What factors converged to create a COVID-19 hot-spot? Lessons from the South Asian community in Ontario
Bibliographic record
Abstract
Abstract Background South Asians represent the largest non-white ethnic group in Canada. The Greater Toronto Area (GTA), home to a high proportion of South Asians, emerged as a COVID-19 hot spot. Early in the pandemic, the South Asian community was identified as having risk factors for exposure and specific barriers to accessing testing and reliable health information, rendering them uniquely vulnerable to SARS-CoV-2 infection. Objectives To investigate the burden of SARS-CoV-2 infection among South Asians in the GTA, and to determine which demographic characteristics were most closely aligned with seropositivity, in this cross-sectional analysis of a prospective cohort study. Methods Participants from the GTA were enrolled between April and July 2021. Seropositivity for anti-spike and anti-nucleocapsid antibodies was determined from dried blood spots, and age and sex standardized to the Ontario South Asian population. Demographics, risk perceptions, and sources of COVID-19 information were collected via questionnaire in a subset. Results Among the 916 South Asians enrolled, mean age 41 years, the age and sex standardized seropositivity was 23.6% (95% CI: 20.8%-26.4%). Approximately one-third identified as essential workers, and 19% reported living in a multi-generational household. Over half perceived high COVID-19 risk due to their geographic location, and 36% due to their type of employment. The top three most trusted sources of COVID-related information included healthcare providers/public health, traditional media sources, and social media. Conclusion By the third wave of the COVID-19 pandemic, approximately one-quarter of a sample of South Asians in Ontario had serologic evidence of prior SARS-CoV-2 infection. Insight into factors that render certain populations at risk can help future pandemic planning and disease control efforts.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".