The impact of COVID-19 on Immigrants and Refugees living with Mental Health and Addiction Disorders: A Population-Based Cohort study: in Ontario, Canada
Bibliographic record
Abstract
Abstract Background: While the COVID-19 pandemic has taken an enormous toll on communities across Canada and the globe, its negative impacts have not been experienced equally. People with mental health and addiction disorders (MH&A) have been found to be at greater risk of COVID-19 infection and worse COVID-19 outcomes. Similarly, although immigrants and refugees contribute to one-quarter of Ontario’s population they make up nearly half of Ontario’s COVID-19 cases. There is a paucity of information on the impact of COVID-19 on people who are at the intersection of MH&A and socioeconomic deprivation. Our study aimed to address this gap. Methods: A population-based retrospective cohort study over a one-year period (January 15, 2020, to Feb 15, 2021) was conducted using multiple linked provincial-administrative databases. The study aimed to determine the differential impact of COVID-19 on immigrants and non-immigrants with MH& A and the general population without MH&A across sociodemographic and health-related factors like age, sex, neighbourhood income, Ontario marginalization index, comorbidities, and access to primary care. We used multivariable regression to adjust for potential confounders. Results: Our cohort comprised 10,994,464 Ontario residents aged 18 or older and of which approximately 17% lived with MH&A, with immigrants and refugees with MH&A making up 2.6%. People with preexisting MH&A were generally younger and more likely to live in deprived neighbourhoods compared to the general population. Immigrants and refugees with MH&A were more likely to reside in neighbourhoods with greater material deprivation, residential instability, and ethnic concentration compared to non-immigrants with MH&A. Even though the COVID-19 testing rate was lower among immigrants living with MH&A compared to non-immigrants with MH&A (32.7% vs. 37.6%), the confirmed positivity was significantly higher (12.4% vs. 4.5%). Adjusting for confounders we also found Covid 19 testing, hospital admission, intensive care admission, and mortality rates related to COVID-19 were considerably higher among people with MH&A than in the general population. Conclusion: Our findings provide evidence of the need to accelerate the development of targeted evidence-based policies that can effectively support and protect people living at the intersection of clinical and social inequities in this and future crises.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".