Factors associated with SARS-CoV-2 positivity in 20 homeless shelters in Toronto, Canada, from April to July 2020: a repeated cross-sectional study
Bibliographic record
Abstract
Background: It is unclear what the best strategy is for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among residents of homeless shelters and what individual factors are associated with testing positive for the virus. We sought to evaluate factors associated with testing positive for SARS-CoV-2 among residents of homeless shelters and to evaluate positivity rates in shelters where testing was conducted in response to coronavirus disease 2019 (COVID-19) outbreaks or for surveillance. Methods: We conducted a retrospective chart audit to obtain repeated cross-sectional data from outreach testing done at homeless shelters between Apr. 1 and July 31, 2020, in Toronto, Ontario, Canada. We compared the SARS-CoV-2 positivity rate for shelters where testing was conducted because of an outbreak (at least 1 known case) with those tested for surveillance (no known cases). A patient-level analysis evaluated differences in demographic, health and behavioural characteristics of residents who did and did not test positive for SARS-CoV-2 at shelters with at least 2 positive cases. Results: One thousand nasopharyngeal swabs were done on 872 unique residents at 20 shelter locations. Among the 504 tests done in outbreak settings, 69 (14%) were positive for SARS-CoV-2 and 1 (0.2%) was indeterminate. Among the 496 tests done for surveillance, 11 (2%) were positive and none were indeterminate. Shelter residents who tested positive for SARS-CoV-2 were significantly less likely to have a health insurance card (54% v. 72%, p = 0.03) or to have visited another shelter in the last 14 days (0% v. 18%, p < 0.01). There was no association between SARS-CoV-2 positivity and medical history or symptoms. Interpretation: Our findings support testing of asymptomatic shelter residents for SARS-CoV-2 when a positive case is identified at the same shelter. Surveillance testing when there are no known positive cases may detect outbreaks, but further research should identify efficient strategies given scarce testing resources.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".