COVID-19 Disparities Among Arab, Middle Eastern, and West Asian Populations in Toronto: Implications for Improving Health Equity Among Middle Eastern and North African Communities in the United States
Bibliographic record
Abstract
INTRODUCTION: Equity-oriented efforts to mitigate and prevent COVID-related disparities are hindered due to methodological limitations of the categorization of racial and ethnic groups, including Arabs and Middle Eastern and North African (MENA) communities, which remain invisible in national data collection efforts. This study highlights the disparities in COVID-related outcomes in Toronto, Canada and supports ongoing calls to collect public health data among MENA communities in the United States. METHODS: Data on racial/ethnic identity and hospitalizations were collected by the Toronto Public Health (TPH) of the Ontario Ministry of Public Health Case between May 20, 2020, and September 30, 2021 from people with a confirmed or probable case of COVID-19. RESULTS: The reported COVID-19 infection rate for Arab, Middle Eastern, West Asians (i.e., categories used to self-identify as MENA in Canada) relative to Whites in Toronto was 3.51. The age-standardized hospitalization rate ratio between Arab, Middle Eastern, West Asians and Whites was 4.59. DISCUSSION: Data from Toronto highlight that Arab, Middle Eastern, and West Asians have higher rates of COVID-19 infections and hospitalizations than their White counterparts. Comparable studies are currently not possible in the United States due to lack of data that can disaggregate MENA individuals. This study underscores the critical need to collect data among MENA communities in the United States to advance our field's goal of promoting and advancing equity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".