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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it