School Absenteeism As an Adjunct Surveillance Indicator: Experience during the Second Wave of the 2009 H1N1 Pandemic in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: A school absenteeism surveillance system was implemented in the province of Quebec, Canada during the second wave of the 2009 H1N1 pandemic. This paper compares this surveillance approach with other available indicators. METHOD: All (3432) elementary and high schools from Quebec were included. Each school was required to report through a web-based system any day where the proportion of students absent for influenza-like illness (ILI) exceeded 10% of current school enrolment. RESULTS: Between October 18 and December 12 2009, 35.6% of all schools met the 10% absenteeism threshold. This proportion was greater in elementary compared to high schools (40% vs 19%) and in smaller compared to larger schools (44% vs 22%). The maximum absenteeism rate was reached the first day of reporting or within the next two days in 55% and 31% of schools respectively. The first reports and subsequent peak in school absenteeism provincially preceded the peak in paediatric hospitalization by two and one weeks, respectively. Trends in school surveillance otherwise mirrored other indicators. CONCLUSION: During a pandemic, school outbreak surveillance based on a 10% threshold appears insufficient to trigger timely intervention within a given affected school. However, school surveillance appears well-correlated and slightly anticipatory compared to other population indicators. As such, school absenteeism warrants further evaluation as an adjunct surveillance indicator whose overall utility will depend upon specified objectives, and other existing capacity for monitoring and response.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".