Emergency Department Surveillance as a Proxy For The Prediction of Circulating Respiratory Viral Disease in Eastern Ontario
Bibliographic record
Abstract
BACKGROUND: Seasonal outbreaks of winter respiratory viruses are responsible for increases in morbidity and mortality in the community. Previous studies have used hospitalizations, intensive care unit and emergency department (ED) visits as indicators of seasonal influenza incidence. OBJECTIVES: To evaluate whether ED visits can be used as a proxy to detect respiratory viral disease outbreaks, as measured by laboratory confirmation. METHODS: An Emergency Department Syndromic Surveillance system was used to collect ED chief complaints in Eastern Ontario from 2006 to 2010. Comparable laboratory-confirmed cases of respiratory viral infections were collected from the Public Health Ontario Laboratory in Kingston, Ontario. Correlations between ED visits and laboratory-confirmed cases were calculated. RESULTS: Laboratory-confirmed cases of selected respiratory viruses were significantly correlated with ED visits for respiratory and fever/influenza-like illness. In particular, respiratory syncytial virus (Spearman's rho = 0.593), rhinovirus (Spearman's rho = 0.280), influenza A (Spearman's rho = 0.528), influenza B (Spearman's rho = 0.426) and pH1N1 (Spearman's rho = 0.470) increased laboratory test levels were correlated with increased volume of ED visits across a number of age demographics. For the entire study population and all studied viruses, the Spearman's rho was 0.702, suggesting a strong correlation with ED visits. Laboratory-confirmed cases lagged in reporting by between one and two weeks for influenza A and pH1N1 compared with ED visit volume. CONCLUSION: These findings support the use of an Emergency Department Syndromic Surveillance system to track the incidence of respiratory viral disease in the community. These methods are efficient and can be performed using automated electronic data entry versus the inherent delays in the primary care sentinel surveillance system, and can aid the timely implementation of preventive and preparatory health interventions.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".