Early in the COVID-19 pandemic, as public health officials and policy-makers tried to understand the new coronavirus, it became clear that certain communities were affected more severely. A strong link was becoming apparent between the social determinants of health, including work, and the risks of COVID-19 infection and poor outcomes.
At Public Health Ontario (PHO), a team led by Drs. Brendan Smith and Erin Hobin saw a need to better understand how work-related exposure to COVID-19, such as working in close proximity to others, differed across—and interacted with—sociodemographic factors (e.g. age, sex, race or ethnicity, immigrant status and household income). The team set out to create an open-source interactive data visualization tool that drew on publicly available data to describe the different risk levels. The result is the Occupational Exposure to COVID-19 Risk Tool, launched in December 2020.
It may be hard to remember, but early in the pandemic, very limited data was available to understand what factors were leading to higher rates of COVID-19 in certain communities, says PHO’s Smith, who co-led a team that included scientists from the Institute for Work & Health (IWH)—an example of system collaboration to answer emerging research questions.
We were trying to figure out what role work played in increasing the risks of COVID-19 across different communities, different racial and ethnic groups, etc.
The tool draws on three sources of data. The provincial/territorial lists of essential services helped the team identify the workers who could work remotely during the pandemic. The second source, the 2016 Canadian Census, provided data on the occupations and industries of 18 million Canadians, as well as information on the sociodemographic profile of 500 different occupation groups.
The third source, the O*NET database in the United States, offered detailed descriptions of job contexts and work descriptions for specific jobs and industries. This information allowed the team to assign scores representing, for each occupation in the tool, whether and to what extent workers are exposed to infectious diseases, do their work outdoors, are in close proximity with others or potentially do their work from home. Smith stressed that this information was developed prior to the pandemic and did not reflect adjustments made in response to COVID—e.g. physical distancing measures, and so on.
The tool allows users to examine data by key sociodemographic and work characteristics, and to find risk profiles across different occupations within a given industry. Importantly, the tool also lets users filter for results specific to the public health units or health regions within provinces.
We wanted to make local or regional data available for people to use in making decisions, Smith explains.
Since launching the tool, Smith has seen it used in ways that went beyond the team’s original intent. Some have used the tool for information on worker populations in specific industry or occupational groups. More recently, the tool helped inform the Ontario Ministry of Health and Long-Term Care’s vaccine prioritization plan for health-care and other sectors. Smith’s team also made available regional data for each public health unit in the province to help identify which health-care occupations were most at risk and how many workers would be affected.
Smith credits the involvement of IWH scientists for their expertise on occupational data sources.
IWH scientists contributed extensive experience working with occupation and industry classification information to the team, he says.
They have been key to helping us interpret findings, especially with regards to which groups of workers were at higher risks.
This collaboration has already led to additional studies exploring patterns of COVID risks during the pandemic, says IWH Senior Scientist Dr. Peter Smith.
Working on this tool connected IWH and PHO, and has led to other projects examining levels of worker protections across industry and demographic groups, and allowed for accurate estimates of COVID infection rates due to workplace outbreaks across industries.