Association Between Pandemic Coronavirus Disease 2019 Public Health Measures and Reduction in Critical Care Utilization Across ICUs in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: The coronavirus disease 2019 pandemic has disrupted critical care services across the world. In anticipation of surges in the need for critical care services, governments implemented "lockdown" measures to preserve and create added critical care capacity. Herein, we describe the impact of lockdown measures on the utilization of critical care services and patient outcomes compared with nonlockdown epochs in a large integrated health region. DESIGN: This was a population-based retrospective cohort study. SETTING: Seventeen adult ICUs across 14 acute care hospitals in Alberta, Canada. PATIENTS: All adult (age ≥ 15 yr) patients admitted to any study ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The main exposure was ICU admission during "lockdown" occurring between March 16, 2020, and June 30, 2020. This period was compared with two nonpandemic control periods: "year prior" (March 16, 2019, to June 30, 2019) and "pre lockdown" immediately prior (November 30, 2019, to March 15, 2020). The primary outcome was the number of ICU admissions. Secondary outcomes included the following: daily measures of ICU utilization, ICU duration of stay, avoidable delay in ICU discharge, and occupancy; and patient outcomes. Mixed multilevel negative binomial regression and interrupted time series regression were used to compare rates of ICU admissions between periods. Multivariable regressions were used to compare patient outcomes between periods. During the lockdown, there were 3,649 ICU admissions (34.1 [8.0] ICU admissions/d), compared with 4,125 (38.6 [9.3]) during the prelockdown period and 3,919 (36.6 [8.7]) during the year prior. Mean bed occupancy declined significantly during the lockdown compared with the nonpandemic periods (78.7%, 95.9%, and 96.4%; p < 0.001). Avoidable ICU discharge delay also decreased significantly (42.0%, 53.2%, and 58.3%; p < 0.001). During the lockdown, patients were younger, had fewer comorbid diseases, had higher acuity, and were more likely to be medical admissions compared with the nonpandemic periods. Adjusted ICU and hospital mortality and ICU and hospital lengths of stay were significantly lower during the lockdown compared with nonpandemic periods. CONCLUSIONS: The coronavirus disease 2019 lockdown resulted in substantial changes to ICU utilization, including a reduction in admissions, occupancy, patient lengths of stay, and mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".