Seropositivity and risk factors for SARS-CoV-2 infection in a South Asian community in Ontario: a cross-sectional analysis of a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Early in the COVID-19 pandemic, the South Asian community in the Greater Toronto Area (GTA) was identified as having risk factors for exposure and specific barriers to accessing testing and reliable health information, rendering them particularly vulnerable to SARS-CoV-2 infection. We sought to investigate the burden of SARS-CoV-2 infection among South Asian people in the GTA, and to characterize the demographic characteristics, risk perceptions and trusted sources of health information in this group. METHODS: We conducted a cross-sectional analysis from the baseline assessment of participants in a prospective cohort study. Participants from the GTA were enrolled from Apr. 14 to July 28, 2021. Seropositivity for antispike and antinucleocapsid antibodies was determined from dried blood spots, and estimates of seropositivity were age and sex standardized to the South Asian population in Ontario. Demographic characteristics, risk perceptions and sources of COVID-19 information were collected via questionnaire and reported descriptively. RESULTS: Among the 916 South Asian participants enrolled (mean age 41 yr), the age- and sex-standardized seropositivity was 23.6% (95% confidence interval 20.8%-26.4%). Of the 693 respondents to the questionnaire, 228 (32.9%) identified as essential workers, and 125 (19.1%) reported living in a multigenerational household. A total of 288 (49.4%) perceived that they were at high COVID-19 risk owing to their geographic location, and 149 (34.3%) owing to their type of employment. The top 3 most trusted sources of information related to COVID-19 included health care providers and public health, traditional media sources and social media. INTERPRETATION: By the third wave of the COVID-19 pandemic, about one-quarter of a sample of South Asian individuals in Ontario had serologic evidence of prior SARS-CoV-2 infection. Insight into factors that put 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".