Influenza‐like illness‐related emergency department visits: Christmas and New Year holiday peaks and relationships with laboratory‐confirmed respiratory virus detections, Edmonton, Alberta, 2004–2014
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) visit volumes can be especially high during the Christmas-New Year holidays, a period occurring during the influenza season in Canada. METHODS: Using daily data, we examined the relationship between ED visits for the chief complaint "cough" (for Edmonton, Alberta residents) and laboratory detections for influenza A and respiratory syncytial virus (RSV) (for Edmonton and surrounding areas), lagged 0-5 days ahead, for non-pandemic years (2004-2008 and 2010-2014) using multivariable linear regression adjusting for temporal variables. We defined these cough-related visits as influenza-like illness (ILI)-related ED visits and, for 2004-2014, compared Christmas-New Year holiday (December 24-January 3) and non-holiday volumes during the influenza season (October-April). RESULTS: Adjusting for temporal variables, ILI-related ED visits were significantly associated with laboratory detections for influenza A and RSV. During non-pandemic years, the highest peak in ILI-related visit volumes always occurred during the holidays. The median number of holiday ILI-related visits/day (42.5) was almost twice the non-holiday median (24) and was even higher in 2012-2013 (80) and 2013-2014 (86). Holiday ILI-related ED visit volumes/100 000 population ranged from 56.0 (2010-2011) to 117.4 (2012-2013). In contrast, lower visit volumes occurred during the holidays of pandemic-affected years (2008-2010). CONCLUSIONS: During non-pandemic years, ILI-related ED visit volumes were associated with variations in detections for influenza A and RSV and always peaked during the Christmas-New Year holidays. This predictability should be used to prepare for, and possibly prevent, this increase in healthcare use; however, interventions beyond disease prevention strategies are likely needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".