This is a summary, written by members of the CITF Secretariat, of:
Mishra S, Ma H, Moloney G, Yiu KCY, Darvin D, Landsman D, Kwong JC, Calzavara A, Straus S, Chan AK, Gournis E, Rilkoff H, Xia Y, Katz A, Williamson T, Malikov K, Kustra R, Maheu-Giroux M, Sander B, Baral SD, on behalf of the COVID-19 Heterogeneity Research Group. Increasing concentration of COVID-19 by socioeconomic determinants and geography in Toronto, Canada: an observational study. medRxiv. 2021 April 6. doi: 10.1101/2021.04.01.21254585
Watson T, Kwong JC, Kornas K, Mishra S, Rosella LC. Neighbourhood characteristics associated with the geographic variation in laboratory confirmed COVID-19 in Ontario, Canada: a multilevel analysis. medRXiv. 2021 April 2. doi: 10.1101/2021.04.06.21254988
The results and/or conclusions contained in the research do not necessarily reflect the views of all CITF members.
Two studies from Ontario highlight the importance of sociodemographic factors and COVID-19 risk
As the COVID-19 pandemic moves into its third wave, it is apparent that socioeconomic determinants of health play a role in determining risk of infection. Using place of residence as a proxy, two studies from Ontario have submitted preprints (therefore not yet peer-reviewed) demonstrating how socioeconomic status is associated with increased risk of getting COVID-19. Both of these studies, done in collaboration with Vaccine Surveillance Reference Group (VSRG) member Dr. Jeff Kwong, highlight that as the pandemic has evolved, so have geographic areas of risk.
COVID-19 is not the great equalizer once predicted
In examining the burden of COVID-19 observed across Canada, it is becoming increasingly clear that all communities do not experience COVID-19 risk equally. A study led by Dr. Sharmistha Mishra and including Dr. Jeff Kwong, sought to quantify the degree to which differences in socioeconomic status (SES) in Toronto affect the risk of contracting SARS-CoV-2, the virus that causes COVID-19, and how this risk has evolved over time.
Based on data related to the social determinants of health, the authors illustrated growing inequities in patterns of COVID-19 cases over time. This paper highlights how, early in the pandemic, locally-acquired cases were concentrated in higher income neighbourhoods, but as the pandemic has progressed, cases have been concentrated in lower income neighbourhoods.
Areas of Toronto particularly burdened by COVID-19 report lower SES and greater proportions of individuals employed in occupations that are not amenable to remote work. The authors suggest that this rapid transition in COVID-19 from an epidemic concentrated among higher income communities to local transmission in lower income communities is due to structural risks that often define where people work and how they live.
The researchers examined 33,992 COVID-19 positive diagnoses throughout Toronto using surveillance data from Ontario’s Case and Contact Management strategy. Cases were analyzed by postal codes. This information was then linked with existing datasets to gain information about social determinants of health such as income and household structure.
The importance of Place
Understanding how neighbourhood-level characteristics influence COVID-19 rates helps public health officials design and implement appropriate responses. In this study led by Dr. Tristan Watson and featuring Dr. Jeff Kwong, the authors measured the role of sociodemographic factors and geography in explaining risk of COVID-19 infections across Ontario.
They found that neighbourhoods where people were more likely to live in crowded housing were associated with a higher incidence of COVID-19 diagnoses. The study also showed associations with types of employment in the neighbourhoods and COVID-19 rates, highlighting how occupational factors can impact exposure to COVID-19 infection. Based on their findings, the authors suggest interventions that support a healthy workforce, including personal protective equipment (PPE), detailed case management tracking, and paid sick leave.
The cohort study was created through the linkage of multiple population and health administrative databases. The primary outcome, defined a priori, was the neighbourhood rate of laboratory-confirmed COVID-19 per 1,000 people in each neighbourhood in Ontario.
This research highlights the importance of recognizing neighbourhood-level characteristics when discussing geographic variation in COVID-19 rates. The authors conclude that any interventions to reduce COVID-19 should address structural and social determinants of health within neighbourhoods.