Characterizing the disproportionate burden of SARS-CoV-2 variants of concern among essential workers in the Greater Toronto Area, Canada
Bibliographic record
Abstract
ABSTRACT Importance The emergence of SARS-CoV-2 Variants of Concern (VOC) across North America has been associated with concerns of increased COVID-19 transmission. Characterizing the distribution of VOCs can inform development of policies and programs to address the prevention needs of disproportionately affected communities. Objective We compared per-capita rates of COVID-19 cases (overall and VOC) from February 3, 2021 to March 10, 2021, across neighborhoods in the health regions of Toronto and Peel, Ontario, by proportion of the population working in essential services and income. Design Descriptive epidemiological analysis, integrating COVID-19 surveillance and census data. Per-capita daily epidemic curves were generated using 7-days rolling averages for cases and deaths. Cumulative per-capita rates were determined using census-reported population of each neighbourhood. Setting The study setting was the city of Toronto and the region of Peel (the City of Brampton, Mississauga, and Caledon), Canada’s largest cities with a combined population of 4.3 million. This area of Canada has had one of the highest incident rates of COVID-19 throughout the pandemic. Participants We used person-level data on laboratory-confirmed COVID-19 community cases (N=22,478) and census data for neighborhood-level attributes. Exposures We stratified neighbourhoods, i.e., dissemination areas which represent geographic areas of approximately 400-700 individuals, into tertiles by ranking the proportion of population in each neighbourhood working in essential services (health, trades, transport, equipment, manufacturing, utilities, sales, services, agriculture); and the per-person equivalent household income. Main Outcome(s) and Measure(s) The primary outcomes were laboratory-confirmed COVID-19 cases overall and VOC positives by neighbourhood. Results During the study period, VOC cases emerged faster in groups with lowest income (growth rate 43.8%, 34.6% and 21.6% by income tertile from lowest to highest), and most essential work (growth rate 18.4%, 30.8% and 50.8% by tertile from lowest tertile of essential workers to highest tertile of essential workers). Conclusions and Relevance The recent introduction of VOC in a large urban area has disproportionately affected neighbourhoods with the most essential workers and lowest income levels. Notably, this is consistent with the increased burden of non-VOC COVID-19 cases suggesting shared risk factors. To date, restrictive public health strategies have been of limited impact in these communities suggesting the need for complementary and well-specified supportive strategies to address disparities and overall incidence of both VOC and non-VOC COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".