Q and A with the experts: collecting race-based and socio-economic data during COVID-19

Thursday, April 30, 2020

Every day we learn about the number of new positive cases, the number of deaths and the number of resolved cases of COVID-19. But is there more information we should be collecting about who the virus is infecting?

Professor Michael Wallace discusses what we can learn from collecting socio-economic and race-based data on COVID-19. 

Michael Wallace, professor in Waterloo's Department of Statistics and Acturial Science

Watch video of Prof. Wallace

What kinds of socio-economic or race-based data could be collected right now, and why might they matter in the fight against COVID-19?

When we approach this kind of data, we usually start with fairly broad categorizations – a patient's membership of a racial and/or ethnic group, for example, or their employment status and household income. It may not seem obvious why such factors should matter in the context of COVID-19 - a virus doesn't check your bank balance before deciding to strike - but these broad classifiers are often correlated with other, more directly relevant aspects of individual lives.

It's well established, for example, that wealth is associated with health. Individuals in low-income households are more likely to experience chronic health conditions which in turn increases their susceptibility to COVID-19. The spread - and impact - of the disease could also be affected by where you live, the structure of your household, whether you're employed, and the type of work you do. All of these are areas where race, ethnicity, and socio-economic status can have a bearing. 

What could socio-economic and race-based data tell us about COVID-19?

This kind of data could help us learn more about who is most vulnerable to the disease, how it is spreading, and how its course might change in the coming weeks, months, and even years. There is growing evidence that this kind of data could provide insights into how COVID-19's impact and spread are felt differently by different communities.

Much of this evidence has emerged from the United States. A Centers for Disease Control and Prevention (CDC) report, for example, suggested that black Americans may be disproportionately affected by the disease. New York City, meanwhile,  reported higher COVID-19 death rates among black/African American and Hispanic/Latino individuals than white or Asian individuals. It has also been suggested the city's lower-income areas are some of the worst-hit. However, more data - and careful analysis - is required to fully contextualize such preliminary observations.

Why is it important to collect this kind of data?

Our most powerful weapon in the fight against COVID-19 is information. Data is information in its purest form. 

We've learned from past pandemics that race, ethnicity, and socio-economic status could matter in the spread of this disease. Not as immediate causes, but as proxies for more direct - but harder to measure - factors. By gathering this data we'll learn more, not only about which communities are most at risk, but also where - and how COVID-19 is spreading.

This information would help inform health policy and practice, from providing more support for those most vulnerable, to arranging targeted testing in communities identified as at the greatest risk.

The University of Waterloo has a number of experts available for comment on various aspects of the COVID-19 pandemic, click here to see the up-to-date list.

MEDIA CONTACT | Rebecca Elming
519-888-4567 x 30031 | @uwaterloonews | uwaterloo.ca/news

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