The Impact of Comorbidities among Ethnic Minorities on COVID-19 Severity and Mortality in Canada and the USA: A Scoping Review
Bibliographic record
Abstract
(1) Current literature on ethnic minorities, comorbidities, and COVID-19 tends to investigate these factors separately, leaving gaps in our understanding about their interactions. Our review seeks to identify a relationship between ethnicity, comorbidities, and severe COVID-19 outcomes (ICU admission and mortality). We hope to enhance our understanding of the various factors that exacerbate COVID-19 severity and mortality in ethnic minorities in Canada and the USA. (2) All articles were received from PubMed, Scopus, CINAHL, and Ovid EMBASE from November 2020 to June 2022. Included articles contain information regarding comorbidities among ethnic minorities in relation to COVID-19 severity and mortality. (3) A total of 59 articles were included that examined various ethnic groups, including Black/African American, Asian, Hispanic, White/Caucasian, and Indigenous people. We found that the most examined comorbidities were diabetes, hypertension, obesity, and chronic kidney disease. A total of 76.9% of the articles (40 out of 52) found a significant association between different races and COVID-19 mortality, whereas 21.2% of the articles (11 out of 52) did not. (4) COVID-19 ICU admissions and mortality affect various ethnic groups differently, with Black patients generally having the most adverse outcomes. These outcomes may also interact with sex and age, though more research is needed assessing these variables together with ethnicity.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".