular neighbourhoods. Finally, our models use a ‘business as usual’ approach to the variables that are included, which means that we do not factor in pandemic-related restrictions or closures. This is useful because when we combine our maps that focus on risk with epidemiologic data about COVID-19 cases, this can help us to understand the potential effectiveness of some of these public health measures in vulnerable neighbourhoods.

This research has been supported by a COVID-19 Rapid Response Grant awarded by the Michael Smith Foundation for Health Research and some additional funding from the BC SUPPORT Unit. Our team members are Dr. Valorie Crooks (Principal Investigator), Dr. Nadine Schuurman (Co-Investigator), Dr. Melissa Giesbrecht (Research Coordinator), Leah Rosenkrantz (PhD trainee), Jessica Tate (Master’s trainee), Kristie Nicol (Patient Partner), and Paul Burgener (Patient Partner). Our team is based out of the Department of Geography at Simon Fraser University.

Questions? Comments? Want technical details about this project? We welcome hearing from you. Connect with Dr. Crooks at  crooks@sfu.ca .