The impact of COVID-19 on Immigrants and Refugees living with Cancer: A Population-Based Cohort Study in Ontario, Canada
Bibliographic record
Abstract
Background: While the COVID-19 pandemic has taken an enormous toll on communities across Canada, its negative impacts have not been experienced equally within immigrant and refugee communities. This disparity emanates from their systematically marginalized social and economic position in Canada. The association of social inequities with adverse COVID-19 outcomes can further be intensified in the context of underlying chronic health conditions like Cancer. There is a paucity of information on the impact of COVID-19 on immigrants living with active cancer. Our study aimed to address this gap. Methods: A population-based retrospective cohort study over 2 years (March 31, 2020, to December 31, 2021) was conducted based on multiple linked provincial-administrative databases. Multivariable regression was utilized to assess the differential impact of COVID-19 on immigrants and non-immigrants with and without active cancer while adjusting for potential socioeconomic and health-related confounders (e.g., age, sex, income, comorbidities, and access to primary care). Results: Our study comprised about 10.4 million Ontario residents aged 18 or older, of which 24% were identified as immigrants and 0.7% lived with an active cancer. Among immigrants with active cancer, 63% were female. A higher proportion of immigrants living with cancer, compared to non-immigrants with cancer, lived in neighbourhoods that had the lowest household income (26% vs 18%), were the most residentially unstable (29% vs 25%), were the most materially deprived (23% vs 17%,) and were the most ethnically diverse (59% vs. 17%). About 15% of immigrants with active cancer also suffered from mental health and addiction disorders. The prevalence of confirmed COVID-19 test results was significantly higher among immigrants than non-immigrants living with cancer. Furthermore, when we adjusted for covariates, immigrants living with active cancer were 3 times more likely to be hospitalized and be admitted to ICU, and 4 times more likely to die from COVID-19 than their peers. Conclusion: Our study provides evidence that immigrants with active cancer were more socially and economically disadvantaged and had worse COVID-19 outcomes compared to their peers. System-level intervention is needed to protect those at the intersection of clinical and social vulnerabilities during pandemic recovery and in future crises.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".