Silent Voices of Immigrants and Refugees Battling with Mental Health and Addiction during COVID-19: A Follow- Up Population-Based Cohort Retrospective Study in Ontario, Canada
Bibliographic record
Abstract
Background: Although the COVID-19 pandemic has affected all communities across Canada, immigrants and refugees have shouldered a disproportionate burden of the disease. This health disparity is not surprising, given their structurally marginalized social and economic positions. Further, immigrants and refugees with chronic health conditions, such as mental health and addiction disorders (MH&A), may be particularly vulnerable to the pandemic's negative impacts due to the preexisting debilitating health conditions. There is limited information in this area. This study is a follow up to our first study that looked at the impact of COVID-19 on immigrants and refugee population living with MH&A over a year of COVID-19 (See DOI: 10.26502/acbr.50170393). Methods: As our initial study only covered the first two waves of COVID-19, a follow up retrospective cohort was conducted using linked Ontario-based administrative databases to expand the timeframe. The differential impact of COVID-19 over the two years (March 31, 2020, to December 31, 2021) on immigrants and non-immigrants with and without MH&A were examined using multivariate regression while controlling for potential socioeconomic and health-related confounders (e.g., age, sex, income quintiles, living in deprived neighbourhoods, region of origin, region of residence in Ontario, comorbidities, and access to primary care). Results: Our study included about 10.4 million Ontario residents aged 18 or older, of which 24% were identified as immigrants and 8.9% lived with MH&A. The average age of immigrants and non-immigrants living with MH&A was around 46 years with nearly 60% identifying as female. While both immigrants and non-immigrants with MH&A were more likely than those without MH&A to be impoverished and reside in socially deprived neighborhoods immigrants with MH&A were more socially disadvantaged than non-immigrant without MH&A (27.2% vs. 17.2%, Std diff=0.242; 31% vs. 23.3%, Std diff=0.175; 23.7%vs. 17%, Std diff=0.2=0.166). The prevalence of confirmed COVID-19 test results was significantly higher among immigrants than non-immigrants living with MH&A (17.7% vs. 9.5%). When we adjusted for potential confounders, immigrants living with MH&A were 52% more likely to be diagnosed with COVID19, over twice as likely to be hospitalized and be admitted to ICU, and 65% more likely to die from COVID-19 non-immigrants without MH&A. Conclusion: Our study provides evidence that the intersection of immigration status and preexisting MH&A significantly influences COVID-19 adverse outcomes. It is crucial that COVID-19 recovery efforts and future crisis responses incorporate targeted upstream interventions and community based-support systems that address the specific needs of structurally and clinically marginalized populations.
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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 | 0.000 |
| 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".