Using population-wide administrative and laboratory data to estimate type- and subtype-specific influenza vaccine effectiveness: a surveillance protocol
Bibliographic record
Abstract
INTRODUCTION: The appropriateness of using routinely collected laboratory data combined with administrative data for estimating influenza vaccine effectiveness (VE) is still being explored. This paper outlines a protocol to estimate influenza VE using linked laboratory and administrative data which could act as a companion to estimates derived from other methods. METHODS AND ANALYSIS: We will use the test-negative design to estimate VE for each influenza type/subtype and season. Province-wide individual-level records of positive and negative influenza tests at the Provincial Laboratory for Public Health in Alberta will be linked, by unique personal health numbers, to administrative databases and vaccination records held at the Ministry of Health in Alberta to determine covariates and influenza vaccination status, respectively. Covariates of interests include age, sex, immunocompromising chronic conditions and healthcare setting. Cases will be defined based on an individual's first positive influenza test during the season, and potential controls will be defined based on an individual's first negative influenza test during the season. One control for each case will be randomly selected based on the week the specimen was collected. We will estimate VE using multivariable logistic regression. ETHICS AND DISSEMINATION: Ethics approval was obtained from the University of Alberta's Health Research Ethics Board-Health Panel under study ID Pro00075997. Results will be disseminated by public health officials in Alberta.
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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 | 0.139 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".