Strengths and limitations of assessing influenza vaccine effectiveness using routinely collected, passive surveillance data in Ontario, Canada, 2007 to 2012: balancing efficiency versus quality
Bibliographic record
Abstract
Prompt evaluation of annual influenza vaccine effectiveness (IVE) is important. IVE is estimated in Ontario using a test-negative design (TND) within a national sentinel surveillance network (SPSN). To explore alternative approaches, we applied the screening method (SM) during five seasons spanning 2007 to 2012 to passive surveillance data to determine whether routinely collected data could provide unbiased IVE estimates. Age-adjusted SM-IVE estimates, excluding 2008/09 pandemic cases and cases with missing immunisation status, were compared with TND-IVE estimates in SPSN participants, adjusted for age, comorbidity, week of illness onset and interval to specimen collection. In four seasons, including the 2009 pandemic, the SM underestimated IVE (22–39% seasonal; 72% pandemic) by 20 to 35% relative to the TND-IVE (58–63% seasonal; 93% pandemic), except for the 2010/11 season when both estimates were low (33% and 30%, respectively). Half of the cases in the routine surveillance data lacked immunisation information; imputing all to be unimmunised better aligned SM-IVE with TND-IVE, instead overestimating in four seasons by 4 to 29%. While the SM approach applied to routine data may offer the advantage of timeliness, ease and efficiency, methodological issues related to completeness of vaccine information and/or case ascertainment may constitute trade-offs in reliability.
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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 | 0.086 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| 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".