Background rates of all-cause mortality, hospitalizations, and emergency department visits among nursing home residents in Ontario, Canada to inform COVID-19 vaccine safety assessments
Bibliographic record
Abstract
Abstract Background Nursing home (NH) residents are prioritized for COVID-19 vaccination. We report monthly mortality, hospitalizations, and emergency department (ED) visit incidence rates (IRs) during 2010-2020 to provide context for COVID-19 vaccine safety assessments. Methods We observed outcomes among NH residents using administrative databases. IRs were calculated by month, sex, and age group. Comparisons between months were assessed using one-sample t-tests; comparisons by age and sex were assessed using chi-squared tests. Results From 2010-2019, there were 83,453 (SD: 652.4) NH residents per month, with an average of 2.3 (SD: 0.28) deaths, 3.1 (SD: 0.16) hospitalizations, and 3.6 (SD: 0.17) ED visits per 100 residents per month. From March to December 2020, mortality IRs were increased, but hospitalization and ED visit IRs were reduced (p<0.05). Conclusion We identified consistent monthly mortality, hospitalization, and ED visit IRs during 2010-2019. Marked differences in these rates were observed during 2020, coinciding with the COVID-19 pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".