A cohort study of patients hospitalised with SARS-CoV-2 infection in Ontario: patient characteristics and outcomes by wave
Bibliographic record
Abstract
INTRODUCTION: Each wave of the COVID-19 pandemic exhibited a unique combination of epidemiological, social and structural characteristics. We explore similarities and differences in wave-over-wave characteristics of patients hospitalised with COVID-19. METHODS: This was a population-based study in Ontario province, Canada. Patients hospitalised with SARS-CoV-2 between 26 February 2020 and 31 March 2022 were included. An admission was considered related to SARS-CoV-2 infection if the provincial inpatient or outpatient hospital databases contained the ICD-10 diagnostic codes U071/U072 or the Ontario Laboratories Information System indicated a positive SARS-CoV-2 test result (PCR or rapid antigen testing) during the admission or up to two weeks prior. The primary outcome was 90-day mortality (modified Poisson regression). Secondary outcomes were use of critical care during the admission (logistic regression) and total length-of-stay (linear regression with heteroskedastic-consistent standard-error estimators). All models were adjusted for demographic characteristics, neighbourhood socioeconomic factors and indicators of illness severity. RESULTS: There were 73,201 SARS-CoV-2-related admissions: 6127 (8%) during wave 1 (wild-type), 14,371 (20%) during wave 2 (wild-type), 16,653 (23%) during wave 3 (Alpha), 5678 (8%) during wave 4 (Delta) and 30,372 (42%) during wave 5 (Omicron). SARS-CoV-2 was the most responsible diagnosis for 70% of admissions during waves 1-2 and 42% in wave 5. The proportion of admitted patients who were long-term care residents was 18% (n = 1111) during wave 1, decreasing to 10% (n = 1468) in wave 2 and <5% in subsequent waves. During waves 1-3, 46% of all admitted patients resided in a neighbourhood assigned to the highest ethnic diversity quintile, which declined to 27% during waves 4-5. Compared to wave 1, 90-day mortality was similar during wave 2 (adjusted risk ratio [aRR]: 1.00 [95% CI: 0.95-1.04]), but lower during wave 3 (aRR: 0.89 [0.85-0.94]), wave 4 (aRR: 0.85 [0.79-0.91]) and wave 5 (aRR: 0.83 [0.80-0.88]). Improvements in survival over waves were observed among elderly patients (p-interaction <0.0001). Critical care admission was significantly less likely during wave 5 than previous waves (adjusted odds ratio: 0.50 [0.47-0.54]). The length of stay was a median of 8.5 (3.6-23.8) days during wave 1 and 5.3 (2.2-12.6) during wave 5. After adjustment, the mean length of stay was on average -10.4 (-11.1 to -9.8) days, i.e. shorter, in wave 5 vs wave 1. CONCLUSION: Throughout the pandemic, sociodemographic characteristics of patients hospitalised with SARS-CoV-2 changed over time, particularly in terms of ethnic diversity, but still disproportionately affected patients from more marginalised regions. Improved survival and reduced use of critical care during the Omicron wave are reassuring.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".