Surge Capacity Associated with Restrictions on Nonurgent Hospital Utilization and Expected Admissions during an Influenza Pandemic: Lessons from the Toronto Severe Acute Respiratory Syndrome Outbreak
Bibliographic record
Abstract
BACKGROUND: Current influenza pandemic models predict a surge in influenza-related hospitalizations in affected jurisdictions. One proposed strategy to increase hospital surge capacity is to restrict elective hospitalizations, yet the degree to which this measure would meet the anticipated is unknown. OBJECTIVES: To compare the reduction in hospitalizations resulting from widespread nonurgent hospital admission restrictions during the Toronto severe acute respiratory syndrome (SARS) outbreak with the expected increase in admissions resulting from an influenza pandemic in Toronto. METHODS: The authors compared the expected influenza-related hospitalizations in the first eight weeks of a mild, moderate, or severe pandemic with the actual reduction in the number of hospital admissions in Toronto, Ontario, during the first eight weeks of the SARS-related restrictions. RESULTS: Influenza modeling for Toronto predicts that there will be 4,819, 8,032, or 11,245 influenza-related admissions in the first eight weeks of a mild, moderate, or severe pandemic, respectively. In the first eight weeks of SARS-related hospital admission restrictions, there were 3,654 fewer hospitalizations than expected in Toronto, representing a modest 12% decrease in the overall admission rate (a reduction of 1.40 admissions per 1,000 population). Therefore, influenza-related admissions could exceed the reduction in admissions resulting from restricted hospital utilization by 1,165 to 7,591 patient admissions, depending on pandemic severity, which corresponds to an excess of 0.44 to 2.91 influenza-related admissions per 1,000 population per eight weeks, and an increase of 4% to 25% in the overall number of admissions, when compared with nonpandemic conditions. CONCLUSIONS: Pandemic modeling for Toronto suggests that influenza-related admissions would exceed the reduction in hospitalizations seen during SARS-related nonurgent hospital admission restrictions, even in a mild pandemic. Sufficient surge capacity in a pandemic will likely require the implementation of other measures, including possibly stricter implementation of hospital utilization restrictions.
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How this classification was reachedexpand
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".