Outcomes of hospitalized COVID-19 patients in Canada: impact of ethnicity, migration status and country of birth
Bibliographic record
Abstract
BACKGROUND: Ethnoracial groups in high-income countries have a 2-fold higher risk of SARS-CoV-2 infection, associated hospitalizations, and mortality than Whites. Migrants are an ethnoracial subset that may have worse COVID-19 outcomes due to additional barriers accessing care, but there are limited data on in-hospital outcomes. We aimed to disaggregate and compare COVID-19 associated hospital outcomes by ethnicity, immigrant status and region of birth. METHODS: Adults with community-acquired SARS-CoV-2 infection, hospitalized March 1-June 30, 2020, at four hospitals in Montréal, Quebec, Canada, were included. Age, sex, socioeconomic status, comorbidities, migration status, region of birth, self-identified ethnicity [White, Black, Asian, Latino, Middle East/North African], intensive care unit (ICU) admissions and mortality were collected. Adjusted hazard ratios (aHR) for ICU admission and mortality by immigrant status, ethnicity and region of birth adjusted for age, sex, socioeconomic status and comorbidities were estimated using Fine and Gray competing risk models. RESULTS: Of 1104 patients (median [IQR] age, 63.0 [51.0-76.0] years; 56% males), 57% were immigrants and 54% were White. Immigrants were slightly younger (62 vs 65 years; p = 0.050), had fewer comorbidities (1.0 vs 1.2; p < 0.001), similar crude ICU admissions rates (33.0% vs 28.2%) and lower mortality (13.3% vs 17.6%; p < 0.001) than Canadian-born. In adjusted models, Blacks (aHR 1.39, 95% confidence interval 1.05-1.83) and Asians (1.64, 1.15-2.34) were at higher risk of ICU admission than Whites, but there was significant heterogeneity within ethnic groups. Asians from Eastern Asia/Pacific (2.15, 1.42-3.24) but not Southern Asia (0.97, 0.49-1.93) and Caribbean Blacks (1.39, 1.02-1.89) but not SSA Blacks (1.37, 0.86-2.18) had a higher risk of ICU admission. Blacks had a higher risk of mortality (aHR 1.56, p = 0.049). CONCLUSIONS: Data disaggregated by region of birth identified subgroups of immigrants at increased risk of COVID-19 ICU admission, providing more actionable data for health policymakers to address health inequities.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".