Heterogeneity in testing, diagnosis and outcome in SARS-CoV-2 infection across outbreak settings in the Greater Toronto Area, Canada: an observational study
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
BACKGROUND: Congregate settings have been disproportionately affected by coronavirus disease 2019 (COVID-19). Our objective was to compare testing for, diagnosis of and death after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection across 3 settings (residents of long-term care homes, people living in shelters and the rest of the population). METHODS: We conducted a population-based prospective cohort study involving individuals tested for SARS-CoV-2 in the Greater Toronto Area between Jan. 23, 2020, and May 20, 2020. We sourced person-level data from COVID-19 surveillance and reporting systems in Ontario. We calculated cumulatively diagnosed cases per capita, proportion tested, proportion tested positive and case-fatality proportion for each setting. We estimated the age- and sex-adjusted rate ratios associated with setting for test positivity and case fatality using quasi-Poisson regression. RESULTS: Over the study period, a total of 173 092 individuals were tested for and 16 490 individuals were diagnosed with SARS-CoV-2 infection. We observed a shift in the proportion of cumulative cases from all cases being related to travel to cases in residents of long-term care homes (20.4% [3368/16 490]), shelters (2.3% [372/16 490]), other congregate settings (20.9% [3446/16 490]) and community settings (35.4% [5834/16 490]), with cumulative travel-related cases at 4.1% (674/16490). Cumulatively, compared with the rest of the population, the diagnosed cases per capita was 64-fold and 19-fold higher among long-term care home and shelter residents, respectively. By May 20, 2020, 76.3% (21 617/28 316) of long-term care home residents and 2.2% (150 077/6 808 890) of the rest of the population had been tested. After adjusting for age and sex, residents of long-term care homes were 2.4 (95% confidence interval [CI] 2.2-2.7) times more likely to test positive, and those who received a diagnosis of COVID-19 were 1.4-fold (95% CI 1.1-1.8) more likely to die than the rest of the population. INTERPRETATION: Long-term care homes and shelters had disproportionate diagnosed cases per capita, and residents of long-term care homes diagnosed with COVID-19 had higher case fatality than the rest of the population. Heterogeneity across micro-epidemics among specific populations and settings may reflect underlying heterogeneity in transmission risks, necessitating setting-specific COVID-19 prevention and mitigation strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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