An interrupted time series study using administrative health data to examine the impact of the COVID-19 pandemic on alternate care level acute hospitalizations in Ontario, Canada
Bibliographic record
Abstract
Background: Many health systems struggle with delayed discharges (known as alternate level of care [ALC] in Canada). Our objectives were to describe and compare patient and hospitalization characteristics by ALC status, and to examine the impact of the initial period of the COVID-19 pandemic on ALC rates in Ontario, Canada. Methods: We conducted an interrupted time series using linked administrative data for acute care hospital discharges in Ontario between Feb. 28, 2018, and Nov. 30, 2020. We measured the monthly ALC rate among discharges before and after the onset of the COVID-19 pandemic (Mar. 1, 2020). We used interrupted time series regressions to examine the association between the onset of the pandemic and average ALC monthly rates. Results: We identified no meaningful differences in patient and admission characteristics, irrespective of time; however, differences were identified by ALC status. The overall average monthly rate of ALC discharges before the COVID-19 pandemic was 4.9% and after the onset of the pandemic was 5.0%. These discharges dropped to 4.3% (n = 3558) in March 2020 but then rebounded to their peak of 5.8% (n = 3915). There was no significant change in the average level of ALC rates per month after the onset of the pandemic (increase of 0.36% average per month, 95% confidence interval [CI] −0.11% to 0.83%) or monthly rate of change (slope) after the onset of the pandemic (−0.08%, 95% CI −0.15 to 0). Interpretation: We identified a continued high rate of hospital discharges with an ALC component despite the considerable efforts in hospital to reduce hospital occupancy during the COVID-19 pandemic. Future research should examine why ALC rates remain high despite hospital 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".